Power distribution terminal distributed arrangement method based on intelligent sensing

Abstract

The application provides a power distribution terminal distributed arrangement method based on intelligent sensing, and belongs to the technical field of intelligent sensing power distribution networks, and comprises the following steps: step 1: collecting the topological structure, load data and equipment operation parameters of a power distribution network based on a preset method; step 2: determining a plurality of load level areas based on the collected topological structure, load data and equipment operation parameters of the power distribution network; step 3: determining the load characteristic data of each load level area based on the load data and equipment operation parameters, and then determining the fault risk degree of each load level area; step 4: determining the corresponding intelligent sensing technology and equipment based on the fault risk degree of each load level area, and then obtaining the real-time electrical parameter data of each load level area; and step 5: performing reliability evaluation based on the real-time electrical parameter data of each load level area, and then determining a power distribution terminal distributed arrangement scheme based on the reliability evaluation result. The response speed and reliability of the power distribution network are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent perception power distribution network, and particularly relates to a power distribution terminal distributed arrangement method based on intelligent perception. BACKGROUND

[0002] With the continuous development of power systems, the application of traditional power distribution networks in intelligentization and automation has gradually attracted attention. In order to improve the reliability and real-time response capability of the power distribution network, the introduction of intelligent perception technology has become a research hotspot.

[0003] The existing technology mainly focuses on monitoring and adjusting the power distribution network based on a centralized control system, but in a distributed system, there is a lack of flexible and efficient power distribution terminal arrangement methods, especially in areas with large load fluctuations, it is difficult to respond to system failures in real time. The existing power distribution network arrangement method usually adopts static configuration, which fails to fully consider the load characteristics and fault risk of each region, resulting in low resource configuration efficiency and lack of pertinence.

[0004] Therefore, the present application provides a power distribution terminal distributed arrangement method based on intelligent perception. SUMMARY

[0005] The present application provides a power distribution terminal distributed arrangement method based on intelligent perception, which accurately divides the load level area and evaluates the fault risk of each area by combining the topology structure, load data and equipment operation parameters of the power distribution network through intelligent perception technology, and then performs reliability evaluation according to real-time electrical parameter data, so as to realize intelligent and distributed arrangement of the power distribution terminal, improve the response speed and reliability of the power distribution network, optimize resource allocation, and effectively solve the problems of inflexible power distribution network arrangement and untimely fault response in the prior art.

[0006] The present application provides a power distribution terminal distributed arrangement method based on intelligent perception, which accurately divides the load level area and evaluates the fault risk of each area by combining the topology structure, load data and equipment operation parameters of the power distribution network through intelligent perception technology, and then performs reliability evaluation according to real-time electrical parameter data, so as to realize intelligent and distributed arrangement of the power distribution terminal, improve the response speed and reliability of the power distribution network, optimize resource allocation, and effectively solve the problems of inflexible power distribution network arrangement and untimely fault response in the prior art.

[0007] Step 1: collecting the topology structure, load data and equipment operation parameters of the power distribution network based on a preset method;

[0008] Step 2: determining a plurality of load level areas based on the collected topology structure, load data and equipment operation parameters of the power distribution network;

[0009] Step 3: determining the load characteristic data of each load level area based on the load data and equipment operation parameters, and then determining the fault risk degree of each load level area;

[0010] Step 4: determining the corresponding intelligent perception technology and equipment based on the fault risk degree of each load level area, and then obtaining the real-time electrical parameter data of each load level area;

[0011] Step 5: performing reliability evaluation based on real-time electrical parameter data of each load level area, and determining the distributed arrangement scheme of the power distribution terminal based on the reliability evaluation result.

[0012] The application provides a smart sensing-based distributed arrangement method of a power distribution terminal, and the topology structure, load data and equipment operation parameter of a power distribution network are collected based on a preset method, including the following steps.

[0013] The information of the equipment in the power distribution network and the connection information of the equipment are determined based on a preset link layer discovery protocol.

[0014] The topology structure of the power distribution network is determined based on the information of the equipment in the power distribution network and the connection information of the equipment.

[0015] The load data of the power distribution network are collected based on a preset data collection system.

[0016] The operation parameter of the equipment in the power distribution network are collected based on a preset sensor array.

[0017] The application provides a smart sensing-based distributed arrangement method of a power distribution terminal, and the topology structure, load data and equipment operation parameter of a power distribution network are collected, and a plurality of load level areas are determined based on the collected topology structure, load data and equipment operation parameter, including the following steps.

[0018] The load coefficient of each node in the power distribution network is determined based on the collected topology structure, load data and equipment operation parameter of the power distribution network.

[0019] The load level of each node is determined based on the load coefficient of each node in the power distribution network and a preset load coefficient-level table.

[0020] The collected topology structure of the power distribution network is analyzed, and a plurality of initial areas are determined.

[0021] Each initial area is divided into a plurality of subareas based on the load level of all nodes of each initial area and the topology structure of the power distribution network.

[0022] The subareas corresponding to all initial areas are determined as load level areas.

[0023] The application provides a smart sensing-based distributed arrangement method of a power distribution terminal, and each initial area is divided into a plurality of subareas based on the load level of all nodes of each initial area and the topology structure of the power distribution network, including the following steps.

[0024] All nodes of each initial area are classified according to the load level, and a plurality of node groups are determined.

[0025] The electrical connection relationship between nodes of each node group is determined based on the topology structure of the power distribution network and a preset graph theory algorithm.

[0026] Determine a plurality of connected subgraphs corresponding to each node group based on a preset graph algorithm, and determine each connected subgraph as an initial sub-region;

[0027] Determine the electrical connection relationship between nodes in each initial sub-region based on the electrical connection relationship between nodes in each node group, and further adjust each initial sub-region based on the electrical connection relationship between nodes in each initial sub-region, and further determine a plurality of sub-regions.

[0028] The application provides a power distribution terminal distributed arrangement method based on intelligent sensing, determines the load coefficient of each node in the power distribution network based on the collected topological structure, load data and equipment operation parameters of the power distribution network, including:

[0029] Determine a plurality of nodes based on the topological structure of the power distribution network, and pretreat the load data of each node;

[0030] Determine the load coefficient of each node based on the pretreated load data of each node and the equipment operation parameters corresponding to each node:

[0031]

[0032] Wherein, F g is the load coefficient of the gth node, D g is the data demand coefficient of the gth node, D max is the total data amount of all nodes, T g is the transmission real-time requirement coefficient of the gth node, I g is the data importance coefficient of the gth node, V g is the data fluctuation coefficient of the gth node, N g is the network interference coefficient of the gth node, C g is the average correlation coefficient of the gth node and other nodes, ∈ is a preset adjustment parameter, V1 is the ideal value of the data fluctuation coefficient, C1 is the ideal value of the network interference coefficient, and C1 is the ideal value of the average correlation coefficient.

[0033] The application provides a power distribution terminal distributed arrangement method based on intelligent sensing, determines the load characteristic data of each load level area based on the load data and equipment operation parameters, and further determines the fault risk degree of each load level area, including:

[0034] Determine the load characteristic data of each load level area based on the load data and equipment operation parameters, wherein the load characteristic data includes a plurality of preset type load characteristic coefficients;

[0035] Determine several failure risk indicators of each load level area based on the load characteristic data of each load level area, and then establish a rule-based failure risk assessment model of each load level area in combination with the historical load characteristic data of the load level area;

[0036] Determine the failure risk level of each load level area based on the rule-based failure risk assessment model of each load level area;

[0037] Determine the failure risk degree of each load level area based on the failure risk level of each load level area and a preset level-degree data table.

[0038] The application provides a smart sensing-based power distribution terminal distributed arrangement method, which performs reliability evaluation based on real-time electrical parameter data of each load level area, and then determines a power distribution terminal distributed arrangement scheme based on the reliability evaluation result, including:

[0039] Determine the reliability coefficient of each load level area based on the real-time electrical parameter data of each load level area:

[0040]

[0041] Wherein, R i represents the reliability coefficient of the i th load level area, λ i represents the failure rate of the i th load level area, MTTR i is the average repair time of the i th load level area, N i is the number of devices in the i th load level area, p ij is the failure probability of the j th device in the i th load level area, r ij represents the redundancy of the j th device in the i th load level area, q ij is the electrical performance index of the j th device in the i th load level area, E ij is the environmental factor influence index of the j th device in the i th load level area, f(E ij ) is the mapping coefficient of the environmental factor, L i is the load rate of the i th load level area, P fi is the failure probability of the i th load level area, k1 is the influence weight of device failure, k2 is the influence weight of the electrical performance of the device, and k3 is the load influence weight;

[0042] Determine the reliability evaluation result based on the reliability coefficient of each load level area;

[0043] Determine the power distribution terminal distributed arrangement scheme based on the reliability evaluation result and a preset result-scheme database.

[0044] The application provides a smart perception-based distributed arrangement method of power distribution terminals and a smart perception-based distributed arrangement scheme of power distribution terminals.

[0045] Compared with the prior art, the application has the following beneficial effects:

[0046] By combining the topology of the power distribution network, the load data and the equipment operation parameters with the smart perception technology, the load level areas are accurately divided and the fault risks of the areas are evaluated, and then the reliability is evaluated according to the real-time electrical parameter data, so that the smart and distributed arrangement of the power distribution terminals can be realized, the response speed and the reliability of the power distribution network are improved, the resource allocation is optimized, and the problems of the inflexible arrangement of the power distribution network and the untimely fault response in the prior art are effectively solved. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0048] Figure 1 FIG. 1 is a flowchart of the smart perception-based distributed arrangement method of power distribution terminals provided by the embodiments of the application. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the application clearer, the following will clearly and completely describe the technical solutions in the application with reference to the drawings in the application. Obviously, the described embodiments are some but not all of the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the application.

[0050] Embodiment 1

[0051] The embodiments of the application provide a smart perception-based distributed arrangement method of power distribution terminals, as shown in FIG. 1, which comprises the following steps. Figure 1

[0052] Step 1: Collecting the topology of the power distribution network, the load data and the equipment operation parameters based on a preset method;

[0053] Step 2: Determining a plurality of load level areas based on the collected topology of the power distribution network, the load data and the equipment operation parameters;

[0054] ​Step 3: Determine the load characteristic data of each load level area based on the load data and equipment operation parameters, and then determine the fault risk degree of each load level area;

[0055] Step 4: Determine the corresponding intelligent sensing technology and equipment based on the fault risk degree of each load level area, and then obtain the real-time electrical parameter data of each load level area;

[0056] Step 5: Perform reliability evaluation based on the real-time electrical parameter data of each load level area, and then determine the distributed arrangement scheme of the power distribution terminal based on the reliability evaluation result.

[0057] In this embodiment, the fault risk degree refers to the possibility and severity of the power distribution network failing in a specific load level area. By analyzing load data, equipment operation parameters, and electrical characteristics, the risk of failure in different working conditions can be evaluated. Specifically, how factors such as load fluctuations, equipment aging or abnormal operation affect system stability. For example, if the power load of a certain load level area is close to or exceeds the carrying capacity of the equipment for a long time, the fault risk degree of that area is high, and the equipment may be overloaded, overheated, or even burned out.

[0058] In this embodiment, intelligent sensing technology and equipment refer to the use of sensors, monitoring devices, and advanced data analysis techniques to collect and analyze real-time operation data of the power distribution network, enabling intelligent sensing and monitoring of equipment status. These technologies and devices can automatically identify and report faults when abnormalities occur, providing efficient prediction and diagnosis capabilities. For example, in areas with high fault risk, intelligent sensors can be used to monitor current, voltage, and other parameters in real time, detecting equipment overload, temperature anomalies, and other conditions in a timely manner to prevent faults. Intelligent sensing technologies such as artificial intelligence algorithms, Internet of Things devices, and data transmission networks can also improve the accuracy and timeliness of fault warning and management.

[0059] The beneficial effects of the above technical solution are: through intelligent sensing technology combined with the topology of the power distribution network, load data and equipment operation parameters, the load level area is accurately divided and the fault risk of each area is evaluated, and then the reliability is evaluated based on real-time electrical parameter data, which can realize the intelligent and distributed arrangement of the power distribution terminal, improve the response speed and reliability of the power distribution network, optimize resource allocation, and effectively solve the problem of inflexible power distribution network arrangement and untimely fault response in the prior art.

[0060] Embodiment 2:

[0061] The embodiment of the present application provides a distributed arrangement method of power distribution terminals based on intelligent sensing, which collects the topology of the power distribution network, load data and equipment operation parameters based on a preset method, including:

[0062] determining information of devices in the power distribution network and connection information of the devices based on a preset link layer discovery protocol;

[0063] determining a topology of the power distribution network based on the information of the devices in the power distribution network and the connection information of the devices;

[0064] collecting load data of the power distribution network based on a preset data collection system;

[0065] collecting operating parameters of the devices in the power distribution network based on a preset sensor array.

[0066] In this embodiment, the information of the devices refers to the basic attributes and functional data of each device in the power distribution network. These information may include device type, model, manufacturer, installation location, working status, performance indicators, etc. For example, the device information of a transformer may include its maximum load capacity, rated voltage, production date, and current operating status (such as whether it is running or in a fault state);

[0067] In this embodiment, the connection information of the devices refers to the electrical connection relationship between devices in the power distribution network, including the connection mode, connection path, connection point, and topology of electrical connection of each device with other devices. For example, the connection information of a transformer may include how it is connected to the upstream transmission line and the downstream distribution line, as well as its input and output voltage and current parameters, helping to establish an electrical network diagram of the entire power grid.

[0068] In this embodiment, the preset data collection system refers to a system designed in advance for automatically collecting required data from the power distribution network, such as load data, device operating status, environmental monitoring data, etc. The system usually consists of multiple sensors, monitoring devices, and data transmission devices, and can collect various data of the power distribution network periodically or in real time. For example, the preset data collection system can include a smart monitoring system of the power distribution network, which transmits data from each measurement point to the central system through a wireless network, helping operators to perform remote monitoring.

[0069] In this embodiment, the preset sensor array refers to a group of sensors arranged in the power distribution network according to pre-set requirements and needs, used to collect operating parameters of electrical equipment such as voltage, current, temperature, humidity, etc. The sensor array is usually composed of multiple sensors arranged on key devices or monitoring points to comprehensively monitor the operating status of the devices and environmental conditions. For example, in the transformer substation of the power distribution network, a sensor array can be arranged to monitor the temperature, current, and voltage parameters of the transformer, so as to timely discover potential faults of the device.

[0070] The beneficial effects of the above technical solution are: by collecting the topology structure, load data and equipment operation parameters of the power distribution network based on a preset method, using intelligent sensing technologies such as link layer discovery protocol, data acquisition system and sensor array, the operation information and connection relationship of the equipment in the power distribution network can be obtained in real time, the operation state of the power distribution network can be accurately mastered, the fault early warning and management capability can be improved, a scientific basis for the distributed arrangement of the power distribution network is provided, the stability and reliability of the power distribution network are improved, and the fault occurrence rate is effectively reduced.

[0071] Embodiment 3:

[0072] The embodiment of the application provides a power distribution terminal distributed arrangement method based on intelligent sensing, determines a plurality of load level areas based on collected topology structure, load data and equipment operation parameters of a power distribution network, and the method comprises the following steps:

[0073] Determine the load coefficient of each node in the power distribution network based on the collected topology structure, load data and equipment operation parameters of the power distribution network;

[0074] Determine the load level of each node based on the load coefficient of each node in the power distribution network and a preset load coefficient-level table;

[0075] Perform structure analysis on the collected topology structure of the power distribution network, and then determine a plurality of initial areas;

[0076] Divide each initial area into a plurality of sub-areas based on the load level of all nodes in each initial area and the topology structure of the power distribution network;

[0077] Determine the sub-areas corresponding to all initial areas as load level areas.

[0078] In this embodiment, the structure analysis on the collected topology structure of the power distribution network means that the connection relationship between the equipment and the lines in the power distribution network is analyzed in detail, so as to understand the network topology characteristics of the power distribution network, including analyzing the electrical connection between the equipment, the positional relationship and interaction of each node (such as a transformer substation, a power distribution box and a load point) in the network, identifying the key nodes and weak connection points of the power distribution network and the like. Through this analysis, the overall structure of the power distribution network and the potential load distribution can be determined, for example, in a power distribution network, by performing structure analysis on the connection relationship of each transformer substation, switch station and terminal load point, the main line, branch line and load node can be identified, so as to understand the power demand and load flow direction of different areas;

[0079] In this embodiment, a plurality of initial regions are determined after analyzing the topology of the power distribution network. Next, according to the power load conditions of each node and line in the power grid, a plurality of initial regions are divided. The initial region is a region roughly determined according to the topology and load conditions. It may include several adjacent nodes or devices, and their load demand is relatively close, or the power flow is relatively concentrated.

[0080] The beneficial effects of the above technical solution are: by collecting the topology, load data and equipment operation parameters of the power distribution network, combining load coefficient analysis and preset grade table, accurately dividing load grade region, through structure analysis and initial region division, the load distribution of the power distribution network can be effectively optimized, the flexibility and accuracy of load management can be improved, energy waste can be reduced, and the stability and reliability of the power distribution network can be improved. This method provides a scientific basis for the reasonable layout and load scheduling of the power distribution network, and enhances the adaptive and efficient operation ability of the power grid.

[0081] Embodiment 4:

[0082] The embodiment of the application provides a power distribution terminal distributed arrangement method based on intelligent sensing, each initial region is divided into a plurality of sub-regions based on the load grade of all nodes in each initial region and the topology of the power distribution network, including:

[0083] Classify all nodes in each initial region according to the load grade, and then determine a plurality of node groups;

[0084] Determine the electrical connection relationship between nodes in each node group based on the topology of the power distribution network and a preset graph theory algorithm;

[0085] Determine a plurality of connected subgraphs corresponding to each node group based on the preset graph theory algorithm, and determine each connected subgraph as an initial sub-region;

[0086] Determine the electrical connection relationship between nodes in each initial sub-region based on the electrical connection relationship between nodes in each node group, and then adjust each initial sub-region based on the electrical connection relationship between nodes in each initial sub-region, and then determine a plurality of sub-regions.

[0087] In this embodiment, all nodes in each initial region are classified according to the load grade, including: for each initial region, all nodes in the region are classified according to their load grade. For example, nodes with a load grade of "light load" are classified into a group, nodes with a load grade of "medium load" are classified into another group, nodes with a load grade of "heavy load" are classified into a group, and nodes with a load grade of "overload" are classified into a group. Data structures such as lists, dictionaries, etc. can be used to store these grouping information.

[0088] In this embodiment, each initial sub-region is adjusted based on the electrical connection relationship between each initial sub-region node, including: for each connected subgraph, ensuring that there is an electrical connection between its nodes, and the load levels of these nodes are the same or similar. If there are isolated nodes (not connected to other nodes of the same level), according to the actual situation, it can be separately as a sub-region, or according to the load level and electrical connection relationship of adjacent nodes, it can be merged into adjacent suitable sub-region.

[0089] The beneficial effects of the above technical solutions are: through intelligent perception and graph algorithm, combining the power distribution network topology structure and node load level, the initial region is divided into several optimized sub-regions. Through load level classification and node electrical connection relationship analysis, the region division can be accurately adjusted, the load balance and reliability of the power grid are improved, the overload risk is avoided, and the resource allocation is optimized. This method improves the intelligent management level of the power distribution network, enhances the self-healing ability and operation efficiency of the power grid.

[0090] Embodiment 5:

[0091] The embodiment of the application provides a power distribution terminal distributed arrangement method based on intelligent perception, determines the load coefficient of each node in the power distribution network based on the collected topology structure, load data and equipment operation parameters of the power distribution network, and includes the following steps:

[0092] Based on the topology structure of the power distribution network, a plurality of nodes are determined, and the load data of each node is preprocessed;

[0093] Based on the preprocessed load data of each node and the equipment operation parameters corresponding to each node, the load coefficient of each node is determined:

[0094]

[0095] Wherein, F g is the load coefficient of the gth node, D g is the data quantity demand coefficient of the gth node, D max is the total data quantity of all nodes, T g is the transmission real-time requirement coefficient of the gth node, I g is the data importance coefficient of the gth node, V g is the data fluctuation coefficient of the gth node, N g is the network interference coefficient of the gth node, C g is the average correlation coefficient of the gth node and other nodes, ∈ is a preset adjustment parameter, V1 is the ideal value of the data fluctuation coefficient, C1 is the ideal value of the network interference coefficient, and C1 is the ideal value of the average correlation coefficient.

[0096] In this embodiment, the transmission real-time requirement coefficient is a coefficient for measuring the demand degree of data transmission of each node in the power distribution network for real-time, which is usually related to the required real-time data update frequency and latency requirement of the node. In the power distribution network, some nodes may require faster response time to ensure the normal operation of the equipment, especially when a fault or load fluctuation occurs, the acquisition of real-time data is crucial for decision-making. For example, assume that the load data of an important substation node in the power distribution network needs to be updated every minute in order to adjust the load distribution and protection strategy of the power grid in real time. If the node has a higher real-time requirement for data transmission, it may be assigned a higher transmission real-time requirement coefficient (e.g., 0.9). For some less important nodes, their data transmission can be delayed for updating, and their transmission real-time requirement coefficient may be lower (e.g., 0.3). The size of this coefficient reflects the sensitivity of the node to real-time data, helping the system to adjust resource allocation and data processing priority according to the needs of different nodes.

[0097] The beneficial effects of the above technical solutions are: by comprehensively considering the topology of the power distribution network, load data and equipment operation parameters, the load coefficient of each node is accurately calculated. This method can effectively quantify the multi-dimensional factors such as data demand, transmission real-time, data importance of the node, improve the efficiency of data transmission and management of the power distribution network, optimize resource allocation, enhance network stability, reduce the risk of faults caused by data fluctuations and network interference, and improve the overall operation reliability and intelligent level of the power distribution network.

[0098] Embodiment 6

[0099] The embodiment of the present application provides a smart sensing-based distributed arrangement method for power distribution terminals, determines the load characteristic data of each load level area based on load data and equipment operation parameters, and further determines the fault risk degree of each load level area, comprising:

[0100] Determine the load characteristic data of each load level area based on load data and equipment operation parameters, wherein the load characteristic data includes a plurality of preset types of load characteristic coefficients;

[0101] Determine a plurality of fault risk indicators of each load level area based on the load characteristic data of each load level area, and further establish a rule-based fault risk assessment model for each load level area in combination with the historical load characteristic data of the load level area;

[0102] Determine the fault risk level of each load level area based on the rule-based fault risk assessment model of each load level area;

[0103] Determine the fault risk degree of each load level area based on the fault risk level of each load level area and a preset level-degree data table.

[0104] In this embodiment, the preset type of load characteristic coefficient refers to a value or parameter preset for different types of load characteristics in the power distribution network, used to describe the behavior pattern of the load in different time periods and operating states. These coefficients can help evaluate the volatility, stability, and load change of the load, providing support for fault risk assessment. Common load characteristics include load volatility, load acceleration and deceleration rate, etc. For example, assume that the load volatility in a certain area of the power distribution network is large, which may cause instability of the load. At this time, a "load volatility coefficient" can be set to measure the frequency and amplitude of load change in the area. For areas with high load volatility, a higher load volatility coefficient (such as 0.8) can be set, while for areas with low load volatility, a lower coefficient (such as 0.2) can be set.

[0105] In this embodiment, the fault risk indicator is a value used to measure the possibility or risk level of a fault occurring in a load level area of the power distribution network. These indicators are calculated based on load data, equipment operating parameters, and historical fault data, reflecting the potential fault threat that the system may face. Fault risk indicators can include equipment aging degree, load fluctuation frequency, overload frequency, etc. For example, if the load fluctuation in a certain area is frequent and the load level is close to the carrying limit of the equipment, the fault risk indicator may be high. For example, if the load change frequency of the area is more than 5 times per hour, and the overload of the equipment exceeds the specified threshold, the fault risk indicator of the area may be set to 0.7, indicating that the fault risk of the area is relatively high.

[0106] The beneficial effects of the above technical solutions are: by collecting load data and equipment operating parameters, combining load characteristics and historical data, establishing a rule-based fault risk assessment model, the fault risk level of each load level area can be accurately evaluated, and the fault risk degree is determined according to the preset level-degree data table, which improves the accuracy of fault prediction, helps to optimize the operation of the power distribution network, improves the stability and reliability of the system, and reduces the possibility of fault occurrence.

[0107] Embodiment 7:

[0108] The embodiment of the present application provides a power distribution terminal distributed arrangement method based on intelligent sensing, which performs reliability evaluation based on real-time electrical parameter data of each load level area, and then determines a power distribution terminal distributed arrangement scheme based on the reliability evaluation result, comprising:

[0109] Determine the reliability coefficient of each load level area based on the real-time electrical parameter data of each load level area:

[0110]

[0111] Among them, R i represents the reliability coefficient of the i-th load level area, λ i represents the failure rate of the i-th load level area, MTTR i is the average repair time of the i-th load level area, N i is the number of equipment in the i-th load level area, p ij is the failure probability of the jth device in the i-th load level area, r ij represents the redundancy of the jth device in the i-th load level area, q ij is the electrical performance index of the jth device in the i-th load level area, E ij is the environmental factor impact index of the jth device in the i-th load level area, f(E ij ) is the mapping coefficient for environmental factors, L i is the load rate of the i-th load level area, P fi is the failure probability of the i-th load level area, k1 is the impact weight of equipment failure, k2 is the impact weight of the electrical performance of the equipment, and k3 is the load impact weight;

[0112] Determining reliability assessment results based on the reliability coefficient of each load level area;

[0113] The distributed layout plan of the distribution terminals is determined based on the reliability assessment results and the preset result-plan database.

[0114] In this embodiment, the failure rate of the i-th load level area is obtained by counting the number of failures occurring in the area within a certain period of time and dividing it by the total operating time. The unit is: number of times / time. The time here is related to the repair time and has the same unit.

[0115] In this embodiment, the time obtained by taking a statistical average of the repair time of equipment failures in the area is the time obtained by taking a statistical average of the repair time of equipment failures in the area;

[0116] In this embodiment, redundancy is for devices without redundancy, r ij =0; for a device with n redundant devices, For example, if a device has a redundant device, then r ij =0.5;

[0117] In this embodiment, the electrical performance index has a value range between 0 and 1, and the closer to 1, the better the performance;

[0118] In this embodiment, the mapping coefficient is used to map the environmental factor into a dimensionless influence coefficient;

[0119] In this embodiment, the load rate of the i-th load level area is obtained by dividing the actual load of the area by the rated load.

[0120] The beneficial effects of the above technical solution are: by calculating the reliability coefficient of each load level area through real-time electrical parameter data, considering multiple dimensions such as equipment failure rate, electrical performance, redundancy and environmental factors, the reliability of the power distribution network can be accurately evaluated. Based on the evaluation results, combined with the preset database, a reasonable power distribution terminal arrangement scheme is formulated, thereby optimizing the stability and fault response capability of the power distribution network, improving the reliability and operation efficiency of the system, reducing the risk of failure, and ensuring the safe and stable operation of the power grid.

[0121] Embodiment 8:

[0122] The embodiment of the present application provides a power distribution terminal distributed arrangement method based on intelligent sensing, and a power distribution terminal distributed arrangement scheme, which comprises: the position layout of the power distribution terminal, the type of the power distribution terminal, the communication mode, the redundancy mode of the power distribution terminal equipment, and the maintenance plan of the power distribution terminal equipment.

[0123] The beneficial effects of the above technical solution are: by reasonably planning the position layout, type, communication mode, equipment redundancy mode and equipment maintenance plan of the power distribution terminal, the reliability and intelligent management level of the power distribution network can be effectively improved, the resource allocation is optimized, the fault tolerance of the system is enhanced, the efficient operation and maintenance of the equipment are ensured, the failure rate is reduced, the stability and emergency response capability of the power grid are improved, and strong support is provided for the intelligent construction of the modern power distribution network.

[0124] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for distributing power distribution terminals based on intelligent sensing, characterized in that, The method comprises the following steps: Step 1: collecting the topology structure, load data and equipment operation parameters of the power distribution network based on a preset method; Step 2: determining a plurality of load level areas based on the collected topology structure, load data and equipment operation parameters of the power distribution network; Specifically comprising: determining the load coefficient of each node in the power distribution network based on the collected topology structure, load data and equipment operation parameters of the power distribution network; determining the load level of each node based on the load coefficient of each node in the power distribution network and a preset load coefficient-level table; performing structure analysis on the collected topology structure of the power distribution network, and then determining a plurality of initial areas; dividing each initial area into a plurality of sub-areas based on the load level of all nodes in each initial area and the topology structure of the power distribution network; determining the sub-areas corresponding to all initial areas as the load level areas; The determination of the load coefficient of each node in the power distribution network based on the collected topology structure, load data and equipment operation parameters of the power distribution network specifically comprises: determining a plurality of nodes based on the topology structure of the power distribution network, and preprocessing the load data of each node; determining the load coefficient of each node based on the preprocessed load data of each node and the equipment operation parameters corresponding to each node: Wherein, F g is the load coefficient of the gth node, D g is the data volume demand coefficient of the gth node, D max is the total data volume of all nodes, T g is the transmission real-time requirement coefficient of the gth node, I g is the data importance coefficient of the gth node, V g is the data fluctuation coefficient of the gth node, N g is the network interference coefficient of the gth node, C g is the average correlation coefficient of the gth node with other nodes, ∈ is a preset adjustment parameter, V1 is the ideal value of the data fluctuation coefficient, C1 is the ideal value of the network interference coefficient, and C1 is the ideal value of the average correlation coefficient. Step 3: determining the load characteristic data of each load level area based on the load data and equipment operation parameters, and then determining the fault risk degree of each load level area; Step 4: determining the corresponding intelligent sensing technology and equipment based on the fault risk degree of each load level area, and then obtaining the real-time electrical parameter data of each load level area; Step 5: performing reliability evaluation based on the real-time electrical parameter data of each load level area, and then determining the distributed arrangement scheme of the power distribution terminal based on the reliability evaluation result; specifically comprising: determining the reliability coefficient of each load level area based on the real-time electrical parameter data of each load level area: wherein R i represents the reliability coefficient of the ith load level area, λ i represents the failure rate of the ith load level area, MTTR i is the average repair time of the ith load level area, N i is the number of devices in the ith load level area, p ij is the failure probability of the jth device in the ith load level area, r ij represents the redundancy of the jth device in the ith load level area, q ij is the electrical performance index of the jth device in the ith load level area, E ij is the environmental factor influence index of the jth device in the ith load level area, f(E ij ) is the mapping coefficient of the environmental factor, L i is the load rate of the ith load level area, P fi is the failure probability of the ith load level area, k1 is the influence weight of device failure, k2 is the influence weight of the electrical performance of the device, and k3 is the load influence weight; determining the reliability evaluation result based on the reliability coefficient of each load level area; determining the distributed arrangement scheme of the power distribution terminal based on the reliability evaluation result and a preset result-scheme database.

2. The smart sensing based power distribution terminal distributed arrangement method according to claim 1, wherein, Collecting the topology structure, load data and equipment operation parameters of the power distribution network based on a preset method comprises: determining the information of the equipment in the power distribution network and the connection information of the equipment based on a preset link layer discovery protocol; determining the topology structure of the power distribution network based on the information of the equipment in the power distribution network and the connection information of the equipment; collecting the load data of the power distribution network based on a preset data collection system; collecting the operation parameters of the equipment in the power distribution network based on a preset sensor array.

3. The smart sensing based power distribution terminal distributed arrangement method according to claim 1, wherein, Dividing each initial area into a plurality of sub-areas based on the load level of all nodes in each initial area and the topology structure of the power distribution network comprises: classifying all nodes in each initial area according to the load level, and then determining a plurality of node groups; determining the electrical connection relationship between nodes in each node group based on the topology structure of the power distribution network and a preset graph theory algorithm; determining a plurality of connected sub-graphs corresponding to each node group based on the preset graph theory algorithm, and determining each connected sub-graph as an initial sub-area; Determine the electrical connection relationship between each initial sub-area node based on the electrical connection relationship between each node group node, and then adjust each initial sub-area based on the electrical connection relationship between each initial sub-area node, and then determine a plurality of sub-areas.

4. The smart sensing based power distribution terminal distributed arrangement method according to claim 1, wherein, Determine the load characteristic data of each load level area based on the load data and equipment operation parameters, and then determine the fault risk degree of each load level area, including: Determine the load characteristic data of each load level area based on the load data and equipment operation parameters, wherein the load characteristic data includes a plurality of preset types of load characteristic coefficients; Determine a plurality of fault risk indicators of each load level area based on the load characteristic data of each load level area, and then establish a rule-based fault risk assessment model for each load level area in combination with the historical load characteristic data of the load level area; Determine the fault risk level of each load level area based on the rule-based fault risk assessment model of each load level area; Determine the fault risk degree of each load level area based on the fault risk level of each load level area and the preset level-degree data table.

5. The smart sensing based power distribution terminal distributed arrangement method as claimed in claim 1 wherein, The distribution terminal distributed arrangement scheme includes: the position layout of the distribution terminal, the type of the distribution terminal, the communication mode, the redundancy mode of the distribution terminal equipment and the maintenance plan of the distribution terminal equipment.

Images