New energy station power quality harmonic suppression device and method based on multi-objective optimization

Abstract

The invention discloses a new energy station power quality harmonic suppression device and method based on multi-objective optimization, and relates to the technical field of power system new energy access. The problem that the self-adaption effect is poor in the electric energy quality treatment process after the new energy station is connected to the power grid is solved. According to the method, Hanning window function processing is carried out on collected signals, then fast Fourier transform is used for extracting harmonic frequency and amplitude information, a wavelet packet decomposition method is introduced, a Daubechies-4 wavelet function is adopted, multi-scale spectrum analysis is carried out, high-precision identification and compensation of 2-25 harmonic waves are achieved, and the THD inhibition rate can be increased to 70% or above; dynamic balance among power loss, voltage deviation and equipment load is realized through a multi-target control method; the system has self-adaptive adjustment and remote communication functions and adapts to different operation conditions. The method is mainly used for new energy station power quality harmonic suppression.

Description

Technical Field

[0001] This invention relates to the field of new energy access technology for power systems. Background Technology

[0002] With the increasing proportion of new energy sources such as photovoltaic and wind power being integrated into the grid, problems such as harmonics, voltage fluctuations, and reactive power imbalances in the power grid are becoming increasingly serious. Traditional power grids are characterized by centralized operation and high inertia, and have a strong tolerance for harmonics. However, new energy power plants have characteristics such as output volatility and electronic inverter power, which leads to prominent power quality problems. In severe cases, this can cause protection malfunctions, equipment overheating, or even failure.

[0003] Existing harmonic mitigation technologies mainly fall into two categories: one is to use passive LC filters to suppress harmonics of specific frequencies, which is low in cost but has poor dynamic performance and is prone to resonance; the other is to use active power filters (APFs) to actively inject compensation current, which has strong compensation capabilities but high equipment costs and significant operating losses. Although some hybrid compensation schemes have achieved certain performance improvements, they still have shortcomings in multi-objective dynamic adjustment, field adaptability, and intelligence.

[0004] Furthermore, existing methods generally employ a single objective (such as minimizing THD) for optimization, neglecting the trade-offs between equipment losses, voltage deviations, and response speeds, making them ill-suited for the dynamic compensation requirements under complex operating conditions. Therefore, there is an urgent need for a novel harmonic mitigation device that integrates multi-objective control, precise detection, and intelligent feedback regulation to meet the practical needs of high-quality, low-cost operation of renewable energy power plants. Summary of the Invention

[0005] This invention aims to address the problem of poor adaptive performance in power quality management after new energy power plants are connected to the power grid. It provides a device and method for harmonic management of power quality in new energy power plants based on multi-objective optimization.

[0006] The first aspect of this application provides a harmonic control device for power quality in new energy power plants based on multi-objective optimization. The device includes: a harmonic detection module, a multi-objective control module, a harmonic compensation device, and a data communication module.

[0007] The harmonic detection module collects voltage and current data from the new energy power station in real time. It uses a combination of wavelet analysis and windowed FFT to perform frequency domain analysis on the voltage and current waveforms, accurately identify each harmonic component, and obtain the total harmonic distortion rate.

[0008] The multi-objective control module takes the minimum total harmonic distortion, the minimum voltage deviation, the minimum reactive power compensation, and the minimum device loss as optimization objectives. It uses an improved particle swarm optimization algorithm or the NSGA-II algorithm to perform a global search for the control strategy, obtains the optimal compensation solution set through the Pareto optimization front, and selects the most suitable control strategy under the current operating conditions from the optimal compensation solution set.

[0009] The harmonic compensation device utilizes the most suitable control strategy under the current operating conditions, employing active power filters and passive filters to generate and inject a compensation current in real time that is equal in magnitude and opposite in direction to the harmonic current of the power grid; directly canceling the dynamically changing harmonic components in the power grid and suppressing broadband harmonics.

[0010] The data communication module communicates with the main station to send out the control strategy after broadband harmonic suppression and transmit data back, thereby realizing closed-loop control and intelligent adjustment.

[0011] Furthermore, in this invention, the active power filter (APF) adopts a current-controlled inverter structure, and the passive filter has a multi-branch structure.

[0012] Furthermore, in this invention, the multi-branch structure of the passive filter filters out the 5th, 7th, and 11th harmonics, respectively.

[0013] Furthermore, in this invention, the data communication module supports Modbus, IEC61850, and CANopen communication protocols to enable control command issuance and data feedback.

[0014] The first aspect of this application provides a method for power quality harmonic mitigation based on the above-mentioned device, including:

[0015] Step S1: Install the harmonic mitigation device on the grid-connected circuit breaker side, and configure three-phase voltage transformers (VT) and current transformers (CT) on the line between the grid-connected circuit breaker of the new energy power station and the harmonic mitigation device to collect the voltage and current data of the new energy power station in real time.

[0016] Step S2: Process the acquired signal using the Hanning window function, then use Fast Fourier Transform (FFT) to extract harmonic frequency and amplitude information, introduce wavelet packet decomposition method, use Daubechies-4 wavelet function to perform multi-scale spectrum analysis, and obtain the total harmonic current distortion rate.

[0017] Step S3: Using the total harmonic current distortion rate, construct a multi-objective optimization function with the objectives of minimizing the total harmonic distortion rate, minimizing the voltage deviation, minimizing the reactive power compensation, and minimizing the device loss.

[0018] Step S4: Based on the multi-objective optimization function, the improved particle swarm optimization algorithm (MOPSO) or NSGA-II algorithm is used to perform a global search for the control strategy. The optimal compensation solution set is obtained through the Pareto optimization front. The control strategy that best fits the current operating conditions is selected from the optimal compensation solution set.

[0019] Step S5: Based on the most suitable control strategy under the current operating conditions, execute the harmonic compensation and reactive power regulation strategy for the harmonic mitigation device to achieve primary power quality harmonic mitigation.

[0020] Furthermore, in this invention, in step S3, the multi-objective optimization function is:

[0021]

[0022] in, The total harmonic current distortion rate is . For voltage deviation, The injected reactive power refers to the absolute value of the reactive power emitted by the active power filter (APF) and the multi-branch passive filter (PF). The total losses of the device include switching losses and conduction losses of the active power filter (APF), as well as copper and dielectric losses of inductors and capacitors in the multi-branch passive filter (PF) branches. , , and These represent the total harmonic current distortion rate, respectively. Weights, voltage deviation The weight of the injected reactive power Weight and total device loss The weight.

[0023] Furthermore, in this invention, the execution process of the multi-objective particle swarm optimization algorithm includes:

[0024] Step S40: Determine the particle structure and generate an initial particle population within the constraints;

[0025] Step S41: Decode each particle into a control command and substitute it into the multi-objective function to calculate the target value of each particle;

[0026] Step S42: Using the target values ​​of each particle, compare the particles according to the concept of dominance, and update the individual's historical best position (Pbest).

[0027] Step S43: Based on the individual's historical best position (Pbest), obtain the non-dominated solution of the population, and use the non-dominated solution to update and prune the external resource pool to maintain the distribution of the solution;

[0028] Step S44: Dynamically select the global optimum from the external resource library until convergence is achieved.

[0029] Furthermore, in this invention, the particle structure adopts:

[0030]

[0031] To achieve, among which, This represents the structure of the i-th particle. The APF requires the injection of 2nd to 25th harmonic current commands. The total reactive power command that the APF needs to issue. Control the switching of each passive filter branch (1 for switching on, 0 for switching off).

[0032] The beneficial effects of this application are as follows: This invention processes the acquired signal using the Hanning window function, then uses Fast Fourier Transform (FFT) to extract harmonic frequency and amplitude information, introduces wavelet packet decomposition, and employs the Daubechies-4 wavelet function for multi-scale spectrum analysis, achieving high-precision identification and compensation of the 2nd to 25th harmonics, with THD suppression rate improved to over 70%; through a multi-objective control method, a dynamic balance is achieved between power loss, voltage deviation, and equipment load; the system has adaptive adjustment and remote communication functions, adapting to different operating conditions; the device can be widely applied in the range of 500kW to 50MW, possessing significant engineering value and economic benefits. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the device described in this invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0035] Specific implementation method one: Refer to Figure 1 This embodiment describes a multi-objective optimization-based harmonic control device for power quality in new energy power plants. The device includes: a harmonic detection module 1, a multi-objective control module 2, a harmonic compensation device 3, and a data communication module 4.

[0036] The harmonic detection module 1 collects voltage and current data of the new energy power station in real time, and uses a combination of wavelet analysis and windowed FFT to perform frequency domain analysis on the voltage and current waveforms, accurately identify each harmonic component, and obtain the total harmonic distortion rate.

[0037] The multi-objective control module 2 takes the minimum total harmonic distortion, the minimum voltage deviation, the minimum reactive power compensation, and the minimum device loss as optimization objectives. It uses an improved particle swarm optimization algorithm or the NSGA-II algorithm to perform a global search for the control strategy, obtains the optimal compensation solution set through the Pareto optimization front, and selects the most suitable control strategy under the current operating conditions from the optimal compensation solution set.

[0038] The harmonic compensation device 3 utilizes the most suitable control strategy under the current operating conditions, employing active power filters and passive filters to generate and inject a compensation current in real time that is equal in magnitude and opposite in direction to the harmonic current of the power grid; directly canceling the dynamically changing harmonic components in the power grid and suppressing broadband harmonics.

[0039] The data communication module 4 communicates with the main station to send out the control strategy after broadband harmonic suppression and transmit data back, thereby realizing closed-loop control and intelligent adjustment.

[0040] Furthermore, in this invention, the active power filter (APF) adopts a current-controlled inverter structure, and the passive filter has a multi-branch structure.

[0041] Furthermore, in this invention, the multi-branch structure of the passive filter filters out the 5th, 7th, and 11th harmonics, respectively.

[0042] Furthermore, in this invention, the data communication module supports Modbus, IEC61850, and CANopen communication protocols to enable control command issuance and data feedback.

[0043] Specific Implementation Method Two: Refer to Figure 1 This embodiment is specifically described. It is implemented based on the device described in Specific Embodiment One. The multi-objective optimization-based harmonic mitigation of power quality at new energy power plants described in this embodiment includes:

[0044] Step S1: Install the harmonic mitigation device on the grid-connected circuit breaker side, and configure three-phase voltage transformers (VT) and current transformers (CT) on the line between the grid-connected circuit breaker of the new energy power station and the harmonic mitigation device to collect the voltage and current data of the new energy power station in real time.

[0045] Step S2: Process the acquired signal using the Hanning window function, then use Fast Fourier Transform (FFT) to extract harmonic frequency and amplitude information, introduce wavelet packet decomposition method, use Daubechies-4 wavelet function to perform multi-scale spectrum analysis, and obtain the total harmonic current distortion rate.

[0046] Step S3: Using the total harmonic current distortion rate, construct a multi-objective optimization function with the objectives of minimizing the total harmonic distortion rate, minimizing the voltage deviation, minimizing the reactive power compensation, and minimizing the device loss.

[0047] Step S4: Based on the multi-objective optimization function, the improved particle swarm optimization algorithm (MOPSO) or NSGA-II algorithm is used to perform a global search for the control strategy. The optimal compensation solution set is obtained through the Pareto optimization front. The control strategy that best fits the current operating conditions is selected from the optimal compensation solution set.

[0048] Step S5: Based on the most suitable control strategy under the current operating conditions, execute the harmonic compensation and reactive power regulation strategy for the harmonic mitigation device to achieve primary power quality harmonic mitigation.

[0049] Furthermore, in this invention, in step S3, the multi-objective optimization function is:

[0050]

[0051] in, The total harmonic current distortion rate is . For voltage deviation, The injected reactive power refers to the absolute value of the reactive power emitted by the active power filter (APF) and the multi-branch passive filter (PF). The total losses of the device include switching losses and conduction losses of the active power filter (APF), as well as copper and dielectric losses of inductors and capacitors in the multi-branch passive filter (PF) branches. , , and These represent the total harmonic current distortion rate, respectively. Weights, voltage deviation The weight of the injected reactive power Weight and total device loss The weight.

[0052] Furthermore, in this invention, the execution process of the multi-objective particle swarm optimization algorithm includes:

[0053] Step S40: Determine the particle structure and generate an initial particle population within the constraints;

[0054] Step S41: Decode each particle into a control command and substitute it into the multi-objective function to calculate the target value of each particle;

[0055] Step S42: Using the target values ​​of each particle, compare the particles according to the concept of dominance, and update the individual's historical best position (Pbest).

[0056] Step S43: Based on the individual's historical best position (Pbest), obtain the non-dominated solution of the population, and use the non-dominated solution to update and prune the external resource pool to maintain the distribution of the solution;

[0057] Step S44: Dynamically select the global optimum from the external resource library until convergence is achieved.

[0058] Furthermore, in this invention, the particle structure adopts:

[0059]

[0060] To achieve, among which, This represents the structure of the i-th particle. The APF requires the injection of 2nd to 25th harmonic current commands. The total reactive power command that the APF needs to issue. Control the switching of each passive filter branch (1 for switching on, 0 for switching off).

[0061] The multi-objective control module of this invention uses the minimum THD, minimum voltage deviation, minimum reactive power compensation, and minimum device loss as optimization objective functions. It employs an improved particle swarm optimization algorithm (MOPSO) or the NSGA-II algorithm to perform a global search for the control strategy. The optimal compensation solution set is obtained through the Pareto optimization front, and the most suitable control strategy under the current operating conditions is selected.

[0062] The compensation device includes an active power filter (APF) and a multi-branch passive filter (PF). The APF uses a current-controlled inverter structure to inject reverse harmonic current in real time to cancel the main harmonics; while the passive filter is mainly used for filtering high-frequency fixed harmonics (such as the 5th, 7th, and 11th harmonics). The two together form a hybrid filter topology to achieve a wider bandwidth and higher efficiency harmonic control effect.

[0063] The data communication module communicates with the master station via protocols such as Modbus, IEC61850, and CANopen to issue control commands and transmit data. The system features adaptive adjustment capabilities, dynamically optimizing parameters under different operating conditions to achieve closed-loop control and intelligent regulation.

[0064] When deployed in photovoltaic or wind power plants, this invention can significantly reduce system harmonic content, improve power factor, and effectively alleviate power quality problems caused by harmonics, ensuring the safe, stable, and economical operation of new energy power plants.

[0065] This invention can specifically achieve harmonic control, reactive power optimization, and voltage stabilization functions for power quality in renewable energy power plants through the following implementation steps. The system deployment is flexible and adaptable to different types of renewable energy power plants. The following section uses a typical centralized photovoltaic power plant as an example to explain the entire system's operation process and implementation details in detail:

[0066] S1: On-site installation and signal acquisition configuration

[0067] A harmonic mitigation device is installed on the grid-connected circuit breaker side, along with three-phase voltage transformers (VT) and current transformers (CT). The sampling device must possess high bandwidth (≥10kHz) and high accuracy (≤0.2%) to ensure signal fidelity. The acquisition period is 20ms, with 2048 points per period.

[0068] S2: Power Quality Analysis and Harmonic Extraction

[0069] The system first processes the acquired signal using a window function (Hanning window), and then uses Fast Fourier Transform (FFT) to extract information such as harmonic frequency and amplitude. To improve dynamic detection capabilities, the system also introduces a wavelet packet decomposition method, employing the Daubechies-4 wavelet function for multi-scale spectral analysis to achieve real-time tracking of harmonics from the 2nd to the 25th order.

[0070] S3: Construct the optimization model and perform the solution.

[0071] The controller constructs the following multi-objective function:

[0072]

[0073] in, The total harmonic current distortion rate is . For voltage deviation, The injected reactive power refers to the absolute value of the reactive power emitted by the active power filter (APF) and the multi-branch passive filter (PF). The total losses of the device include switching losses and conduction losses of the active power filter (APF), as well as copper and dielectric losses of inductors and capacitors in the multi-branch passive filter (PF) branches. , , and These represent the total harmonic current distortion rate, respectively. Weights, voltage deviation The weight of the injected reactive power Weight and total device loss The weight, , , and Configured according to on-site working conditions. The optimization algorithm adopts the multi-objective particle swarm optimization algorithm, with the number of particles set to 30, the maximum number of iterations set to 100, and the output of control quantities such as optimal harmonic compensation current, reactive power injection, and voltage regulation commands.

[0074] Specifically, 1. Initialize the particle swarm.

[0075] (1) Determine the particle

[0076]

[0077] in, The APF requires the injection of 2nd to 25th harmonic current commands. The total reactive power command that the APF needs to issue. Control the switching of each passive filter branch (1 for switching on, 0 for switching off);

[0078] (2) Population initialization: Within the set constraints (determined by the physical capacity of APF and PF), 30 particles are randomly generated to form the initial population.

[0079] 2. Calculate all objective function values ​​for each particle.

[0080] For each particle This is decoded into control commands, which are then substituted into the four defined targets for calculation:

[0081] (Evaluate the effect of harmonic suppression)

[0082] (Assess voltage deviation)

[0083] (Evaluate reactive power output)

[0084] (Assess device wear and tear)

[0085] 3. Evaluate particle dominance relationships and update individual optimality.

[0086] (1) Domination concept: Comparing two particles A and B. If A is no worse than B in all targets and is strictly better than B in at least one target, then A is said to dominate B.

[0087] (2) Update Pbest (Individual Historical Best): Each particle remembers the best position it has ever found. Compare the current position with the remembered Pbest:

[0088] 1) If the current position dominates Pbest, then update Pbest with the current position.

[0089] 2) If Pbest dominates the current position, then keep Pbest unchanged.

[0090] 3) If neither can dominate the other, choose one randomly, or choose based on other strategies (such as crowding).

[0091] 4. Maintain external resource repositories

[0092] (1) This is an elite set used to store all currently found non-dominated solutions (Pareto optimal solutions).

[0093] (2) Whenever particles are updated, the resource library needs to be checked and updated:

[0094] 2.1) Add the new non-dominated solution to the resource pool.

[0095] 2.2) Remove old solutions that are dominated by new solutions.

[0096] 2.3) If the number of solutions in the resource pool exceeds the upper limit, pruning is required to maintain the distribution of solutions (usually based on "crowding distance", solutions distributed in sparse regions are preferred to be retained).

[0097] 5. Select the globally optimal Gbest

[0098] (1) In standard PSO, there is only one Gbest. In MOPSO, since there are multiple non-dominated solutions, Gbest is dynamically selected from an external resource pool.

[0099] (2) The selection strategy is to ensure the diversity and convergence of the search. Commonly used methods include:

[0100] 2.1) Roulette wheel selection: The greater the crowding distance of a solution (i.e., the sparser the surrounding solutions and the better the distribution), the higher the probability of it being selected.

[0101] 2.2) Density estimation: The solution of the region with lower density in the resource pool is preferred as Gbest.

[0102] 6. Update particle velocity and position

[0103] 7. Termination of judgment

[0104] (1) Repeat steps 2-6 until the termination condition is met (such as reaching the maximum iteration number of 100, or the solution set has converged).

[0105] (2) Finally, the Pareto optimal frontier is found and stored in the external resource library.

[0106] 8. Output and Decision

[0107] (1) The algorithm outputs the Pareto optimal solution set. At this point, the controller needs to select a final solution from this solution set based on the current weights w1~w4 or the operating mode.

[0108] (2) For example, in the “THD priority mode”, the THD in the solution set can be selected directly. i The smallest solution will be implemented.

[0109] The controller of this invention drives the APF to output corresponding harmonic currents based on the optimized output results. The current controller uses a combination of PI regulator and hysteresis control to ensure that the injected current is synchronized with the reference waveform. The passive filter is arranged in a parallel LC structure, and the resonant frequency is precisely tuned for the 5th, 7th, and 11th harmonics. Key operating parameters, such as harmonic distortion rate, injected current, and system temperature, are uploaded in real time via fieldbus. If the compensation effect is not ideal (e.g., insufficient THD suppression), the controller will automatically correct the objective function parameters, execute a new round of optimization iterations, and form a dynamic closed-loop regulation.

[0110] Field testing and parameter self-tuning

[0111] During initial commissioning, the system executes a filtering performance verification procedure. A specific harmonic is injected into the simulated load, and the performance indicators before and after compensation are compared. After 48 hours of operation, the controller automatically adjusts the reactive power capacity range and the filter's operating frequency band based on system feedback to ensure optimal operating conditions.

[0112] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A harmonic mitigation device for power quality at new energy power plants based on multi-objective optimization, characterized in that, The device includes: a harmonic detection module (1), a multi-target control module (2), a harmonic compensation device (3), and a data communication module (4). The harmonic detection module (1) collects voltage and current data of the new energy power station in real time, and uses a combination of wavelet analysis and windowed FFT to perform frequency domain analysis on the voltage and current waveforms, identify each harmonic component, and obtain the total harmonic distortion rate. The multi-objective control module (2) takes the minimum total harmonic distortion, the minimum voltage deviation, the minimum reactive power compensation, and the minimum device loss as optimization objectives. It uses an improved particle swarm optimization algorithm or the NSGA-II algorithm to perform a global search for the control strategy. It obtains the optimal compensation solution set through the Pareto optimization front and selects the most suitable control strategy under the current operating conditions from the optimal compensation solution set. The harmonic compensation device (3) utilizes the most suitable control strategy under the current operating conditions, employs active power filters and passive filters, and generates and injects a compensation current that is equal in magnitude and opposite in direction to the harmonic current of the power grid in real time; directly cancels the dynamically changing harmonic components in the power grid and suppresses broadband harmonics. The data communication module (4) communicates with the main station to send out the control strategy after broadband harmonic suppression and transmit data back, so as to realize closed-loop control and intelligent adjustment.

2. The harmonic control device for power quality in new energy power plants based on multi-objective optimization according to claim 1, characterized in that, The source power filter adopts a current-controlled inverter structure, while the passive filter has a multi-branch structure.

3. The harmonic control device for power quality in new energy power plants based on multi-objective optimization according to claim 2, characterized in that, The multi-branch structure of the passive filter filters out the 5th, 7th and 11th harmonics respectively.

4. The harmonic control device for power quality in new energy power plants based on multi-objective optimization according to claim 2, characterized in that, The data communication module (4) supports Modbus, IEC61850 and CANopen communication protocols to realize the issuance of control commands and data feedback.

5. A method for harmonic mitigation of power quality in new energy power plants based on multi-objective optimization, wherein the method is implemented using the device described in any one of claims 1-4, characterized in that, include: Step S1: Install the harmonic mitigation device on the grid-connected circuit breaker side, and configure three-phase voltage transformers and current transformers on the line between the grid-connected circuit breaker and the harmonic mitigation device of the new energy power station to collect voltage and current data of the new energy power station in real time. Step S2: Process the acquired signal using the Hanning window function, then use the fast Fourier transform to extract the harmonic frequency and amplitude information, introduce the wavelet packet decomposition method, and use the Daubechies-4 wavelet function to perform multi-scale spectrum analysis to obtain the total harmonic current distortion rate. Step S3: Using the total harmonic current distortion rate, construct a multi-objective optimization function with the objectives of minimizing the total harmonic distortion rate, minimizing the voltage deviation, minimizing the reactive power compensation, and minimizing the device loss. Step S4: Based on the multi-objective optimization function, the improved particle swarm optimization algorithm or NSGA-II algorithm is used to perform a global search for the control strategy. The optimal compensation solution set is obtained by using the Pareto optimization front. The control strategy that best fits the current operating conditions is selected from the optimal compensation solution set. Step S5: Based on the most suitable control strategy under the current operating conditions, execute the harmonic compensation and reactive power regulation strategy for the harmonic mitigation device to achieve primary power quality harmonic mitigation.

6. The method for harmonic mitigation of power quality in new energy power plants based on multi-objective optimization according to claim 5, characterized in that, In step S3, the multi-objective optimization function is: in, The total harmonic current distortion rate is . For voltage deviation, The injected reactive power refers to the absolute value of the reactive power emitted by the active power filter and the multi-branch passive filter. The total losses of the device include switching losses and conduction losses of the active power filter, as well as copper and dielectric losses of inductors and capacitors in multi-branch passive filters. , , and These represent the total harmonic current distortion rate, respectively. Weights, voltage deviation The weight of the injected reactive power Weight and total device loss The weight.

7. The method for harmonic mitigation of power quality in new energy power plants based on multi-objective optimization according to claim 5, characterized in that, The execution process of the multi-objective particle swarm optimization algorithm includes: Step S40: Determine the particle structure and generate an initial particle population within the constraints; Step S41: Decode each particle into a control command and substitute it into the multi-objective function to calculate the target value of each particle; Step S42: Using the target values ​​of each particle, compare the particles according to the concept of dominance, and update the individual's historical best position; Step S43: Based on the individual's historical best position, obtain the non-dominated solution of the population, and use the non-dominated solution to update and prune the external resource pool to maintain the distribution of the solution; Step S44: Dynamically select the global optimum from the external resource library until convergence is achieved.

8. The method for harmonic mitigation of power quality in new energy power plants based on multi-objective optimization according to claim 7, characterized in that, The particle structure adopts: To achieve, among which, This represents the structure of the i-th particle. The APF requires the injection of 2nd to 25th harmonic current commands. The total reactive power command that the APF needs to issue. Control the switching of each passive filter branch.

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