Intelligent substation auxiliary system comprehensive monitoring platform

Abstract

The application discloses an intelligent substation auxiliary system comprehensive monitoring platform, which comprises: a sensing layer: diversified sensors are arranged for collecting the running state of equipment in a substation, environmental parameters, security information and fire-fighting information in real time, and the sensors are provided with self-diagnosis and self-calibration functions; an edge computing layer: composed of multiple edge computing gateways; a cloud platform: used for deep fusion, storage, analysis and intelligent decision of data uploaded by the edge computing nodes; an application layer: providing an interactive interface and functional modules for users, and the users can log in the technology through various terminal devices. Through the multi-level architecture and intelligent analysis technology, the equipment state is comprehensively sensed, intelligent analysis and automatic control are realized, faults are predicted in advance, downtime is reduced, and the reliability and stability of the power system are improved. The intelligent linkage strategy and the edge computing local decision mechanism shorten the response time, can quickly dispose of sudden safety events and equipment faults, and reduce the accident loss.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid, in particular to a comprehensive monitoring platform for auxiliary systems of a smart substation. BACKGROUND

[0002] In the process of rapid development of smart grid, the efficient and stable operation of smart substations as key nodes of power systems is directly related to the reliability and safety of the power grid. However, the traditional substation auxiliary systems have significant defects: each subsystem such as video monitoring, environmental monitoring, security protection, fire alarm and access control operates independently, forming a data island, which leads to ineffective sharing and collaborative processing of data, not only making the comprehensive management cost high, but also making it difficult to realize comprehensive and intelligent monitoring and management of the substation.

[0003] Although some smart substations have tried to integrate auxiliary systems, there are still two major problems: first, different manufacturers' equipment and systems lack unified standards in communication protocols, data formats, etc., making it difficult for systems to interconnect and interoperate, which seriously restricts the construction and operation of the comprehensive monitoring platform; second, the existing systems cannot quickly and accurately make intelligent decisions and linkage responses when facing complex and variable substation operating environments and diversified equipment states, which makes it difficult to meet the requirements of modern power systems for high reliability and high efficiency operation and maintenance.

[0004] With the acceleration of smart grid construction, the equipment scale and system complexity of substations have increased dramatically, and the disadvantages of traditional decentralized monitoring mode have become more and more obvious. For example, in the tasks of equipment inspection, fault warning and emergency disposal in large-scale substations, the manual inspection and simple equipment monitoring mode is inefficient and prone to omissions, making it difficult to discover potential equipment faults and safety hazards in real time. At the same time, under the background of energy transformation and continuous growth of power demand, smart substations have higher requirements for intelligent and unmanned operation and maintenance, and it has become a key technical problem to be solved to build an efficient, intelligent and integrated comprehensive monitoring platform for auxiliary systems. SUMMARY

[0005] The present application aims to at least solve one of the technical problems in the prior art, and provides a comprehensive monitoring platform for auxiliary systems of a smart substation, which realizes comprehensive perception of equipment state, intelligent analysis and automatic control through multi-level architecture and intelligent analysis technology, predicts faults in advance, reduces downtime, and improves the reliability and stability of the power system. Intelligent linkage strategy and edge computing local decision mechanism shorten the response time, can quickly handle sudden safety incidents and equipment faults, and reduce accident losses.

[0006] Multi-protocol fusion and redundant link design ensure accurate and real-time data transmission, encryption and security protection technology guarantee data security, and provide support for safe operation of substations. Standardized interfaces and scalable architecture realize resource sharing and optimized configuration, intelligent operation and maintenance reduce manual inspection and maintenance workload, and reduce operation and maintenance cost.

[0007] The application also provides the intelligent substation auxiliary system comprehensive monitoring platform, which adopts a multi-level distributed architecture of perception, edge calculation, a cloud platform and an application layer, and specifically comprises the following features.

[0008] The perception layer is provided with a diversified sensor network, the sensor network adopts a dynamic networking technology and can automatically adjust a sampling frequency according to equipment load; the sensor is internally provided with a microprocessor, has self-diagnosis and self-calibration functions, and can obtain calibration parameters through an edge calculation gateway to realize remote calibration;

[0009] The edge calculation layer comprises a distributed computing power cluster composed of multiple edge calculation gateways, each gateway is loaded with a power special AI inference model, supports real-time data cleaning, abnormal data filtering, equipment state trend prediction and local intelligent linkage decision, and has a response delay of less than or equal to 100 ms;

[0010] The cloud platform adopts a power industry customized distributed storage architecture, deeply fuses data uploaded by edge nodes through a space-time correlation algorithm, establishes an equipment life prediction model by using an improved LSTM neural network, and realizes 72-hour early warning of faults;

[0011] The application layer provides a three-dimensional visual interactive interface, integrates a device digital twin module, supports multi-terminal collaborative operation, can realize seamless connection of monitoring data and a power dispatching system, and has an operation permission management mechanism in line with power safety specifications.

[0012] According to the intelligent substation auxiliary system comprehensive monitoring platform provided by the application, the diversified sensors include high-precision temperature and humidity sensors, gas concentration sensors, intelligent image sensors, vibration sensors and various equipment state monitoring sensors.

[0013] According to the intelligent substation auxiliary system comprehensive monitoring platform provided by the application, when an abnormal situation occurs, corresponding alarm and emergency treatment measures can be immediately triggered locally, and related data can be uploaded to the cloud platform.

[0014] According to the intelligent substation auxiliary system comprehensive monitoring platform provided by the application, the cloud platform compares and analyzes historical data and real-time data, predicts the operation trend of equipment, discovers potential fault hidden dangers in advance, generates corresponding maintenance suggestions and warning information, and manages and dispatches resources of the entire system.

[0015] The application layer function module comprises device monitoring, environment monitoring, security management, fire management, access control, intelligent inspection, data analysis report module.

[0016] The intelligent substation auxiliary system comprehensive monitoring platform provided by the application deeply integrates artificial intelligence algorithms such as deep learning and machine learning into the comprehensive monitoring platform, analyzes image data of equipment by using a convolutional neural network in the aspect of equipment state monitoring, adopts a target detection algorithm based on deep learning in security management, and establishes a prediction model of equipment operation state by using a machine learning algorithm.

[0017] The artificial intelligence algorithms comprise an improved YOLO algorithm based on an attention mechanism, a GraphSAGE fault diagnosis model fusing equipment electrical parameters, and an XGBoost load prediction model considering meteorological factors.

[0018] The intelligent substation auxiliary system comprehensive monitoring platform provided by the application has an intelligent linkage function and can realize automatic collaborative work between different subsystems according to preset rules and algorithms.

[0019] The intelligent substation auxiliary system comprehensive monitoring platform provided by the application supports fusion of multiple communication protocols, including Modbus, IEC61850, DL / T104, TCP / IP and MQTT, realizes data interconnection and intercommunication between devices of different protocols through an intelligent protocol conversion gateway, and adopts a redundant communication link design.

[0020] The intelligent substation auxiliary system comprehensive monitoring platform provided by the application adopts multiple encryption technologies in the data transmission and storage process, uses an SSL / TLS encryption protocol for data transmission, adopts an AES-256 encryption algorithm for data storage, and is equipped with a perfect security protection mechanism.

[0021] The intelligent substation auxiliary system comprehensive monitoring platform provided by the application formulates a standardized interface specification, covers data interfaces, communication interfaces and control interfaces, adopts a modular and extensible architecture design, has a flexible hardware expansion interface, and adopts a service-oriented architecture.

[0022] An adaptive communication mechanism is adopted between the edge computing layer and the perception layer: when the monitoring data anomaly degree is greater than a threshold value, the sampling frequency is automatically increased to 10Hz and encrypted transmission is performed; when the data is smooth, the sampling frequency is reduced to 0.1Hz to save bandwidth, and the threshold value is dynamically updated through the cloud platform.

[0023] Compared with the prior art, the intelligent substation auxiliary system comprehensive monitoring platform of the application realizes comprehensive perception of equipment state, intelligent analysis and automatic control through multi-level architecture and intelligent analysis technology, predicts faults in advance, reduces downtime, and improves reliability and stability of the power system.

[0024] Compared with the prior art, the intelligent substation auxiliary system comprehensive monitoring platform of the application shortens response time through intelligent linkage strategy and edge computing local decision mechanism, can quickly handle sudden safety incidents and equipment failures, and reduces accident loss.

[0025] Compared with the prior art, the intelligent substation auxiliary system comprehensive monitoring platform of the application ensures accurate and real-time data transmission through multi-protocol fusion and redundant link design, and guarantees data security through encryption and security protection technology, thereby providing support for safe operation of the substation.

[0026] Compared with the prior art, the intelligent substation auxiliary system comprehensive monitoring platform of the application realizes resource sharing and optimized configuration through standardized interface and extensible architecture, reduces manual inspection and maintenance workload through intelligent operation and maintenance, and reduces operation and maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0027] The application will be further described below in combination with the drawings and examples.

[0028] Figure 1 The figure is a whole architecture diagram of the intelligent substation auxiliary system comprehensive monitoring platform of the application. DETAILED DESCRIPTION

[0029] This part will describe the specific embodiments of the application in detail, and the preferred embodiments of the application are shown in the drawings. The drawings serve to supplement the description in the text part and enable people to intuitively and visually understand each technical feature and the overall technical scheme of the application, but it cannot be understood as a limitation on the protection scope of the application.

[0030] Referring to Figure 1 , the embodiment of the application is an intelligent substation auxiliary system comprehensive monitoring platform, which comprises:

[0031] System building: in the intelligent substation, according to different monitoring needs, reasonably distribute various sensors: install temperature and humidity sensors in high-voltage equipment rooms, low-voltage distribution rooms, capacitor rooms and other areas to monitor the changes of indoor temperature and humidity in real time. Install laser SF6 sensors in GIS equipment rooms to accurately monitor the concentration of SF6 gas. Install intelligent image sensors and intrusion detection sensors on the perimeter of the substation and important entrances to achieve the purpose of security monitoring. All sensors are connected to edge computing gateways through wired or wireless methods.

[0032] Edge computing layer configuration: Select a powerful edge computing gateway, and reasonably divide the coverage range of the edge computing nodes according to the physical layout of the substation and the distribution of the sensors. In the edge computing gateway, pre-install and configure data processing and analysis algorithms, as well as local linkage control rules. Set the communication parameters between the edge computing gateway and the cloud platform to ensure that data can be stably and quickly uploaded to the cloud platform.

[0033] Cloud platform building: On the cloud server, deploy the core software system of the cloud platform, which includes data storage, data analysis, intelligent decision-making, resource management, etc. Use distributed storage technology to build a massive data storage cluster to ensure reliable data storage. Configure big data analysis engines and artificial intelligence algorithm libraries to provide strong support for data mining and intelligent analysis. Set the communication interface and security authentication mechanism between the cloud platform and the edge computing layer and the application layer.

[0034] Application layer development and deployment: According to the actual needs of users, develop PC and mobile application programs. The application program uses advanced visualization technologies such as 3D modeling and virtual reality to build an intuitive and friendly user interface. In the application program, integrate device monitoring, environmental monitoring, security management, fire management, access control, intelligent inspection, data analysis report, etc. Deploy the application program to the server and provide users with services through the network.

[0035] System operation and maintenance include data acquisition and processing. Through various sensors in the perception layer, the running state of the equipment in the substation, environmental parameters, security information, etc. Real-time data acquisition and transmission of collected data to edge computing gateway. The edge computing gateway receives real-time cleaning, abnormal data filtering and preliminary analysis of the received data, and extracts valuable information. For some simple abnormal situations, the edge computing gateway processes and linkage control locally according to the preset rules, and uploads the processed data to the cloud platform.

[0036] Intelligent analysis and decision-making: After the cloud platform receives the data uploaded by the edge computing gateway, it uses big data analysis technology and artificial intelligence algorithms to deeply integrate, analyze and mine the data. Through comparative analysis of historical data and real-time data, predict the running trend of the equipment, discover potential fault hidden dangers in advance, and generate corresponding maintenance suggestions and warning information. According to the intelligent linkage strategy, the cloud platform unifies the scheduling and management of the collaborative work between different subsystems.

[0037] User Interaction and Operation: Users can log in to the comprehensive monitoring platform through PC or mobile application, and can view the running status of the substation in real time, and perform remote control and operation. Specifically, users can remotely view real-time running parameters of equipment, monitor video pictures, and perform remote switching operation, parameter adjustment, etc. At the same time, users can receive warning information and analysis reports sent by the platform, and understand the running status of the substation in a timely manner.

[0038] System Maintenance and Upgrade: Regularly check and maintain the hardware devices of the system to ensure that the devices can operate normally. Update the firmware and driver of the sensor in a timely manner to improve the performance and stability of the sensor. Regularly update and upgrade the software system of the cloud platform, optimize the algorithm model, and add new function modules, so that the system can adapt to the changing needs of the intelligent substation. Strengthen the security protection of the system, regularly perform security vulnerability scanning and repair, and ensure the safe operation of the system.

[0039] Self-diagnosis mechanism of perception layer: Through the built-in temperature compensation circuit and drift correction algorithm, zero drift calibration is automatically performed every hour; when the measurement error is > 3%, local alarm is triggered and fault code is uploaded, and standby sensor switching mechanism is started.

[0040] Local decision-making process of edge computing:

[0041] First-level response (such as fire, equipment short circuit): edge gateway directly executes control instructions (such as starting fire extinguishing device, disconnecting circuit breaker), and synchronously uploads cloud platform;

[0042] Second-level response (such as temperature and humidity exceeding standard): execute predefined adjustment strategy (such as start ventilation), if no improvement after 30 seconds, report to the platform;

[0043] Third-level response (such as slight data fluctuation): only record and upload analysis.

[0044] Deep fusion technology of cloud platform: through space-time label, the electrical parameters (such as current, voltage) of the same device are associated with the environmental parameters (such as temperature, humidity) for analysis, a device state evaluation matrix is established, and an upgrade from "single parameter alarm" to "comprehensive state evaluation" is realized.

[0045] Intelligent linkage strategy example:

[0046] When the SF6 sensor detects that the concentration exceeds the standard and the oxygen sensor is < 19.5%, automatically close the ventilation outlet of the area, start forced ventilation, lock the door of the area, and send a first-level alarm to the operation and maintenance personnel.

[0047] When the video recognition detects that personnel enter the high-voltage area and the corresponding area access is not authorized to open, immediately trigger sound and light alarm, start tracking camera, and synchronously push to the terminal of the on-duty personnel.

[0048] The embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application.

Claims

1. The intelligent substation auxiliary system comprehensive monitoring platform adopts a multi-level distributed architecture of perception, edge computing, cloud platform and application layer, characterized in that, Specifically comprising: The perception layer: a diversified sensor network is deployed, which adopts dynamic networking technology and can automatically adjust the sampling frequency according to the equipment load; the sensor is built-in with a microprocessor, which has self-diagnosis and self-calibration functions, and can obtain calibration parameters through an edge computing gateway to realize remote calibration; The edge computing layer: a distributed computing cluster is composed of multiple edge computing gateways, each gateway carries an AI inference model dedicated to power, supports real-time data cleaning, abnormal data filtering, device state trend prediction and local intelligent linkage decision, and the response delay is ≤100 ms; The cloud platform: a customized distributed storage architecture for the power industry is adopted, the data uploaded by the edge nodes are deeply fused through a spatio-temporal correlation algorithm, an improved LSTM neural network is used to establish a device life prediction model, and a 72-hour early warning of failure is realized; The application layer: a three-dimensional visual interactive interface is provided, a device digital twin module is integrated, multi-terminal collaborative operation is supported, seamless connection between monitoring data and power dispatching system can be realized, and an operation permission management mechanism conforming to the power safety specification is provided; The cloud platform compares and analyzes historical data and real-time data to predict the operation trend of the device, discovers potential hidden troubles in advance, generates corresponding maintenance suggestions and warning information, and manages and dispatches the resources of the entire system; Deep learning, machine learning and other artificial intelligence algorithms are deeply integrated into the comprehensive monitoring platform, a convolutional neural network is used to analyze image data of the device in the aspect of device state monitoring, a target detection algorithm based on deep learning is adopted in security management, and a machine learning algorithm is used to establish a prediction model of the device operation state; The artificial intelligence algorithms include: an improved YOLO algorithm based on attention mechanism, a GraphSAGE fault diagnosis model integrating device electrical parameters, and an XGBoost load prediction model considering meteorological factors; Standardized interface specifications are formulated, covering data interfaces, communication interfaces and control interfaces, the platform adopts a modular and expandable architecture design, the hardware has flexible hardware expansion interfaces, and the software adopts a service-oriented architecture; An adaptive communication mechanism is adopted between the edge computing layer and the perception layer: when the abnormality degree of the monitoring data is greater than a threshold value, the sampling frequency is automatically increased to 10 Hz and encrypted transmission is performed; when the data is stable, the sampling frequency is reduced to 0.1 Hz to save bandwidth, and the threshold value is dynamically updated by the cloud platform. 2.The integrated monitoring platform of the intelligent substation auxiliary system according to claim 1, characterized in that, The diversified sensors include high-precision temperature and humidity sensors, gas concentration sensors, intelligent image sensors, vibration sensors and various device state monitoring sensors. 3.The integrated monitoring platform of the smart substation auxiliary system according to claim 1, characterized in that, The edge computing gateway is built-in with a high-performance computing chip and an optimized algorithm library, when an abnormal situation is detected, the gateway can immediately trigger corresponding alarm and emergency handling measures locally, and upload relevant data to the cloud platform.

4. The intelligent substation auxiliary system integrated monitoring platform of claim 1, wherein, The function modules of the application layer include device monitoring, environment monitoring, security management, fire management, access control, intelligent inspection and data analysis report modules.

5. The intelligent substation auxiliary system integrated monitoring platform of claim 1, wherein, The platform has an intelligent linkage function and can realize automatic collaborative work between different subsystems according to preset rules and algorithms. 6.The integrated monitoring platform of smart substation auxiliary system according to claim 1, characterized in that, Support the integration of multiple communication protocols, including Modbus, IEC61850, DL / T104, TCP / IP, MQTT, through intelligent protocol conversion gateway to realize data interconnection and intercommunication between different protocol devices, and adopt redundant communication link design. 7.The integrated monitoring platform of smart substation auxiliary system according to claim 1, characterized in that, In the process of data transmission and storage, multiple encryption technology is adopted, SSL / TLS encryption protocol is used for data transmission, AES-256 encryption algorithm is used for data storage, and perfect security protection mechanism is equipped.

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