CN119595979B - Intelligent monitoring method, device and system for distribution box - Google Patents
Intelligent monitoring method, device and system for distribution box Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
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- G01R19/16538—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values characterised by the application in AC or DC supplies
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Abstract
The invention relates to the technical field of current fault monitoring, in particular to an intelligent monitoring method, device and system of a distribution box, which comprises the steps of obtaining current monitoring data of the distribution box; the method comprises the steps of carrying out periodic segmentation processing on current monitoring data to obtain periodic monitoring data, carrying out incremental fitting on the periodic monitoring data to obtain fitting results, analyzing the parameter change of a fitting function in the fitting results to obtain incremental fitting influence degree of each current period, carrying out fine granularity analysis on each current period according to the incremental fitting influence degree to obtain dividing duration of each current period, and screening the dividing duration of each current period to obtain a current threshold value. And (3) periodically segmenting and incrementally fitting historical current data by analyzing the current periodicity, and determining an optimal time interval by combining fine granularity analysis. And then, determining a current threshold value by using the fitting allowance, monitoring the current in real time and early warning so as to reduce monitoring errors caused by interference of the current transformer.
Description
Technical Field
The invention relates to the technical field of current fault monitoring, in particular to an intelligent monitoring method, device and system of a distribution box.
Background
In the intelligent monitoring system of the distribution box, a current transformer (Current Transformer, CT for short) can monitor current in real time, help evaluate load conditions, prevent overload, diagnose faults and trigger early warning, and ensure the safety and reliability of the distribution system. The CT data can also be used for statistics and optimization of energy consumption, support scientific maintenance planning and equipment life cycle management, and can be used for harmonic analysis and current imbalance monitoring, so that the electric energy quality is improved. Through remote transmission and historical data storage, intelligent operation and intelligent decision support are realized by CT, and the operation efficiency and stability of the power distribution system are remarkably improved.
But the monitoring accuracy of the current transformer is significantly affected by load and environmental factors. Factors such as secondary load impedance, load variations such as dynamic and nonlinear loads, ambient humidity, electromagnetic interference, vibration, and mechanical stress may affect the stability of the current transformer. Therefore, how to comprehensively control the load and the influence of environmental factors to improve the measurement accuracy of the current transformer is a technical problem to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent monitoring method, device and system for a distribution box. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The intelligent monitoring method for the distribution box comprises the steps of obtaining current monitoring data of the distribution box in a historical time period, conducting periodic segmentation processing on the current monitoring data in the historical time period to obtain periodic monitoring data, wherein the periodic monitoring data are current monitoring data corresponding to a plurality of current periods of the distribution box in the historical time period, conducting incremental fitting on the periodic monitoring data to obtain fitting results, the fitting results comprise fitting functions corresponding to each current period, obtaining incremental fitting influence degree of each current period based on parameter change analysis of the fitting functions in the fitting results, conducting fine granularity analysis on time interval ranges of different time durations of each current period according to the incremental fitting influence degree to obtain dividing time durations of each current period, and screening to obtain current threshold values by utilizing the dividing time durations of each current period, wherein the current threshold values corresponding to each current period are used for monitoring real-time current detection data of the distribution box in each time period.
With reference to the first aspect, in one possible implementation manner, the method for obtaining the incremental fitting influence degree of each current period based on the parameter variation analysis of the fitting function in the fitting result includes extracting a fitting vector based on each fitting function, wherein each element of the fitting vector is a fitting parameter of the fitting function, and calculating the fitting vector corresponding to each current period by using a preset first calculation formula to obtain the incremental fitting influence degree corresponding to each current period.
In combination with the first aspect, in one possible implementation manner, fine granularity analysis of time interval ranges of different time durations is performed on each current period according to the incremental fitting influence degree to obtain divided time durations of each current period, the method comprises the steps of dividing the time interval ranges of different time durations of one current period to obtain a plurality of time intervals under each time interval division, calculating environmental sensitivity corresponding to each time interval under the same time duration based on current variation differences in the plurality of time intervals divided by the same time duration, calculating environmental factor sensitivity corresponding to each time duration based on the incremental fitting influence degree and the environmental sensitivity corresponding to each time interval under the same time duration, calculating peak time duration average value corresponding to each time duration based on the current peak time duration appearance time durations in the plurality of time intervals divided by the same time duration, calculating reference weight corresponding to each time duration based on the peak time duration average value and the environmental factor sensitivity, and calculating weighted average based on the reference weight of each time duration to obtain the divided time duration of one current period.
In combination with the first aspect, in one possible implementation manner, the method comprises the steps of calculating and obtaining the environmental sensitivity corresponding to each time interval under the same time length based on the current change difference in a plurality of time intervals divided by the same time length, calculating and obtaining fitting current data corresponding to the plurality of time intervals divided by the same time length based on the fitting function, sequentially carrying out difference and summation calculation on the fitting current data and the current monitoring data according to a time sequence order to obtain fitting allowance in the plurality of time intervals divided by the same time length, carrying out mean calculation based on the fitting allowance in the plurality of time intervals divided by the same time length to obtain a fitting allowance mean value, and calculating and obtaining the environmental sensitivity corresponding to each time interval based on the fitting allowance mean value corresponding to each time length and the current difference in each time interval.
In combination with the first aspect, in one possible implementation manner, the current threshold is obtained by screening the dividing duration of each current period, and the method comprises the steps of segmenting current monitoring data corresponding to the current period based on the dividing duration to obtain current monitoring data corresponding to a plurality of sections of optimal time intervals, respectively conducting straight line fitting on the current monitoring data corresponding to each section of optimal time interval to obtain a fitting straight line corresponding to each section of optimal time interval, calculating to obtain fitting current data corresponding to each section of optimal time interval based on the fitting straight line corresponding to each section of optimal time interval, conducting difference and summation calculation on the fitting current data corresponding to each section of optimal time interval and the current monitoring data to obtain fitting allowance corresponding to each section of optimal time interval, screening from the fitting allowance corresponding to each section of optimal time interval to obtain a fitting allowance maximum value, conducting proportion of the fitting allowance corresponding to each section of optimal time interval to the fitting allowance maximum value to obtain an allowance ratio, and judging to obtain the current threshold based on the allowance proportion corresponding to each section of optimal time interval and a preset threshold.
The intelligent monitoring device of the distribution box comprises an acquisition module, a period segmentation module and a screening module, wherein the acquisition module is used for acquiring current monitoring data of the distribution box in a historical time period, the period segmentation module is used for carrying out period segmentation processing on the current monitoring data in the historical time period to obtain period monitoring data, the period monitoring data are current monitoring data corresponding to a plurality of current periods of the distribution box in the historical time period, the increment fitting module is used for carrying out increment fitting on the period monitoring data to obtain fitting results, the fitting results comprise fitting functions corresponding to each current period, the increment analysis module is used for analyzing the parameter change of the fitting functions in the fitting results to obtain increment fitting influence degree of each current period, the fine granularity analysis module is used for carrying out fine granularity analysis on time interval ranges of different time durations on each current period according to the increment fitting influence degree, the screening module is used for screening to obtain current thresholds according to the dividing duration of each current period, and the current thresholds corresponding to each current period are used for carrying out monitoring on the current detection data of the distribution box in each time period.
With reference to the second aspect, in one possible implementation manner, the incremental analysis module includes a vector extraction module, configured to extract a fitting vector based on each fitting function, where each element of the fitting vector is a fitting parameter of the fitting function, and a first calculation module, configured to calculate, by using a preset first calculation formula, a fitting vector corresponding to each current period to obtain an incremental fitting influence degree corresponding to each current period.
With reference to the second aspect, in one possible implementation manner, the fine granularity analysis module includes an initial dividing module, a second calculating module, a third calculating module, a crest calculating module, a weight calculating module and a weighting calculating module, wherein the initial dividing module is used for dividing time interval ranges of different time durations of one current period to obtain a plurality of time intervals under each time interval division, the second calculating module is used for calculating environmental sensitivity corresponding to each time interval under the same time duration based on current change differences in the plurality of time intervals of the same time interval division, the third calculating module is used for calculating environmental factor sensitivity based on the increment fitting influence degree and the environmental sensitivity corresponding to each time interval under the same time duration, the crest calculating module is used for calculating crest time duration average corresponding to each time duration based on current crest appearance time duration in the plurality of time intervals of the same time interval division, the weight calculating module is used for calculating to obtain reference weight corresponding to each time duration based on the crest time duration average corresponding to each time duration, and the weighting calculating module is used for carrying out weighted average calculation based on the reference weight of each time duration to obtain the time duration of one current period.
In combination with the second aspect, in one possible implementation manner, the screening module includes a section dividing module, a straight line fitting module, a current calculating module, a margin screening module and a margin ratio module, wherein the section dividing module is used for dividing current monitoring data corresponding to the current period based on the dividing time length to obtain current monitoring data corresponding to a plurality of sections of optimal time sections, the straight line fitting module is used for respectively carrying out straight line fitting on the current monitoring data corresponding to each section of optimal time section to obtain a fitting straight line corresponding to each section of optimal time section, the current calculating module is used for calculating the fitting current data corresponding to each section of optimal time section based on the fitting straight line corresponding to each section of optimal time section, the margin calculating module is used for carrying out difference re-summation calculation on the fitting current data corresponding to each section of optimal time section and the current monitoring data to obtain a fitting margin corresponding to each section of optimal time section, the margin screening module is used for obtaining a fitting margin maximum value from the fitting margin corresponding to each section of optimal time section, the margin ratio module is used for respectively carrying out ratio with the margin maximum value to obtain a margin ratio, and the judgment module is used for judging based on the margin ratio corresponding to each section of optimal time section to obtain a current threshold value.
In a third aspect, the invention also provides an intelligent monitoring system of the distribution box, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for realizing the steps of the intelligent monitoring method of the distribution box according to any one of the above steps when executing the computer program.
The invention has the following beneficial effects:
In the invention, the electricity consumption at every moment is considered to have periodicity in the unit of day. Therefore, the current monitoring data in the historical time period is subjected to periodic segmentation and then incremental fitting treatment, and then the optimal time interval dividing interval is obtained by screening by combining a fine granularity analysis method. In this way, the dividing result is combined with the fitting allowance calculation mode provided by the invention to screen and obtain the threshold value of acceptable current, real-time current data is monitored, and the subsequent relevant processing such as real-time early warning, shutdown and maintenance of relevant equipment is carried out according to the degree that the current change exceeds an acceptable interval, so that the accurate detection of the current in the distribution box is completed, and the occurrence probability of inaccurate current monitoring caused by external interference of the current transformer is reduced.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an intelligent monitoring method of a distribution box according to an embodiment 1 of the present invention;
fig. 2 is a flow chart of step S4 provided in embodiment 1 of the present invention;
fig. 3 is a flowchart of step S5 provided in embodiment 1 of the present invention;
fig. 4 is a flowchart of step S6 provided in embodiment 1 of the present invention;
fig. 5 is a schematic structural diagram of an intelligent monitoring device of a distribution box according to embodiment 2 of the present invention;
fig. 6 is a schematic structural diagram of an intelligent monitoring system of a distribution box according to embodiment 3 of the present invention;
The intelligent monitoring system of the distribution box is shown as 800, the intelligent monitoring system of the distribution box is shown as 801, the processor is shown as 802, the memory is shown as 803, the multimedia component is shown as 804, the I/O interface is shown as 805, and the communication component is shown as 803.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of an intelligent monitoring method, device and system for a distribution box according to the invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Example 1:
The following specifically describes a specific scheme of the intelligent monitoring method of the distribution box provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of an intelligent monitoring method of a distribution box according to an embodiment of the invention is shown, and in this embodiment, steps S1 to S6 are included together.
S1, acquiring current monitoring data of the distribution box in a historical time period.
In the present embodiment, the time span involved is preferably seven day periods. During this time, the acquisition frequency of the current monitoring data is performed every ten seconds. Analog-to-digital conversion is performed through a secondary output signal of the current transformer, and data is transmitted in a wired or wireless communication mode. In addition, for those skilled in the art, the data may also be directly transmitted to a predetermined database for storage, so as to facilitate subsequent reading and analysis.
S2, carrying out periodic segmentation processing on the current monitoring data in the historical time period to obtain periodic monitoring data, wherein the periodic monitoring data are current monitoring data corresponding to a plurality of current periods of the distribution box in the historical time period.
Specifically, the duration of the current period in this embodiment is 60min. However, the duration of the current period may be modified according to the actual situation, and no specific limitation is made in this embodiment.
The timing characteristics of the acquired current monitoring dataset are taken into account in embodiments. The current monitoring dataset reflects the dynamic characteristics of the current over time, the changes of which are affected by various dimensional factors including, but not limited to, load fluctuations, changes in the current itself, humidity changes, and possible environmental factors leading to fluctuations in the current transformer internal monitoring current. Therefore, in the present embodiment, the processing of the current monitoring data is focused on analyzing the difference in the overall trend.
In view of this, in this embodiment, it is proposed to define a current refinement trend range by using an incremental fitting method, so as to effectively reject the influence of environmental factors in the data set. Therefore, the analysis of the influence of environmental factors is of vital importance in the present embodiment, with the aim of extracting effective information from the current monitoring data fluctuations under the influence of different environmental factors.
Based on this, data over multiple current periods was analyzed. The trend of the current monitoring data is mainly affected by the load change of the distribution box in the current period. However, the periodic regular-change portion within the current period is caused not only by the load change but also by environmental factors such as temperature, humidity, vibration, etc. that affect the current transformer, which cause the fluctuation of the regular-change portion, thereby increasing the fluctuation of the overall trend change, which can be regarded as a disturbance change portion.
Therefore, the main task in this embodiment is to extract such a disturbance change portion to eliminate the influence of environmental factors. Specific operations can refer to step S3 and step S4 in the study case. Through these steps, the current monitoring data can be analyzed and processed more accurately, thereby improving the accuracy and reliability of data processing.
And S3, performing incremental fitting on the period monitoring data to obtain a fitting result, wherein the fitting result comprises a fitting function corresponding to each current period.
Incremental fitting is a method of updating a statistical model step by step, in this embodiment, a two-dimensional rectangular coordinate system is first constructed, where the horizontal axis represents time and the vertical axis represents current magnitude. In the two-dimensional rectangular coordinate system, current monitoring data corresponding to one current period are marked. The data points are then connected by means of a straight line fit, resulting in a fitting function. In this fit equation, the number of fitting equations,AndRepresenting the fitting parameters, respectively.
By adopting the incremental fitting mode, small changes of data can be effectively captured. The advantage of this approach is that as the amount of data increases, the fitting process is continually updated, thus making the model more accurate. In this process, the more current periods the current transformer of the distribution box monitors, the more accurate the main trend of the distribution box load represented by the fitting function. In addition, as the amount of data increases, the fit function is also progressively less affected by environmental factors.
Thus, in this embodiment, the fitting parameters corresponding to the last current period in the history period are consideredAndIs the best fit parameter pair. This is because the data point of the last current cycle can reflect the latest load change situation, so that the fitting result is closer to the current actual situation. By this method, the load change of the distribution box can be better understood and predicted, thereby providing powerful support for the optimization and management of the power system.
And S4, analyzing and obtaining the incremental fitting influence degree of each current period based on the parameter change of the fitting function in the fitting result.
Specifically, referring to fig. 2, step S41 and step S42 are further included in step S4 in the present embodiment.
S41, extracting fitting vectors based on each fitting function, wherein each element of the fitting vectors is a fitting parameter of the fitting function.
S42, calculating the fitting vector corresponding to each current period by using a preset first calculation formula to obtain the incremental fitting influence degree corresponding to each current period.
In this embodiment, the variation of the fitting parameter pairs during the incremental fitting is considered. In particular, when the variation of the fitting parameter pair is large, this indicates that the influence of environmental factors causes a significant trend change in the present current period. The more pronounced the effect of such trend changes, the more evidence that the change in current cycle data is due to the difference in the integrity of the current cycle data caused by the change in environmental factors. Thus, in this embodiment, a method based on the difference between one current period and the immediately preceding current period is employed, in combination with the difference between that current period and the best-fit parameter as a weight. Such weights will serve as a basis for constructing the first calculation formula, thereby more accurately reflecting the influence of environmental factors on the current period data change. In this way, the contribution of environmental factors to the current period data change can be more effectively identified and quantified.
Specifically, in the present embodiment, the first calculation formula is:
;
wherein, Represent the firstIncremental fitting influence degree of each current period; Representing a maximum and minimum normalization function; Represent the first Fitting vectors corresponding to the fitting functions of the current periods; Represent the first Fitting vectors corresponding to the fitting functions of the current periods; Representing the fitting vector corresponding to the fitting function of the last current cycle.
In the above-mentioned calculation formula, the calculation formula,Representing vectorsSum vectorThe mode of the difference can be called as the difference between fitting vectors of two adjacent current periods of a current transformer of the distribution box, namely the incremental fitting influence of adjacent current period data is measured, and +0.1 is used for avoiding the situation that when the mode of the difference of the vectors is equal to 0, the denominator is zero; The greater the value of (c) is, the more pronounced the effect of the trend change due to environmental factors in the current cycle is, the more evidence that the current cycle change is due to an overall difference due to environmental factors change.
In this embodiment, although the above steps have been taken, the influence of environmental factors on the performance of the current transformer has not been completely excluded. Thus, to further improve the accuracy and reliability of the present method, the present embodiment employs a fine-grained analysis method to identify and define the negative impact of environmental factors. Through the detailed analysis, the specific influence of the environmental factors on the current transformer data can be more accurately identified, and the distinguishing capability of the system on the abnormal data is further improved on the basis. In particular, such a method of fine-grained analysis includes detailed classification and quantification of environmental factors to better understand their specific impact on current transformer data. These interfering factors can then be more effectively identified and excluded based on these negative effects, thereby improving discrimination of anomalous data. For the detailed steps, please refer to step S5 and step S6.
S5, carrying out fine granularity analysis on time interval ranges with different durations on each current period according to the increment fitting influence degree to obtain the dividing duration of each current period.
In this embodiment, the data is analyzed deeply and accurately by a fine-grain analysis method so that minute differences and details in the data can be captured. Specifically, in the present embodiment, the range in different current periods during the incremental change is analyzed in detail by adjusting the size of the time interval range. The purpose of this method is to extract the influence of the environmental factors on the current period more accurately, i.e. to determine the sensitivity of the magnitude of the range of different time intervals within each current period to the extraction of the environmental factors.
Based on the fine granularity analysis, the current refinement trend range can be effectively defined, so that the influence of environmental factors in the data set is eliminated. This method has the advantage that it can help us more accurately identify and analyze subtle differences in current variations, thereby improving the accuracy and reliability of data processing.
To better illustrate this process, reference may be made to fig. 3. Steps S51 to S56 further included in the present embodiment are illustrated in the figure.
S51, dividing time interval ranges with different time lengths for one current period to obtain a plurality of time intervals under each time length division.
For the convenience of understanding by those skilled in the art, the first step isThe current periods are illustrated. For 60min of the first timeThe current periods are respectively 1min, 2min and 3min, 30min, and the group of the arithmetic series of the equal differences are used as the time interval range, and the following steps are respectively carried outDividing the current periods to obtain、、......And each time interval, wherein n represents the total number of time intervals contained in one time interval range. Wherein if it is unable to integrateFor a current period, the remainder is also taken as a time interval. Meanwhile, for those skilled in the art, the change of the time interval range may be selected according to the actual situation, and the specific limitation is not made in the present embodiment.
To enable those skilled in the art to more clearly understand the specific operation of the present stepThe individual current periods are described in detail as examples. In this example, a time period of 60 minutesA current period. To divide this current period, an arithmetic progression from 1 minute to 30 minutes is used as the time interval range. Specifically, this 60-minute period is divided into 60 time intervals of 1 minute each, 30 time intervals of 2 minutes each, 20 time intervals of 3 minutes each, and so on until divided into 2 time intervals of 30 minutes each.
In practice, if the firstThe current periods cannot be divided, i.e. there is a remainder, and this remainder portion will also be divided separately into a time interval. This ensures that the entire current cycle is covered completely, without omission. Those skilled in the art can flexibly select the division mode of the time interval range according to actual needs and specific situations. In this embodiment, no specific limitation is made to the selection of the time interval range, so as to adapt to different application scenarios and requirements.
S52, calculating the environmental sensitivity corresponding to each time interval under the same time length based on the current change difference in a plurality of time intervals divided by the same time length.
For ease of understanding by those skilled in the art, the present embodiment illustrates how the environmental sensitivity is calculated with one of the time interval range divisions. See step S521-step S524 for details.
S521, calculating to obtain fitting current data corresponding to a plurality of time intervals divided by the same time length based on the fitting function.
And S522, sequentially fitting current data and the current monitoring data according to a time sequence, performing difference and then summing calculation, and obtaining fitting allowance in a plurality of time intervals divided by the same duration.
And S523, carrying out mean value calculation on the fit allowance in a plurality of time intervals divided based on the same time length to obtain a fit allowance mean value.
S524, calculating to obtain the environment sensitivity corresponding to each time interval based on the fitting margin mean value corresponding to each time length and the current difference in each time interval.
Specifically, the calculation formula of the environmental sensitivity is as follows:
;
wherein, Represent the firstThe current period is in the range of time intervalDividing the following thirdThe environmental sensitivity corresponding to the respective time interval,N represents a time interval ofWhen the time interval total number is included; Representing a maximum and minimum normalization function; Indicating a time interval range of When the fitting straight line is at the firstThe fit margin of the current monitoring data and the fit current data of each time interval can be also called a difference accumulation sum in the embodiment; Indicating a time interval range of The average of the fit margin over all time intervals may also be referred to as the average of the difference running sums in this embodiment; Represent the first The incremental fitting of the individual current periods affects the degree.
In the above-mentioned calculation formula, the calculation formula,The larger the time interval range corresponding to the time interval, the greater the sensitivity of the time interval range to the extraction of environmental factors. The fitting allowance represents the calculation result of summing the difference value corresponding to all the time points in the time interval after the difference between the current monitoring data and the fitting current data of each time point in the time interval is obtained.The smaller it is, which is shown atThe time interval range under the environmental influence degree of each current period isThe better the sensitivity performance, the more pronounced the effect of measuring the influence of environmental factors in terms of their span size.
And S53, calculating the sensitivity of the environmental factors based on the incremental fitting influence degree and the sensitivity of the environment corresponding to each time interval under the same duration.
Specifically, the calculation formula of the environmental factor sensitivity is as follows:
;
wherein, Represent the firstThe current period is in the range of time intervalThe sensitivity of corresponding environmental factors; Representing a variance calculation function; Represent the first The current period is in the range of time intervalDividing the environment sensitivity corresponding to all time intervals; Represent the first The incremental fitting of the individual current periods affects the degree.
In the above-mentioned calculation formula, the calculation formula,The smaller it represents the firstThe current period is in the range of time intervalWhen the sensitivity of the environmental influence degree is better in the interval, the addition of 0.1 at the position of the denominator is used for avoiding the special condition that the denominator is zero when the increment fitting influence degree is zero.
In this embodiment, the floating condition of current variations in the current period, which variations are affected by a number of different environmental factor types, is fully considered. These environmental factors are persistent in the time dimension to the effect of current flow, rather than merely transient. Thus, it can be observed that the current distribution exhibits a multimodal character in the time dimension.
Based on the above analysis and description, it can be concluded that if the degree of variation in sensitivity of the environmental factors is relatively low within each particular time interval, this indicates that the influence of that time interval range on the optimal range size is high. In other words, the higher the stability of the sensitivity of the environmental factors, the more pronounced its effect on the current distribution. Therefore, by combining the distribution condition of the current in the time dimension and the change of the sensitivity of the environmental factors, whether a time interval range is reasonable or not can be more accurately estimated and described. The specific evaluation method and steps will be described in detail in the subsequent steps S54 and S56.
S54, calculating the peak duration average value corresponding to each duration based on the current peak occurrence duration in a plurality of time intervals divided by the same duration.
S55, calculating to obtain a reference weight corresponding to each duration based on the peak duration mean value corresponding to each duration and the environmental factor sensitivity.
Specifically, the calculation formula of the reference weight is as follows:
;
wherein, Indicating that the time interval range is of the size ofReference weights at that time; Representing a maximum and minimum normalization function; Indicating that the time interval range is of the size of When the current peak appearing time length in a plurality of time intervals divided on the basis of the same time length is calculated by the peak on the time interval, so as to obtain a peak time length average value corresponding to each time length; Representing the peak duration mean value of all time interval ranges on the interval range coordinate axis, Represent the firstThe current period is in the range of time intervalThe sensitivity of the corresponding environmental factors in the process,Indicating a time interval range ofThe smaller the value of the variation of the sensitivity of the environmental factors, the higher the influence of the variation on the size of the optimal range.
S56, carrying out weighted average calculation based on the reference weight of each time length to obtain the divided time length of one current period.
Specifically, the calculation formula of the division duration is as follows:
;
wherein, Representing the dividing time length; Indicating a time interval range of Is a time period of (2); Indicating that the time interval range is of the size of Reference weights at that time; Representing the number of interval range sizes.
In the step, the dividing duration of each current period is obtained by using weighted average, so that the dividing duration is more universal for extracting environmental factors.
And S6, screening to obtain a current threshold value by utilizing the dividing duration of each current period, wherein the current threshold value corresponding to each current period is used for monitoring real-time current detection data of the distribution box in each time period.
In the step, the dividing time length is obtained through the calculation, and the current threshold value is obtained through the screening of the fitting allowance. Referring to fig. 4, step S6 is shown to include steps S61-S67.
And S61, segmenting the current monitoring data corresponding to the current period based on the dividing time length to obtain current monitoring data corresponding to a plurality of optimal time intervals.
And S62, respectively carrying out straight line fitting on the current monitoring data corresponding to each section of the optimal time interval to obtain a fitting straight line corresponding to each section of the optimal time interval.
And S63, calculating fitting current data corresponding to each section of optimal time interval based on fitting straight lines corresponding to each section of optimal time interval.
And S64, performing difference re-summation calculation on the fitting current data and the current monitoring data corresponding to each section of the optimal time interval, and obtaining the fitting allowance corresponding to each section of the optimal time interval.
S65, screening the fitting allowance corresponding to each optimal time interval to obtain the maximum fitting allowance.
S66, the fitting allowance corresponding to each optimal time interval is respectively compared with the maximum value of the fitting allowance to obtain the allowance ratio.
S67, judging based on the residual duty ratio corresponding to each optimal time interval and a preset threshold value to obtain a current threshold value.
In this step, those optimal time intervals in which the margin ratio exceeds the preset threshold are regarded as abnormal time periods in which abnormal data occur. Maximum and minimum current values are extracted during the abnormal period, and these current values will be used as current thresholds. In addition, it is also necessary to combine the specific time range in which the abnormal time period is located to confirm the current threshold value that should be used when monitoring the distribution box current in this time range.
For example, assume that an abnormal time period is identified as 5 points 10 minutes to 5 points 30 minutes in the morning. During this period, we extracted a minimum current value of 301 amperes (a) and a maximum current value of 352 amperes (a). Then, in the subsequent monitoring process, the current data in the time range of 5 minutes to 5 minutes 30 minutes in the morning is monitored with 301 amperes and 352 amperes as the current data monitoring threshold.
Meanwhile, for the situation that abnormal time periods obtained by screening in the historical time periods coincide, a mean value calculation method can be adopted to balance the conflicts. Specifically, an average value of the current values in the overlapping time period can be calculated as an integrated threshold value, so that erroneous judgment due to a single abnormal value is avoided.
In the embodiment, the accuracy and the reliability of the current monitoring data can be remarkably improved by defining the refined trend range of the current monitoring data in the distribution box and eliminating the influence of environmental factors. The refined analysis method is helpful for enhancing fault diagnosis capability and can early find potential problems in the aspect of current in the distribution box. In addition, the accurate current monitoring data can also support and optimize maintenance and management strategies of the distribution box, so that misjudgment and unnecessary maintenance cost are reduced, the energy efficiency management level of the distribution box is improved, and efficient and stable operation of the distribution system is ensured.
Example 2:
As shown in fig. 5, this embodiment provides an intelligent monitoring device for a distribution box, where the device includes:
The acquisition module is used for acquiring current monitoring data of the distribution box in a historical time period;
The periodic segmentation module is used for carrying out periodic segmentation processing on the current monitoring data in the historical time period to obtain periodic monitoring data, wherein the periodic monitoring data are current monitoring data corresponding to a plurality of current periods of the distribution box in the historical time period;
the incremental fitting module is used for carrying out incremental fitting on the period monitoring data to obtain a fitting result, and the fitting result comprises a fitting function corresponding to each current period;
The incremental analysis module is used for analyzing and obtaining the incremental fitting influence degree of each current period based on the parameter change of the fitting function in the fitting result;
The fine granularity analysis module is used for carrying out fine granularity analysis on the time interval range of different durations on each current period according to the incremental fitting influence degree to obtain the dividing duration of each current period;
and the screening module is used for screening and obtaining a current threshold value by utilizing the dividing duration of each current period, and the current threshold value corresponding to each current period is used for monitoring real-time current detection data of the distribution box in each time period.
In some specific embodiments, the delta analysis module comprises:
The vector extraction module is used for extracting a fitting vector based on each fitting function, and each element of the fitting vector is a fitting parameter of the fitting function;
The first calculation module is used for calculating the fitting vector corresponding to each current period by using a preset first calculation formula to obtain the increment fitting influence degree corresponding to each current period.
In some specific embodiments, the fine grain analysis module comprises:
The initial dividing module is used for dividing the time interval ranges of different durations of one current period to obtain a plurality of time intervals divided by each duration;
the second calculation module is used for calculating the environmental sensitivity corresponding to each time interval under the same duration based on the current change difference in a plurality of time intervals divided by the same duration;
The third calculation module is used for calculating the environmental factor sensitivity based on the incremental fitting influence degree and the environmental sensitivity corresponding to each time interval under the same duration;
the peak calculation module is used for calculating the peak duration mean value corresponding to each duration based on the current peak occurrence duration in a plurality of time intervals divided by the same duration;
the weight calculation module is used for calculating the reference weight corresponding to each time length based on the peak time length mean value corresponding to each time length and the environmental factor sensitivity;
And the weighting calculation module is used for carrying out weighted average calculation based on the reference weight of each time length to obtain the divided time length of one current period.
In some specific embodiments, the screening module comprises:
The interval dividing module is used for dividing the current monitoring data corresponding to the current period based on the dividing time length to obtain current monitoring data corresponding to a plurality of sections of optimal time intervals;
The linear fitting module is used for respectively carrying out linear fitting on the current monitoring data corresponding to each section of optimal time interval to obtain a fitting linear corresponding to each section of optimal time interval;
the current calculation module is used for calculating fitting current data corresponding to each section of optimal time interval based on fitting straight lines corresponding to each section of optimal time interval;
The allowance calculation module is used for carrying out difference re-summation calculation on the fitting current data and the current monitoring data corresponding to each section of optimal time interval to obtain fitting allowance corresponding to each section of optimal time interval;
The allowance screening module is used for screening the fitting allowance corresponding to each section of optimal time interval to obtain the maximum fitting allowance;
The allowance duty ratio module is used for respectively comparing the fitting allowance corresponding to each section of optimal time interval with the maximum value of the fitting allowance to obtain an allowance duty ratio;
And the judging module is used for judging and obtaining a current threshold value based on the allowance duty ratio corresponding to each optimal time interval and a preset threshold value.
Example 3:
corresponding to the above method embodiment, an intelligent monitoring system 800 for a power distribution box is further provided in this embodiment, and the intelligent monitoring system 800 for a power distribution box and the intelligent monitoring method for a power distribution box described below may be referred to correspondingly.
Fig. 6 is a schematic diagram of an intelligent monitoring system 800 of a distribution box according to an exemplary embodiment. As shown in fig. 6, the intelligent monitoring system 800 of the electrical distribution box may include a processor 801, a memory 802. The intelligent monitoring system 800 of the electrical box may also include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the intelligent monitoring system 800 of the electrical box, so as to complete all or part of the steps in the intelligent monitoring method of the electrical box. The memory 802 is used to store various types of data to support the operation of the intelligent monitoring system 800 at the electrical box, which may include, for example, instructions for any application or method operating on the intelligent monitoring system 800 at the electrical box, as well as application related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type of volatile or non-volatile Memory system or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the intelligent monitoring system 800 of the electrical box and other systems. Wireless Communication, such as Wi-Fi, bluetooth, near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more thereof, so the corresponding Communication component 805 may include a Wi-Fi module, a bluetooth module, an NFC module.
In an exemplary embodiment, the intelligent monitoring system 800 of the electrical distribution box may be implemented by one or more Application Specific Integrated Circuits (ASIC), digital Signal Processor (DSP), digital signal processing system (DIGITAL SIGNAL Processing Device, DSPD), programmable logic device (Programmable Logic Device, PLD), field programmable gate array (Field Programmable GATE ARRAY, FPGA), controller, microcontroller, microprocessor, or other electronic components for performing the intelligent monitoring method of the electrical distribution box described above.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the intelligent monitoring method of a power distribution box described above. For example, the computer readable storage medium may be the memory 802 described above that includes program instructions executable by the processor 801 of the intelligent monitoring system 800 of the electrical box to perform the intelligent monitoring method of the electrical box described above.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (8)
1. An intelligent monitoring method for a distribution box, which is characterized by comprising the following steps:
acquiring current monitoring data of the distribution box in a historical time period;
performing periodic segmentation processing on current monitoring data in a historical time period to obtain periodic monitoring data, wherein the periodic monitoring data are current monitoring data corresponding to a plurality of current periods of the distribution box in the historical time period;
performing incremental fitting on the period monitoring data to obtain a fitting result, wherein the fitting result comprises a fitting function corresponding to each current period;
Obtaining the incremental fitting influence degree of each current period based on the parameter change analysis of the fitting function in the fitting result;
carrying out fine granularity analysis on time interval ranges of different durations on each current period according to the incremental fitting influence degree to obtain the dividing duration of each current period;
Screening to obtain a current threshold value by using the dividing duration of each current period, wherein the current threshold value corresponding to each current period is used for monitoring real-time current detection data of the distribution box in each time period;
Obtaining the incremental fitting influence degree of each current period based on the parameter change analysis of the fitting function in the fitting result, wherein the incremental fitting influence degree comprises the following steps:
Extracting a fitting vector based on each fitting function, wherein each element of the fitting vector is a fitting parameter of the fitting function;
and calculating the fitting vector corresponding to each current period by using a preset first calculation formula to obtain the incremental fitting influence degree corresponding to each current period.
2. The intelligent monitoring method of a power distribution box according to claim 1, wherein the fine granularity analysis of the time interval range of different durations is performed on each current period according to the incremental fitting influence degree to obtain the dividing duration of each current period, and the method comprises the following steps:
dividing the time interval ranges of different time lengths of one current period to obtain a plurality of time intervals under each time length division;
Calculating the environmental sensitivity corresponding to each time interval under the same time length based on the current change difference in a plurality of time intervals divided by the same time length;
Calculating the environmental factor sensitivity corresponding to each time period based on the incremental fitting influence degree and the environmental sensitivity corresponding to each time period under the same time period;
Calculating the peak duration average value corresponding to each duration based on the current peak occurrence duration in a plurality of time intervals divided by the same duration;
calculating a reference weight corresponding to each time length based on the peak time length mean value corresponding to each time length and the environmental factor sensitivity;
And carrying out weighted average calculation based on the reference weight of each time length to obtain the divided time length of one current period.
3. The intelligent monitoring method of the distribution box according to claim 2, wherein the calculating the environmental sensitivity corresponding to each time interval under the same duration based on the current change difference in the time intervals divided by the same duration comprises:
Calculating and obtaining fitting current data corresponding to a plurality of time intervals divided by the same time length based on the fitting function;
Sequentially carrying out difference and summation calculation on the fitting current data and the current monitoring data according to a time sequence order to obtain fitting allowance in a plurality of time intervals divided by the same duration;
performing mean value calculation on the fit allowance in a plurality of time intervals divided based on the same time length to obtain a fit allowance mean value;
and calculating the environmental sensitivity corresponding to each time interval based on the fitting margin mean value corresponding to each time length and the current difference in each time interval.
4. The intelligent monitoring method of a power distribution box according to claim 1, wherein the screening to obtain the current threshold by using the dividing duration of each current period comprises:
Segmenting the current monitoring data corresponding to the current period based on the dividing duration to obtain current monitoring data corresponding to a plurality of optimal time intervals;
respectively carrying out straight line fitting on the current monitoring data corresponding to each section of optimal time interval to obtain a fitting straight line corresponding to each section of optimal time interval;
Calculating fitting current data corresponding to each section of optimal time interval based on fitting straight lines corresponding to each section of optimal time interval;
Performing difference and summation calculation on the fitting current data and the current monitoring data corresponding to each section of optimal time interval to obtain fitting allowance corresponding to each section of optimal time interval;
screening from the fitting allowance corresponding to each optimal time interval to obtain the maximum fitting allowance;
The fitting allowance corresponding to each optimal time interval is respectively compared with the maximum value of the fitting allowance to obtain the allowance ratio;
and judging based on the residual duty ratio corresponding to each optimal time interval and a preset threshold value to obtain a current threshold value.
5. Intelligent monitoring device of block terminal, its characterized in that includes:
The acquisition module is used for acquiring current monitoring data of the distribution box in a historical time period;
The periodic segmentation module is used for carrying out periodic segmentation processing on the current monitoring data in the historical time period to obtain periodic monitoring data, wherein the periodic monitoring data are current monitoring data corresponding to a plurality of current periods of the distribution box in the historical time period;
the incremental fitting module is used for carrying out incremental fitting on the period monitoring data to obtain a fitting result, and the fitting result comprises a fitting function corresponding to each current period;
The incremental analysis module is used for analyzing and obtaining the incremental fitting influence degree of each current period based on the parameter change of the fitting function in the fitting result;
The fine granularity analysis module is used for carrying out fine granularity analysis on the time interval range of different durations on each current period according to the incremental fitting influence degree to obtain the dividing duration of each current period;
The screening module is used for screening to obtain a current threshold value by utilizing the dividing duration of each current period, and the current threshold value corresponding to each current period is used for monitoring real-time current detection data of the distribution box in each time period;
The incremental analysis module includes:
The vector extraction module is used for extracting a fitting vector based on each fitting function, and each element of the fitting vector is a fitting parameter of the fitting function;
The first calculation module is used for calculating the fitting vector corresponding to each current period by using a preset first calculation formula to obtain the increment fitting influence degree corresponding to each current period.
6. The intelligent monitoring device of an electrical distribution box of claim 5, wherein the fine grain analysis module comprises:
The initial dividing module is used for dividing the time interval ranges of different durations of one current period to obtain a plurality of time intervals divided by each duration;
the second calculation module is used for calculating the environmental sensitivity corresponding to each time interval under the same duration based on the current change difference in a plurality of time intervals divided by the same duration;
The third calculation module is used for calculating the environmental factor sensitivity based on the incremental fitting influence degree and the environmental sensitivity corresponding to each time interval under the same duration;
the peak calculation module is used for calculating the peak duration mean value corresponding to each duration based on the current peak occurrence duration in a plurality of time intervals divided by the same duration;
the weight calculation module is used for calculating the reference weight corresponding to each time length based on the peak time length mean value corresponding to each time length and the environmental factor sensitivity;
And the weighting calculation module is used for carrying out weighted average calculation based on the reference weight of each time length to obtain the divided time length of one current period.
7. The intelligent monitoring device of a utility box of claim 5, wherein the screening module comprises:
The interval dividing module is used for dividing the current monitoring data corresponding to the current period based on the dividing time length to obtain current monitoring data corresponding to a plurality of sections of optimal time intervals;
The linear fitting module is used for respectively carrying out linear fitting on the current monitoring data corresponding to each section of optimal time interval to obtain a fitting linear corresponding to each section of optimal time interval;
the current calculation module is used for calculating fitting current data corresponding to each section of optimal time interval based on fitting straight lines corresponding to each section of optimal time interval;
The allowance calculation module is used for carrying out difference re-summation calculation on the fitting current data and the current monitoring data corresponding to each section of optimal time interval to obtain fitting allowance corresponding to each section of optimal time interval;
The allowance screening module is used for screening the fitting allowance corresponding to each section of optimal time interval to obtain the maximum fitting allowance;
The allowance duty ratio module is used for respectively comparing the fitting allowance corresponding to each section of optimal time interval with the maximum value of the fitting allowance to obtain an allowance duty ratio;
And the judging module is used for judging and obtaining a current threshold value based on the allowance duty ratio corresponding to each optimal time interval and a preset threshold value.
8. An intelligent monitoring system of block terminal, characterized in that includes:
a memory for storing a computer program;
a processor for implementing the steps of the intelligent monitoring method of a distribution box according to any one of claims 1 to 4 when executing said computer program.
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