Method and device for evaluating emergency peak-shaving standby capacity of photovoltaic synchronous generator power generation system

Abstract

The present invention relates to the technical field of photovoltaic power generation system operation and maintenance. The present invention provides a method and device for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous machine stack power generation system. Through multi-dimensional data acquisition, data feature extraction and algorithm matching, the emergency peak-shaving standby capacity of the photovoltaic synchronous machine stack power generation system is accurately evaluated. The present invention includes the steps of data acquisition, feature extraction, algorithm matching, estimated evaluation, real-time comparison, algorithm optimization and data verification. It can monitor the system status in real time, dynamically adjust the evaluation algorithm, and evaluate the accuracy of the evaluation results. The device includes units such as data acquisition, algorithm matching, evaluation, evaluation optimization and data processing, realizing an automated and intelligent evaluation process, and ensuring the stable operation of the photovoltaic synchronous machine stack power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation system operation and maintenance, and in particular to a method and device for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous machine pile power generation system. Background Art

[0002] Emergency peak-shaving of photovoltaic synchronous generator systems refers to a technical means of flexibly adjusting the power supply in the power system by introducing energy storage systems (such as battery energy storage, compressed air energy storage, etc.) and synchronous generator power generation technology to balance the supply and demand relationship of the power grid when photovoltaic power generation output becomes unstable or power demand peaks due to changing weather conditions (such as rain or night). The peak-shaving method can compensate for the randomness and intermittency of photovoltaic power generation and improve the stable power supply of the power system.

[0003] The emergency peak load backup capacity assessment for a PV synchronous reactor power generation system determines the amount of additional power support the system can provide in an emergency by analyzing factors such as the system's energy storage capacity, the synchronous reactor's response speed and power generation efficiency, and its coordinated control capabilities with the grid. The assessment comprehensively considers the PV array's historical power generation data, the charge and discharge performance of the energy storage equipment, the synchronous reactor's technical parameters, and its operational performance under different operating conditions. It also incorporates the grid's real-time demand and dispatch strategies to simulate system responses in various emergency scenarios. This comprehensive assessment accurately quantifies the PV synchronous reactor power generation system's emergency peak load backup capacity.

[0004] In terms of data processing for evaluating the emergency peak-shaving standby capacity of photovoltaic synchronous machine stack power generation systems, there are technical pain points, including the need to use multiple analysis algorithms, such as machine learning and deep learning, on a large amount of data during the evaluation of the emergency peak-shaving standby capacity of photovoltaic synchronous machine stack power generation systems. In the process of using analysis algorithms to process data, when errors occur in the analysis algorithm data processing, errors will occur in the evaluation of the emergency peak-shaving standby capacity of photovoltaic synchronous machine stack power generation systems. For this reason, the present invention provides a method and device for evaluating the emergency peak-shaving standby capacity of photovoltaic synchronous machine stack power generation systems. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a transient voltage stability control method and an extraction method for a photovoltaic synchronous machine stack power generation system, which solves the problem that when errors occur in the data processing of the analysis algorithm using the analysis algorithm, errors will occur in the evaluation of the emergency peak-shaving standby capacity of the photovoltaic synchronous machine stack power generation system.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] In a first aspect, the present invention provides a method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator stack power generation system, comprising:

[0008] Step S101, obtaining photovoltaic synchronous machine stack power generation system data, the photovoltaic synchronous machine stack power generation system data including photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data and system control data;

[0009] Step S102: Extracting data features from the photovoltaic synchronous machine stack power generation system data to obtain photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features. Matching the photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features in an emergency peak-shaving standby capacity evaluation algorithm knowledge base to obtain an emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features.

[0010] Step S103, using the corresponding emergency peak load standby capacity assessment algorithm to obtain estimated emergency peak load standby capacity assessment data based on the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics;

[0011] Step S104: collect real-time photovoltaic synchronous machine stack power generation system data, compare the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity assessment data, and if the real-time photovoltaic synchronous machine stack power generation system data exceeds the power supply capacity value in the estimated emergency peak-shaving standby capacity assessment data, optimize the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics using the corresponding emergency peak-shaving standby capacity assessment algorithm, and obtain the optimized emergency peak-shaving standby capacity assessment data using the corresponding optimized emergency peak-shaving standby capacity assessment algorithm;

[0012] Step S105, compare the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous machine stack power generation system data. If the real-time photovoltaic synchronous machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, then use the optimized emergency peak-shaving standby capacity assessment data as the data to be tested, collect the real-time photovoltaic synchronous machine stack power generation system parameters, and compare the data to be tested with the real-time photovoltaic synchronous machine stack power generation system parameters. If the data to be tested is within the range of the real-time photovoltaic synchronous machine stack power generation system parameters, then the optimized emergency peak-shaving standby capacity assessment data is accurate.

[0013] Furthermore, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention further includes:

[0014] Compare the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous generator stack power generation system data. If the real-time photovoltaic synchronous generator stack power generation system data exceeds the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, generate an early warning message.

[0015] Retrieve the corresponding data in the warning information to obtain the data set to be detected, preprocess the data in the data set to be detected, filter out the erroneous data in the data set to be detected, extract data features based on the erroneous data in the data set to be detected, and obtain the erroneous data features.

[0016] Furthermore, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention further includes:

[0017] Substituting the error data features into a preset data simulation model to generate simulation data corresponding to the error data, and replacing the to-be-detected data set with the simulation data corresponding to the error data to obtain a modified to-be-detected data set;

[0018] The modified data set to be tested is processed using the emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics to obtain the modified emergency peak-shaving standby capacity evaluation data.

[0019] Furthermore, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention further includes:

[0020] The modified emergency peak-shaving standby capacity assessment data is compared with the estimated emergency peak-shaving standby capacity assessment data. If the data comparison results are inconsistent, the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are matched in the emergency peak-shaving standby capacity assessment algorithm knowledge base to obtain the emergency peak-shaving standby capacity assessment algorithm corresponding to the re-matched photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics.

[0021] Furthermore, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention, said step S103 further includes:

[0022] According to the requirements of the emergency peak-shaving standby capacity assessment algorithm, the photovoltaic array data characteristics, energy storage system data characteristics, synchronous reactor data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics are matched with the corresponding emergency peak-shaving standby capacity assessment algorithm;

[0023] Emergency peak load reserve capacity assessment algorithms include neural network algorithms, support vector machine algorithms, decision tree algorithms, regression analysis algorithms, and time series analysis algorithms;

[0024] The photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are combined with the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the calculated data are sorted and summarized to estimate the emergency peak-shaving standby capacity evaluation data.

[0025] Furthermore, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention, said step S104 further includes:

[0026] Collect real-time data on the photovoltaic synchronous machine stack power generation system, including the real-time power generation of the photovoltaic array, the real-time charge and discharge status of the energy storage system, the real-time power generation power of the synchronous machine stack, the real-time demand of the power grid, and environmental parameters;

[0027] Compare the collected real-time photovoltaic synchronous machine pile power generation system data with the power supply capacity value in the estimated emergency peak load standby capacity assessment data;

[0028] Check whether the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system exceeds the estimated power supply capacity value. If the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system does not exceed the estimated power supply capacity value, the current estimated emergency peak load standby capacity assessment data is accurate;

[0029] If the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system exceeds the estimated power supply capacity value, the current evaluation algorithm is insufficient and needs to be optimized;

[0030] Re-analyze the data characteristics of the photovoltaic array, energy storage system, synchronous reactor, power grid, environment, and system control to identify factors that lead to assessment errors. Based on the identified factors, adjust the emergency peak-shaving reserve capacity assessment algorithm used, including modifying algorithm parameters, introducing new characteristic variables, and optimizing the algorithm's logical structure.

[0031] Using the optimized emergency peak-shaving standby capacity evaluation algorithm, the data characteristics of the photovoltaic array, energy storage system, synchronous machine pile, power grid, environmental data and system control data are re-evaluated to obtain the optimized emergency peak-shaving standby capacity evaluation data.

[0032] Furthermore, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention, said step S105 further includes:

[0033] Compare the data of the real-time photovoltaic synchronous machine stack power generation system with the power supply capacity values ​​in the optimized emergency peak-shaving standby capacity assessment data one by one to check whether the various data of the real-time photovoltaic synchronous machine stack power generation system do not exceed the power supply capacity values ​​in the optimized assessment data;

[0034] If any data of the real-time photovoltaic synchronous machine pile power generation system exceeds the power supply capacity value in the optimized emergency peak load standby capacity assessment data, the optimized assessment data will still be inaccurate;

[0035] If all data of the real-time photovoltaic synchronous machine pile power generation system do not exceed the power supply capacity value in the optimized emergency peak load standby capacity assessment data, the optimized assessment data is consistent with the real-time system data;

[0036] When the real-time photovoltaic synchronous machine pile power generation system data does not exceed the optimized emergency peak load standby capacity assessment data,

[0037] The optimized evaluation data is set as the data to be tested.

[0038] In a second aspect, the present invention provides a device for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous machine stack power generation system, which is applied to a method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous machine stack power generation system, comprising:

[0039] A data acquisition unit acquires data of a photovoltaic synchronous machine stack power generation system, including photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data, and system control data;

[0040] an algorithm matching unit, which extracts data features from the photovoltaic synchronous machine pile power generation system data to obtain photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features, and matches the photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features in an emergency peak-shaving standby capacity evaluation algorithm knowledge base to obtain an emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features;

[0041] An evaluation unit uses a corresponding emergency peak-shaving standby capacity evaluation algorithm to evaluate the characteristics of the photovoltaic array data, the energy storage system data, the synchronous machine reactor data, the power grid data, the environmental data, and the system control data to obtain estimated emergency peak-shaving standby capacity evaluation data;

[0042] An evaluation and optimization unit collects real-time photovoltaic synchronous machine stack power generation system data, compares the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity evaluation data, and if the real-time photovoltaic synchronous machine stack power generation system data exceeds the power supply capacity value in the estimated emergency peak-shaving standby capacity evaluation data, then the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are optimized using the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are obtained using the corresponding optimized emergency peak-shaving standby capacity evaluation algorithm to obtain optimized emergency peak-shaving standby capacity evaluation data;

[0043] The data processing unit compares the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous machine stack power generation system data. If the real-time photovoltaic synchronous machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, the optimized emergency peak-shaving standby capacity assessment data is used as the data to be tested, and the real-time photovoltaic synchronous machine stack power generation system parameters are collected. The data to be tested are compared with the real-time photovoltaic synchronous machine stack power generation system parameters. If the data to be tested is within the range of the real-time photovoltaic synchronous machine stack power generation system parameters, the optimized emergency peak-shaving standby capacity assessment data is accurate.

[0044] Beneficial effects of the present invention;

[0045] By acquiring multi-dimensional data (photovoltaic array data, energy storage system data, synchronous generator data, power grid data, environmental data, and system control data) and performing data feature extraction and algorithm matching, this invention can more comprehensively assess the emergency peak-shaving and standby capacity of photovoltaic synchronous generator systems. Compared with traditional methods, this method avoids the assessment bias that may be caused by a single data source and improves the accuracy of the assessment.

[0046] The present invention adopts a variety of emergency peak-shaving standby capacity assessment algorithms (such as neural network algorithms, support vector machine algorithms, decision tree algorithms, etc.), and dynamically matches and optimizes them according to actual data characteristics. This mechanism enables the system to adapt to different working conditions and environmental changes, improving the adaptability and robustness of the system. By collecting real-time photovoltaic synchronous machine stack power generation system data and comparing it with the estimated emergency peak-shaving standby capacity assessment data, the present invention can monitor the operating status of the system in real time. When it is found that the real-time data exceeds the estimated power supply capacity value, the system can immediately generate early warning information to provide timely decision support for operators.

[0047] When an assessment error is detected, the present invention optimizes the algorithm, improving its processing efficiency and accuracy by adjusting algorithm parameters, introducing new feature variables, or optimizing the algorithm's logical structure. This helps reduce system operating costs and improves overall performance. The device for assessing the emergency peak-shaving and standby capacity of a photovoltaic synchronous generator system, provided by the present invention, enables automatic data collection, processing, and analysis, promoting intelligent operation and maintenance and automated management of photovoltaic power generation systems. This helps reduce manual intervention, improves operation and maintenance efficiency, and reduces operation and maintenance costs.

[0048] By accurately assessing and optimizing the emergency peak-shaving and standby capacity of a photovoltaic synchronous generator system, this invention helps improve the stability and security of the entire power system. During peak power demand or emergency situations, the system can respond quickly and provide reliable power support, avoiding blackouts or power shortages.

[0049] In summary, the present invention has achieved remarkable beneficial effects in improving the accuracy of emergency peak-shaving standby capacity assessment of photovoltaic synchronous generator power generation systems, enhancing the adaptability and robustness of the system, realizing real-time monitoring and early warning, optimizing algorithms to improve processing efficiency, promoting intelligent operation and maintenance and automated management, and improving the stability and safety of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0051] Figure 1 A schematic flow chart of a method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator stack power generation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0053] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0054] In a first aspect, the present invention provides a method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator stack power generation system, comprising:

[0055] Step S101, obtaining photovoltaic synchronous machine stack power generation system data, the photovoltaic synchronous machine stack power generation system data including photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data and system control data;

[0056] PV array data includes information on the panels' operating status, power generation, conversion efficiency under varying light intensity, and the impact of temperature on power generation efficiency. The PV array is the core component of a PV power generation system, and its data directly reflects the system's power generation capacity and efficiency.

[0057] Energy storage system data plays a crucial role in balancing supply and demand and stabilizing output in synchronous photovoltaic (PV) power generation systems. Therefore, it's important to collect data on energy storage equipment, including capacity, current charge, charge / discharge efficiency, and cycle life. This data helps assess the energy storage system's responsiveness and duration during emergency peak load regulation.

[0058] The synchronous reactor is a key device in the system, responsible for regulating and stabilizing power output. Data such as its power generation, efficiency, and operating status (such as speed and temperature) must be collected. This data is crucial for assessing the synchronous reactor's ability to respond quickly and maintain stable output in emergency situations.

[0059] Grid data includes real-time grid demand, load changes, voltage and frequency stability, etc. This data is used to analyze the interaction between the PV synchronous generator system and the grid, and how the system responds to changes in grid demand.

[0060] Environmental factors have a significant impact on photovoltaic power generation systems, such as temperature, humidity, wind speed, wind direction, etc. Collecting this data helps analyze the impact of environmental factors on the system's power generation efficiency and stability.

[0061] System control data includes the system’s control strategy, dispatch instructions, protection action records, etc. These data reflect the system’s operating strategy and response mechanism under different working conditions and are of great significance for evaluating the system’s overall performance and emergency response capabilities.

[0062] By comprehensively collecting these data, a solid foundation can be provided for subsequent data feature extraction and algorithm matching, thereby accurately evaluating the emergency peak-shaving standby capacity of the photovoltaic synchronous stack power generation system.

[0063] Step S102: Extracting data features from the photovoltaic synchronous machine stack power generation system data to obtain photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features. Matching the photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features in an emergency peak-shaving standby capacity evaluation algorithm knowledge base to obtain an emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features.

[0064] The raw data collected from the photovoltaic synchronous generator system is processed to extract key information that can reflect the characteristics of the system, namely data features. These features include:

[0065] Photovoltaic array data characteristics: such as the impact of light intensity and temperature on the output power of photovoltaic panels, the conversion efficiency of photovoltaic panels, etc.

[0066] Energy storage system data characteristics: such as the capacity of the energy storage equipment, current power, charge and discharge rate, cycle life, etc.

[0067] Synchronous reactor data characteristics: such as power generation power, efficiency, response speed, operating status parameters, etc.

[0068] Grid data characteristics: such as real-time grid demand, load change rate, voltage and frequency stability indicators, etc.

[0069] Environmental data characteristics: such as temperature, humidity, wind speed, wind direction and other meteorological conditions.

[0070] System control data characteristics: such as the effectiveness of control strategies, the execution of scheduling instructions, the triggering frequency of protection mechanisms, etc.

[0071] After extracting the data features, the next step is to match them against a knowledge base of emergency peak-shaving reserve capacity assessment algorithms to find the most appropriate assessment algorithm for each feature. This knowledge base includes a variety of algorithms, such as neural networks, support vector machines, decision trees, regression analysis, and time series analysis. Each algorithm has its own applicable scenarios and advantages.

[0072] Matching algorithm type with data characteristics: For example, for time series data, time series analysis algorithms may be preferred. Algorithm accuracy and computational efficiency must be balanced, finding a balance between high accuracy requirements and real-time performance requirements.

[0073] Step S103, using the corresponding emergency peak load standby capacity assessment algorithm to obtain estimated emergency peak load standby capacity assessment data based on the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics;

[0074] In step S103, various data features extracted from the photovoltaic synchronous machine stack power generation system (including photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features and system control data features) are input into the corresponding emergency peak-shaving standby capacity evaluation algorithm obtained by matching previously to calculate the estimated emergency peak-shaving standby capacity evaluation data.

[0075] Data preparation: Ensure that all extracted data features have been preprocessed according to the requirements of the algorithm, such as normalization and standardization, so that the algorithm can process the data correctly.

[0076] Algorithm Application: Each data feature is input into its corresponding evaluation algorithm. These algorithms may include neural networks, support vector machines, decision trees, regression analysis, and time series analysis. Each algorithm outputs a prediction or evaluation result based on the input data features, following its inherent logic and calculation rules.

[0077] Results Summary: All algorithm outputs will be aggregated to form a comprehensive set of estimated emergency peak load backup capacity assessment data. This data will comprehensively reflect the backup capacity of the PV synchronous generator system under emergency peak load conditions, including but not limited to the system's maximum power supply capacity, response time, and stability.

[0078] Data verification: After obtaining the estimated data, some basic verification work may be required, such as checking whether the data is within a reasonable range and whether there are outliers, to ensure the accuracy and reliability of the estimated results.

[0079] Through this step, based on the current data of the photovoltaic synchronous machine pile power generation system, a preliminary estimate of its backup capacity in emergency peak-shaving situations can be made, which has important reference value for the stable operation and emergency response of the power system.

[0080] Step S104: collect real-time photovoltaic synchronous machine stack power generation system data, compare the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity assessment data, and if the real-time photovoltaic synchronous machine stack power generation system data exceeds the power supply capacity value in the estimated emergency peak-shaving standby capacity assessment data, optimize the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics using the corresponding emergency peak-shaving standby capacity assessment algorithm, and obtain the optimized emergency peak-shaving standby capacity assessment data using the corresponding optimized emergency peak-shaving standby capacity assessment algorithm;

[0081] In step S104, the main process is to compare the real-time PV synchronous generator system data with the previously estimated emergency peak load backup capacity assessment data, and perform necessary algorithm optimization based on the comparison results. The following is a detailed explanation of this step:

[0082] Collecting real-time photovoltaic synchronous machine stack power generation system data includes real-time acquisition of photovoltaic array power generation, charge and discharge status of energy storage system, synchronous machine stack power generation, real-time demand of power grid and environmental parameters (such as temperature, humidity, light intensity, etc.).

[0083] The collected real-time photovoltaic synchronous machine stack power generation system data is compared with the estimated emergency peak-shaving standby capacity assessment data obtained in step S103, especially focusing on the comparison of the power supply capacity value (i.e., the maximum power support that the system can provide in an emergency).

[0084] If the real-time PV synchronous generator system data does not exceed the estimated power supply capacity value, then the current estimated data can be considered accurate and no further optimization is required.

[0085] If real-time PV synchronous generator system data exceeds the estimated power supply capacity, the current assessment algorithm may be insufficient and requires optimization. In this case, the corresponding emergency peak-shaving backup capacity assessment algorithm should be reviewed based on the characteristics of the PV array data, energy storage system data, synchronous generator data, power grid data, environmental data, and system control data.

[0086] Identify factors that lead to evaluation errors, which may include improper algorithm parameter settings, unreasonable feature variable selection, or improved algorithm logic structure. Based on the identified factors, adjust the evaluation algorithm. This may include modifying algorithm parameters, introducing new feature variables, or optimizing the algorithm logic structure.

[0087] Using the optimized emergency peak-shaving standby capacity evaluation algorithm, the data characteristics of the photovoltaic array, energy storage system, synchronous machine pile, power grid, environmental data and system control data are re-evaluated to obtain the optimized emergency peak-shaving standby capacity evaluation data.

[0088] Through this step, the accuracy of the emergency peak-shaving standby capacity assessment can be improved, so that the photovoltaic synchronous stack power generation system can better adapt to fluctuations in power demand and emergency situations, and provide strong support for the stable operation of the power system.

[0089] Step S105, compare the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous machine stack power generation system data. If the real-time photovoltaic synchronous machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, then use the optimized emergency peak-shaving standby capacity assessment data as the data to be tested, collect the real-time photovoltaic synchronous machine stack power generation system parameters, and compare the data to be tested with the real-time photovoltaic synchronous machine stack power generation system parameters. If the data to be tested is within the range of the real-time photovoltaic synchronous machine stack power generation system parameters, then the optimized emergency peak-shaving standby capacity assessment data is accurate.

[0090] The optimized emergency peak-shaving standby capacity assessment data is compared with the real-time PV synchronous generator system data, with particular attention paid to the power supply capacity value. This step is to verify whether the optimized assessment data matches the real-time data of the actual system.

[0091] If the real-time PV synchronous generator system data does not exceed the power supply capacity value in the optimized emergency peak-shaving backup capacity assessment data, then the optimized assessment data can be preliminarily considered accurate. At this point, the optimized emergency peak-shaving backup capacity assessment data is used as the test data for further verification.

[0092] In order to more comprehensively verify the optimized evaluation data, it is necessary to collect specific parameters of the real-time photovoltaic synchronous machine stack power generation system, such as the actual output power of the photovoltaic array, the charging and discharging status of the energy storage system, and the actual power generation power of the synchronous machine stack.

[0093] The data to be tested (i.e., the optimized emergency peak-shaving backup capacity assessment data) is compared with the collected real-time PV synchronous generator system parameters. This step is to further verify whether the optimized assessment data is within the reasonable range of the actual system parameters.

[0094] If the data to be tested is within the real-time PV synchronous generator system parameter range and consistent with the real-time system performance, then the optimized emergency peak-shaving backup capacity assessment data can be confirmed to be accurate. This means that the optimized assessment algorithm can accurately reflect the backup capacity of the PV synchronous generator system under emergency peak-shaving conditions.

[0095] Once the optimized emergency peak-shaving reserve capacity assessment data is verified to be accurate, it can be used to guide the actual operation of the photovoltaic synchronous generator system and emergency peak-shaving decisions. This helps improve the system's operating efficiency and stability, and enhances the ability to provide reliable power support during peak power demand or emergency situations.

[0096] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention further includes:

[0097] Compare the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous generator stack power generation system data. If the real-time photovoltaic synchronous generator stack power generation system data exceeds the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, generate an early warning message.

[0098] Retrieve the corresponding data in the warning information to obtain the data set to be detected, preprocess the data in the data set to be detected, filter out the erroneous data in the data set to be detected, extract data features based on the erroneous data in the data set to be detected, and obtain the erroneous data features.

[0099] The system continuously compares optimized emergency peak-shaving backup capacity assessment data with real-time PV synchronous generator system data. If real-time PV synchronous generator system data (such as actual power generation, energy storage status, and grid demand) exceeds the power supply capacity values ​​in the optimized assessment data, this indicates that the system may be at risk of failing to meet power demand or threatening stable operation. In this case, the system immediately generates an early warning to alert operators or automatically trigger an emergency response mechanism.

[0100] The system retrieves the corresponding data in the warning information, which may include the specific time when the power supply capacity value is exceeded, relevant parameter values, system status, etc. The data is organized into a set of data to be tested for further analysis and processing.

[0101] Preprocessing the data in the dataset to be tested may include data cleaning (removing invalid or outliers) and data normalization or standardization to ensure data accuracy and consistency. The system uses this preprocessed data to filter out erroneous data, which may be caused by sensor failure, data transmission errors, or system misjudgment.

[0102] Data features are extracted from the filtered erroneous data to identify the specific causes or patterns that lead to the errors. These features may include sudden changes in data, periodic anomalies, and persistent deviations from normal values. Based on these extracted features, the system can take appropriate measures, such as adjusting sensors, repairing data transmission links, and optimizing evaluation algorithms, to eliminate the source of the erroneous data. These features can also serve as an important reference for future system improvements and optimizations, helping to improve system stability and accuracy.

[0103] Through this series of steps, the transient voltage stability control method of the photovoltaic synchronous machine stack power generation system described in the present invention can not only monitor the system status in real time, but also provide timely warnings when potential problems are found, and continuously improve and optimize system performance by analyzing error data.

[0104] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention further includes:

[0105] Substituting the error data features into a preset data simulation model to generate simulation data corresponding to the error data, and replacing the to-be-detected data set with the simulation data corresponding to the error data to obtain a modified to-be-detected data set;

[0106] The modified data set to be tested is processed using the emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics to obtain the modified emergency peak-shaving standby capacity evaluation data.

[0107] The transient voltage stability control method for a photovoltaic synchronous generator system includes processing erroneous data and re-evaluating the emergency peak-shaving reserve capacity. The following is a detailed description of the steps involved:

[0108] By using the extracted features of the erroneous data, these features are substituted into a pre-designed data simulation model. This model can generate simulated data corresponding to the erroneous data based on the input features.

[0109] This simulated data is used to replace the original erroneous data in the dataset to be tested. This replacement operation results in a corrected dataset that more closely reflects the system's actual operating status. Comprehensive data processing is performed on this corrected dataset using an emergency peak-shaving reserve capacity assessment algorithm based on the characteristics of the PV array, energy storage system, synchronous generator, power grid, environmental, and system control data.

[0110] Through algorithmic processing, a revised set of emergency peak-shaving reserve capacity assessment data is obtained. This data more accurately and reliably reflects the system's emergency peak-shaving reserve capacity after taking into account the correction of erroneous data. This revised and reassessed data more accurately determines the system's emergency peak-shaving reserve capacity, thereby formulating more reasonable and effective operating strategies and control measures, helping to improve system stability.

[0111] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention further includes:

[0112] The modified emergency peak-shaving standby capacity assessment data is compared with the estimated emergency peak-shaving standby capacity assessment data. If the data comparison results are inconsistent, the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are matched in the emergency peak-shaving standby capacity assessment algorithm knowledge base to obtain the emergency peak-shaving standby capacity assessment algorithm corresponding to the re-matched photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics.

[0113] The revised emergency peak load reserve capacity assessment data is compared with the previously estimated emergency peak load reserve capacity assessment data. This step is to check whether the new assessment data is consistent with the original estimated data after taking into account the correction of the erroneous data.

[0114] If the comparison results show inconsistent data, this means that there may be some deviations or deficiencies in the previous evaluation process. In order to solve this problem, the system needs to take further measures to optimize the evaluation algorithm.

[0115] The system then matches the data characteristics of the photovoltaic array, energy storage system, synchronous reactor, power grid, environment, and system control systems with the knowledge base of emergency peak load capacity assessment algorithms. This step aims to find an assessment algorithm that is more suitable for the current system status and data characteristics.

[0116] Through re-matching, the system can obtain emergency peak-shaving reserve capacity assessment algorithms based on the re-matched data characteristics of the PV array, energy storage system, synchronous generator, power grid, environment, and system control. These new algorithms better align with the actual system operation and provide more accurate assessment results.

[0117] The system will use these new evaluation algorithms to process real-time synchronous photovoltaic power generation system data to verify their accuracy and effectiveness. If the new evaluation algorithms provide better evaluation results, they will be adopted as the system's standard evaluation algorithms. This process is a continuous optimization and iteration process. As the system continues to operate and data continues to accumulate, the system can continuously update and optimize the evaluation algorithms to ensure that they always accurately reflect the system's emergency peak-shaving and backup capabilities.

[0118] By introducing this feedback and optimization mechanism, the transient voltage stability control method for a photovoltaic synchronous generator system described in this invention not only promptly processes erroneous data but also dynamically adjusts the evaluation algorithm based on the system's actual operating conditions and data characteristics, thereby ensuring the accuracy and reliability of the evaluation results. This helps improve system stability and security, providing a strong guarantee for the stable operation of the power system.

[0119] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention, step S103 further includes:

[0120] According to the requirements of the emergency peak-shaving standby capacity assessment algorithm, the photovoltaic array data characteristics, energy storage system data characteristics, synchronous reactor data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics are matched with the corresponding emergency peak-shaving standby capacity assessment algorithm;

[0121] Emergency peak load reserve capacity assessment algorithms include neural network algorithms, support vector machine algorithms, decision tree algorithms, regression analysis algorithms, and time series analysis algorithms;

[0122] The photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are combined with the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the calculated data are sorted and summarized to estimate the emergency peak-shaving standby capacity evaluation data.

[0123] Identify and extract data features of key components and factors such as photovoltaic arrays, energy storage systems, synchronous generators, power grids, the environment, and system controls. These data features may include, but are not limited to, power generation, energy storage status, power output, grid demand, ambient temperature, wind speed, and system control instructions.

[0124] Based on the requirements for emergency peak-shaving reserve capacity assessment, an appropriate assessment algorithm is selected from a pre-set library of algorithms. The algorithms mentioned in this paper include neural network algorithms, support vector machine algorithms, decision tree algorithms, regression analysis algorithms, and time series analysis algorithms. Each algorithm has its own unique advantages and applicable scenarios, and the selection should be based on the actual data characteristics and assessment objectives.

[0125] The extracted data features are matched to the selected evaluation algorithm. This step involves inputting the PV array data features, energy storage system data features, synchronous reactor data features, power grid data features, environmental data features, and system control data features into the corresponding evaluation algorithm. For example, a neural network algorithm may be more suitable for handling complex nonlinear relationships, while a time series analysis algorithm is suitable for predicting future trends.

[0126] The matched evaluation algorithm is used to calculate and process the data features. This may include steps such as data cleaning, preprocessing, feature extraction, and model training. The data processed by the algorithm will reflect the system's emergency peak-shaving and standby capacity.

[0127] The emergency peak-shaving reserve capacity data calculated by each algorithm is collated and summarized. This step may involve data fusion, weight assignment, and result verification to ensure the accuracy and reliability of the estimated results. Ultimately, the estimated emergency peak-shaving reserve capacity assessment data is obtained, which will be used for subsequent system control, decision support, or performance evaluation.

[0128] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention, step S104 further includes:

[0129] Collect real-time data on the photovoltaic synchronous machine stack power generation system, including the real-time power generation of the photovoltaic array, the real-time charge and discharge status of the energy storage system, the real-time power generation power of the synchronous machine stack, the real-time demand of the power grid, and environmental parameters;

[0130] Compare the collected real-time photovoltaic synchronous machine pile power generation system data with the power supply capacity value in the estimated emergency peak load standby capacity assessment data;

[0131] Check whether the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system exceeds the estimated power supply capacity value. If the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system does not exceed the estimated power supply capacity value, the current estimated emergency peak load standby capacity assessment data is accurate;

[0132] If the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system exceeds the estimated power supply capacity value, the current evaluation algorithm is insufficient and needs to be optimized;

[0133] Re-analyze the data characteristics of the photovoltaic array, energy storage system, synchronous reactor, power grid, environment, and system control to identify factors that lead to assessment errors. Based on the identified factors, adjust the emergency peak-shaving reserve capacity assessment algorithm used, including modifying algorithm parameters, introducing new characteristic variables, and optimizing the algorithm's logical structure.

[0134] Using the optimized emergency peak-shaving standby capacity evaluation algorithm, the data characteristics of the photovoltaic array, energy storage system, synchronous machine pile, power grid, environmental data and system control data are re-evaluated to obtain the optimized emergency peak-shaving standby capacity evaluation data.

[0135] Collect key data of the real-time photovoltaic synchronous stack power generation system, including but not limited to the real-time power generation of the photovoltaic array, the real-time charging and discharging status of the energy storage system, the real-time power generation power of the synchronous stack, the real-time demand of the power grid and related environmental parameters (such as temperature, light intensity, wind speed, etc.).

[0136] Compare the collected real-time PV synchronous generator system data with the power supply capacity values ​​previously estimated in the emergency peak-shaving reserve capacity assessment data. This step aims to verify the accuracy of the estimated data and the system's actual current power supply capacity.

[0137] Check whether the actual power supply capacity of the real-time PV synchronous generator system exceeds the estimated power supply capacity. If the actual power supply capacity does not exceed the estimated value, the current estimated data is accurate and the evaluation algorithm is effective. If the actual power supply capacity exceeds the estimated value, it indicates that the current evaluation algorithm is insufficient and may not accurately reflect the system's actual emergency peak load backup capacity.

[0138] Conduct a further in-depth analysis of the data characteristics of the photovoltaic array, energy storage system, synchronous reactor, power grid, environmental data, and system control data to identify the specific factors that lead to assessment errors. This may involve multiple aspects such as data quality, feature selection, and the logical structure of the algorithm.

[0139] Based on the identified error factors, the emergency peak load reserve capacity assessment algorithm used is adjusted and optimized. This may include modifying the algorithm parameters, introducing new characteristic variables, optimizing the algorithm's logical structure, or adopting a new algorithm model.

[0140] Using the optimized emergency peak-shaving reserve capacity assessment algorithm, we re-evaluated the data characteristics of the photovoltaic array, energy storage system, synchronous reactor, power grid, environmental data, and system control data. This step aims to verify whether the optimized algorithm can more accurately reflect the system's emergency peak-shaving reserve capacity.

[0141] The optimized emergency peak load reserve capacity assessment data can be applied to the actual control, decision support, or performance evaluation of the system. This helps improve the stability and security of the system and ensures that it can respond quickly and accurately to power demand in emergency situations.

[0142] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous generator stack power generation system according to the present invention, step S105 further includes:

[0143] Compare the data of the real-time photovoltaic synchronous machine stack power generation system with the power supply capacity values ​​in the optimized emergency peak-shaving standby capacity assessment data one by one to check whether the various data of the real-time photovoltaic synchronous machine stack power generation system do not exceed the power supply capacity values ​​in the optimized assessment data;

[0144] If any data of the real-time photovoltaic synchronous machine pile power generation system exceeds the power supply capacity value in the optimized emergency peak load standby capacity assessment data, the optimized assessment data will still be inaccurate;

[0145] If all data of the real-time photovoltaic synchronous machine pile power generation system do not exceed the power supply capacity value in the optimized emergency peak load standby capacity assessment data, the optimized assessment data is consistent with the real-time system data;

[0146] When the real-time photovoltaic synchronous machine stack power generation system data does not exceed the optimized emergency peak-shaving standby capacity assessment data, the optimized assessment data is set as the data to be tested.

[0147] The real-time data of the photovoltaic synchronous generator system (including the real-time power generation of the photovoltaic array, the real-time charge and discharge status of the energy storage system, the real-time power generation of the synchronous generator, and the real-time demand of the power grid) is compared one by one with the power supply capacity values ​​in the optimized emergency peak-shaving backup capacity assessment data. This step is intended to ensure consistency between the actual system operation status and the assessment data.

[0148] Check whether any data from the real-time PV synchronous generator system exceeds the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data. If any data exceeds the assessment value, it indicates that the optimized assessment data is still inaccurate and may contain omissions or misjudgments.

[0149] If all data of the real-time photovoltaic synchronous machine stack power generation system do not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, it can be confirmed that the optimized assessment data is consistent with the real-time system data and the assessment result is reliable.

[0150] Once the real-time PV synchronous generator system data is confirmed to be within the optimized emergency peak-shaving reserve capacity assessment data, this optimized assessment data set is set as the data to be tested. This means that this data has passed preliminary verification and can be used as a reference for subsequent system control, decision support, or performance evaluation.

[0151] For the optimized evaluation data set as the data to be tested, further stability testing, long-term follow-up verification or comparative verification with other evaluation methods can be carried out to ensure its long-term effectiveness and accuracy.

[0152] Since the operating conditions of the photovoltaic synchronous machine stack power generation system may change with time and environmental factors, it is necessary to continuously monitor the real-time photovoltaic synchronous machine stack power generation system data and update the emergency peak-shaving standby capacity assessment data regularly or as needed to ensure the timeliness of the assessment results.

[0153] Through the above steps, the present invention ensures that the optimized emergency peak-shaving reserve capacity assessment data is consistent with the actual operating status of the real-time photovoltaic synchronous generator system, providing strong support for the system's stable operation and rapid response in emergency situations. Furthermore, the method offers a degree of flexibility and scalability, allowing the comparative verification process and standards to be adjusted as needed to meet the application requirements of different scenarios.

[0154] In a second aspect, the present invention provides a device for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous machine stack power generation system, which is applied to a method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous machine stack power generation system, comprising:

[0155] A data acquisition unit acquires data of a photovoltaic synchronous machine stack power generation system, including photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data, and system control data;

[0156] an algorithm matching unit, which extracts data features from the photovoltaic synchronous machine pile power generation system data to obtain photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features, and matches the photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features in an emergency peak-shaving standby capacity evaluation algorithm knowledge base to obtain an emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features;

[0157] An evaluation unit uses a corresponding emergency peak-shaving standby capacity evaluation algorithm to evaluate the characteristics of the photovoltaic array data, the energy storage system data, the synchronous machine reactor data, the power grid data, the environmental data, and the system control data to obtain estimated emergency peak-shaving standby capacity evaluation data;

[0158] An evaluation and optimization unit collects real-time photovoltaic synchronous machine stack power generation system data, compares the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity evaluation data, and if the real-time photovoltaic synchronous machine stack power generation system data exceeds the power supply capacity value in the estimated emergency peak-shaving standby capacity evaluation data, then the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are optimized using the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are obtained using the corresponding optimized emergency peak-shaving standby capacity evaluation algorithm to obtain optimized emergency peak-shaving standby capacity evaluation data;

[0159] The data processing unit compares the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous machine stack power generation system data. If the real-time photovoltaic synchronous machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, the optimized emergency peak-shaving standby capacity assessment data is used as the data to be tested, and the real-time photovoltaic synchronous machine stack power generation system parameters are collected. The data to be tested are compared with the real-time photovoltaic synchronous machine stack power generation system parameters. If the data to be tested is within the range of the real-time photovoltaic synchronous machine stack power generation system parameters, the optimized emergency peak-shaving standby capacity assessment data is accurate.

[0160] The technical solution provided by this invention, through a series of innovative steps, effectively solves the problem of errors that may occur when using analytical algorithms to process data, which can lead to inaccurate assessment of the emergency peak-shaving standby capacity of photovoltaic synchronous power generation systems. The specific solution is as follows:

[0161] Multi-dimensional data is collected from the photovoltaic synchronous generator system, including PV array data, energy storage system data, synchronous generator data, power grid data, environmental data, and system control data. Feature extraction is performed on this data. The extracted features are then matched with algorithms in the emergency peak load reserve capacity assessment knowledge base to select the most appropriate assessment algorithm for each type of data feature.

[0162] A matching algorithm is used to perform preliminary data processing to estimate emergency peak-shaving and standby capacity. Real-time data from the photovoltaic synchronous generator system is collected and compared with the estimated data. If the real-time data exceeds the estimated power supply capacity, this indicates a potential error in the initial assessment. The algorithm is optimized to address this error by adjusting algorithm parameters, introducing new feature variables, or optimizing the algorithm's logical structure to obtain optimized emergency peak-shaving and standby capacity assessment data.

[0163] Compare the optimized assessment data again with the real-time data to ensure the accuracy of the optimized data. If the comparison reveals that the real-time data still exceeds the power supply capacity of the optimized data, an early warning message is generated and the corresponding data in the early warning message is retrieved for detailed analysis. Preprocess the data to filter out erroneous data and extract its characteristics.

[0164] Substitute the erroneous data characteristics into the pre-set data simulation model, generate simulated data to replace the erroneous data, and obtain a corrected data set for testing. Use the optimized algorithm to process the corrected data to obtain more accurate emergency peak-shaving standby capacity assessment data. Compare the modified assessment data with the estimated data. If there is any inconsistency, re-match the algorithm in the algorithm knowledge base to find a more appropriate algorithm. Through a continuous process of data collection, comparison, optimization, and verification, the accuracy and reliability of the emergency peak-shaving standby capacity assessment are ensured.

[0165] The present invention can not only detect and correct errors in a timely manner during data processing and improve the accuracy of the assessment, but also adapt to the ever-changing photovoltaic synchronous machine stack power generation system environment by continuously optimizing the algorithm, thereby improving the effectiveness of the assessment results.

Claims

1. A method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator system, characterized in that: include: Step S101, obtaining photovoltaic synchronous machine stack power generation system data, the photovoltaic synchronous machine stack power generation system data including photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data and system control data; Step S102: Extracting data features from the photovoltaic synchronous machine stack power generation system data to obtain photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features. Matching the photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features in an emergency peak-shaving standby capacity evaluation algorithm knowledge base to obtain an emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data features, energy storage system data features, synchronous machine stack data features, power grid data features, environmental data features, and system control data features. Step S103, using the corresponding emergency peak load standby capacity assessment algorithm to obtain estimated emergency peak load standby capacity assessment data based on the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics; Step S104: collect real-time photovoltaic synchronous machine stack power generation system data, compare the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity assessment data, and if the real-time photovoltaic synchronous machine stack power generation system data exceeds the power supply capacity value in the estimated emergency peak-shaving standby capacity assessment data, optimize the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics using the corresponding emergency peak-shaving standby capacity assessment algorithm, and obtain the optimized emergency peak-shaving standby capacity assessment data using the corresponding optimized emergency peak-shaving standby capacity assessment algorithm; Step S105: Compare the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous machine stack power generation system data. If the real-time photovoltaic synchronous machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, then use the optimized emergency peak-shaving standby capacity assessment data as the data to be tested, collect the real-time photovoltaic synchronous machine stack power generation system parameters, and compare the data to be tested with the real-time photovoltaic synchronous machine stack power generation system parameters. If the data to be tested is within the range of the real-time photovoltaic synchronous machine stack power generation system parameters, then the optimized emergency peak-shaving standby capacity assessment data is accurate. Compare the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous generator stack power generation system data. If the real-time photovoltaic synchronous generator stack power generation system data exceeds the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, generate an early warning message. Retrieving the corresponding data in the warning information to obtain a set of data to be detected, preprocessing the data in the set of data to be detected, filtering out the erroneous data in the set of data to be detected, and extracting data features based on the erroneous data in the set of data to be detected to obtain erroneous data features; Substituting the error data features into a preset data simulation model to generate simulation data corresponding to the error data, and replacing the to-be-detected data set with the simulation data corresponding to the error data to obtain a modified to-be-detected data set; The modified data set to be tested is processed using the emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics to obtain the modified emergency peak-shaving standby capacity evaluation data.

2. The method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator system according to claim 1, wherein: Also includes: The modified emergency peak-shaving standby capacity assessment data is compared with the estimated emergency peak-shaving standby capacity assessment data. If the data comparison results are inconsistent, the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are matched in the emergency peak-shaving standby capacity assessment algorithm knowledge base to obtain the emergency peak-shaving standby capacity assessment algorithm corresponding to the re-matched photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine pile data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics.

3. The method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator system according to claim 1, wherein: The step S103 further includes: According to the requirements of the emergency peak-shaving standby capacity assessment algorithm, the photovoltaic array data characteristics, energy storage system data characteristics, synchronous reactor data characteristics, power grid data characteristics, environmental data characteristics, and system control data characteristics are matched with the corresponding emergency peak-shaving standby capacity assessment algorithm; Emergency peak load reserve capacity assessment algorithms include neural network algorithms, support vector machine algorithms, decision tree algorithms, regression analysis algorithms, and time series analysis algorithms; The photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are combined with the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the calculated data are sorted and summarized to estimate the emergency peak-shaving standby capacity evaluation data.

4. The method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator system according to claim 1, wherein: The step S104 further includes: Collect real-time data on the photovoltaic synchronous machine stack power generation system, including the real-time power generation of the photovoltaic array, the real-time charge and discharge status of the energy storage system, the real-time power generation power of the synchronous machine stack, the real-time demand of the power grid, and environmental parameters; Compare the collected real-time photovoltaic synchronous machine pile power generation system data with the power supply capacity value in the estimated emergency peak load standby capacity assessment data; Check whether the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system exceeds the estimated power supply capacity value. If the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system does not exceed the estimated power supply capacity value, the current estimated emergency peak load standby capacity assessment data is accurate; If the actual power supply capacity of the real-time photovoltaic synchronous machine stack power generation system exceeds the estimated power supply capacity value, the current evaluation algorithm is insufficient and needs to be optimized; Re-analyze the data characteristics of the photovoltaic array, energy storage system, synchronous reactor, power grid, environment, and system control to identify factors that lead to assessment errors. Based on the identified factors, adjust the emergency peak-shaving reserve capacity assessment algorithm used, including modifying algorithm parameters, introducing new characteristic variables, and optimizing the algorithm's logical structure. Using the optimized emergency peak-shaving standby capacity evaluation algorithm, the data characteristics of the photovoltaic array, energy storage system, synchronous machine pile, power grid, environmental data and system control data are re-evaluated to obtain the optimized emergency peak-shaving standby capacity evaluation data.

5. The method for evaluating the emergency peak-shaving standby capacity of a photovoltaic synchronous generator system according to claim 1, wherein: The step S105 further includes: Compare the data of the real-time photovoltaic synchronous machine stack power generation system with the power supply capacity values ​​in the optimized emergency peak-shaving standby capacity assessment data one by one to check whether the various data of the real-time photovoltaic synchronous machine stack power generation system do not exceed the power supply capacity values ​​in the optimized assessment data; If any data of the real-time photovoltaic synchronous machine pile power generation system exceeds the power supply capacity value in the optimized emergency peak load standby capacity assessment data, the optimized assessment data will still be inaccurate; If all data of the real-time photovoltaic synchronous machine pile power generation system do not exceed the power supply capacity value in the optimized emergency peak load standby capacity assessment data, the optimized assessment data is consistent with the real-time system data; When the real-time photovoltaic synchronous machine stack power generation system data does not exceed the optimized emergency peak-shaving standby capacity assessment data, the optimized assessment data is set as the data to be tested.

6. A photovoltaic synchronous machine stack power generation system emergency peak-shaving standby capacity assessment device, applied to the photovoltaic synchronous machine stack power generation system emergency peak-shaving standby capacity assessment method according to any one of claims 1 to 5, characterized in that: include: A data acquisition unit acquires data of a photovoltaic synchronous machine stack power generation system, including photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data, and system control data; an algorithm matching unit, which extracts data features from the photovoltaic synchronous machine pile power generation system data to obtain photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features, and matches the photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features in an emergency peak-shaving standby capacity evaluation algorithm knowledge base to obtain an emergency peak-shaving standby capacity evaluation algorithm corresponding to the photovoltaic array data features, energy storage system data features, synchronous machine pile data features, power grid data features, environmental data features, and system control data features; An evaluation unit uses a corresponding emergency peak-shaving standby capacity evaluation algorithm to evaluate the characteristics of the photovoltaic array data, the energy storage system data, the synchronous machine reactor data, the power grid data, the environmental data, and the system control data to obtain estimated emergency peak-shaving standby capacity evaluation data; An evaluation and optimization unit collects real-time photovoltaic synchronous machine stack power generation system data, compares the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity evaluation data, and if the real-time photovoltaic synchronous machine stack power generation system data exceeds the power supply capacity value in the estimated emergency peak-shaving standby capacity evaluation data, then the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are optimized using the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the photovoltaic array data characteristics, energy storage system data characteristics, synchronous machine stack data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics are obtained using the corresponding optimized emergency peak-shaving standby capacity evaluation algorithm to obtain optimized emergency peak-shaving standby capacity evaluation data; The data processing unit compares the optimized emergency peak-shaving standby capacity assessment data with the real-time photovoltaic synchronous machine stack power generation system data. If the real-time photovoltaic synchronous machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak-shaving standby capacity assessment data, the optimized emergency peak-shaving standby capacity assessment data is used as the data to be tested, and the real-time photovoltaic synchronous machine stack power generation system parameters are collected. The data to be tested are compared with the real-time photovoltaic synchronous machine stack power generation system parameters. If the data to be tested is within the range of the real-time photovoltaic synchronous machine stack power generation system parameters, the optimized emergency peak-shaving standby capacity assessment data is accurate.

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