Fault monitoring method and system for new energy photovoltaic power station power distribution network

Abstract

The invention discloses a fault monitoring method and system for a new energy photovoltaic power station power distribution network, and relates to the technical field of power distribution network fault monitoring. Analyzing and identifying abnormal areas based on the monitoring data of the edge monitoring point of each area, and updating the final monitoring level of each abnormal area; according to the fault detection sequence, the new energy photovoltaic power station power distribution network evaluation information of the monitoring points in all the abnormal areas and the difference information between the adjacent monitoring points are analyzed in sequence, the monitoring points in all the abnormal areas are checked, suspected monitoring points in all the abnormal areas are identified and marked, and a detection and maintenance path is generated. The problems of delayed detection, unreasonable resource allocation, low troubleshooting efficiency, insufficient intelligence, single maintenance strategy and insufficient data utilization in traditional new energy photovoltaic power station power distribution network fault monitoring are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault monitoring, and in particular to a fault monitoring method and system for a new energy photovoltaic power station distribution network. Background Art

[0002] With the growth of global demand for renewable energy, the construction and application of photovoltaic power stations have increased year by year. As an important new energy technology, photovoltaic power stations have gradually become an important part of power supply. The distribution network of photovoltaic power stations must not only meet the efficient transmission of electricity, but also handle the failure of various power equipment and lines. Photovoltaic power stations are usually widely distributed, with increasing scale and complexity, which puts higher requirements on the stability and reliability of their distribution networks.

[0003] Traditional fault detection systems have detection delays and are unable to discover faults in real time. They have a high false alarm rate, resulting in frequent false alarms and unnecessary maintenance. Traditional systems also have data silos and are unable to fully integrate and utilize data from multiple sources. They use the same monitoring strategy for all areas and are unable to assign priorities based on the importance and risk of the areas, resulting in insufficient monitoring of key areas. At the same time, fault troubleshooting relies on manual experience, which is inefficient and may not be accurate enough, and lacks systematic fault detection and maintenance path planning.

[0004] Therefore, in view of the above problems, there is an urgent need for a fault monitoring method and system for a new energy photovoltaic power station distribution network. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a fault monitoring method and system for a new energy photovoltaic power station distribution network, which solves the problems of delayed detection, unreasonable resource allocation, low troubleshooting efficiency, insufficient intelligence, single maintenance strategy and insufficient data utilization in the traditional new energy photovoltaic power station distribution network fault monitoring.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a fault monitoring method for a new energy photovoltaic power station distribution network, comprising the following steps: obtaining structural influencing parameters and historical fault data of each area of ​​the new energy photovoltaic power station distribution network, and then identifying the monitoring importance of each area based on the structural influencing parameters and historical fault data of each area, and setting the initial monitoring level of each area; obtaining the monitoring data of the edge monitoring points of each area, analyzing and identifying the abnormal area based on the monitoring data of the edge monitoring points of each area, and determining the specific abnormal label of the abnormal area, and then updating the final monitoring level of each abnormal area in combination with the initial monitoring level and the specific abnormal label of the abnormal area, and sorting the fault detection of each abnormal area according to the final monitoring level of each abnormal area; analyzing the new energy photovoltaic power station distribution network evaluation information of the monitoring points in each abnormal area and the difference information between adjacent monitoring points in turn according to the fault detection order, checking the monitoring points in each abnormal area, identifying and marking the suspected monitoring points in each abnormal area; generating the detection and maintenance path of the suspected monitoring points in each abnormal area according to the distribution network impact information of the suspected monitoring points.

[0007] Furthermore, the structural influencing parameters of each area of ​​the new energy photovoltaic power station distribution network specifically include: grid structure complexity and load density; the historical fault data specifically include: the number of historical faults and the impact range of historical faults.

[0008] Furthermore, the monitoring importance of each area is identified based on the structural influence parameters and historical fault data of each area, and the specific analysis of setting the initial monitoring level of each area is as follows: the grid structure complexity and load density of each area are calculated and multiplied to obtain the structural influence coefficient of each area; the historical number of faults and the historical fault impact range of each area are calculated and multiplied to obtain the historical fault coefficient of each area; the importance score of each area is obtained by calculating the structural influence coefficient and the historical fault coefficient of each area; the initial monitoring level of each area is divided according to the level division interval of the importance score, and the initial monitoring level of each area specifically includes a first-level monitoring area, a second-level monitoring area and a third-level monitoring area.

[0009] Furthermore, the monitoring data of the edge monitoring points of each area are obtained, the abnormal area is identified based on the monitoring data of the edge monitoring points of each area, and the specific abnormal labels of the abnormal area are determined, and then the final monitoring level of each abnormal area is updated in combination with the initial monitoring level and the specific abnormal label of the abnormal area. The specific analysis is as follows: the monitoring data of the edge monitoring points of each area specifically include the voltage fluctuation amplitude, power factor, harmonic content and power generation deviation value; the monitoring threshold data of the edge monitoring points of each area are obtained, and the monitoring threshold data of the edge monitoring points of each area specifically include the voltage fluctuation amplitude threshold, power factor threshold, harmonic content threshold and power generation deviation value. The voltage fluctuation amplitude of each area is compared with the voltage fluctuation amplitude threshold of the area, and the area with a voltage fluctuation amplitude greater than the voltage fluctuation amplitude threshold is marked as an abnormal area, and the specific abnormal label is voltage fluctuation abnormality; the power factor of each area is compared with the power factor threshold of the area, and the area with a power factor less than the power factor threshold is marked as an abnormal area, and the specific abnormal label is power factor abnormality; the harmonic content of each area is compared with the harmonic content threshold of the area, and the area with a harmonic content greater than the harmonic content threshold is marked as an abnormal area, and the specific abnormal label is harmonic content abnormality; the power generation deviation value of each area is Compare with the power generation deviation threshold of the area, mark the area where the power generation deviation value is greater than the power generation deviation threshold as an abnormal area, and the specific abnormal label is power generation abnormality; count the number of abnormal labels and the initial monitoring level of each abnormal area, the number of abnormal labels specifically includes one abnormal label, two abnormal labels, three abnormal labels and four abnormal labels, and then update the final monitoring level according to the number of abnormal labels and the initial monitoring level: update the final monitoring level of the first-level monitoring area containing one abnormal label to the first monitoring level; update the final monitoring level of the first-level monitoring area containing two abnormal labels and the second-level monitoring area containing one abnormal label The first-level monitoring area with three abnormal labels, the second-level monitoring area with two abnormal labels and the third-level monitoring area with one abnormal label will be updated to the third monitoring level; the first-level monitoring area with four abnormal labels, the second-level monitoring area with three abnormal labels and the third-level monitoring area with two abnormal labels will be updated to the fourth monitoring level; the second-level monitoring area with four abnormal labels and the third-level monitoring area with three abnormal labels will be updated to the fifth monitoring level; the third-level monitoring area with four abnormal labels will be updated to the sixth monitoring level.

[0010] Furthermore, the evaluation information of the new energy photovoltaic power station distribution network at the monitoring points in each abnormal area is specifically energy utilization rate; and the difference information between adjacent monitoring points specifically includes equipment temperature difference value.

[0011] Furthermore, according to the fault detection order, the evaluation information of the new energy photovoltaic power station distribution network of the monitoring points in each abnormal area and the difference information between adjacent monitoring points are analyzed in turn, the monitoring points in each abnormal area are checked, and the suspected monitoring points in each abnormal area are identified and marked. The specific analysis is as follows: according to the fault detection ranking of each abnormal area, the energy utilization rate of the monitoring points in each abnormal area is compared with the energy utilization rate threshold in turn, and the equipment temperature difference value between the monitoring point and the adjacent monitoring point is compared with the equipment temperature difference value compliance range; the monitoring points whose energy utilization rate is lower than the energy utilization rate threshold and whose equipment temperature difference value with the adjacent monitoring point is not within the equipment temperature difference value compliance range are marked as suspected monitoring points.

[0012] Furthermore, the specific analysis of generating the detection and maintenance path of the suspected monitoring points in each abnormal area based on the distribution network impact information of the suspected monitoring points is as follows: the distribution network impact information of the suspected monitoring points specifically includes: the number of connected nodes and the load distribution range; numerical calculations are performed based on the number of connected nodes and the load distribution range to obtain the detection and maintenance demand score, and then the detection and maintenance order of the suspected monitoring points in each abnormal area is generated based on the size of the detection and maintenance demand score.

[0013] A fault monitoring method for a new energy photovoltaic power station distribution network, using the above-mentioned fault monitoring system for a new energy photovoltaic power station distribution network, comprises: a region level setting module, used to obtain structural influencing parameters and historical fault data of each region of the new energy photovoltaic power station distribution network, and then identify the monitoring importance of each region according to the structural influencing parameters and historical fault data of each region, and set the initial monitoring level of each region; an abnormal region identification module, used to obtain the monitoring data of the edge monitoring points of each region, analyze and identify the abnormal region based on the monitoring data of the edge monitoring points of each region, and determine the specific abnormal label of the abnormal region, and then update the final monitoring level of each abnormal region in combination with the initial monitoring level and the specific abnormal label of the abnormal region, and sort the fault detection of each abnormal region according to the final monitoring level of each abnormal region; a suspected monitoring point identification module, used to analyze the evaluation information of the new energy photovoltaic power station distribution network of the monitoring points in each abnormal region and the difference information between adjacent monitoring points in turn according to the fault detection order, check the monitoring points in each abnormal region, identify and mark the suspected monitoring points in each abnormal region; a detection sequence path setting module, used to generate the detection and maintenance path of the suspected monitoring points in each abnormal region according to the distribution network impact information of the suspected monitoring points.

[0014] The present invention has the following beneficial effects:

[0015] The fault monitoring method and system of the new energy photovoltaic power station distribution network can more accurately identify abnormal areas and their specific fault points by integrating the structural influencing parameters, historical fault data and monitoring data of each area, avoiding misjudgment caused by a single factor; identifying abnormal areas based on real-time data of edge monitoring points is helpful to quickly locate problems and improve the timeliness and accuracy of fault detection; setting the initial monitoring level through the structural influencing parameters and historical data of the area is helpful to give priority to key areas when resources are limited; updating the final monitoring level of the abnormal area based on the monitoring data, ensuring that resources are preferentially concentrated in high-risk areas, and realizing effective utilization of resources; sorting the abnormal areas, which can be Faults can be processed in sequence according to priority, reducing processing time and quickly restoring normal operation; detailed inspection of monitoring points based on evaluation information and difference analysis can accurately locate fault points, reduce ineffective work and improve fault repair efficiency; by integrating various data sources and monitoring information, a comprehensive understanding of the operating status of the distribution network can be achieved, potential risks can be discovered in a timely manner, and the probability of faults can be reduced; detection and maintenance paths can be generated based on the distribution network impact information of suspected monitoring points, which is conducive to preventive maintenance and reduces the impact of sudden faults; through data analysis and model-driven methods, the distribution network is intelligently monitored and managed, the intelligence level of the system is improved, the unplanned downtime of the system is reduced, and energy waste is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a fault monitoring method for a new energy photovoltaic power station distribution network.

[0017] Figure 2 This is a structural diagram of a fault monitoring system for a new energy photovoltaic power station distribution network of the present invention. DETAILED DESCRIPTION

[0018] The embodiment of the present application realizes the comprehensive utilization of importance parameters, historical data, real-time monitoring data and system evaluation information through a fault monitoring method and system for the distribution network of a new energy photovoltaic power station, accurately and timely identifies and troubleshoots fault points in the distribution network of a new energy photovoltaic power station, optimizes resource allocation, improves fault response efficiency, enhances the stability and reliability of the distribution network, promotes intelligent and automated management, and achieves the goals of energy conservation, emission reduction and system safety.

[0019] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0020] Collect comprehensive distribution network data, establish a basis for fault monitoring, evaluate the importance and possible risks of the region, and then determine the initial monitoring priority of the region based on data analysis, assign an initial monitoring level to each region, and determine the priority of monitoring and maintenance; adjust the monitoring level of each region based on the real-time monitoring data of the abnormal area to ensure that resources are prioritized in areas with serious abnormalities; sort the abnormal areas based on the updated monitoring level, give priority to high-priority areas, conduct a detailed investigation of the monitoring points in each abnormal area, and identify and mark suspected monitoring points; generate detailed detection and maintenance paths based on the distribution network impact information of the suspected monitoring points to guide maintenance work.

[0021] See also Figure 1 The embodiment of the present invention provides a technical solution: a fault monitoring method for a distribution network of a new energy photovoltaic power station, comprising the following steps: obtaining structural influencing parameters and historical fault data of each area of ​​the distribution network of the new energy photovoltaic power station, and then identifying the monitoring importance of each area based on the structural influencing parameters and historical fault data of each area, and setting the initial monitoring level of each area; obtaining the monitoring data of the edge monitoring points of each area, analyzing and identifying the abnormal area based on the monitoring data of the edge monitoring points of each area, and determining the specific abnormal label of the abnormal area, and then updating the final monitoring level of each abnormal area in combination with the initial monitoring level and the specific abnormal label of the abnormal area, and sorting the fault detection of each abnormal area according to the final monitoring level of each abnormal area; analyzing the evaluation information of the distribution network of the new energy photovoltaic power station of the monitoring points in each abnormal area and the difference information between adjacent monitoring points in turn according to the fault detection order, checking the monitoring points of each abnormal area, identifying and marking the suspected monitoring points of each abnormal area; generating the detection and maintenance path of the suspected monitoring points in each abnormal area according to the distribution network impact information of the suspected monitoring points.

[0022] Specifically, the structural influencing parameters of each area of ​​the new energy photovoltaic power station distribution network include: grid structure complexity and load density; historical fault data include: historical fault times and historical fault impact range.

[0023] According to the structural influence parameters and historical fault data of each region, the monitoring importance of each region is identified, and the specific analysis of setting the initial monitoring level of each region is as follows: the structural influence coefficient of each region is obtained by calculating and multiplying the complexity of the power grid structure and the load density of each region. The structural influence coefficient is used to reflect the complexity of the power grid structure in each region and its potential impact on the overall stability of the power grid. The specific calculation formula is: ΑJ=α*ε, where ΑJ represents the structural influence coefficient, α represents the complexity of the power grid structure, and ε represents the load density; the historical fault number and the historical fault impact range of each region are calculated and multiplied to obtain the historical fault coefficient of each region. The historical fault coefficient is used to reflect the fault history of each region and the impact of each fault. The specific calculation formula is: Where ΑL represents the historical failure coefficient, δ represents the number of historical failures, It represents the impact range of historical faults; the importance score of each area is obtained by calculating the structural influence coefficient and the historical fault coefficient of each area. The importance score is used to evaluate the relative importance and priority monitoring level of each area in the overall power grid. The specific calculation formula is: PZ=ΑJ*κ1+ΑL*κ2, where PZ represents the importance score, κ1 represents the weight value of the structural influence coefficient, and κ2 represents the weight value of the historical fault coefficient; the initial monitoring level of each area is divided according to the level division interval of the importance score, and the initial monitoring level of each area specifically includes the first-level monitoring area, the second-level monitoring area and the third-level monitoring area.

[0024] In this implementation scheme, the weight values ​​corresponding to the structural influence coefficient and the historical failure coefficient are obtained in the following way: experts in the power system or distribution network field evaluate the importance of the structural influence coefficient and the historical failure coefficient based on their own experience and expertise, and give a reasonable weighting ratio. The weight values ​​can also be determined by analyzing historical data, counting the number of failures in different areas, the scope of impact, and the correlation with the complexity of the power grid structure, or using a machine learning algorithm to train the model with a large amount of historical data to automatically determine the optimal weight value. The complexity of the grid structure refers to the connection density and structural complexity of each node and line in the grid. A complex grid structure usually means more nodes and more complex connection relationships, which may increase the risk of fault propagation. Specifically, the topological structure diagram of the grid is obtained from the grid design and operation management system, including detailed information on nodes (substations, distribution boxes, etc.) and lines, and then network analysis tools or software (such as graph theory algorithms) are used to calculate the complexity indicators of the grid, such as node degree distribution, average path length, and connectivity to obtain the complexity of the grid structure. Understanding the structural complexity helps to prioritize monitoring and maintenance of key complex areas and reduce the risk of fault propagation. Load density refers to the amount of power load per unit area or unit length. Areas with higher load density need to carry more power and are prone to overload problems. Specifically, real-time load data is obtained from the load monitoring system of the distribution network, including the load at each monitoring point, and then the total load of each area is calculated and standardized by area or line length. , get the load density value. Understanding the load density can help to reasonably allocate monitoring resources and avoid excessive or insufficient monitoring; the number of historical faults refers to the total number of faults that occurred in each area in the past period of time. By obtaining historical fault records from the power company or the power grid operation management system, including the time, location and number of faults, and then statistically analyzing the historical fault records, the total number of faults in each area is calculated. The number of historical faults helps to predict possible future faults and improve preventive measures; the impact range of historical faults refers to the impact range of each fault on the distribution network, including the affected area and the number of power outage users. It is a key indicator for evaluating the severity and potential impact of the fault. By obtaining the impact range data of the fault from the fault handling report, including the power outage area and the affected users, the fault impact range is regionally counted and analyzed to determine the historical impact range of each area. Understanding the impact range of historical faults can help formulate more effective emergency response plans and reduce the impact of faults on users.

[0025] Specifically, the monitoring data of the edge monitoring points in each area are obtained, the abnormal areas are identified based on the monitoring data analysis of the edge monitoring points in each area, and the specific abnormal labels of the abnormal areas are determined, and then the final monitoring levels of the abnormal areas are updated in combination with the initial monitoring levels and specific abnormal labels of the abnormal areas. The specific analysis is as follows: the monitoring data of the edge monitoring points in each area specifically include the voltage fluctuation amplitude, power factor, harmonic content and power generation deviation value; the monitoring threshold data of the edge monitoring points in each area are obtained, and the monitoring threshold data of the edge monitoring points in each area specifically include the voltage fluctuation amplitude threshold, power factor threshold, harmonic content threshold and power generation deviation threshold value; compare the voltage fluctuation amplitude of each area with the voltage fluctuation amplitude threshold of the area, mark the area where the voltage fluctuation amplitude is greater than the voltage fluctuation amplitude threshold as an abnormal area, and the specific abnormal label is voltage fluctuation abnormality; compare the power factor of each area with the power factor threshold of the area, mark the area where the power factor is less than the power factor threshold as an abnormal area, and the specific abnormal label is power factor abnormality; compare the harmonic content of each area with the harmonic content threshold of the area, mark the area where the harmonic content is greater than the harmonic content threshold as an abnormal area, and the specific abnormal label is harmonic content abnormality; compare the power generation deviation value of each area with the power factor threshold of the area The power generation deviation threshold of the domain is compared, and the area where the power generation deviation value is greater than the power generation deviation threshold is marked as an abnormal area, and the specific abnormal label is power generation abnormality; the number of abnormal labels and the initial monitoring level of each abnormal area are counted, and the number of abnormal labels specifically includes one abnormal label, two abnormal labels, three abnormal labels and four abnormal labels, and then the final monitoring level is updated according to the number of abnormal labels and the initial monitoring level: the first-level monitoring area containing one abnormal label is updated to the first monitoring level; the first-level monitoring area containing two abnormal labels and the second-level monitoring area containing one abnormal label are updated to the third monitoring level. The first-level monitoring area with three abnormal labels, the second-level monitoring area with two abnormal labels and the third-level monitoring area with one abnormal label will be updated to the third monitoring level as the final monitoring level; the first-level monitoring area with four abnormal labels, the second-level monitoring area with three abnormal labels and the third-level monitoring area with two abnormal labels will be updated to the fourth monitoring level as the final monitoring level; the second-level monitoring area with four abnormal labels and the third-level monitoring area with three abnormal labels will be updated to the fifth monitoring level as the final monitoring level; the third-level monitoring area with four abnormal labels will be updated to the sixth monitoring level as the final monitoring level.

[0026] In this implementation scheme, the voltage fluctuation amplitude indicates the range of voltage variation in the power grid. Large voltage fluctuations can cause equipment damage or reduced efficiency. The voltage data in the power grid is obtained through a voltage sensor, and its fluctuation amplitude is calculated, or the voltage data recorded by the data acquisition system is used to analyze the voltage fluctuation range. The voltage fluctuation amplitude helps to promptly detect abnormal voltage fluctuations, prevent possible equipment failures, and improve the overall power quality of the power grid; the power factor indicates the ratio of actual power to apparent power in the power grid, reflecting the efficiency of power use. Low power factor can lead to power loss and increased equipment burden. The power factor in the power grid can be monitored through power factor meters or sensors, or power factor data can be recorded using an energy management system. Obtaining the power factor can help identify and improve areas with low power factor, improve power efficiency, and reduce energy loss. Harmonic content indicates the degree of distortion of the current or voltage waveform in the power grid. Higher harmonic content can cause equipment failure and system instability. Harmonic content in the power grid can be measured using a harmonic analyzer, or the harmonic components of current or voltage signals can be recorded and analyzed. Monitoring harmonic content can help protect power grid equipment, avoid failures caused by harmonics, and improve the power quality of the power grid. The power generation deviation value indicates the difference between actual power generation and expected power generation. A larger deviation value indicates equipment problems or system abnormalities. The actual power generation can be obtained in real time through the power generation monitoring system and compared with the expected power generation, or relevant data can be obtained from the power generation data recording system. Timely discovery of power generation deviations can help take measures to optimize power generation performance, ensure the normal operation of the power generation system, and reduce power generation interruptions.

[0027] The specific method for obtaining the voltage fluctuation amplitude threshold is: determine the normal fluctuation range based on historical voltage fluctuation data, set the threshold, or set a reasonable threshold with reference to industry standards and grid operation specifications; the specific method for obtaining the power factor threshold is: refer to the standards and specifications of the power industry, set the normal range of the power factor, or determine the normal threshold of the power factor based on historical monitoring data; the specific method for obtaining the harmonic content threshold is: set the threshold according to the harmonic control standards and specifications of the power system, or determine the normal range based on historical harmonic data analysis; the specific method for obtaining the power generation deviation threshold is: analyze historical power generation data, determine the normal power generation range and deviation threshold, or use the power generation system operation model to predict the normal power generation deviation range.

[0028] Through multi-dimensional monitoring of voltage fluctuation amplitude, power factor, harmonic content and power generation deviation value, abnormal situations can be captured more comprehensively, and omissions and false alarms can be reduced. Based on the updated monitoring level, monitoring resources and maintenance personnel can be allocated more effectively, and high-risk areas can be given priority, thereby improving management efficiency. The dynamically updated monitoring level can quickly respond to newly discovered abnormal areas, reduce the impact time of faults on the system, and improve the overall stability and reliability of the distribution network of new energy photovoltaic power stations.

[0029] Specifically, the evaluation information of the new energy photovoltaic power station distribution network at the monitoring points in each abnormal area is specifically the energy utilization rate; the difference information between adjacent monitoring points specifically includes the equipment temperature difference value.

[0030] Based on the evaluation information of the new energy photovoltaic power station distribution network of the monitoring points in each abnormal area and the difference information between adjacent monitoring points, the monitoring points in each abnormal area are checked, and the specific analysis of identifying and marking the suspected monitoring points in each abnormal area is as follows: according to the fault detection ranking of each abnormal area, the energy utilization rate of the monitoring points in each abnormal area is compared with the energy utilization rate threshold in turn, and the equipment temperature difference value between the monitoring point and the adjacent monitoring point is compared with the equipment temperature difference value compliance range; the monitoring points whose energy utilization rate is lower than the energy utilization rate threshold and whose equipment temperature difference value with the adjacent monitoring point is not within the equipment temperature difference value compliance range are marked as suspected monitoring points.

[0031] In this implementation scheme, the energy utilization rate represents the ratio of the actual power generation of the photovoltaic power station to the theoretical maximum power generation. A lower energy utilization rate indicates that the equipment is inefficient or faulty. The actual power generation is recorded and calculated in real time by the photovoltaic power generation monitoring system. The theoretical maximum power generation can also be calculated based on the design parameters and environmental conditions of the photovoltaic system, or the actual power generation can be compared with the theoretical power generation to calculate the energy utilization rate. Understanding the energy utilization rate can evaluate the operating efficiency of the photovoltaic power station, promptly discover the reasons for performance degradation, and improve the economic benefits of the photovoltaic power station; the equipment temperature difference value represents the equipment temperature difference between adjacent monitoring points. A large temperature difference indicates that there is a problem with the equipment or the load is uneven. The equipment temperature is monitored in real time by a temperature sensor installed on the equipment, or the temperature data of adjacent monitoring points are recorded and the difference value is calculated. Abnormal identification of temperature difference helps in timely maintenance, helps optimize the load distribution of equipment, and prevents overload.

[0032] The specific method for obtaining the energy utilization rate threshold is: analyzing the historical energy utilization rate data of the photovoltaic power station, determining the normal range and threshold, or setting a reasonable energy utilization rate threshold with reference to industry standards and best practices; the specific method for obtaining the equipment temperature difference value compliance range is: analyzing the historical data of the equipment temperature difference, determining the normal range and the temperature difference compliance range, or setting a reasonable temperature difference range based on the specifications and recommendations provided by the equipment manufacturer.

[0033] Comprehensive consideration of energy utilization and equipment temperature difference values ​​can help to more accurately identify faulty monitoring points and reduce false alarms and missed alarms. After marking suspected monitoring points, maintenance and inspection can be prioritized to improve the efficiency and pertinence of fault handling. By accurately identifying and handling fault points, equipment damage and system downtime can be reduced, thereby improving the overall stability and reliability of new energy photovoltaic power stations. Accurate troubleshooting can reduce unnecessary maintenance work and save maintenance resources and costs.

[0034] Specifically, the specific analysis of the detection and maintenance path of the suspected monitoring points in each abnormal area is generated based on the distribution network impact information of the suspected monitoring points: the distribution network impact information of the suspected monitoring points specifically includes: the number of connected nodes and the load distribution range; numerical calculation is performed based on the number of connected nodes and the load distribution range to obtain the detection and maintenance demand score, and then the detection and maintenance order of the suspected monitoring points in each abnormal area is generated based on the detection and maintenance demand score. The detection and maintenance demand score is used to indicate the degree to which each suspected monitoring point needs to be inspected and maintained first in the abnormal area. The higher the score, the greater the impact of the monitoring point on the power grid, and the priority treatment is required. The specific detection and maintenance demand score calculation formula is: Where JW represents the inspection and maintenance demand score, JD represents the number of connected nodes, FH represents the load distribution range, and JD max Indicates the maximum number of connected nodes, FH max Indicates the maximum load distribution range.

[0035] In this implementation scheme, the number of connected nodes refers to the number of nodes directly connected to a monitoring point in the distribution network, including substations, distribution boxes, and transmission lines. Monitoring points with a large number of connected nodes will have an impact on a larger range of power grids. Specifically, node information is obtained from the design diagram or network topology diagram of the power grid, or the number of connected nodes of each monitoring point is calculated using power grid analysis software; the load distribution range refers to the range of the power load carried by the monitoring point, including the spatial distribution of the load and the size of the load. Monitoring points with a larger load distribution range generally carry a larger power demand, which has a greater impact on the stability of the power grid. The load data of each monitoring point is obtained through a real-time load monitoring system, or the load distribution range is understood by analyzing historical load data. The specific method for obtaining the maximum number of connected nodes is: by analyzing historical power grid structure data, find the maximum number of connected nodes among all monitoring points; the specific method for obtaining the maximum load distribution range is: extract the load distribution range of all monitoring points from historical load data and find the maximum value therein.

[0036] By calculating the inspection and maintenance demand score, each suspected monitoring point can be assigned a priority to ensure that resources are used first for the points that need inspection and maintenance the most. By generating a maintenance sequence based on the score, the use of maintenance resources can be optimized and over-investment in unnecessary points can be avoided, thus saving time and costs. Through a clear inspection and maintenance sequence, the most serious faults can be responded to and handled quickly, reducing system downtime and the impact of faults. Timely maintenance of monitoring points with greater impact will help improve the stability and reliability of the entire distribution network and reduce the expansion and impact of faults.

[0037] A fault monitoring method for a new energy photovoltaic power station distribution network, using the above-mentioned fault monitoring system for a new energy photovoltaic power station distribution network, comprises: a region level setting module, used to obtain structural influencing parameters and historical fault data of each region of the new energy photovoltaic power station distribution network, and then identify the monitoring importance of each region according to the structural influencing parameters and historical fault data of each region, and set the initial monitoring level of each region; an abnormal region identification module, used to obtain the monitoring data of the edge monitoring points of each region, analyze and identify the abnormal region based on the monitoring data of the edge monitoring points of each region, and determine the specific abnormal label of the abnormal region, and then update the final monitoring level of each abnormal region in combination with the initial monitoring level and the specific abnormal label of the abnormal region, and sort the fault detection of each abnormal region according to the final monitoring level of each abnormal region; a suspected monitoring point identification module, used to analyze the evaluation information of the new energy photovoltaic power station distribution network of the monitoring points in each abnormal region and the difference information between adjacent monitoring points in turn according to the fault detection order, check the monitoring points in each abnormal region, identify and mark the suspected monitoring points in each abnormal region; a detection sequence path setting module, used to generate the detection and maintenance path of the suspected monitoring points in each abnormal region according to the distribution network impact information of the suspected monitoring points.

[0038] In summary, this application has at least the following effects:

[0039] By obtaining the structural influencing parameters and historical fault data of each area, targeted analysis can be carried out based on past fault conditions, thereby improving the accuracy of fault identification; data analysis of edge monitoring points can quickly identify abnormal areas, ensure that fault detection can respond quickly, and reduce the possibility of fault spread; the initially set monitoring level can give priority to those areas with greater impact on the power grid, ensuring that limited monitoring resources are used in the most critical areas, improving resource utilization efficiency; sorting abnormal areas based on the final monitoring level helps to reasonably allocate inspection resources and give priority to the most serious faults, thereby reducing downtime and maintenance costs; troubleshooting the monitoring points in each abnormal area based on the evaluation information and the difference information of adjacent monitoring points can quickly locate the problem area, thereby improving the efficiency of fault troubleshooting; by comprehensively evaluating the monitoring points in the abnormal area, possible fault points can be effectively identified, thereby narrowing the inspection scope and improving the accuracy of fault detection; generating detection and maintenance paths based on the distribution network impact information of suspected monitoring points helps to formulate the most optimized maintenance strategy, which not only improves the pertinence of maintenance work, but also minimizes interference with normal operation, effectively ensuring the continuous and stable operation of new energy photovoltaic power stations.

[0040] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods and systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0041] The present invention is described with reference to the flowcharts and structure diagrams of the methods and systems according to the embodiments of the present invention. It should be understood that each process and combination of modules in the flowcharts and structure diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 process or processes and structures Figure 1 A device that specifies functionality in a module or modules.

[0042] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 process or processes and structures Figure 1 A function specified in a module or multiple modules.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 process or processes and structures Figure 1 Steps to specify functionality in a module or multiple modules.

[0044] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0045] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A fault monitoring method for a new energy photovoltaic power station distribution network, characterized in that: The following steps are involved: Obtain the structural influencing parameters and historical fault data of each area of ​​the new energy photovoltaic power station distribution network, and then identify the monitoring importance of each area based on the structural influencing parameters and historical fault data of each area, and set the initial monitoring level of each area; Obtain monitoring data from edge monitoring points in each area, analyze and identify abnormal areas based on the monitoring data from edge monitoring points in each area, and determine specific abnormal labels for the abnormal areas, and then update the final monitoring level of each abnormal area based on the initial monitoring level and specific abnormal labels of the abnormal areas, and sort the fault detection of each abnormal area according to the final monitoring level of each abnormal area; According to the fault detection sequence, the evaluation information of the new energy photovoltaic power station distribution network at the monitoring points in each abnormal area and the difference information between adjacent monitoring points are analyzed in turn, the monitoring points in each abnormal area are checked, and the suspected monitoring points in each abnormal area are identified and marked; The detection and maintenance paths of the suspected monitoring points in each abnormal area are generated based on the distribution network impact information of the suspected monitoring points.

2. A fault monitoring method for a new energy photovoltaic power station distribution network according to claim 1, characterized in that: The structural influencing parameters of each area of ​​the new energy photovoltaic power station distribution network specifically include: grid structure complexity and load density; The historical fault data specifically includes: the number of historical faults and the impact range of historical faults.

3. A fault monitoring method for a new energy photovoltaic power station distribution network according to claim 2, characterized in that: According to the structural influencing parameters and historical fault data of each area, the monitoring importance of each area is identified, and the specific analysis of setting the initial monitoring level of each area is as follows: The structural complexity and load density of each area are calculated and multiplied to obtain the structural influence coefficient of each area; The historical failure coefficient of each area is obtained by calculating the product of the historical failure times and the historical failure impact range of each area; By calculating the structural influence coefficient and historical failure coefficient of each area, the importance score of each area is obtained; The initial monitoring level of each area is divided according to the level division interval of the importance score, and the initial monitoring level of each area specifically includes a primary monitoring area, a secondary monitoring area and a tertiary monitoring area.

4. A fault monitoring method for a new energy photovoltaic power station distribution network according to claim 3, characterized in that: The monitoring data of the edge monitoring points of each area are obtained, and the abnormal areas are identified based on the monitoring data of the edge monitoring points of each area, and the specific abnormal labels of the abnormal areas are determined. Then, the final monitoring level of each abnormal area is updated in combination with the initial monitoring level and the specific abnormal label of the abnormal area. The specific analysis is as follows: The monitoring data of the edge monitoring points in each area specifically include voltage fluctuation amplitude, power factor, harmonic content and power generation deviation value; Acquire monitoring threshold data of edge monitoring points in each area, wherein the monitoring threshold data of edge monitoring points in each area specifically include voltage fluctuation amplitude threshold, power factor threshold, harmonic content threshold and power generation deviation threshold; The voltage fluctuation amplitude of each area is compared with the voltage fluctuation amplitude threshold of the area, and the area where the voltage fluctuation amplitude is greater than the voltage fluctuation amplitude threshold is marked as an abnormal area, and the specific abnormal label is voltage fluctuation abnormality; The power factor of each area is compared with the power factor threshold of the area, and the area with a power factor less than the power factor threshold is marked as an abnormal area, and the specific abnormal label is power factor abnormality; The harmonic content of each area is compared with the harmonic content threshold of the area, and the area with a harmonic content greater than the harmonic content threshold is marked as an abnormal area, and the specific abnormal label is harmonic content abnormality; The power generation deviation value of each area is compared with the power generation deviation threshold of the area, and the area where the power generation deviation value is greater than the power generation deviation threshold is marked as an abnormal area, and the specific abnormal label is power generation abnormality; The number of abnormal labels and the initial monitoring level of each abnormal area are counted. The number of abnormal labels specifically includes one abnormal label, two abnormal labels, three abnormal labels and four abnormal labels. Then the final monitoring level is updated according to the number of abnormal labels and the initial monitoring level: Update the final monitoring level of the first-level monitoring area containing an abnormal label to the first monitoring level; Update the final monitoring level of the first-level monitoring area containing two abnormal labels and the second-level monitoring area containing one abnormal label to the second monitoring level; Update the final monitoring level of the first-level monitoring area containing three abnormal labels, the second-level monitoring area containing two abnormal labels, and the third-level monitoring area containing one abnormal label to the third monitoring level; The first-level monitoring area with four abnormal labels, the second-level monitoring area with three abnormal labels, and the rest of the third-level monitoring area with two abnormal labels will be updated to the fourth monitoring level; Update the final monitoring level of the second-level monitoring area containing four abnormal labels and the third-level monitoring area containing three abnormal labels to the fifth monitoring level; The final monitoring level of the third-level monitoring area containing four abnormal labels will be updated to the sixth monitoring level.

5. A fault monitoring method for a new energy photovoltaic power station distribution network according to claim 1, characterized in that: The evaluation information of the new energy photovoltaic power station distribution network at the monitoring points in each abnormal area is specifically the energy utilization rate; The difference information between adjacent monitoring points specifically includes device temperature difference values.

6. A fault monitoring method for a new energy photovoltaic power station distribution network according to claim 5, characterized in that: According to the fault detection sequence, the evaluation information of the new energy photovoltaic power station distribution network at the monitoring points in each abnormal area and the difference information between adjacent monitoring points are analyzed in turn, the monitoring points in each abnormal area are checked, and the suspected monitoring points in each abnormal area are identified and marked. The specific analysis is as follows: According to the fault detection ranking of each abnormal area, the energy utilization rate of the monitoring point in each abnormal area is compared with the energy utilization rate threshold in turn, and the equipment temperature difference value between the monitoring point and the adjacent monitoring point is compared with the equipment temperature difference value compliance range; A monitoring point whose energy utilization rate is lower than the energy utilization rate threshold and whose equipment temperature difference value with an adjacent monitoring point is not within the equipment temperature difference value compliance range is marked as a suspected monitoring point.

7. A fault monitoring method for a new energy photovoltaic power station distribution network according to claim 1, characterized in that: The specific analysis of generating the detection and maintenance path of the suspected monitoring points in each abnormal area based on the distribution network impact information of the suspected monitoring points is as follows: The distribution network impact information of the suspected monitoring point specifically includes: the number of connected nodes and load distribution range; Numerical calculations are performed based on the number of connected nodes and the load distribution range to obtain the detection and maintenance demand score, and then the detection and maintenance order of the suspected monitoring points in each abnormal area is generated based on the detection and maintenance demand score.

8. A fault monitoring method for a new energy photovoltaic power station distribution network, using a fault monitoring system for a new energy photovoltaic power station distribution network as claimed in any one of claims 1 to 7, characterized in that: include: The regional level setting module is used to obtain the structural influencing parameters and historical fault data of each area of ​​the new energy photovoltaic power station distribution network, and then identify the monitoring importance of each area based on the structural influencing parameters and historical fault data of each area, and set the initial monitoring level of each area; The abnormal area identification module is used to obtain the monitoring data of the edge monitoring points of each area, analyze and identify the abnormal area based on the monitoring data of the edge monitoring points of each area, and determine the specific abnormal label of the abnormal area, and then update the final monitoring level of each abnormal area in combination with the initial monitoring level and the specific abnormal label of the abnormal area, and sort the fault detection of each abnormal area according to the final monitoring level of each abnormal area; Suspected monitoring point identification module is used to analyze the evaluation information of the new energy photovoltaic power station distribution network of the monitoring points in each abnormal area and the difference information between adjacent monitoring points in turn according to the fault detection order, check the monitoring points in each abnormal area, and identify and mark the suspected monitoring points in each abnormal area; The detection sequence path setting module is used to generate the detection and maintenance path of the suspected monitoring points in each abnormal area according to the distribution network impact information of the suspected monitoring points.

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