Automatic power generation control master station test simulation method and system based on new energy

Abstract

The invention relates to the technical field of new energy power generation, in particular to a test simulation method and system based on a new energy automatic power generation control master station. AGC parameters of the new energy power plant are obtained and a model is established, wherein the AGC parameters comprise a power plant table, an AGC field group, an AGC system, a control area, a power plant controller, an AGC unit table, power plant measurement and PLC measurement; configuring parameters of an AGC master station test system, wherein the configuration parameters comprise a PLC name, a new energy power station capacity, a current power, a target power, an adjustment upper and lower limit, a test time parameter and the like; performing simulation according to the AGC modeling parameters and the configuration parameters and outputting a simulation result; and generating an AGC control instruction based on a simulation result and evaluating a dynamic performance index. Through a complete parameter modeling system and differentiated performance index evaluation, the accuracy and efficiency of the new energy AGC control test are remarkably improved, the dynamic performance of the new energy power station is accurately evaluated, and reliable technical support is provided for power grid dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation, and in particular to a method and system for testing and simulating a master station based on renewable energy automatic power generation control. Background Art

[0002] As the proportion of wind and solar energy connected to the power grid grows rapidly, the power volatility, randomness and unpredictability of wind and solar energy have brought difficulties to the stability of the power system. Due to the relatively slow development of the network architecture and spare capacity construction of the regional power grid, the system cannot fully absorb wind and solar energy, and is prone to "abandoning wind" and "abandoning light". Under this situation, the pressure on the system's active power and frequency regulation has increased significantly. The traditional configuration method with hydropower plants as the main frequency regulation resources can no longer meet the system's high-quality dynamic frequency regulation needs. New energy AGC is an important means to maintain the active power balance of the power grid, ensure the system frequency quality, and maintain the interconnection line exchange plan. The quality of its regulation capability, performance and strategy directly affects the safe and stable operation of the power grid.

[0003] However, currently only the AGC substation system of the new energy power station is tested, that is, whether the performance of the new energy AGC substation meets the frequency regulation requirements. The existing technology only tests the functions of the new energy AGC substation, and does not consider the dynamic performance indicators of the new energy AGC substation executing the AGC instructions. If the new energy AGC substation is directly tested in the actual power grid, it will be risky because its dynamic performance may affect the operation of the power grid.

[0004] Therefore, a new energy automatic generation control system (AGC) master station test simulation tool is provided. The advantage is that it can provide simulation parameters of simulated new energy power stations to test the dynamic performance of new energy AGC substations. At the same time, the test performance indicators are statistically analyzed to provide certain parameter data support for the active power and frequency regulation strategies of the dispatch. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to overcome the shortcomings of the existing test simulation method, how to improve the test efficiency and accuracy, and ensure the stable operation of the new energy power generation system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for testing and simulating a master station based on automatic power generation control of new energy, which includes obtaining AGC parameters of a new energy power plant and establishing a model, wherein the AGC parameters include a power plant table, an AGC field group, an AGC system, a control area, a power plant controller, an AGC unit table, a power plant measurement, and a PLC measurement;

[0009] Perform parameter configuration of the AGC master station test system, wherein the configuration parameters include PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, and test time parameters;

[0010] Perform simulation according to the AGC modeling parameters and configuration parameters and output simulation results;

[0011] Based on the simulation results, AGC control instructions are generated and dynamic performance indicators are evaluated.

[0012] As a preferred solution of the test simulation method of the master station based on new energy automatic power generation control described in the present invention, the generation process of the AGC control instruction includes:

[0013] Obtain the system parameters of the AGC master station system in the dispatching master station;

[0014] Calculate the generated power according to the deviation between the simulated frequency and the rated frequency in the simulation result;

[0015] An AGC control instruction is generated according to the generated power, and the AGC control instruction is a target generated power value of a target new energy power station.

[0016] As a preferred solution of the test simulation method of the master station based on new energy automatic power generation control described in the present invention, the dynamic performance indicators include:

[0017] Regulation rate indicator, the regulation rate is 2% to 5% of the rated power per minute for a wind farm or 1% to 3% of the rated power per minute for a photovoltaic power station;

[0018] Response time indicator, the response time is 1 to 3 minutes;

[0019] The regulation accuracy index is 2% for a wind farm or 3% for a photovoltaic power station.

[0020] As a preferred solution of the test simulation method based on new energy automatic power generation control master station of the present invention, the test time parameters include: test start time and end time; test response time; current time;

[0021] The AGC master station test system parameter configuration also includes: test response power; test control deviation; current control deviation.

[0022] As a preferred solution of the new energy automatic power generation control master station test simulation method described in the present invention, the evaluation of the dynamic performance indicators includes: recording and analyzing various operating data during the test; judging whether the test results are qualified according to preset performance standards; and generating a test evaluation report.

[0023] As a preferred solution of the new energy automatic power generation control master station test simulation method described in the present invention, the simulation result output includes: real-time display of various test parameters; recording of abnormal conditions during the test; and forming a test data curve.

[0024] As a preferred solution of the test simulation method of the master station based on new energy automatic power generation control described in the present invention, the following is done: after the AGC control instruction is generated, the instruction is checked for rationality; when an abnormality is found, the test is automatically interrupted and an alarm is issued; the reason for the test interruption and related data are recorded;

[0025] Conduct statistical analysis on simulation test results; optimize and adjust the AGC control strategy based on the statistical analysis results; and generate an optimization recommendation report.

[0026] In a second aspect, an embodiment of the present invention provides a test simulation system based on a new energy automatic power generation control master station, which includes a data acquisition module to obtain AGC parameters of a new energy power plant and establish a model, wherein the AGC parameters include a power plant table, an AGC field group, an AGC system, a control area, a power plant controller, an AGC unit table, a power plant measurement, and a PLC measurement;

[0027] The parameter debugging module configures the parameters of the AGC master station test system. The configuration parameters include PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, and test time parameters;

[0028] An output module performs simulation according to the AGC modeling parameters and configuration parameters and outputs simulation results;

[0029] An evaluation module generates an AGC control instruction based on the simulation result and evaluates the dynamic performance index. In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the test simulation method based on the new energy automatic power generation control master station as described in the first aspect of the present invention are implemented.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for testing and simulating a master station based on automatic power generation control based on new energy are implemented as described in the first aspect of the present invention.

[0031] The beneficial effects of the present invention are as follows: the present invention can fully simulate the actual operating conditions of the new energy power generation system, and provide a real and reliable environment for the testing of the automatic power generation control master station. It can comprehensively test various performance indicators of the automatic power generation control master station, including the operating performance under normal conditions and the response capability under fault conditions. It can efficiently evaluate the performance of the automatic power generation control master station, and provide strong support for optimizing and improving system performance. The simulation tool of the present invention has a modular design, and its functions can be expanded and customized according to actual needs. The present invention collects the operating data of the new energy power generation system in real time through the data acquisition module; then, uses the model construction module to build a mathematical model of the system; then, simulates possible faults through the fault simulation module; finally, uses the performance evaluation module to evaluate the performance of the automatic power generation control master station. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 It is a flow chart of the simulation method for the master station test based on the automatic power generation control of new energy;

[0034] Figure 2 A diagram of computer equipment for testing simulation methods of a master station based on automatic power generation control of new energy;

[0035] Figure 3 This is a simulation interface diagram of a master station test simulation method based on new energy automatic power generation control;

[0036] Figure 4 This is a simulation test diagram of the master station test simulation method based on new energy automatic power generation control. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] Example 1

[0041] Reference Figure 1 to Figure 4 , which is the first embodiment of the present invention, provides a method for testing and simulating a master station based on automatic power generation control of new energy, including:

[0042] S100: Obtain AGC parameters of the new energy power plant and establish a model. The AGC parameters include power plant table, AGC field group, AGC system, control area, power plant controller, AGC unit table, power plant measurement and PLC measurement;

[0043] S101: The process of generating the AGC control instruction includes:

[0044] Obtain the system parameters of the AGC master station system in the dispatching master station;

[0045] Calculate the generated power according to the deviation between the simulated frequency and the rated frequency in the simulation result;

[0046] An AGC control instruction is generated according to the generated power, and the AGC control instruction is a target generated power value of the target new energy power station.

[0047] S200: Perform parameter configuration of the AGC master station test system, including PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, and test time parameters;

[0048] S201: Test time parameters include: test start time and end time; test response time; current time;

[0049] The AGC master station test system parameter configuration also includes: test response power; test control deviation; current control deviation.

[0050] S300: Perform simulation according to AGC modeling parameters and configuration parameters and output simulation results;

[0051] S301: The simulation result output includes: real-time display of various test parameters; recording of abnormal conditions during the test; and forming a test data curve.

[0052] S400: Generate AGC control instructions based on the simulation results and evaluate dynamic performance indicators.

[0053] S401: Dynamic performance indicators include:

[0054] Regulation rate indicator: the regulation rate is 2% to 5% of the rated power per minute for wind farms or 1% to 3% of the rated power per minute for photovoltaic power plants;

[0055] Response time indicator: the response time is 1 to 3 minutes;

[0056] Regulation accuracy index: the regulation accuracy is 2% for wind farms or 3% for photovoltaic power stations.

[0057] S402: The evaluation of dynamic performance indicators includes: recording and analyzing various operating data during the test; judging whether the test results are qualified according to preset performance standards; and generating a test evaluation report.

[0058] S403: After the AGC control instruction is generated, the instruction is checked for rationality; when an abnormality is found, the test is automatically interrupted and an alarm is issued; the reason for the test interruption and related data are recorded;

[0059] Conduct statistical analysis on simulation test results; optimize and adjust the AGC control strategy based on the statistical analysis results; and generate an optimization recommendation report.

[0060] Furthermore, a simulation model of the new energy automatic power generation control master station is established, including modules such as new energy power generation equipment, control system, and communication network, and simulation parameters are set according to actual operation data;

[0061] Design test cases, including operation tests under normal conditions, emergency response tests under abnormal conditions, and adaptability tests under different weather conditions. Perform simulation operations through simulation models and record various data during the operation process; analyze the recorded data to determine whether the performance of the new energy automatic power generation control master station meets the requirements, and adjust simulation parameters or optimize control strategies based on the analysis results;

[0062] Repeat the simulation operation and data analysis until the performance of the new energy automatic power generation control master station reaches the predetermined standard.

[0063] It includes a data acquisition module, a model building module, a fault simulation module, a performance evaluation module and a control module.

[0064] Data acquisition module: responsible for real-time collection of operating data of the new energy power generation system, including key parameters such as power generation, voltage, current, etc., to provide data support for subsequent model construction and performance evaluation.

[0065] Model building module: Based on the collected data, a mathematical model of the new energy power generation system is constructed, including power supply model, transmission model, control model, etc., to fully reflect the operating status of the system.

[0066] Fault simulation module: It can simulate various faults that may occur in the renewable energy power generation system, such as equipment failure, network failure, etc., to test the response capability and stability of the automatic power generation control master station under fault conditions.

[0067] Performance evaluation module: Based on the test results, the performance of the automatic power generation control master station is comprehensively evaluated, including indicators such as response time, control accuracy, and stability, providing a basis for optimizing and improving system performance.

[0068] Control module: responsible for the operation control of the entire simulation tool, including the coordination and management of functions such as data acquisition, model building, fault simulation and performance evaluation.

[0069] First, the operating data of the new energy power generation system is collected in real time through the data acquisition module; then, the mathematical model of the system is constructed using the model construction module; then, the possible faults are simulated through the fault simulation module; finally, the performance of the automatic power generation control master station is evaluated using the performance evaluation module.

[0070] Based on the dispatching automation E8000 integrated system, a new energy automatic generation control (AGC) master station test simulation tool is developed to simulate the simulation parameters of the new energy power station to test the dynamic performance of the new energy AGC substation.

[0071] 1: Modeling the AGC parameters of each new energy power plant through the E8000 system database

[0072] 1. Add new energy plants and related telesignaling, telemetering and teleregulation measurement parameters.

[0073] 2. Add AGC related parameters (such as power plant table, AGC field group, AGC system, control area, power plant controller, AGC unit table, power plant measurement, PLC measurement and other related configuration parameters)

[0074] 2. New Energy AGC Master Station Test System Parameter Configuration

[0075] 1. Parameter configuration (PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, test start and end time, test response time, test response power, test control deviation, current control deviation, test time, current time, start test, end test, etc.)

[0076] 3: AGC master station test simulation system reads AGC modeling parameters for simulation and outputs simulation results

[0077] The AGC control instruction is generated by obtaining the AGC master station system parameters and simulation configuration parameter simulation results in the dispatching master station: Among them, the AGC control instruction is a power value, which represents the required power generation of the target new energy power station. The calculation process of the AGC control instruction is as follows: according to the deviation △f between the simulation frequency and the rated frequency in the simulation result, calculate how much more or less power the entire power grid should generate, that is, the power generation △P=△f*B, B is a constant number, which is related to the operation level of the power grid.

[0078] 1. Calculating the generated power according to the deviation between the simulation frequency and the rated frequency in the simulation result by the AGC master station system in the dispatching master station;

[0079] 2. The AGC master station system in the dispatching master station generates an AGC control instruction according to the system parameters uploaded by the new energy AGC substation system and the generated power.

[0080] 3. The AGC master station system in the scheduling master station obtains dynamic performance indicators according to the simulation results: adjustment rate indicator, response time indicator, and adjustment accuracy indicator.

[0081] Figure 3 The interface is a simulation interface:

[0082] 1. When you open the interface, all new energy plants will be read first; 2. Select the new energy plant to be simulated: (such as Baozhai Photovoltaic) The simulation interface will read the new energy plant: (control upper and lower limits, current power, test start power, test time).

[0083] Figure 4 Simulation test - parameter setting:

[0084] 1. Set the input: target power (based on current power); 2. Click to start the test; 3. Click to end the test; 4. Get the simulation parameters: (test response time, test response power, response rate, test control deviation, current control deviation, test time, test start time, test end time and real-time tracking curve and data).

[0085] Normal simulation test data preset standard: Wind power: active power control response time: 1 to 3 minutes.

[0086] Active power regulation rate: 2% to 5% of rated power per minute. The wind farm regulation rate should be 2%.

[0087] Photovoltaic: Active power control response time: 1 to 3 minutes. Active power regulation rate: 1% to 3% of rated power per minute. The regulation rate of photovoltaic power stations should be 3%.

[0088] A provincial power dispatching and control center was selected as the test environment. The center manages 15 wind farms and 12 photovoltaic power stations with a total installed capacity of 8.5GW. During the test, three typical wind farms and two photovoltaic power stations were selected for AGC function test verification.

[0089] AGC parameter modeling process

[0090] When modeling AGC parameters in the E8000 system database, the hierarchical modeling principle must be strictly followed. First, the overall power grid model is established at the system level, including network topology, voltage level distribution, main equipment parameters, etc. On this basis, a detailed sub-model is established for each new energy power plant. The specific modeling process is as follows:

[0091] First, the basic information of the power plant is modeled. In the power plant table, enter the basic information such as the power plant code, name, type, rated capacity, and grid-connected voltage level. For wind farms, it is also necessary to supplement information such as wind turbine type and control method; for photovoltaic power stations, it is necessary to supplement information such as photovoltaic module type and inverter parameters. These basic information provide necessary parameter support for subsequent AGC control.

[0092] Then AGC cluster modeling is performed. Adjacent new energy power stations are organized into AGC clusters based on geographical location and electrical connection relationships. The power stations in each cluster share the same AGC control strategy and parameters. The cluster model needs to define the cluster number, name, member power station list, priority and other information. In this embodiment, a nearby wind farm and a photovoltaic power station are combined into an AGC cluster to study the coordinated control effects of different types of new energy power stations.

[0093] Then the AGC system model is established. The system model mainly includes parameters such as control area division, frequency regulation dead zone setting, AGC control cycle, and regulation coefficient. According to actual operation experience, the frequency regulation dead zone is set to ±0.02Hz, the AGC control cycle is set to 4 seconds, and the regulation coefficient is allocated in proportion to the installed capacity of each power station.

[0094] In the control area model, it is necessary to define in detail the boundary points, key busbars, tie line power and other information of each area. In this embodiment, the test range is divided into three control areas, each of which contains 1-2 AGC field groups. The areas are connected by 220kV tie lines, and the tie line power limit is set to plus or minus 300MW.

[0095] The power plant controller model is the most critical part, and a complete control loop needs to be established. The controller model contains core parameters such as PID parameter setting values, power limit values, and ramp rate limit. Through actual operation data analysis, the PID parameters are initially set to: Kp = 0.8, Ki = 0.05, Kd = 0.01. The power limit value is set to 95% of the rated power, and the ramp rate limit is set to 3% / minute of the rated power.

[0096] Finally, the measurement model is established, including the definition of key measurement points. Each power station needs to be configured with at least the following basic measurements: active power, reactive power, station frequency, bus voltage, etc. In addition, wind farms need to add meteorological measurements such as wind speed and wind direction, and photovoltaic power stations need to add characteristic measurements such as light intensity and component temperature. The sampling period of all measurement points is uniformly set to 1 second.

[0097] Implementation of parameter configuration system

[0098] The parameter configuration of the AGC master station test system is a systematic process that requires consideration of configuration requirements at multiple levels. In terms of PLC configuration, it is necessary to assign a unique identification code to each controller and establish a mapping relationship with the field equipment. The PLC scan cycle is set to 100ms and the communication timeout is set to 1 second to ensure the real-time nature of the control instructions.

[0099] When configuring the capacity of a new energy power station, the actual available capacity is set to 90% of the rated capacity, taking into account equipment aging and environmental impact. The current power value is collected in real time from the SCADA system with a sampling frequency of 1Hz. The setting range of the target power is limited to ±30% of the current power to avoid system disturbances caused by excessive power regulation.

[0100] The configuration of the upper and lower limits of regulation needs to comprehensively consider the characteristics of the equipment and the system requirements. The upper limit is set to 95% of the actual available capacity, and the lower limit is set to 20% of the rated power for wind farms and 10% of the rated power for photovoltaic power plants. This setting not only ensures the safe operation of the equipment, but also reserves sufficient margin for AGC regulation.

[0101] The configuration of the test time parameters also requires special attention. To ensure the integrity of the test, each test lasts no less than 30 minutes. A 5-minute stabilization period is set at the beginning of the test for the convergence of system parameters. During the test, the response time window of each adjustment instruction is set to 3 minutes. If the target value is not reached within this time window, it is judged as a response timeout.

[0102] Simulation system core algorithm:

[0103] The core of the simulation system is the implementation of the AGC control algorithm. The control algorithm adopts an improved PID control strategy, which adds an adaptive parameter adjustment mechanism based on traditional PID control. The controller output calculation formula uses an incremental PID algorithm, which can effectively avoid the integral saturation problem. At the same time, a feedforward compensation mechanism is introduced to use historical data to predict the power regulation trend and improve control accuracy.

[0104] In terms of power allocation strategy, a priority-based dynamic allocation algorithm is adopted. First, the basic priority is determined according to the regulation capability and response characteristics of each power station, and then dynamically adjusted according to the real-time operating status. When the response characteristics of a power station deteriorate, its allocation weight is automatically reduced, and more regulation tasks are allocated to power stations with better response.

[0105] In order to deal with the randomness of renewable energy generation, the system implements an adaptive filtering algorithm. The collected power data is filtered by a sliding window, and the window length is automatically adjusted according to the degree of power fluctuation, ranging from 10 to 30 seconds. For sudden and large power fluctuations, median filtering is used to effectively suppress the impact of abnormal data.

[0106] Dynamic performance evaluation method:

[0107] Dynamic performance evaluation uses a multi-dimensional indicator system. In addition to the basic regulation rate, response time and regulation accuracy, stability evaluation indicators are also added. The stability of the control process is evaluated by calculating the standard deviation and volatility of the power output. At the same time, the integral time absolute error (ITAE) indicator is introduced to comprehensively evaluate the control quality.

[0108] The performance indicators of wind farms focus on power tracking characteristics. The regulation rate is required to be between 2% and 5% of the rated power, a range that takes into account the mechanical characteristics and life of the wind turbine. The 3-minute response time limit is determined based on the physical constraints of the wind turbine blade angle adjustment. The 2% differential rate takes into account the impact of wind speed fluctuations.

[0109] The performance indicators of photovoltaic power stations focus more on the stability of regulation. The regulation rate is limited to 1% to 3% of the rated power, mainly considering the power regulation characteristics of the inverter. The 3% adjustment rate takes into account the impact of changes in light intensity. The response time is also required to be within 3 minutes, which is mainly limited by the control characteristics of the inverter.

[0110] Exception handling mechanism:

[0111] In order to ensure the reliability and security of the test system, a complete exception handling mechanism is designed. Exception handling is divided into three levels: data exception, control exception and communication exception. In terms of data exception handling, a multi-level data verification mechanism is implemented:

[0112] The first is the rationality check of the measured data. Set upper and lower limits for each measurement point, and data outside the range will be marked. The power data of the wind farm needs to be correlated with the wind speed data. When the corresponding relationship between the power value and the wind speed deviates significantly from the wind turbine power characteristic curve, the data abnormality mark is triggered. The power data of the photovoltaic power station is correlated with the light intensity, taking into account the reasonable range of component conversion efficiency.

[0113] Control exception handling is mainly aimed at the execution process of AGC control instructions. Control exception handling is triggered when the following situations are detected:

[0114] The actual response value of the control command deviates from the target value by more than 5% for three consecutive times; the power adjustment direction is opposite to the command requirement; the adjustment rate exceeds the limit range.

[0115] Emergency stop or protection action occurs:

[0116] Communication exception handling includes communication interruption, data frame check error, communication delay exceeding the limit, etc. The system uses TCP / IP protocol for communication and sets up an automatic reconnection mechanism. When communication interruption is detected, it first tries to reconnect three times. If it still fails, the control unit is marked as offline and an alarm message is sent to the dispatcher.

[0117] Intelligent judgment and optimization strategy:

[0118] The system integrates an intelligent judgment module to evaluate the effectiveness of the test process in real time. Specifically, it includes the following aspects:

[0119] Response characteristic analysis: Evaluate the response of the power station by calculating the power change rate, control deviation and other parameters in real time. When there is response hysteresis, severe overshoot and other situations, the system will automatically adjust the control parameters. For wind farms, dynamically adjust the PID parameters according to the wind speed change trend; for photovoltaic power stations, adjust the control gain according to the change of light intensity.

[0120] Control strategy optimization: Based on historical data accumulation, the system has established a control effect evaluation model. This model takes into account multiple factors such as weather conditions, equipment status, load characteristics, etc., and continuously optimizes the control strategy through machine learning methods. For example, in windy weather conditions, the control response speed of the wind farm is appropriately reduced to improve system stability.

[0121] Performance prediction: By analyzing historical operation data, a performance prediction model is established. This model can predict the adjustable capacity in the future and provide decision support for AGC control. The prediction model uses time series analysis methods and combines meteorological forecast data to achieve a prediction accuracy of more than 85%.

[0122] Data analysis and evaluation system:

[0123] In order to comprehensively evaluate the AGC control effect, a systematic data analysis system was established: Basic data analysis: statistical analysis of the original data, including: the mean, standard deviation, and peak factor of the power data; the execution rate, accuracy, and timeliness of the control instructions; the overshoot, adjustment time, and steady-state error of the system response.

[0124] Advanced feature extraction: Use wavelet transform to analyze power fluctuation characteristics; calculate power spectrum density and analyze system dynamic characteristics; extract characteristic parameters of the control process, such as rise time, peak time, etc.

[0125] Performance evaluation indicators: define a comprehensive performance index with weights of 30% for response speed, 40% for control accuracy, and 30% for system stability; establish a scoring model, standardize each indicator and perform weighted calculation; the performance indicators are divided into four levels: excellent (above 90 points), good (75-90 points), qualified (60-75 points), and unqualified (below 60 points).

[0126] Safety protection mechanism: Taking into account the particularity of renewable energy power generation, the system implements a multi-level safety protection mechanism:

[0127] Equipment protection: monitor key equipment parameters, such as fan bearing temperature, gearbox vibration, inverter temperature, etc.; set graded alarm thresholds, and automatically reduce the AGC adjustment range when the parameter exceeds the warning value; when the protection value is reached, immediately exit AGC control.

[0128] System protection: Real-time monitoring of system frequency and voltage, suspending AGC regulation when it exceeds the safety range; monitoring power flow to prevent line overload; setting power change rate limit to avoid large-scale regulation causing impact on the system.

[0129] Network security: Use encrypted communication protocols to prevent control instructions from being tampered with; set up firewalls to limit external access; establish operation logs to record all control commands and system status changes.

[0130] Furthermore, this embodiment also provides a new energy automatic power generation control master station test simulation system, including:

[0131] Data acquisition module, which obtains AGC parameters of new energy power plants and builds models. AGC parameters include power plant table, AGC field group, AGC system, control area, power plant controller, AGC unit table, power plant measurement and PLC measurement;

[0132] Parameter debugging module, to configure the parameters of the AGC master station test system. The configuration parameters include PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, and test time parameters;

[0133] Output module, performs simulation according to AGC modeling parameters and configuration parameters and outputs simulation results;

[0134] Evaluation module, generates AGC control instructions based on simulation results and evaluates dynamic performance indicators.

[0135] In summary, by obtaining the AGC parameters of new energy power plants and establishing models, including a comprehensive parameter system such as power plant tables, AGC field groups, and AGC systems, accurate modeling of new energy power stations is achieved, avoiding simulation deviations caused by incomplete modeling in traditional technologies, and ultimately achieving the effect of improving simulation accuracy.

[0136] By configuring the AGC master station test system parameters including PLC name, power station capacity, power parameters, etc., a complete parameter configuration system was established, which solved the problem of scattered and unsystematic parameter configuration in traditional technology and ultimately achieved standardization and normalization of the test system.

[0137] Analysis of technical solutions for key dependent claims:

[0138] By calculating the power generation power by using the deviation between the simulated frequency and the rated frequency in the process of generating AGC control instructions, the frequency change is cleverly associated with the power regulation, solving the problem of blind power regulation in traditional technology and achieving the effect of precise control. This effect is unpredictable in the existing technology.

[0139] By setting differentiated dynamic performance indicators (2% to 5% for wind farms and 1% to 3% for photovoltaics), the characteristics of different types of renewable energy generation are fully considered, the defects of "one size fits all" in traditional technologies are overcome, and performance evaluation is achieved according to local conditions. This differentiated design brings about control effects beyond expectations.

[0140] Synergistic effect of the overall technical solution:

[0141] The system adopts modular design (data acquisition module, parameter debugging module, output module, evaluation module), and each module works together, which not only realizes the decoupling of functions, but also brings significant improvement in system maintainability and scalability. This modular design has produced unexpected improvement in maintenance efficiency.

[0142] The specific implementation method of AGC parameter modeling of new energy power plants through the E8000 system database has achieved seamless connection with the existing dispatching automation system, solved the problem of disconnection between traditional test systems and actual production systems, and brought about system compatibility beyond expectations.

[0143] A. Through the real-time evaluation mechanism of dynamic performance indicators, the system can adaptively adjust the control strategy. This adaptive feature is not available in existing technologies and has brought about a significant improvement in control accuracy.

[0144] B. The test data accumulated during the operation of the system provides valuable reference for the subsequent grid-connected commissioning of new energy power stations. This data accumulation effect is a beneficial effect that was not anticipated at the beginning of development.

[0145] C. Through standardized testing processes, the commissioning cycle of new energy power stations has been significantly shortened, saving an average of 30% of the commissioning time. This efficiency improvement exceeds the expected design goals.

[0146] The present invention not only solves the problem of incomplete AGC substation testing in the prior art, but also fundamentally solves the technical difficulties of grid-connected commissioning of new energy power stations through a systematic testing method. The testing process is transformed from passive response to active prediction; the control strategy is transformed from a fixed mode to adaptive adjustment. Performance evaluation is transformed from a single indicator to a multi-dimensional comprehensive evaluation.

[0147] Example 2

[0148] Reference Figure 2 - Figure 4 , which is the second embodiment of the present invention, provides a test simulation method for a master station based on automatic power generation control of new energy. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0149] In order to verify the technical effect of the present invention, a power dispatching and control center of a province in the East China region was selected as the test base, and a six-month test verification was carried out on five typical new energy stations under its jurisdiction. The test objects included three large wind farms (stations A, B, and C, with installed capacities of 200MW each) and two photovoltaic power stations (stations D and E, with installed capacities of 100MW and 150MW respectively). The test adopted a comparative verification method, and the five stations were respectively subjected to performance evaluation using the traditional AGC test method (Scheme 1), the conventional simulation test system (Scheme 2), and the test method of the present invention (Scheme 3).

[0150] The test process is strictly carried out in accordance with the following steps: First, use the E8000 system database to establish three sets of AGC parameter models. The traditional solution only contains basic power plant parameters; the conventional solution adds some control parameters; the solution of the present invention builds a complete parameter system, including all model parameters such as power plant table, AGC field group, AGC system, control area, power plant controller, etc. In the configuration stage, a unified test condition is set: 60% of the rated capacity is used as the basic output point, the adjustment range is ±20% of the rated capacity, and the test cycle is 30 minutes.

[0151] Specific test items include: control accuracy test (given target power, check actual output deviation), response speed test (step response time measurement), regulation stability test (power fluctuation analysis) and anti-disturbance ability test (simulating grid disturbance conditions). To ensure data comparability, each test item is repeated 3 times under similar weather conditions, and the average value is taken as the final result.

[0152]

[0153]

[0154] Through systematic analysis of test data, the solution of the present invention shows significant advantages in multiple key indicators:

[0155] First, in terms of AGC control accuracy, the solution of the present invention reaches 96.5%, which is 13.1% and 7.6% higher than the traditional solution and the conventional solution respectively. This improvement is due to the complete parameter modeling system, which makes the generation of control instructions more accurate. In particular, in the test of wind farm A, even when the wind speed fluctuates frequently, the control deviation can still be maintained within ±1.5%, while the deviation of the traditional solution under the same conditions reaches ±4.8%.

[0156] In terms of response speed, the average response time of the solution of the present invention is only 95 seconds, which is 48.6% shorter than that of the traditional solution. This significant improvement is due to the innovative adaptive control algorithm. Taking photovoltaic power station D as an example, under the condition of sudden change in light intensity, the system can complete power regulation within 78 seconds, while the traditional solution takes 165 seconds to achieve the same effect.

[0157] The control effect of power fluctuation is the most outstanding. The solution of the present invention reduces it to 1.8%, which is 62.5% lower than the 4.8% of the traditional solution. This indicator fully reflects the stability of the system. In the continuous operation test of wind farm B, even in windy weather conditions, the standard deviation of power output remains within 1.2MW, while the fluctuation of the traditional solution reaches 3.5MW.

[0158] The improvement in anti-disturbance capability is also very significant, with a success rate of 94.8%, 20.8 percentage points higher than the traditional solution. Under the simulated grid frequency mutation condition, the system can start responding within 2 seconds and complete power adjustment within 5 seconds, while the traditional solution takes an average of 4 seconds to start responding.

[0159] In terms of actual operation and maintenance, the present invention also shows great advantages. The system debugging time is reduced from 72 hours / station in the traditional solution to 24 hours / station, and the efficiency is improved by 66.7%. The number of parameter optimizations is also reduced from 12 times to 3 times, indicating that the system's adaptive ability is significantly enhanced. These improvements directly lead to a 66.7% reduction in maintenance workload, greatly reducing operation and maintenance costs.

[0160] It is particularly noteworthy that the present invention has achieved an excellent level of 95.7% in terms of system stability, which is 15.9% higher than the traditional solution. This indicator fundamentally reflects the reliability of the system and is of great significance to ensuring the safe and stable operation of the power grid.

[0161] These data fully demonstrate the innovation and superiority of the present invention in the field of new energy AGC control testing. It not only solves the problems existing in traditional technologies, but also achieves a qualitative leap in performance.

[0162] Example 3

[0163] This embodiment also provides a computer device, which is suitable for a method for testing and simulating a master station based on automatic power generation control of new energy, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0164] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0165] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0166] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0167] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0168] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0169] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A test simulation method for a master station based on automatic power generation control of new energy, characterized in that: include, Obtain AGC parameters of new energy power plants and establish models, wherein the AGC parameters include power plant tables, AGC field groups, AGC systems, control areas, power plant controllers, AGC unit tables, power plant measurements, and PLC measurements; Perform parameter configuration of the AGC master station test system, wherein the configuration parameters include PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, and test time parameters; Perform simulation according to the AGC modeling parameters and configuration parameters and output simulation results; Based on the simulation results, AGC control instructions are generated and dynamic performance indicators are evaluated.

2. The method for testing and simulating a master station based on automatic power generation control of new energy as claimed in claim 1, characterized in that: The generation process of the AGC control instruction includes: Obtain the system parameters of the AGC master station system in the dispatching master station; Calculate the generated power according to the deviation between the simulated frequency and the rated frequency in the simulation result; An AGC control instruction is generated according to the generated power, and the AGC control instruction is a target generated power value of a target new energy power station.

3. The method for testing and simulating a master station based on automatic power generation control of new energy as claimed in claim 2, characterized in that: The dynamic performance indicators include: Regulation rate indicator, the regulation rate is 2% to 5% of the rated power per minute for a wind farm or 1% to 3% of the rated power per minute for a photovoltaic power station; Response time indicator, the response time is 1 to 3 minutes; The regulation accuracy index is 2% for a wind farm or 3% for a photovoltaic power station.

4. The method for testing and simulating a master station based on automatic power generation control of new energy as claimed in claim 3, characterized in that: The test time parameters include: test start time and end time; test response time; current time; The AGC master station test system parameter configuration also includes: test response power; test control deviation; current control deviation.

5. The method for testing and simulating a master station based on automatic power generation control of new energy as claimed in claim 4, characterized in that: The evaluation of the dynamic performance indicators includes: recording and analyzing various operating data during the test; judging whether the test results are qualified according to preset performance standards; and generating a test evaluation report.

6. The method for testing and simulating a master station based on automatic power generation control of new energy as claimed in claim 5, characterized in that: The simulation result output includes: real-time display of various test parameters; recording of abnormal conditions during the test; and forming a test data curve.

7. The method for testing and simulating a master station based on automatic power generation control of new energy as claimed in claim 6, characterized in that: After the AGC control instruction is generated, the instruction is checked for rationality; if an abnormality is found, the test is automatically interrupted and an alarm is issued; Record the reasons for test interruption and related data; Conduct statistical analysis on simulation test results; optimize and adjust the AGC control strategy based on the statistical analysis results; and generate an optimization recommendation report.

8. A system for testing and simulating a master station for automatic power generation control based on new energy, based on the method for testing and simulating a master station for automatic power generation control based on new energy according to any one of claims 1 to 7, characterized in that: Also includes, Data acquisition module, which acquires AGC parameters of new energy power plants and establishes models. The AGC parameters include power plant table, AGC field group, AGC system, control area, power plant controller, AGC unit table, power plant measurement and PLC measurement; The parameter debugging module configures the parameters of the AGC master station test system. The configuration parameters include PLC name, new energy power station capacity, current power, target power, upper and lower limits of adjustment, and test time parameters; An output module performs simulation according to the AGC modeling parameters and configuration parameters and outputs simulation results; An evaluation module generates AGC control instructions based on the simulation results and evaluates dynamic performance indicators.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for testing and simulating a master station based on automatic power generation control based on new energy are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for testing and simulating a master station based on automatic power generation control based on new energy are implemented as described in any one of claims 1 to 7.

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