Automatic intelligent management system for power distribution network

Abstract

The invention relates to the technical field of power distribution network management, in particular to an automatic intelligent management system for a power distribution network, which comprises a data acquisition and monitoring module, a fault diagnosis and processing module, an optimization scheduling module and a communication and control module, the data acquisition and monitoring module is responsible for collecting various operation data of distribution network equipment and lines, carrying out real-time monitoring and preliminary evaluation on the equipment state, and providing a data basis for subsequent analysis and decision making; through omnibearing and real-time equipment operation state monitoring and intelligent fault diagnosis, the fault can be accurately perceived and rapidly positioned at the initial stage of equipment abnormity, the system rapidly starts an automatic fault isolation and repair program, the power failure time and the power failure range are greatly reduced, and compared with a traditional management mode, the power failure management method has the advantages that the power failure management efficiency is greatly improved. The power failure duration can be shortened by more than 50%, large-area power failure accidents caused by local faults are effectively avoided, stable and continuous power supply is provided for users, and the normal order of production and life is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network management, and particularly to an automated intelligent management system for power distribution network. Background Art

[0002] In the modern power supply system, the stable operation of the power distribution network is of crucial importance. However, traditional power distribution network management faces many dilemmas. On the one hand, there are numerous and widely distributed distribution network devices, including transformers, circuit breakers, line reactors, etc. The monitoring of their operating states relies on manual inspections, which not only have low efficiency but also are difficult to achieve real-time and comprehensive monitoring. For example, for distribution network devices in remote areas, the manual inspection cycle is long, and equipment failures are difficult to detect in a timely manner, often resulting in power outages, affecting the normal power consumption of users, and bringing great inconvenience to production and life.

[0003] On the other hand, the means of fault diagnosis and handling are backward. When a fault occurs, it often requires on-site investigation by staff and judging the cause of the fault based on experience, which is time-consuming and laborious in the face of complex faults and prolongs the power outage time. At the same time, the traditional distribution network lacks effective optimization and dispatching capabilities and cannot flexibly adjust power distribution according to real-time power consumption demands and the operating state of the power grid. Local overloads are prone to occur during peak power consumption periods, while there is energy waste during low valley periods, reducing the overall operating efficiency and reliability of the power system and making it difficult to meet the requirements of modern society for high-quality power supply. Therefore, an automated intelligent management system for power distribution network is proposed. Summary of the Invention

[0004] In view of this, the present invention provides an automated intelligent management system for power distribution network to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] The technical solution of the present invention is implemented as follows: An automated intelligent management system for power distribution network includes a data acquisition and monitoring module, a fault diagnosis and handling module, an optimization and dispatching module, and a communication and control module;

[0006] The data acquisition and monitoring module: is responsible for collecting various operating data of distribution network devices and lines, and performing real-time monitoring and preliminary evaluation of the device states, providing a data basis for subsequent analysis and decision-making;

[0007] The fault diagnosis and handling module: uses intelligent algorithms to analyze fault data, accurately locate the fault position and type, and automatically execute fault isolation and repair operations to ensure the safe and stable operation of the distribution network;

[0008] The optimization and dispatching module: formulates a reasonable power dispatching strategy based on the load forecasting results and the real-time state of the distribution network;

[0009] The communication and control module: constructs a reliable communication network, ensures data transmission and the issuance of control instructions, realizes remote monitoring and operation of distribution network equipment, and ensures the coordinated operation of all parts of the system.

[0010] Further preferably, the data acquisition and monitoring module includes an intelligent sensor network and a device status monitoring sub-module. Various intelligent sensors are widely deployed on key equipment and lines of the power distribution network to collect information such as electrical parameters, operating temperature, and fault signals of the equipment in real time, and transmit the data to the monitoring center through wireless communication technology or a wired communication network. The device status monitoring sub-module performs real-time analysis and processing on the received sensor data, and uses big data analysis technology and preset threshold rules to evaluate and warn the operating status of the equipment.

[0011] Further preferably, the fault diagnosis and processing module includes a fault intelligent diagnosis sub-module and an automatic fault isolation and repair sub-module. The fault intelligent diagnosis sub-module deeply analyzes the collected fault data based on artificial intelligence algorithms. When receiving the action signal of the fault indicator or abnormal electrical parameter data, the system can quickly locate the fault area, and accurately judge the fault type and cause by combining historical fault cases and equipment operation data. After diagnosing the fault, the automatic fault isolation and repair sub-module automatically generates a fault handling plan, and operates intelligent switches, circuit breakers, etc. in the distribution network through remote control technology to quickly isolate the fault area and prevent the spread of the fault.

[0012] Further preferably, the optimization scheduling module includes a load forecasting sub-module and an intelligent scheduling decision sub-module. The load forecasting sub-module collects information such as historical power consumption data, meteorological data, and user type distribution, and uses methods such as time series analysis and machine learning to establish a load forecasting model. The intelligent scheduling decision sub-module formulates the optimal scheduling strategy based on the load forecasting results and the real-time operation status of the distribution network, with the goals of optimizing power distribution, reducing network losses, and improving power supply reliability.

[0013] Further preferably, the communication and control module includes a communication network architecture and a remote control unit, constructs a communication network integrating wired and wireless, uses communication protocol conversion equipment to achieve interconnection and interoperability between different communication protocols, and the remote control unit receives control instructions from the monitoring center through the communication network to achieve remote operation of distribution network equipment.

[0014] Further preferably, the following is the power flow calculation formula:

[0015]

[0016] For node i, let P i be the active power injected into node i, and Q iThe reactive power injected into node i, V i The voltage magnitude of node i, θ i The voltage phase angle of node i, G ij and B ij are the conductance and susceptance between nodes i and j respectively.

[0017] Due to the adoption of the above technical solutions in the embodiments of the present invention, it has the following advantages:

[0018] First, through all-round and real-time monitoring of the equipment operation status and intelligent fault diagnosis, the present invention can accurately detect and quickly locate faults at the initial stage when the equipment shows abnormalities. The system quickly starts the automatic fault isolation and repair program, greatly reducing the power outage time and scope. Compared with the traditional management method, the power outage duration can be shortened by more than 50%, effectively avoiding large-scale power outages caused by local faults, providing stable and continuous power supply for users, and ensuring the normal order of production and life.

[0019] Second, relying on the high-precision load prediction model and intelligent dispatching decision-making mechanism, the system can accurately grasp the changing trend of electricity demand at different times. During peak electricity consumption periods, it can reasonably allocate power resources to give priority to ensuring the electricity consumption of key users and important areas, avoiding overload and voltage dips; during low electricity consumption periods, it can effectively integrate energy storage devices and distributed energy sources, improve energy utilization efficiency, and reduce power grid losses. According to actual application data, the network loss can be reduced by 15%-20%, achieving dynamic balance between power supply and demand, and promoting the economic and efficient operation of the power system.

[0020] Third, through the remote monitoring and operation function, the present invention enables maintenance personnel to complete equipment inspection, parameter adjustment, and fault handling without having to be on-site. On the one hand, it greatly reduces the on-site workload and business trip frequency of maintenance personnel, reducing labor costs; on the other hand, timely equipment status warnings and accurate fault diagnosis avoid the further deterioration of equipment damage caused by untimely fault troubleshooting, reducing equipment repair and replacement costs. The overall operation and maintenance cost can be reduced by about 30%, while improving the timeliness and accuracy of operation and maintenance work, and enhancing the work efficiency of the operation and maintenance team.

[0021] Fourth, the system of the present invention integrates advanced information technology and intelligent algorithms to realize in-depth mining and analysis of distribution network operation data, providing a scientific basis for distribution network planning and transformation, promoting the continuous upgrade of the distribution network towards intelligence and automation. By continuously optimizing operation strategies and management models, it enhances the adaptability and anti-interference ability of the distribution network, better responds to the complex and changing power market environment and user needs, enhances the core competitiveness of power enterprises, and helps the modernization transformation of the power industry.

[0022] The above summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In the following, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present invention. Therefore, the drawings and the description are considered to be exemplary in nature rather than restrictive.

[0026] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] As Figure 1 shown, the embodiment of the present invention provides an intelligent management system for power distribution network automation, including a data acquisition and monitoring module, a fault diagnosis and processing module, an optimization scheduling module, and a communication and control module;

[0028] Data acquisition and monitoring module: responsible for collecting various operation data of distribution network equipment and lines, and performing real-time monitoring and preliminary evaluation of the equipment status to provide a data basis for subsequent analysis and decision-making;

[0029] Fault diagnosis and processing module: analyzes fault data using intelligent algorithms, accurately locates the fault position and type, and automatically performs fault isolation and repair operations to ensure the safe and stable operation of the distribution network;

[0030] Optimization scheduling module: formulates a reasonable power scheduling strategy based on the load prediction results and the real-time status of the distribution network;

[0031] Communication and control module: constructs a reliable communication network to ensure data transmission and the issuance of control instructions, realizes remote monitoring and operation of distribution network equipment, and ensures the coordinated operation of all parts of the system.

[0032] In one embodiment, the data acquisition and monitoring module includes an intelligent sensor network and a device status monitoring sub-module. A variety of intelligent sensors are widely deployed on key devices and lines of the power distribution network to collect information such as electrical parameters, operating temperature, and fault signals of the devices in real time. The data is transmitted to the monitoring center through wireless communication technology or a wired communication network. The device status monitoring sub-module performs real-time analysis and processing on the received sensor data, and uses big data analysis technology and preset threshold rules to evaluate and warn of the device operating status. The voltage sensor can accurately measure the line voltage with an accuracy of ±0.5%. Once abnormal voltage fluctuations occur, it can upload data within milliseconds, providing timely and accurate data support for subsequent analysis. For example, by continuously monitoring and analyzing data such as transformer oil temperature and load current, a device health model is established. When the oil temperature exceeds the normal threshold by 5°C or the load current reaches 80% of the rated value, an early warning signal is automatically sent to remind the operation and maintenance personnel to pay attention to the device status in time and take corresponding measures to effectively prevent device failures from occurring.

[0033] In one embodiment, the fault diagnosis and processing module includes a fault intelligent diagnosis sub-module and an automatic fault isolation and repair sub-module. The fault intelligent diagnosis sub-module performs in-depth analysis on the collected fault data based on artificial intelligence algorithms. When receiving the action signal of the fault indicator or abnormal electrical parameter data, the system can quickly locate the fault area and accurately judge the fault type and cause by combining historical fault cases and device operation data. After diagnosing the fault, the automatic fault isolation and repair sub-module automatically generates a fault handling plan and operates intelligent switches, circuit breakers, etc. in the distribution network through remote control technology to quickly isolate the fault area and prevent the fault from spreading. For example, when a line short-circuit fault occurs, the system analyzes data such as current mutation and voltage drop at the moment of short circuit, and uses neural network algorithms to quickly determine that the fault location is in a specific section of a certain line. The fault cause may be tree contact, insulation damage, etc., providing accurate basis for subsequent fault handling. At the same time, for some simple faults, such as fuse blowing, the system can automatically trigger the repair program and remotely control the standby fuse to be put into operation, greatly shortening the power outage time. After the fault is repaired, the system can also automatically perform device reset and network reconstruction to restore the normal operation of the distribution network.

[0034] In one embodiment, the optimization scheduling module includes a load forecasting sub-module and an intelligent scheduling decision-making sub-module. The load forecasting sub-module collects information such as historical electricity consumption data, meteorological data, and user type distribution, and uses methods such as time series analysis and machine learning to establish a load forecasting model. For example, during high-temperature periods in summer, by combining temperature data and residents' electricity consumption habits, it can accurately predict the peak electricity consumption period and load magnitude, providing basic data support for optimization scheduling. The intelligent scheduling decision-making sub-module, based on the load forecasting results and the real-time operating status of the distribution network, aims to optimize power distribution, reduce network losses, and improve power supply reliability, and formulates the optimal scheduling strategy. For example, during low electricity consumption periods, it reasonably arranges the charging of energy storage devices; during peak periods, it gives priority to ensuring power supply to important users, and maintains voltage stability and balances power supply and demand by adjusting transformer tap positions, switching reactive power compensation devices, etc., realizing the economic and efficient operation of the distribution network.

[0035] In one embodiment, the communication and control module includes a communication network architecture and a remote control unit. It constructs a communication network that integrates wired and wireless, and uses communication protocol conversion devices to achieve interconnection and interoperability between different communication protocols. The core layer uses fiber optic Ethernet to connect the monitoring center and the main substations to achieve high-speed and large-capacity data transmission; the access layer uses wireless communication technology to cover distribution network device terminals such as smart meters and distributed energy access points to ensure the efficient interaction of data collection and control instructions. The remote control unit receives control instructions from the monitoring center through the communication network to achieve remote operation of distribution network devices. For example, remotely controlling the opening and closing of circuit breakers, adjusting the parameters of reactive power compensation devices, etc. The remote control unit has a security encryption function and uses technologies such as identity authentication and data encryption to prevent illegal operations and data leakage, ensuring the safe and stable operation of distribution network devices.

[0036] In one embodiment, the following is the power flow calculation formula:

[0037]

[0038] For node i, let P i be the active power injected into node i, Q i be the reactive power injected into node i, V i be the voltage amplitude of node i, θ i be the voltage phase angle of node i, G ij and B ij be the conductance and susceptance between nodes i and j respectively.

[0039] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various changes or substitutions thereof, and these should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims described above.

Claims

1. An automatic intelligent management system for power distribution network, characterized by: It includes data acquisition and monitoring module, fault diagnosis and processing module, optimization scheduling module and communication and control module; The data collection and monitoring module is responsible for collecting various operating data of distribution network equipment and lines, and performing real-time monitoring and preliminary evaluation of equipment status to provide a data basis for subsequent analysis and decision-making; The fault diagnosis and processing module: uses intelligent algorithms to analyze fault data, accurately locates the fault location and type, automatically performs fault isolation and repair operations, and ensures safe and stable operation of the distribution network; The optimization dispatching module: formulates a reasonable power dispatching strategy based on load forecasting results and real-time status of the distribution network; The communication and control module builds a reliable communication network, ensures data transmission and the issuance of control instructions, realizes remote monitoring and operation of distribution network equipment, and ensures that all parts of the system work together.

2. The power distribution network automation intelligent management system according to claim 1, characterized in that: The data acquisition and monitoring module includes an intelligent sensor network and an equipment status monitoring submodule. Various intelligent sensors are widely deployed on key equipment and lines of the power distribution network to collect electrical parameters, operating temperature, fault signals and other information of the equipment in real time, and transmit the data to the monitoring center through wireless communication technology or wired communication network. The equipment status monitoring submodule performs real-time analysis and processing on the received sensor data, and uses big data analysis technology and preset threshold rules to evaluate and warn the equipment operation status.

3. The power distribution network automation intelligent management system according to claim 1 is characterized by: The fault diagnosis and processing module includes an intelligent fault diagnosis submodule and an automated fault isolation and repair submodule. The intelligent fault diagnosis submodule performs in-depth analysis on the collected fault data based on an artificial intelligence algorithm. When receiving the action signal of the fault indicator or abnormal electrical parameter data, the system can quickly locate the fault area and accurately determine the type and cause of the fault in combination with historical fault cases and equipment operation data. After diagnosing the fault, the automated fault isolation and repair submodule automatically generates a fault handling plan and operates intelligent switches, circuit breakers and other equipment in the distribution network through remote control technology to achieve rapid isolation of the fault area and prevent the fault from spreading.

4. The power distribution network automation intelligent management system according to claim 1, characterized in that: The optimization scheduling module includes a load prediction submodule and an intelligent scheduling decision submodule. The load prediction submodule collects historical electricity consumption data, meteorological data, user type distribution and other information, and uses time series analysis, machine learning and other methods to establish a load prediction model. The intelligent scheduling decision submodule formulates the optimal scheduling strategy based on the load prediction results and the real-time operating status of the distribution network, with the goal of optimizing power distribution, reducing network losses and improving power supply reliability.

5. The power distribution network automation intelligent management system according to claim 1, characterized in that: The communication and control module includes a communication network architecture and a remote control unit, which constructs a communication network that integrates wired and wireless communications. Communication protocol conversion equipment is used to achieve interconnection between different communication protocols. The control unit receives control instructions from the monitoring center through the communication network to achieve remote operation of the distribution network equipment.

6. The power distribution network automation intelligent management system according to claim 1, characterized in that: The following is the power flow calculation formula: For node i, let P i is the active power injected into node i, Q i is the reactive power injected into node i, V i is the voltage amplitude at node i, θ i is the voltage phase angle of node i, G ij and B ij are the conductance and susceptance between nodes i and j, respectively.

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