On-line evaluation method and system for AGC performance of thermal power plant units

Abstract

The present invention relates to the technical field of performance evaluation, and in particular to a method and system for online evaluation of the performance of an AGC of a thermal power plant unit. The method comprises the following steps: real-time operation parameter collection of the AGC of the thermal power plant unit is performed to obtain the AGC operation load change signal of the thermal power plant unit, the AGC operation adjustment signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit, and the actual power generation power of the AGC operation of the thermal power plant unit; the operation time period affiliation analysis and the operation time sequence range determination of the AGC operation load change signal of the thermal power plant unit are performed, and the operation time sequence synchronization division and operation performance index calculation are performed to obtain the AGC operation performance index under different operation conditions; based on the AGC operation performance index under different operation conditions, the performance of the AGC of the thermal power plant unit is evaluated online to obtain the online evaluation result of the state performance of the AGC system of the thermal power plant unit. The present invention can realize dynamic optimization and adjustment of the AGC.

Description

Technical Field

[0001] The present invention relates to the technical field of performance evaluation, and in particular to an online evaluation method and system for AGC performance of a thermal power plant unit. Background Art

[0002] In the operation of thermal power plants, the automatic generation control (AGC) system is crucial to maintain the stability of power output and respond to changes in grid load. By deploying advanced sensors and data acquisition systems, the AGC system of thermal power plant units is monitored in real time. These data include key indicators such as generator output power, load changes, and control signals. By applying dynamic data analysis technology, the real-time collected data is processed to analyze the response characteristics of the AGC system under different loads and operating conditions, and evaluate its performance stability and response speed. By building an intelligent performance evaluation model based on machine learning and data mining, the model can automatically identify abnormal patterns in the performance of the AGC system and generate corresponding performance evaluation indicators. At the same time, by introducing a real-time early warning mechanism to immediately alert the abnormal or declining performance of the AGC system, it can automatically generate reports and provide improvement suggestions to help operation and maintenance personnel adjust and optimize the AGC settings in a timely manner. However, existing AGC performance evaluation methods usually rely on offline testing and regular inspections. These methods often lack a comprehensive and dynamic evaluation of the performance of the AGC system, which makes it difficult to quickly locate and solve problems when performance deteriorates or fails. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for online evaluation of AGC performance of thermal power plant units to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for online evaluation of AGC performance of thermal power plant units includes the following steps:

[0005] Step S1: collect real-time operating parameters of the AGC of the thermal power plant unit to obtain the AGC operating load change signal of the thermal power plant unit, the AGC operating adjustment signal of the thermal power plant unit, the AGC operating target power of the thermal power plant unit and the AGC operating actual power generation power of the thermal power plant unit;

[0006] Step S2: Based on the AGC of the thermal power plant unit, the AGC operation load change signal of the thermal power plant unit is subjected to an operation condition period membership analysis to obtain the AGC operation load change signal under different operation conditions; the operation condition time sequence range is determined for the AGC operation load change signal under different operation conditions to obtain the operation condition time sequence range intervals corresponding to the different operation conditions; based on the operation condition time sequence range intervals corresponding to the different operation conditions, the AGC operation regulation signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the actual power generation power of the AGC operation of the thermal power plant unit are subjected to the operation condition time sequence synchronization division to obtain the AGC operation regulation signal, the AGC operation target power and the actual power generation power of the AGC operation under different operation conditions;

[0007] Step S3: quantify the load response time and output power of the AGC operation load change signal under different operating conditions to obtain the operation load response time and the operation load output power under different operating conditions; calculate the operation overshoot performance index of the operation load output power and the AGC operation target power under different operating conditions based on the operation load response time under different operating conditions to obtain the AGC operation overshoot performance index under different operating conditions; calculate the operation regulation stability and steady-state error performance index of the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation power under different operating conditions based on the AGC operation load change signal under different operating conditions to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions;

[0008] Step S4: Based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, the performance of the thermal power plant unit AGC is evaluated online to obtain the online evaluation result of the thermal power plant unit AGC system status performance.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: collecting the operating load change signal of the motor operating load process of the AGC of the thermal power plant unit in real time to obtain the operating load change signal of the AGC of the thermal power plant unit;

[0011] Step S12: collecting operation regulation signals of the motor operation regulation process of the AGC of the thermal power plant unit in real time to obtain the AGC operation regulation signals of the thermal power plant unit;

[0012] Step S13: analyzing the operating state change trend of the power generation operation process of the AGC of the thermal power plant unit to obtain the power generation operation state change trend of the AGC of the thermal power plant unit; performing an operating power demand and constraint statistical analysis on the power generation operation process of the AGC of the thermal power plant unit based on the power generation operation state change trend of the AGC of the thermal power plant unit to obtain the power generation operation power demand of the AGC of the thermal power plant unit and the power generation operation constraint conditions of the AGC of the thermal power plant unit;

[0013] Step S14: performing target power forecasting calculation on the AGC power generation operation power demand of the thermal power plant units based on the AGC power generation operation constraint conditions of the thermal power plant units to obtain the AGC operation target power of the thermal power plant units;

[0014] Step S15: monitor and calculate the operating power generation power of the AGC power generation operation process of the thermal power plant unit to obtain the actual power generation power of the AGC operation of the thermal power plant unit.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: Analyze the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units to obtain the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units;

[0017] Step S22: performing a signal time sequence change mode analysis on the AGC operation load change signal of the thermal power plant unit to obtain the time sequence change mode of the AGC operation load signal of the thermal power plant unit; dividing the AGC operation load change signal of the thermal power plant unit into time segments based on the time sequence change mode of the AGC operation load signal of the thermal power plant unit to obtain the AGC operation load change signal under different time segments of the change mode;

[0018] Step S23: Based on the load fluctuation characteristics of the AGC operating condition of the thermal power plant unit, the AGC operating load change signal under different change mode time segments is subjected to operating condition period membership analysis to obtain the AGC operating load change signal under different operating conditions;

[0019] Step S24: determining the starting point and the end point of the operating condition timing of the AGC operating load change signal under different operating conditions, and obtaining the corresponding starting point and end point of the changing signal timing under different operating conditions; determining the operating condition timing range according to the corresponding starting point and end point of the changing signal timing under different operating conditions, and obtaining the corresponding operating condition timing range interval under different operating conditions;

[0020] Step S25: Based on the corresponding operating condition timing range intervals under different operating conditions, the AGC operation adjustment signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the AGC operation actual power generation power of the thermal power plant unit are synchronously divided into operating condition timings to obtain the AGC operation adjustment signal, AGC operation target power and AGC operation actual power generation power under different operating conditions.

[0021] Further, the step S22 of dividing the change mode of the AGC operation load change signal of the thermal power plant unit into time segments based on the time sequence change mode of the AGC operation load signal of the thermal power plant unit includes the following steps:

[0022] The signal change pattern characteristics of the AGC operation load signal time series change pattern of the thermal power plant unit are analyzed to obtain the AGC operation load signal change pattern characteristic set of the thermal power plant unit;

[0023] Draw a time series variation spectrum of the AGC operation load variation signal of the thermal power plant unit to generate a time series variation spectrum of the AGC operation load variation signal of the thermal power plant unit;

[0024] Based on the corresponding time series change pattern characteristics in the change pattern feature set of the AGC operation load signal of the thermal power plant unit, the change pattern turning point of the time series change spectrum of the AGC operation load signal of the thermal power plant unit is determined to obtain the change pattern turning point set of the AGC operation load signal of the thermal power plant unit;

[0025] According to the interval between two adjacent change mode turning points in the change mode turning point set of the AGC operation load signal of the thermal power plant unit, the corresponding time series in the time series change spectrum of the AGC operation load signal of the thermal power plant unit is divided into time series ranges to obtain different AGC operation load signal change mode time series range segments;

[0026] Based on different AGC operation load signal change mode time series range segmentation, the AGC operation load change signal of the thermal power plant unit is divided into change mode time segments to obtain the AGC operation load change signal under different change mode time segments.

[0027] Further, step S23 includes the following steps:

[0028] Step S231: dividing the load fluctuation characteristics of the AGC operating condition of the thermal power plant unit into operating condition periods to obtain a subset of load fluctuation characteristics of the AGC operating condition of the thermal power plant unit in different operating condition periods;

[0029] Step S232: performing feature vector mapping conversion on the load fluctuation feature subsets of the AGC of the thermal power plant unit under different operating conditions, so as to obtain the load fluctuation feature vectors under different operating conditions;

[0030] Step S233: Based on the load fluctuation characteristic vectors under different operating conditions, the AGC operating load change signal under different change mode time segments is subjected to load signal fluctuation matching membership mining analysis to obtain the matching membership relationship between the operating load change signal under different change mode time segments and the load fluctuation of each operating condition; based on the matching membership relationship between the operating load change signal under different change mode time segments and the load fluctuation of each operating condition, the AGC operating load change signal under different change mode time segments is subjected to time period membership calculation to obtain the membership between the operating load change signal under different change mode time segments and each operating condition time period;

[0031] Step S234: constructing a membership matrix according to the membership between the operating load change signal and each operating condition period under different change mode time segments to generate an AGC operating condition period membership matrix;

[0032] Step S235: Based on the AGC operating condition period membership matrix, the AGC operating load change signals under different change mode time segments are subjected to operating condition period optimal membership screening processing to obtain the AGC operating load change signals under different operating conditions.

[0033] Further, step S3 includes the following steps:

[0034] Step S31: performing load response time statistical analysis on the AGC operating load change signal under different operating conditions to obtain the operating load response time under different operating conditions;

[0035] Step S32: performing output power quantization calculation on the AGC operating load change signal under different operating conditions based on the operating load response time under different operating conditions, so as to obtain the operating load output power under different operating conditions;

[0036] Step S33: based on the operating load response time under different operating conditions, the operating power hyperbola is plotted for the operating load output power under different operating conditions and the AGC operating target power, so as to generate a hyperbola of changes between the operating load output power and the target power within the corresponding response time range under different operating conditions;

[0037] Step S34: determining the maximum spacing point between the operating power curves for the changing hyperbola between the operating load output power and the target power within the corresponding response time range under different operating conditions, and obtaining the maximum spacing point between the changing hyperbola within the corresponding response time range under different operating conditions; calculating the operating overshoot performance index of the operating load output power and the AGC operating target power at the corresponding point time under different operating conditions based on the maximum spacing point between the changing hyperbola within the corresponding response time range under different operating conditions, and obtaining the AGC operating overshoot performance index under different operating conditions;

[0038] Step S35: Based on the AGC operation load change signal under different operating conditions, the operation regulation stability and steady-state error performance index of the AGC operation adjustment signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions are calculated to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions.

[0039] Further, step S32 includes the following steps:

[0040] Step S321: performing load signal response timing fluctuation amplitude and frequency statistical analysis on the AGC load change signal under different operating conditions based on the operating load response time under different operating conditions, and obtaining the load change signal timing fluctuation amplitude and load change signal timing fluctuation frequency within the response time range under different operating conditions;

[0041] Step S322: performing initial quantitative calculation of the response time output power of the AGC operation load change signal under different operating conditions based on the load change signal time fluctuation amplitude and the load change signal time fluctuation frequency within the response time range under different operating conditions, and obtaining the operation load fluctuation initial output power within the response time range under different operating conditions;

[0042] Step S323: performing output power fluctuation limit evaluation and analysis on the initial output power of the operating load fluctuation within the response time range under different operating conditions, and obtaining the output power fluctuation limit range of the operating load under different operating conditions;

[0043] Step S324: Based on the operating load output power fluctuation limit range under different operating conditions, a nonlinear fluctuation correction is performed on the operating load fluctuation initial output power within the response time range under different operating conditions to obtain the operating load output power under different operating conditions.

[0044] Further, step S35 includes the following steps:

[0045] Step S351: analyzing the signal change fluctuation trend of the AGC operating load change signal under different operating conditions to obtain the operating load signal change fluctuation trend under different operating conditions;

[0046] Step S352: determining the stable fluctuation trend period of the AGC operating load change signal under different operating conditions based on the fluctuation trend of the operating load signal under different operating conditions, so as to obtain the stable fluctuation trend period of the operating load under different operating conditions;

[0047] Step S353: Based on the stable fluctuation trend period of the operating load under different operating conditions, the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions are subjected to stable period timing synchronization processing to obtain the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions in the same stable operation fluctuation period;

[0048] Step S354: calculating the operation regulation stability performance index of the AGC operation regulation signal in the same stable operation fluctuation period under different operation conditions to obtain the AGC operation regulation stability performance index under different operation conditions;

[0049] Step S355: Calculate the power steady-state error performance index for the AGC operation target power and the AGC operation actual power generation power in the same stable operation fluctuation period under different operating conditions to obtain the AGC operation steady-state error performance index under different operating conditions.

[0050] Furthermore, the present invention also provides an online evaluation system for AGC performance of thermal power plant units, which is used to execute the above-mentioned online evaluation method for AGC performance of thermal power plant units. The online evaluation system for AGC performance of thermal power plant units comprises:

[0051] The AGC operation parameter acquisition module of the thermal power plant unit is used to collect the real-time operation parameters of the AGC of the thermal power plant unit, so as to obtain the AGC operation load change signal of the thermal power plant unit, the AGC operation adjustment signal of the thermal power plant unit, the actual power generation power of the AGC operation of the thermal power plant unit and the AGC operation target power of the thermal power plant unit;

[0052] The module for dividing the timing of different working conditions of the AGC operating parameters is used to perform operating condition period affiliation analysis on the AGC operating load change signal of the thermal power plant unit based on the AGC of the thermal power plant unit, so as to obtain the AGC operating load change signal under different operating conditions; determine the operating condition timing range of the AGC operating load change signal under different operating conditions, and obtain the corresponding operating condition timing range interval under different operating conditions; based on the corresponding operating condition timing range interval under different operating conditions, the AGC operating regulation signal of the thermal power plant unit, the actual power generation power of the AGC operating of the thermal power plant unit, and the AGC operating target power of the thermal power plant unit are synchronously divided into the operating condition timing, so as to obtain the AGC operating regulation signal, the actual power generation power of the AGC operating, and the AGC operating target power under different operating conditions;

[0053] The AGC operation performance index calculation module is used to perform load response time and output power quantitative calculation on the AGC operation load change signal under different operation conditions, so as to obtain the operation load response time and the operation load output power under different operation conditions; based on the operation load response time under different operation conditions, the operation overshoot performance index is calculated on the operation load output power under different operation conditions and the AGC operation target power, so as to obtain the AGC operation overshoot performance index under different operation conditions; based on the AGC operation load change signal under different operation conditions, the operation regulation stability and steady-state error performance index is calculated on the AGC operation regulation signal under different operation conditions, the AGC operation actual power generation power and the AGC operation target power, so as to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operation conditions;

[0054] The AGC performance online evaluation module is used to evaluate the performance of the AGC of the thermal power plant unit online based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, so as to obtain the online evaluation results of the AGC system status performance of the thermal power plant unit.

[0055] Beneficial effects of the present invention:

[0056] 1. The online performance evaluation method of the AGC of a thermal power plant unit proposed in the present invention, compared with the prior art, has the beneficial effect that by real-time acquisition of the operating load change signal of the motor operating load process of the AGC of the thermal power plant unit, the dynamic change of the motor load can be accurately monitored, and a key data basis can be provided for the operation of the thermal power plant. These real-time load change signals can help operators identify load fluctuations early, timely discover existing equipment failures or abnormal operation, and provide a scientific basis for the subsequent online performance evaluation process. Real-time acquisition of the operating adjustment signal of the motor operating adjustment process of the AGC of the thermal power plant unit helps to fully understand the adjustment of the motor during operation. Real-time acquisition of the adjustment signal can provide real-time data of the motor adjustment action, including the frequency, amplitude and response time of the adjustment, etc. These data are of great significance to optimizing the operation of the motor because they can reveal the real-time performance and possible deviations in the motor adjustment process. By predicting and calculating the target power of the AGC power generation process of the thermal power plant unit, the power plant can determine the optimal target power generation. By comprehensively analyzing historical data and operating constraints, the future power demand can be predicted, providing clear goals for the scheduling and operation of the generator set. The core of this step is to convert complex constraints into actual power generation targets, thereby achieving efficient power scheduling. By accurately predicting the target power generation, the power plant can adjust the power generation strategy in time when the power demand changes, avoiding the waste of resources and economic losses caused by excessive or insufficient power generation. At the same time, by monitoring and calculating the power generation power of the AGC power generation process of the thermal power plant unit, the actual power output data of the current generator set can be provided. Through real-time monitoring, the difference between the power output and the target power can be found in time, so as to take necessary adjustment measures to ensure the stability and reliability of the power generation process. By monitoring the actual power generation, the performance of the generator set under different load conditions can be analyzed, and potential problems and optimization space can be identified. This monitoring data can be used to improve the control strategy and help achieve a more accurate and efficient AGC performance evaluation process. Secondly, by analyzing the operating condition period of the load change signal of the thermal power plant unit AGC based on the thermal power plant unit AGC, the operating condition type of the load signal in different time periods can be determined according to different load fluctuation characteristics. This helps to identify the impact of different operating conditions on load changes, thereby associating the load change signal with specific operating conditions. This analysis can provide in-depth insights into load changes under different operating conditions, allowing power plants to optimize their operating strategies according to the characteristics of different operating conditions. By understanding the load fluctuation characteristics under different operating conditions, power plants can formulate more targeted operation plans, thereby improving the flexibility and adaptability of the power system of thermal power plant units. By determining the operating condition time series range of the load change signal under different operating conditions, the specific time range of the load change can be clarified, thereby providing a basis for further analysis and control.Through this determination, the start and end times of load changes can be accurately divided, which helps to formulate more accurate scheduling plans and control strategies. This method can provide timing details of load changes, helping power plants to make more precise adjustments under different operating conditions and avoid resource waste or system instability caused by inaccurate time division. By synchronously dividing the operating condition timing of the AGC operation adjustment signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the actual power generation power of the AGC operation of the thermal power plant unit based on the corresponding operating condition timing range intervals under different operating conditions, the relevant signals under different operating conditions can be systematically compared and analyzed. This synchronous division can help the power plant clarify the changing rules of each signal and their mutual relationship under specific conditions, and then optimize the control strategy and scheduling scheme. By synchronously analyzing the signals under different operating conditions, the power plant can better coordinate the operation of the AGC system, ensure the match between the target power and the actual power generation, and improve the overall efficiency and economy of the power generation system. This method can also improve the system's responsiveness to load fluctuations, reduce operating risks, and ensure the stability and reliability of power supply, thereby achieving a comprehensive and dynamic evaluation of the performance of the AGC system. Then, by performing a load response time statistical analysis on the AGC operating load change signal under different operating conditions, the time characteristics of the load response are determined. This step can reveal the response speed and stability of the AGC system under different load conditions by recording and analyzing the time characteristics of the load change under various conditions in detail. By comparing these response times, the system performance under different conditions can be evaluated, including how the system adjusts the output power to maintain stability when the load fluctuates or the demand changes, thereby improving the overall control performance and reliability. By performing a quantitative calculation of the output power of the AGC operating load change signal under different operating conditions based on the operating load response time under different operating conditions, the output power of the AGC system under each operating condition can be obtained. The main purpose of this step is to link the response time with the actual output power in order to more accurately evaluate the performance of the system. Specifically, by converting the load response time into power output data, the actual power generation capacity under each operating condition can be obtained. This quantitative calculation helps to accurately measure the response capability and power adjustment range of the system in actual operation. The overshoot performance index of the operating load output power under different operating conditions and the AGC operating target power is calculated based on the operating load response time under different operating conditions, and the overshoot of the AGC system under different operating conditions can be evaluated. The key to this step is to calculate the overshoot between the system and the target power at a specific time point, which can reflect the dynamic response capability and stability of the system. By calculating the overshoot in detail, it can be revealed whether the system has an over-response phenomenon during the adjustment process and quantify the extent of this phenomenon, which can help achieve smoother power regulation and more efficient system operation.By calculating the operation regulation stability and steady-state error performance indicators of the AGC operation regulation signal, AGC operation target power and AGC operation actual power generation under different operating conditions based on the AGC operation load change signal under different operating conditions, the regulation stability and steady-state error of the AGC system can be evaluated. The core of this step is to quantify the stability and error range of the system under various operating conditions and provide a comprehensive evaluation of the system performance. By calculating the regulation stability index, we can understand whether the system can remain stable during the adjustment process, and calculating the steady-state error index can evaluate the deviation of the system from the target power in long-term operation. Combining these performance indicators helps to find the shortcomings of the system in practical applications and provide data support for adjustment and optimization. Finally, by performing a performance weighted evaluation calculation of the AGC of the thermal power plant unit based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, the overall performance of the AGC of the thermal power plant unit under various operating conditions can be effectively and comprehensively evaluated. This comprehensive scoring method can help identify the performance differences and potential problems of the system under different operating conditions. In addition, the AGC of the thermal power plant units is evaluated online based on the previously calculated comprehensive operating performance scores under different operating conditions. This allows real-time monitoring and evaluation of the system status and real-time performance feedback of the AGC system. This online evaluation can provide instant system status information, allowing operation and maintenance personnel to quickly identify and respond to abnormal conditions or performance degradation in the system. Through real-time comprehensive performance scoring, control strategies can be dynamically adjusted to optimize system operation and prevent performance degradation or failures. The real-time feedback mechanism can improve the adaptability and flexibility of the AGC system, thereby ensuring the overall operating safety of the thermal power plant units and quickly locating and solving problems when performance degradation or failures occur.

[0057] 2. The AGC performance online evaluation system for thermal power plant units proposed in the present invention is generally composed of an AGC operating parameter acquisition module for thermal power plant units, an AGC operating parameter timing division module for different working conditions, an AGC operating performance index calculation module and an AGC performance online evaluation module. It can implement any AGC performance online evaluation method for thermal power plant units described in the present invention, and is used to combine the operations between computer programs running on various modules to implement the AGC performance online evaluation method for thermal power plant units. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient AGC performance online evaluation process for thermal power plant units, thereby simplifying the operating procedures of the AGC performance online evaluation system for thermal power plant units. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0059] Figure 1 A schematic flow chart of the steps of the on-line evaluation method for AGC performance of thermal power plant units according to the present invention;

[0060] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0061] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0062] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0063] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for online evaluation of AGC performance of a thermal power plant unit, the method comprising the following steps:

[0064] Step S1: collect real-time operating parameters of the AGC of the thermal power plant unit to obtain the AGC operating load change signal of the thermal power plant unit, the AGC operating adjustment signal of the thermal power plant unit, the AGC operating target power of the thermal power plant unit and the AGC operating actual power generation power of the thermal power plant unit;

[0065] Step S2: Based on the AGC of the thermal power plant unit, the AGC operation load change signal of the thermal power plant unit is subjected to an operation condition period membership analysis to obtain the AGC operation load change signal under different operation conditions; the operation condition time sequence range is determined for the AGC operation load change signal under different operation conditions to obtain the operation condition time sequence range intervals corresponding to the different operation conditions; based on the operation condition time sequence range intervals corresponding to the different operation conditions, the AGC operation regulation signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the actual power generation power of the AGC operation of the thermal power plant unit are subjected to the operation condition time sequence synchronization division to obtain the AGC operation regulation signal, the AGC operation target power and the actual power generation power of the AGC operation under different operation conditions;

[0066] Step S3: quantify the load response time and output power of the AGC operation load change signal under different operating conditions to obtain the operation load response time and the operation load output power under different operating conditions; calculate the operation overshoot performance index of the operation load output power and the AGC operation target power under different operating conditions based on the operation load response time under different operating conditions to obtain the AGC operation overshoot performance index under different operating conditions; calculate the operation regulation stability and steady-state error performance index of the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation power under different operating conditions based on the AGC operation load change signal under different operating conditions to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions;

[0067] Step S4: Based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, the performance of the thermal power plant unit AGC is evaluated online to obtain the online evaluation result of the thermal power plant unit AGC system status performance.

[0068] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic flow chart of the steps of the on-line evaluation method for the AGC performance of a thermal power plant unit of the present invention. In this example, the on-line evaluation method for the AGC performance of a thermal power plant unit includes the following steps:

[0069] Step S1: collect real-time operating parameters of the AGC of the thermal power plant unit to obtain the AGC operating load change signal of the thermal power plant unit, the AGC operating adjustment signal of the thermal power plant unit, the AGC operating target power of the thermal power plant unit and the AGC operating actual power generation power of the thermal power plant unit;

[0070] In an embodiment of the present invention, a sensor (such as a load sensor or a current sensor) installed in the AGC of a thermal power plant unit is used to monitor the change signal of the motor load in real time during the motor operation load process. The collected load change signal is transmitted to the data processing center through a data acquisition module, and the data acquisition module is configured to record the load change signal at a high frequency, thereby obtaining the load change signal of the thermal power plant unit AGC operation. By performing real-time adjustment signal acquisition on the motor operation adjustment process of the thermal power plant unit AGC, the signal data in the motor adjustment process, including control instructions and adjustment amounts, is obtained through a set adjustment signal acquisition device (such as a controller signal acquisition instrument). These signal data are transmitted to the data processing center in real time through a data transmission channel to record the actual situation of the adjustment process, thereby obtaining the thermal power plant unit AGC operation adjustment signal. At the same time, the time series analysis method is used to analyze the trend of state changes in the power generation operation process of the AGC of the thermal power plant unit, so as to process the operating status data in the power generation process, including power output, load fluctuation, etc., through data analysis software (such as MATLAB or Python's data analysis library), and apply trend analysis algorithms (such as regression analysis or sliding average method) to perform statistical analysis on the change trend of the operating status data. According to the power generation operation status change trend obtained by the previous analysis, the power generation operation process of the corresponding thermal power plant unit AGC is statistically analyzed for operating power requirements and constraints, so as to analyze the power generation of the thermal power plant unit AGC by using statistical analysis tools (such as Python's NumPy and Pandas libraries). The power demand of the power plant is analyzed in the power operation process, the main power demand of the thermal power plant unit in the power generation process is statistically analyzed, and the corresponding constraints (such as maximum load, minimum load, response time, etc.) are set to generate the power demand and the corresponding constraints. The corresponding power demand is predicted and calculated by using the prediction model by combining the constraints obtained by the previous analysis. The power demand is analyzed by applying the prediction algorithm (such as linear regression or time series prediction model), and the target power value is generated according to the constraints. In the prediction calculation process, the power generation operation constraints are input, and the operation target power of the AGC of the thermal power plant unit is obtained by model prediction, so as to obtain the AGC operation target power of the thermal power plant unit. Then, the power generation operation process of the AGC of the thermal power plant unit is monitored and calculated, so as to record the actual power generation in real time through the installed power monitoring equipment (such as power meter or intelligent meter), and the monitoring data is transmitted to the data processing center through the data acquisition system, and the actual power generation is calculated and displayed by applying the monitoring and analysis tools (such as SCADA system or real-time data analysis platform), and finally the actual power generation of the thermal power plant unit AGC operation is obtained.

[0071] Step S2: Based on the AGC of the thermal power plant unit, the AGC operation load change signal of the thermal power plant unit is subjected to an operation condition period membership analysis to obtain the AGC operation load change signal under different operation conditions; the operation condition time sequence range is determined for the AGC operation load change signal under different operation conditions to obtain the operation condition time sequence range intervals corresponding to the different operation conditions; based on the operation condition time sequence range intervals corresponding to the different operation conditions, the AGC operation regulation signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the actual power generation power of the AGC operation of the thermal power plant unit are subjected to the operation condition time sequence synchronization division to obtain the AGC operation regulation signal, the AGC operation target power and the actual power generation power of the AGC operation under different operation conditions;

[0072] In an embodiment of the present invention, operating condition load data of the AGC of a thermal power plant unit in different time periods, including load change amplitude, frequency and fluctuation period, are collected, and a timing analysis tool is used to perform Fourier transform on these data to extract the frequency characteristics of load fluctuations and identify the main fluctuation frequencies and amplitudes. This process involves using data processing libraries in programming languages ​​such as Matlab or Python, such as NumPy and Pandas, to perform data cleaning and feature extraction, thereby summarizing the obtained load fluctuation characteristics to obtain load fluctuation characteristics related to the AGC operating conditions. At the same time, the timing change pattern of the AGC operation load change signal of the thermal power plant unit previously collected in real time is analyzed by using signal processing tools (such as Wavelet transform or short-time Fourier transform) to analyze the timing characteristics of the AGC operation load change signal of the thermal power plant unit and identify the change pattern of the signal. The load change signal of the corresponding thermal power plant unit is time-segmented by combining the timing change pattern of the operating load signal obtained by the previous analysis, so as to divide the load change signal into several segments according to the turning point of the corresponding timing change pattern. Each segment corresponds to a specific signal timing change pattern. By setting the threshold and time window, the AGC operation load change signal of the thermal power plant unit is segmented to obtain the time period of each change pattern. This process usually involves the use of signal processing software to slide Dynamic window analysis is performed to determine the start and end points of each time period to divide the change signal. The load fluctuation characteristics related to the AGC operating condition obtained in the previous analysis are combined to perform a membership analysis of the AGC operating load change signal in different time segments. In the specific implementation process, the load fluctuation characteristics are described to compare the signal of each change mode time period with the predefined condition characteristics to determine the corresponding operating condition type. This usually involves the use of fuzzy logic or other classification algorithms (such as support vector machines) to determine which operating condition the operating load change signal of each time period belongs to. Data mining tools (such as RapidMiner) can be used to classify the load change signal under the condition, so as to obtain the AGC operating load change signal under different operating conditions through membership division. Then, the operating condition timing is defined for the AGC operating load change signal under different operating conditions obtained by the previous analysis to determine the timing start and end points of the load change signal under different operating conditions, and the timing range is planned and defined based on the corresponding timing start and end points of the change signal under different operating conditions determined previously to plan and determine the operating timing range of the signal change, thereby obtaining the corresponding operating condition timing range intervals under different operating conditions.Finally, by combining the corresponding operating condition timing range intervals under different operating conditions determined previously, the corresponding thermal power plant unit AGC operation regulation signal, thermal power plant unit AGC operation target power and thermal power plant unit AGC operation actual power generation power are divided into timing synchronization, so as to divide these signal data into corresponding operating condition timing segments, and use timing data alignment tools (such as pivot tables in Excel or dedicated timing analysis software) to synchronize the regulation signal, target power and actual power generation power, integrate and compare the data in each operating condition timing segment, so as to analyze the performance under different operating conditions, and finally divide the AGC operation regulation signal, AGC operation target power and AGC operation actual power generation power under different operating conditions.

[0073] Step S3: quantify the load response time and output power of the AGC operation load change signal under different operating conditions to obtain the operation load response time and the operation load output power under different operating conditions; calculate the operation overshoot performance index of the operation load output power and the AGC operation target power under different operating conditions based on the operation load response time under different operating conditions to obtain the AGC operation overshoot performance index under different operating conditions; calculate the operation regulation stability and steady-state error performance index of the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation power under different operating conditions based on the AGC operation load change signal under different operating conditions to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions;

[0074] In an embodiment of the present invention, a high-precision data recorder and analysis software are used to perform a timing analysis on the AGC operating load change signal under different operating conditions previously divided, so as to align the load change signal with the time axis, calculate the time difference from the load change time starting point to the load change signal stabilization, and determine the response time of the AGC operating load under each operating condition, thereby obtaining the operating load response time under different operating conditions. Secondly, by combining the operating load response time under different operating conditions obtained by the previous quantitative calculation, the output power of the AGC operating load change signal under the corresponding operating condition is quantitatively calculated, so as to use a quantitative calculation tool to convert the AGC operating load change signal into output power, and the quantitative calculation includes the integration and transformation operations of the signal, and uses algorithms such as Fourier transform or wavelet transform to convert the AGC operating load change signal from the time domain to the frequency domain, for example: , where , ,in, For in time The output power at the moment, is the length of the time window, is the integral time variable parameter, and are adjustment coefficients, The load change signal for AGC operation is The integral value at The load change signal for AGC operation is The load change response value at Load change response value In frequency The Fourier transform result is: is an imaginary unit, so as to quantitatively calculate the operating load output power under different operating conditions. At the same time, by combining the operating load response time under different operating conditions obtained by the previous analysis, a graphical tool (such as a drawing function in Matlab) is used to draw a hyperbola for the operating load output power and the AGC operating target power under the corresponding operating condition, so as to fit and draw the corresponding operating power hyperbola. The hyperbola graphically shows the relationship between the operating load output power and the AGC target power, and displays the changes within the response time range. The maximum spacing point is determined by combining the previously drawn change hyperbola, so as to determine the maximum spacing point between the operating power curves by comparing the hyperbola graphs under different operating conditions, and using the curve analysis tool. The specific operation includes for each time point within the response time range. The hyperbola between the output power at the position and the target power is numerically solved to find the point with the largest distance between the two curves, and the maximum spacing point is calculated and marked by using data analysis software. The operating overshoot is quantitatively calculated for the operating load output power and the AGC operating target power at the corresponding point time by combining the maximum spacing point between the changing hyperbolas determined previously, so as to determine the operating load output power at this timing point as the maximum, and calculate the difference between it and the AGC target power, so as to quantitatively calculate the maximum amplitude of the output power exceeding the target power to determine the overshoot between the load output power at these points and the AGC target power, so as to obtain the AGC operating overshoot performance index under different operating conditions. Then, by combining the AGC operation load change signal under different operating conditions obtained by the previous division, the AGC operation regulation signal, AGC operation target power and AGC operation actual power generation under the corresponding operating conditions are used to calculate the performance indicators of regulation stability and steady-state power error. By using control system analysis tools (such as the control system toolbox in Matlab) to perform system analysis, and quantitatively calculate the operation regulation stability of the AGC system and the difference between the actual power generation power and the target power under stable operating conditions, finally the AGC operation regulation stability performance indicators and AGC operation steady-state error performance indicators under different operating conditions are obtained.

[0075] Step S4: Based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, the performance of the thermal power plant unit AGC is evaluated online to obtain the online evaluation result of the thermal power plant unit AGC system status performance.

[0076] In an embodiment of the present invention, corresponding weight values ​​are assigned to the AGC operation overshoot performance index, the AGC operation regulation stability performance index and the AGC operation steady-state error performance index previously obtained by quantitative calculation under corresponding operating conditions. These weight values ​​should be set according to the degree of influence of the actual operating conditions on each index. The comprehensive score of the operation performance of the corresponding thermal power plant unit AGC under the corresponding operating condition is quantitatively calculated by using a weighted calculation formula of comprehensive score value = Σ (value of each index × corresponding weight). The operation performance of the corresponding thermal power plant unit AGC is evaluated online according to the specific values ​​of the comprehensive score of the operation performance of the thermal power plant unit AGC under different operating conditions obtained by quantitative calculation previously, so as to evaluate the AGC system performance under the current operating conditions. The comprehensive score of the operation performance calculated in real time is matched and compared with the preset scoring standard. For example, when the comprehensive score of the operation performance is -45 is "poor", 46-75 is "good", and 76-1 The final evaluation result is “excellent”, and the online evaluation results of the status performance of the AGC system of the thermal power plant units are obtained. These results can be displayed on the user interface and trigger corresponding alarms or notifications to prompt operators whether they need to adjust control parameters or perform maintenance.

[0077] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0078] Step S11: collecting the operating load change signal of the motor operating load process of the AGC of the thermal power plant unit in real time to obtain the operating load change signal of the AGC of the thermal power plant unit;

[0079] In an embodiment of the present invention, a sensor (such as a load sensor or a current sensor) installed in the AGC of a thermal power plant unit is used to monitor the change signal of the motor load during the motor operation load process in real time. The collected load change signal is transmitted to a data processing center through a data acquisition module. The data acquisition module is configured to record the load change signal at a high frequency and display the signal change through a real-time monitoring system (such as a SCADA system), and finally the AGC operation load change signal of the thermal power plant unit is obtained.

[0080] Step S12: collecting operation regulation signals of the motor operation regulation process of the AGC of the thermal power plant unit in real time to obtain the AGC operation regulation signals of the thermal power plant unit;

[0081] In an embodiment of the present invention, real-time adjustment signal acquisition is performed on the motor operation adjustment process of the AGC of the thermal power plant unit to obtain signal data in the motor adjustment process through a set adjustment signal acquisition device (such as a controller signal acquisition instrument), including control instructions and adjustment quantities. These signal data are transmitted to a data processing center in real time through a data transmission channel to record the actual situation of the adjustment process, and finally obtain the AGC operation adjustment signal of the thermal power plant unit.

[0082] Step S13: analyzing the operating state change trend of the power generation operation process of the AGC of the thermal power plant unit to obtain the power generation operation state change trend of the AGC of the thermal power plant unit; performing an operating power demand and constraint statistical analysis on the power generation operation process of the AGC of the thermal power plant unit based on the power generation operation state change trend of the AGC of the thermal power plant unit to obtain the power generation operation power demand of the AGC of the thermal power plant unit and the power generation operation constraint conditions of the AGC of the thermal power plant unit;

[0083] In an embodiment of the present invention, a time series analysis method is used to perform a trend analysis of state changes in the power generation operation process of the AGC of the thermal power plant unit, so that the operating state data in the power generation process is processed by data analysis software (such as MATLAB or Python's data analysis library), including power output, load fluctuation, etc., and a trend analysis algorithm (such as regression analysis or sliding average method) is applied to perform a statistical analysis of the change trend of the operating state data, thereby obtaining the change trend of the power generation operation state of the AGC of the thermal power plant unit. At the same time, according to the change trend of the AGC power generation operation status of the thermal power plant units obtained in the previous analysis, the operating power demand and constraints of the AGC power generation operation process of the corresponding thermal power plant units are statistically analyzed, so as to use statistical analysis tools (such as Python's NumPy and Pandas libraries) to analyze the operating power demand of the AGC power generation operation process of the thermal power plant units, so as to statistically analyze the main power demands of the thermal power plant units in the power generation process, and set corresponding constraints (such as maximum load, minimum load, response time, etc.), so as to generate power generation power demand and corresponding constraints, and finally obtain the AGC power generation operation power demand of the thermal power plant units and the AGC power generation operation constraints of the thermal power plant units.

[0084] Step S14: performing target power forecasting calculation on the AGC power generation operation power demand of the thermal power plant units based on the AGC power generation operation constraint conditions of the thermal power plant units to obtain the AGC operation target power of the thermal power plant units;

[0085] In an embodiment of the present invention, a prediction model is used to perform target power prediction calculation on the corresponding AGC power generation operation power demand of the thermal power plant unit by combining the AGC power generation operation constraint conditions of the thermal power plant unit obtained by previous analysis, so as to analyze the power demand data by applying a prediction algorithm (such as linear regression or a time series prediction model), and generate a target power value according to the constraint conditions. In the prediction calculation process, the power generation operation constraint conditions are input, and the operation target power of the AGC of the thermal power plant unit is obtained through model prediction, and finally the AGC operation target power of the thermal power plant unit is obtained.

[0086] Step S15: monitor and calculate the operating power generation power of the AGC power generation operation process of the thermal power plant unit to obtain the actual power generation power of the AGC operation of the thermal power plant unit.

[0087] In an embodiment of the present invention, the power generation operation process of the AGC of the thermal power plant unit is monitored and calculated, so that the actual power generation is recorded in real time through the installed power monitoring equipment (such as a power meter or an intelligent meter), the monitoring data is transmitted to the data processing center through the data acquisition system, and the actual power generation is calculated and displayed by applying a monitoring and analysis tool (such as a SCADA system or a real-time data analysis platform), and finally the actual power generation of the thermal power plant unit AGC operation is obtained.

[0088] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment, step S2 includes the following steps:

[0089] Step S21: Analyze the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units to obtain the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units;

[0090] In an embodiment of the present invention, by collecting the operating condition load data of the AGC of the thermal power plant unit in different time periods, including the load change amplitude, frequency and fluctuation period, and using a time series analysis tool to perform Fourier transform on these data to extract the frequency characteristics of the load fluctuation, and by drawing a load fluctuation spectrum, the main fluctuation frequency and amplitude are identified. This process involves using data processing libraries in programming languages ​​such as Matlab or Python, such as NumPy and Pandas, to perform data cleaning and feature extraction, so as to summarize the obtained load fluctuation characteristics, and finally obtain the load fluctuation characteristics of the operating condition of the AGC of the thermal power plant unit.

[0091] Step S22: performing a signal time sequence change mode analysis on the AGC operation load change signal of the thermal power plant unit to obtain the time sequence change mode of the AGC operation load signal of the thermal power plant unit; dividing the AGC operation load change signal of the thermal power plant unit into time segments based on the time sequence change mode of the AGC operation load signal of the thermal power plant unit to obtain the AGC operation load change signal under different time segments of the change mode;

[0092] In an embodiment of the present invention, a signal processing tool (such as Wavelet transform or short-time Fourier transform) is used to analyze the time series change pattern of the AGC operation load change signal of the thermal power plant unit previously collected in real time, so as to analyze the time series characteristics of the AGC operation load change signal of the thermal power plant unit, and identify the change pattern of the signal, thereby obtaining the time series change pattern of the AGC operation load signal of the thermal power plant unit. At the same time, the load change signal of the corresponding thermal power plant unit is time segmented by combining the time series change pattern of the AGC operation load signal of the thermal power plant unit obtained by the previous analysis, so as to divide the load change signal into several segments according to the change turning point of the corresponding time series change pattern, each segment corresponds to a specific signal time series change pattern, and the AGC operation load change signal of the thermal power plant unit is segmented by setting a threshold and a time window to obtain the time period of each change mode. This process usually involves using signal processing software to perform sliding window analysis, determine the starting and ending points of each time period to divide the change signal, and finally obtain the AGC operation load change signal under different change mode time segments.

[0093] Step S23: Based on the load fluctuation characteristics of the AGC operating condition of the thermal power plant unit, the AGC operating load change signal under different change mode time segments is subjected to operating condition period membership analysis to obtain the AGC operating load change signal under different operating conditions;

[0094] In an embodiment of the present invention, by combining the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units obtained by previous analysis, the AGC operating load change signal under different change mode time segments is subjected to a membership analysis of the operating condition period. In the specific implementation process, the load fluctuation characteristic description is used to compare the signal of each change mode time period with the predefined operating condition characteristics to determine the corresponding operating condition type. This usually involves the use of fuzzy logic or other classification algorithms (such as support vector machines) to determine which operating condition the operating load change signal of each time period belongs to. The load change signal can be classified according to the operating condition with the help of data mining tools (such as RapidMiner), and finally the AGC operating load change signal under different operating conditions is obtained by membership division.

[0095] Step S24: determining the starting point and the end point of the operating condition timing of the AGC operating load change signal under different operating conditions, and obtaining the corresponding starting point and end point of the changing signal timing under different operating conditions; determining the operating condition timing range according to the corresponding starting point and end point of the changing signal timing under different operating conditions, and obtaining the corresponding operating condition timing range interval under different operating conditions;

[0096] In the embodiment of the present invention, the operating condition timing is defined by analyzing the AGC operating load change signal under different operating conditions obtained previously, so as to determine the timing start and end of the load change signal under different operating conditions, and determine the start and end time of the signal change through data point detection and signal trend analysis, so as to obtain the corresponding change signal timing start and change signal timing end under different operating conditions. At the same time, the timing range is planned and defined according to the corresponding change signal timing start and change signal timing end under different operating conditions obtained previously, so as to plan and determine the operating timing range of the signal change, and finally obtain the corresponding operating condition timing range interval under different operating conditions.

[0097] Step S25: Based on the corresponding operating condition timing range intervals under different operating conditions, the AGC operation adjustment signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the AGC operation actual power generation power of the thermal power plant unit are synchronously divided into operating condition timings to obtain the AGC operation adjustment signal, AGC operation target power and AGC operation actual power generation power under different operating conditions.

[0098] In an embodiment of the present invention, the corresponding AGC operation regulation signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the AGC operation actual power generation power of the thermal power plant unit are divided into timing synchronization by combining the corresponding operating condition timing range intervals under different operating conditions determined previously, so as to divide these signal data into corresponding operating condition timing segments, and use a timing data alignment tool (such as a pivot table in Excel or a dedicated timing analysis software) to synchronize the regulation signal, target power and actual power generation power, integrate and compare the data in each operating condition timing segment, so as to analyze the performance under different operating conditions, and finally divide the AGC operation regulation signal, AGC operation target power and AGC operation actual power generation power under different operating conditions.

[0099] Further, the step S22 of dividing the change mode of the AGC operation load change signal of the thermal power plant unit into time segments based on the time sequence change mode of the AGC operation load signal of the thermal power plant unit includes the following steps:

[0100] The signal change pattern characteristics of the AGC operation load signal time series change pattern of the thermal power plant unit are analyzed to obtain the AGC operation load signal change pattern characteristic set of the thermal power plant unit;

[0101] In an embodiment of the present invention, by performing a statistical analysis on the signal change pattern characteristics of the time series change pattern of the AGC (automatic generation control) operation load signal of the thermal power plant unit, a time series analysis tool, such as the time series analysis toolbox in MATLAB, is used to process the corresponding time series change pattern of the AGC operation load signal of the thermal power plant unit, and the mean, variance, periodicity, trend change and other characteristics of the load signal are statistically analyzed. Through statistical analysis and pattern recognition methods (such as principal component analysis PCA or cluster analysis), the change pattern characteristics of the load signal are identified. These feature sets include typical change patterns and abnormal fluctuation patterns of the load signal, and finally a change pattern feature set of the AGC operation load signal of the thermal power plant unit is obtained.

[0102] Preferably, a time series variation spectrum of the AGC operation load variation signal of the thermal power plant unit is drawn to generate a time series variation spectrum of the AGC operation load variation signal of the thermal power plant unit;

[0103] In an embodiment of the present invention, a time series variation graph of the AGC operation load signal of the thermal power plant unit is drawn to perform time series processing on the collected AGC operation load signal of the thermal power plant unit, and a time series graph is drawn by using a data visualization tool (such as the Matplotlib library in Python). The load signal is smoothed to generate a time series variation graph, which shows the change of the load signal over time, including the increase, decrease and stable stages of the load. This process allows intuitive observation and analysis of the change trend of the load signal, identification of characteristic change patterns in different time periods, and finally drawing and generating a time series variation graph of the AGC operation load signal of the thermal power plant unit.

[0104] Preferably, based on the corresponding time series change pattern characteristics in the change pattern characteristic set of the AGC operation load signal of the thermal power plant unit, the change pattern turning point of the time series change spectrum of the AGC operation load signal of the thermal power plant unit is determined to obtain the change pattern turning point set of the AGC operation load signal of the thermal power plant unit;

[0105] In an embodiment of the present invention, the corresponding time series change spectrum is determined by combining the corresponding time series change pattern features in the change pattern feature set of the thermal power plant unit AGC operation load signal obtained by previous statistical analysis, so as to detect the turning points in the load signal by applying a time series analysis algorithm (such as moving average or Hilbert transform), and the key features in the change pattern feature set, such as the change rate mutation point, are used to determine the turning points of each change pattern, and automatic identification and marking of turning points are achieved through automated programming tools (such as Python scripts) to obtain a change pattern turning point set, which reflects the key change moments of the load signal, and finally obtains the change pattern turning point set of the thermal power plant unit AGC operation load signal.

[0106] Preferably, the time series corresponding to the time series variation spectrum of the AGC operation load signal of the thermal power plant unit is segmented according to the interval between two adjacent change mode turning points in the change mode turning point set of the AGC operation load signal of the thermal power plant unit, so as to obtain different AGC operation load signal change mode time series range segments;

[0107] In an embodiment of the present invention, the time series corresponding to the time series in the timing change spectrum of the AGC operation load signal of the thermal power plant unit is synchronously divided between two adjacent change pattern turning point positions in the previously determined turning point set of the AGC operation load signal change pattern of the thermal power plant unit, so as to extract the time period between the turning points, and use a data segmentation tool (such as the data segmentation function in MATLAB) to segment the time series data, and divide the signal data into a number of time series range segments according to the time interval between adjacent turning points, each of which represents a specific change pattern. This process ensures that the time series of different change patterns can be accurately segmented, and finally obtains different AGC operation load signal change pattern timing range segments.

[0108] Preferably, the AGC operation load change signal of the thermal power plant unit is divided into change mode time segments based on different AGC operation load signal change mode timing range segments to obtain the AGC operation load change signal under different change mode time segments.

[0109] In an embodiment of the present invention, the AGC operation load change signal of the corresponding thermal power plant unit is divided into time segments by combining the different AGC operation load signal change mode time series range segments obtained by previous division, so as to match the segmented data with the change mode, and use statistical analysis tools (such as SPSS or R language) to perform further feature analysis on each time series range to identify the signal change characteristics under each segment, apply these characteristics to time segment division, classify according to the characteristics of different change modes, generate the time period operation load change signal corresponding to each change mode, and record the load change situation under each mode. This process ensures that the time series data of the load signal in different modes are accurately divided, and finally obtains the AGC operation load change signal under different change mode time segments.

[0110] Further, step S23 includes the following steps:

[0111] Step S231: dividing the load fluctuation characteristics of the AGC operating condition of the thermal power plant unit into operating condition periods to obtain a subset of load fluctuation characteristics of the AGC operating condition of the thermal power plant unit in different operating condition periods;

[0112] In an embodiment of the present invention, the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units obtained by the previous analysis are classified and divided into operating conditions, so as to divide different operating time periods according to their load fluctuation characteristics. The load fluctuation of the thermal power plant units under different operating conditions is recorded by using historical operating data. By analyzing the frequency, amplitude, duration and other characteristics of the load fluctuation, these fluctuation characteristics are divided into time periods to form load fluctuation characteristic subsets under respective operating conditions. For example, if the load fluctuation amplitude in a certain period is large, it is marked as a "high fluctuation" condition. If the load changes slowly, it is marked as a "low fluctuation" condition. And so on, finally, a load fluctuation characteristic subset of the AGC of the thermal power plant units under different operating condition time periods is obtained.

[0113] Step S232: performing feature vector mapping conversion on the load fluctuation feature subsets of the AGC of the thermal power plant unit under different operating conditions, so as to obtain the load fluctuation feature vectors under different operating conditions;

[0114] In an embodiment of the present invention, the load fluctuation feature subsets of the thermal power plant unit AGC under different operating conditions are mapped and converted into feature vectors by previously dividing them, so as to map the original load fluctuation data into feature vectors by selecting principal component analysis (PCA) or other feature extraction methods. In this process, the load fluctuation data of each time period is analyzed by a feature extraction tool to extract the main feature parameters, such as mean, standard deviation, maximum value, minimum value, etc., to form a feature vector describing the load fluctuation of the time period. For example, for a "high fluctuation" condition, the feature vector of its high-frequency fluctuation component can be extracted. The feature vector obtained in this way can better represent the load fluctuation of the time period, and finally the load fluctuation feature vectors under different operating conditions are obtained.

[0115] Step S233: Based on the load fluctuation characteristic vectors under different operating conditions, the AGC operating load change signal under different change mode time segments is subjected to load signal fluctuation matching membership mining analysis to obtain the matching membership relationship between the operating load change signal under different change mode time segments and the load fluctuation of each operating condition; based on the matching membership relationship between the operating load change signal under different change mode time segments and the load fluctuation of each operating condition, the AGC operating load change signal under different change mode time segments is subjected to time period membership calculation to obtain the membership between the operating load change signal under different change mode time segments and each operating condition time period;

[0116] In an embodiment of the present invention, a matching analysis of load signal fluctuations is performed on the AGC operating load change signal corresponding to different change mode time segments by combining the load fluctuation feature vectors under different operating conditions obtained by previous conversion, so as to preprocess the load change signal of each time period, including denoising and standardization, and match the feature vector with the load change signal, and use a matching algorithm (such as Euclidean distance, cosine similarity, etc.) to calculate the matching affiliation between the load signal of each time period and the load fluctuations of different operating conditions. For example, for a "medium fluctuation" time period, its similarity with each feature vector is calculated to find the best matching condition, thereby obtaining the matching affiliation between the operating load change signal under different change mode time segments and the load fluctuations of each operating condition. At the same time, by combining the matching membership relationship between the operating load change signal and the load fluctuation of each operating condition under different change mode time segments obtained in the previous analysis, the fuzzy membership calculation method is used to calculate the membership value of the load signal in each time period to each operating condition period. By establishing a membership function, the characteristic vector of the time period load signal and the characteristic vector of each operating condition are quantitatively calculated for feature matching, that is, the characteristic vector distance value between the two characteristic vectors, to obtain the membership value. For example, if the load signal of a certain time period has a high similarity with the characteristic vector of the "low fluctuation" condition, then the membership value of the time period is higher under the "low fluctuation" condition, and finally the membership between the operating load change signal and each operating condition period under different change mode time segments is obtained.

[0117] Step S234: constructing a membership matrix according to the membership between the operating load change signal and each operating condition period under different change mode time segments to generate an AGC operating condition period membership matrix;

[0118] In an embodiment of the present invention, a corresponding membership matrix is ​​constructed by obtaining the membership between the operating load change signal and each operating condition period under different change mode time segments obtained by previous quantitative calculation to sort out the membership value of each time period, and forming a matrix according to the arrangement order of the time periods and the operating condition periods. For example, for the membership values ​​between each time period and each operating condition period, a two-dimensional matrix is ​​created, in which each row represents a time period, each column represents a working condition period, and each matrix element represents a corresponding membership value, and finally an AGC operating condition period membership matrix is ​​constructed.

[0119] Step S235: Based on the AGC operating condition period membership matrix, the AGC operating load change signals under different change mode time segments are subjected to operating condition period optimal membership screening processing to obtain the AGC operating load change signals under different operating conditions.

[0120] In an embodiment of the present invention, the AGC operating load change signals corresponding to different change mode time segments are screened for optimal membership by combining the previously constructed AGC operating condition period membership matrix, so as to determine the optimal membership operating condition period of the load signal in each change mode time segment by analyzing the membership value in the membership matrix, so as to select the operating condition period with the highest membership as the optimal membership condition of the time segment. For example, for a time segment, if the value of the "high fluctuation" condition in its membership matrix is ​​the highest, then this condition is selected as its most matching membership condition, so as to more accurately control the load fluctuation of the unit, and finally obtain the AGC operating load change signal under different operating conditions.

[0121] Further, step S3 includes the following steps:

[0122] Step S31: performing load response time statistical analysis on the AGC operating load change signal under different operating conditions to obtain the operating load response time under different operating conditions;

[0123] In an embodiment of the present invention, a high-precision data recorder and analysis software are used to perform timing analysis on the AGC operating load change signal under different operating conditions previously divided, so as to align the load change signal with the time axis, calculate the time difference from the starting point of the load change time to the stabilization of the load change signal, and determine the response time length of the AGC operating load under each operating condition, and finally obtain the operating load response time under different operating conditions.

[0124] Step S32: performing output power quantization calculation on the AGC operating load change signal under different operating conditions based on the operating load response time under different operating conditions, so as to obtain the operating load output power under different operating conditions;

[0125] In the embodiment of the present invention, the output power of the AGC operating load change signal under the corresponding operating condition is quantitatively calculated by combining the operating load response time under different operating conditions obtained by previous quantitative calculation, so as to convert the AGC operating load change signal into output power using a quantitative calculation tool. The quantitative calculation includes integration and transformation operations on the signal, and the AGC operating load change signal is converted from the time domain to the frequency domain using an algorithm such as Fourier transform or wavelet transform. For example, the quantitative calculation tool is specifically: , where , ,in, For in time The output power at the moment, is the length of the time window, is the integral time variable parameter, and are adjustment coefficients, The load change signal for AGC operation is The integral value at The load change signal for AGC operation is The load change response value at Load change response value In frequency The Fourier transform result is: is an imaginary unit, and the final quantitative calculation is to obtain the operating load output power under different operating conditions.

[0126] Step S33: based on the operating load response time under different operating conditions, the operating power hyperbola is plotted for the operating load output power under different operating conditions and the AGC operating target power, so as to generate a hyperbola of changes between the operating load output power and the target power within the corresponding response time range under different operating conditions;

[0127] In an embodiment of the present invention, a graphical tool (such as a drawing function in Matlab) is used to draw a hyperbola of the operating load output power and the AGC operating target power under the corresponding operating condition in combination with the operating load response time under different operating conditions obtained by previous analysis, so as to fit and draw a corresponding operating power hyperbola. The hyperbola illustrates the relationship between the operating load output power and the AGC target power to show the changes within the response time range. The specific implementation method is to fit the output power data and the target power data in combination with the time series data to generate a hyperbola, and draw a corresponding graph under each operating condition, and finally generate a hyperbola of the changes between the operating load output power and the target power within the corresponding response time range under different operating conditions.

[0128] Step S34: determining the maximum spacing point between the operating power curves for the changing hyperbola between the operating load output power and the target power within the corresponding response time range under different operating conditions, and obtaining the maximum spacing point between the changing hyperbola within the corresponding response time range under different operating conditions; calculating the operating overshoot performance index of the operating load output power and the AGC operating target power at the corresponding point time under different operating conditions based on the maximum spacing point between the changing hyperbola within the corresponding response time range under different operating conditions, and obtaining the AGC operating overshoot performance index under different operating conditions;

[0129] In an embodiment of the present invention, the maximum spacing point is determined by combining the previously determined hyperbolic curves of the change between the operating load output power and the target power within the corresponding response time range under different operating conditions, so as to determine the maximum spacing point between the operating power curves by comparing the hyperbolic curves under different operating conditions and using a curve analysis tool. The specific operation includes numerically solving the hyperbola between the output power and the target power at each time point within the response time range, finding the point with the largest distance between the two curves, and calculating and marking the maximum spacing point using data analysis software. These points can clearly define the operating performance differences under different conditions, thereby obtaining the maximum spacing points between the changing hyperbolic curves within the corresponding response time range under different operating conditions. At the same time, by combining the maximum spacing points between the changing hyperbolas within the corresponding response time range under different operating conditions determined previously, the operating overshoot of the operating load output power at the corresponding point time and the AGC operating target power is quantitatively calculated to determine the operating load output power at this timing point as the maximum, and the difference between it and the AGC target power is calculated, so as to quantitatively calculate the maximum amplitude of the output power exceeding the target power to determine the overshoot between the load output power at these points and the AGC target power, and finally obtain the AGC operating overshoot performance index under different operating conditions.

[0130] Step S35: Based on the AGC operation load change signal under different operating conditions, the operation regulation stability and steady-state error performance index of the AGC operation adjustment signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions are calculated to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions.

[0131] In an embodiment of the present invention, by combining the AGC operation load change signal under different operating conditions obtained by the previous division, the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation power under the corresponding operating condition are used to calculate the performance indicators of regulation stability and steady-state power error, so as to perform system analysis by using a control system analysis tool (such as the control system toolbox in Matlab), and quantify the operation regulation stability of the AGC system and the difference between the actual power generation power and the target power under the stable operating state, and finally obtain the AGC operation regulation stability performance indicator and the AGC operation steady-state error performance indicator under different operating conditions.

[0132] Further, step S32 includes the following steps:

[0133] Step S321: performing load signal response timing fluctuation amplitude and frequency statistical analysis on the AGC load change signal under different operating conditions based on the operating load response time under different operating conditions, and obtaining the load change signal timing fluctuation amplitude and load change signal timing fluctuation frequency within the response time range under different operating conditions;

[0134] In an embodiment of the present invention, by combining the operating load response time under different operating conditions obtained by previous analysis, the signal response fluctuation amplitude and frequency of the AGC operating load change signal under the corresponding operating condition are statistically analyzed, so as to record the load response time series under different conditions by using a high-precision data acquisition instrument, and by using a statistical analysis tool, such as a data analysis library in MATLAB or Python, the load change signal is statistically analyzed for the timing fluctuation amplitude and frequency. Specifically, the load fluctuation amplitude at each time point is first calculated, that is, the difference between the maximum and minimum values ​​of the signal in a time period, and then the load fluctuation frequency is calculated, that is, the number of fluctuation cycles per unit time, to ensure that the timing fluctuation amplitude and frequency of the load change signal are accurately measured, and finally the load change signal timing fluctuation amplitude and the load change signal timing fluctuation frequency within the response time range under different operating conditions are quantitatively calculated.

[0135] Step S322: performing initial quantitative calculation of the response time output power of the AGC operation load change signal under different operating conditions based on the load change signal time fluctuation amplitude and the load change signal time fluctuation frequency within the response time range under different operating conditions, and obtaining the operation load fluctuation initial output power within the response time range under different operating conditions;

[0136] In an embodiment of the present invention, the output power of the corresponding AGC operation load change signal is quantified by combining the load change signal timing fluctuation amplitude and the load change signal timing fluctuation frequency within the response time range under different operating conditions obtained by previous quantitative calculation, so as to map the load fluctuation amplitude and frequency to the output power through an algorithm model (such as a linear regression or a neural network model), and by using a tool such as Simulink in MATLAB for modeling and calculation, the initial response timing output power is calculated according to historical data and the fluctuation amplitude. In this step, the fluctuation amplitude and frequency under each operating condition are used as input parameters, and the corresponding initial output power quantization value is solved by using a calculation tool to ensure that the load fluctuation characteristics under each operating condition are accurately reflected. For example, , where is the initial output power of the operating load fluctuation, The load change signal for AGC operation is The load change signal timing fluctuation amplitude at The load fluctuation amplitude affects the adjustment parameters. is the response time range, is the time variable parameter of output power, is the output power integration time variable parameter, The load change signal for AGC operation is The load change signal timing fluctuation amplitude at The load change signal for AGC operation is The load change signal timing fluctuation frequency at The load change signal for AGC operation is The load fluctuation weight value at is the weighted integral influence coefficient of the load fluctuation amplitude within the response time range, and finally the initial output power of the operating load fluctuation within the response time range under different operating conditions is obtained.

[0137] Step S323: performing output power fluctuation limit evaluation and analysis on the initial output power of the operating load fluctuation within the response time range under different operating conditions, and obtaining the output power fluctuation limit range of the operating load under different operating conditions;

[0138] In an embodiment of the present invention, the fluctuation limit of the output power is evaluated by evaluating the initial output power of the operating load fluctuation within the response time range under different operating conditions obtained by previous quantitative calculation, so as to simulate the initial output power under each condition by using professional power system analysis software (such as DIgSILENT PowerFactory or ETAP) to evaluate its fluctuation limit, and determine the output power fluctuation range under given conditions by setting simulation parameters and running limit analysis. The specific operation includes defining simulation conditions and constraints, and then performing multiple simulations to obtain a stable fluctuation limit range. These data are used to evaluate the range of load fluctuations, ensure a comprehensive analysis of the fluctuation limits under different operating conditions, and finally obtain the operating load output power fluctuation limit range under different operating conditions.

[0139] Step S324: Based on the operating load output power fluctuation limit range under different operating conditions, a nonlinear fluctuation correction is performed on the operating load fluctuation initial output power within the response time range under different operating conditions to obtain the operating load output power under different operating conditions.

[0140] In an embodiment of the present invention, a nonlinear fluctuation correction calculation is performed on the initial output power of the operating load fluctuation within the response time range under the corresponding operating condition by combining the operating load output power fluctuation limit range under different operating conditions determined previously, so as to adjust the initial output power by using a nonlinear correction algorithm (such as a Kalman filter or a particle filter). The specific operation is to input the initial output power into a nonlinear correction model and process the initial output power by a correction algorithm. The process includes selecting appropriate correction parameters and running the algorithm to reduce nonlinear errors, thereby calculating and outputting the corrected load fluctuation output power, ensuring that the final output power meets the specified fluctuation limit range, and finally obtaining the operating load output power under different operating conditions.

[0141] Further, step S35 includes the following steps:

[0142] Step S351: analyzing the signal change fluctuation trend of the AGC operating load change signal under different operating conditions to obtain the operating load signal change fluctuation trend under different operating conditions;

[0143] In an embodiment of the present invention, a statistical analysis of the signal change fluctuation trend is performed on the AGC operating load change signal under different operating conditions previously divided, so as to obtain and organize the load change signals under each operating condition, and filter and denoise them through data processing software. Then, time series analysis techniques, such as autoregressive moving average model (ARMA) or autoregressive integrated moving average model (ARIMA), are applied to model the fluctuation trend of the load change signal. These models can extract the periodicity and trend characteristics of the load signal, thereby depicting the fluctuation trend diagram under different working conditions to show the load change characteristics under each working condition, and finally obtain the fluctuation trend of the operating load signal under different operating conditions.

[0144] Step S352: determining the stable fluctuation trend period of the AGC operating load change signal under different operating conditions based on the fluctuation trend of the operating load signal under different operating conditions, so as to obtain the stable fluctuation trend period of the operating load under different operating conditions;

[0145] In an embodiment of the present invention, the stable fluctuation trend time period of the AGC operating load change signal under different operating conditions is determined by combining the fluctuation trend of the operating load signal under different operating conditions obtained by previous analysis, so as to identify the stable time period interval of the load signal in the fluctuation trend obtained previously. This is usually accomplished by setting a stability threshold. The threshold can be set according to the standard deviation or fluctuation amplitude of the load signal. The fluctuation trend is segmented using a sliding window method. If the fluctuation measure in each window is lower than the threshold, the window is marked as a stable period. The length of the period of the stable fluctuation trend is determined by statistical analysis. These periods are compared under different working conditions to determine the stable fluctuation period under each working condition, and finally the stable fluctuation trend period of the operating load under different operating conditions is obtained.

[0146] Step S353: Based on the stable fluctuation trend period of the operating load under different operating conditions, the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions are subjected to stable period timing synchronization processing to obtain the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions in the same stable operation fluctuation period;

[0147] In an embodiment of the present invention, by combining the stable fluctuation trend period of the operating load under different operating conditions determined previously, the AGC operation regulation signal, AGC operation target power and AGC operation actual power generation under the corresponding operating conditions are synchronously processed to synchronously extract the regulation signal, target power and actual power generation data in each stable period, and convert them into a unified time scale, and use a time synchronization algorithm, such as linear interpolation or spline interpolation, to fill the time gaps in the data, thereby ensuring that all signals are aligned at the same time node. Through the aligned data, detailed time series data in the stable operation fluctuation period can be obtained, and finally the AGC operation regulation signal, AGC operation target power and AGC operation actual power generation in the same stable operation fluctuation period under different operating conditions are obtained.

[0148] Step S354: calculating the operation regulation stability performance index of the AGC operation regulation signal in the same stable operation fluctuation period under different operation conditions to obtain the AGC operation regulation stability performance index under different operation conditions;

[0149] In an embodiment of the present invention, by performing statistical calculation of the operation regulation stability performance index on the AGC operation regulation signal in the same stable operation fluctuation period under different operating conditions obtained after timing synchronization, regulation stability indexes such as regulation accuracy and response time are defined, and using statistical analysis tools, the root mean square error (RMSE) or standard deviation of the regulation signal in each stable period is calculated to evaluate the stability of the AGC operation regulation under different conditions, and to obtain the stability performance index value, and finally to obtain the AGC operation regulation stability performance index under different operating conditions.

[0150] Step S355: Calculate the power steady-state error performance index for the AGC operation target power and the AGC operation actual power generation power in the same stable operation fluctuation period under different operating conditions to obtain the AGC operation steady-state error performance index under different operating conditions.

[0151] In an embodiment of the present invention, a power error is quantified by calculating the power error between the AGC operation target power and the AGC operation actual power generation power in the same stable operation fluctuation period under different operating conditions previously obtained after timing synchronization, so as to define a steady-state error index, such as a steady-state deviation or an absolute error, and an error is calculated between the target power and the actual power generation power to calculate the error value between the target power and the actual power generation power in each stable period, and at the same time, statistical tools are used to calculate indicators such as the root mean square value (RMSE) and the mean absolute error (MAE) of the error to quantify the power steady-state error, thereby obtaining corresponding performance indicators, and finally obtaining the AGC operation steady-state error performance indicators under different operating conditions.

[0152] Further, step S4 includes the following steps:

[0153] Step S41: performing a weighted performance evaluation calculation on the AGC of the thermal power plant unit based on the AGC operation overshoot performance index, the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions, and obtaining a comprehensive operating performance score of the AGC of the thermal power plant unit under different operating conditions;

[0154] In an embodiment of the present invention, corresponding weight values ​​are assigned to the AGC operation overshoot performance index, the AGC operation regulation stability performance index and the AGC operation steady-state error performance index previously quantified and calculated under corresponding operating conditions. These weight values ​​should be set according to the degree of influence of the actual operating conditions on each index. The weighted calculation formula is used to quantify the comprehensive score of the operation performance of the corresponding thermal power plant unit AGC under the corresponding operating condition, and finally the comprehensive score of the operation performance of the thermal power plant unit AGC under different operating conditions is obtained.

[0155] Step S42: Performing an online performance evaluation on the AGC of the thermal power plant units according to the comprehensive operating performance scores of the AGC of the thermal power plant units under different operating conditions, so as to obtain an online evaluation result of the state performance of the AGC system of the thermal power plant units.

[0156] In the embodiment of the present invention, the operation performance of the corresponding thermal power plant unit AGC is evaluated online according to the specific values ​​of the comprehensive operating performance score values ​​of the thermal power plant unit AGC under different operating conditions obtained by quantitative calculation previously, so as to evaluate the AGC system performance under the current operating conditions. This step is to match and compare the comprehensive operating performance score values ​​calculated in real time with the preset scoring standards by programming a real-time calculation module. For example, when the comprehensive operating performance score value is -45 is "poor", 46-75 is "good", and 76-1 The final evaluation result is “excellent”, and the online evaluation results of the status performance of the AGC system of the thermal power plant units are obtained. These results can be displayed on the user interface and trigger corresponding alarms or notifications to prompt operators whether they need to adjust control parameters or perform maintenance.

[0157] Furthermore, the present invention also provides an online evaluation system for AGC performance of thermal power plant units, which is used to execute the above-mentioned online evaluation method for AGC performance of thermal power plant units. The online evaluation system for AGC performance of thermal power plant units comprises:

[0158] The AGC operation parameter acquisition module of the thermal power plant unit is used to collect the real-time operation parameters of the AGC of the thermal power plant unit, so as to obtain the AGC operation load change signal of the thermal power plant unit, the AGC operation adjustment signal of the thermal power plant unit, the actual power generation power of the AGC operation of the thermal power plant unit and the AGC operation target power of the thermal power plant unit;

[0159] The module for dividing the timing of different working conditions of the AGC operating parameters is used to perform operating condition period affiliation analysis on the AGC operating load change signal of the thermal power plant unit based on the AGC of the thermal power plant unit, so as to obtain the AGC operating load change signal under different operating conditions; determine the operating condition timing range of the AGC operating load change signal under different operating conditions, and obtain the corresponding operating condition timing range interval under different operating conditions; based on the corresponding operating condition timing range interval under different operating conditions, the AGC operating regulation signal of the thermal power plant unit, the actual power generation power of the AGC operating of the thermal power plant unit, and the AGC operating target power of the thermal power plant unit are synchronously divided into the operating condition timing, so as to obtain the AGC operating regulation signal, the actual power generation power of the AGC operating, and the AGC operating target power under different operating conditions;

[0160] The AGC operation performance index calculation module is used to perform load response time and output power quantitative calculation on the AGC operation load change signal under different operation conditions, so as to obtain the operation load response time and the operation load output power under different operation conditions; based on the operation load response time under different operation conditions, the operation overshoot performance index is calculated on the operation load output power under different operation conditions and the AGC operation target power, so as to obtain the AGC operation overshoot performance index under different operation conditions; based on the AGC operation load change signal under different operation conditions, the operation regulation stability and steady-state error performance index is calculated on the AGC operation regulation signal under different operation conditions, the AGC operation actual power generation power and the AGC operation target power, so as to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operation conditions;

[0161] The AGC performance online evaluation module is used to evaluate the performance of the AGC of the thermal power plant unit online based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, so as to obtain the online evaluation results of the AGC system status performance of the thermal power plant unit.

[0162] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for online evaluation of AGC performance of thermal power plant units. It is characterized in that The following steps are involved: Step S1: collect real-time operating parameters of the AGC of the thermal power plant unit to obtain the AGC operating load change signal of the thermal power plant unit, the AGC operating adjustment signal of the thermal power plant unit, the AGC operating target power of the thermal power plant unit and the AGC operating actual power generation power of the thermal power plant unit; Step S2: Based on the AGC of the thermal power plant unit, the AGC operation load change signal of the thermal power plant unit is subjected to an operation condition period membership analysis to obtain the AGC operation load change signal under different operation conditions; the operation condition time sequence range is determined for the AGC operation load change signal under different operation conditions to obtain the operation condition time sequence range intervals corresponding to the different operation conditions; based on the operation condition time sequence range intervals corresponding to the different operation conditions, the AGC operation regulation signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the actual power generation power of the AGC operation of the thermal power plant unit are subjected to the operation condition time sequence synchronization division to obtain the AGC operation regulation signal, the AGC operation target power and the actual power generation power of the AGC operation under different operation conditions; Step S3: quantifying the load response time and output power of the AGC operating load change signal under different operating conditions to obtain the operating load response time and the operating load output power under different operating conditions; calculating the operating overshoot performance index of the operating load output power under different operating conditions and the AGC operating target power based on the operating load response time under different operating conditions to obtain the AGC operating overshoot performance index under different operating conditions; Based on the AGC operation load change signal under different operating conditions, the AGC operation regulation signal under different operating conditions, the AGC operation target power and the AGC operation actual power generation are calculated to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions; Step S4: Based on the AGC operation overshoot performance index, the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions, the AGC of the thermal power plant unit is evaluated online to obtain the online evaluation result of the state performance of the AGC system of the thermal power plant unit; Step S1 includes the following steps: Step S11: collecting the operating load change signal of the motor operating load process of the AGC of the thermal power plant unit in real time to obtain the operating load change signal of the AGC of the thermal power plant unit; Step S12: collecting operation regulation signals of the motor operation regulation process of the AGC of the thermal power plant unit in real time to obtain the AGC operation regulation signals of the thermal power plant unit; Step S13: analyzing the operating state change trend of the power generation operation process of the AGC of the thermal power plant unit to obtain the power generation operation state change trend of the AGC of the thermal power plant unit; performing an operating power demand and constraint statistical analysis on the power generation operation process of the AGC of the thermal power plant unit based on the power generation operation state change trend of the AGC of the thermal power plant unit to obtain the power generation operation power demand of the AGC of the thermal power plant unit and the power generation operation constraint conditions of the AGC of the thermal power plant unit; Step S14: performing target power forecasting calculation on the AGC power generation operation power demand of the thermal power plant units based on the AGC power generation operation constraint conditions of the thermal power plant units to obtain the AGC operation target power of the thermal power plant units; Step S15: monitoring and calculating the operating power generation of the AGC of the thermal power plant unit during the power generation operation process to obtain the actual power generation of the AGC of the thermal power plant unit; Step S2 includes the following steps: Step S21: Analyze the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units to obtain the load fluctuation characteristics of the AGC operating conditions of the thermal power plant units; Step S22: performing a signal time sequence change mode analysis on the AGC operation load change signal of the thermal power plant unit to obtain the time sequence change mode of the AGC operation load signal of the thermal power plant unit; dividing the AGC operation load change signal of the thermal power plant unit into time segments based on the time sequence change mode of the AGC operation load signal of the thermal power plant unit to obtain the AGC operation load change signal under different time segments of the change mode; Step S23: Based on the load fluctuation characteristics of the AGC operating condition of the thermal power plant unit, the AGC operating load change signal under different change mode time segments is subjected to operating condition period membership analysis to obtain the AGC operating load change signal under different operating conditions; Step S24: determining the starting point and the end point of the operating condition timing of the AGC operating load change signal under different operating conditions, and obtaining the corresponding starting point and end point of the changing signal timing under different operating conditions; determining the operating condition timing range according to the corresponding starting point and end point of the changing signal timing under different operating conditions, and obtaining the corresponding operating condition timing range interval under different operating conditions; Step S25: Based on the corresponding operating condition timing range intervals under different operating conditions, the AGC operation adjustment signal of the thermal power plant unit, the AGC operation target power of the thermal power plant unit and the AGC operation actual power generation power of the thermal power plant unit are synchronously divided into operating condition timings to obtain the AGC operation adjustment signal, the AGC operation target power and the AGC operation actual power generation power under different operating conditions; The step S22 of dividing the change mode time segment of the AGC operation load change signal of the thermal power plant unit based on the time sequence change mode of the AGC operation load signal of the thermal power plant unit comprises the following steps: The signal change pattern characteristics of the AGC operation load signal time series change pattern of the thermal power plant unit are analyzed to obtain the AGC operation load signal change pattern characteristic set of the thermal power plant unit; Draw a time series variation spectrum of the AGC operation load variation signal of the thermal power plant unit to generate a time series variation spectrum of the AGC operation load variation signal of the thermal power plant unit; Based on the corresponding time series change pattern characteristics in the change pattern feature set of the AGC operation load signal of the thermal power plant unit, the change pattern turning point of the time series change spectrum of the AGC operation load signal of the thermal power plant unit is determined to obtain the change pattern turning point set of the AGC operation load signal of the thermal power plant unit; According to the interval between two adjacent change mode turning points in the change mode turning point set of the AGC operation load signal of the thermal power plant unit, the corresponding time series in the time series change spectrum of the AGC operation load signal of the thermal power plant unit is divided into time series ranges to obtain different AGC operation load signal change mode time series range segments; Based on different AGC operation load signal change mode time series range segmentation, the AGC operation load change signal of the thermal power plant unit is divided into change mode time segments to obtain the AGC operation load change signal under different change mode time segments; Step S23 includes the following steps: Step S231: dividing the load fluctuation characteristics of the AGC operating condition of the thermal power plant unit into operating condition periods to obtain a subset of load fluctuation characteristics of the AGC operating condition of the thermal power plant unit in different operating condition periods; Step S232: performing feature vector mapping conversion on the load fluctuation feature subsets of the AGC of the thermal power plant unit under different operating conditions, so as to obtain the load fluctuation feature vectors under different operating conditions; Step S233: Based on the load fluctuation characteristic vectors under different operating conditions, the AGC operating load change signal under different change mode time segments is subjected to load signal fluctuation matching membership mining analysis to obtain the matching membership relationship between the operating load change signal under different change mode time segments and the load fluctuation of each operating condition; based on the matching membership relationship between the operating load change signal under different change mode time segments and the load fluctuation of each operating condition, the AGC operating load change signal under different change mode time segments is subjected to time period membership calculation to obtain the membership between the operating load change signal under different change mode time segments and each operating condition time period; Step S234: constructing a membership matrix according to the membership between the operating load change signal and each operating condition period under different change mode time segments to generate an AGC operating condition period membership matrix; Step S235: performing optimal membership screening processing for the AGC operating load change signals under different change mode time segments based on the AGC operating condition period membership matrix to obtain the AGC operating load change signals under different operating conditions; Step S3 includes the following steps: Step S31: performing load response time statistical analysis on the AGC operating load change signal under different operating conditions to obtain the operating load response time under different operating conditions; Step S32: performing output power quantization calculation on the AGC operating load change signal under different operating conditions based on the operating load response time under different operating conditions, so as to obtain the operating load output power under different operating conditions; Step S33: based on the operating load response time under different operating conditions, the operating power hyperbola is plotted for the operating load output power under different operating conditions and the AGC operating target power, so as to generate a hyperbola of changes between the operating load output power and the target power within the corresponding response time range under different operating conditions; Step S34: determining the maximum spacing point between the operating power curves for the changing hyperbola between the operating load output power and the target power within the corresponding response time range under different operating conditions, and obtaining the maximum spacing point between the changing hyperbola within the corresponding response time range under different operating conditions; calculating the operating overshoot performance index of the operating load output power and the AGC operating target power at the corresponding point time under different operating conditions based on the maximum spacing point between the changing hyperbola within the corresponding response time range under different operating conditions, and obtaining the AGC operating overshoot performance index under different operating conditions; Step S35: Calculate the operation regulation stability and steady-state error performance index of the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions based on the AGC operation load change signal under different operating conditions, and obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions; Step S32 includes the following steps: Step S321: performing load signal response timing fluctuation amplitude and frequency statistical analysis on the AGC load change signal under different operating conditions based on the operating load response time under different operating conditions, and obtaining the load change signal timing fluctuation amplitude and load change signal timing fluctuation frequency within the response time range under different operating conditions; Step S322: performing initial quantitative calculation of the response time output power of the AGC operation load change signal under different operating conditions based on the load change signal time fluctuation amplitude and the load change signal time fluctuation frequency within the response time range under different operating conditions, and obtaining the operation load fluctuation initial output power within the response time range under different operating conditions; Step S323: performing output power fluctuation limit evaluation and analysis on the initial output power of the operating load fluctuation within the response time range under different operating conditions, and obtaining the output power fluctuation limit range of the operating load under different operating conditions; Step S324: performing nonlinear fluctuation correction on the initial output power of the operating load fluctuation within the response time range under different operating conditions based on the operating load output power fluctuation limit range under different operating conditions, so as to obtain the operating load output power under different operating conditions; Step S35 includes the following steps: Step S351: analyzing the signal change fluctuation trend of the AGC operating load change signal under different operating conditions to obtain the operating load signal change fluctuation trend under different operating conditions; Step S352: determining the stable fluctuation trend period of the AGC operating load change signal under different operating conditions based on the fluctuation trend of the operating load signal under different operating conditions, so as to obtain the stable fluctuation trend period of the operating load under different operating conditions; Step S353: Based on the stable fluctuation trend period of the operating load under different operating conditions, the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions are subjected to stable period timing synchronization processing to obtain the AGC operation regulation signal, the AGC operation target power and the AGC operation actual power generation under different operating conditions in the same stable operation fluctuation period; Step S354: calculating the operation regulation stability performance index of the AGC operation regulation signal in the same stable operation fluctuation period under different operation conditions to obtain the AGC operation regulation stability performance index under different operation conditions; Step S355: Calculate the power steady-state error performance index for the AGC operation target power and the AGC operation actual power generation power in the same stable operation fluctuation period under different operating conditions to obtain the AGC operation steady-state error performance index under different operating conditions; Step S4 includes the following steps: Step S41: performing a weighted performance evaluation calculation on the AGC of the thermal power plant unit based on the AGC operation overshoot performance index, the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operating conditions, and obtaining a comprehensive operating performance score of the AGC of the thermal power plant unit under different operating conditions; Step S42: Performing an online performance evaluation on the AGC of the thermal power plant units according to the comprehensive operating performance scores of the AGC of the thermal power plant units under different operating conditions, so as to obtain an online evaluation result of the state performance of the AGC system of the thermal power plant units.

2. An online evaluation system for AGC performance of thermal power plant units, It is characterized in that Used to execute the AGC performance online evaluation method for thermal power plant units according to claim 1, the AGC performance online evaluation system for thermal power plant units comprises: The AGC operation parameter acquisition module of the thermal power plant unit is used to collect the real-time operation parameters of the AGC of the thermal power plant unit, so as to obtain the AGC operation load change signal of the thermal power plant unit, the AGC operation adjustment signal of the thermal power plant unit, the actual power generation power of the AGC operation of the thermal power plant unit and the AGC operation target power of the thermal power plant unit; The module for dividing the timing of different working conditions of the AGC operating parameters is used to perform operating condition period affiliation analysis on the AGC operating load change signal of the thermal power plant unit based on the AGC of the thermal power plant unit, so as to obtain the AGC operating load change signal under different operating conditions; determine the operating condition timing range of the AGC operating load change signal under different operating conditions, and obtain the corresponding operating condition timing range interval under different operating conditions; based on the corresponding operating condition timing range interval under different operating conditions, the AGC operating regulation signal of the thermal power plant unit, the actual power generation power of the AGC operating of the thermal power plant unit, and the AGC operating target power of the thermal power plant unit are synchronously divided into the operating condition timing, so as to obtain the AGC operating regulation signal, the actual power generation power of the AGC operating, and the AGC operating target power under different operating conditions; The AGC operation performance index calculation module is used to perform load response time and output power quantitative calculation on the AGC operation load change signal under different operation conditions, so as to obtain the operation load response time and the operation load output power under different operation conditions; based on the operation load response time under different operation conditions, the operation overshoot performance index is calculated on the operation load output power under different operation conditions and the AGC operation target power, so as to obtain the AGC operation overshoot performance index under different operation conditions; based on the AGC operation load change signal under different operation conditions, the operation regulation stability and steady-state error performance index is calculated on the AGC operation regulation signal under different operation conditions, the AGC operation actual power generation power and the AGC operation target power, so as to obtain the AGC operation regulation stability performance index and the AGC operation steady-state error performance index under different operation conditions; The AGC performance online evaluation module is used to evaluate the performance of the AGC of the thermal power plant unit online based on the AGC operation overshoot performance index, AGC operation regulation stability performance index and AGC operation steady-state error performance index under different operating conditions, so as to obtain the online evaluation results of the AGC system status performance of the thermal power plant unit.

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