Power distribution network line state analysis method and system

Abstract

The invention discloses a power distribution network line state analysis method and system, and relates to the technical field of power distribution network analysis, and the method comprises the following steps: S1, calculating the distribution value of each region, S2, setting a power grid partitioning mode, S3, analyzing an intelligent partition, S4, adjusting the sensitivity, and S5, generating an optimal scheme. The appropriate target is selected by setting the seasonal factor and the daytime and night factor, due to the fact that different new energy sources have seasonal differences and are different in daytime and night, difference division and targeted control are necessary to be carried out on the new energy sources so as to adapt to different scenes, meanwhile, the power grid partitioning modes are set to be multiple, and the power grid partitioning efficiency is improved. According to the method, different control nodes can flexibly select a partition method according to needs, effective adjustment of regional power grid voltage is realized, safe use of a line is ensured, different strategy schemes are cyclically verified, an optimal strategy scheme is formed, the coordination capability for different types of distributed power supplies in power distribution network planning is improved, and stable line state is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network analysis, and in particular to a distribution network line status analysis method and system. Background Art

[0002] When the distribution network lines are in use, a large number of distributed power sources are connected to the grid. Due to the volatility and intermittent characteristics of their output, if they are not guided and controlled, the reliability of the distribution network power supply will be reduced. It is necessary to ensure the stability of the line state and the safe and stable operation of the power grid through the construction of active distribution network active perception, so as to realize the rapid transformation of the distribution network to active management and lay a solid foundation for the construction of a new power system. Since most of the current distribution networks are radial lines or use closed-loop design and open-loop operation wiring methods, their essence is a single-ended power supply mode. The access of distributed energy has changed the original single-ended power supply mode, resulting in significant changes in the distribution network flow and short-circuit current distribution, thereby affecting the fault handling strategy of the distribution line; The traditional dispatching mode and requirements can no longer adapt to the distribution network flow mode under the new power system, which brings great uncertainty to the control application and greatly increases the risk of failure and the operating cost pressure of personnel monitoring. With the large-scale access of distributed power sources to the distribution network, the distribution network has changed from passive to active, and the original single flow has become a two-way interaction. On the one hand, it increases the risk of node voltage exceeding the limit, and on the other hand, it leads to increased node voltage volatility. The connected lines will be affected, which is not conducive to safe operation. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for analyzing the state of a power distribution network line, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for analyzing the state of a distribution network line, comprising the following steps: S1. Calculate the allocation value of each region: Use the reactive power allocation algorithm of the wind farm to analyze the allocation value of the current wind farm according to the quarterly factor, the day and night factor and the theoretical reactive power remaining adjustable capacity of the wind farm; use the reactive power allocation algorithm of the photovoltaic power station to analyze the allocation value of the current photovoltaic power station according to the quarterly factor, the day and night factor and the theoretical reactive power remaining adjustable capacity of the photovoltaic power station; S2. Set the grid partitioning method: There are five ways to partition the grid, namely geographical partitioning, voltage level partitioning, cascade topology partitioning, sensitivity partitioning and intelligent partitioning. Geographical partitioning is to count the geographical locations of all power sources within the regional grid, and divide the power sources within the set interval into the same area. According to the voltage level partitioning, the voltage level level is first divided vertically, and then divided horizontally to obtain the corresponding power source set at each voltage level; S3. Analyze intelligent partitioning: According to intelligent partitioning, the electric coupling strength matrix is ​​calculated according to the power transmission capacity and equivalent impedance between different nodes using the matrix generation algorithm, the power supply is partitioned using the Kmeans algorithm, and the electrical modularity value is calculated through the electrical modularity extraction algorithm; S4, adjust sensitivity: lock the area of ​​node overvoltage, overload, and gateway overload, and calculate the reactive adjustable margin of the current equipment and the sensitivity of the node, perform ground adjustment and new energy station subordinate partition according to the response characteristics of various reactive adjustable resources, obtain the state matrix and time response matrix in the switching timing matrix of each reactive source, map the two one by one and sort and reorganize the elements to form the switching timing of the reactive sources of the ground adjustment and new energy station, use the sensitivity to optimize the switching timing and increment of each reactive source, and continuously update it online; S5. Generate the optimal solution: Obtain the intersection of various reactive sources in the timing matrix, increment matrix, and sensitivity matrix to obtain the final switching timing and increment of each reactive source. Use the time-sharing weighted matrix to further optimize and evaluate reactive sources with the same priority. Cycle and verify different strategy solutions to form the optimal strategy solution. After execution, monitor the execution results of the optimal solution and record abnormal behaviors.

[0005] Preferably, the step S1 specifically includes the following steps: S101, after obtaining the current date and time data, the season and day and night corresponding to these data are determined, and corresponding factors are analyzed according to the day and night; S102, receiving the rated power, quarterly night active output average and wind farm theoretical reactive remaining adjustable capacity of the designated wind farm, using the wind farm reactive power allocation algorithm to calculate the current corresponding quarterly factor according to the rated power and quarterly night active output average of the wind farm, and analyzing the current wind farm allocation value through the quarterly factor, the day and night factor and the wind farm theoretical reactive remaining adjustable capacity, the wind farm reactive power allocation algorithm is specifically as follows:

[0006] in, represents the quarterly factor, represents the rated power of the wind farm, It represents the quarterly average nighttime active output of the wind farm. Indicates the reactive power that the wind farm can bear. represents the quarterly factor, represents the day-night factor, It indicates the theoretical reactive remaining adjustable capacity of the wind farm; S103, receiving the designated photovoltaic power station's quarterly daytime active output average, rated power and photovoltaic power station's theoretical reactive remaining adjustable capacity, using the photovoltaic power station reactive power allocation algorithm to calculate the current corresponding quarterly factor according to the photovoltaic power station's quarterly daytime active output average and rated power, and analyzing the current photovoltaic power station's allocation value through the quarterly factor, the daytime and night factor and the photovoltaic power station's theoretical reactive remaining adjustable capacity, the photovoltaic power station reactive power allocation algorithm is specifically as follows:

[0007] in, Indicates the reactive power that the photovoltaic power station can bear. represents the quarterly factor, represents the day-night factor, It indicates the theoretical reactive remaining adjustable capacity of the photovoltaic power station. represents the daytime factor, represents the night factor, It indicates the average active output of the photovoltaic power station during the daytime in the quarter. Indicates the rated power of the PV power station.

[0008] Preferably, the step S2 specifically includes the following steps: S201, after obtaining the grid topology relationship and the estimated value of each device state, store them in a database; S202, according to geographical division, the geographical locations of all power sources in the regional power grid are counted, and the distance between each power source and other power sources is determined. If the distance is less than or equal to a set distance value, the two power sources are divided into the same area.

[0009] Preferably, the step S2 specifically further comprises the following steps: S203, partitioning by voltage level is firstly performing a vertical voltage level division to obtain each voltage level, and then performing a horizontal division on each voltage level to obtain a corresponding power source set on each voltage level; S204, according to the cascade topology partitioning, after performing topological analysis on the power grid, setting the correlation of different cascade areas, if the correlations of different power sources are in the same range, they are divided into the same power grid partition; S205, partitioning by sensitivity is to calculate the voltage response of each power source to each grid node through a sensitivity analysis algorithm, and then partition the power sources according to the voltage response.

[0010] Preferably, the step S3 specifically includes the following steps: S301, according to the intelligent partitioning, a matrix generation algorithm is used to calculate an electric coupling strength matrix according to power transmission capacity and equivalent impedance between different nodes; S302, inputting the matrix into a deep learning sparse autoencoder, reducing the dimension of the matrix, and partitioning the power supply using a Kmeans algorithm; S303, after calculating the electrical modularity value according to the electrical coupling strength and the sum between different nodes through the electrical modularity extraction algorithm, if the electrical modularity value is greater than or equal to the threshold, it is determined that the partitioning is successful, otherwise the partitioning is performed again.

[0011] Preferably, the step S4 specifically includes the following steps: S401, obtaining grid data, comparing the analog quantities and digital quantities of all nodes with corresponding thresholds, obtaining the equipment that needs to be blocked in the grid, and blocking the areas with node overvoltage, overload, and gateway overload; S402, obtaining the reactive output of the current device, the reference value and the unified Jacobian matrix after the power flow converges, and calculating the reactive adjustable margin of the current device and the sensitivity of the node; S403, perform subordinate partitioning of ground-based regulation and new energy stations according to the response characteristics of various reactive adjustable resources, obtain the state matrix and time response matrix in each reactive source switching sequence matrix, map the two one by one and perform element sorting and reorganization to form the switching sequence of reactive sources of ground-based regulation and new energy stations; S404, obtaining the state matrix and capacity matrix in each reactive source switching increment matrix, mapping the two one by one and obtaining the eigenvalue of the mapped matrix, using the eigenvalue as the weight of the reactive output of the corresponding reactive source, thereby obtaining the switching increment of each reactive source of the ground-regulating and new energy station; S405, directly generate corresponding regulation sensitivity based on the voltage support degree of each reactive source to the node, use the sensitivity to optimize the switching timing and increment of each reactive source that has been formed, and continuously update them online.

[0012] Preferably, the step S5 specifically includes the following steps: S501, finding the intersection of various reactive sources in the timing matrix, the increment matrix, and the sensitivity matrix to obtain the final switching timing and increment of each reactive source; S502, if there is no intersection or no intersection between two sets, then reactive power increment priority is satisfied, and reactive power sources with the same priority are further optimized and evaluated using a time-sharing weighted matrix. Within a set time range, one point is added for each successful adjustment to form a time-sharing weighted matrix. If the set time range is exceeded, the weighted value is reset to zero and counted again; S503, cyclically verify different strategy solutions to form an optimal strategy solution, monitor the execution results of the optimal solution after execution, and record abnormal behaviors.

[0013] The distribution network line status analysis system comprises a distribution value calculation unit, a partition mode setting unit, an intelligent partition processing unit, a sensitivity optimization unit and a scheme execution unit; The allocation value calculation unit uses the wind farm reactive power allocation algorithm to analyze the current wind farm allocation value according to the quarterly factor, the day and night factor and the wind farm theoretical reactive power remaining adjustable capacity, and uses the photovoltaic power station reactive power allocation algorithm to analyze the current photovoltaic power station allocation value according to the quarterly factor, the day and night factor and the photovoltaic power station theoretical reactive power remaining adjustable capacity; The partitioning mode setting unit partitions the power grid in five ways, namely, geographical partitioning, voltage level partitioning, cascade topology partitioning, sensitivity partitioning and intelligent partitioning. Geographical partitioning is to count the geographical locations of all power sources within the regional power grid, and divide the power sources within a set interval into the same area. Partitioning by voltage level is to first divide the voltage level vertically, and then divide horizontally to obtain the corresponding power source set at each voltage level. The intelligent partition processing unit calculates the electric coupling strength matrix according to the power transmission capacity and equivalent impedance between different nodes using a matrix generation algorithm according to the intelligent partition, partitions the power supply using a Kmeans algorithm, and calculates the electrical modularity value using an electrical modularity extraction algorithm; The sensitivity optimization unit locks the area of ​​node overvoltage, overload, and gateway overload, and calculates the reactive adjustable margin of the current equipment and the sensitivity of the node, performs ground adjustment and subdivision of new energy stations according to the response characteristics of various reactive adjustable resources, obtains the state matrix and time response matrix in the switching timing matrix of each reactive source, maps the two one by one and performs element sorting and reorganization to form the switching timing of the reactive sources of the ground adjustment and new energy stations, optimizes the switching timing and increment of each reactive source formed by sensitivity, and continuously updates them online; The scheme execution unit obtains the intersection of various reactive sources in the timing matrix, increment matrix, and sensitivity matrix to obtain the final switching timing and increment of each reactive source, further optimizes and evaluates reactive sources with equal priority using a time-sharing weighted matrix, cyclically verifies different strategy schemes, forms an optimal strategy scheme, monitors the execution results of the optimal scheme after execution, and records abnormal behaviors.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention selects a suitable target by setting a quarterly factor and a daytime and nighttime factor. Because different new energy sources have seasonal differences and the daytime and nighttime are also different, it is necessary to divide them differently and perform targeted control to adapt to different scenarios. At the same time, the grid partitioning methods are set to multiple, so that different control nodes can flexibly select the partitioning method according to needs, so as to achieve effective regulation of the regional grid voltage and ensure the safe use of the line. Different strategy schemes are cyclically verified to form the optimal strategy scheme, thereby improving the coordination ability of different types of distributed power sources in the distribution network planning and ensuring the stability of the line state. During the planning and design, the personnel and traffic investment in the on-site and surrounding power supply condition survey are also reduced, providing basic support for the adaptability of various power sources to the distribution network planning, reducing safety issues such as poor power supply quality, phase asynchrony, and phase-lack power supply for users, and ensuring the efficient use of the distribution network lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Provide an overall method flow chart for an embodiment of the present invention; Figure 2 A flow chart of a method for calculating the allocation value of each region provided by an embodiment of the present invention; Figure 3 A flow chart of a method for setting a power grid partition mode provided by an embodiment of the present invention; Figure 4 A flow chart of a method for analyzing intelligent partitions provided by an embodiment of the present invention; Figure 5 A flow chart of a method for adjusting sensitivity provided by an embodiment of the present invention; Figure 6 A flow chart of a method for generating an optimal solution provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0017] See also Figure 1-6 The present invention provides a technical solution: a method for analyzing the state of a distribution network line, comprising the following steps: S1. Calculate the allocation value of each region: Use the reactive power allocation algorithm of the wind farm to analyze the allocation value of the current wind farm according to the quarterly factor, the day and night factor and the theoretical reactive power remaining adjustable capacity of the wind farm; use the reactive power allocation algorithm of the photovoltaic power station to analyze the allocation value of the current photovoltaic power station according to the quarterly factor, the day and night factor and the theoretical reactive power remaining adjustable capacity of the photovoltaic power station; S2. Set the grid partitioning method: There are five ways to partition the grid, namely geographical partitioning, voltage level partitioning, cascade topology partitioning, sensitivity partitioning and intelligent partitioning. Geographical partitioning is to count the geographical locations of all power sources within the regional grid, and divide the power sources within the set interval into the same area. According to the voltage level partitioning, the voltage level level is first divided vertically, and then divided horizontally to obtain the corresponding power source set at each voltage level; S3. Analyze intelligent partitioning: According to intelligent partitioning, the electric coupling strength matrix is ​​calculated according to the power transmission capacity and equivalent impedance between different nodes using the matrix generation algorithm, the power supply is partitioned using the Kmeans algorithm, and the electrical modularity value is calculated through the electrical modularity extraction algorithm; S4, adjust sensitivity: lock the area of ​​node overvoltage, overload, and gateway overload, and calculate the reactive adjustable margin of the current equipment and the sensitivity of the node, perform ground adjustment and new energy station subordinate partition according to the response characteristics of various reactive adjustable resources, obtain the state matrix and time response matrix in the switching timing matrix of each reactive source, map the two one by one and sort and reorganize the elements to form the switching timing of the reactive sources of the ground adjustment and new energy station, use the sensitivity to optimize the switching timing and increment of each reactive source, and continuously update it online; S5. Generate the optimal solution: Obtain the intersection of various reactive sources in the timing matrix, increment matrix, and sensitivity matrix to obtain the final switching timing and increment of each reactive source. Use the time-sharing weighted matrix to further optimize and evaluate reactive sources with the same priority. Cycle and verify different strategy solutions to form the optimal strategy solution. After execution, monitor the execution results of the optimal solution and record abnormal behaviors.

[0018] Step S1 specifically includes the following steps: S101, after obtaining the current date and time data, the season and day and night corresponding to these data are determined, and corresponding factors are analyzed according to the day and night; S102, receiving the rated power, quarterly night active output average and wind farm theoretical reactive remaining adjustable capacity of the designated wind farm, using the wind farm reactive power allocation algorithm to calculate the current corresponding quarterly factor according to the rated power and quarterly night active output average of the wind farm, and analyzing the current wind farm allocation value through the quarterly factor, the day and night factor and the wind farm theoretical reactive remaining adjustable capacity. The wind farm reactive power allocation algorithm is specifically as follows:

[0019] in, represents the quarterly factor, represents the rated power of the wind farm, It represents the quarterly average nighttime active output of the wind farm. Indicates the reactive power that the wind farm can bear. represents the quarterly factor, represents the day-night factor, It indicates the theoretical reactive remaining adjustable capacity of the wind farm; S103, receiving the designated photovoltaic power station's quarterly daytime active output average, rated power and photovoltaic power station's theoretical reactive remaining adjustable capacity, using the photovoltaic power station reactive power allocation algorithm to calculate the current corresponding quarterly factor according to the photovoltaic power station's quarterly daytime active output average and rated power, and analyzing the current photovoltaic power station's allocation value through the quarterly factor, day and night factor and photovoltaic power station's theoretical reactive remaining adjustable capacity. The photovoltaic power station reactive power allocation algorithm is specifically as follows:

[0020] in, Indicates the reactive power that the photovoltaic power station can bear. represents the quarterly factor, represents the day-night factor, It indicates the theoretical reactive remaining adjustable capacity of the photovoltaic power station. represents the daytime factor, represents the night factor, It indicates the average active output of the photovoltaic power station during the daytime in the quarter. Indicates the rated power of the photovoltaic power station; Step S2 specifically includes the following steps: S201, after obtaining the grid topology relationship and the estimated value of each device state, store them in a database; S202, according to geographical division, the geographical locations of all power sources in the regional power grid are counted, and the distance between each power source and other power sources is determined. If the distance is less than or equal to the set distance value, the two power sources are divided into the same area; Step S2 specifically also includes the following steps: S203, partitioning by voltage level is firstly performing a vertical voltage level division to obtain each voltage level, and then performing a horizontal division on each voltage level to obtain a corresponding power source set on each voltage level; S204, according to the cascade topology partitioning, after performing topological analysis on the power grid, setting the correlation of different cascade areas, if the correlations of different power sources are in the same range, they are divided into the same power grid partition; S205, partitioning by sensitivity is to calculate the voltage response of each power source to each grid node through a sensitivity analysis algorithm, and then partition the power sources according to the voltage response. The sensitivity analysis algorithm is specifically as follows:

[0021] in, Indicates different reactive power sources, Represents different nodes in the power grid, Reactive power source For Node Voltage regulation sensitivity; Reactive power source The reactive power regulation involved, Indicates corresponding to a single reactive source After participating in the adjustment, the node The magnitude of the voltage effect; Step S3 specifically includes the following steps: S301, according to the intelligent partitioning, a matrix generation algorithm is used to calculate an electric coupling strength matrix according to power transmission capacity and equivalent impedance between different nodes; S302, inputting the matrix into a deep learning sparse autoencoder, reducing the dimension of the matrix, and partitioning the power supply using a Kmeans algorithm; S303, after calculating the electrical modularity value according to the electrical coupling strength and the sum between different nodes through the electrical modularity extraction algorithm, if the electrical modularity value is greater than or equal to the threshold, the partition is determined to be successful, otherwise the partition is re-performed. The electrical modularity extraction algorithm is specifically as follows:

[0022] in, Indicates electrical modularity, Representation Node The electrical coupling strength between Representation Node The sum of the electrical coupling strengths of the remaining nodes, Representation Node The sum of the electrical coupling strengths of the remaining nodes, express The sum of the electrical coupling strengths of all nodes in the part, Indicates the parameter, when the node and nodes In the same cluster, ,otherwise ; Step S4 specifically includes the following steps: S401, obtaining grid data, comparing the analog quantities and digital quantities of all nodes with corresponding thresholds, obtaining the equipment that needs to be blocked in the grid, and blocking the areas with node overvoltage, overload, and gateway overload; S402, obtaining the reactive output of the current device, the reference value and the unified Jacobian matrix after the power flow converges, and calculating the reactive adjustable margin of the current device and the sensitivity of the node; S403, perform subordinate partitioning of ground-based regulation and new energy stations according to the response characteristics of various reactive adjustable resources, obtain the state matrix and time response matrix in each reactive source switching sequence matrix, map the two one by one and perform element sorting and reorganization to form the switching sequence of reactive sources of ground-based regulation and new energy stations; S404, obtaining the state matrix and capacity matrix in each reactive source switching increment matrix, mapping the two one by one and obtaining the eigenvalue of the mapped matrix, using the eigenvalue as the weight of the reactive output of the corresponding reactive source, thereby obtaining the switching increment of each reactive source of the ground-regulating and new energy station; S405, directly generating corresponding regulation sensitivity based on the voltage support degree of each reactive source to the node, optimizing the switching timing and increment of each reactive source that has been formed by using the sensitivity, and continuously updating them online; Step S5 specifically includes the following steps: S501, finding the intersection of various reactive sources in the timing matrix, the increment matrix, and the sensitivity matrix to obtain the final switching timing and increment of each reactive source; S502, if there is no intersection or no intersection between two sets, then reactive power increment priority is satisfied, and reactive power sources with the same priority are further optimized and evaluated using a time-sharing weighted matrix. Within a set time range, one point is added for each successful adjustment to form a time-sharing weighted matrix. If the set time range is exceeded, the weighted value is reset to zero and counted again; S503, cyclically verify different strategy solutions to form an optimal strategy solution, monitor the execution results of the optimal solution after execution, and record abnormal behaviors; A distribution network line status analysis system, comprising a distribution value calculation unit, a partition mode setting unit, an intelligent partition processing unit, a sensitivity optimization unit and a scheme execution unit; The allocation value calculation unit uses the wind farm reactive power allocation algorithm to analyze the current wind farm allocation value according to the quarterly factor, the day and night factor and the wind farm theoretical reactive power remaining adjustable capacity, and uses the photovoltaic power station reactive power allocation algorithm to analyze the current photovoltaic power station allocation value according to the quarterly factor, the day and night factor and the photovoltaic power station theoretical reactive power remaining adjustable capacity; There are five ways for the zoning mode setting unit to zone the power grid, namely geographical zoning, voltage level zoning, cascade topology zoning, sensitivity zoning and intelligent zoning. Geographical zoning is to count the geographical locations of all power sources within the regional power grid, and divide the power sources within the set interval into the same area. According to the voltage level zoning, the voltage level is divided vertically first, and then horizontally, to obtain the corresponding power source set at each voltage level; The intelligent partition processing unit calculates the electric coupling strength matrix according to the power transmission capacity and equivalent impedance between different nodes using the matrix generation algorithm, partitions the power supply using the Kmeans algorithm, and calculates the electrical modularity value through the electrical modularity extraction algorithm; The sensitivity optimization unit blocks the areas of node overvoltage, overload, and overload at the gateway, and calculates the reactive adjustable margin of the current equipment and the sensitivity of the node. It divides the local regulation and the new energy station into subordinate zones according to the response characteristics of various reactive adjustable resources, obtains the state matrix and time response matrix in the switching timing matrix of each reactive source, maps the two one by one, and sorts and reorganizes the elements to form the switching timing of the reactive sources of the local regulation and the new energy station, and uses the sensitivity to optimize the switching timing and increment of each reactive source, and continuously updates it online. The scheme execution unit obtains the intersection of various reactive sources in the timing matrix, increment matrix, and sensitivity matrix to obtain the final switching timing and increment of each reactive source. The reactive sources with the same priority are further optimized and evaluated using the time-sharing weighted matrix. Different strategy schemes are cyclically checked to form the optimal strategy scheme. After execution, the execution results of the optimal scheme are monitored and abnormal behaviors are recorded.

[0023] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0024] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing the state of a distribution network line, characterized in that: The method comprises the following steps: S1. Calculate the allocation value of each region: Use the reactive power allocation algorithm of the wind farm to analyze the allocation value of the current wind farm according to the quarterly factor, the day and night factor and the theoretical reactive power remaining adjustable capacity of the wind farm; use the reactive power allocation algorithm of the photovoltaic power station to analyze the allocation value of the current photovoltaic power station according to the quarterly factor, the day and night factor and the theoretical reactive power remaining adjustable capacity of the photovoltaic power station; S2. Set the grid partitioning method: There are five ways to partition the grid, namely geographical partitioning, voltage level partitioning, cascade topology partitioning, sensitivity partitioning and intelligent partitioning. Geographical partitioning is to count the geographical locations of all power sources within the regional grid, and divide the power sources within the set interval into the same area. According to the voltage level partitioning, the voltage level level is first divided vertically, and then divided horizontally to obtain the corresponding power source set at each voltage level; S3. Analyze intelligent partitioning: According to intelligent partitioning, the electric coupling strength matrix is ​​calculated according to the power transmission capacity and equivalent impedance between different nodes using the matrix generation algorithm, the power supply is partitioned using the Kmeans algorithm, and the electrical modularity value is calculated through the electrical modularity extraction algorithm; S4, adjust sensitivity: lock the area of ​​node overvoltage, overload, and gateway overload, and calculate the reactive adjustable margin of the current equipment and the sensitivity of the node, perform ground adjustment and new energy station subordinate partition according to the response characteristics of various reactive adjustable resources, obtain the state matrix and time response matrix in the switching timing matrix of each reactive source, map the two one by one and sort and reorganize the elements to form the switching timing of the reactive sources of the ground adjustment and new energy station, use the sensitivity to optimize the switching timing and increment of each reactive source, and continuously update it online; S5. Generate the optimal solution: Obtain the intersection of various reactive sources in the timing matrix, increment matrix, and sensitivity matrix to obtain the final switching timing and increment of each reactive source. Use the time-sharing weighted matrix to further optimize and evaluate reactive sources with the same priority. Cycle and verify different strategy solutions to form the optimal strategy solution. After execution, monitor the execution results of the optimal solution and record abnormal behaviors.

2. A distribution network line status analysis method according to claim 1, characterized in that: The step S1 specifically includes the following steps: S101, after obtaining the current date and time data, the season and day and night corresponding to these data are determined, and corresponding factors are analyzed according to the day and night; S102, receiving the rated power, the average active output at night in the quarter, and the theoretical reactive remaining adjustable capacity of the designated wind farm, using the wind farm reactive power allocation algorithm to calculate the current corresponding quarterly factor according to the rated power of the wind farm and the average active output at night in the quarter, and analyzing the current allocation value of the wind farm through the quarterly factor, the day and night factor, and the theoretical reactive remaining adjustable capacity of the wind farm; S103, receiving the designated photovoltaic power station's quarterly daytime average active output, rated power and the photovoltaic power station's theoretical reactive remaining adjustable capacity, using the photovoltaic power station reactive power allocation algorithm to calculate the current corresponding quarterly factor according to the photovoltaic power station's quarterly daytime average active output and rated power, and analyzing the current photovoltaic power station's allocation value through the quarterly factor, day and night factor and the photovoltaic power station's theoretical reactive remaining adjustable capacity.

3. A distribution network line status analysis method according to claim 1, characterized in that: The step S2 specifically includes the following steps: S201, after obtaining the grid topology relationship and the estimated value of each device state, store them in a database; S202, according to geographical division, the geographical locations of all power sources in the regional power grid are counted, and the distance between each power source and other power sources is determined. If the distance is less than or equal to a set distance value, the two power sources are divided into the same area.

4. A distribution network line status analysis method according to claim 3, characterized in that: The step S2 specifically further comprises the following steps: S203, partitioning by voltage level is firstly performing a vertical voltage level division to obtain each voltage level, and then performing a horizontal division on each voltage level to obtain a corresponding power source set on each voltage level; S204, according to the cascade topology partitioning, after performing topological analysis on the power grid, setting the correlation of different cascade areas, if the correlations of different power sources are in the same range, they are divided into the same power grid partition; S205, partitioning by sensitivity is to calculate the voltage response of each power source to each grid node through a sensitivity analysis algorithm, and then partition the power sources according to the voltage response.

5. A distribution network line status analysis method according to claim 1, characterized in that: The step S3 specifically comprises the following steps: S301, according to the intelligent partitioning, a matrix generation algorithm is used to calculate an electric coupling strength matrix according to power transmission capacity and equivalent impedance between different nodes; S302, inputting the matrix into a deep learning sparse autoencoder, reducing the dimension of the matrix, and partitioning the power supply using a Kmeans algorithm; S303, after calculating the electrical modularity value according to the electrical coupling strength and the sum between different nodes through the electrical modularity extraction algorithm, if the electrical modularity value is greater than or equal to the threshold, it is determined that the partitioning is successful, otherwise the partitioning is performed again.

6. A distribution network line status analysis method according to claim 1, characterized in that: The step S4 specifically comprises the following steps: S401, obtaining grid data, comparing the analog quantities and digital quantities of all nodes with corresponding thresholds, obtaining the equipment that needs to be blocked in the grid, and blocking the areas with node overvoltage, overload, and gateway overload; S402, obtaining the reactive output of the current device, the reference value and the unified Jacobian matrix after the power flow converges, and calculating the reactive adjustable margin of the current device and the sensitivity of the node; S403, perform subordinate partitioning of ground-based regulation and new energy stations according to the response characteristics of various reactive adjustable resources, obtain the state matrix and time response matrix in each reactive source switching sequence matrix, map the two one by one and perform element sorting and reorganization to form the switching sequence of reactive sources of ground-based regulation and new energy stations; S404, obtaining the state matrix and capacity matrix in each reactive source switching increment matrix, mapping the two one by one and obtaining the eigenvalue of the mapped matrix, using the eigenvalue as the weight of the reactive output of the corresponding reactive source, thereby obtaining the switching increment of each reactive source of the ground-regulating and new energy station; S405, directly generate corresponding regulation sensitivity based on the voltage support degree of each reactive source to the node, use the sensitivity to optimize the switching timing and increment of each reactive source that has been formed, and continuously update them online.

7. A distribution network line status analysis method according to claim 1, characterized in that: The step S5 specifically comprises the following steps: S501, finding the intersection of various reactive sources in the timing matrix, the increment matrix, and the sensitivity matrix to obtain the final switching timing and increment of each reactive source; S502, if there is no intersection or no intersection between two sets, then reactive power increment priority is satisfied, and reactive power sources with the same priority are further optimized and evaluated using a time-sharing weighted matrix. Within a set time range, one point is added for each successful adjustment to form a time-sharing weighted matrix. If the set time range is exceeded, the weighted value is reset to zero and counted again; S503, cyclically verify different strategy solutions to form an optimal strategy solution, monitor the execution results of the optimal solution after execution, and record abnormal behaviors.

8. A distribution network line status analysis system, characterized in that: The line state analysis system is applicable to a distribution network line state analysis method according to any one of claims 1 to 7, comprising a distribution value calculation unit, a partition mode setting unit, an intelligent partition processing unit, a sensitivity optimization unit and a scheme execution unit; The allocation value calculation unit uses the wind farm reactive power allocation algorithm to analyze the current wind farm allocation value according to the quarterly factor, the day and night factor and the wind farm theoretical reactive power remaining adjustable capacity, and uses the photovoltaic power station reactive power allocation algorithm to analyze the current photovoltaic power station allocation value according to the quarterly factor, the day and night factor and the photovoltaic power station theoretical reactive power remaining adjustable capacity; The partitioning mode setting unit partitions the power grid in five ways, namely, geographical partitioning, voltage level partitioning, cascade topology partitioning, sensitivity partitioning and intelligent partitioning. Geographical partitioning is to count the geographical locations of all power sources within the regional power grid, and divide the power sources within a set interval into the same area. Partitioning by voltage level is to first divide the voltage level vertically, and then divide horizontally to obtain the corresponding power source set at each voltage level. The intelligent partition processing unit calculates the electric coupling strength matrix according to the power transmission capacity and equivalent impedance between different nodes using a matrix generation algorithm according to the intelligent partition, partitions the power supply using a Kmeans algorithm, and calculates the electrical modularity value using an electrical modularity extraction algorithm; The sensitivity optimization unit locks the area of ​​node overvoltage, overload, and gateway overload, and calculates the reactive adjustable margin of the current equipment and the sensitivity of the node, performs ground adjustment and subdivision of new energy stations according to the response characteristics of various reactive adjustable resources, obtains the state matrix and time response matrix in the switching timing matrix of each reactive source, maps the two one by one and performs element sorting and reorganization to form the switching timing of the reactive sources of the ground adjustment and new energy stations, optimizes the switching timing and increment of each reactive source formed by sensitivity, and continuously updates them online; The scheme execution unit obtains the intersection of various reactive sources in the timing matrix, increment matrix, and sensitivity matrix to obtain the final switching timing and increment of each reactive source, further optimizes and evaluates reactive sources with equal priority using a time-sharing weighted matrix, cyclically verifies different strategy schemes, forms an optimal strategy scheme, monitors the execution results of the optimal scheme after execution, and records abnormal behaviors.

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