Control method and system for power distribution cabinet cluster

Abstract

The invention relates to the technical field of power equipment control, in particular to a control method and system for a power distribution cabinet cluster. The method comprises the following steps: performing data acquisition on a power distribution cabinet by using a sensor to obtain power distribution cabinet data; preprocessing the power distribution cabinet data to obtain standard power distribution cabinet data; performing multi-parameter feature identification on the standard power distribution cabinet data to obtain power distribution cabinet multi-parameter features; performing electrical parameter extraction on the multi-parameter characteristics of the power distribution cabinet to generate electrical parameter information of the power distribution cabinet; performing electrical function identification on the electrical parameter information of the power distribution cabinet to obtain electrical function data of the power distribution cabinet; through the data processing technology, the real-time monitoring technology and the intelligent regulation and control technology, the abnormal condition of the power distribution cabinet cluster is found in time; and the power distribution cabinet cluster is intelligently adjusted and controlled according to specific abnormal conditions, so that the stability of the power distribution cabinet cluster is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment control, and in particular to a control method and system for a power distribution cabinet cluster. Background Art

[0002] The distribution cabinet cluster is designed in a modular way and can be flexibly combined to meet the power distribution needs in different scenarios. The introduction of automation technology enables the distribution cabinet to be automatically controlled to achieve basic power distribution functions. Today's distribution cabinets integrate more intelligent control technologies, such as PLC (programmable logic controller) and intelligent sensors, to facilitate remote monitoring, fault diagnosis and automated operation. Today, the distribution cabinet cluster uses fieldbus technology to enable networking and communication capabilities of equipment, so that it has a certain degree of integration and communication efficiency. However, the existing control technology makes it difficult to detect abnormal conditions of the distribution cabinet cluster in a timely manner; and it is difficult to perform intelligent adjustment and control of the distribution cabinet cluster according to specific abnormal conditions, resulting in low stability of the distribution cabinet cluster. Summary of the invention

[0003] Based on this, it is necessary to provide a control method and system for a power distribution cabinet cluster to solve at least one of the above technical problems.

[0004] To achieve the above object, a control method for a power distribution cabinet cluster is provided, the method comprising the following steps: Step S1: using sensors to collect data from the distribution cabinet to obtain distribution cabinet data; preprocessing the distribution cabinet data to obtain standard distribution cabinet data; performing multi-parameter feature recognition on the standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; Step S2: extract electrical parameters from the multi-parameter features of the distribution cabinet to generate electrical parameter information of the distribution cabinet; identify electrical functions of the electrical parameter information of the distribution cabinet to obtain electrical function data of the distribution cabinet; map the multi-parameter features of the distribution cabinet to the distribution cabinet protection circuit according to the electrical function data of the distribution cabinet to generate the protection circuit data of the distribution cabinet; perform circuit short circuit detection on the protection circuit data of the distribution cabinet to obtain circuit short circuit detection data; Step S3: Perform distribution cabinet cluster feature recognition on the multi-parameter features of the distribution cabinet to obtain distribution cabinet cluster feature data; perform cluster division on the distribution cabinet cluster feature data to generate distribution cabinet cluster division data; perform cluster topology analysis on the distribution cabinet cluster division data to obtain cluster topology data; perform inter-cluster electric power measurement on the distribution cabinet cluster feature data according to the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data; Step S4: Based on the circuit short-circuit detection data, inter-cluster electric power data and distribution cabinet energy efficiency data, the distribution cabinet cluster performance is measured on the multi-parameter characteristics of the distribution cabinet to generate the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent regulation data; the distribution cabinet is clustered and controlled by using the distribution cabinet cluster intelligent regulation data to generate a distribution cabinet cluster control plan.

[0005] The present invention uses sensors to collect data from a distribution cabinet, and can accurately obtain distribution cabinet data, specifically including key parameters such as voltage, current and power; pre-process the distribution cabinet data to remove noise and abnormal values, thereby obtaining standardized distribution cabinet data, providing a data basis for subsequent analysis; perform multi-parameter feature recognition on standard distribution cabinet data, extract key performance features of the distribution cabinet, so as to reflect the operating status of the distribution cabinet; electrical parameter extraction and electrical function recognition further refine the operating characteristics of the distribution cabinet, and distribution cabinet protection circuit mapping and circuit short circuit detection can identify the short circuit risk in the distribution cabinet. The electrical parameter extraction of the multi-parameter feature of the distribution cabinet makes the electrical parameter information of the distribution cabinet clear; the electrical parameter information includes but is not limited to current, voltage and power, which is the basis for the normal operation and fault diagnosis of the distribution cabinet; the electrical function recognition further links the parameter information with the specific function of the distribution cabinet, thereby generating the electrical function data of the distribution cabinet, which provides a basis for the functional analysis and abnormality detection of the distribution cabinet; the circuit short circuit detection is performed on the distribution cabinet protection circuit data, so that the circuit short circuit event is discovered in time to ensure the safe and stable operation of the distribution system. The multi-parameter characteristics of the distribution cabinet are used to identify the characteristics of the distribution cabinet cluster, so that the cluster characteristics of the distribution cabinet can be clarified; the distribution cabinet cluster characteristic data is clustered to clearly divide the distribution cabinet cluster; the distribution cabinet cluster division data is clustered for topological analysis, so that the network structure information of the distribution cabinet cluster is provided; the inter-cluster electric power is measured for the distribution cabinet cluster characteristic data according to the cluster topology data to obtain the inter-cluster electric power data; the distribution cabinet energy efficiency is calculated for the inter-cluster electric power data to obtain the distribution cabinet energy efficiency data. The distribution cabinet cluster performance is measured based on the circuit short-circuit detection data, the inter-cluster electric power data and the distribution cabinet energy efficiency data, so that the performance status of the distribution cabinet cluster can be comprehensively evaluated, so as to obtain the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data, so that the distribution cabinet cluster can be intelligently regulated; the distribution cabinet is clustered through the distribution cabinet cluster intelligent regulation data to generate the distribution cabinet cluster control plan, which improves the operation efficiency of the distribution cabinet and enhances the stability and reliability of the distribution system. Therefore, the present invention uses data processing technology, real-time monitoring technology and intelligent control technology to timely discover abnormal conditions of the distribution cabinet cluster; and performs intelligent adjustment and control of the distribution cabinet cluster according to specific abnormal conditions, thereby improving the stability of the distribution cabinet cluster.

[0006] Preferably, step S2 comprises the following steps: Step S21: extract the voltage level of the multi-parameter features of the distribution cabinet to obtain the voltage level data of the distribution cabinet; determine the rated current of the voltage level data of the distribution cabinet to obtain the rated current data of the distribution cabinet; identify the frequency of the distribution cabinet based on the voltage level data and the rated current data of the distribution cabinet to generate the frequency data of the distribution cabinet; integrate the voltage level data, the rated current data and the frequency data of the distribution cabinet to obtain the electrical parameter information of the distribution cabinet; Step S22: performing power distribution function identification on the electrical parameter information of the power distribution cabinet to generate power distribution function data; performing electrical parameter monitoring function identification on the power distribution function data to obtain electrical parameter monitoring function data; performing voltage control function identification on the electrical parameter monitoring function data to obtain voltage control function data; integrating the power distribution function data, the electrical parameter monitoring function data and the voltage control function data to obtain the electrical function data of the power distribution cabinet; Step S23: identifying the protection circuit characteristics of the distribution cabinet based on the multi-parameter characteristics of the distribution cabinet to obtain the protection circuit characteristics of the distribution cabinet; performing protection circuit mapping on the protection circuit characteristics of the distribution cabinet based on the electrical function data of the distribution cabinet to generate protection circuit data of the distribution cabinet; Step S24: Perform circuit short-circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short-circuit detection data.

[0007] The present invention extracts the voltage level of the multi-parameter characteristics of the distribution cabinet, and can obtain the voltage level data of the distribution cabinet; clarifies the voltage range that the distribution cabinet can withstand; the rated current is determined based on the voltage level data, which provides a current limiting standard for the safe operation of the distribution cabinet; the frequency identification of the distribution cabinet combines the voltage level and rated current data, and can identify the operating characteristics of the distribution cabinet at different frequencies; the electrical parameter information of the distribution cabinet is identified for the power distribution function, and it is clear how the distribution cabinet distributes and manages electric energy; the electrical parameter monitoring function is identified for the power distribution function data, and the monitoring capability of the distribution cabinet can be further refined; the voltage control function is identified for the electrical parameter monitoring function data, so that the voltage control function is clarified; the protection circuit characteristics of the distribution cabinet are identified for the multi-parameter characteristics of the distribution cabinet, and the key components used to protect the circuit in the distribution cabinet are clarified; the protection circuit is mapped to the protection circuit characteristics of the distribution cabinet based on the electrical function data of the distribution cabinet, so that the protection mechanism of the distribution cabinet is clearly coordinated with the electrical function; the circuit short-circuit detection is performed on the protection circuit data of the distribution cabinet, and the circuit short-circuit situation can be detected, thereby obtaining the circuit short-circuit detection data.

[0008] Preferably, step S24 includes the following steps: Step S241: monitoring the power distribution of the power distribution cabinet protection circuit data through the electrical parameter monitoring function data to generate power distribution monitoring data; identifying the circuit branch load condition of the power distribution monitoring data to obtain the circuit branch load data; Step S242: determining the load demand of the circuit branch load data to generate circuit load demand data; calculating the voltage stabilization degree of the voltage control function data according to the circuit load demand data to obtain circuit voltage control degree data; performing current parameter mapping on the circuit voltage control degree data to generate voltage control current mapping data; Step S243: Based on the circuit voltage control degree data and the voltage stabilization current mapping data, the circuit load demand data is measured to generate circuit load degree data; the circuit load degree data is used to determine the distribution cabinet load change to obtain the distribution cabinet load change data; Step S244: Perform circuit voltage anomaly detection on the load change data of the distribution cabinet to obtain circuit voltage anomaly data; perform circuit current anomaly detection on the load change data of the distribution cabinet in combination with the circuit voltage anomaly data to generate circuit current anomaly data; perform circuit short circuit detection on the load change data of the distribution cabinet based on the circuit voltage anomaly data and the circuit current anomaly data to obtain circuit short circuit detection data.

[0009] The present invention monitors the power distribution of the protection circuit data of the distribution cabinet through the electrical parameter monitoring function data, ensures the real-time monitoring of the power distribution of each branch of the distribution cabinet, and helps to identify the imbalance in the power distribution; identifies the circuit branch load condition of the power distribution monitoring data to clarify the circuit branch load condition; determines the load demand of the circuit branch load data to clarify the load demand of each branch and provide a basis for voltage control; calculates the voltage stability of the voltage control function data according to the circuit load demand data to clarify the voltage stability of the distribution cabinet under different load conditions; maps the circuit voltage control degree data to current parameters to obtain voltage control current mapping data; and The voltage stabilization degree data and voltage stabilization current mapping data are used to measure the load degree of the circuit load demand data, ensuring the load adaptability of the distribution cabinet under different operating conditions; the circuit load degree data is used to determine the load change of the distribution cabinet, which can timely reflect the load change of the distribution cabinet; the circuit voltage anomaly detection is performed on the load change data of the distribution cabinet, which can timely discover the voltage anomaly in the circuit and ensure the safe operation of the distribution cabinet; the circuit current anomaly detection is performed on the load change data of the distribution cabinet in combination with the circuit voltage anomaly data, which can further confirm the abnormality in the circuit; the circuit short circuit detection is performed on the load change data of the distribution cabinet based on the circuit voltage anomaly data and the circuit current anomaly data to clarify the circuit short circuit detection situation.

[0010] Preferably, step S3 comprises the following steps: Step S31: performing geometric feature recognition on the multi-parameter features of the distribution cabinet to obtain the geometric features of the distribution cabinet; performing boundary feature marking on the geometric features of the distribution cabinet to obtain boundary marking data of the distribution cabinet; performing single distribution cabinet position determination on the boundary marking data of the distribution cabinet to generate single distribution cabinet position data; Step S32: Based on the single distribution cabinet location data, the distribution cabinet multi-parameter characteristics are used to identify the distribution cabinet cluster characteristics to obtain the distribution cabinet cluster characteristic data; based on the power distribution function data, the distribution capacity of the distribution cabinet cluster characteristic data is measured to generate the distribution cabinet cluster distribution capacity data; the power distribution distance of the distribution cabinet cluster distribution capacity data is calculated to obtain the cluster power distribution distance; the distribution cabinet cluster characteristic data is clustered according to the cluster power distribution distance to generate the distribution cabinet cluster division data; Step S33: marking the distribution range of the distribution cabinet on the distribution cabinet cluster division data to obtain the distribution range data of the distribution cabinet; performing structural hierarchical division on the distribution range data of the distribution cabinet to generate the structural hierarchical data of the distribution cabinet; performing cluster topological structure identification on the structural hierarchical data of the distribution cabinet to obtain the cluster topological data; Step S34: perform inter-cluster electric power measurement on the distribution cabinet cluster characteristic data according to the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data.

[0011] The present invention identifies the geometric features of the distribution cabinet based on the multi-parameter features of the distribution cabinet, accurately obtains the geometric features of the distribution cabinet, including physical properties such as the size and shape of the distribution cabinet; marks the boundary features of the distribution cabinet based on the geometric features of the distribution cabinet, and clarifies the edges and boundaries of the distribution cabinet; determines the position of a single distribution cabinet based on the boundary marking data of the distribution cabinet, thereby obtaining the position of a single distribution cabinet; identifies the cluster features of the distribution cabinet based on the multi-parameter features of the distribution cabinet based on the position data of the single distribution cabinet, and clarifies the mutual relationship and overall layout of the distribution cabinets in the cluster; determines the distribution capacity of the distribution cabinet cluster feature data based on the power distribution function data, so as to evaluate the power distribution capacity of the distribution cabinet cluster; calculates the power distribution distance of the distribution cabinet cluster distribution capacity data, and clarifies the cluster power distribution distance; and determines the power distribution capacity of the cluster based on the cluster power distribution capacity data. The characteristic data of the distribution cabinet cluster is clustered according to the distance, and the distribution cabinet cluster is divided into smaller management units to improve management efficiency and response speed; the distribution range of the distribution cabinet cluster division data is marked to clarify the distribution of the distribution cabinet in the physical space; the distribution range data of the distribution cabinet is divided into structural hierarchies to reflect the structural hierarchy of the distribution cabinet; the cluster topology structure of the distribution cabinet structural hierarchy data is identified to understand the connection relationship and information flow path between the distribution cabinets; the inter-cluster electric power of the distribution cabinet cluster characteristic data is calculated according to the cluster topology data to obtain the inter-cluster electric power data, which is helpful to evaluate the electric energy flow and electric power distribution efficiency of the distribution cabinet cluster; the energy efficiency of the distribution cabinet is calculated on the inter-cluster electric power data to evaluate the energy efficiency level of the distribution cabinet.

[0012] Preferably, step S34 includes the following steps: Step S341: performing cluster node identification on the distribution cabinet cluster feature data according to the cluster topology data to obtain the distribution cabinet cluster node data; performing electrical parameter collection on the distribution cabinet cluster node data to obtain cluster node electrical parameters; performing node frequency parameter extraction on the cluster node electrical parameters to generate cluster node frequency parameters; Step S342: extract node voltage parameters from cluster node electrical parameters through cluster node frequency parameters to obtain cluster node voltage parameters; extract node current parameters from cluster node electrical parameters according to cluster node voltage parameters to obtain cluster node current parameters; perform inter-cluster electric power measurement on distribution cabinet cluster characteristic data based on cluster node voltage parameters and cluster node current parameters to obtain inter-cluster electric power data; Step S343: performing summary calculation of the electric power of each cluster node on the inter-cluster electric power data to obtain cluster electric power summary data; performing power factor calculation on the cluster electric power summary data to generate cluster power factor information; performing electric energy flow per unit time on the cluster power factor information to obtain cluster electric energy flow data; Step S344: measuring the power consumption per unit time of the cluster power flow data to generate the cluster power consumption; performing energy efficiency calculation on the cluster power consumption and the unit time to obtain the power distribution cabinet energy efficiency data.

[0013] The present invention identifies cluster nodes of distribution cabinet cluster characteristic data according to cluster topology data, and clarifies the position and function of each node in the distribution cabinet cluster; collects electrical parameters of distribution cabinet cluster node data, which helps to collect detailed electrical characteristics of distribution cabinet cluster nodes, such as voltage, current and power; extracts node frequency parameters of cluster node electrical parameters, which can identify the frequency characteristics of distribution cabinet cluster nodes; extracts node voltage parameters of cluster node electrical parameters through cluster node frequency parameters, which helps to identify the voltage characteristics of distribution cabinet cluster nodes, which plays an important role in the subsequent voltage stability and regulation of distribution cabinets; extracts node current parameters of cluster node electrical parameters according to cluster node voltage parameters, which can identify the current characteristics of distribution cabinet cluster nodes, which is important for the subsequent current control and optimization of distribution cabinets. It is of vital importance; based on the cluster node voltage parameters and cluster node current parameters, the inter-cluster electric power measurement of the distribution cabinet cluster characteristic data is helpful to evaluate the power flow and distribution efficiency between the distribution cabinet clusters; the inter-cluster electric power data is aggregated and calculated for each cluster node, which can integrate the power consumption information of the distribution cabinet cluster; the power factor is calculated on the cluster power summary data, which can evaluate the power quality of the distribution cabinet cluster; the cluster power factor information is recorded for the power flow per unit time, so as to monitor the power flow of the distribution cabinet cluster per unit time; the cluster power flow data is measured for the power consumption per unit time, which can evaluate the power consumption of the distribution cabinet cluster per unit time; the cluster power consumption and unit time are calculated for energy efficiency, which can comprehensively evaluate the energy efficiency level of the distribution cabinet.

[0014] Preferably, step S4 comprises the following steps: Step S41: Monitor the frequency of short circuit occurrence on the circuit short circuit detection data to obtain a short circuit frequency index; calculate the uniformity of electric power distribution between clusters on the electric power data between clusters to generate an electric power uniformity index; measure the cluster energy efficiency ratio on the energy efficiency data of the distribution cabinet to obtain a cluster energy efficiency ratio index; measure the distribution cabinet cluster performance index on the multi-parameter characteristics of the distribution cabinet based on the short circuit frequency index, the electric power uniformity index and the cluster energy efficiency ratio index to generate distribution cabinet cluster performance data; Step S42: dynamically adjust and control the cluster short circuit of the power distribution cabinet according to the performance data of the power distribution cabinet cluster, and obtain dynamic control measures for the circuit short circuit; Step S43: performing intelligent optimization control of cluster energy efficiency of the power distribution cabinets according to the performance data of the power distribution cabinet cluster, and obtaining cluster energy efficiency optimization control measures; Step S44: Integrate the circuit short circuit dynamic control measures and the cluster energy efficiency optimization control measures to generate distribution cabinet cluster intelligent control data; Step S45: cluster control of the distribution cabinets is performed through the distribution cabinet cluster intelligent control data to generate a distribution cabinet cluster control plan.

[0015] The present invention monitors the frequency of short circuits on circuit short circuit detection data, and clearly identifies the frequency of short circuit events in the distribution cabinet; calculates the uniformity of electric power distribution between clusters on the electric power data between clusters, so as to evaluate the balance of electric energy distribution in the distribution cabinet cluster, which is helpful to optimize the electric energy distribution; measures the cluster energy efficiency ratio of the distribution cabinet energy efficiency data, and can measure the energy efficiency level of the distribution cabinet cluster; measures the distribution cabinet cluster performance index based on the multi-parameter characteristics of the distribution cabinet based on the circuit short circuit frequency index, the electric power uniformity index and the cluster energy efficiency ratio index, which comprehensively reflects the overall performance of the distribution cabinet cluster; dynamically adjusts and controls the cluster short circuit of the distribution cabinet according to the performance data of the distribution cabinet cluster, and can dynamically respond to short circuit events in the distribution cabinet cluster and adjust the distribution strategy in time. Strategy, reduce the impact of short circuit on the distribution system, improve the safety and reliability of the distribution cabinet cluster; intelligently optimize the energy efficiency of the distribution cabinet cluster according to the performance data of the distribution cabinet cluster, which can optimize the operating parameters and strategies of the distribution cabinet, improve the energy efficiency of the distribution cabinet cluster, reduce energy consumption, and achieve energy conservation and emission reduction; integrate circuit short-circuit dynamic control measures and cluster energy efficiency optimization control measures to generate distribution cabinet cluster intelligent control data. These data combine the control strategies of short-circuit protection and energy efficiency optimization to provide a comprehensive intelligent control solution for the distribution cabinet cluster; cluster control of the distribution cabinet is carried out through the distribution cabinet cluster intelligent control data, thereby generating a distribution cabinet cluster control solution, which can realize the refined management of the distribution cabinet cluster, optimize the power distribution, and improve the stability of the distribution system.

[0016] Preferably, step S42 includes the following steps: Step S421: monitor the performance data of the power distribution cabinet cluster in real time to obtain real-time performance monitoring data; track the operation status of the power distribution cabinet based on the real-time performance monitoring data to generate the operation status data of the power distribution cabinet; Step S422: Based on the distribution cabinet operation status data, the distribution cabinet cluster feature data is monitored for cluster overload status to obtain cluster overload status data; cluster circuit short circuit risk feature identification is performed on the cluster overload status data to obtain cluster circuit short circuit risk feature; Step S423: determining the short circuit location point of the cluster circuit short circuit risk feature to generate cluster short circuit location information; measuring the short circuit abnormal voltage value of the cluster short circuit location information to obtain the cluster short circuit voltage value; measuring the short circuit abnormal current value of the cluster short circuit location information to obtain the cluster short circuit current value; Step S424: Perform circuit dynamic voltage stabilization adjustment on the power distribution cabinet according to the cluster short-circuit voltage value, and generate circuit dynamic voltage stabilization adjustment measures; perform circuit dynamic current limiting control on the power distribution cabinet according to the cluster short-circuit current value, and generate circuit dynamic current limiting control measures; integrate circuit dynamic voltage stabilization adjustment measures and circuit dynamic current limiting control measures to obtain circuit short-circuit dynamic regulation measures.

[0017] The present invention monitors the performance data of the distribution cabinet cluster in real time, and can ensure the real-time monitoring of the operating status of the distribution cabinet cluster; tracks the operating status of the distribution cabinet based on the real-time performance monitoring data, and clarifies the operating status of the distribution cabinet; monitors the cluster overload status of the distribution cabinet cluster feature data based on the distribution cabinet operating status data, and can timely discover whether there is an overload in the distribution cabinet cluster; identifies the cluster circuit short-circuit risk characteristics of the cluster overload status data, and can identify the risk factors that cause the short circuit; determines the circuit short-circuit position point based on the cluster circuit short-circuit risk characteristics, and can accurately locate the specific location where the short circuit occurs; The abnormal short-circuit voltage value is measured based on the cluster short-circuit position information to clarify the characteristics of the cluster short-circuit voltage; the abnormal short-circuit current value is measured based on the cluster short-circuit position information to specifically determine the cluster short-circuit current value; the distribution cabinet is dynamically adjusted for circuit voltage stabilization according to the cluster short-circuit voltage value to ensure that when a short circuit occurs, the voltage is dynamically adjusted to maintain stable operation of the system and reduce the impact of voltage fluctuations on the equipment; the distribution cabinet is dynamically controlled for circuit current limiting according to the cluster short-circuit current value to prevent equipment damage caused by excessive current; the circuit dynamic voltage stabilization adjustment measures and circuit dynamic current limiting control measures are integrated to obtain circuit short-circuit dynamic regulation measures.

[0018] Preferably, step S43 includes the following steps: Step S431: monitor the electric power characteristics of the power distribution cabinet cluster performance data to obtain the cluster electric power characteristics; extract the power peak value of the cluster electric power characteristics to obtain the cluster power peak data; extract the power valley value of the cluster electric power characteristics to obtain the cluster power valley value data; calculate the average power of the cluster electric power characteristics to obtain the cluster average power data; Step S432: performing cluster power fluctuation identification on the cluster electric power characteristics based on the cluster power peak data, the cluster power valley data and the cluster average power data to generate cluster power fluctuation data; performing cluster power anomaly detection on the cluster power fluctuation data to obtain cluster power anomaly data; performing cluster anomaly energy efficiency calculation on the cluster power anomaly data to obtain cluster anomaly energy efficiency data; Step S433: Identify the power consumption mode of the distribution cabinet based on the multi-parameter characteristics of the distribution cabinet according to the cluster abnormal energy efficiency data, and generate the power consumption mode data of the distribution cabinet; set the cluster energy efficiency data threshold for the power consumption mode data of the distribution cabinet to obtain the cluster energy efficiency data threshold; perform difference calculation between the cluster abnormal energy efficiency data and the cluster energy efficiency data threshold to generate cluster energy efficiency difference data; Step S434: Based on the power distribution cabinet power consumption mode data and the cluster energy efficiency difference data, the cluster energy efficiency intelligent optimization control is performed on the distribution cabinet to obtain cluster energy efficiency optimization control measures.

[0019] The present invention monitors the electric power characteristics of the distribution cabinet cluster performance data to clarify the cluster electric power characteristics; extracts the power peak value of the cluster electric power characteristics to identify the maximum power consumption point of the distribution cabinet cluster during operation; extracts the power valley value of the cluster electric power characteristics to clarify the minimum power consumption point of the distribution cabinet cluster during operation; calculates the average power of the cluster electric power characteristics, which is helpful to evaluate the overall energy consumption level of the distribution cabinet cluster; identifies the cluster power fluctuation based on the cluster power peak data, cluster power valley data and cluster average power data, so as to clarify the cluster power fluctuation; performs cluster power anomaly detection on the cluster power fluctuation data to timely discover the cluster power anomaly; The energy efficiency of the distribution cabinet cluster can be measured based on the abnormal rate data, which can evaluate the energy efficiency level of the distribution cabinet cluster under abnormal conditions; the power consumption pattern of the distribution cabinet is identified based on the multi-parameter characteristics of the distribution cabinet according to the cluster abnormal energy efficiency data, and the power consumption habits and patterns of the distribution cabinet are clarified; the cluster energy efficiency data threshold is set for the power consumption pattern data of the distribution cabinet, which is helpful to set reasonable energy efficiency goals for the distribution cabinet cluster and provide goal guidance for the energy efficiency optimization of the distribution cabinet; the difference between the cluster abnormal energy efficiency data and the cluster energy efficiency data threshold is calculated to quantify the energy efficiency difference of the distribution cabinet cluster; the cluster energy efficiency of the distribution cabinet is intelligently optimized and controlled based on the power consumption pattern data of the distribution cabinet and the cluster energy efficiency difference data, and the cluster energy efficiency optimization and regulation measures are obtained to achieve energy efficiency optimization of the distribution cabinet cluster.

[0020] Preferably, step S45 includes the following steps: Step S451: perform real-time monitoring of the cluster circuit status of the power distribution cabinet to obtain cluster circuit status monitoring data; extract short-circuit voltage status monitoring information from the cluster circuit status monitoring data to obtain short-circuit voltage status monitoring information; extract short-circuit current status monitoring information from the cluster circuit status monitoring data to obtain short-circuit current status monitoring information; Step S452: Based on the intelligent control data of the power distribution cabinet cluster, a circuit dynamic voltage stabilization adjustment measure is executed for the short-circuit voltage state monitoring situation, and circuit dynamic voltage stabilization execution data is generated; based on the intelligent control data of the power distribution cabinet cluster, a circuit dynamic current limiting control measure is executed for the short-circuit current state monitoring situation, and circuit dynamic current limiting execution data is generated; Step S453: Perform real-time monitoring of the power consumption mode of the distribution cabinet to obtain the power consumption mode monitoring data of the distribution cabinet; perform real-time detection of cluster abnormal energy efficiency of the distribution cabinet through the power consumption mode monitoring data of the distribution cabinet to generate cluster abnormal energy efficiency monitoring data; perform cluster energy efficiency optimization and control measures on the cluster abnormal energy efficiency monitoring data based on the distribution cabinet cluster intelligent control data to generate cluster energy efficiency optimization execution data; Step S454: Integrate circuit dynamic voltage stabilization execution data, circuit dynamic current limiting execution data and cluster energy efficiency optimization execution data to obtain a distribution cabinet cluster control solution.

[0021] The present invention performs real-time monitoring of the status of cluster circuits in the power distribution cabinets, ensuring continuous and real-time understanding of the status of circuits in the power distribution cabinet cluster; extracts short-circuit voltage status monitoring conditions from cluster circuit status monitoring data to clarify the short-circuit voltage status monitoring conditions; extracts short-circuit current status monitoring conditions from cluster circuit status monitoring data to master the short-circuit current status monitoring conditions; performs circuit dynamic voltage stabilization adjustment measures on the short-circuit voltage status monitoring conditions based on the intelligent control data of the power distribution cabinet cluster, ensuring that when a voltage anomaly is detected, the voltage of the power distribution cabinet cluster is maintained stable by dynamically adjusting the voltage, thereby reducing the impact of voltage fluctuations on equipment; performs circuit dynamic current limiting control measures on the short-circuit current status monitoring conditions based on the intelligent control data of the power distribution cabinet cluster, ensuring that when a current anomaly is detected, the voltage anomaly is maintained by limiting the current, thereby reducing the impact of voltage fluctuations on equipment. Protect the circuit to prevent equipment damage caused by excessive current; conduct real-time monitoring of the power consumption mode of the distribution cabinet, promptly discover abnormal energy efficiency in the distribution cabinet cluster, and provide a basis for energy efficiency optimization of the distribution cabinet; conduct real-time detection of cluster abnormal energy efficiency of the distribution cabinet through the power consumption mode monitoring data of the distribution cabinet, and generate cluster abnormal energy efficiency monitoring data; perform cluster energy efficiency optimization and control measures on the cluster abnormal energy efficiency monitoring data based on the intelligent control data of the distribution cabinet cluster, which can ensure that when energy efficiency abnormalities are detected, the energy efficiency of the distribution cabinet cluster can be improved and energy consumption can be reduced through optimization measures; integrate the circuit dynamic voltage stabilization execution data, the circuit dynamic current limiting execution data and the cluster energy efficiency optimization execution data to obtain the distribution cabinet cluster control plan, which can realize the refined management of the distribution cabinet cluster, optimize the power distribution, and improve the stability of the distribution cabinet cluster.

[0022] In this specification, a control system for a distribution cabinet cluster is provided, which is used to execute the above-mentioned control method for a distribution cabinet cluster. The control system for a distribution cabinet cluster includes: The distribution cabinet data acquisition module is used to collect data from the distribution cabinet using sensors to obtain distribution cabinet data; pre-process the distribution cabinet data to obtain standard distribution cabinet data; perform multi-parameter feature recognition on the standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; The power distribution cabinet circuit short circuit detection module is used to extract electrical parameters from the multi-parameter features of the power distribution cabinet to generate electrical parameter information of the power distribution cabinet; perform electrical function identification on the electrical parameter information of the power distribution cabinet to obtain electrical function data of the power distribution cabinet; perform power distribution cabinet protection circuit mapping on the multi-parameter features of the power distribution cabinet according to the electrical function data of the power distribution cabinet to generate power distribution cabinet protection circuit data; perform circuit short circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short circuit detection data; The power distribution cabinet energy efficiency calculation module is used to identify the power distribution cabinet cluster characteristics based on the multi-parameter characteristics of the power distribution cabinet to obtain the power distribution cabinet cluster characteristic data; cluster the power distribution cabinet cluster characteristic data to generate the power distribution cabinet cluster division data; perform cluster topology analysis on the power distribution cabinet cluster division data to obtain the cluster topology data; perform inter-cluster electric power calculation on the power distribution cabinet cluster characteristic data according to the cluster topology data to obtain the inter-cluster electric power data; perform power distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate the power distribution cabinet energy efficiency data; The distribution cabinet cluster intelligent adjustment and control module is used to measure the distribution cabinet cluster performance based on the multi-parameter characteristics of the distribution cabinets based on the circuit short-circuit detection data, the electric power data between the clusters and the energy efficiency data of the distribution cabinets, and generate the distribution cabinet cluster performance data; perform distribution cabinet cluster intelligent adjustment and control on the distribution cabinets according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent regulation data; perform cluster control on the distribution cabinets through the distribution cabinet cluster intelligent regulation data to generate the distribution cabinet cluster control plan.

[0023] The present invention uses a distribution cabinet data acquisition module and a sensor to collect data from the distribution cabinet, so as to accurately obtain the distribution cabinet data, specifically including key parameters such as voltage, current and power; pre-process the distribution cabinet data to remove noise and abnormal values, so as to obtain standardized distribution cabinet data, providing a data basis for subsequent analysis; perform multi-parameter feature recognition on the standard distribution cabinet data, extract the key performance characteristics of the distribution cabinet, so as to reflect the operating status of the distribution cabinet; electrical parameter extraction and electrical function recognition further refine the operating characteristics of the distribution cabinet, and distribution cabinet protection circuit mapping and circuit short-circuit detection can identify the short-circuit risk in the distribution cabinet. Through the distribution cabinet circuit short circuit detection module, the electrical parameter information of the distribution cabinet can be clarified; the electrical parameter information includes but is not limited to current, voltage and power, which is the basis for the normal operation and fault diagnosis of the distribution cabinet; the electrical function identification further links the parameter information with the specific function of the distribution cabinet, thereby generating the electrical function data of the distribution cabinet, which provides a basis for the functional analysis and abnormal detection of the distribution cabinet; the circuit short circuit detection is performed on the protection circuit data of the distribution cabinet, so that the circuit short circuit event can be discovered in time to ensure the safe and stable operation of the distribution system. Through the distribution cabinet cluster intelligent adjustment control module, the distribution cabinet cluster feature identification is performed on the multi-parameter characteristics of the distribution cabinet, so that the cluster characteristics of the distribution cabinet can be clarified; the distribution cabinet cluster feature data is clustered, which can clearly divide the distribution cabinet cluster; the distribution cabinet cluster division data is clustered topologically analyzed, so that the network structure information of the distribution cabinet cluster is provided; the distribution cabinet cluster feature data is measured between clusters according to the cluster topology data to obtain the inter-cluster electric power data; the distribution cabinet energy efficiency is calculated on the inter-cluster electric power data, so as to obtain the distribution cabinet energy efficiency data. Through the intelligent adjustment and control module of the distribution cabinet cluster, the distribution cabinet cluster performance is measured based on the multi-parameter characteristics of the distribution cabinet based on the circuit short-circuit detection data, the electric power data between clusters and the energy efficiency data of the distribution cabinet, which can comprehensively evaluate the performance status of the distribution cabinet cluster, thereby obtaining the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data, and the distribution cabinet cluster can be intelligently regulated; the distribution cabinet is clustered and controlled through the distribution cabinet cluster intelligent regulation data, and the distribution cabinet cluster control scheme is generated, which improves the operating efficiency of the distribution cabinet and enhances the stability and reliability of the distribution system. Therefore, the present invention timely discovers the abnormal situation of the distribution cabinet cluster through data processing technology, real-time monitoring technology and intelligent regulation technology; and performs intelligent adjustment and control of the distribution cabinet cluster according to specific abnormal situations, thereby improving the stability of the distribution cabinet cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of a control method for a power distribution cabinet cluster; Figure 2 for Figure 1Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 2 Detailed implementation steps of step S34 are shown in the flowchart; 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

[0025] The technical method of the present invention is described clearly and completely below 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.

[0026] 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 networks and / or processor methods and / or microcontroller methods.

[0027] 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, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a control method for a power distribution cabinet cluster, the method comprising the following steps: Step S1: using sensors to collect data from the distribution cabinet to obtain distribution cabinet data; preprocessing the distribution cabinet data to obtain standard distribution cabinet data; performing multi-parameter feature recognition on the standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; Step S2: extract electrical parameters from the multi-parameter features of the distribution cabinet to generate electrical parameter information of the distribution cabinet; identify electrical functions of the electrical parameter information of the distribution cabinet to obtain electrical function data of the distribution cabinet; map the multi-parameter features of the distribution cabinet to the distribution cabinet protection circuit according to the electrical function data of the distribution cabinet to generate the protection circuit data of the distribution cabinet; perform circuit short circuit detection on the protection circuit data of the distribution cabinet to obtain circuit short circuit detection data; Step S3: Perform distribution cabinet cluster feature recognition on the multi-parameter features of the distribution cabinet to obtain distribution cabinet cluster feature data; perform cluster division on the distribution cabinet cluster feature data to generate distribution cabinet cluster division data; perform cluster topology analysis on the distribution cabinet cluster division data to obtain cluster topology data; perform inter-cluster electric power measurement on the distribution cabinet cluster feature data according to the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data; Step S4: Based on the circuit short-circuit detection data, inter-cluster electric power data and distribution cabinet energy efficiency data, the distribution cabinet cluster performance is measured on the multi-parameter characteristics of the distribution cabinet to generate the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent regulation data; the distribution cabinet is clustered and controlled by using the distribution cabinet cluster intelligent regulation data to generate a distribution cabinet cluster control plan.

[0029] The present invention uses sensors to collect data from a distribution cabinet, and can accurately obtain distribution cabinet data, specifically including key parameters such as voltage, current and power; pre-process the distribution cabinet data to remove noise and abnormal values, thereby obtaining standardized distribution cabinet data, providing a data basis for subsequent analysis; perform multi-parameter feature recognition on standard distribution cabinet data, extract key performance features of the distribution cabinet, so as to reflect the operating status of the distribution cabinet; electrical parameter extraction and electrical function recognition further refine the operating characteristics of the distribution cabinet, and distribution cabinet protection circuit mapping and circuit short circuit detection can identify the short circuit risk in the distribution cabinet. The electrical parameter extraction of the multi-parameter feature of the distribution cabinet makes the electrical parameter information of the distribution cabinet clear; the electrical parameter information includes but is not limited to current, voltage and power, which is the basis for the normal operation and fault diagnosis of the distribution cabinet; the electrical function recognition further links the parameter information with the specific function of the distribution cabinet, thereby generating the electrical function data of the distribution cabinet, which provides a basis for the functional analysis and abnormality detection of the distribution cabinet; the circuit short circuit detection is performed on the distribution cabinet protection circuit data, so that the circuit short circuit event is discovered in time to ensure the safe and stable operation of the distribution system. The multi-parameter characteristics of the distribution cabinet are used to identify the characteristics of the distribution cabinet cluster, so that the cluster characteristics of the distribution cabinet can be clarified; the distribution cabinet cluster characteristic data is clustered to clearly divide the distribution cabinet cluster; the distribution cabinet cluster division data is clustered for topological analysis, so that the network structure information of the distribution cabinet cluster is provided; the inter-cluster electric power is measured for the distribution cabinet cluster characteristic data according to the cluster topology data to obtain the inter-cluster electric power data; the distribution cabinet energy efficiency is calculated for the inter-cluster electric power data to obtain the distribution cabinet energy efficiency data. The distribution cabinet cluster performance is measured based on the circuit short-circuit detection data, the inter-cluster electric power data and the distribution cabinet energy efficiency data, so that the performance status of the distribution cabinet cluster can be comprehensively evaluated, so as to obtain the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data, so that the distribution cabinet cluster can be intelligently regulated; the distribution cabinet is clustered through the distribution cabinet cluster intelligent regulation data to generate the distribution cabinet cluster control plan, which improves the operation efficiency of the distribution cabinet and enhances the stability and reliability of the distribution system. Therefore, the present invention uses data processing technology, real-time monitoring technology and intelligent control technology to timely discover abnormal conditions of the distribution cabinet cluster; and performs intelligent adjustment and control of the distribution cabinet cluster according to specific abnormal conditions, thereby improving the stability of the distribution cabinet cluster.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a control method for a power distribution cabinet cluster of the present invention. In this example, the control method for a power distribution cabinet cluster includes the following steps: Step S1: using sensors to collect data from the distribution cabinet to obtain distribution cabinet data; preprocessing the distribution cabinet data to obtain standard distribution cabinet data; performing multi-parameter feature recognition on the standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; In an embodiment of the present invention, various types of sensors are used to monitor different parameters; for example, a temperature sensor is used to monitor the temperature change inside the distribution cabinet, a pressure sensor is used to detect the pressure difference inside and outside the distribution cabinet, and a current and voltage sensor is used to monitor the current and voltage levels in the circuit; specifically, temperature sensors and current sensors are deployed to monitor the status of the distribution cabinet in real time; the temperature sensor converts the temperature reading into a digital signal through an analog-to-digital converter and transmits it to a central monitoring system through Ethernet; from the data received from the sensor, the interquartile range method is used to identify and process outliers, and data points outside the normal range are marked as outliers and processed; then, for missing data, the mean filling or median filling method is used to complete the data; for classified data, the unique hot encoding method is used for conversion; the principal component analysis technology is used to perform multi-parameter feature recognition on the standard distribution cabinet data, thereby obtaining the multi-parameter feature of the distribution cabinet.

[0031] Step S2: extract electrical parameters from the multi-parameter features of the distribution cabinet to generate electrical parameter information of the distribution cabinet; identify electrical functions of the electrical parameter information of the distribution cabinet to obtain electrical function data of the distribution cabinet; map the multi-parameter features of the distribution cabinet to the distribution cabinet protection circuit according to the electrical function data of the distribution cabinet to generate the protection circuit data of the distribution cabinet; perform circuit short circuit detection on the protection circuit data of the distribution cabinet to obtain circuit short circuit detection data; In an embodiment of the present invention, feature extraction technology is used to extract electrical parameters of a distribution cabinet, including resistance, inductance, capacitance and conductance; by analyzing the electrical function parameters extracted from the distribution cabinet, the electrical functions of each circuit branch are specifically identified, for example, the current branch responsible for overload protection and the circuit branch responsible for short-circuit protection, and these data will be used to map the protection circuit of the distribution cabinet; the protection circuit of the distribution cabinet is mapped according to the electrical function data of the distribution cabinet, and the electrical parameters are specifically matched with the specific protection functions; for example, for a specific overload protection branch, the electrical parameters that trigger the circuit breakage under overload conditions are extracted; the protection circuit data of the distribution cabinet is subjected to circuit short-circuit detection, and specifically, in the actual operation of the distribution cabinet, a circuit detector is used to detect the short-circuit condition in the circuit, and a fault detection mark is specifically provided to indicate whether there is an overvoltage condition on its input.

[0032] Step S3: Perform distribution cabinet cluster feature recognition on the multi-parameter features of the distribution cabinet to obtain distribution cabinet cluster feature data; perform cluster division on the distribution cabinet cluster feature data to generate distribution cabinet cluster division data; perform cluster topology analysis on the distribution cabinet cluster division data to obtain cluster topology data; perform inter-cluster electric power measurement on the distribution cabinet cluster feature data according to the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data; In the embodiment of the present invention, boundary feature points are respectively set at the four corner points of the distribution cabinet panel to obtain the distribution cabinet panel image containing the boundary feature points; the system is calibrated by a binocular camera to obtain the parameters of the left camera and the right camera in the binocular camera, and a stereo calibration is performed to obtain the relative position relationship matrix between the left and right cameras; the G vector partitioning method is used for cluster partitioning, specifically, the dimension of G is the total number of lines in the system, each element of which corresponds to the relationship between a line and a cluster, the number "1" indicates that the line is an intra-cluster line, and "0" indicates an inter-cluster line; the neighborhood of the solution G is obtained by special The elements in a few positions are changed from 0 to 1 or from 1 to 0 to obtain; when performing cluster division, the fitness of the solution G is quantitatively calculated by the comprehensive performance index of the cluster, and the larger the value, the better the fitness of the solution; cluster topology analysis is performed on the distribution cabinet cluster division data, and a cluster division method based on source-load-storage power coordination is specifically adopted to obtain cluster topology data; based on the cluster topology data, the inter-cluster electric power of the distribution cabinet cluster characteristic data is calculated, and the electric power is calculated by optimizing the photovoltaic converter; the distribution cabinet energy efficiency is calculated for the inter-cluster electric power data to generate the distribution cabinet energy efficiency data.

[0033] Step S4: Based on the circuit short-circuit detection data, inter-cluster electric power data and distribution cabinet energy efficiency data, the distribution cabinet cluster performance is measured on the multi-parameter characteristics of the distribution cabinet to generate the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent regulation data; the distribution cabinet is clustered and controlled by using the distribution cabinet cluster intelligent regulation data to generate a distribution cabinet cluster control plan.

[0034] In the embodiment of the present invention, the main component analysis method is used to measure the performance of the distribution cabinet cluster based on the circuit short circuit detection data, the inter-cluster electric power data and the distribution cabinet energy efficiency data of the distribution cabinet multi-parameter characteristics; the key parameters such as the current, voltage, temperature of the distribution cabinet and the environmental conditions in the distribution room are monitored in real time to obtain the distribution cabinet cluster performance data; according to the distribution cabinet cluster performance data, the ABB Ability EAM cloud platform is used to perform intelligent adjustment and control of the distribution cabinet cluster. The platform can realize the intelligent operation and maintenance management of the power distribution in the communication room; specifically, by accessing the cloud platform, the remote monitoring and control of the distribution cabinet is realized to obtain the intelligent control data of the distribution cabinet cluster; through the intelligent control data of the distribution cabinet cluster, the ABB Ability iPMS intelligent distribution management system is used to control the distribution cabinet cluster, specifically, the iPMS intelligent distribution management system mainly involves the management of medium-voltage switchgear, diesel generator sets and their distribution equipment, transformer equipment, low-voltage switchgear, UPS and supporting battery packs, ATS power switching devices and PDUs, etc., to obtain the distribution cabinet cluster control solution.

[0035] Preferably, step S2 comprises the following steps: Step S21: extract the voltage level of the multi-parameter features of the distribution cabinet to obtain the voltage level data of the distribution cabinet; determine the rated current of the voltage level data of the distribution cabinet to obtain the rated current data of the distribution cabinet; identify the frequency of the distribution cabinet based on the voltage level data and the rated current data of the distribution cabinet to generate the frequency data of the distribution cabinet; integrate the voltage level data, the rated current data and the frequency data of the distribution cabinet to obtain the electrical parameter information of the distribution cabinet; Step S22: performing power distribution function identification on the electrical parameter information of the power distribution cabinet to generate power distribution function data; performing electrical parameter monitoring function identification on the power distribution function data to obtain electrical parameter monitoring function data; performing voltage control function identification on the electrical parameter monitoring function data to obtain voltage control function data; integrating the power distribution function data, the electrical parameter monitoring function data and the voltage control function data to obtain the electrical function data of the power distribution cabinet; Step S23: identifying the protection circuit characteristics of the distribution cabinet based on the multi-parameter characteristics of the distribution cabinet to obtain the protection circuit characteristics of the distribution cabinet; performing protection circuit mapping on the protection circuit characteristics of the distribution cabinet based on the electrical function data of the distribution cabinet to generate protection circuit data of the distribution cabinet; Step S24: Perform circuit short-circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short-circuit detection data.

[0036] In an embodiment of the present invention, an electrical parameter acquisition module is used to measure and monitor various electrical parameters in the power system, including voltage, current, power factor and frequency; further, the voltage level of the distribution cabinet is extracted to obtain the voltage level data of the distribution cabinet; according to the rated current value of the nameplate information of the distribution cabinet, specifically, the rated current of the distribution cabinet is determined according to the rated current of the primary component with the smallest current in the cabinet. For example, if the rated currents of the circuit breaker and the current transformer are different, the rated current of the distribution cabinet is determined according to the smaller one; the frequency parameters of the power grid are specifically monitored through the electrical parameter acquisition module; based on the voltage level data of the distribution cabinet and the rated current data of the distribution cabinet, combined with the frequency parameters of the power grid, the frequency of the distribution cabinet is identified for the multi-parameter characteristics of the distribution cabinet, thereby obtaining the frequency data of the distribution cabinet; the electrical parameter information of the distribution cabinet is identified for the power distribution function to generate the power distribution function data; the electrical parameter acquisition module is used to obtain and record the key parameters in the distribution cabinet in real time; the voltage control function is identified for the electrical parameter monitoring function data to obtain the voltage control function data; the protection circuit characteristics of the distribution cabinet are identified based on the electrical function data of the distribution cabinet, and the real-time data of power, temperature, current, voltage, harmonics and power quality are specifically identified; the voltage and current parameters of the distribution cabinet are monitored, and when the measured data exceeds the preset limit value, for example, the preset voltage limit value is set to 300V, and the preset current limit value is set to 100A; an alarm is automatically issued when the preset limit value is exceeded, thereby obtaining circuit short circuit detection data.

[0037] Preferably, step S24 includes the following steps: Step S241: monitoring the power distribution of the power distribution cabinet protection circuit data through the electrical parameter monitoring function data to generate power distribution monitoring data; identifying the circuit branch load condition of the power distribution monitoring data to obtain the circuit branch load data; Step S242: determining the load demand of the circuit branch load data to generate circuit load demand data; calculating the voltage stabilization degree of the voltage control function data according to the circuit load demand data to obtain circuit voltage control degree data; performing current parameter mapping on the circuit voltage control degree data to generate voltage control current mapping data; Step S243: Based on the circuit voltage control degree data and the voltage stabilization current mapping data, the circuit load demand data is measured to generate circuit load degree data; the circuit load degree data is used to determine the distribution cabinet load change to obtain the distribution cabinet load change data; Step S244: Perform circuit voltage anomaly detection on the load change data of the distribution cabinet to obtain circuit voltage anomaly data; perform circuit current anomaly detection on the load change data of the distribution cabinet in combination with the circuit voltage anomaly data to generate circuit current anomaly data; perform circuit short circuit detection on the load change data of the distribution cabinet based on the circuit voltage anomaly data and the circuit current anomaly data to obtain circuit short circuit detection data.

[0038] In an embodiment of the present invention, current and voltage sensors are deployed in a power distribution cabinet to monitor the changes of current and voltage in real time, and the data is transmitted to a monitoring system through a collector to generate power distribution monitoring data; the load demand is determined according to a circuit load demand determination technology, such as the selection of inductance parameters in a DC-DC buck converter; a voltage regulator testing technology is used to measure the circuit voltage control degree data; specifically, according to the design requirements of the DC-DC buck converter, the circuit load demand is determined, including the load current size and the current ripple limit; then, by testing the voltage regulation degree of a linear voltage regulator, such as a 78L09 regulator, the circuit voltage control degree data is measured, and current parameter mapping is performed; based on the circuit voltage regulation degree data and the voltage regulation current mapping data, an abnormal value monitoring technology of the power system, such as an autoencoder, is used to measure the load degree of the circuit load demand data; specifically, an abnormal value monitoring of the power distribution monitoring data is performed using an autoencoder to determine the load degree of the circuit; further, by designing a short circuit detection circuit, such as using an optical coupler to detect a short circuit, the load change data of the power distribution cabinet is determined; and the application based on Faster R-CNN's non-intrusive load identification method analyzes the load change data of the distribution cabinet, detects circuit voltage anomalies, and obtains circuit voltage anomaly data; combined with the circuit voltage anomaly data, it detects circuit current anomalies, thereby generating circuit current anomaly data.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes: Step S31: performing geometric feature recognition on the multi-parameter features of the distribution cabinet to obtain the geometric features of the distribution cabinet; performing boundary feature marking on the geometric features of the distribution cabinet to obtain boundary marking data of the distribution cabinet; performing single distribution cabinet position determination on the boundary marking data of the distribution cabinet to generate single distribution cabinet position data; Step S32: Based on the single distribution cabinet location data, the distribution cabinet multi-parameter characteristics are used to identify the distribution cabinet cluster characteristics to obtain the distribution cabinet cluster characteristic data; based on the power distribution function data, the distribution capacity of the distribution cabinet cluster characteristic data is measured to generate the distribution cabinet cluster distribution capacity data; the power distribution distance of the distribution cabinet cluster distribution capacity data is calculated to obtain the cluster power distribution distance; the distribution cabinet cluster characteristic data is clustered according to the cluster power distribution distance to generate the distribution cabinet cluster division data; Step S33: marking the distribution range of the distribution cabinet on the distribution cabinet cluster division data to obtain the distribution range data of the distribution cabinet; performing structural hierarchical division on the distribution range data of the distribution cabinet to generate the structural hierarchical data of the distribution cabinet; performing cluster topological structure identification on the structural hierarchical data of the distribution cabinet to obtain the cluster topological data; Step S34: perform inter-cluster electric power measurement on the distribution cabinet cluster characteristic data according to the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data.

[0040] In an embodiment of the present invention, computer vision technology is used to obtain the geometric features of the distribution cabinet through image processing; the boundary feature points in the distribution cabinet panel image are extracted by using image processing software, and perspective projection transformation is performed to correct the image to clearly identify the boundary marking data of the distribution cabinet; based on the position data of a single distribution cabinet, a taboo search algorithm is applied to perform cluster division to obtain the distribution cabinet cluster feature data; and according to the electric energy distribution function data, the distribution capacity of the distribution cabinet cluster is determined to generate the distribution cabinet cluster distribution capacity data; according to the distribution cabinet cluster division data, the distribution range of the distribution cabinet is marked; network topology recognition technology, such as topology recognition of a multi-layer delay network, is used to perform structural hierarchical division of the distribution range data of the distribution cabinet; based on the structural hierarchical data, the topological structure of the distribution cabinet cluster is identified; using the power system voltage control technology, based on the cluster topology data, the electric power between clusters is calculated to obtain the electric power data between clusters; further, according to the energy consumption calculation formula, the distribution cabinet energy efficiency is calculated for the electric power data between clusters to generate the distribution cabinet energy efficiency data.

[0041] As an example of the present invention, refer to Figure 3 As shown, in this example, step S34 includes: Step S341: performing cluster node identification on the distribution cabinet cluster feature data according to the cluster topology data to obtain the distribution cabinet cluster node data; performing electrical parameter collection on the distribution cabinet cluster node data to obtain cluster node electrical parameters; performing node frequency parameter extraction on the cluster node electrical parameters to generate cluster node frequency parameters; Step S342: extract node voltage parameters from cluster node electrical parameters through cluster node frequency parameters to obtain cluster node voltage parameters; extract node current parameters from cluster node electrical parameters according to cluster node voltage parameters to obtain cluster node current parameters; perform inter-cluster electric power measurement on distribution cabinet cluster characteristic data based on cluster node voltage parameters and cluster node current parameters to obtain inter-cluster electric power data; Step S343: performing summary calculation of the electric power of each cluster node on the inter-cluster electric power data to obtain cluster electric power summary data; performing power factor calculation on the cluster electric power summary data to generate cluster power factor information; performing electric energy flow per unit time on the cluster power factor information to obtain cluster electric energy flow data; Step S344: measuring the power consumption per unit time of the cluster power flow data to generate the cluster power consumption; performing energy efficiency calculation on the cluster power consumption and the unit time to obtain the power distribution cabinet energy efficiency data.

[0042] In an embodiment of the present invention, a data-driven and deep neural network-based intelligent distribution network topology identification technology is used, combined with a self-organizing map (SOM) and a convolutional neural network (CNN) deep learning framework, to identify cluster nodes of distribution cabinet cluster feature data; an outlier monitoring technology of the power system, such as an autoencoder in Python data analysis, is used to collect electrical parameters of cluster nodes; key features are extracted through a convolutional neural network (CNN), specifically including node voltage parameters; a long short-term memory (LSTM) neural network is used to establish a mapping relationship between key characteristics and power generation, and inter-cluster electric power is calculated for distribution cabinet cluster feature data; based on inter-cluster electric power data, an electric energy efficiency measurement technology is applied to perform cluster electric power summary calculation to obtain cluster electric power summary data; then the power factor is calculated to generate cluster power factor information; based on cluster electric energy flow data, the electric energy consumption per unit time is recorded; based on the electric energy efficiency level and influencing factors of the data center, the cluster electric energy consumption and unit time are energy efficiency calculated to obtain distribution cabinet energy efficiency data.

[0043] Preferably, step S4 comprises the following steps: Step S41: Monitor the frequency of short circuit occurrence on the circuit short circuit detection data to obtain a short circuit frequency index; calculate the uniformity of electric power distribution between clusters on the electric power data between clusters to generate an electric power uniformity index; measure the cluster energy efficiency ratio on the energy efficiency data of the distribution cabinet to obtain a cluster energy efficiency ratio index; measure the distribution cabinet cluster performance index on the multi-parameter characteristics of the distribution cabinet based on the short circuit frequency index, the electric power uniformity index and the cluster energy efficiency ratio index to generate distribution cabinet cluster performance data; Step S42: dynamically adjust and control the cluster short circuit of the power distribution cabinet according to the performance data of the power distribution cabinet cluster, and obtain dynamic control measures for the circuit short circuit; Step S43: performing intelligent optimization control of cluster energy efficiency of the power distribution cabinets according to the performance data of the power distribution cabinet cluster, and obtaining cluster energy efficiency optimization control measures; Step S44: Integrate the circuit short circuit dynamic control measures and the cluster energy efficiency optimization control measures to generate distribution cabinet cluster intelligent control data; Step S45: cluster control of the distribution cabinets is performed through the distribution cabinet cluster intelligent control data to generate a distribution cabinet cluster control plan.

[0044] In an embodiment of the present invention, a short circuit in a distribution cabinet is detected to obtain a short circuit frequency index; an electric power uniformity index is calculated by analyzing the electric power data between clusters; then, based on the cluster energy efficiency ratio index, these data are combined to measure the performance index of the multi-parameter characteristics of the distribution cabinet to generate distribution cabinet cluster performance data; based on the distribution cabinet cluster performance data, the intelligent fusion terminal technology is used to dynamically adjust and control the cluster short circuit of the distribution cabinet to obtain dynamic short circuit control measures; based on the distribution cabinet cluster performance data, an energy efficiency optimization algorithm based on a GPU cluster is applied to perform cluster energy efficiency intelligent optimization control on the distribution cabinet to obtain cluster energy efficiency optimization control measures; using data fusion technology, the dynamic short circuit control measures and the cluster energy efficiency optimization control measures are integrated to form distribution cabinet cluster intelligent control data; through the distribution cabinet cluster intelligent control data, the distribution cabinet is clustered and controlled, thereby obtaining a distribution cabinet cluster control solution.

[0045] Preferably, step S42 includes the following steps: Step S421: monitor the performance data of the power distribution cabinet cluster in real time to obtain real-time performance monitoring data; track the operation status of the power distribution cabinet based on the real-time performance monitoring data to generate the operation status data of the power distribution cabinet; Step S422: Based on the distribution cabinet operation status data, the distribution cabinet cluster feature data is monitored for cluster overload status to obtain cluster overload status data; cluster circuit short circuit risk feature identification is performed on the cluster overload status data to obtain cluster circuit short circuit risk feature; Step S423: determining the short circuit location point of the cluster circuit short circuit risk feature to generate cluster short circuit location information; measuring the short circuit abnormal voltage value of the cluster short circuit location information to obtain the cluster short circuit voltage value; measuring the short circuit abnormal current value of the cluster short circuit location information to obtain the cluster short circuit current value; Step S424: Perform circuit dynamic voltage stabilization adjustment on the power distribution cabinet according to the cluster short-circuit voltage value, and generate circuit dynamic voltage stabilization adjustment measures; perform circuit dynamic current limiting control on the power distribution cabinet according to the cluster short-circuit current value, and generate circuit dynamic current limiting control measures; integrate circuit dynamic voltage stabilization adjustment measures and circuit dynamic current limiting control measures to obtain circuit short-circuit dynamic regulation measures.

[0046] In an embodiment of the present invention, various types of high-precision sensors are deployed in the distribution cabinet to collect current and voltage parameters in real time, and the Internet of Things (IoT) technology is used to monitor the operating status of the distribution cabinet in real time, including electrical parameters such as current, voltage, and power factor; by tracking the operating status of the distribution cabinet, the operating status data of the distribution cabinet is obtained; according to the operating status data of the distribution cabinet, the overload status of the distribution cabinet cluster is monitored to obtain the cluster overload status data; the current detection technology based on the precision operational amplifier is applied to identify the short-circuit risk characteristics of the cluster circuit; the battery short circuit, short circuit to ground, open circuit and short circuit faults are detected to determine the cluster short circuit location information; according to the short-circuit voltage and current abnormality detection method in the lithium battery, the cluster short-circuit voltage value and the cluster short-circuit current value are determined; according to the cluster short-circuit voltage value, the dynamic voltage adjustment technology is used to adjust the output voltage to compensate for load transients and provide stricter regulation, and generate circuit dynamic voltage regulation adjustment measures; according to the cluster short-circuit current value, the overcurrent protection circuit design is applied to dynamically adjust the current limit value and start the overload protection to obtain the circuit dynamic current limiting control measures.

[0047] Preferably, step S43 includes the following steps: Step S431: monitor the electric power characteristics of the power distribution cabinet cluster performance data to obtain the cluster electric power characteristics; extract the power peak value of the cluster electric power characteristics to obtain the cluster power peak data; extract the power valley value of the cluster electric power characteristics to obtain the cluster power valley value data; calculate the average power of the cluster electric power characteristics to obtain the cluster average power data; Step S432: performing cluster power fluctuation identification on the cluster electric power characteristics based on the cluster power peak data, the cluster power valley data and the cluster average power data to generate cluster power fluctuation data; performing cluster power anomaly detection on the cluster power fluctuation data to obtain cluster power anomaly data; performing cluster anomaly energy efficiency calculation on the cluster power anomaly data to obtain cluster anomaly energy efficiency data; Step S433: Identify the power consumption mode of the distribution cabinet based on the multi-parameter characteristics of the distribution cabinet according to the cluster abnormal energy efficiency data, and generate the power consumption mode data of the distribution cabinet; set the cluster energy efficiency data threshold for the power consumption mode data of the distribution cabinet to obtain the cluster energy efficiency data threshold; perform difference calculation between the cluster abnormal energy efficiency data and the cluster energy efficiency data threshold to generate cluster energy efficiency difference data; Step S434: Based on the power distribution cabinet power consumption mode data and the cluster energy efficiency difference data, the cluster energy efficiency intelligent optimization control is performed on the distribution cabinet to obtain cluster energy efficiency optimization control measures.

[0048] In an embodiment of the present invention, an intelligent monitor is deployed in a distribution cabinet to collect electrical parameters such as current and voltage in real time; Fourier transform technology is applied to analyze these parameters to extract power peaks and valleys; average power is calculated based on power spectrum density to obtain cluster electric power characteristics; distributed photovoltaic cluster power generation power fluctuation pattern recognition technology is applied to analyze cluster electric power characteristics and identify power fluctuations; cluster power anomalies are identified using an outlier detection method based on a Boosting integrated framework; cluster power anomalies are calculated based on cluster power anomaly data to obtain cluster anomaly energy efficiency data; based on an intelligent distribution room monitoring system, the power consumption mode of the distribution cabinet is identified; a cluster energy efficiency data threshold is set, for example, the cluster energy efficiency data threshold is set to 1.6; further, the difference between the cluster abnormal energy efficiency data and the cluster energy efficiency data threshold is calculated to generate cluster energy efficiency difference data; based on the power consumption mode data of the distribution cabinet and the cluster energy efficiency difference data, the distribution cabinet is intelligently optimized and controlled to obtain cluster energy efficiency optimization and regulation measures.

[0049] Preferably, step S45 includes the following steps: Step S451: perform real-time monitoring of the cluster circuit status of the power distribution cabinet to obtain cluster circuit status monitoring data; extract short-circuit voltage status monitoring information from the cluster circuit status monitoring data to obtain short-circuit voltage status monitoring information; extract short-circuit current status monitoring information from the cluster circuit status monitoring data to obtain short-circuit current status monitoring information; Step S452: Based on the intelligent control data of the power distribution cabinet cluster, a circuit dynamic voltage stabilization adjustment measure is executed for the short-circuit voltage state monitoring situation, and circuit dynamic voltage stabilization execution data is generated; based on the intelligent control data of the power distribution cabinet cluster, a circuit dynamic current limiting control measure is executed for the short-circuit current state monitoring situation, and circuit dynamic current limiting execution data is generated; Step S453: Perform real-time monitoring of the power consumption mode of the distribution cabinet to obtain the power consumption mode monitoring data of the distribution cabinet; perform real-time detection of cluster abnormal energy efficiency of the distribution cabinet through the power consumption mode monitoring data of the distribution cabinet to generate cluster abnormal energy efficiency monitoring data; perform cluster energy efficiency optimization and control measures on the cluster abnormal energy efficiency monitoring data based on the distribution cabinet cluster intelligent control data to generate cluster energy efficiency optimization execution data; Step S454: Integrate circuit dynamic voltage stabilization execution data, circuit dynamic current limiting execution data and cluster energy efficiency optimization execution data to obtain a distribution cabinet cluster control solution.

[0050] In the embodiment of the present invention, various sensors, collectors, concentrators and other field equipment terminals are used to perform one-to-one real-time monitoring of the circuit status of the distribution cabinet cluster; specifically, a UHF partial discharge sensor is used to monitor the frequency band of 300 to 1500 MHz and the measurement range of -60 to 60 dBm to extract the short-circuit voltage status monitoring situation; a pulse current partial discharge sensor is used to monitor the frequency band of 3 to 30 MHz and the measurement range of 1 to 100000 pC to extract the short-circuit current status monitoring situation; based on the intelligent control data of the distribution cabinet cluster, the dynamic voltage adjustment (DVS) technology is adopted to adjust the output voltage to compensate for load transients and provide stricter regulation; an overcurrent protection circuit design with adjustable current limit value is used, based on the current detection of the precision operational amplifier and the MCU processing The AD sampling and operation of the unit and the MOSFET shutdown control circuit are used to execute the dynamic current limiting control of the circuit; the LC1000 distributed distribution automation technology is used to monitor the power consumption mode of the distribution cabinet through the remote I / O unit RIO600 and sensors; the neural network optimal transportation module is used to monitor the abnormal energy efficiency of the cluster, so as to obtain the abnormal energy efficiency monitoring data of the cluster; based on the power consumption mode data of the distribution cabinet and the cluster energy efficiency difference data, the cluster energy efficiency optimization method is used, including hardware energy consumption optimization, software-level energy efficiency improvement, data center environmental regulation, and energy management strategy optimization; specifically, according to the power consumption mode data of the distribution cabinet and the cluster abnormal energy efficiency monitoring data, the cluster energy efficiency optimization method is applied to perform intelligent optimization control to generate cluster energy efficiency optimization execution data.

[0051] In this specification, a control system for a distribution cabinet cluster is provided, which is used to execute the above-mentioned control method for a distribution cabinet cluster. The control system for a distribution cabinet cluster includes: The distribution cabinet data acquisition module is used to collect data from the distribution cabinet using sensors to obtain distribution cabinet data; pre-process the distribution cabinet data to obtain standard distribution cabinet data; perform multi-parameter feature recognition on the standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; The power distribution cabinet circuit short circuit detection module is used to extract electrical parameters from the multi-parameter features of the power distribution cabinet to generate electrical parameter information of the power distribution cabinet; perform electrical function identification on the electrical parameter information of the power distribution cabinet to obtain electrical function data of the power distribution cabinet; perform power distribution cabinet protection circuit mapping on the multi-parameter features of the power distribution cabinet according to the electrical function data of the power distribution cabinet to generate power distribution cabinet protection circuit data; perform circuit short circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short circuit detection data; The power distribution cabinet energy efficiency calculation module is used to identify the power distribution cabinet cluster characteristics based on the multi-parameter characteristics of the power distribution cabinet to obtain the power distribution cabinet cluster characteristic data; cluster the power distribution cabinet cluster characteristic data to generate the power distribution cabinet cluster division data; perform cluster topology analysis on the power distribution cabinet cluster division data to obtain the cluster topology data; perform inter-cluster electric power calculation on the power distribution cabinet cluster characteristic data according to the cluster topology data to obtain the inter-cluster electric power data; perform power distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate the power distribution cabinet energy efficiency data; The distribution cabinet cluster intelligent adjustment and control module is used to measure the distribution cabinet cluster performance based on the multi-parameter characteristics of the distribution cabinets based on the circuit short-circuit detection data, the electric power data between the clusters and the energy efficiency data of the distribution cabinets, and generate the distribution cabinet cluster performance data; perform distribution cabinet cluster intelligent adjustment and control on the distribution cabinets according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent regulation data; perform cluster control on the distribution cabinets through the distribution cabinet cluster intelligent regulation data to generate the distribution cabinet cluster control plan.

[0052] The present invention uses a distribution cabinet data acquisition module and a sensor to collect data from the distribution cabinet, so as to accurately obtain the distribution cabinet data, specifically including key parameters such as voltage, current and power; pre-process the distribution cabinet data to remove noise and abnormal values, so as to obtain standardized distribution cabinet data, providing a data basis for subsequent analysis; perform multi-parameter feature recognition on the standard distribution cabinet data, extract the key performance characteristics of the distribution cabinet, so as to reflect the operating status of the distribution cabinet; electrical parameter extraction and electrical function recognition further refine the operating characteristics of the distribution cabinet, and distribution cabinet protection circuit mapping and circuit short-circuit detection can identify the short-circuit risk in the distribution cabinet. Through the distribution cabinet circuit short circuit detection module, the electrical parameter information of the distribution cabinet can be clarified; the electrical parameter information includes but is not limited to current, voltage and power, which is the basis for the normal operation and fault diagnosis of the distribution cabinet; the electrical function identification further links the parameter information with the specific function of the distribution cabinet, thereby generating the electrical function data of the distribution cabinet, which provides a basis for the functional analysis and abnormal detection of the distribution cabinet; the circuit short circuit detection is performed on the protection circuit data of the distribution cabinet, so that the circuit short circuit event can be discovered in time to ensure the safe and stable operation of the distribution system. Through the distribution cabinet cluster intelligent adjustment control module, the distribution cabinet cluster feature identification is performed on the multi-parameter characteristics of the distribution cabinet, so that the cluster characteristics of the distribution cabinet can be clarified; the distribution cabinet cluster feature data is clustered, which can clearly divide the distribution cabinet cluster; the distribution cabinet cluster division data is clustered topologically analyzed, so that the network structure information of the distribution cabinet cluster is provided; the distribution cabinet cluster feature data is measured between clusters according to the cluster topology data to obtain the inter-cluster electric power data; the distribution cabinet energy efficiency is calculated on the inter-cluster electric power data, so as to obtain the distribution cabinet energy efficiency data. Through the intelligent adjustment and control module of the distribution cabinet cluster, the distribution cabinet cluster performance is measured based on the multi-parameter characteristics of the distribution cabinet based on the circuit short-circuit detection data, the electric power data between clusters and the energy efficiency data of the distribution cabinet, which can comprehensively evaluate the performance status of the distribution cabinet cluster, thereby obtaining the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data, and the distribution cabinet cluster can be intelligently regulated; the distribution cabinet is clustered and controlled through the distribution cabinet cluster intelligent regulation data, and the distribution cabinet cluster control scheme is generated, which improves the operating efficiency of the distribution cabinet and enhances the stability and reliability of the distribution system. Therefore, the present invention timely discovers the abnormal situation of the distribution cabinet cluster through data processing technology, real-time monitoring technology and intelligent regulation technology; and performs intelligent adjustment and control of the distribution cabinet cluster according to specific abnormal situations, thereby improving the stability of the distribution cabinet cluster.

[0053] 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.

[0054] 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 control method for a power distribution cabinet cluster, characterized in that: The following steps are involved: Step S1: using sensors to collect data from the power distribution cabinet to obtain power distribution cabinet data; pre-processing the power distribution cabinet data to obtain standard power distribution cabinet data; Perform multi-parameter feature recognition on standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; Step S2: extract electrical parameters from the multi-parameter features of the distribution cabinet to generate electrical parameter information of the distribution cabinet; identify electrical functions of the electrical parameter information of the distribution cabinet to obtain electrical function data of the distribution cabinet; map the multi-parameter features of the distribution cabinet to the distribution cabinet protection circuit according to the electrical function data of the distribution cabinet to generate protection circuit data of the distribution cabinet; Performing circuit short-circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short-circuit detection data; Step S3: performing distribution cabinet cluster feature recognition on the multi-parameter features of the distribution cabinet to obtain distribution cabinet cluster feature data; performing cluster division on the distribution cabinet cluster feature data to generate distribution cabinet cluster division data; Perform cluster topology analysis on the distribution cabinet cluster division data to obtain cluster topology data; perform inter-cluster electric power measurement on the distribution cabinet cluster feature data based on the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data; Step S4: Based on the circuit short-circuit detection data, the inter-cluster electric power data and the distribution cabinet energy efficiency data, the distribution cabinet cluster performance is measured for the multi-parameter characteristics of the distribution cabinet to generate the distribution cabinet cluster performance data; the distribution cabinet cluster is intelligently adjusted and controlled according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent control data; The distribution cabinets are clustered and controlled through the intelligent control data of the distribution cabinet cluster to generate a distribution cabinet cluster control plan.

2. The control method for a power distribution cabinet cluster according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extract the voltage level of the multi-parameter features of the distribution cabinet to obtain the voltage level data of the distribution cabinet; determine the rated current of the voltage level data of the distribution cabinet to obtain the rated current data of the distribution cabinet; identify the frequency of the distribution cabinet based on the voltage level data and the rated current data of the distribution cabinet to generate the frequency data of the distribution cabinet; integrate the voltage level data, the rated current data and the frequency data of the distribution cabinet to obtain the electrical parameter information of the distribution cabinet; Step S22: performing power distribution function identification on the electrical parameter information of the power distribution cabinet to generate power distribution function data; performing electrical parameter monitoring function identification on the power distribution function data to obtain electrical parameter monitoring function data; performing voltage control function identification on the electrical parameter monitoring function data to obtain voltage control function data; integrating the power distribution function data, the electrical parameter monitoring function data and the voltage control function data to obtain the electrical function data of the power distribution cabinet; Step S23: identifying the protection circuit characteristics of the distribution cabinet based on the multi-parameter characteristics of the distribution cabinet to obtain the protection circuit characteristics of the distribution cabinet; performing protection circuit mapping on the protection circuit characteristics of the distribution cabinet based on the electrical function data of the distribution cabinet to generate protection circuit data of the distribution cabinet; Step S24: Perform circuit short-circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short-circuit detection data.

3. The control method for a power distribution cabinet cluster according to claim 2, characterized in that: Step S24 includes the following steps: Step S241: monitoring the power distribution of the power distribution cabinet protection circuit data through the electrical parameter monitoring function data to generate power distribution monitoring data; identifying the circuit branch load condition of the power distribution monitoring data to obtain the circuit branch load data; Step S242: determining the load demand of the circuit branch load data to generate circuit load demand data; calculating the voltage stabilization degree of the voltage control function data according to the circuit load demand data to obtain circuit voltage control degree data; performing current parameter mapping on the circuit voltage control degree data to generate voltage control current mapping data; Step S243: Based on the circuit voltage control degree data and the voltage stabilization current mapping data, the circuit load demand data is measured to generate circuit load degree data; the circuit load degree data is used to determine the distribution cabinet load change to obtain the distribution cabinet load change data; Step S244: Perform circuit voltage anomaly detection on the load change data of the distribution cabinet to obtain circuit voltage anomaly data; perform circuit current anomaly detection on the load change data of the distribution cabinet in combination with the circuit voltage anomaly data to generate circuit current anomaly data; perform circuit short circuit detection on the load change data of the distribution cabinet based on the circuit voltage anomaly data and the circuit current anomaly data to obtain circuit short circuit detection data.

4. The control method for a power distribution cabinet cluster according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing geometric feature recognition on the multi-parameter features of the distribution cabinet to obtain the geometric features of the distribution cabinet; performing boundary feature marking on the geometric features of the distribution cabinet to obtain boundary marking data of the distribution cabinet; performing single distribution cabinet position determination on the boundary marking data of the distribution cabinet to generate single distribution cabinet position data; Step S32: Based on the single distribution cabinet position data, the distribution cabinet multi-parameter characteristics are used to identify the distribution cabinet cluster characteristics to obtain the distribution cabinet cluster characteristic data; based on the power distribution function data, the distribution capacity of the distribution cabinet cluster characteristic data is measured to generate the distribution cabinet cluster distribution capacity data; the power distribution distance of the distribution cabinet cluster distribution capacity data is calculated to obtain the cluster power distribution distance; the distribution cabinet cluster characteristic data is clustered according to the cluster power distribution distance to generate the distribution cabinet cluster division data; Step S33: marking the distribution range of the distribution cabinet on the distribution cabinet cluster division data to obtain the distribution range data of the distribution cabinet; performing structural hierarchical division on the distribution range data of the distribution cabinet to generate the structural hierarchical data of the distribution cabinet; performing cluster topological structure identification on the structural hierarchical data of the distribution cabinet to obtain the cluster topological data; Step S34: perform inter-cluster electric power measurement on the distribution cabinet cluster characteristic data according to the cluster topology data to obtain inter-cluster electric power data; perform distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate distribution cabinet energy efficiency data.

5. The control method for a power distribution cabinet cluster according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: performing cluster node identification on the distribution cabinet cluster feature data according to the cluster topology data to obtain the distribution cabinet cluster node data; performing electrical parameter collection on the distribution cabinet cluster node data to obtain cluster node electrical parameters; performing node frequency parameter extraction on the cluster node electrical parameters to generate cluster node frequency parameters; Step S342: extract node voltage parameters from cluster node electrical parameters through cluster node frequency parameters to obtain cluster node voltage parameters; extract node current parameters from cluster node electrical parameters according to cluster node voltage parameters to obtain cluster node current parameters; perform inter-cluster electric power measurement on distribution cabinet cluster characteristic data based on cluster node voltage parameters and cluster node current parameters to obtain inter-cluster electric power data; Step S343: performing summary calculation of the electric power of each cluster node on the inter-cluster electric power data to obtain cluster electric power summary data; performing power factor calculation on the cluster electric power summary data to generate cluster power factor information; performing electric energy flow per unit time on the cluster power factor information to obtain cluster electric energy flow data; Step S344: measuring the power consumption per unit time of the cluster power flow data to generate the cluster power consumption; performing energy efficiency calculation on the cluster power consumption and the unit time to obtain the power distribution cabinet energy efficiency data.

6. The control method for a power distribution cabinet cluster according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Monitor the frequency of short circuit occurrence on the circuit short circuit detection data to obtain a short circuit frequency index; calculate the uniformity of electric power distribution between clusters on the electric power data between clusters to generate an electric power uniformity index; measure the cluster energy efficiency ratio on the energy efficiency data of the distribution cabinet to obtain a cluster energy efficiency ratio index; measure the distribution cabinet cluster performance index on the multi-parameter characteristics of the distribution cabinet based on the short circuit frequency index, the electric power uniformity index and the cluster energy efficiency ratio index to generate distribution cabinet cluster performance data; Step S42: dynamically adjust and control the cluster short circuit of the power distribution cabinet according to the performance data of the power distribution cabinet cluster, and obtain dynamic control measures for the circuit short circuit; Step S43: performing intelligent optimization control of cluster energy efficiency of the power distribution cabinets according to the performance data of the power distribution cabinet cluster, and obtaining cluster energy efficiency optimization control measures; Step S44: Integrate the circuit short circuit dynamic control measures and the cluster energy efficiency optimization control measures to generate distribution cabinet cluster intelligent control data; Step S45: cluster control of the distribution cabinets is performed through the distribution cabinet cluster intelligent control data to generate a distribution cabinet cluster control plan.

7. The control method for a power distribution cabinet cluster according to claim 6, characterized in that: Step S42 includes the following steps: Step S421: monitor the performance data of the power distribution cabinet cluster in real time to obtain real-time performance monitoring data; track the operation status of the power distribution cabinet based on the real-time performance monitoring data to generate the operation status data of the power distribution cabinet; Step S422: Based on the distribution cabinet operation status data, the distribution cabinet cluster feature data is monitored for cluster overload status to obtain cluster overload status data; cluster circuit short circuit risk feature identification is performed on the cluster overload status data to obtain cluster circuit short circuit risk feature; Step S423: determining the short circuit location point of the cluster circuit short circuit risk feature to generate cluster short circuit location information; measuring the short circuit abnormal voltage value of the cluster short circuit location information to obtain the cluster short circuit voltage value; measuring the short circuit abnormal current value of the cluster short circuit location information to obtain the cluster short circuit current value; Step S424: Perform circuit dynamic voltage stabilization adjustment on the power distribution cabinet according to the cluster short-circuit voltage value, and generate circuit dynamic voltage stabilization adjustment measures; perform circuit dynamic current limiting control on the power distribution cabinet according to the cluster short-circuit current value, and generate circuit dynamic current limiting control measures; integrate circuit dynamic voltage stabilization adjustment measures and circuit dynamic current limiting control measures to obtain circuit short-circuit dynamic regulation measures.

8. The control method for a power distribution cabinet cluster according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: monitor the electric power characteristics of the power distribution cabinet cluster performance data to obtain the cluster electric power characteristics; extract the power peak value of the cluster electric power characteristics to obtain the cluster power peak data; extract the power valley value of the cluster electric power characteristics to obtain the cluster power valley value data; calculate the average power of the cluster electric power characteristics to obtain the cluster average power data; Step S432: performing cluster power fluctuation identification on the cluster electric power characteristics based on the cluster power peak data, the cluster power valley data and the cluster average power data to generate cluster power fluctuation data; performing cluster power anomaly detection on the cluster power fluctuation data to obtain cluster power anomaly data; performing cluster anomaly energy efficiency calculation on the cluster power anomaly data to obtain cluster anomaly energy efficiency data; Step S433: Identify the power consumption mode of the distribution cabinet based on the multi-parameter characteristics of the distribution cabinet according to the cluster abnormal energy efficiency data, and generate the power consumption mode data of the distribution cabinet; set the cluster energy efficiency data threshold for the power consumption mode data of the distribution cabinet to obtain the cluster energy efficiency data threshold; perform difference calculation between the cluster abnormal energy efficiency data and the cluster energy efficiency data threshold to generate cluster energy efficiency difference data; Step S434: Based on the power distribution cabinet power consumption mode data and the cluster energy efficiency difference data, the cluster energy efficiency intelligent optimization control is performed on the distribution cabinet to obtain cluster energy efficiency optimization control measures.

9. The control method for a power distribution cabinet cluster according to claim 6, characterized in that: Step S45 includes the following steps: Step S451: perform real-time monitoring of the cluster circuit status of the power distribution cabinet to obtain cluster circuit status monitoring data; extract short-circuit voltage status monitoring information from the cluster circuit status monitoring data to obtain short-circuit voltage status monitoring information; extract short-circuit current status monitoring information from the cluster circuit status monitoring data to obtain short-circuit current status monitoring information; Step S452: Based on the intelligent control data of the power distribution cabinet cluster, a circuit dynamic voltage stabilization adjustment measure is executed for the short-circuit voltage state monitoring situation, and circuit dynamic voltage stabilization execution data is generated; based on the intelligent control data of the power distribution cabinet cluster, a circuit dynamic current limiting control measure is executed for the short-circuit current state monitoring situation, and circuit dynamic current limiting execution data is generated; Step S453: Perform real-time monitoring of the power consumption mode of the distribution cabinet to obtain the power consumption mode monitoring data of the distribution cabinet; perform real-time detection of cluster abnormal energy efficiency of the distribution cabinet through the power consumption mode monitoring data of the distribution cabinet to generate cluster abnormal energy efficiency monitoring data; perform cluster energy efficiency optimization and control measures on the cluster abnormal energy efficiency monitoring data based on the distribution cabinet cluster intelligent control data to generate cluster energy efficiency optimization execution data; Step S454: Integrate circuit dynamic voltage stabilization execution data, circuit dynamic current limiting execution data and cluster energy efficiency optimization execution data to obtain a distribution cabinet cluster control solution.

10. A control system for a power distribution cabinet cluster, characterized in that: Used to execute the control method for a power distribution cabinet cluster according to claim 1, the control system for a power distribution cabinet cluster comprises: The distribution cabinet data acquisition module is used to collect data from the distribution cabinet using sensors to obtain distribution cabinet data; pre-process the distribution cabinet data to obtain standard distribution cabinet data; perform multi-parameter feature recognition on the standard distribution cabinet data to obtain multi-parameter features of the distribution cabinet; The power distribution cabinet circuit short circuit detection module is used to extract electrical parameters from the multi-parameter features of the power distribution cabinet to generate electrical parameter information of the power distribution cabinet; perform electrical function identification on the electrical parameter information of the power distribution cabinet to obtain electrical function data of the power distribution cabinet; perform power distribution cabinet protection circuit mapping on the multi-parameter features of the power distribution cabinet according to the electrical function data of the power distribution cabinet to generate power distribution cabinet protection circuit data; perform circuit short circuit detection on the protection circuit data of the power distribution cabinet to obtain circuit short circuit detection data; The power distribution cabinet energy efficiency calculation module is used to identify the power distribution cabinet cluster characteristics based on the multi-parameter characteristics of the power distribution cabinet to obtain the power distribution cabinet cluster characteristic data; cluster the power distribution cabinet cluster characteristic data to generate the power distribution cabinet cluster division data; perform cluster topology analysis on the power distribution cabinet cluster division data to obtain the cluster topology data; perform inter-cluster electric power calculation on the power distribution cabinet cluster characteristic data according to the cluster topology data to obtain the inter-cluster electric power data; perform power distribution cabinet energy efficiency calculation on the inter-cluster electric power data to generate the power distribution cabinet energy efficiency data; The distribution cabinet cluster intelligent adjustment and control module is used to measure the distribution cabinet cluster performance based on the multi-parameter characteristics of the distribution cabinets based on the circuit short-circuit detection data, the electric power data between the clusters and the energy efficiency data of the distribution cabinets, and generate the distribution cabinet cluster performance data; perform distribution cabinet cluster intelligent adjustment and control on the distribution cabinets according to the distribution cabinet cluster performance data to obtain the distribution cabinet cluster intelligent regulation data; perform cluster control on the distribution cabinets through the distribution cabinet cluster intelligent regulation data to generate the distribution cabinet cluster control plan.

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