Comprehensive monitoring platform for auxiliary system of intelligent substation

Abstract

The invention discloses a comprehensive monitoring platform for an auxiliary system of an intelligent substation, and the platform comprises a sensing layer which is provided with diversified sensors and is used for collecting the operation state, environmental parameters, security and protection information and fire-fighting information of equipment in the substation in real time, and the sensors have the self-diagnosis and self-calibration functions; the edge computing layer is composed of a plurality of edge computing gateways; the cloud platform is used for carrying out deep fusion, storage, analysis and intelligent decision making on the data uploaded by the edge computing node; and the application layer is used for providing an interactive interface and a functional module for a user, and the user can log in the technology through various terminal devices. Through a multi-level architecture and an intelligent analysis technology, comprehensive perception, intelligent analysis and automatic control of equipment states are realized, faults are predicted in advance, downtime is reduced, and reliability and stability of a power system are improved. The response time is shortened by an intelligent linkage strategy and an edge computing local decision-making mechanism, sudden safety events and equipment faults can be quickly handled, and accident loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a comprehensive monitoring platform for smart substation auxiliary systems. Background Art

[0002] With the rapid development of smart grids, smart substations, as key nodes in power systems, are crucial for the reliability and security of the grid. However, traditional substation auxiliary systems have significant drawbacks: subsystems such as video surveillance, environmental monitoring, security, fire alarms, and access control operate independently, creating data silos that prevent effective data sharing and collaborative processing. This not only drives up overall management costs but also makes it difficult to achieve comprehensive, intelligent monitoring and management of substations.

[0003] Although some smart substations have attempted to integrate auxiliary systems, two core issues remain: First, equipment and systems from different manufacturers lack unified standards in terms of communication protocols and data formats, making interoperability between systems difficult and severely restricting the construction and operation of integrated monitoring platforms; Second, existing systems are unable to make intelligent decisions and coordinated responses quickly and accurately in the face of the complex and ever-changing substation operating environment and diverse equipment conditions, making it difficult to meet the requirements of modern power systems for high-reliability and high-efficiency operation and maintenance.

[0004] With the accelerated development of smart grids, the scale of substation equipment and system complexity have increased dramatically, making the drawbacks of traditional decentralized monitoring models increasingly apparent. For example, in tasks such as equipment inspection, fault warning, and emergency response in large-scale substations, relying on manual inspections and simple equipment monitoring is inefficient and prone to oversights, making it difficult to detect potential equipment failures and safety hazards in real time. Furthermore, amidst the energy transition and continued growth in electricity demand, smart substations are placing higher demands on intelligent and unmanned operation and maintenance. Building an efficient, intelligent, and integrated comprehensive monitoring platform for auxiliary systems has become a key technical challenge that urgently needs to be addressed. Summary of the Invention

[0005] The present invention aims to address at least one of the technical problems existing in the prior art by providing a comprehensive monitoring platform for intelligent substation auxiliary systems. Through a multi-level architecture and intelligent analysis technology, this platform enables comprehensive device status perception, intelligent analysis, and automated control, enabling early failure prediction, reducing downtime, and improving power system reliability and stability. Intelligent linkage strategies and edge computing local decision-making mechanisms shorten response times, enabling rapid handling of sudden safety incidents and equipment failures, thereby minimizing losses.

[0006] Multi-protocol integration and redundant link design ensure accurate and real-time data transmission, while encryption and security protection technologies guarantee data security, supporting safe substation operation. Standardized interfaces and scalable architecture enable resource sharing and optimized configuration, while intelligent operation and maintenance reduce manual inspection and maintenance workload, lowering operation and maintenance costs.

[0007] The present invention also provides a comprehensive monitoring platform for the intelligent substation auxiliary system, which adopts a multi-level distributed architecture of perception, edge computing, cloud platform, and application layer, and is characterized by specifically including: Perception layer: A diversified sensor network is deployed. The sensor network uses dynamic networking technology and can automatically adjust the sampling frequency according to the device load. The sensors have built-in microprocessors, self-diagnosis and self-calibration functions, and can obtain calibration parameters through the edge computing gateway for remote calibration. Edge computing layer: A distributed computing cluster consisting of multiple edge computing gateways. Each gateway is equipped with a power-specific AI inference model that supports real-time data cleaning, abnormal data filtering, equipment status trend prediction (prediction accuracy ≤ 5%), and local intelligent linkage decision-making, with a response delay of ≤ 100ms. Cloud platform: Adopting a distributed storage architecture customized for the power industry, it deeply integrates data uploaded by edge nodes through a spatiotemporal correlation algorithm and uses an improved LSTM neural network to establish an equipment life prediction model, enabling 72-hour advance warning of failures. Application layer: Provides a three-dimensional visual interactive interface, integrates a device digital twin module, supports multi-terminal collaborative operation, enables seamless connection between monitoring data and the power dispatching system, and has an operation authority management mechanism that complies with power safety regulations.

[0008] According to the integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention, the diversified sensors include high-precision temperature and humidity sensors, gas concentration sensors, intelligent image sensors, vibration sensors, and various equipment status monitoring sensors.

[0009] According to the integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention, when a situation occurs, corresponding alarms and emergency treatment measures can be immediately triggered locally, and relevant data can be uploaded to the cloud platform at the same time.

[0010] According to the comprehensive monitoring platform for the intelligent substation auxiliary system provided by the present invention, the cloud platform predicts the operating trends of equipment through comparative analysis of historical data and real-time data, discovers potential fault hazards in advance, and generates corresponding maintenance suggestions and early warning information, while managing and scheduling the resources of the entire system.

[0011] According to the integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention, the functional modules of the application layer include equipment monitoring, environmental monitoring, security management, fire management, access control, intelligent inspection, and data analysis and reporting modules.

[0012] The integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention deeply integrates artificial intelligence algorithms such as deep learning and machine learning into the integrated monitoring platform. In terms of equipment status monitoring, convolutional neural networks are used to analyze equipment image data. In security management, a target detection algorithm based on deep learning is adopted, and a prediction model for equipment operating status is established through machine learning algorithms. The artificial intelligence algorithms include: an improved YOLO algorithm based on the attention mechanism (with an accuracy rate of ≥98% for identifying hot spots in substation equipment), a GraphSAGE fault diagnosis model that integrates equipment electrical parameters, and an XGBoost load forecasting model that takes meteorological factors into account.

[0013] According to the comprehensive monitoring platform for the intelligent substation auxiliary system provided by the present invention, the platform has an intelligent linkage function and can realize automatic collaborative work between different subsystems according to preset rules and algorithms.

[0014] The integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention supports the integration of multiple communication protocols, including Modbus, IEC61850, DL / T104, TCP / IP, and MQTT. It realizes data interconnection between devices with different protocols through an intelligent protocol conversion gateway and adopts a redundant communication link design.

[0015] According to the integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention, multiple encryption technologies are adopted in the data transmission and storage process. The data transmission uses the SSL / TLS encryption protocol, and the data storage uses the AES-256 encryption algorithm, and is equipped with a complete security protection mechanism.

[0016] The integrated monitoring platform for the intelligent substation auxiliary system provided by the present invention formulates standardized interface specifications covering data interface, communication interface, and control interface. The platform adopts a modular and scalable architecture design, the hardware has a flexible hardware expansion interface, 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 of the monitored data is greater than the threshold, the sampling frequency is automatically increased to 10Hz and the transmission is encrypted; when the data is stable, it is reduced to 0.1Hz to save bandwidth. The threshold is dynamically updated through the cloud platform.

[0017] Compared with the existing technology, the intelligent substation auxiliary system integrated monitoring platform of the present invention realizes comprehensive perception of equipment status, 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.

[0018] Compared with the existing technology, the intelligent substation auxiliary system comprehensive monitoring platform of the present invention shortens the response time through intelligent linkage strategy and edge computing local decision-making mechanism, and can quickly deal with sudden safety incidents and equipment failures, thereby reducing accident losses.

[0019] Compared with the existing technology, the intelligent substation auxiliary system integrated monitoring platform of the present invention ensures accurate and real-time data transmission through multi-protocol fusion and redundant link design, and encryption and security protection technology ensures data security, providing support for the safe operation of the substation.

[0020] Compared with the existing technology, the intelligent substation auxiliary system integrated monitoring platform of the present invention realizes resource sharing and optimized configuration through standardized interfaces and scalable architecture. Intelligent operation and maintenance reduces the workload of manual inspection and maintenance, thereby lowering operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is the overall architecture diagram of the integrated monitoring platform for the intelligent substation auxiliary system of the present invention. DETAILED DESCRIPTION

[0022] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it should not be understood as a limitation on the scope of protection of the present invention.

[0023] Reference Figure 1 The embodiment of the present invention provides a comprehensive monitoring platform for an intelligent substation auxiliary system, which includes: System Setup: Within the smart substation, various sensors are strategically distributed based on different monitoring needs. Temperature and humidity sensors are installed in areas such as the high-voltage equipment room, low-voltage distribution room, and capacitor room to monitor indoor temperature and humidity in real time. Laser-based SF6 sensors are installed in the GIS equipment room to accurately monitor SF6 gas concentrations. Smart image sensors and intrusion detection sensors are installed around the substation perimeter and at key entrances and exits to achieve security monitoring. All sensors are connected to the edge computing gateway via wired or wireless connections.

[0024] Edge computing layer configuration: Select a high-performance edge computing gateway and rationally divide the coverage area of ​​edge computing nodes according to the physical layout of the substation and the distribution of sensors. Pre-install and configure data processing and analysis algorithms, as well as local linkage control rules, in the edge computing gateway. Configure communication parameters between the edge computing gateway and the cloud platform to ensure stable and rapid data upload to the cloud platform.

[0025] Cloud Platform Construction: Deploy the cloud platform's core software system on cloud servers. This system includes modules such as data storage, data analysis, intelligent decision-making, and resource management. Use distributed storage technology to build a massive data storage cluster to ensure reliable data storage. Configure a big data analysis engine and artificial intelligence algorithm library to provide strong support for data mining and intelligent analysis. Configure the communication interface and security authentication mechanism between the cloud platform and the edge computing and application layers.

[0026] Application layer development and deployment: Develop PC and mobile applications based on actual user needs. These applications utilize advanced visualization technologies, such as 3D modeling and virtual reality, to create intuitive and user-friendly interfaces. These applications integrate functional modules such as equipment monitoring, environmental monitoring, security management, fire management, access control, intelligent inspections, and data analysis and reporting. These applications are then deployed to servers and made available to users over the network.

[0027] System operation and maintenance involves data collection and processing. Various sensors in the perception layer collect real-time data on substation equipment operating status, environmental parameters, security information, and more. This data is then sent to the edge computing gateway. The edge computing gateway performs real-time cleaning, anomaly filtering, and preliminary analysis of the received data to extract valuable information. For simple anomalies, the edge computing gateway performs local processing and coordinated control based on pre-set rules, and simultaneously uploads the processed data to the cloud platform.

[0028] Intelligent Analysis and Decision-Making: After receiving data uploaded by the edge computing gateway, the cloud platform uses big data analytics and artificial intelligence algorithms to deeply integrate, analyze, and mine the data. By comparing and analyzing historical and real-time data, it predicts equipment operating trends, identifies potential faults in advance, and generates corresponding maintenance recommendations and early warning information. Based on intelligent linkage strategies, the cloud platform centrally schedules and manages the collaborative work between different subsystems.

[0029] User Interaction and Operation: By logging into the integrated monitoring platform through a PC or mobile application, users can view the substation's operating status in real time and perform remote control and operation. Specifically, users can remotely view real-time equipment operating parameters, monitor video footage, and remotely turn equipment on and off, adjust parameters, and more. Furthermore, users can receive early warning information and analytical reports from the platform, providing timely insights into substation operations.

[0030] System maintenance and upgrades: Regularly inspect and maintain the system's hardware to ensure proper operation. Update sensor firmware and drivers promptly to improve performance and stability. Regularly update and upgrade the cloud platform's software system, optimizing algorithm models and adding new functional modules to adapt the system to the evolving needs of smart substations. Strengthen system security, regularly scan for and repair security vulnerabilities, and ensure safe system operation.

[0031] Perception layer self-diagnosis mechanism: Through the built-in temperature compensation circuit and drift correction algorithm, zero drift calibration is automatically performed once an hour; when a measurement error > 3% is detected, a local alarm is immediately triggered and a fault code is uploaded, while the backup sensor switching mechanism is activated.

[0032] Edge computing local decision-making process: Level 1 response (e.g., fire, equipment short circuit): The edge gateway directly executes control commands (e.g., activates fire extinguishing devices, disconnects circuit breakers) and simultaneously uploads them to the cloud platform. Secondary response (e.g., temperature and humidity exceeding the standard): Execute predefined adjustment strategies (e.g., start ventilation), and report if no improvement occurs after 30 seconds. Level 3 response (such as minor data fluctuations): Only record and upload for analysis.

[0033] Cloud platform deep integration technology: Uses spatiotemporal tags to correlate and analyze the electrical parameters (such as current and voltage) of the same device with environmental parameters (such as temperature and humidity), establishes an equipment status assessment matrix, and achieves an upgrade from "single parameter alarm" to "comprehensive status assessment."

[0034] Smart linkage strategy example: When the SF6 sensor detects that the concentration exceeds the standard and the oxygen sensor is less than 19.5%, the ventilation outlet of the area is automatically closed, forced exhaust is started, the access control of the area is locked, and a level 1 alarm is sent to the operation and maintenance personnel. When the video recognizes that a person enters a high-voltage area and the access control of the corresponding area is opened without authorization, an audible and visual alarm is immediately triggered, the tracking camera is started, and the information is pushed to the on-duty personnel terminal at the same time.

[0035] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the technical field without departing from the scope of the present invention.

Claims

1. The integrated monitoring platform for the intelligent substation auxiliary system adopts a multi-level distributed architecture of perception, edge computing, cloud platform, and application layer, which is characterized by: Specifically include: Perception layer: A diversified sensor network is deployed. The sensor network uses dynamic networking technology and can automatically adjust the sampling frequency according to the device load. The sensors have built-in microprocessors, self-diagnosis and self-calibration functions, and can obtain calibration parameters through the edge computing gateway for remote calibration. Edge computing layer: A distributed computing cluster consisting of multiple edge computing gateways. Each gateway is equipped with a power-specific AI inference model that supports real-time data cleaning, abnormal data filtering, equipment status trend prediction (prediction accuracy ≤ 5%), and local intelligent linkage decision-making, with a response delay of ≤ 100ms. Cloud platform: Adopting a distributed storage architecture customized for the power industry, it deeply integrates data uploaded by edge nodes through a spatiotemporal correlation algorithm and uses an improved LSTM neural network to establish an equipment life prediction model, enabling 72-hour advance warning of failures. Application layer: Provides a three-dimensional visual interactive interface, integrates a device digital twin module, supports multi-terminal collaborative operation, enables seamless connection between monitoring data and the power dispatching system, and has an operation authority management mechanism that complies with power safety regulations.

2. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1 is characterized in that: The diversified sensors include high-precision temperature and humidity sensors, gas concentration sensors, intelligent image sensors, vibration sensors and various equipment status monitoring sensors.

3. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1 is characterized in that: The edge computing gateway has built-in high-performance computing chips and optimized algorithm libraries. When an abnormal situation is detected, it can immediately trigger corresponding alarms and emergency response measures locally, and upload relevant data to the cloud platform.

4. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1, characterized in that: The cloud platform predicts the operating trends of equipment by comparing and analyzing historical data and real-time data, detects potential fault hazards in advance, and generates corresponding maintenance suggestions and early warning information, while managing and scheduling the resources of the entire system.

5. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1 is characterized in that: The functional modules of the application layer include equipment monitoring, environmental monitoring, security management, fire management, access control, intelligent inspection, and data analysis and reporting modules.

6. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1, characterized in that: Deeply integrate artificial intelligence algorithms such as deep learning and machine learning into the comprehensive monitoring platform. In terms of equipment status monitoring, convolutional neural networks are used to analyze equipment image data. In security management, deep learning-based target detection algorithms are adopted, and machine learning algorithms are used to establish a predictive model for equipment operating status. The artificial intelligence algorithms include: an improved YOLO algorithm based on the attention mechanism (with an accuracy rate of ≥98% for identifying hot spots in substation equipment), a GraphSAGE fault diagnosis model that integrates equipment electrical parameters, and an XGBoost load forecasting model that takes meteorological factors into account.

7. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1, characterized in that: The platform has intelligent linkage functions, which can realize automatic collaboration between different subsystems based on preset rules and algorithms.

8. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1, characterized in that: It supports the integration of multiple communication protocols, including Modbus, IEC61850, DL / T104, TCP / IP, and MQTT. It realizes data interconnection between devices with different protocols through an intelligent protocol conversion gateway and adopts a redundant communication link design.

9. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1, characterized in that: Multiple encryption technologies are used during data transmission and storage. Data transmission uses the SSL / TLS encryption protocol, and data storage uses the AES-256 encryption algorithm, equipped with a complete security protection mechanism.

10. The integrated monitoring platform for the intelligent substation auxiliary system according to claim 1, characterized in that: Standardized interface specifications have been formulated, covering data interfaces, communication interfaces, and control interfaces. The platform adopts a modular and scalable 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 of the monitored data is greater than the threshold, the sampling frequency is automatically increased to 10Hz and the transmission is encrypted; when the data is stable, it is reduced to 0.1Hz to save bandwidth. The threshold is dynamically updated through the cloud platform.

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