CN116805210B - Intelligent power grid risk identification management and control method based on big data - Google Patents

Abstract

The invention belongs to the technical field of power grid risk management and control, and particularly discloses a power grid risk intelligent identification and management and control method based on big data.

Description

An intelligent identification and control method for power grid risks based on big data

Technical field

The invention belongs to the technical field of power grid risk management and control, and relates to a method for intelligent identification and control of power grid risks based on big data.

Background technique

The power industry occupies a very important position in the development of society. Once an accident occurs in the power grid, it will bring a lot of inconvenience to people's lives and have a great social impact on power supply enterprises. With the rapid development of urbanization, the structure of the existing power supply network is gradually expanding, and there are more and more risk factors that affect the operation safety of the power grid. In order to improve the reliability and safety of power supply, it is necessary to control the operation risks of the power grid. Carry out identification and control.

However, current identification of power grid operation risks generally focuses on risks arising from the operation of the power grid itself, such as line operation overload, overvoltage, line equipment operation failure, etc., while ignoring operational risks caused by severe weather, such as strong winds, heavy rain, heavy snow, etc. , will lead to line damage, failure of important power equipment, and in severe cases, fires and casualties. It can be seen that the harm to power grid operation caused by bad weather is still relatively large. If the monitoring of bad weather is ignored during power grid operation, Invisibly, the coverage of power grid operation risk identification will be reduced, resulting in limited and incomplete identification, which will affect the timeliness and accuracy of power grid operation risk identification to a certain extent, and is not conducive to subsequent power grid operation risk management and control.

Furthermore, the current power grid operation risk management and control only uses the identified operation risks as the basis for management and control. When the operation risk is high, power outages are basically used to facilitate grid management, and there is a lack of consideration of the continuity of power supply at the consumer end. , this is because different power consumers have different demands for power supply continuity. For some precision electrical equipment, they are very sensitive to the continuity and stability of power supply. A sudden power outage will cause damage to the equipment, which may be irreparable or costly. Therefore, it is obviously not scientific and reasonable to simply use the identified operational risks as the basis for control, and it is easy to form a one-size-fits-all phenomenon, resulting in a low degree of adaptability between the control results and the power consumption end, resulting in poor control effects and an invisible increase in costs. The incidence of ineffective controls.

Contents of the invention

In view of this, an intelligent identification and control method for power grid risks based on big data is now proposed, which effectively solves the problems raised in the above background technology.

The purpose of the present invention can be achieved through the following technical solutions: The present invention proposes a method for intelligent identification and control of power grid risks based on big data, which includes the following steps: A. Count the number of power supply networks existing in the target area, and obtain the corresponding data of each power supply network. The power consumption information and power supply management and control end include the power consumption area area, the power consumption period of the power consumption area and the main load level corresponding to each power consumption period.

B. Real-time monitoring of the line operation information, equipment operation information and weather information of the areas where the lines pass corresponding to each power supply network, and based on this, it is judged whether there are operation risks in each power supply network at each monitoring time. If it is judged that a certain power supply network has operation risks at a certain monitoring time, , then the power supply network is recorded as a risk power supply network, the monitoring time is recorded as a risk moment, and the operation risk direction of the risk power supply network at the risk moment is identified at the same time.

C. Based on the operation risk point of the risk power supply network at the risk moment, predict the power outage demand index of the risk power supply network at the risk moment.

D. Evaluate the allowable outage index of the risk power supply network at the risk time based on the risk time and power consumption information corresponding to the risk power supply network.

E. Based on the power outage demand index and allowed outage index of the risk power supply network at the risk moment, judge whether the risk power supply network needs power outage processing, and analyze the non-power outage corresponding to the risk power supply network when judging that the risk power supply network does not need power outage processing. processing method.

F. Upload the power outage processing evaluation results and non-power outage treatment methods corresponding to the risk power supply network to the corresponding power supply control terminal.

In an optional way, the power consumption period of the power consumption area includes a peak power consumption period of the power consumption area, a normal power consumption period of the power consumption area and a low power consumption period of the power consumption area, where the subject corresponding to each power consumption period The acquisition process corresponding to the load level is as follows: count the number of power equipment existing in the power consumption area, and identify the load level of each power equipment.

Set the monitoring time period, and then count the power equipment in operation in each power consumption period on each monitoring day corresponding to the monitoring time period, as the operating power equipment corresponding to each power consumption period.

The operating power equipment on each monitoring day within the same power consumption period is compared with each other, and then the same operating power equipment is deduplicated to obtain a number of operating power equipment corresponding to each power consumption period.

Based on the load level of each power equipment, the load level of each operating power equipment corresponding to each power consumption period is obtained, and the load levels of each power consumption period corresponding to each operating power equipment are arranged in order of importance, and then the ranking is obtained The load level in the first place is used as the main load level corresponding to each power consumption period.

In an optional way, the line operation information includes line load rate, line overvoltage rate and line power supply fluctuation, the equipment operation information includes operation indicators of each equipment on the line, and the weather information includes weather type and weather performance. degree value.

In an optional way, the specific division method of dividing the monitoring time is: setting the start monitoring time, and then dividing according to the set time interval from the start monitoring time within one day to obtain several monitoring times. .

In an optional way, the evaluation of whether there is an operation risk in each power supply network at each monitoring moment specifically includes the following steps: extracting the line limited operation information and line equipment corresponding to each power supply network in the target area from the power supply management database. indicators of normal operation.

Compare the line operation information of each power supply network at each monitoring time with the line limit operation information corresponding to each power supply network, and calculate the line operation over-limit index of each power supply network at each monitoring time. , the expression is , formula in/> ,/> ,/> are respectively expressed as the line load rate, line overvoltage rate, and line power supply fluctuation of the i-th power supply network at the t-th monitoring time, i represents the number of the power supply network,/> , t represents the number of the monitoring time,/> ,/> , ,/> are respectively expressed as the limited line load rate, limited line overvoltage rate, and limited line power supply fluctuation corresponding to the i-th power supply network, and e is expressed as a natural constant.

Compare the equipment operation information of each power supply network at each monitoring time with the normal operation indicators of each equipment in the corresponding lines of each power supply network, and calculate the equipment operation abnormality index of each power supply network at each monitoring time. The expression is: , formula in/> expressed as the operating indication of the k-th equipment of the i-th power supply network at the t-th monitoring time, k represents the equipment number,/> ,/> It is expressed as the normal operation indication of the k-th equipment on the line corresponding to the i-th power supply network.

Extract weather types from weather information, and then filter out the weather influencing factors of each power supply network at each power supply moment from the power supply management library. .

The weather performance degree value is extracted from the weather information, and the weather influencing factors of each power supply network at each monitoring time are combined with the weather performance degree value through the expression Obtain the severe weather index of each power supply network at each monitoring time/> ,/> It is expressed as the weather performance value of the i-th power supply network at the t-th monitoring time.

The line operation over-limit index, equipment operation abnormality index and severe weather index of each power supply network at each monitoring time are compared with the allowable line operation over-limit index, allowable equipment operation abnormality index and allowable severe weather index corresponding to each power supply network in the power supply management database. For comparison, if the line operation overrun index, equipment operation abnormality index and weather severe index of a power supply network at a certain monitoring time are all less than or equal to the allowable line operation overrun index, allowable equipment operation abnormality index and allowable weather corresponding to the power supply network. If the severity index is high, the power supply network will be judged to have no operational risk; otherwise, the power supply network will be judged to have operational risks.

In an optional way, the specific identification method of the operation risk direction of the risk power supply network at the risk moment is to import the line operation over-limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment into the identification model. , get the operation risk direction of the risk power supply network at the risk moment/> ,/> ,/> ,/> They are respectively expressed as the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment,/> ,/> ,/> They are respectively expressed as the allowable line operation limit index, the allowable equipment operation abnormality index, and the allowable severe weather index corresponding to the risk power supply network.

In an optional way, the prediction of the power outage demand index of the risk power supply network at the risk moment refers to the following process: pointing the operation risk of the risk power supply network at the risk moment to various operational risks stored in the power supply management library. Point to the corresponding power outage impact proportion factor for matching, and match the power outage impact proportion factor of the risk power supply network at the risk moment. .

Based on the operation risk direction of the risk power supply network at the risk moment, the operation risk index corresponding to the risk power supply network is screened out from the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment. .

Leverage predictive expressions Obtain the power outage demand index of the risk power supply network at the risk moment/> ,/> It is expressed as the allowable operation risk index corresponding to the operation risk point of the risk power supply network.

In an optional way, the assessment of the allowable power outage index of the risk power supply network at the risk moment refers to the following steps: extracting the area of the power consumption area from the power consumption information corresponding to the risk power supply network and combining it with the target area The area is calculated to obtain the proportion of the electricity consumption area corresponding to the risk power supply network. .

Extract the power consumption period of the power consumption area from the electricity consumption information corresponding to the risk power supply network, and then match the risk time corresponding to the risk power supply network with the power consumption period corresponding to the risk power supply network, and match the corresponding risks of the risk power supply network. The power consumption period of the power consumption area to which the time belongs is recorded as a specific power consumption period.

Match the specific power consumption period corresponding to the risk power supply network with the power supply interruption hazard factors corresponding to various power consumption periods stored in the power supply management library, and match the power supply interruption hazard factors of the risk power supply network at the risk moment. .

Based on the specific power consumption period corresponding to the risk power supply network, the main load level of the risk power supply network in the specific power consumption period is extracted from the power consumption information.

Match the main load level of the risk power supply network during a specific power consumption period with the demand power supply reliability corresponding to various load levels in the power supply management database, and then match the demand power supply reliability of the risk power supply network at the risk moment. .

Will ,/> and/> Import expression/> , calculate the demand power supply continuity index of the risk power supply network at the risk moment/> ,U represents a preset constant, and U>1.

Compare the demand power supply continuity index of the risk power supply network at the risk moment with the allowable power outage index corresponding to various demand power supply continuity indexes stored in the power supply management library, and obtain the allowable power outage index of the risk power supply network at the risk moment.

In an optional way, the evaluation of whether the risk power supply network requires power outage processing is implemented by comparing the power outage demand index of the risk power supply network at the risk moment with the allowed power outage index. If the power outage demand index is greater than If the allowable power outage index is used, it is judged that the risk power supply network needs to be cut off, otherwise it is judged that the risk power supply network does not need to be cut off.

In an optional method, the non-power outage processing method corresponding to the analysis of the risk power supply network refers to the following process: the operation risk point of the risk power supply network at the risk moment is introduced into the analysis algorithm , obtain the non-power outage processing method R corresponding to the risk power supply network.

Compared with the existing technology, the beneficial effects of the present invention are as follows: (1) When identifying power grid operation risks, the present invention performs real-time monitoring from the three dimensions of power supply line operating status, equipment operating status and weather status of the area where the line passes. Then, risk judgment and identification are carried out based on the monitoring results, and comprehensive identification of power grid operation risks is achieved by expanding the coverage of power grid operation risk identification, effectively avoiding the shortcomings of limited power grid risk identification in the existing technology, and improving power grid operation to a certain extent. The timeliness and accuracy of risk identification can provide a reliable reference for power grid operation risk management and control.

(2) When performing power grid operation risk management and control, the present invention obtains the information of the power consumer, thereby analyzing the power consumer's demand for power supply continuity, so as to combine the identified operational risks with the power consumer's demand for power supply continuity. The needs are integrated and the power grid risk management is comprehensively carried out, making the power grid risk management and control more scientific and reasonable, greatly improving the adaptability between the management and control results and the power consumption end, making the management and control effect better, and greatly reducing the incidence of ineffective management and control. , and at the same time, it also ensures the operation safety of electrical equipment to a certain extent, which is helpful to reduce losses caused to power consumers due to sudden power outages.

(3) The present invention performs management and control processing based on the identified power grid operation risks and the demand for continuity of power supply at the power end. When the management and control processing result is uninterruptible power supply processing, it adds an analysis of the non-power outage processing method, which can provide uninterrupted power supply. The processed power grid operation safety provides targeted and reliable guarantee measures, which can reduce the risk of power grid operation as much as possible under the condition of uninterrupted power supply, and is conducive to maintaining the continuous operation of the power grid.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the drawings needed to describe the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic diagram of the method steps of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Please refer to Figure 1. The present invention proposes a method for intelligent identification and control of power grid risks based on big data, which includes the following steps: A. Count the number of power supply networks existing in the target area, and obtain the corresponding power consumption information of each power supply network. And the power supply management and control end, where the electricity consumption information includes the area of the electricity consumption area, the electricity consumption period of the electricity consumption area and the main load level corresponding to each power consumption period.

In a further embodiment, the power consumption period of the power consumption area includes a peak power consumption period of the power consumption area, a normal power consumption period of the power consumption area and a low power consumption period of the power consumption area.

It should be noted that the above-mentioned division of the power consumption period of the power consumption area is divided into 24 hours corresponding to the day. For example, the peak power consumption period of the power consumption area is 9:00-17:00, and the normal power consumption period of the power consumption area is It is 17:00-22:00, and the low power consumption period in the power consumption area is 22:00-8:00.

The acquisition process of the subject load level corresponding to each power consumption period is as follows: count the number of power equipment existing in the power consumption area, and identify the load level of each power equipment. The specific identification method is to combine each power equipment existing in the power consumption area. The name is matched with several power equipment names corresponding to various load levels in the power supply management database, thereby identifying the load level of each power equipment.

What needs to be known is that the load level is the classification of electric load according to the requirements for power supply reliability and the degree of loss or impact of interruption of power supply on politics and economy. It can be divided into first-level load, second-level load and third-level load, among which The importance of the first-level load is greater than that of the second-level load, and the importance of the second-level load is greater than that of the third-level load.

Set the monitoring time period, and then count the power equipment in operation in each power consumption period on each monitoring day corresponding to the monitoring time period, as the operating power equipment corresponding to each power consumption period.

The operating power equipment on each monitoring day within the same power consumption period is compared with each other, and then the same operating power equipment is deduplicated to obtain a number of operating power equipment corresponding to each power consumption period.

Based on the load level of each power equipment, the load level of each operating power equipment corresponding to each power consumption period is obtained, and the load levels of each power consumption period corresponding to each operating power equipment are arranged in order of importance, and then the ranking is obtained The load level in the first place is used as the main load level corresponding to each power consumption period.

B. Real-time monitoring of the line operation information, equipment operation information and weather information of the areas the lines pass through corresponding to each power supply network. The line operation information includes line load rate, line overvoltage rate and line power supply fluctuation. The equipment operation information includes each equipment on the line. The weather information includes weather type and weather performance degree value. Based on this, it is judged whether there is operation risk in each power supply network at each monitoring time. If it is judged that a certain power supply network has operation risk at a certain monitoring time, the power supply network will be recorded. For the risk power supply network, the monitoring moment is recorded as the risk moment, and the operation risk direction of the risk power supply network at the risk moment is identified.

In the specific implementation of the present invention, the specific division method of monitoring time is: setting the start monitoring time, and then dividing according to the set time interval from the start monitoring time within one day to obtain several monitoring time.

As an example of the above solution, assume that the monitoring start time is 7:00, and the set time interval is 10 minutes. In this case, the divided monitoring time is 7:10-7:20, 7:20-7:30 , 7:30-7:40….

Further, the line load rate in the line operation information is obtained by obtaining the actual load of the line, using the formula Obtained, the way to obtain the line overvoltage rate is to obtain the actual operating voltage of the line, and use the formula/> , the line power supply fluctuation is obtained by collecting line power quality indicators, including frequency deviation, voltage deviation, and grid harmonic rate, using the expression

Furthermore, the equipment existing on the line includes but is not limited to circuit breakers, transformers, mutual inductors...

Furthermore, weather types include rainy days, windy days, sunny days, snowy days, etc., and the weather performance degree value is the performance degree under the corresponding weather type. When the weather type is rainy day, the weather performance degree value is , when the weather type is windy, the weather performance degree value is/> , when the weather type is sunny, the weather performance degree value is , when the weather type is snow, the weather performance degree value is/> .

It should be noted that the above weather information is obtained from the meteorological center of the area where the line passes.

In the above technical solution, judging whether each power supply network has operational risks at each monitoring moment specifically includes the following steps: extracting the limited operation information of the lines corresponding to each power supply network in the target area and the normal operation indicators of each equipment on the line from the power supply management database .

Compare the line operation information of each power supply network at each monitoring time with the line limit operation information corresponding to each power supply network, and calculate the line operation over-limit index of each power supply network at each monitoring time. , the expression is , formula in/> ,/> ,/> are respectively expressed as the line load rate, line overvoltage rate, and line power supply fluctuation of the i-th power supply network at the t-th monitoring time, i represents the number of the power supply network,/> , t represents the number of the monitoring time,/> ,/> , ,/> are respectively expressed as the limited line load rate, limited line overvoltage rate, and limited line power supply fluctuation corresponding to the i-th power supply network, and e is expressed as a natural constant.

Compare the equipment operation information of each power supply network at each monitoring time with the normal operation indicators of each equipment in the corresponding lines of each power supply network, and calculate the equipment operation abnormality index of each power supply network at each monitoring time. The expression is: , formula in/> expressed as the operating indication of the k-th equipment of the i-th power supply network at the t-th monitoring time, k represents the equipment number,/> ,/> It is expressed as the normal operation indication of the k-th equipment on the line corresponding to the i-th power supply network.

Extract weather types from weather information, and then filter out the weather influencing factors of each power supply network at each power supply moment from the power supply management library. , where the worse the weather type, the greater the weather influence factor.

The weather performance degree value is extracted from the weather information, and the weather influencing factors of each power supply network at each monitoring time are combined with the weather performance degree value through the expression Obtain the severe weather index of each power supply network at each monitoring time/> ,/> It is expressed as the weather performance value of the i-th power supply network at the t-th monitoring time.

The line operation over-limit index, equipment operation abnormality index and severe weather index of each power supply network at each monitoring time are compared with the allowable line operation over-limit index, allowable equipment operation abnormality index and allowable severe weather index corresponding to each power supply network in the power supply management database. For comparison, if the line operation overrun index, equipment operation abnormality index and weather severe index of a power supply network at a certain monitoring time are all less than or equal to the allowable line operation overrun index, allowable equipment operation abnormality index and allowable weather corresponding to the power supply network. If the severity index is high, the power supply network will be judged to have no operational risk; otherwise, the power supply network will be judged to have operational risks.

Based on the above scheme, the specific identification method of the operation risk direction of the risky power supply network at the risk moment is to import the line operation limit index, equipment operation abnormality index and severe weather index of the risky power supply network at the risk moment into the identification model. , obtain the operation risk direction of the risk power supply network at the risk moment ,/> ,/> ,/> They are respectively expressed as the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment,/> ,/> ,/> They are respectively expressed as the allowable line operation limit index, the allowable equipment operation abnormality index, and the allowable severe weather index corresponding to the risk power supply network.

When identifying the power grid operation risk, the present invention conducts real-time monitoring from the three dimensions of power supply line operation status, equipment operation status and weather status of the area where the line passes, and then performs risk judgment and identification based on the monitoring results. By expanding the coverage of power grid operation risk identification The scope realizes the comprehensive identification of power grid operation risks, effectively avoids the shortcomings of limited power grid risk identification in the existing technology, improves the timeliness and accuracy of power grid operation risk identification to a certain extent, and can provide information for power grid operation risk management and control. Reliable reference.

C. Predict the power outage demand index of the risk power supply network at the risk time based on the operation risk points of the risk power supply network at the risk moment. The specific prediction process is as follows: compare the operation risk points of the risk power supply network at the risk moment with each stored in the power supply management library. Each operation risk point is matched with the corresponding power outage impact proportion factor, and the power outage impact proportion factor of the risk power supply network at the risk moment is matched. .

The power outage impact factors corresponding to the various operational risks mentioned above range from 0 to 1.

Based on the operation risk direction of the risk power supply network at the risk moment, the operation risk index corresponding to the risk power supply network is screened out from the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment. .

Leverage predictive expressions Obtain the power outage demand index of the risk power supply network at the risk moment ,/> It is expressed as the allowable operation risk index corresponding to the operation risk point of the risk power supply network.

Specifically, the operation risk direction of the risk power supply network at the risk moment is when the line operation exceeds the limit, the operation risk index corresponding to the risk power supply network is the line operation over limit index, the allowable operation risk index is the allowable line operation over limit index, and the risk power supply network is When the operation risk direction at the risk moment is abnormal equipment operation, the operation risk index corresponding to the risk power supply network is the equipment operation abnormality index, the allowed operation risk index is the allowable equipment operation abnormality index, and the operation risk direction of the risk power supply network at the risk moment is bad weather. When , the operation risk index corresponding to the risky power supply network is the severe weather index, and the allowable operation risk index is the allowable severe weather index.

D. Evaluate the allowable power outage index of the risk power supply network at the risk time based on the risk time and power consumption information corresponding to the risk power supply network, including the following steps: extract the power area area from the power consumption information corresponding to the risk power supply network, and Divide it by the area of the target area to calculate the proportion of the electricity consumption area corresponding to the risk power supply network. .

Extract the power consumption period of the power consumption area from the electricity consumption information corresponding to the risk power supply network, and then match the risk time corresponding to the risk power supply network with the power consumption period corresponding to the risk power supply network, and match the corresponding risks of the risk power supply network. The power consumption period of the power consumption area to which the time belongs is recorded as a specific power consumption period.

Match the specific power consumption period corresponding to the risk power supply network with the power supply interruption hazard factors corresponding to various power consumption periods stored in the power supply management library, and match the power supply interruption hazard factors of the risk power supply network at the risk moment. .

Based on the specific power consumption period corresponding to the risk power supply network, the main load level of the risk power supply network in the specific power consumption period is extracted from the power consumption information.

Match the main load level of the risk power supply network during a specific power consumption period with the demand power supply reliability corresponding to various load levels in the power supply management database, and then match the demand power supply reliability of the risk power supply network at the risk moment. .

Will ,/> and/> Import expression/> , calculate the demand power supply continuity index of the risk power supply network at the risk moment/> ,U represents a preset constant, and U>1.

Compare the demand power supply continuity index of the risk power supply network at the risk moment with the allowable power outage index corresponding to various demand power supply continuity indexes stored in the power supply management library, and obtain the allowable power outage index of the risk power supply network at the risk moment.

E. Based on the power outage demand index and allowed outage index of the risk power supply network at the risk moment, judge whether the risk power supply network needs power outage processing, and analyze the non-power outage corresponding to the risk power supply network when judging that the risk power supply network does not need power outage processing. processing method.

In the above scheme, the way to judge whether the risk power supply network needs power outage processing is to compare the power outage demand index of the risk power supply network at the risk moment with the allowed power outage index. If the power outage demand index is greater than the allowed power outage index, then If the power supply network is judged to be at risk, it needs to be cut off. On the contrary, the power supply network to be judged to be at risk does not need to be cut off.

When performing power grid operation risk management and control processing, the present invention obtains power-consuming end information, thereby analyzing the power-consuming end's demand for power supply continuity, thereby integrating the identified operational risks with the power-consuming end's demand for power supply continuity. , comprehensively carry out power grid risk management and control, making power grid risk management and control more scientific and reasonable, greatly improving the adaptability between the management and control results and the power consumption end, making the management and control effect better, greatly reducing the incidence of ineffective management and control, and at the same time also It ensures the operation safety of electrical equipment to a certain extent and helps reduce losses caused to power consumers due to sudden power outages.

Preferably, to analyze the non-power outage processing method corresponding to the risk power supply network, refer to the following process:

Introduce the operational risk points of the risky power supply network at risk moments into the analytical algorithm , obtain the non-power outage processing method R corresponding to the risk power supply network.

The present invention performs management and control processing based on the identified power grid operation risks and the power supply continuity requirements of the power consumption end. When the management and control processing result is uninterruptible power supply processing, it adds an analysis of the non-power outage processing method, and can provide a power grid with uninterruptible power supply processing. Operational safety provides targeted and reliable guarantee measures, which can reduce power grid operation risks as much as possible without interruption of power supply, and is conducive to maintaining the continuous operation of the power grid.

F. Upload the power outage processing evaluation results and non-power outage treatment methods corresponding to the risk power supply network to the corresponding power supply control terminal.

During the implementation process of the present invention, a power supply management library is also used to store the allowable line operation limit index, the allowable equipment operation abnormality index and the allowable severe weather index corresponding to each power supply network, and to store the line rated load and line corresponding to each power supply network. Rated operating voltage and normal frequency deviation, normal voltage deviation, normal power grid harmonic rate, storage of influencing factors corresponding to various weather types, storage of line limited operation information corresponding to each power supply network and normal operation indicators of each line equipment, storage of various Each operation risk points to the corresponding power outage impact proportion factor, stores the power supply interruption hazard factors corresponding to various power consumption periods, stores the demand power supply reliability corresponding to various load levels, and stores several power equipment names corresponding to various load levels. And store the allowable power outage index corresponding to various demand power supply continuity indexes.

The above contents are only examples and explanations of the concept of the invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or substitute them in similar ways, as long as they do not deviate from the concept of the invention. Or beyond the scope defined by the present invention, all shall belong to the protection scope of the present invention.

Claims (6)

1. An intelligent identification and control method for power grid risks based on big data, which is characterized by including the following steps: A. Count the number of power supply networks that exist in the target area, and obtain the power consumption information and power supply management and control terminals corresponding to each power supply network. The power consumption information includes the area of the power consumption area, the power consumption period of the power consumption area, and the corresponding power consumption period. Main body load level; B. Real-time monitoring of the line operation information, equipment operation information and weather information of the areas where the lines pass corresponding to each power supply network, and based on this, it is judged whether there are operation risks in each power supply network at each monitoring time. If it is judged that a certain power supply network has operation risks at a certain monitoring time, , then the power supply network is recorded as a risk power supply network, the monitoring time is recorded as a risk moment, and the operation risk direction of the risk power supply network at the risk moment is identified at the same time; The evaluation of whether there are operational risks in each power supply network at each monitoring time specifically includes the following steps: Extract the limited operation information of the lines corresponding to each power supply network in the target area and the normal operation indicators of each equipment on the lines from the power supply management database; Compare the line operation information of each power supply network at each monitoring time with the line limit operation information corresponding to each power supply network, and calculate the line operation over-limit index of each power supply network at each monitoring time. , the expression is , formula in/> ,/> , are respectively expressed as the line load rate, line overvoltage rate, and line power supply fluctuation of the i-th power supply network at the t-th monitoring time, i represents the number of the power supply network,/> , t represents the number of the monitoring time,/> ,/> ,/> ,/> are respectively expressed as the limited line load rate, limited line overvoltage rate, and limited line power supply fluctuation corresponding to the i-th power supply network, and e is expressed as a natural constant; Compare the equipment operation information of each power supply network at each monitoring time with the normal operation indicators of each equipment in the corresponding lines of each power supply network, and calculate the equipment operation abnormality index of each power supply network at each monitoring time. The expression is: , formula in/> expressed as the operating indication of the k-th equipment of the i-th power supply network at the t-th monitoring time, k represents the equipment number,/> ,/> Expressed as the normal operation indication of the k-th equipment on the line corresponding to the i-th power supply network; Extract weather types from weather information, and then filter out the weather influencing factors of each power supply network at each power supply moment from the power supply management library. ; The weather performance degree value is extracted from the weather information, and the weather influencing factors of each power supply network at each monitoring time are combined with the weather performance degree value through the expression Obtain the severe weather index of each power supply network at each monitoring time/> ,/> Expressed as the weather performance value of the i-th power supply network at the t-th monitoring time; The line operation over-limit index, equipment operation abnormality index and severe weather index of each power supply network at each monitoring time are compared with the allowable line operation over-limit index, allowable equipment operation abnormality index and allowable severe weather index corresponding to each power supply network in the power supply management database. For comparison, if the line operation overrun index, equipment operation abnormality index and weather severe index of a power supply network at a certain monitoring time are all less than or equal to the allowable line operation overrun index, allowable equipment operation abnormality index and allowable weather corresponding to the power supply network. If the severity index is high, then the power supply network is judged to have no operational risk; otherwise, the power supply network is judged to have operational risk; The specific identification method of the operation risk direction of the risk power supply network at the risk moment is to import the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment into the identification model. , obtain the operation risk direction of the risk power supply network at the risk moment ,/> ,/> ,/> They are respectively expressed as the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment,/> ,/> ,/> They are respectively expressed as the allowable line operation limit index, the allowable equipment operation abnormality index, and the allowable severe weather index corresponding to the risk power supply network; Based on the operation risk pointer of the risk power supply network at the risk moment, predict the power outage demand index of the risk power supply network at the risk moment; The predicted power outage demand index of the risk power supply network at the risk moment refers to the following process: Match the operation risk direction of the risk power supply network at the risk moment with the power outage impact proportion factors corresponding to various operation risk directions stored in the power supply management library, and match the power outage impact proportion factor of the risk power supply network at the risk moment. ; Based on the operation risk direction of the risk power supply network at the risk moment, the operation risk index corresponding to the risk power supply network is screened out from the line operation limit index, equipment operation abnormality index and severe weather index of the risk power supply network at the risk moment. ; Leverage predictive expressions Obtain the power outage demand index of the risk power supply network at the risk moment/> , Represented as the allowable operation risk index corresponding to the operation risk point of the risk power supply network; Evaluate the allowable outage index of the risk power supply network at the risk moment based on the risk moment and power consumption information corresponding to the risk power supply network; To assess the allowable power outage index of the risk power supply network at risk moments, please refer to the following steps: Extract the area of the electricity consumption area from the electricity consumption information corresponding to the risk power supply network, and combine it with the area of the target area to calculate the proportion of the electricity consumption area corresponding to the risk power supply network. ; Extract the power consumption period of the power consumption area from the electricity consumption information corresponding to the risk power supply network, and then match the risk time corresponding to the risk power supply network with the power consumption period corresponding to the risk power supply network, and match the corresponding risks of the risk power supply network. The power consumption period of the power consumption area to which the time belongs is recorded as a specific power consumption period; Match the specific power consumption period corresponding to the risk power supply network with the power supply interruption hazard factors corresponding to various power consumption periods stored in the power supply management library, and match the power supply interruption hazard factors of the risk power supply network at the risk moment. ; Based on the specific power consumption period corresponding to the risk power supply network, extract the main load level of the risk power supply network during the specific power consumption period from the power consumption information; Match the main load level of the risk power supply network during a specific power consumption period with the demand power supply reliability corresponding to various load levels in the power supply management database, and then match the demand power supply reliability of the risk power supply network at the risk moment. ; Will ,/> and/> Import expression/> , calculate the demand power supply continuity index of the risk power supply network at the risk moment/> ,U represents a preset constant, and U>1; Compare the demand power supply continuity index of the risk power supply network at the risk moment with the allowable power outage index corresponding to the various demand power supply continuity indexes stored in the power supply management library, and obtain the allowable power outage index of the risk power supply network at the risk moment; E. Based on the power outage demand index and allowed outage index of the risk power supply network at the risk moment, judge whether the risk power supply network needs power outage processing, and analyze the non-power outage corresponding to the risk power supply network when judging that the risk power supply network does not need power outage processing. processing method; F. Upload the power outage processing evaluation results and non-power outage treatment methods corresponding to the risk power supply network to the corresponding power supply control terminal. 2. A method for intelligent identification and control of power grid risks based on big data as claimed in claim 1, characterized in that: the power consumption period of the power consumption area includes the peak power consumption period of the power consumption area and the normal power consumption period of the power consumption area. and the low power consumption period in the power consumption area. The acquisition process of the main load level corresponding to each power consumption period is as follows: Count the number of electrical equipment existing in the power consumption area and identify the load level of each electrical equipment; Set the monitoring time period, and then count the power equipment in operation in each power consumption period on each monitoring day corresponding to the monitoring time period, as the operating power equipment corresponding to each power consumption period; Compare the operating power equipment on each monitoring day within the same power consumption period with each other, and then deduplicate the same operating power equipment to obtain a number of operating power equipment corresponding to each power consumption period; Based on the load level of each power equipment, the load level of each operating power equipment corresponding to each power consumption period is obtained, and the load levels of each power consumption period corresponding to each operating power equipment are arranged in order of importance, and then the ranking is obtained The load level in the first place is used as the main load level corresponding to each power consumption period. 3. A method for intelligent identification and control of power grid risks based on big data as claimed in claim 1, characterized in that: the line operation information includes line load rate, line overvoltage rate and line power supply fluctuation, and the equipment operation information includes The operating indicators of each equipment on the line, and the weather information includes weather type and weather performance degree values. 4. A method for intelligent identification and control of power grid risks based on big data as claimed in claim 1, characterized in that: the specific division method of dividing the monitoring time is: setting the starting monitoring time, and then starting from within one day. Divide according to the set time interval from the start of monitoring time to obtain several monitoring time. 5. A method for intelligent identification and control of power grid risks based on big data as claimed in claim 1, characterized in that: the method for judging whether a risk power supply network needs power outage processing is to cut off the risk power supply network at the risk moment. The electricity demand index is compared with the allowable power outage index. If the power outage demand index is greater than the allowable power outage index, it is judged that the risk power supply network needs to be cut off, otherwise it is judged that the risk power supply network does not need to be cut off. 6. A method for intelligent identification and control of power grid risks based on big data as claimed in claim 1, characterized in that: the non-power outage processing method corresponding to the analytical risk power supply network refers to the following process: Introduce the operational risk points of the risky power supply network at risk moments into the analytical algorithm , obtain the non-power outage processing method R corresponding to the risk power supply network.