Method and system for generating optimal patrol path of medium-voltage power distribution network, and medium

Abstract

The invention relates to the technical field of data path generation, in particular to a medium-voltage power distribution network optimal patrol path generation method and system and a medium. The method comprises the following steps: acquiring an original data set of the medium-voltage power distribution network; performing data preprocessing on the medium-voltage power distribution network original data set to generate a medium-voltage power distribution network processing data set; acquiring real-time voltage data; reactive power is extracted from the medium-voltage power distribution network processing data set, and medium-voltage feeder reactive power data is generated; performing coefficient correlation on the reactive power data and the real-time voltage data of the medium-voltage feeder line by using a preset equipment switching frequency and service life relation model to generate an equipment service life-reactive voltage coefficient; therefore, through multi-level data processing and analysis, the problems of low efficiency and inaccurate path planning in the traditional medium-voltage power distribution network inspection path generation are solved, and the intelligence and optimality of the inspection path are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data path generation, and in particular to a method, system and medium for generating an optimal patrol path for a medium-voltage distribution network. Background Art

[0002] The traditional methods for patrol path generation in medium-voltage distribution networks have evolved from preliminary static models to more complex dynamic adjustment models during their development. However, these methods still have significant deficiencies in data processing and path optimization. Initially, the methods mainly relied on static data analysis and preset rules, which could not effectively cope with the real-time changes in equipment status in actual operation, resulting in the inability to flexibly adjust path planning. With the advancement of technology, the methods gradually introduced the concepts of dynamic factors and local optimization, but it is still difficult to achieve comprehensive and accurate dynamic response when processing equipment life, reactive power and voltage data. Although these advances have improved the effect of path planning to a certain extent, traditional methods still have problems such as insufficient data timeliness processing, limited dynamic adjustment capabilities and poor optimization effects. These deficiencies limit the application efficiency and effectiveness of patrol path generation methods in complex and dynamic environments. In general, traditional patrol path generation methods have obvious deficiencies in data processing and path optimization, including lack of dynamic adaptability, failure to fully consider data timeliness and local optimization requirements, and failure to effectively integrate dynamic factors in the path optimization process. These deficiencies limit the effectiveness and efficiency of traditional methods in practical applications, and they are in urgent need of improvement and optimization. Summary of the invention

[0003] Based on this, it is necessary to provide a method, system and medium for generating an optimal patrol path for a medium-voltage distribution network to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above object, a method for generating an optimal patrol path for a medium voltage distribution network is provided, the method comprising the following steps:

[0005] Step S1: obtaining a raw data set of a medium-voltage distribution network; performing data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network;

[0006] Step S2: acquiring real-time voltage data; extracting reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; using a preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient;

[0007] Step S3: Distributed optimization is performed on the reactive power data and real-time voltage data of the medium voltage feeder to generate distributed data of the medium voltage distribution network; time-sharing calculation is performed on the distributed data of the medium voltage distribution network based on a preset intelligent decision-making algorithm to generate reactive power-voltage control instructions;

[0008] Step S4: construct a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a medium-voltage distribution network patrol path algorithm; use the medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processing data set to generate a medium-voltage distribution network patrol path; output a preset equipment switching number and life relationship model, and output medium-voltage distribution network life data; match the life status and reactive voltage of the medium-voltage distribution network life data, and output life status-reactive voltage matching data; evaluate and adjust the optimal path of the medium-voltage distribution network patrol path through the life status-reactive voltage matching data to generate the optimal path of the medium-voltage distribution network.

[0009] The beneficial effect of the present invention is that by extracting real-time voltage data and reactive power data, a medium-voltage feeder reactive power data set is constructed, and combined with a preset equipment switching number and life relationship model, an equipment life-reactive voltage coefficient is generated. In this process, the multi-dimensional association of data significantly improves the expression of the relationship between reactive power and equipment life, ensuring the accuracy and consistency of the data set. The application of distributed optimization technology refines reactive power data and real-time voltage data to generate distributed optimized distribution network data, ensuring the dynamic adaptability of the control strategy. At the same time, the time-sharing calculation strategy based on the intelligent decision-making algorithm not only enhances the timeliness of reactive-voltage control instructions, but also optimizes the voltage reactive control operation in multiple time periods. In the generation and evaluation process of the patrol path, the joint application of equipment life-reactive voltage coefficient and reactive-voltage control instructions realizes the efficient construction of the patrol path algorithm, and further uses the life state-reactive voltage matching data to optimally evaluate and adjust the generated patrol path, effectively improving the patrol path planning effect of the medium-voltage distribution network. Through this method, the matching of the life status of the equipment and the reactive voltage is fully considered, ensuring the accuracy and operability of the optimal path. In addition, the life data matching mechanism introduced by the present invention can not only realize real-time monitoring of the health status of the equipment, but also provide a scientific basis for the dynamic adjustment of the optimal patrol path. The implementation of this step makes the operation path of the complex system more efficient, accurate and intelligent through deep mining and processing at the data level. Therefore, the present invention solves the problems of low efficiency and inaccurate path planning in the generation of patrol paths in traditional medium-voltage distribution networks through multi-level data processing and analysis, and improves the intelligence and optimality of the patrol path.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire the original data set of the medium voltage distribution network;

[0012] Step S12: performing data redundancy reduction processing on the original data set of the medium-voltage distribution network to generate a medium-voltage distribution network redundancy reduction data set; performing data set calibration on the medium-voltage distribution network redundancy reduction data set to generate a medium-voltage distribution network calibration data set;

[0013] Step S13: performing data set standardization processing on the medium voltage distribution network calibration data set to generate a medium voltage distribution network processed data set.

[0014] The present invention effectively removes redundant data in the original data set of the medium-voltage distribution network through data redundancy reduction processing, and generates a medium-voltage distribution network redundancy reduction data set. The core of this process is to reduce data redundancy, thereby avoiding the negative impact of the expansion of the data volume on the subsequent analysis and processing process. The removal of data redundancy not only reduces the storage demand, but also improves the efficiency and accuracy of data processing. Next, the data set after redundancy reduction is calibrated to generate a medium-voltage distribution network calibration data set. Data set calibration is to ensure the accuracy and reliability of the data by correcting and checking the consistency of the data. At this stage, through the calibration process, the errors and deviations in the data are effectively corrected, so that the data set is more in line with the actual situation, and high-quality basic data is provided for subsequent analysis. Finally, the calibrated data set is standardized to generate a medium-voltage distribution network processing data set. Data standardization is to ensure the consistency and comparability of the data by converting the data into a unified standard format. This processing step greatly improves the availability of data and the accuracy of analysis, and lays a solid foundation for subsequent complex data analysis and model building.

[0015] Preferably, step S2 comprises the following steps:

[0016] Step S21: acquiring real-time voltage data and historical operation data; extracting reactive power from the medium voltage distribution network processing data set to generate medium voltage feeder reactive power data;

[0017] Step S22: using a preset equipment switching times and life relationship model to correlate the reactive power data and real-time voltage data of the medium voltage feeder to generate a preliminary correlation coefficient between equipment life and reactive voltage;

[0018] Step S23: Use a dynamic coefficient matching algorithm to perform voltage-power correlation analysis on the equipment life-reactive voltage preliminary correlation coefficient and historical operation data to generate an equipment life-reactive voltage coefficient.

[0019] The present invention generates medium voltage feeder reactive power data by extracting real-time voltage data and historical operation data, and combining the reactive power extracted from the medium voltage distribution network processing data set. This process ensures the synchronous analysis of historical data and real-time data, not only providing a more comprehensive perspective for the equipment operation status, but also providing high-quality data support for subsequent correlation analysis. Based on the preset equipment switching times and life relationship model, the medium voltage feeder reactive power data and real-time voltage data are coefficient-correlated, and the equipment life-reactive voltage preliminary correlation coefficient is generated. This link utilizes the inherent relationship between the switching times and the equipment life, and through the fine correlation of data, the synchronization and correlation of the life and reactive voltage changes are significantly improved. Through the dynamic coefficient matching algorithm, the preliminary correlation coefficient and the historical operation data are deeply analyzed for voltage-power correlation, and the final equipment life-reactive voltage coefficient is generated. The introduction of the dynamic coefficient matching algorithm makes the data analysis process more flexible, can respond to the changes in the equipment operation status in real time, dynamically adjust the correlation coefficient, and ensure the accuracy and real-time performance of the analysis results.

[0020] Preferably, step S3 comprises the following steps:

[0021] Step S31: regionally grouping medium-voltage feeder reactive power data and real-time voltage data to generate medium-voltage distribution network regional grouping data; performing distributed optimization on the medium-voltage distribution network regional grouping data to generate medium-voltage distribution network distributed data;

[0022] Step S32: Perform time-sharing calculation on the distributed data of the medium-voltage distribution network to generate reactive voltage data for the medium-voltage distribution network during a period of time; perform comparative analysis on the reactive voltage data for the medium-voltage distribution network during a period of time and the historical operation data to generate comparative analysis data for the medium-voltage distribution network; perform reactive compensation processing on the comparative analysis data of the medium-voltage distribution network using a preset equipment switching times and life relationship model to generate distributed supplementary processing data; use an intelligent decision-making algorithm to generate voltage control instructions for the distributed supplementary processing data to generate reactive-voltage control instructions.

[0023] The present invention generates medium-voltage distribution network regional grouping data by regionally grouping medium-voltage feeder reactive power data and real-time voltage data. This process divides the entire distribution network into multiple independent regions, so that the data processing and optimization of each region can be more accurate, reducing the complexity of large-scale data processing and enhancing the local flexibility of processing. Through regional distributed optimization, the distributed data of the medium-voltage distribution network is generated, thereby further improving the efficiency and reliability of the overall reactive power distribution. The distributed data is deeply analyzed in time periods through time-sharing calculation, and the time period reactive voltage data of the medium-voltage distribution network is generated. Through this time period division, the reactive power regulation of the distribution network becomes more refined and can dynamically respond to the voltage fluctuations in each time period. Next, the time period reactive voltage data and historical operation data are compared and analyzed, and the comparative analysis data of the medium-voltage distribution network is generated, ensuring the synchronization of historical data with the current operating state, and providing a basis for the accuracy of reactive compensation. Through the reactive compensation processing of the comparative analysis data, distributed supplementary processing data is generated, and the reactive power allocation strategy is further optimized. Finally, an intelligent decision-making algorithm is used to generate voltage control instructions for distributed supplementary processing data, realizing automated and intelligent reactive-voltage control instruction issuance.

[0024] Preferably, step S31 includes the following steps:

[0025] Step S311: extracting feeder geographical location data and load distribution data from the original data set of the medium voltage distribution network, and generating feeder geographical location data and medium voltage distribution network load distribution data;

[0026] Step S312: performing regional grouping on the medium voltage feeder reactive power data and the real-time voltage data to generate medium voltage distribution network regional grouping data;

[0027] Step S313: using the feeder geographic location data to perform distributed optimization calculations on the medium voltage distribution network regional grouping data, and analyzing it in combination with the medium voltage distribution network load distribution data to generate medium voltage distribution network distributed data.

[0028] The present invention generates feeder geographic location data and medium voltage distribution network load distribution data by extracting feeder geographic location and load distribution data from the original data set of the medium voltage distribution network. This process provides basic information of geographic space and load distribution for subsequent optimization analysis, so that the data processing of the distribution network has the support of dual dimensions of space and load. The introduction of geographic location data ensures that the geographical characteristics of different regions can be considered in the feeder optimization process, avoiding the neglect of spatial differences in the traditional single parameter optimization process. The reactive power data and real-time voltage data of the medium voltage feeder are regionally grouped to generate regional grouping data of the medium voltage distribution network. This step divides the entire distribution network into different regions, so that the data processing of each region can be more finely processed according to the geographic location and load characteristics, thereby enhancing the local flexibility and optimization efficiency of the data. The regional grouping data of the medium voltage distribution network is distributedly optimized and calculated using the geographic location data of the feeder, and in-depth analysis is performed in combination with the load distribution data to generate distributed data of the medium voltage distribution network. This process comprehensively considers the feeders and their load characteristics in different geographic locations within the region, making the allocation and scheduling of reactive power more reasonable. By combining and analyzing geographic location data with load distribution data, the present invention optimizes the regional load balance and voltage stability of the distribution network while ensuring the accuracy of reactive power regulation, thereby improving the overall operating efficiency and safety of the distribution network.

[0029] Preferably, step S4 comprises the following steps:

[0030] Step S41: constructing a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a patrol path algorithm for the medium-voltage distribution network; performing patrol path analysis on the medium-voltage distribution network processing data set using the patrol path algorithm for the medium-voltage distribution network to generate a patrol path for the medium-voltage distribution network;

[0031] Step S42: Output the preset equipment switching times and life relationship model to generate equipment life data; associate the equipment life data with medium-voltage distribution equipment to generate medium-voltage distribution network life data; match the life state and reactive voltage of the medium-voltage distribution network life data to output life state-reactive voltage matching degree data;

[0032] Step S43: the optimal path evaluation and optimization of the patrol path of the medium-voltage distribution network is performed through the life state-reactive voltage matching data, the optimal path of the medium-voltage distribution network is generated, and the optimal patrol path generation operation is completed.

[0033] The present invention generates a patrol path algorithm for a medium-voltage distribution network by combining the equipment life-reactive voltage coefficient with the reactive-voltage control instruction. This algorithm is based on the correlation between the equipment life and its reactive voltage operating state, ensures the scientificity and rationality of the path planning, can design the optimal patrol path according to the life state of different equipment, and avoids the problem of unclear equipment state or lack of pertinence in the path planning during the traditional patrol process. After analyzing the processed data set of the medium-voltage distribution network using the algorithm, a specific patrol path is generated. This step enables the patrol work of the distribution network to fully consider the health status and operation of the equipment, greatly improving the efficiency and effect of the patrol. The preset equipment switching number and life relationship model is applied to the generation of equipment life data, and further through the correlation analysis of the medium-voltage distribution equipment, the overall life data of the distribution network is generated. This life data not only reflects the current health status of the equipment, but also provides the constraint conditions of the life status for the control of the reactive voltage, thereby realizing the deep coupling of life management and voltage control. On this basis, the life status and reactive voltage matching are analyzed, and the life status-reactive voltage matching data is output. This data directly reflects the matching degree between the equipment life status and voltage control, making the subsequent patrol path optimization more accurate. Through the life status-reactive voltage matching data, the generated patrol path is evaluated and optimized for the optimal path, further ensuring the close integration of the patrol path with the equipment health status and voltage control requirements, and generating the optimal patrol path for the medium-voltage distribution network.

[0034] Preferably, step S41 includes the following steps:

[0035] Step S411: combining the equipment life-reactive voltage coefficient and the reactive-voltage control instruction by coefficient-instruction, generating matching coefficient-control instruction data; constructing a patrol path algorithm for the matching coefficient-control instruction data, and generating a medium-voltage distribution network patrol path algorithm framework by combining a preset equipment switching times and life relationship model;

[0036] Step S412: using the distributed data of the medium-voltage distribution network to adjust the algorithm parameter weight distribution of the medium-voltage distribution network patrol path algorithm framework, and generating a medium-voltage distribution network patrol path algorithm;

[0037] Step S413: using a medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processed data set to generate a medium-voltage distribution network patrol path.

[0038] The present invention generates matching coefficient-control instruction data by combining equipment life-reactive voltage coefficient with reactive-voltage control instruction. This step provides a basis for comprehensive consideration of equipment life and voltage control state for subsequent algorithm construction by data coupling of equipment life with reactive voltage state and control instruction, so that the generated patrol path can dynamically adapt to the health status and real-time operating conditions of the equipment. Then, based on these data, combined with the preset equipment switching times and life relationship model, a patrol path algorithm framework for medium-voltage distribution network is constructed. This framework not only provides dual constraints of equipment life and control requirements for path planning, but also enhances the flexibility and scalability of the path algorithm, which is helpful for effective algorithm optimization in complex distribution network operation environment. The parameter weights of the patrol path algorithm framework are allocated and adjusted using the distributed data of the medium-voltage distribution network, and a complete patrol path algorithm is generated. This step further optimizes the algorithm parameter weights of each region through in-depth analysis of distributed data, so that the algorithm can be adaptively adjusted according to the characteristics of different regions of the distribution network, especially in the scenario of distributed load and reactive power regulation, which enhances the rationality and accuracy of path optimization. Through this dynamic weight adjustment of parameters, the algorithm can not only better match the equipment life with the voltage control instructions, but also more effectively cope with the changes in load distribution and voltage fluctuations in different regions. The patrol path analysis of the medium-voltage distribution network processing data set was performed using the generated patrol path algorithm to generate the final patrol path of the medium-voltage distribution network. This process uses an optimization algorithm based on equipment life and voltage control requirements to make the generated patrol path more in line with actual operation requirements, and can dynamically adapt to the real-time data of the distribution network, avoiding the reliance on static parameters in traditional path planning. The design of the entire algorithm not only improves the efficiency of patrol work, but also reduces the problem of shortened equipment life due to excessive use, ensuring the operational reliability of the distribution network and the accuracy of life management.

[0039] Preferably, step S43 includes the following steps:

[0040] Step S431: Acquire historical data of equipment switching times; perform unstable state analysis on the patrol path of the medium voltage distribution network through life state-reactive voltage matching data, and generate life state unstable node data;

[0041] Step S432: Preliminary optimization of the life state unstable node data is performed using the path evaluation algorithm, and node adjustment is performed using the historical data of the number of equipment switching times to generate a preliminary path dynamic adjustment factor; the preliminary path dynamic adjustment factor is fitted and analyzed using the reactive voltage data of the medium voltage distribution network period to generate a dynamic path adjustment factor of the medium voltage distribution network;

[0042] Step S433: using the dynamic path adjustment factor of the medium-voltage distribution network to perform optimal path evaluation and optimization on the patrol path of the medium-voltage distribution network, generating the optimal path of the medium-voltage distribution network, and completing the optimal patrol path generation operation.

[0043] The present invention obtains the historical data of the number of equipment switching times, and uses the life state-reactive voltage matching data to analyze the unstable state of the patrol path of the medium-voltage distribution network, and generates the life state unstable node data. This process identifies the key nodes that affect the operation stability and efficiency in the medium-voltage distribution network through a comprehensive analysis of the equipment life state and the reactive voltage matching degree, ensures the accuracy of the path planning in the equipment operation life, and avoids the instability risk caused by ignoring the aging state of the equipment in the traditional path planning. The generated unstable node data is preliminarily optimized by using the path evaluation algorithm, and the node is adjusted in combination with the historical data of the number of equipment switching times, and the preliminary path dynamic adjustment factor is generated. This process improves the adaptive ability of path planning to the change of equipment operation state by introducing historical data, so that the algorithm can dynamically adjust the node according to the actual number of equipment switching times, and prevent excessive switching or unreasonable path arrangement from having an adverse effect on the equipment life. At the same time, through the fitting analysis of the reactive voltage data of the medium-voltage distribution network period, the preliminary path dynamic adjustment factor is further optimized, and a dynamic path adjustment factor of the medium-voltage distribution network that is more in line with the real-time voltage state is generated. This process achieves a deep integration of equipment life, reactive voltage and path planning, enhances the real-time and adaptability of path planning, and ensures that the path can dynamically respond to voltage fluctuations and changes in equipment life status. The dynamic path adjustment factor of the medium-voltage distribution network is used to evaluate and optimize the patrol path, and the final optimal path of the medium-voltage distribution network is generated. This step comprehensively considers the life status of the equipment, voltage control requirements, and dynamic adjustment of historical data, so that the generated optimal path not only improves the efficiency of the patrol work, but also ensures the safety of equipment operation and the stability of the power system. Compared with traditional static path planning, this algorithm greatly improves the accuracy and flexibility of path planning, and can effectively avoid planning errors caused by over-reliance on static data.

[0044] In this specification, a medium-voltage distribution network transfer operation identification system is provided, which is used to execute the above-mentioned medium-voltage distribution network transfer operation identification method, and the medium-voltage distribution network transfer operation identification system includes:

[0045] The raw data acquisition and processing module is used to obtain the raw data set of the medium-voltage distribution network; perform data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network;

[0046] Real-time voltage and reactive power analysis module: used to obtain real-time voltage data; extract reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; use the preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient;

[0047] Distributed optimization and control instruction generation module: used to perform distributed optimization on medium-voltage feeder reactive power data and real-time voltage data, and generate distributed data on the medium-voltage distribution network; perform time-sharing calculations on the distributed data on the medium-voltage distribution network based on a preset intelligent decision-making algorithm, and generate reactive-voltage control instructions;

[0048] The patrol path construction and optimal path evaluation module is used to construct the patrol path algorithm of the equipment life-reactive voltage coefficient and the reactive-voltage control instructions to generate the patrol path algorithm of the medium-voltage distribution network; the patrol path analysis of the medium-voltage distribution network processing data set is performed using the medium-voltage distribution network patrol path algorithm to generate the patrol path of the medium-voltage distribution network; the preset equipment switching times and life relationship model is output, and the medium-voltage distribution network life data is output; the life status and reactive voltage are matched for the medium-voltage distribution network life data, and the life status-reactive voltage matching degree data is output; the optimal path evaluation and adjustment of the patrol path of the medium-voltage distribution network is performed through the life status-reactive voltage matching degree data to generate the optimal path of the medium-voltage distribution network.

[0049] The beneficial effect of the present invention is that by extracting real-time voltage data and reactive power data, a medium-voltage feeder reactive power data set is constructed, and combined with a preset equipment switching number and life relationship model, an equipment life-reactive voltage coefficient is generated. In this process, the multi-dimensional association of data significantly improves the expression of the relationship between reactive power and equipment life, ensuring the accuracy and consistency of the data set. The application of distributed optimization technology refines reactive power data and real-time voltage data to generate distributed optimized distribution network data, ensuring the dynamic adaptability of the control strategy. At the same time, the time-sharing calculation strategy based on the intelligent decision-making algorithm not only enhances the timeliness of reactive-voltage control instructions, but also optimizes the voltage reactive control operation in multiple time periods. In the generation and evaluation process of the patrol path, the joint application of equipment life-reactive voltage coefficient and reactive-voltage control instructions realizes the efficient construction of the patrol path algorithm, and further uses the life state-reactive voltage matching data to optimally evaluate and adjust the generated patrol path, effectively improving the patrol path planning effect of the medium-voltage distribution network. Through this method, the matching of the life status of the equipment and the reactive voltage is fully considered, ensuring the accuracy and operability of the optimal path. In addition, the life data matching mechanism introduced by the present invention can not only realize real-time monitoring of the health status of the equipment, but also provide a scientific basis for the dynamic adjustment of the optimal patrol path. The implementation of this step makes the operation path of the complex system more efficient, accurate and intelligent through deep mining and processing at the data level. Therefore, the present invention solves the problems of low efficiency and inaccurate path planning in the generation of patrol paths in traditional medium-voltage distribution networks through multi-level data processing and analysis, and improves the intelligence and optimality of the patrol path. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic flow chart of the steps of a method, system and medium for generating an optimal patrol path for a medium voltage distribution network;

[0051] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0052] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0053] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0054] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different network and-or processor methods and-or microcontroller methods.

[0057] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly a second unit may be referred to as a first unit. The term "and-or" used herein includes any and all combinations of one or more of the listed associated items.

[0058] To achieve this, please refer to Figures 1 to 4 , a method for generating an optimal patrol path for a medium voltage distribution network, the method comprising the following steps:

[0059] Step S1: obtaining a raw data set of a medium-voltage distribution network; performing data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network;

[0060] Step S2: acquiring real-time voltage data; extracting reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; using a preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient;

[0061] Step S3: Distributed optimization is performed on the reactive power data and real-time voltage data of the medium voltage feeder to generate distributed data of the medium voltage distribution network; time-sharing calculation is performed on the distributed data of the medium voltage distribution network based on a preset intelligent decision-making algorithm to generate reactive power-voltage control instructions;

[0062] Step S4: construct a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a medium-voltage distribution network patrol path algorithm; use the medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processing data set to generate a medium-voltage distribution network patrol path; output a preset equipment switching number and life relationship model, and output medium-voltage distribution network life data; match the life status and reactive voltage of the medium-voltage distribution network life data, and output life status-reactive voltage matching data; evaluate and adjust the optimal path of the medium-voltage distribution network patrol path through the life status-reactive voltage matching data to generate the optimal path of the medium-voltage distribution network.

[0063] The beneficial effect of the present invention is that by extracting real-time voltage data and reactive power data, a medium-voltage feeder reactive power data set is constructed, and combined with a preset equipment switching number and life relationship model, an equipment life-reactive voltage coefficient is generated. In this process, the multi-dimensional association of data significantly improves the expression of the relationship between reactive power and equipment life, ensuring the accuracy and consistency of the data set. The application of distributed optimization technology refines reactive power data and real-time voltage data to generate distributed optimized distribution network data, ensuring the dynamic adaptability of the control strategy. At the same time, the time-sharing calculation strategy based on the intelligent decision-making algorithm not only enhances the timeliness of reactive-voltage control instructions, but also optimizes the voltage reactive control operation in multiple time periods. In the generation and evaluation process of the patrol path, the joint application of equipment life-reactive voltage coefficient and reactive-voltage control instructions realizes the efficient construction of the patrol path algorithm, and further uses the life state-reactive voltage matching data to optimally evaluate and adjust the generated patrol path, effectively improving the patrol path planning effect of the medium-voltage distribution network. Through this method, the matching of the life status of the equipment and the reactive voltage is fully considered, ensuring the accuracy and operability of the optimal path. In addition, the life data matching mechanism introduced by the present invention can not only realize real-time monitoring of the health status of the equipment, but also provide a scientific basis for the dynamic adjustment of the optimal patrol path. The implementation of this step makes the operation path of the complex system more efficient, accurate and intelligent through deep mining and processing at the data level. Therefore, the present invention solves the problems of low efficiency and inaccurate path planning in the generation of patrol paths in traditional medium-voltage distribution networks through multi-level data processing and analysis, and improves the intelligence and optimality of the patrol path.

[0064] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of the method for generating the optimal patrol path of the medium voltage distribution network of the present invention. In this example, the method for generating the optimal patrol path of the medium voltage distribution network includes the following steps:

[0065] Step S1: obtaining a raw data set of a medium-voltage distribution network; performing data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network;

[0066] In an embodiment of the present invention, noise and abnormal data in the original data set are identified and removed. This process is completed by applying statistical analysis and anomaly detection algorithms, for example, by using the 3σ criterion based on mean and variance, or by using cluster analysis, distance-based anomaly detection and other technical means to identify and process those data points that significantly deviate from the normal distribution. The key goal of this step is to ensure that the acquired data set has sufficient accuracy and consistency to avoid the adverse effects of abnormal data on subsequent analysis results. The original data set is subjected to redundancy reduction processing. This technical means analyzes the correlation between different data dimensions and removes redundant data that is irrelevant to the main features or has a high correlation to simplify the data structure. Commonly used methods include principal component analysis (PCA) or feature selection algorithms, which can effectively reduce the data dimension and retain the most informative feature data. This not only reduces the complexity of subsequent calculations, but also improves the overall quality of the data set. Standardization of the data set is also one of the core links of this step. Since the data dimensions of different parameters are different, such as voltage, power, equipment operation time, etc., which differ greatly in order of magnitude, it is necessary to normalize or standardize these data to ensure that all data are analyzed and calculated under the same dimension. Commonly used standardization methods include minimum-maximum standardization, Z-score standardization, etc. This technical means ensures the stability and accuracy of subsequent analysis and algorithm processing by making each data dimension have the same dimension and range. The processed data set will generate a high-quality, low-noise, de-redundant, and standardized medium-voltage distribution network processing data set.

[0067] Step S2: acquiring real-time voltage data; extracting reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; using a preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient;

[0068] In an embodiment of the present invention, real-time voltage data is obtained, which is one of the key dynamic parameters in the operation of the distribution network and is obtained through an online monitoring device. The real-time voltage data can not only reflect the voltage level fluctuations of the distribution network in different time periods, but also capture the changes in the operating status of the equipment. In order to further improve the accuracy and timeliness of the real-time voltage data, the data acquisition device needs to use high-precision sensors and combine time synchronization technology to ensure timely and continuous update of the data. Reactive power data is extracted from the processed medium-voltage distribution network data set. The extraction process of reactive power mainly depends on the calculation of the power factor. Based on the phase difference between voltage and current, the reactive power of each feeder can be calculated. Specifically, when extracting reactive power, it is necessary to analyze the voltage and current waveforms at different time points to ensure that the extracted data not only accurately reflects the current electrical operation, but also supports subsequent dynamic optimization processing. After completing the extraction of reactive power data, the next step is to associate it with the life of the equipment. In order to achieve this goal, a preset equipment switching number and life relationship model is introduced. The model is based on the operation history of the equipment, considering the number of equipment switching operations and the impact of each switching on the equipment life. This model is constructed using algorithms such as regression analysis and machine learning. Through the fitting analysis of a large amount of historical operating data, the quantitative relationship between the number of switching times of the equipment and the life decay is obtained. The core purpose of this model is to predict the life change of the equipment under a specific number of switching times, so as to provide a reference for the subsequent reactive power regulation. The model is used to associate the reactive power data and real-time voltage data of the medium-voltage feeder with coefficients. This coefficient association process is completed through multivariate regression analysis or matching technology based on optimization algorithms. Specifically, the historical number of switching times of the equipment and its corresponding life changes are first analyzed through the model, and then the correlation between the life decay rate of the equipment and reactive power regulation is calculated in combination with the current real-time voltage fluctuation. In this process, the model will dynamically adjust the parameters to ensure that the life changes of different equipment under different voltage conditions can accurately match the reactive power data. Finally, the equipment life-reactive voltage coefficient is generated through the above technical means.

[0069] Step S3: Distributed optimization is performed on the reactive power data and real-time voltage data of the medium voltage feeder to generate distributed data of the medium voltage distribution network; time-sharing calculation is performed on the distributed data of the medium voltage distribution network based on a preset intelligent decision-making algorithm to generate reactive power-voltage control instructions;

[0070] In an embodiment of the present invention, the reactive power data and real-time voltage data of the medium voltage feeder are distributedly optimized to generate distributed data of the medium voltage distribution network. This process involves the application of a distributed optimization algorithm, which is intended to optimize the reactive power and voltage data of the distribution network in space and time. The distributed optimization of data is based on a distributed computing framework, such as MapReduce or Spark, which can efficiently process large-scale data sets. In a distributed computing environment, the reactive power data and voltage data are first divided into multiple subsets, and then processed in parallel on each computing node. This process includes local optimization of the data, such as adjusting the distribution of reactive power to achieve voltage balance across the entire network. The distributed optimization algorithm takes into account the electrical parameters and constraints in the network, and applies, for example, linear programming, nonlinear programming, or heuristic optimization methods to adjust the reactive power output of each feeder, so that the entire distribution network achieves the effect of optimal configuration while meeting the requirements of equipment capacity, line stability, and voltage level. The optimization process involves real-time updating and feedback of data. Real-time voltage data is integrated with reactive power data in real time through continuous monitoring and dynamic adjustment mechanisms. Through dynamic data fusion technology, such as Kalman filtering or recursive least squares algorithm, real-time voltage data and reactive power data are updated synchronously to reflect the latest changes in the operating status of the distribution network. The distributed optimization algorithm continuously adjusts the strategy to optimize the operating parameters of each node, thereby generating optimized distributed data of the medium-voltage distribution network. The generated distributed data of the medium-voltage distribution network is then used as input to enter the calculation process of the intelligent decision-making algorithm. The preset intelligent decision-making algorithm performs time-sharing calculations based on these data, mainly including prediction models and decision support systems based on time series analysis. These algorithms include dynamic programming, predictive control and intelligent decision-making models, etc., which predict future load demand and voltage fluctuations by combining historical data with real-time data. The intelligent decision-making algorithm uses these prediction results to formulate reactive-voltage control instructions to optimize the voltage control and reactive power regulation strategies of the distribution network.

[0071] Step S4: construct a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a medium-voltage distribution network patrol path algorithm; use the medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processing data set to generate a medium-voltage distribution network patrol path; output a preset equipment switching number and life relationship model, and output medium-voltage distribution network life data; match the life status and reactive voltage of the medium-voltage distribution network life data, and output life status-reactive voltage matching data; evaluate and adjust the optimal path of the medium-voltage distribution network patrol path through the life status-reactive voltage matching data to generate the optimal path of the medium-voltage distribution network.

[0072] In the embodiment of the present invention, the combination process of equipment life-reactive voltage coefficient and reactive-voltage control instruction involves the comprehensive modeling of the algorithm. The equipment life-reactive voltage coefficient provides the life prediction data of the equipment under different voltage conditions, while the reactive-voltage control instruction represents the specific instruction for reactive compensation and voltage regulation of the equipment. These data are weighted and integrated to construct the basic framework of the patrol path algorithm. The algorithm uses weighted summation or multi-objective optimization methods to convert the correlation between equipment life and control instructions into decision variables for optimizing the path. The construction process involves complex algorithm design, such as the shortest path algorithm, genetic algorithm or particle swarm optimization algorithm in graph theory, to ensure that the generated patrol path can effectively cover all key areas and take into account the dual goals of equipment life and voltage control. The patrol path algorithm of the medium-voltage distribution network is used to perform patrol path analysis on the medium-voltage distribution network processing data set. This step performs path optimization analysis on each data in the processing data set by applying the aforementioned algorithm framework. During the analysis process, the algorithm comprehensively considers the location, life status, reactive power demand and voltage control requirements of the equipment to generate an optimized patrol path. This process relies on path planning techniques, such as the Dijkstra algorithm or the A* algorithm, to find the best patrol path while satisfying various constraints, thereby ensuring that each area of ​​the distribution network is effectively inspected and maintained. Based on the preset equipment switching times and life relationship model, the medium-voltage distribution network life data is output. This process involves the application of the model and the extraction of data. The equipment switching times and life relationship model predicts the remaining life of the equipment under specific operating conditions based on historical data and statistical analysis, and generates corresponding life data. These data are output through the model to evaluate the health status and maintenance needs of the equipment. On this basis, the life status and reactive voltage are matched for the medium-voltage distribution network life data to generate life status-reactive voltage matching data. Specifically, this step involves data matching analysis, which generates matching data by comparing the equipment life status and the actual reactive voltage data. This matching data is obtained through correlation analysis, regression model or data fusion technology to reveal the relationship between equipment life and actual voltage regulation. Finally, the optimal path evaluation and adjustment of the medium-voltage distribution network patrol path is carried out through the life status-reactive voltage matching data. This process uses matching data to optimize the patrol path to ensure that the patrol path not only covers all areas that need to be inspected, but also achieves the optimal path planning under the dual constraints of equipment life and voltage control. The optimal path evaluation and adjustment applies optimization algorithms, such as linear programming or nonlinear programming, to adjust the patrol path strategy to generate the final optimal path for the medium-voltage distribution network.

[0073] Preferably, step S1 comprises the following steps:

[0074] Step S11: Acquire the original data set of the medium voltage distribution network;

[0075] Step S12: performing data redundancy reduction processing on the original data set of the medium-voltage distribution network to generate a medium-voltage distribution network redundancy reduction data set; performing data set calibration on the medium-voltage distribution network redundancy reduction data set to generate a medium-voltage distribution network calibration data set;

[0076] Step S13: performing data set standardization processing on the medium voltage distribution network calibration data set to generate a medium voltage distribution network processed data set.

[0077] In an embodiment of the present invention, the obtained feeder real-time data and substation real-time data are original real-time observation data, which contain information measured by sensors and recorded by the monitoring system. There are missing values ​​in these data, which are caused by sensor failure, communication problems or data recording errors. The feeder real-time data and substation real-time data are processed for missing values, and interpolation, mean filling or other statistical methods are used to complete the missing data. The processed data are called feeder missing value processing data and substation missing value processing data. After the missing value processing, the Z-score method is further used to detect outliers on the processed data. Z-score is a standardized measure based on the standard deviation of the mean deviation of the data point and the data set, which is used to identify and remove outliers. By calculating the Z-score of each data point, abnormal data points exceeding the preset threshold are detected, and removing these data points can improve the quality and accuracy of the data set. This process generates feeder outlier processing data and substation outlier processing data. Interpolation time alignment processing is performed on the feeder outlier processing data and substation outlier processing data. Time alignment is to ensure that data from different data sources have consistent timestamps, so that effective comparison and analysis can be performed on the same time scale. Interpolation methods include linear interpolation, spline interpolation or other time series interpolation techniques, which complete or adjust the data according to the time axis so that the feeder time alignment data and the substation time alignment data are consistent in the time dimension. Next, the time-aligned data is standardized. Standardization is to convert the data into a standard normal distribution with a mean of 0 and a variance of 1 by subtracting the mean and dividing by the standard deviation. Standardization generates feeder standardized data and substation standardized data, which helps to eliminate the scale differences between different data dimensions and improve the comparability and analysis of the data. Finally, the feeder standardized data and substation standardized data are used to construct a data set. This step involves fusing different data types together to form a unified processing data set, namely the substation feeder processing data set.

[0078] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0079] Step S21: acquiring real-time voltage data and historical operation data; extracting reactive power from the medium voltage distribution network processing data set to generate medium voltage feeder reactive power data;

[0080] Step S22: using a preset equipment switching times and life relationship model to correlate the reactive power data and real-time voltage data of the medium voltage feeder to generate a preliminary correlation coefficient between equipment life and reactive voltage;

[0081] Step S23: Use a dynamic coefficient matching algorithm to perform voltage-power correlation analysis on the equipment life-reactive voltage preliminary correlation coefficient and historical operation data to generate an equipment life-reactive voltage coefficient.

[0082] In an embodiment of the present invention, it involves obtaining real-time voltage data and historical operation data, and extracting reactive power data from a medium-voltage distribution network processing data set. These real-time voltage data and historical operation data provide dynamic and static power system operation states for analysis, and the reactive power data extracted from the data set reflects the reactive load of the system at different time points. The key technology of this step is the collection and preliminary arrangement of data to ensure that all relevant data sources can be effectively used for subsequent analysis. The medium-voltage feeder reactive power data and real-time voltage data are coefficient-correlated using a preset equipment switching times and life relationship model. This process involves combining reactive power data with voltage data, and calculating a correlation coefficient based on a model of the relationship between the equipment switching times and life. This model evaluates the relationship between the equipment's life and its operating parameters based on the equipment's operating history and the frequency of switching operations. The generated equipment life-reactive voltage preliminary correlation coefficient provides basic data for further analysis, and the key to this step is the establishment of the model and the accurate association of the data. The voltage-power correlation analysis is performed on the equipment life-reactive voltage preliminary correlation coefficient and historical operation data using a dynamic coefficient matching algorithm. This dynamic coefficient matching algorithm identifies the specific relationship between voltage and reactive power under different operating conditions by comprehensively analyzing the preliminary equipment life-reactive voltage correlation coefficient and historical operating data. This algorithm dynamically adjusts the correlation coefficient to reflect the actual power system operation under different historical backgrounds, thereby generating the final equipment life-reactive voltage coefficient.

[0083] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0084] Step S31: regionally grouping medium-voltage feeder reactive power data and real-time voltage data to generate medium-voltage distribution network regional grouping data; performing distributed optimization on the medium-voltage distribution network regional grouping data to generate medium-voltage distribution network distributed data;

[0085] Step S32: Perform time-sharing calculation on the distributed data of the medium-voltage distribution network to generate reactive voltage data for the medium-voltage distribution network during a period of time; perform comparative analysis on the reactive voltage data for the medium-voltage distribution network during a period of time and the historical operation data to generate comparative analysis data for the medium-voltage distribution network; perform reactive compensation processing on the comparative analysis data of the medium-voltage distribution network using a preset equipment switching times and life relationship model to generate distributed supplementary processing data; use an intelligent decision-making algorithm to generate voltage control instructions for the distributed supplementary processing data to generate reactive-voltage control instructions.

[0086] In an embodiment of the present invention, medium-voltage feeder reactive power data and real-time voltage data are regionally grouped. This process involves dividing the data into several regions according to geographical location, load characteristics or other relevant factors, so as to perform more targeted analysis. The generated medium-voltage distribution network regional grouping data can better reflect the power load and reactive power conditions in different regions. Then, these regional grouping data are distributedly optimized. The technical means of this step include using distributed computing methods and optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to improve the distribution state of data, reduce system load fluctuations, and improve resource allocation efficiency. The distributed data of the medium-voltage distribution network generated by this process helps to adjust the distribution of power resources to the optimal state, thereby improving the overall operation efficiency of the system. The distributed data of the medium-voltage distribution network is calculated in time division. This step uses time series analysis technology to process the data according to time periods to generate reactive voltage data of the medium-voltage distribution network period. The data reflects the dynamic changes of reactive power and voltage in different time periods. Subsequently, the reactive voltage data of the medium-voltage distribution network period is compared and analyzed using historical operation data. Comparative analysis technology is used here to identify trends and anomalies in the system through retrospective comparison of historical data, thereby generating comparative analysis data for the medium-voltage distribution network.

[0087] Preferably, step S31 includes the following steps:

[0088] Step S311: extracting feeder geographical location data and load distribution data from the original data set of the medium voltage distribution network, and generating feeder geographical location data and medium voltage distribution network load distribution data;

[0089] Step S312: performing regional grouping on medium voltage feeder reactive power data and real-time voltage data to generate medium voltage distribution network regional grouping data;

[0090] Step S313: using the feeder geographic location data to perform distributed optimization calculations on the medium voltage distribution network regional grouping data, and analyzing it in combination with the medium voltage distribution network load distribution data to generate medium voltage distribution network distributed data.

[0091] In an embodiment of the present invention, feeder geographic location and load distribution data are extracted from the original data set of the medium-voltage distribution network. This process uses data mining technology and information extraction methods, the purpose of which is to extract key geographic and load information from the original data. The feeder geographic location data provides the specific geographic distribution information of the feeder in the power system, while the load distribution data shows the distribution of the power load on each feeder. These data are the basis for subsequent analysis and optimization, and can effectively reflect the spatial and load characteristics of the power system. The medium-voltage feeder reactive power data and real-time voltage data are regionally grouped. This process involves using geographic information system (GIS) technology and regional division algorithms to group data according to geographic regions or load characteristics to facilitate data processing and analysis in local areas. The generated medium-voltage distribution network regional grouping data can make the reactive power and voltage conditions in each area clearer, thereby laying the foundation for subsequent optimization calculations. The feeder geographic location data is further used to perform distributed optimization calculations on the medium-voltage distribution network regional grouping data. Distributed optimization calculations use optimization algorithms (such as linear programming, nonlinear programming, genetic algorithms, etc.) to optimize the configuration and scheduling of power resources based on the feeder geographic location data and regional grouping data. This calculation not only takes into account the actual geographical location, but also combines the medium-voltage distribution network load distribution data to optimize the reactive power distribution and voltage level in the system. The resulting medium-voltage distribution network distributed data is the result of optimizing the performance of the power system in each region.

[0092] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0093] Step S41: constructing a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a patrol path algorithm for the medium-voltage distribution network; performing patrol path analysis on the medium-voltage distribution network processing data set using the patrol path algorithm for the medium-voltage distribution network to generate a patrol path for the medium-voltage distribution network;

[0094] Step S42: Output the preset equipment switching times and life relationship model to generate equipment life data; associate the equipment life data with medium-voltage distribution equipment to generate medium-voltage distribution network life data; match the life state and reactive voltage of the medium-voltage distribution network life data to output life state-reactive voltage matching degree data;

[0095] Step S43: the optimal path evaluation and optimization of the patrol path of the medium-voltage distribution network is performed through the life state-reactive voltage matching data, the optimal path of the medium-voltage distribution network is generated, and the optimal patrol path generation operation is completed.

[0096] In an embodiment of the present invention, the equipment life-reactive voltage coefficient is combined with the reactive-voltage control instruction to construct a patrol path algorithm. Specifically, this process involves taking the equipment life-reactive voltage coefficient as a key input parameter, and generating a patrol path algorithm framework for a medium-voltage distribution network through algorithm design and calculation methods (such as graph theory algorithms, shortest path algorithms or heuristic algorithms). The algorithm framework can plan an optimized patrol path according to the life status of the equipment and the voltage control requirements to ensure the effective maintenance of the equipment and the stable operation of the power system. Subsequently, the constructed patrol path algorithm is used to analyze the processed data set of the medium-voltage distribution network to generate a patrol path for the medium-voltage distribution network. This analysis process performs actual path calculations through the algorithm to ensure that a reasonable patrol path is formed under specific data conditions, thereby achieving the purpose of improving equipment maintenance efficiency and grid stability. The technical means include outputting a preset equipment switching number and life relationship model to generate equipment life data. This process uses the relationship between the equipment switching number model and the life data to generate equipment life data through mathematical modeling and statistical analysis (such as regression analysis or time series analysis). Next, the generated equipment life data is associated with the medium-voltage distribution equipment to generate medium-voltage distribution network life data. This step integrates the equipment life data with the operating status of the distribution equipment through data fusion technology to obtain complete medium-voltage distribution network life data. Furthermore, the life status and reactive voltage are matched for the medium-voltage distribution network life data, and the life status-reactive voltage matching data is generated through the matching algorithm. The matching process uses data mining and pattern recognition technology to compare and analyze the equipment life status with the voltage control requirements to generate matching data. By analyzing the life status-reactive voltage matching data, the optimal path of the medium-voltage distribution network patrol path is evaluated and optimized. This process uses an optimization algorithm (such as an optimal control algorithm, a genetic algorithm, or a simulated annealing algorithm) to evaluate the effectiveness of the existing patrol path based on the matching data, and adjusts and optimizes the path to generate the optimal path of the medium-voltage distribution network.

[0097] Preferably, step S41 includes the following steps:

[0098] Step S411: combining the equipment life-reactive voltage coefficient and the reactive-voltage control instruction by coefficient-instruction, generating matching coefficient-control instruction data; constructing a patrol path algorithm for the matching coefficient-control instruction data, and generating a medium-voltage distribution network patrol path algorithm framework by combining a preset equipment switching times and life relationship model;

[0099] Step S412: using the distributed data of the medium-voltage distribution network to adjust the algorithm parameter weight distribution of the medium-voltage distribution network patrol path algorithm framework, and generating a medium-voltage distribution network patrol path algorithm;

[0100] Step S413: using a medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processed data set to generate a medium-voltage distribution network patrol path.

[0101] In an embodiment of the present invention, the equipment life-reactive voltage coefficient is combined with the reactive-voltage control instruction to generate matching coefficient-control instruction data. This process involves matching the life characteristics of the equipment with the reactive voltage control instruction. Technically, data fusion technology and algorithms (such as weighted average, linear combination or complex multivariate regression model) are used to generate matching coefficient-control instruction data. These data reflect the impact of equipment life on voltage control and provide basic data for subsequent path algorithm construction. Afterwards, the patrol path algorithm is constructed using these matching coefficient-control instruction data, and the patrol path algorithm framework of the medium-voltage distribution network is generated by designing an algorithm framework (such as the shortest path algorithm, optimal path algorithm or heuristic algorithm in graph theory) and combining the preset equipment switching number and life relationship model. The construction of this framework not only takes into account the actual needs of equipment life and voltage control, but also combines the switching history data of the equipment to form an algorithm model for optimizing the patrol path. The distributed data of the medium-voltage distribution network is used to adjust the weight distribution of the algorithm parameters of the patrol path algorithm framework to generate a medium-voltage distribution network patrol path algorithm. The technical means here include parameter weight optimization of distributed data. The specific operation involves using optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, or Bayesian optimization) to adjust the weight parameters in the algorithm to more accurately reflect the actual operation and maintenance needs of the medium-voltage distribution network. The goal of this step is to generate a patrol path algorithm suitable for the medium-voltage distribution network by refining the parameters of the algorithm framework to make it more adaptable and effective. The generated patrol path algorithm for the medium-voltage distribution network is used to perform patrol path analysis on the processed data set of the medium-voltage distribution network to generate a patrol path for the medium-voltage distribution network. This process involves applying the optimized patrol path algorithm to analyze and calculate the processed data set. The actual path planning is performed through the algorithm to generate a patrol path that meets the data requirements.

[0102] Preferably, step S43 includes the following steps:

[0103] Step S431: Acquire historical data of equipment switching times; perform unstable state analysis on the patrol path of the medium voltage distribution network through life state-reactive voltage matching data, and generate life state unstable node data;

[0104] Step S432: Preliminary optimization of the life state unstable node data is performed using the path evaluation algorithm, and node adjustment is performed using the historical data of the number of equipment switching times to generate a preliminary path dynamic adjustment factor; the preliminary path dynamic adjustment factor is fitted and analyzed using the reactive voltage data of the medium voltage distribution network period to generate a dynamic path adjustment factor of the medium voltage distribution network;

[0105] Step S433: using the dynamic path adjustment factor of the medium-voltage distribution network to perform optimal path evaluation and optimization on the patrol path of the medium-voltage distribution network, generating the optimal path of the medium-voltage distribution network, and completing the optimal patrol path generation operation.

[0106] In an embodiment of the present invention, historical data of the number of equipment switching times are obtained, which provide the operation history records of the equipment in different time periods. Using these historical data, the operating status and usage frequency of the equipment can be analyzed in detail. The unstable state analysis is performed through the life state-reactive voltage matching data, aiming to identify the potential unstable nodes in the medium voltage distribution network. These data reflect the matching of the equipment life state and reactive voltage, and reveal the nodes that cause system instability. Technically, this involves anomaly detection algorithms (such as cluster analysis, outlier detection) to identify nodes with unstable life states and generate life state unstable node data. The data processing of this step provides basic information for subsequent path optimization. The path evaluation algorithm is used to perform preliminary optimization on the life state unstable node data. The path evaluation algorithm (such as Dijkstra algorithm, A* algorithm) is used to optimize the identified unstable nodes to determine the most appropriate adjustment measures. The historical data of the number of equipment switching times is used for node adjustment to generate preliminary path dynamic adjustment factors. These factors adjust the nodes based on historical operation data to improve the stability of the path. Furthermore, the dynamic adjustment factor of the preliminary path is fitted and analyzed by the reactive voltage data of the medium-voltage distribution network period to generate the dynamic path adjustment factor of the medium-voltage distribution network. The fitting analysis uses a regression model or other data fitting techniques to combine the actual reactive voltage data with the adjustment factor to optimize the accuracy and effectiveness of the dynamic path adjustment factor. The dynamic path adjustment factor of the medium-voltage distribution network is used to evaluate and optimize the optimal path of the patrol path of the medium-voltage distribution network. The patrol path is evaluated and optimized by the dynamic path adjustment factor to determine the best patrol path to improve the operating efficiency and stability of the system. Technically, this involves the application of a path optimization algorithm, combined with a dynamic adjustment factor, to re-evaluate and adjust the patrol path to ensure its optimal performance in actual operation. Ultimately, the generated optimal path for the medium-voltage distribution network will provide an efficient patrol path solution based on the latest data analysis and optimization strategies.

[0107] In this specification, a medium voltage distribution network optimal patrol path generation system is provided, which is used to execute the above-mentioned medium voltage distribution network optimal patrol path generation method, and the medium voltage distribution network optimal patrol path generation system includes:

[0108] The raw data acquisition and processing module is used to obtain the raw data set of the medium-voltage distribution network; perform data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network;

[0109] Real-time voltage and reactive power analysis module: used to obtain real-time voltage data; extract reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; use the preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient;

[0110] Distributed optimization and control instruction generation module: used to perform distributed optimization on medium-voltage feeder reactive power data and real-time voltage data, and generate distributed data on the medium-voltage distribution network; perform time-sharing calculations on the distributed data on the medium-voltage distribution network based on a preset intelligent decision-making algorithm, and generate reactive-voltage control instructions;

[0111] The patrol path construction and optimal path evaluation module is used to construct the patrol path algorithm of the equipment life-reactive voltage coefficient and the reactive-voltage control instructions to generate the patrol path algorithm of the medium-voltage distribution network; the patrol path analysis of the medium-voltage distribution network processing data set is performed using the medium-voltage distribution network patrol path algorithm to generate the patrol path of the medium-voltage distribution network; the preset equipment switching times and life relationship model is output, and the medium-voltage distribution network life data is output; the life status and reactive voltage are matched for the medium-voltage distribution network life data, and the life status-reactive voltage matching degree data is output; the optimal path evaluation and adjustment of the patrol path of the medium-voltage distribution network is performed through the life status-reactive voltage matching degree data to generate the optimal path of the medium-voltage distribution network.

[0112] A computer-readable storage medium stores a computer program, which, when executed, implements the above-mentioned method for generating an optimal patrol path for a medium-voltage distribution network.

[0113] The beneficial effect of the present invention is that by extracting real-time voltage data and reactive power data, a medium-voltage feeder reactive power data set is constructed, and combined with a preset equipment switching number and life relationship model, an equipment life-reactive voltage coefficient is generated. In this process, the multi-dimensional association of data significantly improves the expression of the relationship between reactive power and equipment life, ensuring the accuracy and consistency of the data set. The application of distributed optimization technology refines reactive power data and real-time voltage data to generate distributed optimized distribution network data, ensuring the dynamic adaptability of the control strategy. At the same time, the time-sharing calculation strategy based on the intelligent decision-making algorithm not only enhances the timeliness of reactive-voltage control instructions, but also optimizes the voltage reactive control operation in multiple time periods. In the generation and evaluation process of the patrol path, the joint application of equipment life-reactive voltage coefficient and reactive-voltage control instructions realizes the efficient construction of the patrol path algorithm, and further uses the life state-reactive voltage matching data to optimally evaluate and adjust the generated patrol path, effectively improving the patrol path planning effect of the medium-voltage distribution network. Through this method, the matching of the life status of the equipment and the reactive voltage is fully considered, ensuring the accuracy and operability of the optimal path. In addition, the life data matching mechanism introduced by the present invention can not only realize real-time monitoring of the health status of the equipment, but also provide a scientific basis for the dynamic adjustment of the optimal patrol path. The implementation of this step makes the operation path of the complex system more efficient, accurate and intelligent through deep mining and processing at the data level. Therefore, the present invention solves the problems of low efficiency and inaccurate path planning in the generation of patrol paths in traditional medium-voltage distribution networks through multi-level data processing and analysis, and improves the intelligence and optimality of the patrol path.

[0114] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0115] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for generating an optimal patrol path for a medium voltage distribution network, characterized in that: The following steps are involved: Step S1: obtaining a raw data set of a medium-voltage distribution network; performing data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network; Step S2: acquiring real-time voltage data; extracting reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; using a preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient; Step S3: Distributed optimization is performed on the reactive power data and real-time voltage data of the medium voltage feeder to generate distributed data of the medium voltage distribution network; time-sharing calculation is performed on the distributed data of the medium voltage distribution network based on a preset intelligent decision-making algorithm to generate reactive power-voltage control instructions; Step S4: construct a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a medium-voltage distribution network patrol path algorithm; use the medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processing data set to generate a medium-voltage distribution network patrol path; output a preset equipment switching number and life relationship model, and output medium-voltage distribution network life data; match the life status and reactive voltage of the medium-voltage distribution network life data, and output life status-reactive voltage matching data; evaluate and adjust the optimal path of the medium-voltage distribution network patrol path through the life status-reactive voltage matching data to generate the optimal path of the medium-voltage distribution network.

2. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire the original data set of the medium voltage distribution network; Step S12: performing data redundancy reduction processing on the original data set of the medium-voltage distribution network to generate a medium-voltage distribution network redundancy reduction data set; performing data set calibration on the medium-voltage distribution network redundancy reduction data set to generate a medium-voltage distribution network calibration data set; Step S13: performing data set standardization processing on the medium voltage distribution network calibration data set to generate a medium voltage distribution network processed data set.

3. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: acquiring real-time voltage data and historical operation data; extracting reactive power from the medium voltage distribution network processing data set to generate medium voltage feeder reactive power data; Step S22: using a preset equipment switching times and life relationship model to correlate the reactive power data and real-time voltage data of the medium voltage feeder to generate a preliminary correlation coefficient between equipment life and reactive voltage; Step S23: Use a dynamic coefficient matching algorithm to perform voltage-power correlation analysis on the equipment life-reactive voltage preliminary correlation coefficient and historical operation data to generate an equipment life-reactive voltage coefficient.

4. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: regionally grouping medium-voltage feeder reactive power data and real-time voltage data to generate medium-voltage distribution network regional grouping data; performing distributed optimization on the medium-voltage distribution network regional grouping data to generate medium-voltage distribution network distributed data; Step S32: Perform time-sharing calculation on the distributed data of the medium-voltage distribution network to generate reactive voltage data for the medium-voltage distribution network during a period of time; perform comparative analysis on the reactive voltage data for the medium-voltage distribution network during a period of time and the historical operation data to generate comparative analysis data for the medium-voltage distribution network; perform reactive compensation processing on the comparative analysis data of the medium-voltage distribution network using a preset equipment switching times and life relationship model to generate distributed supplementary processing data; use an intelligent decision-making algorithm to generate voltage control instructions for the distributed supplementary processing data to generate reactive-voltage control instructions.

5. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 4, characterized in that: Step S31 includes the following steps: Step S311: extracting feeder geographical location data and load distribution data from the original data set of the medium voltage distribution network, and generating feeder geographical location data and medium voltage distribution network load distribution data; Step S312: performing regional grouping on the medium voltage feeder reactive power data and the real-time voltage data to generate medium voltage distribution network regional grouping data; Step S313: using the feeder geographic location data to perform distributed optimization calculations on the medium voltage distribution network regional grouping data, and analyzing it in combination with the medium voltage distribution network load distribution data to generate medium voltage distribution network distributed data.

6. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing a patrol path algorithm for the equipment life-reactive voltage coefficient and the reactive-voltage control instruction to generate a patrol path algorithm for the medium-voltage distribution network; performing patrol path analysis on the medium-voltage distribution network processing data set using the patrol path algorithm for the medium-voltage distribution network to generate a patrol path for the medium-voltage distribution network; Step S42: Output the preset equipment switching times and life relationship model to generate equipment life data; associate the equipment life data with medium-voltage distribution equipment to generate medium-voltage distribution network life data; match the life state and reactive voltage of the medium-voltage distribution network life data to output life state-reactive voltage matching degree data; Step S43: the optimal path evaluation and optimization of the patrol path of the medium-voltage distribution network is performed through the life state-reactive voltage matching data, the optimal path of the medium-voltage distribution network is generated, and the optimal patrol path generation operation is completed.

7. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 6, characterized in that: Step S41 includes the following steps: Step S411: combining the equipment life-reactive voltage coefficient and the reactive-voltage control instruction by coefficient-instruction, generating matching coefficient-control instruction data; constructing a patrol path algorithm for the matching coefficient-control instruction data, and generating a medium-voltage distribution network patrol path algorithm framework by combining a preset equipment switching times and life relationship model; Step S412: using the distributed data of the medium-voltage distribution network to adjust the algorithm parameter weight distribution of the medium-voltage distribution network patrol path algorithm framework, and generating a medium-voltage distribution network patrol path algorithm; Step S413: using a medium-voltage distribution network patrol path algorithm to perform patrol path analysis on the medium-voltage distribution network processed data set to generate a medium-voltage distribution network patrol path.

8. The method for generating an optimal patrol path for a medium voltage distribution network according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: Acquire historical data of equipment switching times; perform unstable state analysis on the patrol path of the medium voltage distribution network through life state-reactive voltage matching data, and generate life state unstable node data; Step S432: Preliminary optimization of the life state unstable node data is performed using the path evaluation algorithm, and node adjustment is performed using the historical data of the number of equipment switching times to generate a preliminary path dynamic adjustment factor; the preliminary path dynamic adjustment factor is fitted and analyzed using the reactive voltage data of the medium voltage distribution network period to generate a dynamic path adjustment factor of the medium voltage distribution network; Step S433: using the dynamic path adjustment factor of the medium-voltage distribution network to perform optimal path evaluation and optimization on the patrol path of the medium-voltage distribution network, generating the optimal path of the medium-voltage distribution network, and completing the optimal patrol path generation operation.

9. A system for generating an optimal patrol path for a medium voltage distribution network, characterized in that: Used to execute the method for generating an optimal patrol path for a medium-voltage distribution network as claimed in claim 1, the system for generating an optimal patrol path for a medium-voltage distribution network comprises: The raw data acquisition and processing module is used to obtain the raw data set of the medium-voltage distribution network; perform data preprocessing on the raw data set of the medium-voltage distribution network to generate a processed data set of the medium-voltage distribution network; Real-time voltage and reactive power analysis module: used to obtain real-time voltage data; extract reactive power from the medium-voltage distribution network processing data set to generate medium-voltage feeder reactive power data; use the preset equipment switching times and life relationship model to perform coefficient correlation on the medium-voltage feeder reactive power data and real-time voltage data to generate equipment life-reactive voltage coefficient; Distributed optimization and control instruction generation module: used to perform distributed optimization on medium-voltage feeder reactive power data and real-time voltage data, and generate distributed data on the medium-voltage distribution network; perform time-sharing calculations on the distributed data on the medium-voltage distribution network based on a preset intelligent decision-making algorithm, and generate reactive-voltage control instructions; The patrol path construction and optimal path evaluation module is used to construct the patrol path algorithm of the equipment life-reactive voltage coefficient and the reactive-voltage control instructions to generate the patrol path algorithm of the medium-voltage distribution network; the patrol path analysis of the medium-voltage distribution network processing data set is performed using the medium-voltage distribution network patrol path algorithm to generate the patrol path of the medium-voltage distribution network; the preset equipment switching times and life relationship model is output, and the medium-voltage distribution network life data is output; the life status and reactive voltage are matched for the medium-voltage distribution network life data, and the life status-reactive voltage matching degree data is output; the optimal path evaluation and adjustment of the patrol path of the medium-voltage distribution network is performed through the life status-reactive voltage matching degree data to generate the optimal path of the medium-voltage distribution network.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the method for generating an optimal patrol path for a medium voltage distribution network as claimed in any one of claims 1 to 8 is implemented.

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