A method for suppressing broadband oscillation of wind power grid connection through grid-connected energy storage

Abstract

The present invention relates to the technical field of suppressing broadband oscillations of wind power grid-connected power, specifically a method for suppressing broadband oscillations of wind power grid-connected power through grid-connected energy storage, including system initialization, data acquisition and processing, energy storage system management, grid-connected control, oscillation suppression, communication coordination, safety protection and simulation testing steps: through data filtering and spectrum analysis technology, subsynchronous oscillation frequency can be accurately identified, dynamic programming or genetic algorithm is used to optimize the charging and discharging process of the energy storage system, and grid-connected parameters are dynamically adjusted through feedforward and feedback control algorithms. Combined with MPC model predictive control, adaptive resonant control and DQN deep learning algorithm, the effect of oscillation suppression is significantly improved, and overcurrent and overvoltage conditions of the energy storage system and grid-connected points can be detected and prevented. RTDS real-time digital simulation system and HIL technology are provided to perform comprehensive testing and verification of the energy storage system and control algorithm, ensuring the effectiveness and reliability of the solution in practical applications and facilitating operation and maintenance management.

Description

Technical Field

[0001] The present invention relates to the technical field of suppressing broadband oscillation of wind power grid connection, and in particular to a method for suppressing broadband oscillation of wind power grid connection by grid-connected energy storage. Background Art

[0002] Grid-connected energy storage is a control technology for power electronic equipment. Its core goal is to maintain a constant or near-constant internal potential phasor. This technology can play a key role in areas with high renewable energy penetration and weak power grids. By outputting high power (for example, reaching 300% of rated power in 10 seconds), it can actively support the power grid. Grid-connected energy storage not only has the "ideal" synchronous voltage source characteristics similar to traditional generators, but can also achieve synchronous operation, instantaneous response to active and reactive power changes, provide inertia support, and perform frequency and voltage regulation. Compared with traditional generators, grid-connected energy storage has stronger system support and stability capabilities, faster regulation speeds, and more rapid black starts.

[0003] Suppressing broadband oscillations in wind power grid connection refers to reducing or eliminating the oscillation phenomenon in a wide frequency range that may be caused when wind farms are connected to the grid. These oscillations may be caused by a variety of factors, including the interaction between wind turbines, converters and the grid. The main reasons for suppressing broadband oscillations in wind power grid connection are as follows: First, broadband oscillations threaten the stable operation of the grid, which may lead to a decline in power quality or even system collapse; second, such oscillations damage wind power equipment and grid facilities, increasing maintenance costs; finally, oscillations also affect the grid connection efficiency and power quality of wind power, thereby having a negative impact on the overall performance of the power system.

[0004] Traditional methods for suppressing broadband oscillations in wind power grid-connected devices mainly include optimizing wind turbine controller parameters and improving control strategies, as well as suppressing subsynchronous oscillations through additional damping control. These methods can effectively reduce oscillations in some cases, but they are usually designed for specific frequencies or frequency ranges, and have limited effect on oscillation suppression outside the design range. Their disadvantages lie in the limitations of their suppression effects and their dependence on specific conditions. Existing subsynchronous oscillation suppression methods are difficult to meet the requirements for broadband and frequency-varying oscillation suppression. In addition, with the increase in wind power installed capacity and the extension of grid-connected distances, remote monitoring and control functions are imperfect, making operation and maintenance management inconvenient.

[0005] In summary, it is necessary to propose a method to suppress the broadband oscillation of wind power grid connection by grid-connected energy storage to solve the above problems. Summary of the Invention

[0006] The object of the present invention is to provide a method for suppressing broadband oscillation of wind power grid connection by grid-connected energy storage, so as to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for suppressing broadband oscillations of wind power grid-connected power by using grid-connected energy storage, the method being implemented based on a grid-connected energy storage control system and comprising the following steps:

[0009] S1. Initialize the system, start the energy storage management and control system, initialize each module, and confirm that the power monitoring data acquisition module, voltage monitoring unit, current monitoring unit, and frequency monitoring unit are operating normally;

[0010] S2. Data acquisition and processing: Collect voltage, current, and frequency data. Filter the collected data using a data filtering unit to remove noise and interference. Use a Fourier transform algorithm to perform spectral analysis on the data to identify subsynchronous oscillation frequencies. Use a KNN algorithm to detect abnormal oscillations and generate an alarm signal.

[0011] S3. Energy storage system management: Using dynamic programming or genetic algorithms to control the charging and discharging process of the energy storage system, balance grid power fluctuations, calculate the optimal energy scheduling strategy in real time to ensure efficient operation of the energy storage system, and monitor the battery status of energy storage devices to ensure safe and reliable system operation.

[0012] S4. Grid-connected control: Using feedforward and feedback control algorithms, the inverter's output voltage and current are controlled, grid-connected parameters, including reactance and resistance, are dynamically adjusted to optimize grid-connected performance, regulate the inverter's reactive power output, and improve the grid power factor.

[0013] S5. Oscillation suppression: Using the MPC model predictive control algorithm to predict and suppress the main subsynchronous oscillation frequency, and based on the adaptive resonance control algorithm to suppress oscillations in a wide frequency range, the DQN deep learning algorithm is used to adjust the control parameters in real time to improve the suppression effect.

[0014] S6. Communication coordination: Through Ethernet and optical fiber, high-speed data transmission between modules is achieved, the work of each module is coordinated, the overall efficient operation of the system is ensured, remote monitoring and control are achieved, and operation and maintenance management is facilitated;

[0015] S7. Safety protection: Detect and prevent overcurrent in the energy storage system and grid connection points, detect and prevent overvoltage in the grid and energy storage system, monitor system short circuits in real time, and promptly disconnect fault points to protect equipment safety.

[0016] S8. Simulation testing: Use the RTDS real-time digital simulation system to simulate the grid operating status, test the effectiveness of the control algorithm, use HIL technology to conduct comprehensive testing and verification of the energy storage system and control algorithm, create a virtual grid and energy storage system environment, and conduct testing and evaluation under various operating conditions.

[0017] Preferably, the grid-type energy storage management and control system includes an electric energy monitoring data acquisition module, a data processing and analysis module, an energy storage system management module, a grid connection control module, an oscillation suppression control module, a communication coordination module, a safety protection module, and a simulation test module;

[0018] The electric energy monitoring data acquisition module is used for voltage monitoring, current monitoring and frequency monitoring;

[0019] The data processing and analysis module is used for data filtering, data analysis and anomaly detection;

[0020] The energy storage system management module is used for charge and discharge control, energy scheduling and status monitoring;

[0021] The grid-connected control module is used for inverter control, grid-connected parameter adjustment and power factor correction;

[0022] The oscillation suppression control module is used for main oscillation control, broadband suppression control and real-time adjustment of control parameters;

[0023] The communication coordination module is used to achieve high-speed data transmission, coordinated control and remote monitoring between modules;

[0024] The safety protection module is used to provide overcurrent protection, overvoltage protection and short circuit protection;

[0025] The simulation test module is used for real-time simulation, hardware-in-the-loop testing and virtual testing.

[0026] Preferably, the electric energy monitoring data acquisition module further includes a voltage monitoring unit, a current monitoring unit and a frequency monitoring unit;

[0027] The voltage monitoring unit is used to monitor the voltage data of the power grid and the wind power grid connection point in real time through the voltage sensor, and send the data to the data processing unit through the data collector;

[0028] The current monitoring unit is used to monitor the current data of the grid connection point through the current sensor, and the data is sent to the data processing unit through the data collector;

[0029] The frequency monitoring unit is used to monitor the changes in the power grid frequency, and the data is sent to the data processing unit through the data collector.

[0030] Preferably, the data processing and analysis module further includes a data filtering unit, a data analysis unit and an anomaly detection unit;

[0031] The data filtering unit is used to filter the collected data to remove noise and interference to ensure data accuracy;

[0032] The data analysis unit is used to perform spectrum analysis on the data by Fourier transform FFT algorithm to identify the subsynchronous oscillation frequency;

[0033] The abnormality detection unit is used to detect abnormal oscillation through the KNN machine learning algorithm and generate an alarm signal.

[0034] Preferably, the energy storage system management module further includes a charge and discharge control unit, an energy scheduling unit and a status monitoring unit;

[0035] The charge and discharge control unit controls the charge and discharge process of the energy storage system based on dynamic programming or genetic algorithm to balance power fluctuations of the power grid;

[0036] The energy scheduling unit calculates the optimal energy scheduling strategy in real time to ensure the efficient operation of the energy storage system;

[0037] The status monitoring unit is used to monitor the battery status of the energy storage device to ensure safe and reliable operation of the system.

[0038] Preferably, the grid-connected control module further includes an inverter control unit, a grid-connected parameter adjustment unit and a power factor correction unit;

[0039] The inverter control unit is based on feedforward control and feedback control algorithms to control the output voltage and current of the inverter;

[0040] The grid-connected parameter adjustment unit is used to dynamically adjust the grid-connected parameters, including reactance and resistance values, to optimize grid-connected performance;

[0041] The power factor correction unit is used to improve the power factor of the power grid by adjusting the reactive power output of the inverter.

[0042] Preferably, the oscillation suppression control module further includes a main oscillation control unit, a broadband suppression control unit and an intelligent parameter adjustment unit;

[0043] The main oscillation control unit is used to predict and suppress the main subsynchronous oscillation frequency through an MPC model predictive control algorithm;

[0044] The broadband suppression control unit is based on an adaptive resonance control algorithm and is used to suppress oscillations within a broadband range;

[0045] The intelligent parameter adjustment unit uses the DQN deep learning algorithm to adjust the control parameters in real time to improve the suppression effect.

[0046] Preferably, the communication coordination module further includes a communication interface unit, a coordination control unit and a remote monitoring unit;

[0047] The communication interface unit is used to achieve high-speed data transmission between modules through Ethernet and optical fiber;

[0048] The coordination control unit is used to coordinate the work of each module to ensure the efficient operation of the entire system;

[0049] The remote monitoring unit is used to implement remote monitoring and control, facilitating operation and maintenance management.

[0050] Preferably, the safety protection module further includes an overcurrent protection unit, an overvoltage protection unit and a short circuit protection unit;

[0051] The overcurrent protection unit is used to detect and prevent overcurrent conditions in the energy storage system and the grid connection point;

[0052] The overvoltage protection unit is used to detect and prevent overvoltage conditions in the power grid and energy storage system;

[0053] The short-circuit protection unit is used to monitor the short-circuit situation of the system in real time, cut off the fault point in time, and protect the safety of the equipment.

[0054] Preferably, the simulation test module further includes a real-time simulation unit, a hardware-in-the-loop test unit and a virtual test unit;

[0055] The real-time simulation unit simulates the power grid operation state through the RTDS real-time digital simulation system to test the effectiveness of the control algorithm;

[0056] The hardware-in-the-loop test unit is used to perform comprehensive testing and verification of the energy storage system and control algorithm through HIL technology;

[0057] The virtual test unit is used to create a virtual power grid and energy storage system environment to perform tests and evaluations under various operating conditions.

[0058] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention can accurately identify subsynchronous oscillation frequencies through data filtering and spectrum analysis, and effectively detect abnormal oscillations using the KNN algorithm. It uses dynamic programming or genetic algorithms to optimize the charging and discharging process of the energy storage system, and can calculate the optimal energy scheduling strategy in real time, which not only balances the power fluctuations of the power grid, but also ensures the efficient and safe operation of the energy storage system. It dynamically adjusts the grid-connected parameters through feedforward and feedback control algorithms, optimizes the grid-connected performance, and effectively improves the power factor of the power grid. It combines MPC model predictive control, adaptive resonant control and DQN deep learning algorithm to significantly improve the oscillation suppression effect. Through high-speed data transmission and coordination between modules, it ensures the efficient operation of the entire system, and realizes remote monitoring, facilitates operation and maintenance management, and greatly reduces operating costs. By detecting and preventing overcurrent, overvoltage and short circuit conditions, the fault point is promptly cut off, which significantly improves the safety of the energy storage system and the power grid. It provides an RTDS real-time digital simulation system and HIL technology for comprehensive testing and verification of the energy storage system and control algorithm, ensuring the effectiveness and reliability of the solution in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a topological diagram of the network-type energy storage management and control system of the present invention;

[0060] Figure 2 This is a flow chart of the method for suppressing broadband oscillation of wind power grid connection through grid-connected energy storage according to the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying 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 making creative efforts are within the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 The present invention proposes a grid-type energy storage management and control system, which is composed of an electric energy monitoring data acquisition module, a data processing and analysis module, an energy storage system management module, a grid connection control module, an oscillation suppression control module, a communication coordination module, a safety protection module, and a simulation test module;

[0064] Among them, this system performs voltage monitoring, current monitoring and frequency monitoring through the power monitoring data acquisition module, this system performs data filtering, data analysis and anomaly detection through the data processing and analysis module, this system performs charging and discharging control, energy scheduling and status monitoring through the energy storage system management module, this system performs inverter control, grid parameter adjustment and power factor correction through the grid connection control module, this system performs main oscillation control, broadband suppression control and real-time adjustment of control parameters through the oscillation suppression control module, this system realizes high-speed data transmission, coordinated control and remote monitoring between modules through the communication coordination module, this system provides overcurrent protection, overvoltage protection and short-circuit protection through the safety protection module, and this system uses the simulation test module for real-time simulation, hardware-in-the-loop testing and virtual testing.

[0065] In this embodiment, it should also be noted that the power monitoring data acquisition module of this system also includes a voltage monitoring unit, a current monitoring unit and a frequency monitoring unit. The voltage monitoring unit is used to monitor the voltage data of the power grid and the wind power grid connection point in real time through a voltage sensor, and send it to the data processing unit through a data collector. The current monitoring unit is used to monitor the current data of the grid connection point through a current sensor, and the data is sent to the data processing unit through a data collector. The frequency monitoring unit is used to monitor changes in the power grid frequency, and the data is sent to the data processing unit through a data collector.

[0066] In this embodiment, it should also be noted that the data processing and analysis module of this system also includes a data filtering unit, a data analysis unit and an anomaly detection unit. The data filtering unit is used to filter the collected data to remove noise and interference and ensure data accuracy. The data analysis unit is used to perform spectral analysis on the data through the Fourier transform FFT algorithm to identify the subsynchronous oscillation frequency. The anomaly detection unit is used to detect abnormal oscillation conditions through the KNN machine learning algorithm and generate an alarm signal.

[0067] In this embodiment, it should also be noted that the energy storage system management module of this system also includes a charge and discharge control unit, an energy scheduling unit and a status monitoring unit. The charge and discharge control unit controls the charge and discharge process of the energy storage system based on dynamic programming or genetic algorithm to balance power fluctuations in the power grid. The energy scheduling unit calculates the optimal energy scheduling strategy in real time to ensure the efficient operation of the energy storage system. The status monitoring unit is used to monitor the battery status of the energy storage equipment to ensure safe and reliable operation of the system.

[0068] In this embodiment, it should also be noted that the grid-connected control module of this system also includes an inverter control unit, a grid-connected parameter adjustment unit and a power factor correction unit. The inverter control unit is based on feedforward control and feedback control algorithms to control the output voltage and current of the inverter. The grid-connected parameter adjustment unit is used to dynamically adjust the grid-connected parameters. The grid-connected parameters include reactance values ​​and resistance values ​​to optimize grid-connected performance. The power factor correction unit is used to improve the power factor of the power grid by adjusting the reactive power output of the inverter.

[0069] In this embodiment, it should also be noted that the oscillation suppression control module of this system also includes a main oscillation control unit, a wideband suppression control unit and an intelligent parameter adjustment unit. The main oscillation control unit is used to predict and suppress the main sub-synchronous oscillation frequency through the MPC model predictive control algorithm. The wideband suppression control unit is based on an adaptive resonance control algorithm and is used to suppress oscillations within a wide frequency range. The intelligent parameter adjustment unit uses the DQN deep learning algorithm to adjust the control parameters in real time to improve the suppression effect.

[0070] In this embodiment, it should also be noted that the communication coordination module of this system also includes a communication interface unit, a coordination control unit and a remote monitoring unit. The communication interface unit is used to realize high-speed data transmission between modules through Ethernet and optical fiber. The coordination control unit is used to coordinate the work of each module to ensure the overall efficient operation of the system. The remote monitoring unit is used to realize remote monitoring and control to facilitate operation and maintenance management.

[0071] In this embodiment, it should also be noted that the safety protection module of this system also includes an overcurrent protection unit, an overvoltage protection unit and a short-circuit protection unit. The overcurrent protection unit is used to detect and prevent overcurrent conditions in the energy storage system and the grid connection point. The overvoltage protection unit is used to detect and prevent overvoltage conditions in the power grid and the energy storage system. The short-circuit protection unit is used to monitor the system short-circuit conditions in real time, cut off the fault point in time, and protect the safety of the equipment.

[0072] In this embodiment, it should also be noted that the simulation test module of this system also includes a real-time simulation unit, a hardware-in-the-loop test unit and a virtual test unit. The real-time simulation unit simulates the operating status of the power grid through the RTDS real-time digital simulation system to test the effectiveness of the control algorithm. The hardware-in-the-loop test unit is used to perform comprehensive testing and verification of the energy storage system and control algorithm through HIL technology. The virtual test unit is used to create a virtual power grid and energy storage system environment for testing and evaluation under various operating conditions.

[0073] Example 2

[0074] See also Figure 2 In actual application, the method of suppressing broadband oscillation of wind power grid connection through grid-connected energy storage based on the grid-connected energy storage control system specifically includes the following steps:

[0075] S1. Initialize the system:

[0076] S1.1. Start the energy storage control system:

[0077] First, turn on the main power of the energy storage management and control system and start the system control software;

[0078] Load system configuration files, which contain basic parameters and settings for system operation;

[0079] Perform system self-tests to check the hardware device connection status and software module loading status to ensure that the system is in an operational state;

[0080] S1.2. Initialize each module:

[0081] Initialize the power monitoring data acquisition module, set the data acquisition frequency and accuracy parameters, and prepare to start data acquisition;

[0082] Initialize the voltage monitoring unit and calibrate the voltage sensor to ensure the accuracy of voltage measurement;

[0083] Initialize the current monitoring unit and calibrate the sensor to ensure the reliability of the current data;

[0084] Initialize the frequency monitoring unit and set the appropriate sampling rate and data processing method to accurately monitor the grid frequency;

[0085] S1.3. Confirm that the monitoring unit is operating normally:

[0086] Send test signals to each monitoring unit to check whether its response is correct;

[0087] Observe the data returned by each monitoring unit, verify whether it is within the normal range, and compare it with the expected value;

[0088] If any abnormality is found, troubleshoot until all monitoring units are functioning normally;

[0089] S2. Data Collection and Processing:

[0090] S2.1. Data collection:

[0091] Use the power monitoring data acquisition module to continuously collect voltage, current, and frequency data from the power grid. This data changes in real time, so it needs to be collected at a sufficiently high sampling rate to ensure that all important dynamic changes are captured;

[0092] The collected data is stored in the system's memory for subsequent processing and analysis;

[0093] S2.2. Data filtering processing:

[0094] The raw data is processed by the data filtering unit to remove noise and interference signals introduced by equipment noise and electromagnetic interference factors;

[0095] Digital filters can be used, including low-pass filters, high-pass filters or band-pass filters, and the appropriate filter type is selected according to the characteristics of the signal;

[0096] The filtered data will be smoother, which is beneficial for subsequent signal analysis and processing;

[0097] S2.3. Data Analysis and Anomaly Detection:

[0098] The Fourier transform (FFT) algorithm is used to perform spectrum analysis on the filtered data; the formula of Fourier transform is shown in formula (1):

[0099]

[0100] Where f(ω) represents the spectrum of the signal, and f(t) represents the time domain signal. Through FFT, the time domain signal can be converted into a frequency domain signal, thereby identifying the subsynchronous oscillation frequency.

[0101] Use the KNN (K-Nearest Neighbors) algorithm to detect abnormal oscillations. The KNN algorithm calculates the distance between the test instance and the instances in the training set, finds the K nearest neighbors, and predicts the label of the test instance based on the labels of these neighbors. KNN can be used to identify abnormal patterns that are significantly different from normal oscillation patterns.

[0102] Once abnormal oscillation is detected, the system immediately generates an alarm signal, which can trigger further analysis or action, notify maintenance personnel, and automatically adjust control parameters to ensure stable operation of the power grid.

[0103] S3. Energy storage system management:

[0104] S3.1. Charge and discharge process control:

[0105] Dynamic programming or genetic algorithm application: Based on the real-time demand of the power grid and the status of the energy storage system, dynamic programming or genetic algorithms are used to control the charging and discharging process of the energy storage system. Dynamic programming solves complex problems by breaking them down into relatively simple sub-problems, while genetic algorithms simulate the natural selection and genetic mechanisms of biological evolution to find the optimal solution.

[0106] Specifically:

[0107] Dynamic programming algorithm:

[0108] Dynamic programming is a method for solving optimization problems in multi-stage decision-making processes. In the charge and discharge control of energy storage systems, state transition equations can be defined to determine the optimal charge and discharge strategy for each time period.

[0109] State transition equation:

[0110] Assuming f(n) represents the optimal state of the energy storage system in the nth time period, including maximum energy storage or minimum cost, the state transition equation is shown in Equation (2):

[0111] f(n)=max / min{f(n-1)+cost / benefit(n,decision)} (2);

[0112] In the formula, cost / benefit(n,decision) represents the cost or benefit of taking a decision in the nth time period;

[0113] Genetic Algorithm:

[0114] A genetic algorithm is a search algorithm that simulates the biological evolution process. In the charge and discharge control of energy storage systems, a genetic algorithm can be applied to find the optimal charge and discharge strategy. The process is as follows:

[0115] 1) Initialize the population: Randomly generate a series of charging and discharging strategies as the initial population;

[0116] 2) Fitness function: Define a fitness function to evaluate the pros and cons of each charging and discharging strategy, including factors based on grid power balance and energy storage system efficiency;

[0117] 3) Selection operation: select the best charging and discharging strategy as the parent generation based on the fitness value;

[0118] 4) Crossover operation: The selected parent strategy generates a new strategy through crossover operation;

[0119] 5) Mutation operation: mutate the new strategy to increase the diversity of the population;

[0120] 6) New population generation: Generate a new charging and discharging strategy population through selection, crossover, and mutation operations;

[0121] 7) Charge and discharge strategy formulation: Based on the algorithm calculation results, formulate an appropriate charge and discharge strategy to balance grid power fluctuations, discharging during peak load periods and charging during low load periods to smooth the grid's power demand;

[0122] 8) Implement control: Convert the developed charge and discharge strategy into specific control instructions, and execute these instructions through the energy storage system's control system to achieve precise control of the energy storage device's charge and discharge process;

[0123] S3.2. Calculation of optimal energy scheduling strategy:

[0124] Real-time data collection: Continuously collects grid load data, electricity price information, and energy storage system status data, including power, temperature, and pressure;

[0125] Optimal dispatch strategy calculation: Based on the collected data, optimization algorithms, including linear and nonlinear programming, are used to calculate the optimal energy dispatch strategy in real time. This strategy aims to minimize operating costs, maximize the utilization efficiency of the energy storage system, and meet the power requirements of the grid.

[0126] Strategy update and implementation: Based on the calculation results, the energy storage system's energy dispatch strategy is updated regularly or in real time, and these strategies are implemented through the control system;

[0127] S3.3. Energy storage equipment status monitoring:

[0128] State parameter collection: Continuously collect key state parameters of energy storage equipment through sensors and monitoring systems, including voltage, current, temperature, and pressure;

[0129] Fault diagnosis and early warning: Using data analysis technology, the collected status parameters are processed and analyzed to promptly detect abnormal conditions and perform fault diagnosis. Once a potential safety hazard or fault is detected, an early warning mechanism is immediately triggered.

[0130] Maintenance and care: Develop a reasonable maintenance and care plan based on the operating status and usage of the energy storage equipment to extend the service life of the equipment and ensure its safe and reliable operation;

[0131] S4. Grid connection control:

[0132] S4.1. Application of feedforward control and feedback control algorithms:

[0133] Feedforward control:

[0134] Selection principle: When there are high-frequency, large-amplitude, measurable but uncontrollable disturbances in the system, feedforward control can be used;

[0135] Implementation method: By measuring disturbances, including grid voltage fluctuations and load changes, the inverter is controlled in advance to reduce the impact of disturbances on output voltage and current.

[0136] Feedback control:

[0137] Control principle: Based on the deviation of the inverter's output voltage and current from the set value, the inverter's control signal is adjusted through a feedback control algorithm to stabilize the output voltage and current near the set value;

[0138] Feedback control is performed through a PID (proportional-integral-derivative) controller, see formula (3):

[0139]

[0140] Where u(t) is the control signal, e(t) is the deviation signal, Kp, Ki, and Kd are the proportional, integral, and differential coefficients respectively;

[0141] S4.2. Dynamically adjust grid-connected parameters:

[0142] Adjustment of reactance and resistance values:

[0143] In order to optimize grid-connected performance, reduce harmonics and losses, and improve power quality, the reactance and resistance values ​​of the grid-connected connection point are dynamically adjusted according to the actual conditions of the grid and the operating status of the inverter. This is achieved by changing the parameters of the inductor or resistor.

[0144] Parameter optimization:

[0145] The genetic algorithm in step S3 is again used to find the best combination of reactance and resistance values, and the optimal solution is searched in the parameter space by simulating the natural selection or group behavior mechanism;

[0146] S4.3. Regulating the inverter's reactive power output and improving the grid's power factor:

[0147] Reactive power regulation:

[0148] Regulating reactive power output by controlling the reactive component of the inverter's output current; this can be achieved by changing the inverter's control strategy, including adjusting the phase and amplitude of the PWM (pulse width modulation) signal;

[0149] Power factor improvement: Power factor is an important indicator for measuring the efficiency of power grid operation. Improving the power factor can reduce reactive power loss in the power grid and improve energy utilization efficiency. By optimizing the inverter control strategy, it can output more active power and reduce reactive power output, thereby improving the power factor of the power grid. This is achieved by adjusting the phase relationship between the inverter's output voltage and current.

[0150] S5. Oscillation suppression:

[0151] S5.1. Suppress the main subsynchronous oscillation frequency:

[0152] Model building and prediction:

[0153] Determine A and B based on historical data and system characteristics, use the above model to predict future states, and select appropriate prediction and control time domains;

[0154] The prediction model can be expressed as formula (4):

[0155] x(k+1)=Ax(k)+Bu(k) (4);

[0156] Where x(k) is the current state, u(k) is the control input, and A and B are system matrices;

[0157] Optimization solution:

[0158] Define the objective function J, including the tracking error and the cost of the control input. At each sampling time, solve the optimal control sequence U through quadratic programming and implement the first control action of the control sequence.

[0159] The optimization problem is shown in formula (5):

[0160]

[0161] Where x ref is the reference trajectory, Q and R are weight matrices;

[0162] S5.2. Suppress oscillations in a wide frequency range:

[0163] Oscillation frequency identification:

[0164] Collect the system response signal, apply FFT to analyze the signal, identify the main oscillation frequency, and identify the oscillation frequency through Fourier transform (FFT), see formula (6):

[0165]

[0166] Where x(t) is the time domain signal;

[0167] Adaptive adjustment of resonance parameters:

[0168] Adjust ω according to the identified oscillation frequency n , adaptively adjust the damping ratio ζ and gain K to optimize the suppression effect, realize the coordinated operation of multiple resonant controllers, and cover a wide frequency range;

[0169] The transfer function of the resonant controller is expressed as formula (7):

[0170]

[0171] Where K is the gain, ζ is the damping ratio, and ω n is the natural frequency;

[0172] S5.3. Real-time adjustment of control parameters:

[0173] Define the state and action space:

[0174] Define the state space, including characteristics such as oscillation amplitude and frequency, define the action space, that is, the adjustment range of the control parameters, initialize the Q value function, and the Q value function update rule is formula (8):

[0175] Q(s,a)←Q(s,a)+α[r+γmax a' Q(s',a')-Q(s,a)](8);

[0176] Where s is the state, a is the action, r is the reward, and γ is the discount factor;

[0177] Training the DQN model:

[0178] Use historical data to train the DQN model, select actions through the ε-greedy strategy, balance exploration and exploitation, and use experience replay and target networks to stabilize the learning process;

[0179] Adjust control parameters in real time:

[0180] In actual operation, the optimal action is selected according to the current system state, the control parameters are adjusted in real time according to the output of the DQN model, and continuous monitoring and adjustment are made to optimize the suppression effect;

[0181] S6. Communication coordination: Through Ethernet and optical fiber, high-speed data transmission between modules is achieved, the work of each module is coordinated, the overall efficient operation of the system is ensured, remote monitoring and control are achieved, and operation and maintenance management is facilitated;

[0182] S7. Safety protection: Detect and prevent overcurrent in the energy storage system and grid connection points, detect and prevent overvoltage in the grid and energy storage system, monitor system short circuits in real time, and promptly disconnect fault points to protect equipment safety.

[0183] S8. Simulation testing: Use the RTDS real-time digital simulation system to simulate the grid operating status, test the effectiveness of the control algorithm, use HIL technology to conduct comprehensive testing and verification of the energy storage system and control algorithm, create a virtual grid and energy storage system environment, and conduct testing and evaluation under various operating conditions.

[0184] Through the above steps, the present invention can accurately identify subsynchronous oscillation frequencies through data filtering and spectrum analysis, effectively detect abnormal oscillations using the KNN algorithm, optimize the charging and discharging process of the energy storage system using dynamic programming or genetic algorithms, and calculate the optimal energy scheduling strategy in real time, which not only balances the power fluctuations of the power grid, but also ensures the efficient and safe operation of the energy storage system. It dynamically adjusts the grid connection parameters through feedforward and feedback control algorithms, optimizes the grid connection performance, and effectively improves the power factor of the power grid. It combines MPC model predictive control, adaptive resonant control and DQN deep learning algorithm to significantly improve the oscillation suppression effect. Through high-speed data transmission and coordination between modules, it ensures the efficient operation of the entire system, and realizes remote monitoring, facilitates operation and maintenance management, and greatly reduces operating costs. By detecting and preventing overcurrent, overvoltage and short circuit conditions, the fault point is promptly cut off, significantly improving the safety of the energy storage system and the power grid. It provides an RTDS real-time digital simulation system and HIL technology for comprehensive testing and verification of the energy storage system and control algorithm, ensuring the effectiveness and reliability of the solution in practical applications and facilitating operation and maintenance management.

[0185] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for suppressing broadband oscillation of wind power grid connection by grid-connected energy storage, the method being implemented based on a grid-connected energy storage management and control system, characterized in that: The following steps are involved: S1. Initialize the system, start the energy storage management and control system, initialize each module, and confirm that the power monitoring data acquisition module, voltage monitoring unit, current monitoring unit, and frequency monitoring unit are operating normally; S2. Data acquisition and processing: Collect voltage, current, and frequency data. Filter the collected data using a data filtering unit to remove noise and interference. Use a Fourier transform algorithm to perform spectral analysis on the data to identify subsynchronous oscillation frequencies. Use a KNN algorithm to detect abnormal oscillations and generate an alarm signal. S3. Energy storage system management: Using dynamic programming or genetic algorithms to control the charging and discharging process of the energy storage system, balance grid power fluctuations, calculate the optimal energy scheduling strategy in real time to ensure efficient operation of the energy storage system, and monitor the battery status of energy storage devices to ensure safe and reliable system operation. S4. Grid-connected control: Using feedforward and feedback control algorithms, the inverter's output voltage and current are controlled, grid-connected parameters, including reactance and resistance, are dynamically adjusted to optimize grid-connected performance, regulate the inverter's reactive power output, and improve the grid power factor. S5. Oscillation suppression: Using the MPC model predictive control algorithm to predict and suppress the main subsynchronous oscillation frequency, and based on the adaptive resonance control algorithm to suppress oscillations in a wide frequency range, the DQN deep learning algorithm is used to adjust the control parameters in real time to improve the suppression effect. S6. Communication coordination: Through Ethernet and optical fiber, high-speed data transmission between modules is achieved, the work of each module is coordinated, the overall efficient operation of the system is ensured, remote monitoring and control are achieved, and operation and maintenance management is facilitated; S7. Safety protection: Detect and prevent overcurrent in the energy storage system and grid connection points, detect and prevent overvoltage in the grid and energy storage system, monitor system short circuits in real time, and promptly disconnect fault points to protect equipment safety. S8. Simulation testing: Use the RTDS real-time digital simulation system to simulate the grid operating status, test the effectiveness of the control algorithm, use HIL technology to conduct comprehensive testing and verification of the energy storage system and control algorithm, create a virtual grid and energy storage system environment, and conduct testing and evaluation under various operating conditions.

2. The method of suppressing broadband oscillation of wind power grid connection by grid-connected energy storage according to claim 1, characterized in that: The grid-connected energy storage management and control system includes an electric energy monitoring data acquisition module, a data processing and analysis module, an energy storage system management module, a grid connection control module, an oscillation suppression control module, a communication coordination module, a safety protection module, and a simulation test module. The electric energy monitoring data acquisition module is used for voltage monitoring, current monitoring and frequency monitoring; The data processing and analysis module is used for data filtering, data analysis and anomaly detection; The energy storage system management module is used for charge and discharge control, energy scheduling and status monitoring; The grid-connected control module is used for inverter control, grid-connected parameter adjustment and power factor correction; The oscillation suppression control module is used for main oscillation control, broadband suppression control and real-time adjustment of control parameters; The communication coordination module is used to achieve high-speed data transmission, coordinated control and remote monitoring between modules; The safety protection module is used to provide overcurrent protection, overvoltage protection and short circuit protection; The simulation test module is used for real-time simulation, hardware-in-the-loop testing and virtual testing.

3. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 2, characterized in that: The electric energy monitoring data acquisition module also includes a voltage monitoring unit, a current monitoring unit and a frequency monitoring unit; The voltage monitoring unit is used to monitor the voltage data of the power grid and the wind power grid connection point in real time through the voltage sensor, and send the data to the data processing unit through the data collector; The current monitoring unit is used to monitor the current data of the grid connection point through the current sensor, and the data is sent to the data processing unit through the data collector; The frequency monitoring unit is used to monitor the changes in the power grid frequency, and the data is sent to the data processing unit through the data collector.

4. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 3, characterized in that: The data processing and analysis module also includes a data filtering unit, a data analysis unit and an anomaly detection unit; The data filtering unit is used to filter the collected data to remove noise and interference to ensure data accuracy; The data analysis unit is used to perform spectrum analysis on the data using a Fourier transform (FFT) algorithm to identify the subsynchronous oscillation frequency; The abnormality detection unit is used to detect abnormal oscillation through the KNN machine learning algorithm and generate an alarm signal.

5. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 4, characterized in that: The energy storage system management module also includes a charge and discharge control unit, an energy scheduling unit and a status monitoring unit; The charge and discharge control unit controls the charge and discharge process of the energy storage system based on dynamic programming or genetic algorithm to balance power fluctuations of the power grid; The energy scheduling unit calculates the optimal energy scheduling strategy in real time to ensure the efficient operation of the energy storage system; The status monitoring unit is used to monitor the battery status of the energy storage device to ensure safe and reliable operation of the system.

6. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 5, characterized in that: The grid-connected control module also includes an inverter control unit, a grid-connected parameter adjustment unit and a power factor correction unit; The inverter control unit is based on feedforward control and feedback control algorithms to control the output voltage and current of the inverter; The grid-connected parameter adjustment unit is used to dynamically adjust the grid-connected parameters, including reactance and resistance values, to optimize grid-connected performance; The power factor correction unit is used to improve the power factor of the power grid by adjusting the reactive power output of the inverter.

7. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 6, characterized in that: The oscillation suppression control module also includes a main oscillation control unit, a broadband suppression control unit and an intelligent parameter adjustment unit; The main oscillation control unit is used to predict and suppress the main subsynchronous oscillation frequency through an MPC model predictive control algorithm; The broadband suppression control unit is based on an adaptive resonance control algorithm and is used to suppress oscillations within a broadband range; The intelligent parameter adjustment unit uses the DQN deep learning algorithm to adjust the control parameters in real time to improve the suppression effect.

8. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 7, characterized in that: The communication coordination module also includes a communication interface unit, a coordination control unit and a remote monitoring unit; The communication interface unit is used to achieve high-speed data transmission between modules through Ethernet and optical fiber; The coordination control unit is used to coordinate the work of each module to ensure the efficient operation of the entire system; The remote monitoring unit is used to implement remote monitoring and control, facilitating operation and maintenance management.

9. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 8, characterized in that: The safety protection module also includes an overcurrent protection unit, an overvoltage protection unit and a short circuit protection unit; The overcurrent protection unit is used to detect and prevent overcurrent conditions in the energy storage system and the grid connection point; The overvoltage protection unit is used to detect and prevent overvoltage conditions in the power grid and energy storage system; The short-circuit protection unit is used to monitor the short-circuit situation of the system in real time, cut off the fault point in time, and protect the safety of the equipment.

10. The method of suppressing broadband oscillation of wind power grid-connected power by grid-connected energy storage according to claim 9, characterized in that: The simulation test module also includes a real-time simulation unit, a hardware-in-the-loop test unit and a virtual test unit; The real-time simulation unit simulates the operation state of the power grid through the RTDS real-time digital simulation system to test the effectiveness of the control algorithm; The hardware-in-the-loop test unit is used to perform comprehensive testing and verification of the energy storage system and control algorithm through HIL technology; The virtual test unit is used to create a virtual power grid and energy storage system environment to perform tests and evaluations under various operating conditions.

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