CN105134386A - On-line monitoring method for gas turbine combustion system based on measuring-point weighted value - Google Patents

Abstract

The invention relates to an online monitoring method of a gas turbine combustion system based on weighted values of measuring points, belonging to the field of gas turbine combustion system monitoring. It is difficult for the existing combustion monitoring system to judge the changing trend of the combustion state. An on-line monitoring method of gas turbine combustion system based on the weighted value of the measurement points, n temperature measurement points are evenly arranged in the circumferential direction of the turbine outlet of the gas turbine, and the exhaust temperature data T _i of normal operation in the period t _m is obtained; increase T _i and The correlation factor α _i,1 of T ₁ , according to the relationship function between T _i and T ₁ , respectively get the predicted value of T _i ; calculate the theoretical value T ₁ ˊ of exhaust temperature of temperature measuring point 1 without failure; let the theoretical value of temperature measuring point 1 The difference between the value T ₁ ˊ and the measured value T _{1 of} temperature measuring point ₁ is △T ₁ which satisfies the normal distribution with mean value 0 and standard deviation σ _{1 ;} The relationship between the monitoring of the temperature measuring point. The invention realizes the on-line monitoring of the exhaust temperature of the gas turbine, makes full use of the correlation between each measuring point of the exhaust temperature, and accurately detects the abnormal evolution process.

Description

On-line Monitoring Method of Gas Turbine Combustion System Based on Measurement Point Weighted Value

technical field

The invention relates to an online monitoring method of a combustion system of a gas turbine based on weighted values of measuring points.

Background technique

As a new type of power equipment, gas turbine has the advantages of compact structure, stable operation, safety and reliability, quick start and load driving, and high thermal efficiency. It has been widely used in aviation, ground and ships. Anomaly detection is of great significance to production practice. During the operation of gas turbine units, more than 50% of the faults in the abnormal detection of gas turbine combustion system are related to the combustion chamber. Since the combustor, combustor and other components work in the high temperature area of 1600 °C for a long time, the working environment is harsh, and once the equipment is defective, it may pose a threat to the safety of the downstream nozzle and moving blade components. Therefore, it is necessary to monitor the working condition of the combustion chamber.

Once the combustion system fails, the outlet temperature of the combustion chamber will be abnormal. Therefore, we can monitor the operation status of the combustion system by detecting the outlet temperature of the combustion chamber. However, conventional temperature measuring elements cannot work in such a high temperature area for a long time. Therefore, several exhaust temperature measuring thermocouples are evenly arranged circumferentially in the turbine exhaust passage of the unit. The temperature measured by the thermocouples is the exhaust temperature of the gas turbine. temperature. The schematic diagram of the combustion chamber and thermocouple layout is shown in Fig. 1. Judging whether the working condition of the combustion cylinder is abnormal through the exhaust temperature.

The MARKVI combustion monitoring system developed by GE defines S as the allowable dispersion of exhaust temperature, and considers that S is a function of the average exhaust temperature T ₄ ^* at the outlet of the gas turbine and the temperature T ₄ ^* at the outlet of the compressor. The specific function is an empirical formula :

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 60</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.145</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.08</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; |</span>\begin{array}{c}\mathrm{<span\; class="notranslate">\; 750</span>}\\ \mathrm{<span\; class="notranslate">\; 50</span>}\end{array}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\begin{array}{c}\mathrm{<span\; class="notranslate">\; 150</span>}\\ \mathrm{<span\; class="notranslate">\; 50</span>}\end{array}<span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; )</span>$

In this formula, temperature is measured in °F. The 100 at the right end of the formula has brackets, which means that this item is only added under variable working conditions.

In addition, the MARKVI combustion monitoring system also defines: S1 is the difference between the highest reading and the lowest reading of the exhaust temperature thermocouple; S2 is the difference between the highest reading and the second lowest reading of the exhaust temperature thermocouple; S3 is The difference between the highest reading and the 3rd lowest reading of the discharge temperature thermocouple.

Based on the above formulas and definitions, the discrimination principle of MARKⅥ combustion monitoring and protection system is shown in Figure 2. In Fig. 2, K ₁ , K ₂ , and K ₃ are three parameters defined based on experience. Typically:

K ₁ =1.0; K ₂ =5.0; K ₃ =0.8

The discriminant principle of combustion monitoring is shown in Figure 2; in practical applications, it is found that this method has a serious "post-event" diagnosis phenomenon, that is, the combustion system has been seriously damaged when the detection system sends out an alarm.

Contents of the invention

The purpose of the present invention is to solve the problem that the existing combustion monitoring system cannot pass the detection of the abnormal evolution process and make judgments on the combustion state change trend, and it can be detected in the early stage of the fault, and a method based on the weighted value of the measuring point is proposed On-line monitoring method of gas turbine combustion system.

A gas turbine combustion system online monitoring method based on the weighted value of the measuring point, characterized in that: the online monitoring method of the gas turbine combustion system based on the weighted value of the measuring point is realized by the following steps:

Step 1. Evenly arrange n temperature measuring points in the circumferential direction of the turbine outlet of the gas turbine. The gas turbine operates normally within the t _m period, and the exhaust temperature data T=[T ₁ ,T ₂ ,…,T during the t _m period is obtained _i ,...T _n ]; where T _i represents the exhaust temperature data measured at each time point of the i-th temperature measuring point in the t _m period, $T_1=\&lsqb;T_{1t_1},T_{1t_2},...,T_{1t_m}\&rsqb;,T_2=\&lsqb;T_{2t_1},T_{2t_2},...,T_{2t_m}\&rsqb;,$ $T_i=\&lsqb;T_{{\mathrm{it}}_1},T_{{\mathrm{it}}_2},...,T_{{\mathrm{it}}_m}\&rsqb;,T_{\mathrm{no}}=\&lsqb;T_{{\mathrm{nt}}_1},T_{{\mathrm{nt}}_2},...,T_{{\mathrm{nt}}_m}\&rsqb;;$

Step 2, using the least squares method to obtain the relationship function between the exhaust temperature data T _i and the exhaust temperature data T ₁ , namely:

T ₁ =f _i,1 (T _i ), i=2,3,4,...,n

Among them, f _i,1 represents the relationship function between exhaust temperature data T _i and exhaust temperature data T ₁ ; satisfying: T ₁ =k _i,1 T _i +b _i,1 ,i=2,3,4,... ,n, where k _i,1 and b _i,1 can be obtained by the least square method, namely:

${\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

${\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; ;</span>$

Step 3: Use the Pearson product moment coefficient to express the correlation between the exhaust temperature data T _i and the exhaust temperature data T ₁ , obtain the correlation factor α _i,1 between the exhaust temperature data T _i and the exhaust temperature data T ₁ , and obtain :

The correlation factor between T ₁ and T ₂ is α _2,1 ,

The correlation factor between T ₁ and T ₃ is α _3,1 ,

The correlation factor between T ₁ and T _n is α _n,1 ;

Step 4: According to the relationship function between exhaust temperature data T _i and exhaust temperature data T ₁ obtained in step 2, respectively bring exhaust temperature data T ₂ to T _n measured at time t into the relationship function: T ₁ =f _{i ,1} (T _i ), the predicted values T _2,1 ,T _3,1 ,…T _i,1 …,T _n,1 of the exhaust temperature data T ₁ are respectively obtained:

T _i,1 =f _i,1 (T _i ), i=2,3,4,...,n

Among them, f _i,1 represents the relationship function between exhaust temperature data T _i obtained in step 2 and exhaust temperature data T ₁ ; time t refers to a time from t ₁ to t _m ;

Step 5: Calculate the theoretical exhaust temperature T ₁ ' of temperature measuring point 1 when the gas turbine has no fault at time t;

Step 6: Define the difference between the theoretical value T ₁ ' of temperature measuring point 1 and the measured value T ₁ of temperature measuring point 1 at each moment when the unit has no faults as △T ₁ : △T ₁ =T ₁ '-T ₁ ; the gas turbine The exhaust temperature data T=[T ₁ ,T ₂ ,…,T _i ,…T _n ] in the time period t _m is substituted into step 4 and step 5, and △T ₁ satisfies the normal state with the mean value of 0 and standard deviation of σ ₁ Distribution, that is, △T ₁ ～N(0,σ ₁ );

Step 7: Monitoring of the temperature measuring point 1 to be measured when the gas turbine is in operation: Substituting the exhaust temperature data measured at each moment into steps 4 and 5 to obtain the theoretical value T ₁ of the temperature measuring point 1 when the unit is running without failure '; The difference between the theoretical value T ₁ ' of temperature measuring point 1 and the measured value T ₁ of temperature measuring point 1 is △T ₁ , if △T ₁ is in [-3σ ₁ ,3σ ₁ ] If it is within the range of [-3σ ₁ ,3σ ₁ ], it means that the unit has no fault;

Step 8: Monitor the temperature measuring points 2 to n to be measured when the gas turbine enters the operation stage: Repeat steps 2 to 6 to obtain the theoretical values T ₂ ', T ₃ ', respectively, of the temperature measuring points 2 to n when the unit is not faulty. …,T _n ', and the standard deviation of the difference between the theoretical value and the measured value △T ₂ ,△T ₃ ,…,△T _n △σ ₂ ,△σ ₃ ,…,△σ _n ; correspondingly, if △T ₂ ,△T ₃ ,…,△T _n are all within the range of [-3σ _n ,3σ _n ], indicating that the unit has no faults, and if it exceeds the range of [-3σ ₁ ,3σ ₁ ], it indicates that the unit is malfunctioning.

The beneficial effects of the present invention are:

When there is an abnormality in the combustion tube, the result of the exhaust temperature will also be abnormal. The temperature of the exhaust temperature can be used to judge whether the working condition of the combustion tube is abnormal, so as to monitor the exhaust temperature of the gas turbine to indirectly monitor the working conditions in the combustion chamber, and obtain Monitor the health of the combustion system.

The present invention considers the correlation between different temperature measuring points, and through the addition of correlation factors when calculating the correlation between different temperature measuring points, the weight of those measuring points with strong correlation with a certain measuring point is enhanced, and the weight of those measuring points with strong correlation is weakened. The weights of those measuring points that are weakly correlated with the measuring point are given. Compared with the prior art, the method of the invention realizes the on-line monitoring of the exhaust temperature of the gas turbine, makes full use of the correlation between each measuring point of the exhaust temperature, and accurately detects the abnormal evolution process. The present invention can better realize the abnormal monitoring of the gas turbine exhaust temperature, find the fault in time or even earlier, thereby reducing the possibility that the gas turbine cannot be found in time due to the fault.

Description of drawings

Fig. 1 is the combustion chamber and the thermocouple arrangement situation schematic diagram that background technology of the present invention relates to;

Fig. 2 is the discriminative schematic diagram of the combustion monitoring involved in the background technology of the present invention;

Fig. 3 is a flowchart of the present invention;

Detailed ways

Specific implementation mode one:

The gas turbine combustion system online monitoring method based on the weighted value of the measuring point in this embodiment is realized by the following steps in combination with the online monitoring method of the gas turbine combustion system based on the weighted value of the measuring point described in FIG. 3 :

Step 1. Evenly arrange n temperature measuring points in the circumferential direction of the turbine outlet of the gas turbine. The gas turbine operates normally within the t _m period, and the exhaust temperature data T=[T ₁ ,T ₂ ,…,T during the t _m period is obtained _i ,...T _n ]; where T _i represents the exhaust temperature data measured at each time point of the i-th temperature measuring point in the t _m period, $T_1=\&lsqb;T_{1t_1},T_{1t_2},...,T_{1t_m}\&rsqb;,T_2=\&lsqb;T_{2t_1},T_{2t_2},...,T_{2t_m}\&rsqb;,$ $T_i=\&lsqb;T_{{\mathrm{it}}_1},T_{{\mathrm{it}}_2},...,T_{{\mathrm{it}}_m}\&rsqb;,T_{\mathrm{no}}=\&lsqb;T_{{\mathrm{nt}}_1},T_{{\mathrm{nt}}_2},...,T_{{\mathrm{nt}}_m}\&rsqb;;$

Step 2: Use the least squares method to obtain the relationship functions between exhaust temperature data T ₂ , T ₃ ...T _i ..., T _n and exhaust temperature data T ₁ , namely:

T ₁ =f _i,1 (T _i ), i=2,3,4,...,n

Among them, f _i,1 represents the relationship function between exhaust temperature data T _i and exhaust temperature data T ₁ ; satisfying, T ₁ =k _i,1 T _i +bi _,1 ,i=2,3,4,... ,n, where k _i,1 and b _i,1 can be obtained by the least square method, namely:

${\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

${\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; ;</span>$

Step 3: Use the Pearson product moment coefficient to express the correlation between the exhaust temperature data T ₂ , T ₃ ... T _i ... T _n and the exhaust temperature data T ₁ to obtain the exhaust temperature data T ₂ , T ₃ , The correlation factor α _i,1 of ...T _i ...T _n and exhaust temperature data T ₁ , and obtained:

The correlation factor between T ₁ and T ₂ is α _2,1 ,

The correlation factor between T ₁ and T ₃ is α _3,1 ,

The correlation factor between T ₁ and T _n is α _n,1 ;

Step 4: According to the relationship function between exhaust temperature data T ₂ , T ₃ , ... T _i ... T _n and exhaust temperature data T ₁ obtained in step 2, the exhaust temperature data T ₂ measured at time t to T _n into the relationship function: T _i,1 = f _i,1 (T _i ), i=2,3,4,...,n, to obtain exhaust temperature data T ₂ , T ₃ , .. .T _i ...T _n predictive value T _2,1 ,T _3,1 ,...T _i,1 ...,T _{n,1 for T 1 :}

T _2,1 = f _2,1 (T ₁ ),

T _3,1 = f _3,1 (T ₁ ),

…

T _i,1 = f _i,1 (T ₁ ),

T _n,1 = f _n,1 (T ₁ );

Among them, f _i,1 represents the relationship function between exhaust temperature data T _i obtained in step 2 and exhaust temperature data T ₁ ; time t refers to a time from t ₁ to t _m ;

Step 5: Calculate the theoretical exhaust temperature T ₁ ' of temperature measuring point 1 when the gas turbine has no fault at time t;

Step 6: Define the difference between the theoretical value T ₁ ' of temperature measuring point 1 and the measured value T ₁ of temperature measuring point 1 at each moment when the unit has no faults as △T ₁ : △T ₁ =T ₁ '-T ₁ ; the gas turbine The exhaust temperature data T=[T ₁ ,T ₂ ,…,T _i ,…T _n ] in the time period t _m is substituted into step 4 and step 5, and △T ₁ satisfies the normal state with the mean value of 0 and standard deviation of σ ₁ Distribution, that is, △T ₁ ～N(0,σ ₁ );

Step 7: Monitoring of the temperature measuring point 1 to be measured when the gas turbine is in operation: Substituting the exhaust temperature data measured at each moment into steps 4 and 5 to obtain the theoretical value T ₁ of the temperature measuring point 1 when the unit is running without failure '; The difference between the theoretical value T ₁ ' of temperature measuring point 1 and the measured value T ₁ of temperature measuring point 1 is △T ₁ , if △T ₁ is in [-3σ ₁ ,3σ ₁ ] If it is within the range of [-3σ ₁ ,3σ ₁ ], it means that the unit has no fault;

Step 8: Monitor the temperature measuring points 2 to n to be measured when the gas turbine enters the operation stage: Repeat steps 2 to 6 to obtain the theoretical values T ₂ ', T ₃ ', respectively, of the temperature measuring points 2 to n when the unit is not faulty. …,T _n ', and the standard deviation of the difference between the theoretical value and the measured value △T ₂ ,△T ₃ ,…,△T _n △σ ₂ ,△σ ₃ ,…,△σ _n ; correspondingly, if △T ₂ ,△T ₃ ,…,△T _n are all within the range of [-3σ _n ,3σ _n ], indicating that the unit has no faults, and if it exceeds the range of [-3σ ₁ ,3σ ₁ ], it indicates that the unit is malfunctioning.

Specific implementation mode two:

The difference from the first specific embodiment is that in the online monitoring method of the gas turbine combustion system based on the weighted value of the measuring points in this embodiment, the exhaust temperature data (T ₂ , T ₃ , ...)T _i (... , T _n ) and the correlation factor α _i,1 of exhaust temperature data T ₁ are calculated as follows:

${\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}{\sqrt{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\sqrt{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$

Wherein, j represents the first time point to the m-th time point within the time period t _m , j=t ₁ ...t _m .

Specific implementation mode three:

The difference from the specific embodiment 1 or 2 is that the online monitoring method of the combustion system of the gas turbine based on the weighted value of the measuring points in this embodiment,

The process of calculating the theoretical exhaust temperature T ₁ ' of the temperature measuring point 1 at time t when the gas turbine has no faults as described in step five is, according to the calculation formula of the theoretical exhaust temperature:

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}$

Calculate the exhaust temperature theoretical value T ₁ ' of temperature measuring point 1 at time t when the gas turbine has no faults.

Claims (3)

1. a gas turbine combustion system on-line monitoring method based on measuring point weighted value, it is characterized in that: the gas turbine combustion system on-line monitoring method based on measuring point weighted value is realized by the following steps: Step 1. Evenly arrange n temperature measuring points in the circumferential direction of the turbine outlet of the gas turbine. The gas turbine operates normally during the t _m period, and the exhaust temperature data T=[T ₁ ,T ₂ ,…,T during the t _m period is obtained _i ,...T _n ]; where T _i represents the exhaust temperature data measured at each time point of the i-th temperature measuring point in the t _m period, $T_1=\&lsqb;T_{1t_1},T_{1t_2},...,T_{1t_m}\&rsqb;,T_2=\&lsqb;T_{2t_1},T_{2t_2},...,T_{2t_m}\&rsqb;,$ $T_i=\&lsqb;T_{{\mathrm{it}}_1},T_{{\mathrm{it}}_2},...,T_{{\mathrm{it}}_m}\&rsqb;,T_{\mathrm{no}}=\&lsqb;T_{{\mathrm{nt}}_1},T_{{\mathrm{nt}}_2},...,T_{{\mathrm{nt}}_m}\&rsqb;;$ Step 2, using the least squares method to obtain the relationship function between the exhaust temperature data T _i and the exhaust temperature data T ₁ , namely: T ₁ =f _i,1 (T _i ), i=2,3,4,...,n Among them, f _i,1 represents the relationship function between exhaust temperature data T _i and exhaust temperature data T ₁ ; satisfying: T ₁ =k _i,1 T _i +b _i,1 ,i=2,3,4,... ,n, where k _i,1 and b _i,1 can be obtained by the least square method, namely: ${\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span>}{\underset{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$ ${\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; ;</span>$ Step 3: Use the Pearson product moment coefficient to express the correlation between the exhaust temperature data T _i and the exhaust temperature data T ₁ , obtain the correlation factor α _i,1 between the exhaust temperature data T _i and the exhaust temperature data T ₁ , and obtain : The correlation factor between T ₁ and T ₂ is α _2,1 , The correlation factor between T ₁ and T ₃ is α _3,1 , The correlation factor between T ₁ and T _n is α _n,1 ; Step 4: According to the relationship function between exhaust temperature data T _i and exhaust temperature data T ₁ obtained in step 2, respectively bring exhaust temperature data T ₂ to T _n measured at time t into the relationship function: T ₁ =f _{i ,1} (T _i ), the predicted values T _2,1 ,T _3,1 ,…T _i,1 …,T _n,1 of the exhaust temperature data T ₁ are respectively obtained: T _i,1 =f _i,1 (T _i ), i=2,3,4,...,n Among them, f _i,1 represents the relationship function between exhaust temperature data T _i obtained in step 2 and exhaust temperature data T ₁ ; time t refers to a time from t ₁ to t _m ; Step 5: Calculate the theoretical exhaust temperature T ₁ ' of temperature measuring point 1 when the gas turbine has no fault at time t; Step 6: Define the difference between the theoretical value T ₁ ' of the temperature measuring point 1 and the measured value T ₁ of the temperature measuring point 1 at each moment when the unit has no faults as ΔT ₁ : ΔT ₁ =T ₁ '-T ₁ ; the gas turbine t _m The exhaust temperature data T=[T ₁ , T ₂ ,...,T _i ,...T _n ] in the time period is substituted into step 4 and step 5, and ΔT ₁ satisfies the normal distribution with mean value 0 and standard deviation σ ₁ , namely ΔT ₁ ～N(0,σ ₁ ); Step 7: Monitoring of the temperature measuring point 1 to be measured when the gas turbine is in operation: Substituting the exhaust temperature data measured at each moment into steps 4 and 5 to obtain the theoretical value T ₁ of the temperature measuring point 1 when the unit is running without failure '; The difference between the theoretical value T ₁ ' of temperature measuring point 1 and the measured value T ₁ of temperature measuring point 1 is ΔT ₁ , if ΔT ₁ is in the range of [-3σ ₁ ,3σ ₁ ] If it is within the range of [-3σ ₁ ,3σ ₁ ], it means that the unit has no fault; Step 8: Monitor the temperature measuring points 2 to n to be measured when the gas turbine enters the operation stage: Repeat steps 2 to 6 to obtain the theoretical values T ₂ ', T ₃ ', respectively, of the temperature measuring points 2 to n when the unit is not faulty. …,T _n ', and the standard deviation of the difference between the theoretical value and the measured value ΔT ₂ ,ΔT ₃ ,…,ΔT _n Δσ ₂ ,Δσ ₃ ,…,Δσ _n ; correspondingly, if ΔT ₂ ,ΔT ₃ ,…, If ΔT _n is in the range of [-3σ _n , 3σ _n ], it means that the unit has no faults. If it exceeds the range of [-3σ ₁ , 3σ ₁ ], it means that the unit has a fault. 2. The gas turbine combustion system online monitoring method based on the weighted value of measuring points according to claim 1, characterized in that: the calculation method of the correlation factor α _i,1 of the exhaust temperature data T _i and exhaust temperature data T ₁ described in step 3 as follows: ${\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}{\sqrt{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\sqrt{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$ Wherein, j represents the first time point to the m-th time point within the time period t _m , j=t ₁ ...t _m . 3. The on-line monitoring method of gas turbine combustion system based on the weighted value of measuring points according to claim 1 or 2, characterized in that: in step 5, the theoretical exhaust temperature value T ₁ ' of temperature measuring point 1 when the gas turbine has no failure at time t The process is, according to the calculation formula of the theoretical value of exhaust temperature: ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}}$ Calculate the exhaust temperature theoretical value T ₁ ' of temperature measuring point 1 at time t when the gas turbine has no faults.