Method and device for evaluating emergency peak regulation reserve capacity of photovoltaic synchronous generator pile power generation system

Abstract

The invention relates to the technical field of operation and maintenance of a photovoltaic power generation system, and provides a method and a device for evaluating the emergency peak regulation standby capacity of a photovoltaic synchronous machine pile power generation system, which are used for accurately evaluating the emergency peak regulation standby capacity of the photovoltaic synchronous machine pile power generation system through multi-dimensional data acquisition, data feature extraction and algorithm matching. The method comprises the steps of data acquisition, feature extraction, algorithm matching, pre-estimation and evaluation, real-time comparison, algorithm optimization, data verification and the like, the system state can be monitored in real time, the evaluation algorithm and the accuracy of the evaluation result can be dynamically adjusted, and the device comprises a data acquisition unit, an algorithm matching unit, an evaluation unit, an evaluation optimization unit, a data processing unit and the like. The automatic and intelligent evaluation process is realized, and the stable operation of the photovoltaic synchronous generator pile power generation system is realized.

Description

Technical Field

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

[0002] Emergency peak-shaving of photovoltaic synchronous power generation systems refers to a technical means of flexibly adjusting the power supply to balance the supply and demand of the power grid by introducing energy storage systems (such as battery energy storage, compressed air energy storage, etc.) and synchronous power generation technologies when photovoltaic power generation becomes unstable in output or when power demand peaks due to changes in 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 reserve capacity assessment of the photovoltaic synchronous machine pile power generation system determines the amount of additional power support that the system can provide in an emergency by analyzing factors such as the system's energy storage capacity, the response speed and power generation efficiency of the synchronous machine pile, and the coordination and control capabilities with the power grid. The assessment process will comprehensively consider the historical power generation data of the photovoltaic array, the charging and discharging performance of the energy storage equipment, the technical parameters of the synchronous machine pile and its operating performance under different working conditions, and combine the real-time demand and dispatch strategy of the power grid to simulate the system response in various emergency scenarios. Through this series of comprehensive assessments, the reserve capacity of the photovoltaic synchronous machine pile power generation system in emergency peak load regulation can be accurately quantified.

[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, for a large amount of data during the evaluation of the emergency peak-shaving standby capacity of photovoltaic synchronous machine stack power generation systems. When errors occur in the data processing of the analysis algorithm, errors will occur in the evaluation of the emergency peak-shaving standby capacity of the photovoltaic synchronous machine stack power generation systems. To this end, 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 view of the deficiencies in the prior art, the present invention provides a transient voltage stability control method and an extraction method for a photovoltaic synchronous machine stack power generation system, so as to solve the problem that when an error occurs in the data processing of the analysis algorithm using the analysis algorithm, an error will occur in the assessment 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, a method for evaluating emergency peak load standby capacity of a photovoltaic synchronous machine pile power generation system of the present invention comprises:

[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 of the photovoltaic synchronous machine stack power generation system data, obtaining 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 load standby capacity evaluation algorithm knowledge base, and obtaining an emergency peak load 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 for 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;

[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, 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, then 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, obtain 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 optimized emergency peak-shaving standby capacity assessment algorithm, and obtain the optimized emergency peak-shaving standby capacity assessment data;

[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 transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of the present invention further includes:

[0014] The optimized emergency peak load standby capacity assessment data is compared with the real-time photovoltaic synchronous machine pile power generation system data. If the real-time photovoltaic synchronous machine pile power generation system data exceeds the power supply capacity value in the optimized emergency peak load standby capacity assessment data, an early warning message is generated;

[0015] Retrieve the corresponding data in the warning information to obtain a 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 erroneous data features.

[0016] Furthermore, the transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of 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 assessment 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 assessment data.

[0019] Furthermore, the transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of 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 transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of the present invention, the step S103 further includes:

[0022] According to the requirements of the emergency peak load standby capacity evaluation algorithm, 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 with the corresponding emergency peak load standby capacity evaluation 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 data characteristics of the photovoltaic array, the energy storage system, the synchronous machine pile, the power grid, the environmental data and the system control data are integrated with the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the calculated data are sorted and summarized to obtain the estimated emergency peak-shaving standby capacity evaluation data.

[0025] Furthermore, the transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of the present invention, the step S104 further includes:

[0026] Collect real-time data of the photovoltaic synchronous machine stack power generation system, including 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 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 photovoltaic arrays, energy storage systems, synchronous reactors, power grids, environment, and system control to identify factors that lead to evaluation errors. Based on the identified factors, adjust the emergency peak load reserve capacity evaluation algorithm used, including modifying the 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 machine stack power generation system according to the present invention, said step S105, further comprises:

[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 load standby capacity assessment data one by one, and 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 is still 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 photovoltaic synchronous machine pile power generation system emergency peak load standby capacity assessment device, which is applied to a photovoltaic synchronous machine pile power generation system emergency peak load standby capacity assessment method, comprising:

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

[0040] The algorithm matching unit extracts data features of 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, and matches 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 load standby capacity evaluation algorithm knowledge base to obtain an emergency peak load 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;

[0041] The evaluation unit uses the corresponding emergency peak load standby capacity evaluation algorithm to obtain the estimated emergency peak load standby capacity evaluation data by using the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine pile data characteristics, the power grid data characteristics, the environmental data characteristics and the system control data characteristics;

[0042] An evaluation and optimization unit collects real-time photovoltaic synchronous machine stack power generation system data, and compares the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity evaluation data. 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, the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine stack data characteristics, the power grid data characteristics, the environmental data characteristics, and the system control data characteristics are optimized using the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine stack data characteristics, the power grid data characteristics, the environmental data characteristics, and the system control data characteristics are obtained using the corresponding optimized emergency peak-shaving standby capacity evaluation algorithm to obtain the 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 is 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 machine data, power grid data, environmental data, and system control data), and performing data feature extraction and algorithm matching, the present invention can more comprehensively evaluate the emergency peak-shaving standby capacity of the photovoltaic synchronous machine power generation system. Compared with traditional methods, this avoids the evaluation bias that may be caused by a single data source and improves the accuracy of the evaluation.

[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 according to actual data characteristics. This mechanism enables the system to adapt to different working conditions and environmental changes, and improves 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 evaluation error is found, the present invention will optimize the algorithm, and improve the processing efficiency and accuracy of the algorithm by adjusting algorithm parameters, introducing new feature variables, or optimizing the algorithm logic structure. This helps to reduce system operating costs and improve overall efficiency. The photovoltaic synchronous machine pile power generation system emergency peak load standby capacity evaluation device provided by the present invention can realize automatic data collection, processing and analysis, and promote the intelligent operation and maintenance and automated management of the photovoltaic power generation system. This helps to reduce manual intervention, improve operation and maintenance efficiency, and reduce operation and maintenance costs.

[0048] By accurately evaluating and optimizing the emergency peak load reserve capacity of the photovoltaic synchronous machine pile power generation system, the present invention helps to improve the stability and safety of the entire power system. In the case of peak power demand or emergency, the system can respond quickly and provide reliable power support to avoid power outages 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 machine pile 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 machine stack power generation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments 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, a method for evaluating emergency peak load standby capacity of a photovoltaic synchronous machine pile power generation system of the present invention comprises:

[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] Photovoltaic array data includes the working status of photovoltaic panels, power generation, conversion efficiency under light intensity, the impact of temperature on power generation efficiency, etc. The photovoltaic array is the core part of the photovoltaic power generation system, and its data directly reflects the power generation capacity and efficiency of the system.

[0057] Energy storage system data has the function of balancing supply and demand and stabilizing output in the photovoltaic synchronous machine pile power generation system. Therefore, it is necessary to collect data such as the capacity, current power, charge and discharge efficiency, and cycle life of the energy storage equipment. These data are helpful in evaluating the response capability and duration of the energy storage system during emergency peak regulation.

[0058] The synchronous reactor is a key device in the system for regulating and stabilizing output. It is necessary to collect data on its power generation, efficiency, operating status (such as speed, temperature, etc.). These data are crucial for evaluating the rapid response and stable output capabilities of the synchronous reactor in emergency situations.

[0059] Grid data includes real-time grid demand, load changes, voltage and frequency stability, etc. These data are used to analyze the interaction between the photovoltaic synchronous machine pile power generation 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 system control strategies, dispatching instructions, protection action records, etc. These data reflect the system's operating strategies and response mechanisms 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 power generation system.

[0063] Step S102, extracting data features of the photovoltaic synchronous machine stack power generation system data, obtaining 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 load standby capacity evaluation algorithm knowledge base, and obtaining an emergency peak load 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 machine stack power generation system is processed to extract key information that can reflect the system characteristics, 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, 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 in the emergency peak load reserve capacity assessment algorithm knowledge base to find the most suitable assessment algorithm for each feature. This knowledge base contains a variety of algorithms, such as neural network algorithms, support vector machine algorithms, decision tree algorithms, regression analysis algorithms, and time series analysis algorithms, each of which has its applicable scenarios and advantages.

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

[0073] Step S103, using the corresponding emergency peak load standby capacity assessment algorithm to obtain estimated emergency peak load standby capacity assessment data for 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;

[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: Input each data feature into its corresponding evaluation algorithm. These algorithms may include neural network algorithms, support vector machine algorithms, decision tree algorithms, regression analysis algorithms, and time series analysis algorithms. Each algorithm will output a prediction or evaluation result based on the input data features, according to its internal logic and calculation rules.

[0077] Results summary: The results of all algorithm outputs will be summarized to form a complete set of estimated emergency peak load reserve capacity assessment data. These data will fully reflect the reserve capacity of the photovoltaic synchronous machine pile power generation system under emergency peak load conditions, including but not limited to the system's maximum power supply capacity, response time, stability and other indicators.

[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, etc., 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 conditions 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, 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, then 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, obtain 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 optimized emergency peak-shaving standby capacity assessment algorithm, and obtain the optimized emergency peak-shaving standby capacity assessment data;

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

[0082] The collection of real-time photovoltaic synchronous machine stack power generation system data includes real-time acquisition of photovoltaic array power generation, charging and discharging status of energy storage system, synchronous machine stack power generation, real-time demand of the 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 load standby capacity assessment data obtained in step S103, especially focusing on the comparison of 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 machine stack power generation 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 the real-time PV synchronous machine power generation system data exceeds the estimated power supply capacity, it means that the current evaluation algorithm may be insufficient and needs to be optimized. At this time, it is necessary to review the corresponding emergency peak load standby capacity evaluation algorithm based on the PV array data characteristics, energy storage system data characteristics, synchronous machine data characteristics, power grid data characteristics, environmental data characteristics and system control data characteristics.

[0086] Identify factors that lead to evaluation errors, which may include improper algorithm parameter settings, unreasonable feature variable selection, or algorithm logic structure that needs to be improved. Adjust the evaluation algorithm based on the identified factors. This may include modifying the algorithm's parameters, introducing new feature variables, or optimizing the algorithm's 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 machine stack power generation system can better adapt to fluctuations in electricity 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 load reserve capacity assessment data is compared with the real-time photovoltaic synchronous machine pile power generation system data, especially focusing on 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 machine stack power generation system data does not exceed the power supply capacity value in the optimized emergency peak load standby capacity assessment data, then it can be preliminarily considered that the optimized assessment data is accurate. At this time, the optimized emergency peak load standby capacity assessment data is used as the data to be tested and is prepared 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 load reserve capacity evaluation data) is compared with the collected real-time photovoltaic synchronous machine pile power generation system parameters. This step is to further verify whether the optimized evaluation 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 machine pile power generation system parameter range and is consistent with the performance of the real-time system, then it can be confirmed that the optimized emergency peak load reserve capacity assessment data is accurate. This means that the optimized assessment algorithm can accurately reflect the reserve capacity of the PV synchronous machine pile power generation system under emergency peak load conditions.

[0095] When the optimized emergency peak load reserve capacity assessment data is verified to be accurate, it can be used to guide the actual operation of the photovoltaic synchronous machine pile power generation system and emergency peak load decision-making. This helps to improve the operating efficiency and stability of the system and enhance the ability to provide reliable power support during peak power demand or emergency situations.

[0096] Specifically, the transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of the present invention also includes:

[0097] The optimized emergency peak load standby capacity assessment data is compared with the real-time photovoltaic synchronous machine pile power generation system data. If the real-time photovoltaic synchronous machine pile power generation system data exceeds the power supply capacity value in the optimized emergency peak load standby capacity assessment data, an early warning message is generated;

[0098] Retrieve the corresponding data in the warning information to obtain a 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 erroneous data features.

[0099] The system continuously compares the optimized emergency peak load reserve capacity assessment data with the real-time PV synchronous machine pile power generation system data. If the real-time PV synchronous machine pile power generation system data (such as actual power generation, energy storage status, grid demand, etc.) exceeds the power supply capacity value in the optimized assessment data, it means that the system may face the risk of not being able to meet power demand or the stable operation is threatened. In this case, the system immediately generates an early warning message to remind the operator or automatically trigger the 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 detected for further analysis and processing.

[0101] Preprocessing the data in the data set to be tested may include data cleaning (removing invalid or outliers), data normalization or standardization, etc., to ensure the accuracy and consistency of the data. Through the preprocessed data, the system filters out erroneous data, which may be caused by sensor failure, data transmission error or system misjudgment.

[0102] Data feature extraction is performed on the screened error data to identify the specific cause or pattern that caused the error. Error data features may include data mutation, periodic anomalies, and continuous deviation from normal values. Based on the extracted error data features, the system can take corresponding measures, such as adjusting sensors, repairing data transmission links, optimizing evaluation algorithms, etc., to eliminate the source of error data. At the same time, these error data features can also serve as an important reference for future system improvements and optimizations, helping to improve the stability and accuracy of the system.

[0103] Through this series of steps, the transient steady-state 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 transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of the present invention also 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 assessment 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 assessment data.

[0107] The transient voltage stability control method of the photovoltaic synchronous machine pile power generation system includes the link of processing the erroneous data and re-evaluating the emergency peak load reserve capacity. The following is a detailed step description of this link:

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

[0109] These simulated data are used to replace the original erroneous data in the data set to be tested. After such replacement operation, we obtain a corrected data set to be tested, which is closer to the actual operating status of the system. The emergency peak load 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 is used to perform comprehensive data processing on the corrected data set to be tested.

[0110] Through algorithm processing, a set of modified emergency peak load standby capacity assessment data is obtained, which more accurately and reliably reflects the system's emergency peak load standby capacity after taking into account the correction of erroneous data. The corrected and re-evaluated data can more accurately judge the system's emergency peak load standby capacity, thereby formulating more reasonable and effective operation strategies and control measures, which will help improve the stability of the system.

[0111] Specifically, the transient voltage stability control method of the photovoltaic synchronous machine stack power generation system of the present invention also 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 modified emergency peak load reserve capacity assessment data is comprehensively 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 matches the characteristics of photovoltaic array data, energy storage system data, synchronous machine data, power grid data, environmental data and system control data with the knowledge base of emergency peak load reserve capacity evaluation algorithm again. This step is to find an evaluation algorithm that is more suitable for the current system status and data characteristics.

[0116] Through re-matching, the system can obtain the emergency peak load reserve capacity evaluation algorithms 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. These new algorithms are more in line with the actual operating conditions of the system and can provide more accurate evaluation results.

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

[0118] By introducing this feedback and optimization mechanism, the transient voltage stability control method of the photovoltaic synchronous machine pile power generation system described in the present invention can not only process erroneous data in a timely manner, but also dynamically adjust the evaluation algorithm according to the actual operating conditions and data characteristics of the system, thereby ensuring the accuracy and reliability of the evaluation results. This helps to improve the stability and safety of the system and provide a strong guarantee for the stable operation of the power system.

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

[0120] According to the requirements of the emergency peak load standby capacity evaluation algorithm, 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 with the corresponding emergency peak load standby capacity evaluation 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 data characteristics of the photovoltaic array, the energy storage system, the synchronous machine pile, the power grid, the environmental data and the system control data are integrated with the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the calculated data are sorted and summarized to obtain the estimated 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 machine piles, power grids, environment, and system control. These data features may include but are not limited to power generation, energy storage status, power output, grid demand, ambient temperature, wind speed, system control instructions, etc.

[0124] According to the needs of emergency peak load reserve capacity assessment, a suitable assessment algorithm is selected from a preset algorithm library. The algorithms mentioned in the present invention 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 needs to be selected based on actual data characteristics and assessment objectives.

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

[0126] Use the matched evaluation algorithm 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 load standby capacity.

[0127] The emergency peak load reserve capacity data calculated by each algorithm are collated and summarized. This step may involve data fusion, weight allocation, result verification and other processes to ensure the accuracy and reliability of the estimated results. Finally, the estimated emergency peak load reserve capacity evaluation 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 machine stack power generation system according to the present invention, the step S104 further includes:

[0129] Collect real-time data of the photovoltaic synchronous machine stack power generation system, including 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 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 photovoltaic arrays, energy storage systems, synchronous reactors, power grids, environment, and system control to identify factors that lead to evaluation errors. Based on the identified factors, adjust the emergency peak load reserve capacity evaluation algorithm used, including modifying the 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 real-time photovoltaic synchronous machine stack power generation system, including but not limited to real-time power generation of photovoltaic array, real-time charging and discharging status of energy storage system, real-time power generation power of synchronous machine stack, real-time demand of power grid and related environmental parameters (such as temperature, light intensity, wind speed, etc.).

[0136] The collected real-time PV synchronous machine pile power generation system data is compared with the power supply capacity value in the previously estimated emergency peak load reserve capacity assessment data. This step is to verify the accuracy of the estimated data and the current actual power supply capacity of the system.

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

[0138] The data characteristics of photovoltaic arrays, energy storage systems, synchronous reactors, power grids, environment and system control are analyzed again to identify the specific factors that lead to evaluation errors. This may involve data quality, feature selection, algorithm logic structure and other aspects.

[0139] According to 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 logic structure or adopting a new algorithm model.

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

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

[0142] Specifically, the method for controlling transient voltage stability of a photovoltaic synchronous machine stack power generation system according to the present invention, the 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 load standby capacity assessment data one by one, and 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 is still 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 various data of the real-time photovoltaic synchronous machine pile power generation system (including 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 of the synchronous machine pile, the real-time demand of the power grid, etc.) are compared one by one with the power supply capacity values ​​in the optimized emergency peak load standby capacity assessment data. This step is to ensure the consistency between the actual operating status of the system and the assessment data.

[0148] Check whether 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. If any data exceeds the assessment value, it means that the optimized assessment data is still inaccurate and there may be 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] When it is confirmed that the real-time PV synchronous machine pile power generation system data does not exceed the optimized emergency peak load reserve capacity assessment data, this set of optimized assessment data is set as the data to be tested. This means that this set of data has passed the 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 can ensure that the optimized emergency peak load reserve capacity assessment data is consistent with the actual operating conditions of the real-time photovoltaic synchronous machine pile power generation system, providing strong support for the stable operation of the system and rapid response in emergency situations. At the same time, the method also has certain flexibility and scalability, and the comparison and verification process and standards can be adjusted according to actual needs to adapt to application requirements in different scenarios.

[0154] In a second aspect, the present invention provides a photovoltaic synchronous machine pile power generation system emergency peak load standby capacity assessment device, which is applied to a photovoltaic synchronous machine pile power generation system emergency peak load standby capacity assessment method, comprising:

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

[0156] The algorithm matching unit extracts data features of 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, and matches 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 load standby capacity evaluation algorithm knowledge base to obtain an emergency peak load 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;

[0157] The evaluation unit uses the corresponding emergency peak load standby capacity evaluation algorithm to obtain the estimated emergency peak load standby capacity evaluation data by using the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine pile data characteristics, the power grid data characteristics, the environmental data characteristics and the system control data characteristics;

[0158] An evaluation and optimization unit collects real-time photovoltaic synchronous machine stack power generation system data, and compares the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity evaluation data. 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, the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine stack data characteristics, the power grid data characteristics, the environmental data characteristics, and the system control data characteristics are optimized using the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine stack data characteristics, the power grid data characteristics, the environmental data characteristics, and the system control data characteristics are obtained using the corresponding optimized emergency peak-shaving standby capacity evaluation algorithm to obtain the 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 is 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 the present invention effectively solves the problem of inaccurate assessment of the emergency peak load standby capacity of the photovoltaic synchronous machine pile power generation system through a series of innovative steps, which may occur when the analysis algorithm is used to process data. The specific solution is as follows:

[0161] Acquire multi-dimensional data from the photovoltaic synchronous machine pile power generation system, including photovoltaic array data, energy storage system data, synchronous machine pile data, power grid data, environmental data, and system control data. Extract features from the data, and then match the extracted features with the algorithms in the emergency peak load reserve capacity evaluation algorithm knowledge base to select the most appropriate evaluation algorithm for different types of data features.

[0162] Use the matching algorithm to preliminarily process the data and estimate the emergency peak load standby capacity assessment data. Collect real-time photovoltaic synchronous machine pile power generation system data and compare it with the estimated data. If it is found that the real-time data exceeds the power supply capacity value of the estimated data, it means that there may be errors in the preliminary assessment. According to the errors, the algorithm used is optimized, and the optimized emergency peak load standby capacity assessment data is obtained by adjusting the algorithm parameters, introducing new feature variables or optimizing the algorithm logic structure.

[0163] Compare the optimized evaluation data with the real-time data again to ensure that the optimized data is accurate. If it is found during the comparison process that the real-time data still exceeds the power supply capacity value 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, filter out the erroneous data, and extract the features of the erroneous data.

[0164] Substitute the erroneous data features into the preset data simulation model, generate simulated data to replace the erroneous data, and obtain the corrected data set to be tested. 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 suitable algorithm. Through continuous data collection, comparison, optimization and verification processes, the accuracy and reliability of the emergency peak-shaving standby capacity assessment are ensured.

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

Claims

1. A method for evaluating the emergency peak load 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 of the photovoltaic synchronous machine stack power generation system data, obtaining 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 load standby capacity evaluation algorithm knowledge base, and obtaining an emergency peak load 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 for 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; 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, 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, then 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, obtain 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 optimized emergency peak-shaving standby capacity assessment algorithm, and obtain the optimized emergency peak-shaving standby capacity assessment data; 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.

2. The transient voltage stability control method of the photovoltaic synchronous machine stack power generation system according to claim 1, characterized in that: Also includes: The optimized emergency peak load standby capacity assessment data is compared with the real-time photovoltaic synchronous machine pile power generation system data. If the real-time photovoltaic synchronous machine pile power generation system data exceeds the power supply capacity value in the optimized emergency peak load standby capacity assessment data, an early warning message is generated; Retrieve the corresponding data in the warning information to obtain a 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 erroneous data features.

3. The transient voltage stability control method of the photovoltaic synchronous machine stack power generation system according to claim 2, characterized in that: Also includes: 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 assessment 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 assessment data.

4. The transient voltage stability control method of the photovoltaic synchronous machine stack power generation system according to claim 3, characterized in that: 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.

5. The transient voltage stability control method of the photovoltaic synchronous machine stack power generation system according to claim 1, characterized in that: The step S103 further includes: According to the requirements of the emergency peak load standby capacity evaluation algorithm, 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 with the corresponding emergency peak load standby capacity evaluation 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 data characteristics of the photovoltaic array, the energy storage system, the synchronous machine pile, the power grid, the environmental data and the system control data are integrated with the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the calculated data are sorted and summarized to obtain the estimated emergency peak-shaving standby capacity evaluation data.

6. The transient voltage stability control method of the photovoltaic synchronous machine stack power generation system according to claim 1, characterized in that: The step S104 further includes: Collect real-time data of the photovoltaic synchronous machine stack power generation system, including 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 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 photovoltaic arrays, energy storage systems, synchronous reactors, power grids, environment, and system control to identify factors that lead to evaluation errors. Based on the identified factors, adjust the emergency peak load reserve capacity evaluation algorithm used, including modifying the 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.

7. The transient voltage stability control method of the photovoltaic synchronous machine stack power generation system according to claim 1, characterized in that: 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 load standby capacity assessment data one by one, and check whether the 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 is still 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.

8. A photovoltaic synchronous machine pile power generation system emergency peak load standby capacity assessment device, applied to a photovoltaic synchronous machine pile power generation system emergency peak load standby capacity assessment method according to any one of claims 1 to 7, characterized in that: include: A data acquisition unit acquires data of a photovoltaic synchronous machine stack power generation system, wherein the photovoltaic synchronous machine stack power generation system data includes photovoltaic array data, energy storage system data, synchronous machine stack data, power grid data, environmental data, and system control data; The algorithm matching unit extracts data features of 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, and matches 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 load standby capacity evaluation algorithm knowledge base to obtain an emergency peak load 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; The evaluation unit uses the corresponding emergency peak load standby capacity evaluation algorithm to obtain the estimated emergency peak load standby capacity evaluation data by using the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine pile data characteristics, the power grid data characteristics, the environmental data characteristics and the system control data characteristics; An evaluation and optimization unit collects real-time photovoltaic synchronous machine stack power generation system data, and compares the real-time photovoltaic synchronous machine stack power generation system data with the estimated emergency peak-shaving standby capacity evaluation data. 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, the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine stack data characteristics, the power grid data characteristics, the environmental data characteristics, and the system control data characteristics are optimized using the corresponding emergency peak-shaving standby capacity evaluation algorithm, and the photovoltaic array data characteristics, the energy storage system data characteristics, the synchronous machine stack data characteristics, the power grid data characteristics, the environmental data characteristics, and the system control data characteristics are obtained using the corresponding optimized emergency peak-shaving standby capacity evaluation algorithm to obtain the 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 is 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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