A distributed photovoltaic regulation system and method

Abstract

The application relates to a distributed photovoltaic regulation system and method, which comprises a distributed photovoltaic monitoring module for monitoring the running state and data of photovoltaic equipment; a distributed photovoltaic prediction module for realizing short-term and medium-term distributed new energy power generation prediction of a feeder and a transformer area; a distributed photovoltaic group regulation and control module with a regional photovoltaic control auxiliary peak shaving function; a photovoltaic carrying capacity evaluation module for evaluating the carrying capacity of a power grid to photovoltaic power generation access; a voltage quality analysis module for real-time monitoring of the voltage level of each node of the power grid; and a protection automatic analysis function module for quickly and accurately detecting fault conditions in the power grid and automatically calculating the setting value of a protection device. The application can accurately predict distributed photovoltaic and evaluate the carrying capacity of distributed photovoltaic.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of safe operation of power grids, and particularly relates to a distributed photovoltaic regulation system and method. BACKGROUND

[0002] With the wide construction of high-proportion renewable distributed photovoltaics and the proposal of "carbon peak" and "carbon neutral", new energy will become an important part of the electricity market, and it is urgent to build a new power system with new energy as the main body. Distributed photovoltaic power generation has the characteristics of cleanliness, low carbon, safety and high efficiency, and can be widely used in industrial parks, residential buildings and the like to solve the local user's electricity demand. However, distributed photovoltaic installation is scattered, and manual operation and maintenance is difficult, so it is necessary to regulate distributed photovoltaics.

[0003] The existing distributed photovoltaic regulation method can monitor distributed photovoltaics, predict distributed photovoltaics, regulate and control distributed photovoltaic groups, evaluate photovoltaic carrying capacity, analyze voltage quality, and protect automatic analysis, so as to realize the "observable, measurable, adjustable and controllable" goal of distributed photovoltaics, improve power production efficiency, and ensure safe and stable operation of power grids.

[0004] However, the existing distributed photovoltaic regulation method still has the defects and deficiencies that the accuracy of distributed photovoltaic prediction is not high.

[0005] After searching, no existing technical disclosure literature identical or similar to the present application has been found. SUMMARY

[0006] The present application aims to overcome the deficiencies in the prior art and proposes a distributed photovoltaic regulation system and method that can solve the technical problems of insufficient accuracy of distributed photovoltaic prediction and insufficient accuracy of distributed photovoltaic carrying capacity evaluation.

[0007] The present application solves the practical problems by adopting the following technical solutions:

[0008] A distributed photovoltaic regulation system comprises:

[0009] A distributed photovoltaic monitoring module is configured to monitor the operating state and data of photovoltaic equipment.

[0010] A distributed photovoltaic prediction module is configured to establish a distributed new energy power generation prediction model based on distributed new energy historical power generation, grid meteorology, date type and other data, and to realize short-term and medium-term distributed new energy power generation prediction of feeders and transformer areas.

[0011] The distributed photovoltaic group control module has regional photovoltaic control auxiliary peak-shaving function, realizes the generation of regional photovoltaic control strategy for setting target peak-shaving power, has the ability to set peak-shaving targets by day-ahead and time-period to support planned peak-shaving, automatically analyzes the de-grid / grid-connected photovoltaic sequence according to the set target value and regional analysis rules, and supports the batch restoration of de-grid / grid-connected photovoltaics to the grid;

[0012] The photovoltaic carrying capacity assessment module is used to assess the solar energy resources of a region, including sunshine hours and solar radiation, to determine the region's solar energy potential; and to analyze the existing structure and operating status of the power grid to assess the grid's carrying capacity for photovoltaic power generation.

[0013] The voltage quality analysis module monitors the voltage levels at each node of the power grid in real time to ensure they are within the specified range; analyzes voltage fluctuations and identifies the causes of voltage fluctuations; detects the harmonic content in the power grid and assesses the impact of harmonics on the power grid voltage quality.

[0014] The automatic analysis module is used to quickly and accurately detect faults in the power grid, automatically calculate the settings of protection devices to ensure correct operation of protection devices, analyze the operating logic of protection devices to ensure consistency with power grid protection strategies, and assess the scope and extent of the impact of faults on the power grid.

[0015] A distributed photovoltaic control method includes the following steps:

[0016] Step 1: Calculate and compile statistics on distributed photovoltaic monitoring information;

[0017] Step 2: Based on the distributed photovoltaic monitoring information obtained in Step 1, make predictions about distributed photovoltaic power.

[0018] Step 3: Configure the distributed photovoltaic group control function;

[0019] Step 5: Analyze the voltage quality;

[0020] Step 4: Assess the photovoltaic load-bearing capacity;

[0021] Step 6: Perform protection analysis on the 10kV line.

[0022] Furthermore, the specific steps of step 1 include:

[0023] (1) Based on the zoning-substation-busbar-line system, realize multi-level hierarchical aggregation of photovoltaic power across the entire range;

[0024] (2) Compile a comprehensive ledger of distributed photovoltaic power generation and output information:

[0025] (3) Real-time calculation of the actual load curve according to the dispatching criteria;

[0026] (4) Collect the distributed photovoltaic power generation calendar, including: monthly total power generation, total installed capacity, daily power generation interval information;

[0027] (5) Calculate and display multi-dimensional indicators, including: maximum power generation simultaneous rate indicator, utilization hour number indicator, photovoltaic maximum output in load ratio indicator, photovoltaic power generation, photovoltaic maximum power information.

[0028] Moreover, the specific steps of step 2 include:

[0029] (1) Divide the photovoltaic cluster grid to obtain a plurality of photovoltaic clusters;

[0030] Considering the consistency of meteorological conditions within a certain area, the 95 counties, cities and districts in the administrative region are divided into more than 200 photovoltaic clusters according to the administrative region and combined with the provincial distribution grid division method;

[0031] (2) Select a value reference station in the plurality of photovoltaic clusters;

[0032] Each photovoltaic cluster selects 1-4 photovoltaic reference stations, and more than 400 centralized and non-centralized distributed photovoltaic power stations are selected in the administrative region.

[0033] (3) Collect the reference station photovoltaic output historical data and low-voltage distributed photovoltaic output historical data, and calculate the reference station proportion coefficient;

[0034] (4) Based on the artificial intelligence algorithm, the correlation characteristics between the reference station photovoltaic output historical data and the low-voltage distributed photovoltaic output historical data are extracted, and a K value model is obtained;

[0035] (5) Calculate the real-time value of the low-voltage distributed photovoltaic output of each region;

[0036] (6) Predict the output of the main station and the reference station;

[0037] (7) Calculate the low-voltage distributed photovoltaic power prediction value of each region.

[0038] Moreover, the specific method of step 2 (6) is:

[0039] The output prediction of the main station is obtained by using the artificial intelligence power prediction algorithm of all reference stations, combined with numerical weather prediction data, to predict the power output curve of the reference station, and the main station output prediction is composed of the power prediction of the reference station;

[0040] The output prediction of the reference station is obtained by the reference station power prediction system to the main station;

[0041] Based on the neural network algorithm training, the meta-algorithm of the reference station prediction output and the main station prediction output model is combined to improve the accuracy of the final prediction value of the integrated model output.

[0042]

[0043] h i represents the output of neuron i, X represents the input vector, X i represents the input vector corresponding to neuron i, i represents the neuron;

[0044] Moreover, the specific method of step 3 is:

[0045] For 10kV and above photovoltaic, with the ability to accept power regulation center control adjustment instructions, distributed power supply should have group control and group regulation function; for 380V photovoltaic, reserve uploading and control of grid connection point switch state and adjustment of power generation power, with adjustable and controllable ability.

[0046] Moreover, the specific steps of step 4 include:

[0047] (1) Evaluate existing photovoltaic and planned access photovoltaic:

[0048] Calculate 6 types of bearing capacity indexes such as voltage deviation, current out-of-limit, line loss, distribution transformer overcapacity, short-circuit current out-of-limit and reverse load rate, and visualize the weak links of distribution network;

[0049] (2) Evaluate the maximum bearing capacity of distribution network photovoltaic:

[0050] Maximum access at line head, maximum access in line, maximum access at line end, maximum bearing capacity of line, maximum bearing capacity of low-voltage side of line.

[0051] Moreover, the specific method of step 5 is to analyze the voltage quality, including: voltage deviation, voltage out-of-limit.

[0052] Moreover, the specific steps of step 6 include:

[0053] (1) 10kV line protection analysis:

[0054] When the line access distributed photovoltaic capacity exceeds 80% of the rated capacity of the line, the overload protection of the substation outgoing switch can increase the direction criterion to prevent reverse misoperation; when the length of overhead line exceeds 5km, according to the system impedance, line parameters and photovoltaic access capacity, the sensitivity of overload protection is checked; when the sensitivity is insufficient, it is solved by hierarchical protection.

[0055] (2) 10kV feeder automation influence analysis:

[0056] When the downstream photovoltaic access capacity of the terminal exceeds 67% of the rated capacity of the line, adjust the overcurrent alarm setting value of the distribution terminal; for the problem of sensitivity reduction caused by external drainage, it is solved by hierarchical protection of distribution line.

[0057] (10) 10kV line reclosing analysis:

[0058] If it is a system side, the photovoltaic access capacity is greater than the minimum load of the line in the normal operation mode, the reclosing time of the outgoing switch is adjusted, and the distributed photovoltaic anti-islanding protection time is matched to ensure that the outgoing switch is reclosed after the distributed photovoltaic off-grid; if it is a user side, the acceptance of the user distributed photovoltaic anti-islanding protection function is strengthened.

[0059] Advantages and beneficial effects of the present application:

[0060] 1. The present application proposes a distributed photovoltaic regulation method, which accurately predicts the output of distributed photovoltaic through neural network algorithm, and accurately evaluates the carrying capacity of distributed photovoltaic by combining the photovoltaic output situation.

[0061] 2. The present application proposes a distributed photovoltaic regulation method, which can optimize the operation mode and configuration through real-time monitoring, data analysis and dispatching of distributed photovoltaic power station, improve the efficiency of power production, promote the development of renewable energy, reduce the dependence on fossil fuels, and reduce environmental pollution. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 The processing flowchart of step 2 of the present application. DETAILED DESCRIPTION

[0063] The embodiments of the present application are further described below in combination with the drawings:

[0064] A distributed photovoltaic regulation system comprises:

[0065] A distributed photovoltaic monitoring module is used to monitor the running state and data of photovoltaic equipment, such as current, voltage, power, power generation, power generation duration, etc.

[0066] A distributed photovoltaic prediction module is used to establish a distributed new energy power generation prediction model based on distributed new energy historical power generation, grid weather, date type and other data, to realize short-term (future 3 days) and medium-term (future 10 days) distributed new energy power generation prediction of feeder and transformer area.

[0067] A distributed photovoltaic group regulation and control module is used to have regional photovoltaic control auxiliary peak shaving function, realize regional photovoltaic control strategy generation for setting target peak shaving power, support plan peak shaving with day-ahead time period setting peak shaving target, automatically analyze off-grid / parallel photovoltaic sequence according to setting target value and regional level analysis rule, and support batch recovery of off-grid / parallel photovoltaic.

[0068] A photovoltaic carrying capacity evaluation module evaluates the solar energy resources of the region, including sunshine hours, solar radiation, etc., to determine the solar potential of the region; analyzes the existing structure and operating state of the power grid, and evaluates the carrying capacity of the power grid for the access of photovoltaic power generation;

[0069] A voltage quality analysis module monitors the voltage level of each node of the power grid in real time to ensure that it is within the specified range; analyzes the voltage fluctuation situation and identifies the causes of voltage fluctuation, such as load changes, power fluctuations, etc.; detects the harmonic content in the power grid and evaluates the impact of harmonics on the voltage quality of the power grid;

[0070] A protection automatic analysis function module is used to quickly and accurately detect fault conditions in the power grid, automatically calculate the setting values of protection devices, ensure correct action of protection devices, analyze the action logic of protection devices to ensure consistency with the power grid protection strategy, and evaluate the range and degree of the impact of faults on the power grid.

[0071] A distributed photovoltaic regulation method, comprising the following steps:

[0072] Step 1, calculate and count distributed photovoltaic monitoring information;

[0073] The specific steps of step 1 include:

[0074] (1) According to the zoning-substation-bus-line, realize multi-level hierarchical convergence of full-caliber photovoltaic;

[0075] Among them, the substation-level distributed photovoltaic information convergence includes: substation internal basic information, internal bus list and substation detailed list;

[0076] Among them, the substation internal basic information includes: substation name, voltage level, dispatching agency, belonging to the power grid, commissioning date, state, number of main transformers, number of bus bars, etc.

[0077] (2) Count the full-caliber distributed photovoltaic account and output information:

[0078] The full-caliber distributed photovoltaic account and output information includes: maximum simultaneous rate, utilization hours, etc.

[0079] (3) Real-time calculation of dispatching caliber real load curve;

[0080] (4) Collect the distributed photovoltaic generation calendar, including: monthly total generation, total installed capacity, daily generation interval (6000 or less, 6000-12000, 12000 or more) and other information;

[0081] (5) Calculate and display multi-dimensional indicators, including: maximum simultaneous generation rate indicator, utilization hours indicator, photovoltaic maximum output in load ratio indicator, photovoltaic power generation, photovoltaic maximum power, etc.

[0082] Step 2, predicting the distributed photovoltaic based on the distributed photovoltaic monitoring information obtained in step 1;

[0083] The specific steps of the step 2 include:

[0084] (1) dividing photovoltaic cluster grids to obtain a plurality of photovoltaic clusters;

[0085] Considering the consistency of meteorological conditions in a certain area, the 95 counties (cities, districts) in the administrative region are divided into more than 200 photovoltaic clusters in the manner of combining the provincial distribution grid division according to the administrative region.

[0086] (2) selecting a value reference station in the plurality of photovoltaic clusters;

[0087] 1-4 photovoltaic reference stations are selected for each photovoltaic cluster, and more than 400 centralized distributed photovoltaic power stations are selected in the administrative region.

[0088] (3) collecting photovoltaic output historical data of the reference station and low-voltage distributed photovoltaic output historical data, and calculating a reference station proportionality coefficient;

[0089] The photovoltaic output historical data of all reference stations are obtained from dispatching, and the low-voltage distributed photovoltaic output historical data are obtained from marketing;

[0090] The reference station proportionality coefficient is calculated, which is the proportion of the photovoltaic data of the reference station to the total photovoltaic quantity;

[0091] (4) extracting the correlation characteristics between the photovoltaic output historical data of the reference station and the low-voltage distributed photovoltaic output historical data based on an artificial intelligence algorithm to obtain a K value model;

[0092] The K value model refers to a parameter in the K-Nearest Neighbors (KNN) algorithm for determining the number of neighboring samples considered when evaluating new samples.

[0093] In the embodiment, the artificial intelligence algorithm is adopted, the influence of the correlation characteristics, temperature and irradiance is considered, and optimization is performed in the day-ahead and intra-day dimensions.

[0094] (5) calculating the real-time value of the low-voltage distributed photovoltaic output of each region;

[0095] The real-time value of the low-voltage distributed photovoltaic output of each region is calculated by using the reference station proportionality coefficient, combining the real-time photovoltaic output of the reference station directly collected in the regulation and control cloud, the real-time irradiance and the real-time temperature.

[0096] (6) predicting the output of the master station and the reference station;

[0097] The specific method of step 2 step (6) is:

[0098] The output prediction of the master station is predicted by all reference stations using an artificial intelligence power prediction algorithm combined with numerical weather prediction data to predict the power output curve of the reference station, and the output prediction of the master station is combined from the power prediction of the reference station;

[0099] The output prediction of the reference station is sent to the master station by the reference station power prediction system;

[0100] Based on the neural network algorithm training, the meta-algorithm of the reference station prediction output and the master station prediction output model is combined to improve the accuracy of the final prediction value of the integrated model output;

[0101]

[0102] h i The output of neuron i is represented by X, the input vector is represented by X i The input vector corresponding to neuron i is represented by i, which represents the neuron;

[0103] (7) Calculate the low-voltage distributed photovoltaic power prediction value of each region;

[0104] The low-voltage distributed photovoltaic power prediction value of each region is calculated.

[0105] Step 3, set the distributed photovoltaic group control function;

[0106] The specific method of step 3 is:

[0107] For 10kV and above photovoltaic, it has the ability to accept power regulation center control adjustment instructions, and the distributed power source should have group control and group regulation functions; for 380V photovoltaic, it is reserved to upload and control the switch state of the grid connection point and adjust the power generation power, and has the ability to adjust and control.

[0108] Step 4, evaluate the photovoltaic carrying capacity;

[0109] The specific method of step 4 is:

[0110] Based on PMS, use sampling, D5000 and other systems to obtain the actual topology and operation data of the distribution network, and develop photovoltaic carrying capacity index evaluation to measure the maximum accessible capacity of photovoltaic and visualize the weak links of the distribution network.

[0111] The photovoltaic carrying capacity index evaluation includes:

[0112] (1) Evaluate the existing photovoltaic and planned access photovoltaic:

[0113] The voltage deviation, current overrun, line loss, distribution transformer overcapacity, short-circuit current overrun, reverse load rate and other 6 types of bearing capacity indexes are calculated, and the weak link of distribution network is visualized and positioned.

[0114] (2) Assessing the maximum bearing capacity of the distribution network photovoltaic:

[0115] The maximum access of the line head, the maximum access of the line middle, the maximum access of the line end, the maximum bearing capacity of the line, and the maximum bearing capacity of the low-voltage side of the line.

[0116] Step 5, voltage quality analysis;

[0117] The specific method of the step 5 is:

[0118] The voltage quality is analyzed, including: voltage deviation, voltage overrun and the like.

[0119] Step 6, protection analysis of the 10kV line;

[0120] The specific steps of the step 6 include:

[0121] (1) 10kV line protection analysis:

[0122] When the line access distributed photovoltaic capacity exceeds 80% of the rated capacity of the line, the overload protection of the substation outgoing line switch can increase the direction criterion to prevent reverse misoperation; when the length of the overhead line exceeds 5km, the overload protection sensitivity is checked according to the system impedance, line parameters and photovoltaic access capacity; when the sensitivity is insufficient, it can be solved by hierarchical protection.

[0123] (2) 10kV feeder automation influence analysis:

[0124] When the downstream photovoltaic access capacity of the terminal exceeds 67% of the rated capacity of the line, adjust the overcurrent alarm setting value of the distribution terminal; for the problem of sensitivity reduction caused by external pumping, the hierarchical protection of the distribution line can be used to solve.

[0125] (10) 10kV line reclosing analysis:

[0126] If it is the system side, the photovoltaic access capacity is greater than the minimum load of the line in the normal operation mode, the reclosing time of the outgoing line switch (>2s) is adjusted, which is matched with the distributed photovoltaic anti-islanding protection time to ensure that the outgoing line switch recloses after the distributed photovoltaic is off the network; if it is the user side, the acceptance of the user distributed photovoltaic anti-islanding protection function is strengthened.

[0127] It should be emphasized that the embodiments described in the present application are illustrative rather than limiting, and therefore the present application includes but is not limited to the embodiments described in the specific embodiments, and any other embodiments derived by those skilled in the art from the technical solutions of the present application also belong to the scope of protection of the present application.

Claims

1. A control method based on a distributed photovoltaic control system, characterized in that: The system includes: Distributed photovoltaic monitoring module, used to monitor the operating status and data of photovoltaic equipment; The distributed photovoltaic forecasting module is used to establish a distributed renewable energy power forecasting model based on historical power generation of distributed renewable energy sources, gridded meteorological data, and date-type data, so as to realize short-term and medium-term distributed renewable energy power generation forecasting for feeders and transformer areas. The distributed photovoltaic group control module has regional photovoltaic control auxiliary peak-shaving function, realizes the generation of regional photovoltaic control strategy for setting target peak-shaving power, has the ability to set peak-shaving targets by day-ahead and time-period to support planned peak-shaving, automatically analyzes the de-grid / grid-connected photovoltaic sequence according to the set target value and regional analysis rules, and supports the batch restoration of de-grid / grid-connected photovoltaics to the grid; The photovoltaic carrying capacity assessment module is used to assess the solar energy resources of a region, including sunshine hours and solar radiation, to determine the region's solar energy potential; and to analyze the existing structure and operating status of the power grid to assess the grid's carrying capacity for photovoltaic power generation. The voltage quality analysis module monitors the voltage levels at each node of the power grid in real time to ensure they are within the specified range; analyzes voltage fluctuations and identifies the causes of voltage fluctuations; detects the harmonic content in the power grid and assesses the impact of harmonics on the power grid voltage quality. The automatic analysis module is used to quickly and accurately detect faults in the power grid, automatically calculate the settings of protection devices to ensure the correct operation of protection devices, analyze the operation logic of protection devices to ensure consistency with power grid protection strategies, and assess the scope and extent of the impact of faults on the power grid. The control method includes the following steps: Step 1: Calculate and compile statistics on distributed photovoltaic monitoring information; Step 2: Based on the distributed photovoltaic monitoring information obtained in Step 1, predict the distributed photovoltaic power generation. Step 3: Configure the distributed photovoltaic group control function; Step 4: Assess the photovoltaic load-bearing capacity; Step 5: Analyze the voltage quality; Step 6: Perform protection analysis on the 10kV line; The specific steps of step 2 include: (1) Divide the photovoltaic cluster grid to obtain multiple photovoltaic clusters; Considering the consistency of meteorological conditions within a certain area, the 95 counties, cities, and districts in the administrative region are divided into more than 200 photovoltaic clusters according to administrative regions and in combination with the provincial power grid grid division method; (2) Select a valuable reference station from multiple photovoltaic clusters; Each photovoltaic cluster selected 1-4 photovoltaic benchmark stations; (3) Collect historical data of photovoltaic power output of the reference station and historical data of low-voltage distributed photovoltaic power output, and calculate the proportional coefficient of the reference station; (4) Based on artificial intelligence algorithms, the correlation features between historical photovoltaic power output data of the benchmark station and historical photovoltaic power output data of low-voltage distributed photovoltaic power generation are extracted to obtain the K-value model; (5) Calculate the real-time output of low-voltage distributed photovoltaic power in each region; (6) Predict the power output of the main station and the base station; (7) Calculate the predicted low-voltage distributed photovoltaic power in each region; The specific method for step (6) of step 2 is as follows: All reference stations employ artificial intelligence power prediction algorithms, combined with numerical weather forecast data, to predict the power output curves of the reference stations. The power output prediction of the main station is a combination of the power predictions from the reference stations.

2. The control method based on a distributed photovoltaic control system according to claim 1, characterized in that: The specific steps of step 1 include: (1) Based on the zoning-substation-busbar-line system, realize multi-level hierarchical aggregation of photovoltaic power across the entire range; (2) Compile a comprehensive ledger of distributed photovoltaic power generation and output information: (3) Real-time calculation of the actual load curve according to the dispatching criteria; (4) Collect the power generation calendar of distributed photovoltaics, including: monthly total power generation, total installed capacity, and daily power generation interval information; (5) Calculate and display multi-dimensional indicators, including: maximum power generation simultaneity rate, utilization hours, the proportion of photovoltaic maximum output in the load, photovoltaic power generation, and photovoltaic maximum power.

3. The control method based on a distributed photovoltaic control system according to claim 1, characterized in that: The specific method for step 3 is as follows: For 10kV and above photovoltaic systems, they should have the ability to receive control and regulation commands from the power dispatching center, and distributed power sources should have group control and regulation functions; for 380V photovoltaic systems, they should reserve the ability to upload and control the grid connection point switch status and regulate power generation, and have adjustable and controllable capabilities.

4. The control method based on a distributed photovoltaic control system according to claim 1, characterized in that: The specific steps of step 4 include: (1) Assess existing photovoltaic and planned photovoltaic grid connection: Calculate six types of load-bearing capacity indicators, including voltage deviation, current exceeding limits, line loss, transformer overcapacity, short-circuit current exceeding limits, and reverse load rate, and visualize and locate weak links in the distribution network. (2) Assess the maximum carrying capacity of the photovoltaic distribution network: Maximum access at the beginning of the line, maximum access in the middle of the line, maximum access at the end of the line, and maximum carrying capacity of the line.

5. The control method based on a distributed photovoltaic control system according to claim 1, characterized in that: The specific method of step 5 is as follows: analyze the voltage quality, including voltage deviation and voltage exceeding limits.

6. The control method based on a distributed photovoltaic control system according to claim 1, characterized in that: The specific steps of step 6 include: (1) 10kV line protection analysis: When the capacity of distributed photovoltaic power connected to a line exceeds 80% of the line's rated capacity, the overload protection of the substation's outgoing line switch can be augmented with a directional criterion to prevent reverse maloperation. When the length of the overhead line exceeds 5km, the sensitivity of the overload protection should be checked based on the system impedance, line parameters, and photovoltaic capacity. If the sensitivity is insufficient, it can be addressed through tiered protection. (2) Impact analysis of 10kV feeder automation: When the downstream photovoltaic access capacity of the terminal exceeds 67% of the line's rated capacity, the overcurrent alarm setting of the distribution terminal is adjusted; to address the issue of decreased sensitivity caused by external water intake, the problem is solved through graded protection of the distribution line. (3) Analysis of reclosing on 10kV lines: If it is on the system side, when the photovoltaic access capacity is greater than the minimum load of the line under normal operation, adjust the reclosing time of the outgoing switch to coordinate with the anti-islanding protection time of the distributed photovoltaic system to ensure that the outgoing switch recloses after the distributed photovoltaic system is disconnected from the grid; if it is on the user side, strengthen the acceptance of the user's distributed photovoltaic anti-islanding protection function.

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