Method and system for precise active reserve control of grid-connected photovoltaic power station

Abstract

The application discloses a network-constructed photovoltaic station precise active reserve control method and system, relates to the photovoltaic power generation and power system control technical field, and the method comprises the following steps: in the MPPT mode, a global iterative search is carried out on the power-voltage curve of the photovoltaic array by using an intelligent optimization algorithm, and the maximum power point of the photovoltaic array is located; whether the intelligent optimization algorithm converges to the maximum power point is judged by using a multi-index convergence monitoring condition, and the target reserve power is calculated in the case that the intelligent optimization algorithm has converged; the network-constructed photovoltaic station enters a weighted soft switching transition period, and the duty cycle is adjusted based on a dynamic weight; after the weighted soft switching transition period ends, the network-constructed photovoltaic station is switched to an active reserve mode, and in the case that the environment mutates, the network-constructed photovoltaic station is switched back to the MPPT mode. The application realizes precise reservation, stable tracking and fast frequency modulation response of the active reserve of the network-constructed photovoltaic unit.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation and power system control technology, specifically to a precise active power reserve control method and system for grid-type photovoltaic power plants. Background Technology

[0002] Traditional grid-type photovoltaic (PV) power plants typically operate in maximum power point tracking (MPPT) mode. In this mode, the output power of the PV array reaches its maximum value under the current irradiance and temperature conditions. With no active power reserve, it cannot respond to fluctuations in grid frequency and is unable to participate in grid frequency regulation, thus limiting the support capacity of grid-type PV power plants for the power system. To enable grid-type PV power plants to have frequency regulation capabilities, existing technologies generally employ active power reserve control technology. This involves shifting the PV operating point from the maximum power point to the right side of the power-voltage curve, reserving a portion of active power as backup power for grid frequency regulation. This backup power is then quickly released during grid frequency fluctuations, achieving frequency support.

[0003] However, existing photovoltaic active power reserve control methods still have many technical shortcomings when applied to grid-type photovoltaic power plants, making it difficult to meet the requirements for accurate active power reserve and stable tracking:

[0004] Low accuracy of maximum power point tracking (MPPT): Under complex conditions such as partial shading and cloud cover, the power-voltage curve of the photovoltaic array exhibits multi-peak characteristics. Traditional methods are prone to getting trapped in local optima, which leads to deviations in the estimation of maximum power point, distortion of the reference value for active power reserve control, and consequently, insufficient accuracy of active power reserve.

[0005] Mode switching can cause shocks: When switching from MPPT mode to active power reserve mode, existing technologies mostly use hard switching. The sudden change in duty cycle can easily cause violent fluctuations in photovoltaic output power and DC voltage oscillations. This not only affects the stable operation of grid-type photovoltaic power plants themselves, but also impacts the power grid.

[0006] Poor stability of active power reserve point tracking: When the traditional fixed-step perturbation observation method is used for active power reserve point tracking, it is difficult to balance dynamic response speed and steady-state control accuracy. If the step size is too large, it will cause the grid-type photovoltaic power station to oscillate near the active power reserve point. If the step size is too small, the grid-type photovoltaic power station will respond slowly to the power deviation rate and will not be able to achieve stable tracking of reserve power.

[0007] Poor environmental adaptability: When photovoltaic operating environment parameters such as irradiance and temperature change abruptly, existing methods lack effective restart and re-search mechanisms, the original active power reserve points will lose their effectiveness, and grid-type photovoltaic power plants will be unable to continue to provide accurate and stable active power reserve to the grid, resulting in a significant decrease in frequency regulation capability;

[0008] High implementation cost: Some improved active power reserve control methods require the addition of hardware equipment such as irradiance and temperature sensors to collect environmental parameters to assist in control, which increases hardware costs and debugging difficulty, making it unsuitable for retrofitting and applying to existing grid-type photovoltaic power stations.

[0009] In addition, there are currently two main methods to achieve active power reserve control of grid-connected photovoltaic power plants under varying irradiance, but both have significant limitations: one is to set up a control strategy with a reference array and a standby array. Although this can achieve accurate reserve capacity and active grid frequency adjustment under varying irradiance, the reference array does not participate in frequency regulation and the standby array operates with long-term load reduction, which significantly reduces the overall power generation efficiency and economy of grid-connected photovoltaic power plants; the other is to switch back to the MPPT algorithm at fixed intervals and use the active power reserve algorithm to track the new standby operating point. However, under complex illumination conditions, intermittent maximum power point tracking cannot capture the global maximum power point of the photovoltaic array in real time, which can easily cause the standby capacity reference benchmark to be inaccurate, thereby weakening the accuracy and reliability of the grid frequency regulation response.

[0010] To address the aforementioned issues, there is an urgent need to develop a high-precision, high-stability, and highly environmentally adaptable grid-type photovoltaic active power reserve control method. This method would enable precise reservation and stable tracking of active power reserves, while simultaneously reducing engineering implementation costs and enhancing the ability of grid-type photovoltaic power plants to participate in grid frequency regulation. Summary of the Invention

[0011] To address the technical problems of inaccurate maximum power point positioning, unsmooth mode switching, poor tracking stability, and poor environmental adaptability in traditional active power reserve control, this invention proposes a precise active power reserve control method and system for grid-type photovoltaic power plants, which achieves precise reservation, stable tracking, and rapid frequency regulation response for active power reserves in grid-type photovoltaic power plants.

[0012] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0013] The present invention provides a precise active power reserve control method for grid-connected photovoltaic power stations, comprising:

[0014] In MPPT mode, an intelligent optimization algorithm is used to perform a global iterative search on the power-voltage curve of the photovoltaic array to locate the maximum power point of the photovoltaic array under the current environment;

[0015] Based on the convergence monitoring conditions of multiple indicators, it is determined whether the intelligent optimization algorithm has converged to the maximum power point. If the intelligent optimization algorithm has converged, the target reserve power is calculated according to the preset active power reserve ratio.

[0016] Grid-type photovoltaic power stations are entering a weighted soft switching transition period, and the duty cycle is adjusted based on dynamic weights;

[0017] After the weighted soft switching transition period ends, the grid-type photovoltaic power station switches to active power reserve mode, and generates frequency regulation power in combination with the grid frequency deviation, corrects the target reserve power, and obtains the final power command in active power reserve mode.

[0018] Real-time monitoring of the rate of change in photovoltaic output power, and mode switching based on the rate of change in photovoltaic output power.

[0019] A further improvement of this invention is that the multi-index convergence monitoring conditions include:

[0020] The rate of change index of the optimal solution, i.e., continuous In each iteration, the relative change of the optimal duty cycle is less than the duty cycle change rate threshold.

[0021] Power volatility index, i.e., continuous In each iteration, the relative change in photovoltaic output power was less than the power fluctuation rate threshold.

[0022] Population diversity index, which is the current population's positional standard deviation being less than the population diversity threshold.

[0023] A further improvement of this invention is that the calculation expression for the target reserve power based on the preset active power reserve ratio is:

[0024]

[0025] in: For target backup power, This is the maximum power value. This is the active power reserve ratio.

[0026] A further improvement of this invention is that the calculation expression for the duty cycle based on dynamic weight adjustment is:

[0027] ;

[0028] ;

[0029] in: This represents the weighted soft handover transition period. Duty cycle, , They are respectively , Dynamic weights at time points, The duty cycle output by the intelligent optimization algorithm. The duty cycle output by the improved variable step size perturbation observation method.

[0030] A further improvement of this invention is that, in active power reserve mode, an improved variable step-size perturbation observation method is used to track the final power command, adaptively adjust the duty cycle, and perform amplitude limiting processing on the duty cycle to ensure stable operation of the photovoltaic array at the active power reserve point. The adaptive step size of the adaptively adjusted duty cycle and the absolute value of the power deviation rate satisfy the following:

[0031]

[0032] in: The power deviation rate between the current photovoltaic output power and the final power command. This is the proportionality coefficient. For adaptive step size, To maximize the adjustment step size, This is the minimum adjustment step size.

[0033] A further improvement of the present invention is that the improved variable step size perturbation observation method is equipped with a power dead zone, when the absolute value of the power deviation rate is... When the power dead zone threshold is less than the preset threshold, the adaptive step size is set to the preset minimum adjustment step size.

[0034] The present invention provides a precise active power backup control system for grid-connected photovoltaic power plants, comprising:

[0035] The data acquisition module is used to collect the output voltage and output current of the photovoltaic array in real time and calculate the photovoltaic output power;

[0036] The MPPT module is used to perform a global iterative search on the power-voltage curve of the photovoltaic array using an intelligent optimization algorithm, locate the maximum power point of the photovoltaic array under the current environment, and output the maximum power value and the corresponding optimal duty cycle.

[0037] The convergence monitoring module is used to determine whether the intelligent optimization algorithm has converged to the maximum power point, and outputs a switching command based on the determination result;

[0038] The soft handover module is used to receive handover instructions, enter the weighted soft handover transition period, and adjust the duty cycle based on dynamic weights.

[0039] The environmental monitoring module is used to monitor the rate of change of photovoltaic output power in real time. When the rate of change of photovoltaic output power exceeds the preset threshold and continues to meet the conditions, it is determined that the environment has changed drastically and a restart signal is triggered to the MPPT module.

[0040] The frequency response module is used to detect the power grid frequency in real time, calculate the frequency deviation, and generate a primary frequency regulation power command based on the frequency regulation power when the frequency deviation exceeds the dead zone ±0.033Hz.

[0041] The active power reserve module is used to adaptively adjust the duty cycle by using an improved variable step size perturbation observation method to track the final power command after superimposing the target reserve power with the frequency modulation power, so that the photovoltaic array operates at the active power reserve point.

[0042] The PWM generation module is used to receive the duty cycle sent by the MPPT module, soft switching module or active power reserve module to generate PWM drive pulses, and control the on and off of the switching transistors in the Boost converter.

[0043] A further improvement of the present invention is that: after receiving the restart signal, the MPPT module uses the set interval where the historical best duty cycle obtained from the previous round of search by the MPPT module is located to reinitialize the population of the intelligent optimization algorithm and perform a global iterative search.

[0044] The beneficial effects of this invention are as follows: This invention employs an intelligent optimization algorithm for global iterative search of the maximum power point, effectively avoiding the problem of traditional perturbation observation methods easily getting trapped in local optima under multi-peak power-voltage curves. Even under complex conditions such as local shading and cloud cover, it can still accurately locate the actual maximum power point of the photovoltaic array. Simultaneously, by combining multi-index convergence monitoring to determine the convergence status of the intelligent optimization algorithm, the accuracy of the maximum power point location is further guaranteed, providing a reliable benchmark for active power reserve. This invention achieves a smooth transition between MPPT mode and active power reserve mode through a weighted soft switching strategy. The duty cycle changes linearly and smoothly with dynamic weights, completely avoiding power surges and DC voltage oscillations caused by hard switching. Combined with grid frequency response and primary frequency regulation control, the photovoltaic output power command, i.e., the final power command, can be quickly and dynamically adjusted according to the grid frequency deviation. This invention achieves accurate tracking of the active power reserve point through an improved variable step-size perturbation observation method, which not only improves the operational stability of the grid-connected photovoltaic power plant itself but also reduces the impact on the grid, enhancing the stability of the photovoltaic grid-connected system. By monitoring the rate of change of photovoltaic output power in real time, the system can accurately determine drastic changes in the photovoltaic operating environment, automatically re-search for the maximum power point and restore active power reserve control. This effectively solves the problem of active power reserve point failure caused by sudden changes in irradiance and temperature, ensuring that grid-connected photovoltaic power plants can still provide accurate and stable active power reserve to the grid after environmental changes, thus improving the environmental adaptability and continuous frequency regulation capability of grid-connected photovoltaic power plants. This invention only requires the acquisition of the output voltage and current signals of the photovoltaic array, without the need for additional hardware such as irradiance and temperature sensors. It can be directly implemented on existing grid-connected photovoltaic inverters through software upgrades, resulting in low modification costs, simple debugging, and ease of large-scale engineering application and promotion. Attached Figure Description

[0045] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2This is a simulation diagram of the active power reserve strategy in an embodiment of the present invention; Figure 3 This is a simulation diagram of frequency response control in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0047] like Figure 1 As shown in the figure, this embodiment provides a precise active power reserve control method for grid-connected photovoltaic power plants under complex lighting conditions. It is based on a two-stage grid-connected photovoltaic structure, where the front stage is a Boost converter and the rear stage is a grid-connected inverter controlled by a virtual synchronous generator (VSG). The two stages are coupled by a DC bus. The method includes the following steps:

[0048] Step 1: In MPPT mode, an intelligent optimization algorithm is used to perform a global iterative search on the power-voltage curve of the photovoltaic array to locate the maximum power point of the photovoltaic array under the current irradiance and temperature conditions. The population initialization of the intelligent optimization algorithm adopts a uniform distribution strategy. The search range of the duty cycle is set to [value missing] in the first population initialization. Population size set to To ensure the comprehensiveness of the search, each iteration of the intelligent optimization algorithm outputs the candidate duty cycle values ​​in the population sequentially and maintains a preset sampling period. After the grid-type photovoltaic power station enters a steady state, the corresponding photovoltaic output power is measured and used as the fitness value of the intelligent optimization algorithm to improve the accuracy of the fitness value. After the search is completed, the maximum power value is recorded. And the corresponding optimal duty cycle.

[0049] Step 2: Based on the multi-index convergence monitoring conditions, determine whether the intelligent optimization algorithm has converged to the maximum power point. If the intelligent optimization algorithm has converged to the maximum power point, then based on the preset active power reserve ratio... Calculate the target reserve power, including the active power reserve ratio. The range of values ​​is It can be flexibly adjusted according to the frequency regulation needs of the power grid. The multi-index convergence monitoring conditions include the optimal solution change rate index, the power fluctuation rate index, and the population diversity index. When all three indicators meet the conditions, it is determined that the intelligent optimization algorithm has converged.

[0050] Step 3: After the intelligent optimization algorithm has converged, the grid-type photovoltaic power station enters the weighted soft switching transition period to achieve a smooth transition from MPPT mode to active power reserve mode. During the weighted soft switching transition period, the duty cycle is obtained by mixing the duty cycle output by the intelligent optimization algorithm and the duty cycle output by the improved variable step size perturbation observation method according to the dynamic weight. The dynamic weight decreases linearly from 1 to 0 with the transition period, so that the duty cycle smoothly transitions from the output value of the intelligent optimization algorithm to the output value of the improved variable step size perturbation observation method.

[0051] Step 4: After the weighted soft handover transition period ends, the grid-type photovoltaic power station switches to active power reserve mode. Frequency regulation power is generated by combining the grid frequency deviation, and the target reserve power is corrected to obtain the final power command in active power reserve mode. An improved variable step size perturbation observation method is adopted to track the final power command as the target. By adaptively adjusting the duty cycle, the photovoltaic array is stably operated at the active power reserve point, continuously reserving active power reserve for the grid.

[0052] Step 5: Throughout the active power reserve mode, monitor the rate of change of photovoltaic output power in real time and switch modes based on the rate of change of photovoltaic output power.

[0053] The process of switching a grid-connected photovoltaic power station from MPPT mode to active power reserve mode, implemented through the method in this embodiment, is as follows: Figure 2 As shown.

[0054] The above method is based on a precision active power reserve control system for grid-type photovoltaic power plants. The system includes:

[0055] The data acquisition module is used to collect the output voltage and output current of the photovoltaic array in real time and calculate the photovoltaic output power;

[0056] The MPPT module is used to perform a global iterative search on the power-voltage curve of the photovoltaic array using an intelligent optimization algorithm, locate the maximum power point of the photovoltaic array under the current environment, and output the maximum power value and the corresponding optimal duty cycle.

[0057] The convergence monitoring module is used to determine whether the intelligent optimization algorithm has converged to the maximum power point, and outputs a switching command based on the determination result;

[0058] The soft handover module is used to receive handover instructions, enter the weighted soft handover transition period, and adjust the duty cycle based on dynamic weights.

[0059] The environmental monitoring module is used to monitor the rate of change of photovoltaic output power in real time. When the rate of change of photovoltaic output power exceeds the preset threshold and continues to meet the conditions, it is determined that the environment has changed drastically and a restart signal is triggered to the MPPT module.

[0060] The frequency response module is used to detect the power grid frequency in real time, calculate the frequency deviation, and generate a primary frequency regulation power command based on the frequency regulation power when the frequency deviation exceeds the dead zone ±0.033Hz.

[0061] The active power reserve module is used to adaptively adjust the duty cycle by using an improved variable step size perturbation observation method to track the final power command after superimposing the target reserve power with the frequency modulation power, so that the photovoltaic array operates at the active power reserve point.

[0062] The PWM generation module is used to receive the duty cycle sent by the MPPT module, soft switching module or active power reserve module to generate PWM drive pulses, and control the on and off of the switching transistors in the Boost converter.

[0063] In this embodiment, after receiving a restart signal, the MPPT module uses the set interval containing the historical best duty cycle obtained from the previous round of search by the MPPT module to reinitialize the population of the intelligent optimization algorithm and perform a global iterative search.

[0064] In this embodiment, the Grey Wolf optimization algorithm is used for maximum power point tracking. The search process specifically includes:

[0065] Set the parameters for the gray wolf optimization algorithm: population size , No. In the nth iteration The position of the gray wolf is That is, the candidate value of duty cycle and the maximum number of iterations. Duty cycle search range Initialize the population. A uniform distribution strategy is adopted:

[0066]

[0067] in: This refers to an individual gray wolf within the population. , These are the minimum duty cycle and the maximum duty cycle, respectively.

[0068] Each iteration of the Grey Wolf optimization algorithm includes:

[0069] The candidate values ​​of each duty cycle in the population are output sequentially, and each duty cycle is maintained for 35 sampling periods to ensure that the grid-type photovoltaic power station enters a steady state;

[0070] Measure the photovoltaic output power corresponding to each duty cycle. , as the fitness value, where The output voltage of the photovoltaic array. This refers to the output current of the photovoltaic array;

[0071] Based on fitness values, determine the current optimal three wolves: Wolves are the best. Wolves are the second best. Wolves are the third best;

[0072] according to Wolf, Wolf, The wolf's location is updated, along with the locations of other individual gray wolves:

[0073]

[0074] in: The updated locations of the other individual gray wolves. , , They are respectively Wolf, Wolf, The wolves' positions are respectively by Wolf, Wolf, The wolf calculated this using a mathematical model of how it surrounds its prey.

[0075] Record the current status after each iteration update. Wolf's duty cycle and power value Store it in a circular history array. When consecutive The Grey Wolf Optimization Algorithm is considered to have converged when the following three conditions are met simultaneously in the next iteration. A convergence flag is then output and the convergence flag bit is set. :

[0076] Optimal solution rate of change index: ;

[0077] Power volatility index: ;

[0078] Population diversity indicators: .

[0079] in: For the first The position of the gray wolf. This represents the average position of all individuals in the population. , for Wolf , The duty cycle of the next iteration. In this embodiment, we take... Duty cycle change rate threshold Power volatility threshold Population diversity threshold When all three conditions are met simultaneously, a switching preparation signal is triggered. .

[0080] The process for transitioning from MPPT mode to active power reserve mode in this embodiment includes:

[0081] Initialize the spare parameters:

[0082] Obtain the maximum power value from the MPPT module. and the corresponding optimal duty cycle, based on the preset reserve ratio. Calculate the target reserve power :

[0083]

[0084] The target reserve power needs to be dynamically adjusted according to grid frequency fluctuations, with the adjustment amount being... Calculate the final power command .

[0085] ;

[0086] ;

[0087] in: For frequency deviation, This represents the maximum frequency deviation.

[0088] like Figure 3 As shown, in this embodiment, a power grid frequency disturbance condition is set: after 1.5 seconds of simulation, the power grid frequency drops from the rated value of 50Hz to 49.9Hz, i.e., a frequency deviation. It remains at the rated value for 1.5 seconds, and then returns to the rated value.

[0089] Setting parameters for the improved variable step size perturbation observation method: scaling factor Minimum adjustment step size Maximum adjustment step size Power dead zone threshold .

[0090] Weighted soft handover transition period:

[0091] Set the weighted soft handover transition period. The sampling period is 20. Initialize the dynamic weights. During each sampling period of the weighted soft handover transition, the following is executed:

[0092] Measure the current photovoltaic output power The current duty cycle is calculated using the improved variable step size perturbation observation method. ;

[0093] Calculate the final output duty cycle: ;

[0094] Update weights: ;

[0095] Output duty cycle To the PWM generation module.

[0096] Power deviation rate calculation:

[0097] After the transition period, it enters steady-state active power reserve tracking. The current photovoltaic output power is measured. Calculate the current photovoltaic output power Power deviation rate from final power command:

[0098]

[0099] in: The power deviation rate between the current photovoltaic output power and the final power command;

[0100] Adaptive step size calculation:

[0101] Calculate the adaptive step size based on the absolute value of the power deviation rate:

[0102]

[0103] in: For adaptive step size;

[0104] Power dead zone detection:

[0105] Determine if the absolute value of the power deviation rate is less than the power dead zone threshold. .like If the step size is positive, the adaptive step size is the minimum adjustment step size; otherwise, the adaptive step size is calculated from the adaptive step size.

[0106] Duty cycle direction adjustment:

[0107] Determine the duty cycle adjustment direction based on the sign of the power deviation rate:

[0108] like If the current photovoltaic output power is higher than the final power command, then reduce the photovoltaic output power and increase the duty cycle in the operating region to the right of the maximum power point (MPP). ,in This is the adjusted duty cycle. The duty cycle before adjustment;

[0109] like If the current photovoltaic output power is lower than the final power command, then increase the photovoltaic output power and decrease the duty cycle. .

[0110] Duty cycle limited output:

[0111] The calculated duty cycle is limited to ensure it remains within a safe range.

[0112]

[0113] in: This is the duty cycle after the amplitude limiting process.

[0114] Output duty cycle to PWM generation module and update , .

[0115] Environmental change monitoring:

[0116] During active power reserve mode, the rate of change of photovoltaic output power is calculated in real time:

[0117]

[0118] in: The rate of change of photovoltaic output power , The first , The photovoltaic output power of the next iteration.

[0119] If the rate of change in photovoltaic output power is detected three times consecutively If a drastic change in the environment is detected, the active power standby mode is immediately interrupted, the Grey Wolf optimization algorithm state is reset, and the MPPT mode is re-executed.

[0120] At this point, the empirical value recorded when the Grey Wolf optimization algorithm converged last time, i.e., the optimal duty cycle, is used. The population was reinitialized with a fluctuation range of ±20%.

[0121]

[0122] If no valid experience value is available, the default range will be used for re-initialization. This mechanism effectively utilizes historical information to speed up the re-search and avoids starting from scratch.

[0123] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0124] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A precise active power reserve control method for grid-type photovoltaic power stations, characterized in that the method include: In MPPT mode, an intelligent optimization algorithm is used to perform a global iterative search on the power-voltage curve of the photovoltaic array to locate the maximum power point of the photovoltaic array under the current environment; Based on the convergence monitoring conditions of multiple indicators, it is determined whether the intelligent optimization algorithm has converged to the maximum power point. If the intelligent optimization algorithm has converged, the target reserve power is calculated according to the preset active power reserve ratio. Grid-type photovoltaic power stations are entering a weighted soft switching transition period, and the duty cycle is adjusted based on dynamic weights; After the weighted soft switching transition period ends, the grid-type photovoltaic power station switches to active power reserve mode, and generates frequency regulation power in combination with the grid frequency deviation, corrects the target reserve power, and obtains the final power command in active power reserve mode. Real-time monitoring of the rate of change in photovoltaic output power, and mode switching based on the rate of change in photovoltaic output power; Multi-indicator convergence monitoring conditions include: The rate of change index of the optimal solution, i.e., continuous In each iteration, the relative change of the optimal duty cycle is less than the duty cycle change rate threshold. Power volatility index, i.e., continuous In each iteration, the relative change in photovoltaic output power was less than the power fluctuation rate threshold. Population diversity index, i.e., the current population's positional standard deviation is less than the population diversity threshold; The calculation formula for the target reserve power based on the preset active power reserve ratio is as follows: ； in: For target backup power, This is the maximum power value. The active power reserve ratio; The calculation expression for the duty cycle based on dynamic weight adjustment is: ； ； in: This represents the weighted soft handover transition period. Duty cycle, , They are respectively , Dynamic weights at time points, The duty cycle output by the intelligent optimization algorithm. The duty cycle output for the improved variable step size perturbation observation method; In active power reserve mode, an improved variable step-size perturbation observation method is adopted to track the final power command, adaptively adjust the duty cycle, and limit the duty cycle to ensure stable operation of the photovoltaic array at the active power reserve point. The adaptive step size of the adaptive duty cycle adjustment and the absolute value of the power deviation rate satisfy the following: ； in: The power deviation rate between the current photovoltaic output power and the final power command. This is the proportionality coefficient. For adaptive step size, To maximize the adjustment step size, This is the minimum adjustment step size.

2. The precise active power reserve control method for grid-type photovoltaic power stations according to claim 1, characterized in that, The improved variable step size perturbation observation method incorporates a power dead zone, which is applied when the absolute value of the power deviation rate is... When the power dead zone threshold is less than the preset threshold, the adaptive step size is set to the minimum adjustment step size.

3. A precise active power backup control system for grid-connected photovoltaic power plants based on the method described in any one of claims 1 to 2, characterized in that, include: The data acquisition module is used to collect the output voltage and output current of the photovoltaic array in real time and calculate the photovoltaic output power; The MPPT module is used to perform a global iterative search on the power-voltage curve of the photovoltaic array using an intelligent optimization algorithm, locate the maximum power point of the photovoltaic array under the current environment, and output the maximum power value and the corresponding optimal duty cycle. The convergence monitoring module is used to determine whether the intelligent optimization algorithm has converged to the maximum power point, and outputs a switching command based on the determination result; The soft handover module is used to receive handover instructions, enter the weighted soft handover transition period, and adjust the duty cycle based on dynamic weights. The environmental monitoring module is used to monitor the rate of change of photovoltaic output power in real time. When the rate of change of photovoltaic output power exceeds the preset threshold and continues to meet the conditions, it is determined that the environment has changed drastically and a restart signal is triggered to the MPPT module. The frequency response module is used to detect the power grid frequency in real time, calculate the frequency deviation, and generate a primary frequency regulation power command based on the frequency regulation power when the frequency deviation exceeds the dead zone ±0.033Hz. The active power reserve module is used to adaptively adjust the duty cycle by using an improved variable step size perturbation observation method to track the final power command after superimposing the target reserve power with the frequency modulation power, so that the photovoltaic array operates at the active power reserve point. The PWM generation module is used to receive the duty cycle sent by the MPPT module, soft switching module or active power reserve module to generate PWM drive pulses, and control the on and off of the switching transistors in the Boost converter.

4. The precise active power backup control system for grid-type photovoltaic power plants according to claim 3, characterized in that, After receiving the restart signal, the MPPT module uses the set interval containing the historical best duty cycle obtained from the previous round of search by the MPPT module to reinitialize the population of the intelligent optimization algorithm and perform a global iterative search.

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