Photovoltaic disturbance-based voltage quality optimization control method and system

Abstract

The invention relates to the technical field of photovoltaic power generation control, and discloses a photovoltaic disturbance-based voltage quality optimization control method and system, and the method comprises the steps: collecting the operation data of a photovoltaic system, and constructing a photovoltaic system simulation model; global optimization is carried out by using a genetic algorithm, and control parameters for optimizing voltage quality are found; key nodes are selected, and local key node optimization is carried out; and testing the optimized system, evaluating the electric energy quality of the system, and performing feedback iteration. The method has the beneficial effects that the global electric energy quality problem of the photovoltaic system is comprehensively improved, the control precision of the key nodes is further improved through local optimization, and the dual optimization effect of global and local combination is achieved. Compared with a traditional method, the problems of voltage fluctuation, harmonic distortion and frequency fluctuation caused after the photovoltaic system is connected to the power grid can be effectively solved, and the stability of the power system and the photovoltaic power generation efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation control, and in particular to a method and system for optimizing voltage quality control based on photovoltaic disturbances. Background Art

[0002] With the rapid development of renewable energy, photovoltaic (PV) power generation has become an integral part of the global power mix. Advances in PV technology and reduced installation costs have led to widespread adoption of PV systems in distributed generation and large-scale power plants. However, due to the intermittent and unstable nature of PV power generation, its output power is significantly affected by environmental conditions (such as solar radiation intensity, temperature, and wind speed). This characteristic leads to frequent power quality issues such as voltage fluctuations, harmonic distortion, and frequency fluctuations during PV grid integration, which can threaten grid stability in severe cases. Therefore, effectively controlling the power quality of PV systems, particularly optimizing voltage fluctuations, has become a key research area in PV grid integration technology. Traditional PV system power quality control methods typically employ fixed-value control or control strategies based on preset rules. These methods often fail to achieve optimal results when dealing with complex environmental changes. Furthermore, existing PV system control algorithms often focus on single-objective optimization, neglecting the coordinated control of multiple power quality indicators. This makes it difficult to simultaneously meet the requirements of voltage stability, harmonic suppression, and frequency control.

[0003] Existing methods for optimizing photovoltaic power generation voltage quality still have several shortcomings. First, most methods only consider a single optimization strategy, which is limited in global optimization and coordinated control of local key nodes, and cannot fully utilize the system's disturbance information to improve power quality. In addition, existing technologies often rely on static control parameter settings and fail to dynamically adjust optimization strategies in the complex operating environment of photovoltaic systems, resulting in insufficient system adaptability to changes in lighting conditions and loads. Second, existing control methods fail to fully integrate simulation modeling technology and advanced optimization algorithms, making it impossible to accurately simulate and optimize the nonlinear characteristics of photovoltaic systems, thereby limiting the accuracy and effectiveness of power quality optimization. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing photovoltaic disturbance voltage quality control technology mainly has problems such as a single optimization strategy, poor dynamic adaptability and insufficient simulation accuracy.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a voltage quality optimization control method based on photovoltaic disturbance, comprising:

[0007] Collect photovoltaic system operation data and build a photovoltaic system simulation model;

[0008] Use genetic algorithms for global optimization to find the control parameters that optimize voltage quality;

[0009] Select key nodes and perform local key node optimization;

[0010] Test the optimized system, evaluate the system's power quality, and perform feedback iterations.

[0011] As a preferred solution of the photovoltaic disturbance-based voltage quality optimization control method of the present invention, wherein: the collecting of photovoltaic system operation data includes collecting photovoltaic system operation data using a data collector of a photovoltaic power station data acquisition system;

[0012] Install current and voltage sensors on the photovoltaic array to monitor the output power, output voltage, and output current of the photovoltaic modules; install environmental sensors, including solar radiation intensity sensors, ambient temperature sensors, wind speed and humidity sensors, to monitor the external conditions of photovoltaic power generation;

[0013] Install power quality sensors at the grid connection points, and install harmonic detectors, voltage sensors and frequency meters at key nodes where the photovoltaic system is connected to the grid to monitor power quality parameters in real time.

[0014] As a preferred embodiment of the photovoltaic disturbance-based voltage quality optimization control method of the present invention, the step of constructing a photovoltaic system simulation model includes, after data collection is completed, constructing a photovoltaic system simulation model based on the collected operating data and system structure, and using the simulation model to simulate the behavior of the photovoltaic system under different conditions;

[0015] Construct a photovoltaic cell model and a power quality model; the photovoltaic cell model includes a single diode model, which is suitable for scenarios with limited computing power. The formula is:

[0016]

[0017] Among them, I ph represents the photocurrent, I0 represents the saturation current, R s is the series resistance, n is the ideality factor of the diode, V t Represents the thermal voltage; the double-diode model reflects the nonlinear characteristics of photovoltaic cells and is suitable for high-precision scenarios. The formula is:

[0018]

[0019] Among them, I 01 ,I 02 represents the saturation current of the two diodes, n1 and n2 represent the ideality factors of the diodes;

[0020] The power quality model includes a voltage fluctuation model, a harmonic model, and a frequency fluctuation model. The voltage fluctuation model uses actual measurement data to establish a voltage deviation model to simulate the voltage fluctuation range after photovoltaic power is connected to the grid. The harmonic model uses Fourier transform to analyze the harmonic components of the photovoltaic grid connection point, establishes a harmonic distortion model, and quantifies the impact of harmonics on power quality. The frequency fluctuation model simulates the frequency response behavior of photovoltaic grid connection when the grid frequency changes.

[0021] Based on historical data and current-voltage characteristic curves, nonlinear fitting techniques are used to estimate model parameters. Simulation experiments are performed, and if there is a deviation between the simulation results and the actual data, the parameters in the model are adjusted.

[0022] As a preferred solution of the photovoltaic disturbance-based voltage quality optimization control method of the present invention, the global optimization using a genetic algorithm includes global optimization of the photovoltaic disturbance voltage quality, with the main goal of minimizing power quality problems, voltage fluctuations, harmonic distortion, and frequency fluctuations, while ensuring the maximization of the system's power generation efficiency;

[0023] Set constraints, select encoding methods, and express the control parameters of the photovoltaic system, the voltage set point of the inverter, the reactive power compensation amount, and the MPPT control parameters in the form of chromosomes; each chromosome is defined as a parameter vector:

[0024] x i =[P i1 ,P i2 ,…,P in ]

[0025] Among them, P i1 ,P i2 ,…,P in Indicates the control parameters that need to be optimized, including inverter voltage set point, MPPT adjustment parameters, and reactive power compensation;

[0026] Randomly generate an initial population, where each individual in the population corresponds to a combination of control parameters; randomly generate a group of initial values ​​as the population within the defined range of each control parameter, and set the value range of each control parameter;

[0027] A fitness function is defined for each individual, and the fitness function is calculated based on multiple power quality indicators; selection, crossover, and mutation operations are performed; and when the genetic algorithm converges, the individual with the highest fitness is output, which is the optimal combination of photovoltaic system control parameters.

[0028] As a preferred embodiment of the photovoltaic disturbance-based voltage quality optimization control method of the present invention, the local key node optimization includes determining key nodes in the photovoltaic system with large voltage fluctuations and severe harmonic distortion based on the results of global optimization;

[0029] Using the simulated annealing algorithm, the initial control parameter solutions for key nodes are generated from the global optimization results. The control parameters include the voltage set point and the reactive power regulation. An adaptive temperature decay function is used to dynamically adjust the temperature decay rate according to the changes in the solution. The formula is expressed as:

[0030] T k+1 =T k ×(1-α·ΔE)

[0031] Where α is the temperature adjustment coefficient, ΔE is the energy change of the current solution, and the initial temperature T is set. The temperature determines whether to accept a worse solution in each iteration. The temperature decay method is set to gradually reduce the temperature after each iteration.

[0032] Based on the current solution, a neighborhood solution is generated by random perturbation, and different perturbation sizes are defined for different neighborhoods. In the early stage, a large-scale perturbation is used to explore a larger solution space, and in the later stage, a small-scale perturbation is used to fine-tune the solution. The multi-neighborhood search implementation formula is expressed as:

[0033] x new =x current +δ×N(0,1)

[0034] Here, δ represents the disturbance coefficient, which gradually decreases with the number of iterations, and N(0,1) represents a normal distribution with mean zero and variance one. The energy difference between the new solution and the current solution is calculated, and the algorithm terminates when the temperature drops to the set threshold. The optimal control parameter solution finally found is used as the adjustment parameter of the key node for fine-tuning voltage fluctuations, harmonic distortion, and frequency fluctuations.

[0035] As a preferred embodiment of the photovoltaic disturbance-based voltage quality optimization control method of the present invention, the power quality of the system is evaluated by applying optimized control parameters to a simulation model of the photovoltaic system to simulate the system response under different illumination and load conditions; implementing the optimized control parameters in an actual photovoltaic system to monitor power quality indicators in real time, including voltage fluctuation, harmonic distortion, and frequency fluctuation;

[0036] Calculate the RMS value of the voltage deviation using the formula:

[0037]

[0038] Among them, V i Represents the voltage value at the i-th moment, Vref It represents the reference voltage;

[0039] Use total harmonic distortion (THD) as an evaluation indicator to calculate the optimized harmonic distortion. If THD exceeds the set threshold, further optimization is required:

[0040]

[0041] Among them, V n represents the amplitude of the nth harmonic, and V1 represents the amplitude of the fundamental wave;

[0042] Frequency stability assessment, calculate the frequency deviation. If the frequency deviation is large, it means the frequency is unstable and needs to be adjusted. The formula is:

[0043] Δf max =max(|f i -f ref |)

[0044] Among them, f i Represents the frequency value at the i-th moment, f ref Indicates the reference frequency; if all evaluation indicators meet the preset goals, the optimization is considered successful; if the indicators do not meet the threshold requirements, it is determined that further optimization is needed and feedback iterative adjustments are performed.

[0045] As a preferred solution of the photovoltaic disturbance-based voltage quality optimization control method of the present invention, the feedback iteration includes adjusting the initial population and fitness function of the global optimization if the local optimization is not good;

[0046] During the iteration process, the population size is dynamically increased to cover a larger solution space. When the optimization is found to be stuck in a local optimum, the population diversity is increased by increasing the population size, randomly introducing new individuals, or mutating existing individuals. Different individual initialization methods are combined to improve population diversity. The following formula is used to randomly generate initial individuals:

[0047] x i =x min +(x max -x min )×rand(0,1)

[0048] Among them, rand(0,1) represents a random number between 0 and 1;

[0049] Dynamically adjust the weights in the fitness function so that they change over the iteration process. In the early stages of optimization, more global indicators are given greater weight. In the later stages of iteration, the weights of local indicators are increased, allowing the algorithm to focus more on fine-grained optimization. Voltage deviation, harmonic distortion, and frequency fluctuation are optimized as independent targets, and the optimal solution is selected through the Pareto front.

[0050] Introducing the elite preservation strategy: In each generation of evolution, the individuals with the highest fitness are directly retained to the next generation to prevent the optimal solution from being destroyed by mutation or crossover operations. The formula is expressed as:

[0051] P new =P elite ∪P evolved

[0052] Among them, P elite represents the elite individuals retained, P evolved Represents a newly generated individual.

[0053] A photovoltaic disturbance-based voltage quality optimization control system using any method of the present invention comprises:

[0054] Data management and modeling, the module provides simulation environment and data support for global optimization module and local optimization module;

[0055] The global optimization module first performs a global search, provides a better initial solution to the local optimization module, and makes adjustments based on the optimization result feedback;

[0056] The local optimization module performs fine-grained optimization on key nodes and returns the results to the global optimization module, forming a feedback iterative process;

[0057] The evaluation iteration module evaluates the results of each round of optimization and feeds the evaluation results back to the global optimization module and the local optimization module to adjust the optimization strategy for the next round of iteration.

[0058] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0059] A computer-readable storage medium stores a computer program thereon, comprising: steps of implementing any one of the methods of the present invention when the computer program is executed by a processor.

[0060] The beneficial effects of this invention include the formation of a systematic voltage quality optimization strategy through global optimization and local optimization of key nodes. This not only achieves comprehensive improvements to the overall power quality of the photovoltaic system, but also further enhances the control accuracy of key nodes through local optimization, achieving a dual optimization effect combining global and local optimization. Compared with traditional methods, this method can more effectively address the voltage fluctuations, harmonic distortion, and frequency fluctuations caused by the photovoltaic system being connected to the grid, significantly improving the stability of the power system and the efficiency of photovoltaic power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0062] Figure 1 This is an overall flow chart of a voltage quality optimization control method based on photovoltaic disturbances provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0064] Example 1, with reference to Figure 1 , as an embodiment of the present invention, provides a voltage quality optimization control method based on photovoltaic disturbance, comprising:

[0065] S1: Collect photovoltaic system operation data and build a photovoltaic system simulation model.

[0066] Furthermore, collecting the photovoltaic system operation data includes using a data collector of a photovoltaic power station data acquisition system to collect the photovoltaic system operation data.

[0067] Furthermore, current and voltage sensors are installed on the photovoltaic array to monitor the output power, output voltage and output current of the photovoltaic module; environmental sensors, including solar radiation intensity sensors, ambient temperature sensors, wind speed and humidity sensors, are installed to monitor the external conditions of photovoltaic power generation.

[0068] Furthermore, power quality sensors are installed at the grid connection points, and harmonic detectors, voltage sensors and frequency meters are installed at key nodes where the photovoltaic system is connected to the grid to monitor power quality parameters in real time.

[0069] Furthermore, the construction of the photovoltaic system simulation model includes, after data collection is completed, constructing a simulation model of the photovoltaic system based on the collected operating data and system structure, and using the simulation model to simulate the behavior of the photovoltaic system under different conditions.

[0070] Furthermore, a photovoltaic cell model and a power quality model are constructed. The photovoltaic cell model includes a single diode model, which is suitable for scenarios with limited computing power. The formula is:

[0071]

[0072] Among them, I ph represents the photocurrent, I0 represents the saturation current, R s is the series resistance, n is the ideality factor of the diode, V t Represents the thermal voltage; the double-diode model reflects the nonlinear characteristics of photovoltaic cells and is suitable for high-precision scenarios. The formula is:

[0073]

[0074] Among them, I 01 ,I 02 represents the saturation current of the two diodes, and n1 and n2 represent the ideality factors of the diodes.

[0075] Furthermore, the power quality model includes a voltage fluctuation model, a harmonic model, and a frequency fluctuation model. The voltage fluctuation model uses actual measurement data to establish a voltage deviation model to simulate the voltage fluctuation range after photovoltaic power is connected to the grid. The harmonic model uses Fourier transform to analyze the harmonic components of the photovoltaic grid connection point, establishes a harmonic distortion model, and quantifies the impact of harmonics on power quality. The frequency fluctuation model simulates the frequency response behavior of photovoltaic grid connection when the grid frequency changes.

[0076] It should be noted that the model parameters are estimated using nonlinear fitting technology based on historical data and current-voltage characteristic curves; simulation experiments are performed, and if there is a deviation between the simulation results and the actual data, the parameters in the model are adjusted.

[0077] S2: Use genetic algorithm to perform global optimization to find the control parameters that optimize voltage quality.

[0078] Furthermore, the global optimization using genetic algorithm includes the global optimization of photovoltaic disturbance voltage quality, with the main goal of minimizing power quality problems, voltage fluctuation, harmonic distortion, and frequency fluctuation, while ensuring the maximization of the system's power generation efficiency.

[0079] Furthermore, constraints are set and real number encoding is used. The control parameters of photovoltaic systems are typically continuous variables, and real number encoding can more accurately represent the values ​​of these continuous parameters. Real number encoding directly represents the control parameters (inverter voltage setpoint, reactive power compensation, MPPT parameters) as real number vectors. For example, a chromosome can be represented by the vector [V_set, Q_comp, MPPT_param], where v_set is the voltage setpoint, Q_comp is the reactive power compensation, and MPPT param is the MPPT control parameter. This encoding method is intuitive and facilitates the crossover and mutation operations of the genetic algorithm, making better use of the continuous nature of the parameters for optimization.

[0080] Furthermore, the control parameters of the photovoltaic system, the voltage set point of the inverter, the reactive power compensation amount, and the MPPT control parameters are expressed in the form of chromosomes; each chromosome is defined as a parameter vector:

[0081] x i =[P i1 ,P i2 ,…,P in ]

[0082] Among them, P i1 ,P i2 ,…,P in Indicates the control parameters that need to be optimized, including the inverter voltage set point, MPPT adjustment parameters, and reactive power compensation.

[0083] Furthermore, an initial population is randomly generated, where each individual in the population corresponds to a combination of control parameters. In the initial phase of the genetic algorithm, an initial population is generated, where each individual represents a combination of control parameters. Within a predefined range, a value is randomly generated for each control parameter.

[0084] Furthermore, a population is randomly generated. Within the defined range of each control parameter, a set of initial values ​​is randomly generated as the population, and each control parameter sets a value range. Parameter range setting: Assume there are three control parameters, namely, the inverter voltage set point V set , reactive power compensation Q comp and MPPT control parameter P mppt The range of these parameters is set to [V min ,V max ]、[Q min ,Q max ]、[P min ,P max ]. The formula is:

[0085]

[0086] Among them, r1, r2, and r3 are random numbers in the range [0,1]. For each individual i in the population, a set of control parameters is generated

[0087] Define the fitness function for the individual, which is calculated based on multiple power quality indicators; perform selection, crossover, and mutation operations. The crossover operation is used to combine the genes of two parent individuals to generate new offspring individuals. Single-point crossover, select two individuals and

[0088] With a certain crossover probability P c Select a crossover point, and the control parameters before the point are provided by parent 1, and the control parameters after the point are provided by parent 2. The offspring after the crossover is

[0089] It should be noted that when the genetic algorithm converges, it outputs the individual with the highest fitness, that is, the optimal photovoltaic system control parameter combination, which is expressed as

[0090] S3: Select key nodes and perform local key node optimization.

[0091] Furthermore, local key node optimization is performed, including determining the key nodes in the photovoltaic system where voltage fluctuations are large and harmonic distortion is severe through the results of global optimization.

[0092] Furthermore, a simulated annealing algorithm is used to generate initial control parameter solutions for key nodes from the results of global optimization. The control parameters include the voltage set point and the reactive power regulation. An adaptive temperature decay function is used to dynamically adjust the temperature decay rate according to the changes in the solution. The formula is expressed as:

[0093] T k+1 =T k ×(1-α·ΔE)

[0094] Where α is the temperature adjustment coefficient, ΔE is the energy change of the current solution, and the initial temperature T is set. The temperature determines whether a poor solution is accepted in each iteration. The temperature decay method is set to gradually reduce the temperature after each iteration.

[0095] Furthermore, from the results of global optimization, the initial control parameters of the key nodes are extracted as the initial solution S0, which is expressed as follows:

[0096] S0=(V set ,Q comp )

[0097] Among them, V set is the voltage set point, Qcomp = is the reactive power regulation. Set the initial temperature T0: Select a higher initial temperature so that the algorithm can accept poor solutions in the early stages of the search to escape the local optimum. Temperature adjustment coefficient α: Used to control the gradual decay of temperature, generally ranging from 0.8 to 0.99.

[0098] Adaptive temperature decay function dynamically adjusts the temperature decay rate according to the change of the solution. The temperature decay formula is expressed as:

[0099] T new =α·T old

[0100] Among them, T old is the current temperature, T new is the updated temperature, and the attenuation coefficient α can be adjusted according to the optimization progress.

[0101] Neighborhood solution generation: Based on the current solution S, a new neighborhood solution S′ is generated through random perturbation. The formula is expressed as:

[0102] S′=S+σ·N(0,1)

[0103] Where σ is the perturbation coefficient, and N(0,1) represents a normally distributed random variable with a mean of zero and a variance of one. The perturbation size σ gradually decreases according to the number of iterations.

[0104]

[0105] Where σ0 is the initial perturbation coefficient, β is the attenuation factor, and k is the current number of iterations. A larger σ is used in the early stages to explore a larger solution space, while a smaller σ is gradually reduced in the later stages to fine-tune the solution.

[0106] Calculate the energy difference ΔE (i.e., the change in the objective function) between the new solution S′ and the current solution S. The objective function can be based on a comprehensive evaluation function of voltage fluctuation, harmonic distortion, and frequency fluctuation:

[0107] ΔE=E(S′)-E(S)

[0108] If ΔE<0, it means that the new solution S′ is better than the current solution S, then the new solution is accepted; otherwise, the probability Accept the new solution S′, where T is the current temperature. This acceptance criterion allows the algorithm to escape from the local optimal solution when the temperature is high.

[0109] Furthermore, based on the current solution, a neighborhood solution is generated by random perturbation, and different perturbation sizes are defined for different neighborhoods. In the early stage, a large-scale perturbation is used to explore a larger solution space, and in the later stage, a small-scale perturbation is used to fine-tune the solution. The multi-neighborhood search implementation formula is expressed as:

[0110] x new =x current +δ×N(0,1)

[0111] Here, δ represents the disturbance coefficient, which gradually decreases with the number of iterations, and N(0,1) represents a normal distribution with mean zero and variance one. The energy difference between the new solution and the current solution is calculated, and the algorithm terminates when the temperature drops to the set threshold. The optimal control parameter solution finally found is used as the adjustment parameter of the key node for fine-tuning voltage fluctuations, harmonic distortion, and frequency fluctuations.

[0112] It should be noted that the temperature iteration and termination condition are to gradually reduce the temperature T after each iteration. When the temperature drops to the set threshold T min Or when the energy change ΔE is less than the given threshold for several consecutive times, the algorithm terminates. The optimal solution S at this time is * As the final adjustment parameter of key nodes, it is used to finely optimize voltage fluctuation, harmonic distortion and frequency fluctuation.

[0113] S4: Test the optimized system, evaluate the power quality of the system, and perform feedback iteration.

[0114] Furthermore, evaluating the power quality of the system includes applying the optimized control parameters to a simulation model of the photovoltaic system to simulate the system response under different lighting and load conditions; implementing the optimized control parameters in the actual photovoltaic system and monitoring power quality indicators in real time, including voltage fluctuations, harmonic distortion, and frequency fluctuations.

[0115] Furthermore, the root mean square value (RMS) of the voltage deviation is calculated as follows:

[0116]

[0117] Among them, V i Represents the voltage value at the i-th moment, V ref It represents the reference voltage.

[0118] Furthermore, the total harmonic distortion (THD) is used as an evaluation indicator to calculate the optimized harmonic distortion. If the THD exceeds the set threshold, further optimization is required:

[0119]

[0120] Among them, V n It represents the amplitude of the nth harmonic, and V1 represents the amplitude of the fundamental wave.

[0121] Furthermore, the frequency stability evaluation calculates the frequency deviation. If the frequency deviation is large, it means that the frequency is unstable and needs to be adjusted. The formula is expressed as:

[0122] Δf max =max(|f i -f ref |)

[0123] Among them, f i Represents the frequency value at the i-th moment, f ref Indicates the reference frequency; if all evaluation indicators meet the preset goals, the optimization is considered successful; if the indicators do not meet the threshold requirements, it is determined that further optimization is needed and feedback iterative adjustments are performed.

[0124] Furthermore, feedback iteration includes adjusting the initial population and fitness function of global optimization if the local optimization is not good.

[0125] Furthermore, the population size is dynamically increased during the iteration process to cover a larger solution space. When the optimization is found to be stuck in a local optimum, the population diversity is increased by increasing the population size, randomly introducing new individuals, or mutating existing individuals. Different individual initialization methods are combined to improve population diversity. The following formula is used to randomly generate initial individuals:

[0126] x i =x min +(x max -x min )×rand(0,1)

[0127] Among them, rand(0,1) represents a random number between 0 and 1.

[0128] Furthermore, the weights in the fitness function are dynamically adjusted so that they change with the iterative process. In the early stages of optimization, more global indicators are given greater weights. In the later stages of iteration, the weights of local indicators are increased so that the algorithm focuses more on fine-grained optimization. Voltage deviation, harmonic distortion, and frequency fluctuation are optimized as independent targets, and the optimal solution is selected through the Pareto front.

[0129] Furthermore, an elite preservation strategy is introduced: in each generation of evolution, the individuals with the highest fitness are directly retained to the next generation to prevent the optimal solution from being destroyed by mutation or crossover operations. The formula is expressed as:

[0130] P new =P elite ∪P evolved

[0131] Among them, P elite represents the elite individuals retained, P evolved Represents a newly generated individual.

[0132] It should be noted that iterations continue until all power quality assessment indicators meet the preset targets, or the algorithm terminates if no significant improvement is achieved after a certain number of iterations. The algorithm then outputs the optimal control parameter combination, optimized after multiple feedback iterations, for application in the photovoltaic system to achieve ultimate voltage quality optimization.

[0133] On the other hand, this embodiment also provides a voltage quality optimization control system based on photovoltaic disturbances, which includes:

[0134] The data management and modeling module provides simulation environment and data support for use by the global optimization module and the local optimization module.

[0135] The global optimization module first performs a global search, provides a better initial solution to the local optimization module, and makes adjustments based on the optimization result feedback.

[0136] The local optimization module performs fine-grained optimization on key nodes and returns the results to the global optimization module, forming a feedback iterative process.

[0137] The evaluation iteration module evaluates the results of each round of optimization and feeds the evaluation results back to the global optimization module and the local optimization module to adjust the optimization strategy for the next round of iteration.

[0138] If the above functions are implemented in the form of software functional units and sold or used as independent products, they 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 enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0139] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0140] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be 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.

[0141] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] Example 2: The following is an embodiment of the present invention, which provides a voltage quality optimization control method based on photovoltaic disturbance. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0143] The purpose of this experiment is to verify the effectiveness of the voltage quality optimization control method based on photovoltaic disturbances in improving the power quality of photovoltaic systems. The experiment is divided into the following steps:

[0144] The experiment was conducted at a 1MW photovoltaic power plant. The photovoltaic array, current, and voltage sensors were used to monitor the system's output power, output voltage, and output current. Environmental sensors were used to monitor external conditions such as solar radiation intensity, ambient temperature, wind speed, and humidity.

[0145] Harmonic detectors, voltage sensors, and frequency meters were installed at the PV system's grid connection point to record power quality parameters in real time. Data collection lasted two weeks, gathering operational data under various lighting and load conditions. Based on this data, a PV system simulation model was constructed, including a single-diode model for the PV cell and a power quality model (voltage fluctuation, harmonics, and frequency fluctuation).

[0146] During the optimization process, the fitness function is used to evaluate the performance of individual nodes. This fitness function integrates the RMS voltage deviation, harmonic distortion (THD), and frequency fluctuation. Based on the global optimization results, three key nodes with significant voltage fluctuations and harmonic distortion are identified. Simulated annealing is then used to fine-tune the control parameters of these nodes.

[0147] Use genetic algorithm for global optimization, the optimization goal is to minimize voltage fluctuation, harmonic distortion and frequency fluctuation, while ensuring the maximum power generation efficiency. Set the inverter voltage set point (V set ), reactive power compensation (Q comp ) and MPPT control parameters (P mppt ) is the optimization variable. An initial population of 100 individuals is randomly generated, and the control parameter range of each individual is set in V set ∈[210,240]V,Q comp ∈[-0.2,0.2]MVAr,P mppt ∈[0.9,1.1] The fitness function is used to evaluate the quality of individuals during the optimization process. The fitness function integrates the root mean square value of voltage deviation, harmonic distortion (THD) and frequency fluctuation.

[0148] Table 1 Experimental data record table

[0149]

[0150] Comparing experimental data shows that the voltage quality optimization control method based on photovoltaic disturbances significantly improves the power quality of the photovoltaic system. The optimized mean voltage deviation decreased from 6.1V to 3.4V, a reduction of approximately 44.3%. This indicates that optimizing control parameters significantly reduces system voltage fluctuations. This demonstrates that the method can effectively control the photovoltaic system's impact on grid voltage, improving power supply quality.

[0151] In terms of harmonic distortion, total harmonic distortion (THD) decreased from 4.63% before optimization to 2.9%, a 37.3% reduction. This demonstrates that the optimized system is able to better suppress the generation of higher-order harmonics, reducing their negative impact on power quality. In particular, at key node 2, the harmonic distortion rate decreased by 37.5%, further validating the effectiveness of the simulated annealing algorithm in optimizing local key nodes.

[0152] Frequency deviation was also significantly reduced. After optimization, the average frequency deviation dropped from 0.32Hz to 0.15Hz, a decrease of approximately 53.1%, significantly improving the system's frequency stability. This demonstrates that optimized control of PV system disturbances can better cope with the impact of load changes on frequency.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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 voltage quality optimization control method based on photovoltaic disturbance, characterized in that: include: Collect photovoltaic system operation data and build a photovoltaic system simulation model; Use genetic algorithms for global optimization to find the control parameters that optimize voltage quality; Select key nodes and perform local key node optimization; Test the optimized system, evaluate the system's power quality, and perform feedback iterations.

2. The method for optimizing voltage quality based on photovoltaic disturbance according to claim 1, wherein: The collecting of photovoltaic system operation data includes collecting photovoltaic system operation data using a data collector of a photovoltaic power station data collection system; Install current and voltage sensors on the photovoltaic array to monitor the output power, output voltage, and output current of the photovoltaic modules; install environmental sensors, including solar radiation intensity sensors, ambient temperature sensors, wind speed and humidity sensors, to monitor the external conditions of photovoltaic power generation; Install power quality sensors at the grid connection points, and install harmonic detectors, voltage sensors and frequency meters at key nodes where the photovoltaic system is connected to the grid to monitor power quality parameters in real time.

3. The method for optimizing voltage quality based on photovoltaic disturbance according to claim 2, wherein: The constructing of the photovoltaic system simulation model includes, after the data collection is completed, constructing the simulation model of the photovoltaic system based on the collected operating data and system structure, and using the simulation model to simulate the behavior of the photovoltaic system under different conditions; Construct a photovoltaic cell model and a power quality model; the photovoltaic cell model includes a single diode model, which is suitable for scenarios with limited computing power. The formula is: Among them, I ph represents the photocurrent, I0 represents the saturation current, R s is the series resistance, n is the ideality factor of the diode, V t Represents the thermal voltage; the double-diode model reflects the nonlinear characteristics of photovoltaic cells and is suitable for high-precision scenarios. The formula is: Among them, I 01 ,I 02 represents the saturation current of the two diodes, n1 and n2 represent the ideality factors of the diodes; The power quality model includes a voltage fluctuation model, a harmonic model, and a frequency fluctuation model. The voltage fluctuation model uses actual measurement data to establish a voltage deviation model to simulate the voltage fluctuation range after photovoltaic power is connected to the grid. The harmonic model uses Fourier transform to analyze the harmonic components of the photovoltaic grid connection point, establishes a harmonic distortion model, and quantifies the impact of harmonics on power quality. The frequency fluctuation model simulates the frequency response behavior of photovoltaic grid connection when the grid frequency changes. Based on historical data and current-voltage characteristic curves, nonlinear fitting techniques are used to estimate model parameters. Simulation experiments are performed, and if there is a deviation between the simulation results and the actual data, the parameters in the model are adjusted.

4. The method for optimizing voltage quality based on photovoltaic disturbances according to claim 3, wherein: The global optimization using genetic algorithms includes global optimization of photovoltaic disturbance voltage quality, with the main goal of minimizing power quality issues, voltage fluctuations, harmonic distortion, and frequency fluctuations, while ensuring that the power generation efficiency of the system is maximized; Set constraints, select encoding methods, and express the control parameters of the photovoltaic system, the voltage set point of the inverter, the reactive power compensation amount, and the MPPT control parameters in the form of chromosomes; each chromosome is defined as a parameter vector: x i =[P i1 ,P i2 ,…,P in ] Among them, P i1 ,P i2 ,…,P in Indicates the control parameters that need to be optimized, including inverter voltage set point, MPPT adjustment parameters, and reactive power compensation; Randomly generate an initial population, where each individual in the population corresponds to a combination of control parameters; randomly generate a group of initial values ​​as the population within the defined range of each control parameter, and set the value range of each control parameter; A fitness function is defined for each individual, and the fitness function is calculated based on multiple power quality indicators; selection, crossover, and mutation operations are performed; and when the genetic algorithm converges, the individual with the highest fitness is output, which is the optimal combination of photovoltaic system control parameters.

5. The method for optimizing voltage quality based on photovoltaic disturbance according to claim 4, wherein: The local key node optimization includes determining the key nodes in the photovoltaic system where voltage fluctuations are large and harmonic distortion is severe based on the results of the global optimization; Using the simulated annealing algorithm, the initial control parameter solutions for key nodes are generated from the global optimization results. The control parameters include the voltage set point and the reactive power regulation. An adaptive temperature decay function is used to dynamically adjust the temperature decay rate according to the changes in the solution. The formula is expressed as: T k+1 =T k ×(1-α·ΔE) Where α is the temperature adjustment coefficient, ΔE is the energy change of the current solution, and the initial temperature T is set. The temperature determines whether to accept a worse solution in each iteration. The temperature decay method is set to gradually reduce the temperature after each iteration. Based on the current solution, a neighborhood solution is generated by random perturbation, and different perturbation sizes are defined for different neighborhoods. In the early stage, a large-scale perturbation is used to explore a larger solution space, and in the later stage, a small-scale perturbation is used to fine-tune the solution. The multi-neighborhood search implementation formula is expressed as: x new =x current +δ×N(0,1) Here, δ represents the disturbance coefficient, which gradually decreases with the number of iterations, and N(0,1) represents a normal distribution with mean zero and variance one. The energy difference between the new solution and the current solution is calculated, and the algorithm terminates when the temperature drops to the set threshold. The optimal control parameter solution finally found is used as the adjustment parameter of the key node for fine-tuning voltage fluctuations, harmonic distortion, and frequency fluctuations.

6. The method for optimizing voltage quality based on photovoltaic disturbance according to claim 5, wherein: The power quality evaluation system includes applying the optimized control parameters to a simulation model of the photovoltaic system to simulate the system response under different lighting and load conditions; Implement optimized control parameters in actual photovoltaic systems to monitor power quality indicators in real time, including voltage fluctuations, harmonic distortion, and frequency fluctuations; Calculate the RMS value of the voltage deviation using the formula: Among them, V i Represents the voltage value at the i-th moment, V ref It represents the reference voltage; Use total harmonic distortion (THD) as an evaluation indicator to calculate the optimized harmonic distortion. If THD exceeds the set threshold, further optimization is required: Among them, V n represents the amplitude of the nth harmonic, and V1 represents the amplitude of the fundamental wave; Frequency stability assessment, calculate the frequency deviation. If the frequency deviation is large, it means the frequency is unstable and needs to be adjusted. The formula is: Δf max =max(|f i -f ref |) Among them, f i Represents the frequency value at the i-th moment, f ref Indicates the reference frequency; if all evaluation indicators meet the preset goals, the optimization is considered successful; if the indicators do not meet the threshold requirements, it is determined that further optimization is needed and feedback iterative adjustments are performed.

7. The method for optimizing voltage quality based on photovoltaic disturbance according to claim 6, wherein: The feedback iteration includes adjusting the initial population and fitness function of the global optimization if the local optimization is not good; During the iteration process, the population size is dynamically increased to cover a larger solution space. When the optimization is found to be stuck in a local optimum, the population diversity is increased by increasing the population size, randomly introducing new individuals, or mutating existing individuals. Different individual initialization methods are combined to improve population diversity. The following formula is used to randomly generate initial individuals: x i =x min +(x max -x min )×rand(0,1) Among them, rand(0,1) represents a random number between 0 and 1; Dynamically adjust the weights in the fitness function so that they change over the iteration process. In the early stages of optimization, more global indicators are given greater weight. In the later stages of iteration, the weights of local indicators are increased, allowing the algorithm to focus more on fine-grained optimization. Voltage deviation, harmonic distortion, and frequency fluctuation are optimized as independent targets, and the optimal solution is selected through the Pareto front. Introducing the elite preservation strategy: In each generation of evolution, the individuals with the highest fitness are directly retained to the next generation to prevent the optimal solution from being destroyed by mutation or crossover operations. The formula is expressed as: P new =P elite ∪P evolved Among them, P elite represents the elite individuals retained, P evolved Represents a newly generated individual.

8. A photovoltaic disturbance-based voltage quality optimization control system using the method according to any one of claims 1 to 7, characterized in that: Data management and modeling, the module provides simulation environment and data support for global optimization module and local optimization module; The global optimization module first performs a global search, provides a better initial solution to the local optimization module, and makes adjustments based on the optimization result feedback; The local optimization module performs fine-grained optimization on key nodes and returns the results to the global optimization module, forming a feedback iterative process; The evaluation iteration module evaluates the results of each round of optimization and feeds the evaluation results back to the global optimization module and the local optimization module to adjust the optimization strategy for the next round of iteration.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the voltage quality optimization control method based on photovoltaic disturbances according to any one of claims 1 to 7 are implemented.

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 voltage quality optimization control method based on photovoltaic disturbances are implemented.

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