Photovoltaic micro-grid distributed control method and system based on power grid protection

Abstract

The invention discloses a photovoltaic micro-grid distributed control method and system based on power grid protection, and relates to the technical field of power grid control, and the method comprises the steps: setting a micro-grid protection region, setting distributed control nodes based on circuit distribution data in the micro-grid protection region, and constructing a micro-grid network topology. Meanwhile, circuit state information data of each circuit are collected in real time based on the constructed micro-grid network topology, the collected circuit state information data are processed in a data processing mode, and the processed circuit state information data are analyzed through a data analysis method; the method comprises the following steps: analyzing the circuit state information data to obtain analyzed circuit state information data, collecting photovoltaic historical power generation data in real time, predicting photovoltaic power generation data through a prediction analysis method, finally summarizing the analyzed circuit state information data and the predicted photovoltaic power generation data, calculating and determining a grid connection position of the predicted photovoltaic power generation data, and performing distributed control management. And the rationality of distributed control of the photovoltaic micro-grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid control, and in particular to a photovoltaic microgrid distributed control method and system based on power grid protection. Background Art

[0002] Due to the intermittent and uncertain characteristics of photovoltaic power generation, the microgrid formed after photovoltaic power generation is connected to the grid is likely to cause increased volatility in grid load, leading to voltage fluctuations and decreased grid stability.

[0003] Prior art, such as the invention patent application with publication number CN107370187A, discloses a photovoltaic microgrid system and a photovoltaic microgrid system control method. The method includes: multiple two-stage photovoltaic power generation subsystems connected in parallel; each of the two-stage photovoltaic power generation subsystems includes: multiple power optimizers, multiple power optimizer control units, a centralized inverter, and a centralized inverter control unit. The photovoltaic microgrid system control method includes: for each photovoltaic power generation subsystem, obtaining the active power and reactive power output by the centralized inverter; obtaining the maximum available active power and maximum available reactive power of the centralized inverter; obtaining a reference voltage; and generating a control signal for the centralized inverter.

[0004] From the above solutions, it can be seen that the current photovoltaic microgrid control methods mostly focus on areas such as their own power control, ignoring the voltage fluctuations and decreased grid stability caused by photovoltaic grid connection, which has certain limitations. Summary of the Invention

[0005] The purpose of the present invention is to provide a photovoltaic microgrid distributed control method and system based on grid protection, which solves the problems of voltage fluctuation and grid stability degradation in the background technology.

[0006] To solve the above-mentioned technical problems of voltage fluctuation and grid stability degradation, the present invention adopts the following technical solution: The present invention provides a photovoltaic microgrid distributed control method based on grid protection, which specifically includes the following steps:

[0007] S1. Setting a microgrid protection area, setting distributed control nodes based on circuit distribution data within the microgrid protection area, and constructing a microgrid network topology according to the set distributed control node locations;

[0008] S2. collecting circuit status information data of each circuit in real time based on the constructed microgrid network topology, and processing the collected circuit status information data through a data processing method to obtain processed circuit status information data;

[0009] The circuit status information data includes: circuit energy storage information, circuit current information and circuit voltage information;

[0010] S3, collect historical photovoltaic power generation data in real time, and predict photovoltaic power generation data through predictive analysis methods;

[0011] S4. Analyze the processed circuit state information data using a data analysis method to obtain analyzed circuit state information data;

[0012] S41. Perform circuit fault analysis on the processed circuit status information data based on a power grid protection algorithm, and output a fault analysis result;

[0013] S42. Based on the fault analysis results, locate the circuit fault location using a control variable method and a data analysis method;

[0014] S43, summarizing the fault analysis results and circuit fault location data to obtain analyzed circuit status information data;

[0015] S5. Summarize and analyze the circuit status information data and the predicted photovoltaic power generation data, and determine the grid connection location of the predicted photovoltaic power generation data through intelligent calculation;

[0016] S6. Perform distributed control management on the photovoltaic microgrid based on the determined grid-connected location of the predicted photovoltaic power generation data.

[0017] Preferably, the setting of the microgrid protection area, setting the distributed control nodes based on the circuit distribution data within the microgrid protection area, and constructing the microgrid network topology according to the set distributed control node positions includes the following steps:

[0018] Real-time collection of circuit distribution data within the microgrid protection area;

[0019] The circuit distribution data includes: circuit line length, circuit end point, and circuit starting point;

[0020] Setting a plurality of distributed control nodes based on circuit line length data in the circuit distribution data, setting intervals between adjacent distributed control nodes to be equal, and saving corresponding distributed control node positions;

[0021] Build and save the microgrid network topology based on circuit distribution data and corresponding distributed control node locations;

[0022] A set of four-tuples M = {A, B, C, D} is set to save the microgrid network topology, where M represents the constructed microgrid network topology, A represents the circuit number, B represents the circuit distribution data of the corresponding numbered circuit, C represents the distributed control node number, and D represents the location of the corresponding numbered distributed control node.

[0023] Preferably, the circuit status information data of each circuit is collected in real time based on the constructed microgrid network topology, and the collected circuit status information data is processed by a data processing method to obtain the processed circuit status information data, which includes the following steps:

[0024] Processing the collected circuit status information data through data standardization;

[0025] The data normalization formula is as follows:

[0026] ;

[0027] in, Represents the i-th group of circuit status information data, represents the mean value of the collected circuit status information data, represents the variance of the circuit state information data, represents the normalized circuit status information data of the i-th group;

[0028] Rounding the normalized circuit state information data by rounding down;

[0029] The rounded circuit state information data is set as the processed circuit state information data.

[0030] Preferably, the real-time collection of historical photovoltaic power generation data and the prediction of photovoltaic power generation data by a prediction analysis method include the following steps:

[0031] Collect sunlight intensity in real time and calculate the power generation of photovoltaic power generation equipment based on the sunlight intensity;

[0032] The formula for calculating the power generation of photovoltaic power generation equipment is as follows:

[0033] ;

[0034] in, Indicates the power generation of photovoltaic power generation equipment, the unit is kw·h, Indicates the total solar radiation of photovoltaic power generation equipment, unit kw·h / m 2 , Indicates the storage capacity of photovoltaic power generation equipment, the unit is kw, It represents the total solar radiation under standard conditions, with a constant of 1kw·h / m 2 , represents the comprehensive efficiency coefficient;

[0035] Collect the working time of photovoltaic power generation equipment and calculate the utilization rate of photovoltaic power generation equipment based on the working time of photovoltaic power generation ;

[0036] ;

[0037] The historical photovoltaic power generation data is the power generation of the photovoltaic power generation equipment multiplied by the utilization rate of the photovoltaic power generation equipment;

[0038] Summarize the most recent 30 sets of photovoltaic historical power generation data to construct a data matrix, with one set representing one day;

[0039] The 30 most recent sets of photovoltaic historical power generation data were fitted iteratively using the least squares method, and photovoltaic power generation data were predicted based on the fitting results;

[0040] Summarize the fitting results and calculate the average value, and set the calculated average value as the predicted photovoltaic power generation data.

[0041] Preferably, performing circuit fault analysis on the processed circuit status information data based on the power grid protection algorithm and outputting the fault analysis result comprises the following steps:

[0042] estimating voltage loss in the power grid based on circuit voltage information in the processed circuit status information data;

[0043] The voltage loss calculation formula is as follows:

[0044] ;

[0045] Among them, setting Represents two groups of adjacent distributed control nodes, Indicates the first The circuit voltage information of each distributed control node, Indicates the The circuit voltage information of each distributed control node, Indicates the A distributed control node and the The distance between distributed control nodes, Indicates the A distributed control node and the Voltage loss function between distributed control nodes;

[0046] estimating circuit voltage information of all distributed control nodes in the power grid topology based on the voltage loss function, and estimating circuit current information based on the estimated circuit voltage information;

[0047] Comparing the real-time collected circuit voltage information and circuit current information with the estimated circuit voltage information and circuit current information;

[0048] A comparison threshold range is set. When the comparison result exceeds the set comparison threshold range, it indicates that the circuit segment corresponding to the distributed control node has a fault; otherwise, it is normal.

[0049] Preferably, the method of locating the circuit fault position based on the fault analysis result by using a control variable method and a data analysis method comprises the following steps:

[0050] Select the positions of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the circuit between two adjacent distributed control nodes, and monitor and record the voltage and current fluctuations detected by the two adjacent distributed control nodes when the positions of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the circuit are damaged;

[0051] When a circuit fault is detected, the voltage and current fluctuations corresponding to the fault current are recorded, and the recorded voltage and current fluctuations are compared with the voltage and current fluctuations when each location is damaged. The circuit fault location is located based on the comparison results.

[0052] Preferably, the circuit status information data and predicted photovoltaic power generation data after the summary analysis are used to determine the grid connection position of the predicted photovoltaic power generation data by intelligent calculation, including the following steps:

[0053] S51, summarizing and analyzing the circuit status information data and the predicted photovoltaic power generation data to construct an initial grid-connected position population;

[0054] The conditions for constructing the initial grid-connected location population are set as follows: there is no fault in the grid-connected location circuit and the circuit energy storage information of the grid-connected location circuit is greater than the predicted photovoltaic power generation data;

[0055] S52. Based on the constructed initial grid-connected position population, set the genetic population size, number of iterations, and chromosome encoding;

[0056] Each population is set as a set of grid-connected location information data;

[0057] S53, randomly select from the constructed initial grid-connected position population The initial population is generated by the grid-connected location information data ;

[0058] S54, constructing a grid connection location decision model and using the constructed grid connection location decision model as a fitness function; and calculating the fitness of each individual in the population based on the fitness function;

[0059] S55. Select the best individuals from all individuals based on the roulette wheel method;

[0060] S56, based on the chromosome coding of the selected excellent individuals, cross the selected excellent individuals in a sequential crossover manner, and set the new population generated after the crossover to be ;

[0061] S57. Randomly select an individual in the population to mutate with a set probability, and set the population after the mutation to be ;

[0062] S58. Compare the fitness difference between the initial population and the population after genetic algorithm crossover mutation ;

[0063] when <0, indicating that the fitness of the mutated population is higher than that of the initial population, and the population is accepted. ≥0, indicating that the fitness of the mutated population is lower than that of the initial population, and the population is rejected;

[0064] S59, judging whether the maximum number of iterations has been reached according to the number of iterations of the algorithm, outputting the optimal solution if the maximum number of iterations has been reached, and continuing to execute step S55 if the maximum number of iterations has not been reached;

[0065] The optimal solution of the output is set to determine the grid connection location of the predicted photovoltaic power generation data.

[0066] Preferably, the step of constructing a grid connection location decision model and using the constructed grid connection location decision model as a fitness function; and calculating the fitness of each individual in the population based on the fitness function comprises the following steps:

[0067] The grid connection location decision model is as follows:

[0068] ;

[0069] in, represents the grid connection distance minimization function, Indicates the distance between the photovoltaic energy storage location and the grid-connected location, represents the grid-connected energy storage maximization function, Indicates the The circuit energy storage information of each grid-connected location, Energy storage information indicating the location of photovoltaic energy storage;

[0070] Assume that each individual represents a set of grid-connected location information data;

[0071] The formula for calculating the fitness of an individual is as follows:

[0072] ;

[0073] in, Represents population The fitness of the vth individual in .

[0074] Preferably, the distributed control management of the photovoltaic microgrid based on the determination of the grid-connected location of the predicted photovoltaic power generation data comprises the following steps:

[0075] After determining the grid-connected location of the predicted photovoltaic power generation data, the photovoltaic power generation data is summarized in real time, and the real-time summarized photovoltaic power generation data is connected to the circuit corresponding to the determined grid-connected location; after the connection is completed, the connected circuit is monitored in real time through the distributed control node.

[0076] The present invention also discloses a photovoltaic microgrid distributed control system based on grid protection, which is used to implement a photovoltaic microgrid distributed control method based on grid protection. The system includes: a data collection module, a data processing module, a photovoltaic prediction module, a fault analysis module, a photovoltaic grid-connected module, and a control management module;

[0077] The data collection module is used to collect circuit status information data and photovoltaic power generation data of each circuit in real time;

[0078] The data processing module is used to process the circuit status information data collected from each circuit;

[0079] The photovoltaic prediction module is used to analyze photovoltaic power generation data and make predictions;

[0080] The fault analysis module is used to perform fault analysis on the circuit based on the processed circuit status information data;

[0081] The photovoltaic grid-connected module is used to summarize and analyze the circuit status information data and the predicted photovoltaic power generation data, and determine the photovoltaic grid-connected position through intelligent calculation;

[0082] The control management module is used to perform distributed control management on the photovoltaic microgrid.

[0083] The beneficial effects of the present invention are:

[0084] (1) The present invention sets a microgrid protection area, sets distributed control nodes based on circuit distribution data within the microgrid protection area, and constructs a microgrid network topology. At the same time, based on the constructed microgrid network topology, the circuit status information data of each circuit is collected in real time, and the collected circuit status information data is processed by a data processing method. At the same time, the processed circuit status information data is analyzed by a data analysis method to obtain the analyzed circuit status information data and collect photovoltaic historical power generation data in real time. The photovoltaic power generation data is predicted by a prediction analysis method. Finally, the analyzed circuit status information data and the predicted photovoltaic power generation data are summarized, the grid-connected position of the predicted photovoltaic power generation data is calculated and determined, and distributed control management is performed, thereby improving the rationality of the distributed control of the photovoltaic microgrid.

[0085] (2) The present invention improves the efficiency of photovoltaic microgrid management by collecting circuit distribution data within the microgrid protection area in real time and setting the locations of distributed control nodes to construct and save the network topology.

[0086] (3) The present invention collects the working time of photovoltaic power generation equipment and the photovoltaic power generation per unit time, and predicts the photovoltaic power generation data by iteratively using the least squares fitting method, thereby improving the reliability of photovoltaic power generation grid connection processing.

[0087] (4) The present invention performs circuit fault analysis on the processed circuit status information data through the power grid protection algorithm, and simultaneously performs fault analysis on each proportional position of the circuit fault through the control variable method and data analysis method, and determines the circuit fault position according to the fault analysis results, thereby improving the accuracy of the circuit fault analysis.

[0088] (5) The present invention summarizes and analyzes the circuit status information data and the predicted photovoltaic power generation data, and determines the grid connection position of the predicted photovoltaic power generation data through intelligent calculation, thereby improving the stability of the photovoltaic microgrid grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0090] Figure 1 This is a flow chart of the photovoltaic microgrid distributed control method of the present invention. DETAILED DESCRIPTION

[0091] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0092] In a specific embodiment of the present invention,

[0093] Reference Figure 1 As shown, the present invention provides a photovoltaic microgrid distributed control method based on grid protection, comprising the following steps:

[0094] S1. Setting a microgrid protection area, setting distributed control nodes based on circuit distribution data within the microgrid protection area, and constructing a microgrid network topology according to the set distributed control node locations;

[0095] S2. collecting circuit status information data of each circuit in real time based on the constructed microgrid network topology, and processing the collected circuit status information data through a data processing method to obtain processed circuit status information data;

[0096] The circuit status information data includes: circuit energy storage information, circuit current information and circuit voltage information;

[0097] S3, collect historical photovoltaic power generation data in real time, and predict photovoltaic power generation data through predictive analysis methods;

[0098] S4. Analyze the processed circuit state information data using a data analysis method to obtain analyzed circuit state information data;

[0099] S41. Perform circuit fault analysis on the processed circuit status information data based on a power grid protection algorithm, and output a fault analysis result;

[0100] S42. Based on the fault analysis results, locate the circuit fault location using a control variable method and a data analysis method;

[0101] S43, summarizing the fault analysis results and circuit fault location data to obtain analyzed circuit status information data;

[0102] S5. Summarize and analyze the circuit status information data and the predicted photovoltaic power generation data, and determine the grid connection location of the predicted photovoltaic power generation data through intelligent calculation;

[0103] S6. Performing distributed control management on the photovoltaic microgrid based on the grid connection location determined by the predicted photovoltaic power generation data;

[0104] Further, refer to Figure 1 As shown, setting a microgrid protection area, setting a distributed control node based on circuit distribution data within the microgrid protection area, and constructing a microgrid network topology according to the set distributed control node positions includes the following steps:

[0105] Real-time collection of circuit distribution data within the microgrid protection area;

[0106] The circuit distribution data includes: circuit line length, circuit end point, and circuit starting point;

[0107] Further, a plurality of distributed control nodes are set based on the circuit length data in the circuit distribution data, the intervals between adjacent distributed control nodes are set to be equal, and the positions of the corresponding distributed control nodes are saved;

[0108] Furthermore, a microgrid network topology is constructed and saved based on the circuit distribution data and the corresponding distributed control node locations;

[0109] Set a set of four-tuples M = {A, B, C, D} to save the microgrid network topology, where M represents the constructed microgrid network topology, A represents the circuit number, B represents the circuit distribution data of the corresponding numbered circuit, C represents the distributed control node number, and D represents the location of the corresponding numbered distributed control node;

[0110] Further, refer to Figure 1 As shown, based on the constructed microgrid network topology, the circuit status information data of each circuit is collected in real time, and the collected circuit status information data is processed by a data processing method to obtain the processed circuit status information data, including the following steps:

[0111] Processing the collected circuit status information data through data standardization;

[0112] The data normalization formula is as follows:

[0113] ;

[0114] in, Represents the i-th group of circuit status information data, represents the mean value of the collected circuit status information data, represents the variance of the circuit state information data, represents the normalized circuit status information data of the i-th group;

[0115] Further, the normalized circuit state information data is rounded off by rounding down;

[0116] Setting the rounded circuit state information data as the processed circuit state information data;

[0117] Further, refer to Figure 1 As shown, real-time collection of historical photovoltaic power generation data and prediction of photovoltaic power generation data through a prediction analysis method include the following steps:

[0118] Collect sunlight intensity in real time and calculate the power generation of photovoltaic power generation equipment based on the sunlight intensity;

[0119] The formula for calculating the power generation of photovoltaic power generation equipment is as follows:

[0120] ;

[0121] in, Indicates the power generation of photovoltaic power generation equipment, the unit is kw·h, Indicates the total solar radiation of photovoltaic power generation equipment, unit kw·h / m 2 , Indicates the storage capacity of photovoltaic power generation equipment, the unit is kw, It represents the total solar radiation under standard conditions, with a constant of 1kw·h / m 2 , represents the comprehensive efficiency coefficient;

[0122] Collect the working time of photovoltaic power generation equipment and calculate the utilization rate of photovoltaic power generation equipment based on the working time of photovoltaic power generation ;

[0123] ;

[0124] Furthermore, the photovoltaic historical power generation data is the power generation of the photovoltaic power generation equipment multiplied by the utilization rate of the photovoltaic power generation equipment;

[0125] Furthermore, the most recent 30 sets of historical photovoltaic power generation data were aggregated to construct a data matrix, with one set representing one day;

[0126] The 30 most recent sets of photovoltaic historical power generation data were fitted iteratively using the least squares method, and photovoltaic power generation data were predicted based on the fitting results;

[0127] The least squares fitting formula is as follows:

[0128]

[0129] in, For historical photovoltaic power generation data The nonlinear least squares function of represents transpose, The matrix representing the historical photovoltaic power generation data at time t, Indicates historical power generation data The transposed error matrix, Indicates historical power generation data The error matrix of

[0130] Furthermore, the fitting results are summarized and the average value is calculated, and the calculated average value is set as the predicted photovoltaic power generation data;

[0131] Further, refer to Figure 1 As shown, performing circuit fault analysis on the processed circuit status information data based on the power grid protection algorithm and outputting the fault analysis results includes the following steps:

[0132] estimating voltage loss in the power grid based on circuit voltage information in the processed circuit status information data;

[0133] The voltage loss calculation formula is as follows:

[0134] ;

[0135] Among them, setting Represents two groups of adjacent distributed control nodes, Indicates the first The circuit voltage information of each distributed control node, Indicates the The circuit voltage information of each distributed control node, Indicates the A distributed control node and the The distance between distributed control nodes, Indicates the A distributed control node and the Voltage loss function between distributed control nodes;

[0136] Furthermore, circuit voltage information of all distributed control nodes in the power grid topology is estimated based on the voltage loss function, and circuit current information is estimated based on the estimated circuit voltage information;

[0137] Assuming the resistance R of the microgrid protection area is a fixed value, the current monitoring formula is as follows:

[0138] ;

[0139] in, Indicates estimated circuit current information;

[0140] Furthermore, the real-time collected circuit voltage information and circuit current information are compared with the estimated circuit voltage information and circuit current information;

[0141] A comparison threshold range is set. When the comparison result exceeds the set comparison threshold range, it indicates that the circuit segment corresponding to the distributed control node has a fault; otherwise, it is normal.

[0142] Further, refer to Figure 1 As shown, based on the fault analysis results, locating the circuit fault position by using the control variable method and data analysis method includes the following steps:

[0143] Select the positions of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the circuit between two adjacent distributed control nodes, and monitor and record the voltage and current fluctuations detected by the two adjacent distributed control nodes when the positions of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the circuit are damaged;

[0144] Furthermore, when a circuit fault is detected, the voltage and current fluctuations corresponding to the fault current are recorded, and the recorded voltage and current fluctuations are compared with the voltage and current fluctuations when each position is damaged, and the circuit fault position is located according to the comparison results;

[0145] Further, refer to Figure 1 As shown, the circuit status information data and predicted photovoltaic power generation data after analysis are summarized and determined by intelligent calculation to determine the grid connection position of the predicted photovoltaic power generation data, including the following steps:

[0146] S51, summarizing and analyzing the circuit status information data and the predicted photovoltaic power generation data to construct an initial grid-connected position population;

[0147] The conditions for constructing the initial grid-connected location population are set as follows: there is no fault in the grid-connected location circuit and the circuit energy storage information of the grid-connected location circuit is greater than the predicted photovoltaic power generation data;

[0148] S52. Based on the constructed initial grid-connected position population, set the genetic population size, number of iterations, and chromosome encoding;

[0149] Each population is set as a set of grid-connected location information data;

[0150] S53, randomly select from the constructed initial grid-connected position population The initial population is generated by the grid-connected location information data ;

[0151] S54, constructing a grid connection location decision model and using the constructed grid connection location decision model as a fitness function; and calculating the fitness of each individual in the population based on the fitness function;

[0152] The grid connection location decision model is as follows:

[0153] ;

[0154] in, represents the grid connection distance minimization function, Indicates the distance between the photovoltaic energy storage location and the grid-connected location, represents the grid-connected energy storage maximization function, Indicates the The circuit energy storage information of each grid-connected location, Energy storage information indicating the location of photovoltaic energy storage;

[0155] Assume that each individual represents a set of grid-connected location information data;

[0156] The formula for calculating the fitness of an individual is as follows:

[0157] ;

[0158] in, Represents population The fitness of the vth individual in ;

[0159] S55. Select the best individuals from all individuals based on the roulette wheel method;

[0160] The calculation formula for selecting the best individual among all individuals in the roulette wheel method is as follows:

[0161] ;

[0162] in, Represents population The probability that the vth individual is selected is, represents the population size;

[0163] S56, based on the chromosome coding of the selected excellent individuals, cross the selected excellent individuals in a sequential crossover manner, and set the new population generated after the crossover to be ;

[0164] S57. Randomly select an individual in the population to mutate with a set probability, and set the population after the mutation to be ;

[0165] S58. Compare the fitness difference between the initial population and the population after genetic algorithm crossover mutation ;

[0166] when <0, indicating that the fitness of the mutated population is higher than that of the initial population, and the population is accepted. ≥0, indicating that the fitness of the mutated population is lower than that of the initial population, and the population is rejected;

[0167] S59, judging whether the maximum number of iterations has been reached according to the number of iterations of the algorithm, outputting the optimal solution if the maximum number of iterations has been reached, and continuing to execute step S55 if the maximum number of iterations has not been reached;

[0168] The optimal solution of the output is set to determine the grid connection location of the predicted photovoltaic power generation data;

[0169] Further, refer to Figure 1 As shown, distributed control management of a photovoltaic microgrid based on the determination of a grid-connected location based on predicted photovoltaic power generation data includes the following steps:

[0170] After determining the grid-connected location of the predicted photovoltaic power generation data, the photovoltaic power generation data is summarized in real time and connected to the circuit corresponding to the determined grid-connected location. After the connection is completed, the connected circuit is monitored in real time through the distributed control node;

[0171] In a specific embodiment, the photovoltaic microgrid distributed control system based on grid protection is used to implement a photovoltaic microgrid distributed control method based on grid protection, and the system includes: a data collection module, a data processing module, a photovoltaic prediction module, a fault analysis module, a photovoltaic grid-connected module, and a control management module;

[0172] The data collection module is used to collect circuit status information data and photovoltaic power generation data of each circuit in real time;

[0173] The data processing module is used to process the circuit status information data collected from each circuit;

[0174] The photovoltaic prediction module is used to analyze photovoltaic power generation data and make predictions;

[0175] The fault analysis module is used to perform fault analysis on the circuit based on the processed circuit status information data;

[0176] The photovoltaic grid-connected module is used to summarize and analyze the circuit status information data and the predicted photovoltaic power generation data, and determine the photovoltaic grid-connected position through intelligent calculation;

[0177] The control management module is used to perform distributed control management on the photovoltaic microgrid.

[0178] It should be noted that

[0179] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A photovoltaic microgrid distributed control method based on grid protection, characterized in that: The following steps are involved: S1. Setting a microgrid protection area, setting distributed control nodes based on circuit distribution data within the microgrid protection area, and constructing a microgrid network topology according to the set distributed control node locations; S2. collecting circuit status information data of each circuit in real time based on the constructed microgrid network topology, and processing the collected circuit status information data through a data processing method to obtain processed circuit status information data; The circuit status information data includes: circuit energy storage information, circuit current information and circuit voltage information; S3, collect historical photovoltaic power generation data in real time, and predict photovoltaic power generation data through predictive analysis methods; S4. Analyze the processed circuit state information data using a data analysis method to obtain analyzed circuit state information data; S41. Perform circuit fault analysis on the processed circuit status information data based on a power grid protection algorithm, and output a fault analysis result; S42. Based on the fault analysis results, locate the circuit fault location using a control variable method and a data analysis method; S43, summarizing the fault analysis results and circuit fault location data to obtain analyzed circuit status information data; S5. Summarize and analyze the circuit status information data and the predicted photovoltaic power generation data, and determine the grid connection location of the predicted photovoltaic power generation data through intelligent calculation; S6. Perform distributed control management on the photovoltaic microgrid based on the determined grid-connected location of the predicted photovoltaic power generation data.

2. A photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The step of setting a microgrid protection area, setting a distributed control node based on circuit distribution data within the microgrid protection area, and constructing a microgrid network topology according to the set distributed control node positions includes the following steps: Real-time collection of circuit distribution data within the microgrid protection area; The circuit distribution data includes: circuit line length, circuit end point, and circuit starting point; Setting a plurality of distributed control nodes based on circuit line length data in the circuit distribution data, setting intervals between adjacent distributed control nodes to be equal, and saving corresponding distributed control node positions; Build and save the microgrid network topology based on circuit distribution data and corresponding distributed control node locations; A set of four-tuples M = {A, B, C, D} is set to save the microgrid network topology, where M represents the constructed microgrid network topology, A represents the circuit number, B represents the circuit distribution data of the corresponding numbered circuit, C represents the distributed control node number, and D represents the location of the corresponding numbered distributed control node.

3. A photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The method of collecting circuit status information data of each circuit in real time based on the constructed microgrid network topology and processing the collected circuit status information data by a data processing method to obtain the processed circuit status information data includes the following steps: Processing the collected circuit status information data through data standardization; The data normalization formula is as follows: ； in, Represents the i-th group of circuit status information data, represents the mean value of the collected circuit status information data, represents the variance of the circuit state information data, represents the normalized circuit status information data of the i-th group; Rounding the normalized circuit state information data by rounding down; The rounded circuit state information data is set as the processed circuit state information data.

4. A photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The real-time collection of historical photovoltaic power generation data and the prediction of photovoltaic power generation data by a prediction analysis method include the following steps: Collect sunlight intensity in real time and calculate the power generation of photovoltaic power generation equipment based on the sunlight intensity; The formula for calculating the power generation of photovoltaic power generation equipment is as follows: ； in, Indicates the power generation of photovoltaic power generation equipment, the unit is kw·h, Indicates the total solar radiation of photovoltaic power generation equipment, unit kw·h / m 2 , Indicates the storage capacity of photovoltaic power generation equipment, the unit is kw, It represents the total solar radiation under standard conditions, with a constant of 1kw·h / m 2 , represents the comprehensive efficiency coefficient; Collect the working time of photovoltaic power generation equipment and calculate the utilization rate of photovoltaic power generation equipment based on the working time of photovoltaic power generation ; ； The historical photovoltaic power generation data is the power generation of the photovoltaic power generation equipment multiplied by the utilization rate of the photovoltaic power generation equipment; Summarize the most recent 30 sets of photovoltaic historical power generation data to construct a data matrix, with one set representing one day; The 30 most recent sets of photovoltaic historical power generation data were fitted iteratively using the least squares method, and photovoltaic power generation data were predicted based on the fitting results; Summarize the fitting results and calculate the average value, and set the calculated average value as the predicted photovoltaic power generation data.

5. The photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The method of performing circuit fault analysis on the processed circuit status information data based on the power grid protection algorithm and outputting the fault analysis result includes the following steps: estimating voltage loss in the power grid based on circuit voltage information in the processed circuit status information data; The voltage loss calculation formula is as follows: ； Among them, setting Represents two groups of adjacent distributed control nodes, Indicates the first The circuit voltage information of each distributed control node, Indicates the The circuit voltage information of each distributed control node, Indicates the A distributed control node and the The distance between distributed control nodes, Indicates the A distributed control node and Voltage loss function between distributed control nodes; estimating circuit voltage information of all distributed control nodes in the power grid topology based on the voltage loss function, and estimating circuit current information based on the estimated circuit voltage information; Comparing the real-time collected circuit voltage information and circuit current information with the estimated circuit voltage information and circuit current information; A comparison threshold range is set. When the comparison result exceeds the set comparison threshold range, it indicates that the circuit segment corresponding to the distributed control node has a fault; otherwise, it is normal.

6. A photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The method of locating the circuit fault position based on the fault analysis result by using the control variable method and the data analysis method includes the following steps: Select the positions of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the circuit between two adjacent distributed control nodes, and monitor and record the voltage and current fluctuations detected by the two adjacent distributed control nodes when the positions of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the circuit are damaged; When a circuit fault is detected, the voltage and current fluctuations corresponding to the fault current are recorded, and the recorded voltage and current fluctuations are compared with the voltage and current fluctuations when each location is damaged. The circuit fault location is located based on the comparison results.

7. A photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The circuit status information data and predicted photovoltaic power generation data after aggregation and analysis, and determining the grid connection position of the predicted photovoltaic power generation data by intelligent calculation include the following steps: S51, summarizing and analyzing the circuit status information data and the predicted photovoltaic power generation data to construct an initial grid-connected position population; The conditions for constructing the initial grid-connected location population are set as follows: there is no fault in the grid-connected location circuit and the circuit energy storage information of the grid-connected location circuit is greater than the predicted photovoltaic power generation data; S52. Based on the constructed initial grid-connected position population, set the genetic population size, number of iterations, and chromosome encoding; Each population is set as a set of grid-connected location information data; S53, randomly select from the constructed initial grid-connected position population The initial population is generated by the grid-connected location information data ; S54, constructing a grid connection location decision model and using the constructed grid connection location decision model as a fitness function; and calculating the fitness of each individual in the population based on the fitness function; S55. Select the best individuals from all individuals based on the roulette wheel method; S56, based on the chromosome coding of the selected excellent individuals, cross the selected excellent individuals in a sequential crossover manner, and set the new population generated after the crossover to be ; S57. Randomly select an individual in the population to mutate with a set probability, and set the population after the mutation to be ; S58. Compare the fitness difference between the initial population and the population after genetic algorithm crossover mutation ; when <0, indicating that the fitness of the mutated population is higher than that of the initial population, and the population is accepted. ≥0, indicating that the fitness of the mutated population is lower than that of the initial population, and the population is rejected; S59, judging whether the maximum number of iterations has been reached according to the number of iterations of the algorithm, outputting the optimal solution if the maximum number of iterations has been reached, and continuing to execute step S55 if the maximum number of iterations has not been reached; The optimal solution of the output is set to determine the grid connection location of the predicted photovoltaic power generation data.

8. A photovoltaic microgrid distributed control method based on grid protection according to claim 7, characterized in that: The method of constructing a grid connection location decision model and using the constructed grid connection location decision model as a fitness function; and calculating the fitness of each individual in the population based on the fitness function includes the following steps: The grid connection location decision model is as follows: ； in, represents the grid connection distance minimization function, Indicates the distance between the photovoltaic energy storage location and the grid-connected location, represents the grid-connected energy storage maximization function, Indicates the The circuit energy storage information of each grid-connected location, Energy storage information indicating the location of photovoltaic energy storage; Assume that each individual represents a set of grid-connected location information data; The formula for calculating individual fitness is as follows: ； in, Represents population The fitness of the vth individual in .

9. The photovoltaic microgrid distributed control method based on grid protection according to claim 1, characterized in that: The distributed control management of the photovoltaic microgrid based on the determination of the grid-connected location of the predicted photovoltaic power generation data includes the following steps: After determining the grid-connected location of the predicted photovoltaic power generation data, the photovoltaic power generation data is summarized in real time, and the real-time summarized photovoltaic power generation data is connected to the circuit corresponding to the determined grid-connected location; after the connection is completed, the connected circuit is monitored in real time through the distributed control node.

10. A system for implementing the photovoltaic microgrid distributed control method based on grid protection according to any one of claims 1 to 9, characterized in that: include: Data collection module, data processing module, photovoltaic prediction module, fault analysis module, photovoltaic grid connection module and control management module; The data collection module is used to collect circuit status information data and photovoltaic power generation data of each circuit in real time; The data processing module is used to process the circuit status information data collected from each circuit; The photovoltaic prediction module is used to analyze photovoltaic power generation data and make predictions; The fault analysis module is used to perform fault analysis on the circuit based on the processed circuit status information data; The photovoltaic grid-connected module is used to summarize and analyze the circuit status information data and the predicted photovoltaic power generation data, and determine the photovoltaic grid-connected position through intelligent calculation; The control management module is used to perform distributed control management on the photovoltaic microgrid.

Images