Intelligent state information analysis system for centralized control station equipment monitoring

Abstract

The invention discloses an intelligent state information analysis system for centralized control station equipment monitoring, and mainly relates to the technical field of power systems and intelligent monitoring. Which comprises an information analysis management module and an event handling management module, and is characterized by further comprising a service tracking management and control module, the information analysis management module is used for carrying out event processing on alarm signals, and the information analysis management module is provided with a power monitoring knowledge base; and the power monitoring knowledge base is used for collecting, sorting, managing and analyzing data and knowledge related to monitoring, and supporting system monitoring, event management, problem diagnosis and decision making. The method has the beneficial effect that the health management level of the power transformation equipment can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems and intelligent monitoring, and particularly relates to an intelligent status information analysis system for monitoring equipment in a centralized control station. Background Art

[0002] In modern power systems, substations are an important link in power transmission and distribution, and are responsible for converting high-voltage electricity into electricity suitable for users. As the scale of power networks continues to expand, the complexity and quantity of substation equipment have also increased. Traditional manual monitoring methods can no longer meet the needs of modern power systems. In existing substation equipment monitoring work, monitors evaluate the status of equipment by monitoring a large amount of signals and alarm information. The amount of information is complex and difficult to make accurate analysis and judgments in real time, resulting in poor timeliness in fault handling and possible serious power supply interruptions. Therefore, how to realize the intelligence, unmanned operation and automation of substations has become one of the key needs for the development of the industry. At this stage, substation operation and maintenance management faces multiple challenges:

[0003] Numerous monitoring screen signals make analysis difficult: Monitors must process a large amount of monitoring screen information daily, including real-time alarm signals and substation operating data. According to statistics, some large substations can have tens of thousands of alarm information points. Monitors must visually interpret these numerous alarm signals in a short period of time, which can easily cause visual fatigue, increase the risk of misjudgment and missed judgments, and affect the accuracy and timeliness of fault handling.

[0004] Weak situational awareness and difficulty identifying events: In existing systems, alarm signals are typically listed in chronological order, lacking direct and accurate reflection of equipment failures and preventing effective situational awareness. Especially when faced with complex power grid anomalies, monitors often rely solely on experience and lack the support of intelligent tools, making it difficult to conduct in-depth analysis of equipment health and prevent and promptly address potential failure risks.

[0005] Collaborative interaction is difficult, cumbersome, and time-consuming: When a substation fails, a coordinated response from all parties is required to ensure rapid power restoration. However, the current system has limited automation, complex event handling processes, low communication efficiency, and a lack of effective collaborative mechanisms. This is especially true when dealing with high-voltage substations, such as 110kV equipment failures. Information confirmation and resolution often take a long time, resulting in prolonged recovery times and increased waste of manpower and material resources.

[0006] Insufficient intelligence and low efficiency: Although modern substations have achieved partial automation and monitoring, their overall intelligence level remains low. This is particularly true for the identification and handling of complex alarm events, which still require manual analysis and decision-making. As power grids grow in size and complexity, traditional methods alone are no longer sufficient to meet the requirements for safe substation operation. Therefore, there is an urgent need to introduce artificial intelligence (AI) to enhance the intelligence of substation operation monitoring and fault handling.

[0007] To address these issues, State Grid Corporation of China has repeatedly emphasized the need to upgrade power equipment operation and maintenance models and pilot centralized control stations in recent years. For example, according to Document No. 57 on Equipment Change

[2020] , the new "unmanned operation + centralized monitoring" operation and maintenance management model is being promoted to accelerate the implementation of intelligent management of equipment throughout its lifecycle. In 2022, State Grid also proposed accelerating the transformation of its operation and maintenance model, building an intelligent centralized control system, and further expanding the scope of remote operation and monitoring of auxiliary equipment to improve operation and maintenance efficiency and fault handling capabilities.

[0008] Application number 202110563952.4, publication number CN 113409162 A, invention title: A centralized control station equipment monitoring system and control method based on intelligent event processing, comprising an information acquisition module, a fault knowledge base, a state analysis module, and an event processing module; the information acquisition module collects power grid monitoring data in real time; the fault knowledge base stores information parameters related to the equipment and the disposal measures for the equipment under the corresponding information parameters; the state analysis module analyzes and judges the data collected by the information acquisition module based on the information parameters stored in the fault knowledge base, and generates an event disposal plan for the equipment; the event processing module handles the event according to the event disposal plan. The fault knowledge base of the centralized control station equipment monitoring system stores event disposal measures derived from behavioral experience. Operators handle power grid events according to the standardized disposal process of the stored event disposal measures, thereby improving the intelligence level of the overall power grid event disposal process, improving the efficiency and accuracy of power grid accident disposal, and reducing the pressure on dispatchers to handle faults.

[0009] The status analysis module of the aforementioned patent application does not utilize combined primary and secondary monitoring to obtain primary, secondary, and specific monitoring. It also fails to utilize the monitoring rule library within the power monitoring knowledge base to filter out interference, invalid, and associated information. The auxiliary control system does not bundle signals of the same type into a unified alarm event, nor does it label alarm information. Furthermore, it fails to correlate multiple data sets to infer specific events.

[0010] In this context, it is particularly necessary to develop an intelligent status information analysis system for centralized control station equipment monitoring based on the next-generation centralized control system. This system can achieve real-time monitoring and intelligent analysis of substation operating status, effectively improving the health management level of substation equipment, reducing the occurrence of failures, and improving overall operation and maintenance efficiency. Summary of the Invention

[0011] The purpose of the present invention is to provide an intelligent status information analysis system for centralized control station equipment monitoring, which can realize real-time monitoring and intelligent analysis of the operating status of substations, effectively improve the health management level of substation equipment, reduce the occurrence rate of failures, and improve overall operation and maintenance efficiency.

[0012] To achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions:

[0013] Provided is an intelligent status information analysis system for centralized control station equipment monitoring, comprising an information analysis and management module, an event handling and management module, and a business tracking and control module. The information analysis and management module is used to convert alarm signals into events. The information analysis and management module is provided with a power monitoring knowledge base, which is used to collect, organize, manage and analyze monitoring-related data and knowledge, supporting system monitoring, event management, problem diagnosis and decision-making.

[0014] The workflow of the information analysis and management module is as follows:

[0015] S1: Acquire the main equipment monitoring signal, auxiliary monitoring signal, and specific monitoring signal through the main and auxiliary joint monitoring, and use the monitoring rule library in the power monitoring knowledge base to filter out interference information, invalid information, and accompanying information;

[0016] S2: Performs preliminary filtering on various signals collected by the auxiliary control system to remove noise and invalid signals, bundles signals of the same type to form a unified alarm event, and converts the processed signals into events, combining multiple related signals into one event. Alarms are then graded based on their severity, urgency, and impact range.

[0017] S3: Classify and manage alarm information through tags;

[0018] Perform preliminary screening and preprocessing of alarm and measurement data to remove noise and irrelevant information. Based on the grid topology and event knowledge base, a real-time reasoning algorithm is used for comprehensive analysis. The reasoning results are further converted into event-based alarm information. Event-based alarm information integrates relevant alarm and measurement data to generate a complete description of the event.

[0019] The workflow of the event handling management module is as follows:

[0020] S4: Data input sources include alarm information, risk predictions, manual experience, and data analysis. By actively sensing this information, we can obtain real-time equipment operating status, risk points, and historical operating experience.

[0021] S5: By correlating information and combining it with the event rule base, the collected information is judged and events of various categories are identified;

[0022] S6: Further handle the incident through impact analysis, trend warning, and disposal decision-making;

[0023] When an alarm event is triggered, the business tracking and control module performs a status check before and after the device operation, that is, it checks the status of the device before and after the operation is performed.

[0024] Preferably, the process of establishing the power monitoring knowledge base is as follows:

[0025] Through natural language processing (NLP), automatic speech recognition (ASR), and image recognition technologies, power grid monitoring rules, historical simulation cases, and accident plan rules are extracted and converted to form an initial power monitoring knowledge base.

[0026] Use machine learning algorithms to analyze historical operating data, extract the implicit knowledge contained in the data, and update the power monitoring knowledge base.

[0027] Preferably, in step S1, only the auxiliary control level 1 alarm is displayed, and the level 1 alarm information is displayed through the interface; when the user clicks on the level 1 alarm, its detailed information is displayed, including the source of the level 1 alarm, the related signals involved, and the historical occurrence; an alarm history record is provided to help the user analyze the root cause and pattern of the alarm event, assist in decision-making and problem handling; and according to the alarm level and historical information, the monitoring personnel are provided with response measures to be taken.

[0028] Preferably, in step S2, classifying and managing the alarm information by means of tags includes:

[0029] Step S21: pre-define a set of standard tags according to business requirements;

[0030] Step S22: Automatically assign labels based on the alarm content using machine learning or rule-based algorithms;

[0031] Step S23: Add a tagging function to the existing monitoring or operation and maintenance platform.

[0032] Preferably, in step S3, the comprehensive analysis using a real-time reasoning algorithm includes using a rule engine, causal analysis or a machine learning model to associate multiple data to infer specific events.

[0033] Preferably, step S3 further comprises: identifying fault events in the power grid, and detecting equipment anomalies, equipment defects, and power grid operation events;

[0034] Analyze and identify fault events in the power grid, and classify them into line faults, busbar faults, transformer faults, capacitor / reactor faults, standby automatic switching / internal bridge switch faults, faulty load shedding / low-frequency load reduction device faults, grounded transformer / used transformer faults, load transfer device faults, and busbar / busbar bay switch faults;

[0035] Based on the analysis results of graded events, general events, medium events, and serious events are automatically generated, and the event handling strategies for each level are intelligently matched to carry out emergency repairs.

[0036] Preferably, various faults, anomalies or operational behaviors in power grid operation are analyzed and classified to classify events; comprehensive analysis and judgment are conducted through real-time collected alarm data, measurement data and topological relationships, combined with the event knowledge base; intelligent algorithms are used to identify the nature, scope and potential impact of events, and according to pre-defined classification rules, events are classified into general events, medium events and severe events;

[0037] General incidents: The impact only affects a single device or a local area and does not cause systemic risks;

[0038] Moderate incident: The impact involves multiple devices or areas, but is highly controllable;

[0039] Serious incidents: Possing a major threat to the safe and stable operation of the power grid, causing large-scale power outages or equipment damage.

[0040] Preferably, predefined event handling strategies are matched according to the level of the event; based on the event knowledge base and historical experience, handling strategies are formulated, covering the emergency repair process, resource scheduling and safety measures for events of each level;

[0041] General incidents: Use the conventional handling process, which includes dispatching a single-person inspection team to investigate or remotely controlling the fault.

[0042] Medium incidents: Coordinate multiple departments, deploy maintenance resources within the area, and activate backup power or load transfer solutions to minimize the impact of the fault on users.

[0043] Serious incidents: Activate the emergency plan, quickly assemble a repair team, and collaborate with the dispatch center and relevant government departments. At the same time, issue a power outage notice and restoration progress report.

[0044] Preferably, under the guidance of the event handling strategy, perform the following operations:

[0045] Accurately locate fault points based on real-time data and topological relationships;

[0046] Allocate emergency repair personnel, vehicles, and equipment based on the incident level and repair needs;

[0047] Generate specific operation instructions and send them to relevant personnel or equipment;

[0048] Monitor the repair progress in real time and dynamically adjust the handling strategy according to the situation.

[0049] Preferably, step S5 further includes: classifying these events into an event pool, and classifying these events into defect events, fault events, abnormal events, and operation events; information correlation is to analyze information from different sources to identify hidden faults or potential problems; the event rule base is a standardized knowledge base for judging and processing events, and determines the nature and level of events through predefined rules;

[0050] In step S6, the impact scope analysis of the event is used to understand the equipment scope or business scope affected by the event; trend warning is used to judge the development trend of the event in advance and take preventive measures; event handling decisions are based on the handling decision library, providing the best handling plan for the event to ensure timely and effective problem resolution; the handling decision library supports event handling; the processed events are used to further optimize the handling decision library, that is, the judgment and handling rules of the handling decision library are continuously optimized and updated through the processed events to improve the intelligence level and response capabilities.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] 1. It can realize real-time monitoring and intelligent analysis of the substation operation status, effectively improve the health management level of substation equipment, reduce the occurrence rate of failures, and improve the overall operation and maintenance efficiency.

[0053] 2. Through intelligent analysis of equipment status information, it provides real-time monitoring, status assessment, alarm processing and decision-making support functions, thereby improving the degree of automation and operational reliability of equipment management in the centralized control station.

[0054] 3. The present invention provides an AI operator robot for monitoring and analyzing the operating status of substation equipment, which realizes all-round real-time monitoring and intelligent analysis of the operating status of substations, effectively improves the health management level of substation equipment, reduces the occurrence rate of failures, and improves the overall operation and maintenance efficiency.

[0055] 4. By integrating situational awareness and event identification functions, the processing accuracy and response speed of alarm information can be greatly improved, thereby achieving the goal of unmanned smart substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a structural diagram of the monitoring function module of the present invention;

[0057] Figure 2 This is the structural diagram of the power monitoring knowledge base;

[0058] Figure 3 To monitor the process of the on-duty assistant. DETAILED DESCRIPTION

[0059] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0060] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention, and should not be understood as limiting the present invention.

[0061] In the present invention, terms such as "fixed connection," "connected," and "connection" should be interpreted broadly to mean a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediary. Relevant researchers or technicians in this field may determine the specific meanings of these terms in the present invention based on specific circumstances, and they should not be construed as limitations of the present invention.

[0062] Example:

[0063] like Figure 1-3 As shown, the present invention discloses an intelligent status information analysis system for centralized control station equipment monitoring. This patent belongs to the field of power system and intelligent monitoring technology, specifically to intelligent status information monitoring, analysis and decision support for centralized control station equipment. This technology is mainly used for equipment operation monitoring in power centralized control centers or substations. By intelligently analyzing the status information of the equipment, it provides real-time monitoring, status assessment, alarm processing and decision support functions, thereby improving the automation level and operational reliability of the centralized control station equipment management, and belongs to the category of power system automation and intelligent operation and maintenance management.

[0064] Based on the next-generation centralized control system, it is particularly necessary to develop an AI-powered operator robot for monitoring and analyzing the operating status of substation equipment. This robot will feature intelligent monitoring, intelligent handling, intelligent inspection, intelligent interaction, and intelligent reporting modules, enabling comprehensive real-time monitoring and intelligent analysis of substation operating status. This will effectively improve the health management of substation equipment, reduce failure rates, and enhance overall O&M efficiency. Furthermore, by integrating situational awareness and event identification capabilities, it will significantly enhance the accuracy and response speed of alarm information processing, thereby achieving the goal of an unmanned smart substation.

[0065] 1. The system function display interface includes:

[0066] Intelligent monitoring: including monitoring information standardization, monitoring information labeling, event qualitative classification, event correlation display, system automatic patrol, etc.

[0067] Intelligent handling: Responsible for processing alarm and fault information, including primary and secondary SMS push, intelligent handling of faults and defects, intelligent handling of anomalies, intelligent linkage, etc.

[0068] Intelligent operation: realize the automation and safe operation of equipment, such as automatic generation of remote control tickets, safety and anti-error verification, programmed control, automatic unlocking, automatic hanging and removing of tags, etc.

[0069] Intelligent inspection: including inspection task configuration, automatic inspection, manual inspection, abnormal alarm, inspection report generation and other functions.

[0070] Intelligent duty: including intelligent task scheduling, intelligent reporting, monitoring duty assistant, system operation monitoring, monitoring information overview, etc.

[0071] Intelligent interaction: supports intelligent prompts, intelligent voice navigation, intelligent business Q&A, intelligent outbound calls, real-time call translation, voice message processing, etc.

[0072] Mobile applications: Supports mobile power grid event processing, task viewing, APP fault card, online data sharing, on-site fault acceptance and other functions, improving the flexibility and convenience of mobile operations.

[0073] 2 Monitoring process is the monitoring function module of the present invention, which can be divided into three modules according to the monitoring process: information analysis and management module, event handling and management module and business tracking and control module.

[0074] 2.1 Information Analysis and Management Module

[0075] The information analysis and management module realizes the eventization of five types of alarm signals, and its core part is the power monitoring knowledge base.

[0076] A power monitoring knowledge base is a centralized information resource or data storage used to collect, organize, manage, and analyze monitoring-related data and knowledge. It is typically used to support tasks such as system monitoring, event management, problem diagnosis, and decision-making. The process for establishing a power monitoring knowledge base is as follows: Using technologies such as natural language processing (NLP), automatic speech recognition (ASR), and image recognition, grid monitoring rules, historical simulation cases, and emergency response plans are extracted and converted to form an initial power monitoring knowledge base. Subsequently, machine learning algorithms are used to analyze historical operating data, extracting the implicit knowledge contained within the data and updating the power monitoring knowledge base. This enables the integration, storage, updating, and application of power grid knowledge, providing support for grid trip handling strategies and data retrieval.

[0077] The workflow of the information analysis and management module is as follows:

[0078] ① Obtain main equipment monitoring, auxiliary monitoring and specific monitoring through main and auxiliary joint monitoring.

[0079] Primary equipment monitoring involves critical signals such as accident tripping, abnormality, position change, over-limit, and notification signals. Auxiliary control monitoring encompasses fire protection, environmental monitoring, security, and video monitoring. Specific monitoring includes information on severe weather, power supply, and new substation commissioning plans. Subsequently, the monitoring rule library within the power monitoring knowledge base is utilized to filter out interference, invalid, and associated information, replacing manual information optimization and screening.

[0080] The auxiliary control system collects various signals (fire alarms, dynamic and environmental equipment status signals, security intrusion signals, and video surveillance) through preliminary filtering to remove noise and invalid signals. Signals of the same type are bundled to form a unified alarm event, reducing the processing of redundant signals. The processed signals are then converted into events, combining multiple related signals into a single event. Alarms are then classified based on their severity, urgency, and impact. The system only displays Level 1 auxiliary control alarms, presenting the most important alarm information through a simple interface. This interface allows auxiliary control monitoring personnel to quickly identify the main alarms currently in the system. Clicking on a Level 1 alarm displays detailed information, including the source, related signals involved, and historical occurrences. The system provides alarm history records to help users analyze the root causes and patterns of alarm events, assisting in decision-making and problem resolution. Based on the alarm level and historical information, monitoring personnel are provided with information on appropriate response measures. After processing, alarms are archived and tagged.

[0081] ②Alarm information labeling

[0082] To efficiently organize, retrieve, and analyze alert information, thereby improving operational efficiency, reducing response time, and enabling deeper data analysis, we categorize and manage alert information using tags. First, we predefine a set of standard tags based on business requirements. Then, we leverage machine learning or rule-based algorithms to automatically assign tags based on alert content. Finally, we integrate tagging functionality into existing monitoring or operations platforms.

[0083] ③ Monitoring information event

[0084] During system operation, this data is collected in real time and input into the analysis system. The system performs preliminary screening and preprocessing of alarm and measurement data to remove noise and irrelevant information. Subsequently, based on the grid topology and event knowledge base, the system employs a real-time inference algorithm for comprehensive analysis. This inference process utilizes a rule engine, causal analysis, or machine learning models to correlate multiple data sets to infer specific events. The inference results are then converted into event-based alarm information. Unlike traditional single alarms, event-based alarm information integrates multiple related alarms and measurement data to generate a complete description of the event. This alarm format is more aligned with actual O&M needs, helping grid operators quickly understand the event context and take action. Through this process, the system can not only identify fault events within the grid, but also detect equipment anomalies, defects, and grid operation events. Ultimately, this intelligent, real-time alarm generation mechanism significantly improves the safety and reliability of grid operations.

[0085] The system analyzes and judges 9 types of power grid fault events of 14 types of equipment: including line faults, busbar faults, transformer faults, capacitor / reactor faults, standby automatic transfer / internal bridge switch faults, fault separation / low-frequency load reduction device faults, grounding transformer / used transformer faults, load transfer device faults, and bus splitter / bus coupler bay switch faults.

[0086] Based on the analysis results of graded events, general events, medium events, and serious events are automatically generated, and the handling strategies of different levels of events are intelligently matched to carry out emergency repairs.

[0087] The system analyzes and categorizes various faults, anomalies, and operational behaviors in power grid operations to classify them. The system uses real-time collected alarm data, measurement data, and topological relationships, combined with an event knowledge base, for comprehensive analysis and judgment. This process utilizes intelligent algorithms (such as rule engines, causal reasoning, and machine learning models) to identify the nature, scope, and potential impact of events. It then categorizes events into general, moderate, and severe categories based on predefined classification rules.

[0088] General events: Local equipment abnormalities that do not trigger protection actions and can be automatically restored or quickly handled manually. Usually only involve a single device or a single power supply line, including: single device alarms, local parameter abnormalities, and auxiliary system failures. The above abnormalities do not cause systemic risks.

[0089] Moderate event: Regional impact, requiring the activation of emergency plans, with the risk of chain reactions, including: failure of key equipment, changes in network structure, and deterioration of system parameters. However, these events can be effectively controlled by activating corresponding special disposal plans and manual adjustments to operating modes by dispatchers.

[0090] Serious incidents: System stability is damaged, and there is a risk of large-scale power outages, including: main grid failure, stability damage, and catastrophic accidents.

[0091] The system intelligently matches predefined event handling strategies based on the incident level. Based on the incident knowledge base and historical experience, the handling strategies cover emergency repair processes, resource scheduling, and safety measures for incidents of different levels.

[0092] General incidents: Use conventional handling procedures, such as dispatching a single-person inspection team to investigate, or remotely controlling the fault.

[0093] Moderate incidents: Coordinate multiple departments, deploy maintenance resources within the area, and activate backup power or load transfer solutions to ensure that the impact of the fault on users is minimized.

[0094] Serious incidents: Activate the emergency plan, quickly assemble a repair team, and collaborate with the dispatch center, relevant government departments, etc., while issuing power outage notices and restoration progress reports.

[0095] Under the guidance of the event handling strategy, the system performs the following operations:

[0096] Fault location: Based on real-time data and topological relationships, accurately locate the fault point and reduce troubleshooting time.

[0097] Emergency repair resource scheduling: Automatically allocate emergency repair personnel, vehicles, and equipment based on the incident level and emergency repair needs.

[0098] Dynamic instruction generation: Generate specific operation instructions and send them to relevant personnel or equipment (such as remote operation of circuit breakers or disconnect switches).

[0099] Task tracking and feedback: Monitor the repair progress in real time and dynamically adjust the handling strategy according to the situation.

[0100] After troubleshooting is complete, the system records and analyzes the repair process, updating the event knowledge base and handling strategy library. Through continuous learning and optimization of event data, the system's automation level and event handling efficiency are gradually improved.

[0101] We design two monitoring interfaces:

[0102] ① For management personnel. A monitoring and management cockpit with a clear overview of the monitoring situation is built to display the real-time monitoring status to monitoring and management personnel. It provides monitoring items such as main equipment alarms, power grid operation status, remote control operation, information acceptance, defect statistics, load rate statistics, monitoring scale, duty logs, maintenance, and operation, so that they can grasp the overall monitoring status of the centralized control station.

[0103] ② For monitors. Build a smart monitoring duty station with one-screen control of monitoring indicators. This platform displays the status and measurement values of all monitoring equipment to monitor duty personnel in real time. It also provides monitoring indicators such as main equipment alarms, auxiliary equipment alarms, suppression of card placement operations, data label alarm windows, operation and maintenance, on-duty status, and limit-exceeding information, so that they can understand the real-time monitoring status of the centralized control station.

[0104] 2.2 Event Handling Management Module

[0105] First, data input sources primarily include alarm information, risk forecasts, human experience, and data analysis. By actively sensing this information, the system can obtain real-time information on equipment operating status, potential risk points, and historical operational experience, providing a data foundation for intelligent event processing. In the intelligent event judgment phase, the system uses information correlation and an event rule library to identify different event categories based on the collected information. These events are then grouped into event pools, categorized as defect events, failure events, abnormal events, and operational events. Information correlation involves analyzing information from different sources to identify hidden faults or potential problems. The event rule library is a standardized knowledge base used by the system to judge and handle events, using predefined rules to determine the nature and severity of events. During the event analysis phase, the system further processes events through impact analysis, trend warnings, and action decisions. Impact analysis helps identify the range of equipment or business areas that an event may affect. Trend warnings help predict the potential development of an event in advance, facilitating preventive measures. Event action decisions, based on the action decision library, provide the optimal response plan for the event, ensuring timely and effective resolution. The disposal decision library supports event processing and uses the processed events to further optimize the system's rule library. That is, it continuously optimizes and updates its own judgment and disposal rules through processed events to improve the overall system's intelligence level and response capabilities.

[0106] After an incident occurs, intelligent information push will be enabled, including a primary SMS push, a secondary SMS push, and a decision-making push. For the primary SMS push, within 1 minute of the incident, the system will automatically push the first SMS with detailed information about the equipment failure, or automatically call the relevant units to quickly respond to the incident. For the secondary SMS push, after manual confirmation of the equipment failure event, within 5 minutes of the incident, the system will automatically push a second SMS with a briefing of the corresponding fault recording. The system will also automatically load a graphical representation of the fault recording for further analysis. For the decision-making push, the voice assistant automatically wakes up and announces the details of the incident after the incident. The system automatically notifies the substation operation and maintenance personnel to conduct an on-site inspection, receives and records the inspection results, and automatically pushes monitoring and disposal suggestions based on intelligent alarm events and a database of typical equipment fault monitoring and decision-making experts to help relevant personnel make timely decisions and take appropriate measures.

[0107] This module also features intelligent operation capabilities. First, the system interacts with the networked command system to receive operational instructions from the dispatch center. These dispatch instructions are automatically processed by the system and used to generate a remote control operation ticket, which details the specific content and sequence of each operation step. The generated remote control ticket then enters a review process, where it is reviewed by relevant management personnel or the system to ensure that all operations listed in the ticket comply with safety and operating procedures.

[0108] After approval, the system issues operational instructions through a networked command system, combined with an automated telephone notification mechanism to notify on-site executors of the planned operation. This process ensures that on-site personnel clearly understand the required operations and steps, effectively reducing errors in information transmission. This entire process makes the transmission of operational instructions more efficient and accurate, significantly reducing the complexity of manual operation ticket generation, assisting supervisors in completing ticketing tasks, and improving the security and traceability of dispatch instruction execution.

[0109] In addition, the system provides a series of functions to assist monitors and simplify various tasks during actual operation. These functions include equipment unlocking, which manages the locked status of equipment to ensure that the equipment is unlocked before operation or properly locked during maintenance; and maintenance tagging, which helps monitors clearly identify the equipment under maintenance during maintenance to prevent misoperation.

[0110] 2.3 Business tracking and control module

[0111] When an alarm event is triggered, the business tracking and control module performs a pre- and post-operation status check of the equipment. This means verifying the equipment status before and after the operation to ensure that all operations are carried out smoothly and as planned without any anomalies. This feature significantly improves the safety and reliability of equipment operations. The system also features a historical statistics module that records detailed information about each operation, including the content, time, and operator. This historical record provides monitors with ample data support for future fault analysis and operational optimization. Furthermore, this statistical data supports decision-making by managers, allowing them to evaluate and improve operational processes, further enhancing the safety and efficiency of system operations.

[0112] 3 Accessibility

[0113] 3.1 Intelligent notification of alarm information

[0114] To address the unique demands of the night shift, the system has designed a comprehensive intelligent alarm management mechanism. During the night shift, the system automatically adjusts alarm handling based on the level and priority of the comprehensive intelligent alarm events. For less serious equipment events, the system suspends voice alarms and phone notifications to reduce interruptions to night shift monitors in low-risk situations, allowing them to focus on handling urgent and important incidents. Suspended incidents are accumulated in a list and centrally handled by day shift personnel during the day shift. This strategy not only improves the efficiency of night shift monitoring but also effectively leverages the time difference between night and day shifts to distribute workload efficiently.

[0115] The system schedules and handles various tasks through task pool management. The task pool aggregates all alarm events, signal records, and other operations and maintenance tasks that require processing. The task pool's management mechanism enables unified scheduling of all tasks, determining the order in which they are processed by setting task priorities. Task priorities are arranged based on factors such as alarm level, scope of impact, and urgency, ensuring that the most important and urgent tasks are handled first. This allows monitors, whether working the night or day shift, to prioritize tasks individually, ensuring the safe and stable operation of the power grid system to the greatest extent possible.

[0116] The intelligent push function is a key part of the entire task management process. Tasks scheduled in the task pool are intelligently pushed to relevant personnel or system modules based on their priority, ensuring that tasks are processed as planned. Intelligent push not only ensures the accurate transmission of information and tasks, but also reduces the complexity and potential delays of manual task assignment. Through the system's intelligent push, monitors and operation and maintenance personnel can receive appropriate task reminders at the right time, thereby maintaining an efficient and smooth workflow.

[0117] The specific contents of the minor device event list are shown in Table 1 below;

[0118] Table 1:

[0119]

[0120] Minor equipment incidents are not listed one by one.

[0121] 3.2 Monitoring Duty Assistant

[0122] The system provides on-duty personnel with basic functions such as shift scheduling, shift handover management, and duty log management. It integrates intelligent equipment monitoring, event handling, equipment operation, and monitoring information linkage. Shift scheduling helps optimize staff work schedules; shift handover management automatically generates reports to ensure smooth information flow; and duty log management automatically records operations and events to ensure completeness and accuracy. The system integrates equipment monitoring, fault handling, and operation, providing real-time alarm information and action guides to assist on-duty personnel in completing their tasks according to optimal procedures. The system also supports remote equipment operation, automatic log generation, and defect reporting, significantly reducing workload and improving efficiency and safety.

[0123] 3.3 Intelligent reporting tools and monitoring data analysis

[0124] Supports automatic generation, online editing, online preview, and export of statistical reports such as daily business reports (daily, weekly, monthly, and annual reports), information analysis reports, incident handling reports, and special analysis reports. Create custom report templates, support template modification, import, export, and content visualization, and customize query conditions.

[0125] Through real-time equipment data analysis, equipment historical data analysis, and power grid event analysis, we can conduct in-depth operational information mining on equipment monitoring data, maintenance data, defect data, etc., greatly improving the intelligence level of monitoring information analysis and providing more effective and practical technical support for centralized monitoring operations.

[0126] 3.4 Intelligent Voice Assistant

[0127] Intelligent interaction based on the voice platform accurately identifies the dispatcher's intentions and improves the efficiency of human-computer interaction; the intelligent decision-making brain assists in monitoring business development and improves business processing efficiency; intelligent documents and customized reports reduce the pressure of daily dispatch work; independent CS architecture micro-applications and multi-source system integration promote the intensification of daily dispatch work.

[0128] Preferably, in step A of the combined primary and secondary monitoring, the auxiliary monitoring includes a camera and an alarm installed within the power control cabinet, and the primary equipment monitoring also includes a monitoring current transformer and a monitoring voltage transformer installed inside or outside the power control cabinet. The monitoring current transformer is mounted on the ground or neutral wire of the power control cabinet and is connected to the system. When an animal, such as a mouse, enters the cabinet, it may cause a short-term current to flow in the ground or neutral wire of the power control cabinet. The power monitoring knowledge base contains corresponding comparison data for comparing whether an animal has entered, and can also be combined with camera data to jointly identify faults. The ground or neutral wire of the power control cabinet is also separately grounded via a monitoring voltage transformer. The monitoring voltage transformer, camera, and alarm are connected to the system. The camera is used to monitor whether a person has entered the power control cabinet, and the monitoring voltage transformer is used to monitor whether the ground or neutral wire is energized. The power monitoring knowledge base contains corresponding comparison data. When a person enters and the ground or neutral wire is energized, the event handling management module immediately controls the alarm to sound an alarm, alerting the entrant. The camera is directly facing the door of the power control cabinet. Once, a power station experienced a malfunction. When maintenance personnel entered to repair the system, they found that the neutral wire was still energized, even though they had disconnected the circuit breaker and disconnector. This resulted in the maintenance personnel's immediate death and a serious safety incident. When power equipment malfunctions or is improperly operated, the ground or neutral wire may become energized. The measures described above can mitigate the risk of accidents (see Figure omitted). The monitoring current transformer and monitoring voltage transformer described here are not the main current transformer and main voltage transformer in the three-phase circuit of the power control cabinet.

[0129] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent modifications or substitutions to the transaction features between nodes without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. An intelligent status information analysis system for centralized control station equipment monitoring, including an information analysis management module and an event handling management module, characterized in that: It also includes a business tracking and control module. The information analysis and management module is used to convert alarm signals into events. The information analysis and management module is provided with a power monitoring knowledge base. The power monitoring knowledge base is used to collect, organize, manage and analyze data and knowledge related to monitoring, and support system monitoring, event management, problem diagnosis and decision making; The workflow of the information analysis and management module is as follows: S1: Acquire the main equipment monitoring signal, auxiliary monitoring signal, and specific monitoring signal through the main and auxiliary joint monitoring, and use the monitoring rule library in the power monitoring knowledge base to filter out interference information, invalid information, and accompanying information; S2: Performs preliminary filtering on various signals collected by the auxiliary control system to remove noise and invalid signals, bundles signals of the same type to form a unified alarm event, and converts the processed signals into events, combining multiple related signals into one event. Alarms are then graded based on their severity, urgency, and impact range. S3: Classify and manage alarm information through tags; Perform preliminary screening and preprocessing of alarm and measurement data to remove noise and irrelevant information. Based on the grid topology and event knowledge base, a real-time reasoning algorithm is used for comprehensive analysis. The reasoning results are further converted into event-based alarm information. Event-based alarm information integrates relevant alarm and measurement data to generate a complete description of the event. The workflow of the event handling management module is as follows: S4: Data input sources include alarm information, risk predictions, manual experience, and data analysis. By actively sensing this information, we can obtain real-time equipment operating status, risk points, and historical operating experience. S5: By correlating information and combining it with the event rule base, the collected information is judged and events of various categories are identified; S6: Further handle the incident through impact analysis, trend warning, and disposal decision-making; When an alarm event is triggered, the business tracking and control module performs a status check before and after the device operation, that is, it checks the status of the device before and after the operation is performed.

2. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 1, characterized in that: The process of establishing the power monitoring knowledge base is as follows: Through natural language processing (NLP), automatic speech recognition (ASR), and image recognition technologies, power grid monitoring rules, historical simulation cases, and accident plan rules are extracted and converted to form an initial power monitoring knowledge base. Use machine learning algorithms to analyze historical operating data, extract the implicit knowledge contained in the data, and update the power monitoring knowledge base.

3. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 1, characterized in that: In step S1, only the auxiliary control level 1 alarm is displayed, and the level 1 alarm information is displayed through the interface; When the user clicks on a level one alarm, detailed information is displayed, including the source of the level one alarm, the related signals involved, and historical occurrences; alarm history records are provided to help users analyze the root causes and patterns of alarm events, assist in decision-making and problem handling; and response measures are provided to monitoring personnel based on the alarm level and historical information.

4. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 1, characterized in that: In step S2, classifying and managing the alarm information by means of tags includes: Step S21: pre-define a set of standard tags according to business requirements; Step S22: Automatically assign labels based on the alarm content using machine learning or rule-based algorithms; Step S23: Add a tagging function to the existing monitoring or operation and maintenance platform.

5. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 1 is characterized by: In step S3, a comprehensive analysis is performed using a real-time reasoning algorithm, including using a rule engine, causal analysis, or machine learning model to associate multiple data to infer specific events.

6. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 5, characterized in that: Step S3 also includes: identifying fault events in the power grid, and detecting equipment anomalies, equipment defects, and power grid operation events; Analyze and identify fault events in the power grid, and classify them into line faults, busbar faults, transformer faults, capacitor / reactor faults, standby automatic switching / internal bridge switch faults, faulty load shedding / low-frequency load reduction device faults, grounded transformer / used transformer faults, load transfer device faults, and busbar / busbar bay switch faults; Based on the analysis results of graded events, general events, medium events, and serious events are automatically generated, and the event handling strategies for each level are intelligently matched to carry out emergency repairs.

7. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 6, characterized in that: Analyze and classify various faults, anomalies, or operational behaviors in power grid operations to classify events. Comprehensively analyze and judge events based on real-time collected alarm data, measurement data, and topological relationships, combined with an event knowledge base. Use intelligent algorithms to identify the nature, scope, and potential impact of events, and classify them into general, moderate, and severe events based on pre-defined classification rules. General incidents: The impact only affects a single device or a local area and does not cause systemic risks; Moderate incident: The impact involves multiple devices or areas, but is highly controllable; Serious incidents: Possing a major threat to the safe and stable operation of the power grid, causing large-scale power outages or equipment damage.

8. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 7, characterized in that: Match predefined event handling strategies based on the event level; Develop response strategies based on the event knowledge base and historical experience, covering emergency repair processes, resource scheduling, and safety measures for incidents of all levels; General incidents: Use the conventional handling process, which includes dispatching a single-person inspection team to investigate or remotely controlling the fault. Medium incidents: Coordinate multiple departments, deploy maintenance resources within the area, and activate backup power or load transfer solutions to minimize the impact of the fault on users. Serious incidents: Activate the emergency plan, quickly assemble a repair team, and collaborate with the dispatch center and relevant government departments. At the same time, issue a power outage notice and restoration progress report.

9. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 8, characterized in that: Under the guidance of the incident handling strategy, perform the following operations: Accurately locate fault points based on real-time data and topological relationships; Allocate emergency repair personnel, vehicles, and equipment based on the incident level and repair needs; Generate specific operation instructions and send them to relevant personnel or equipment; Monitor the repair progress in real time and dynamically adjust the handling strategy according to the situation.

10. The intelligent status information analysis system for centralized control station equipment monitoring according to claim 1, characterized in that: Step S5 also includes: classifying these events into an event pool, and classifying them into defect events, failure events, abnormal events, and operation events; information correlation, analyzing information from different sources to identify hidden faults or potential problems; and an event rule base, a standardized knowledge base for judging and processing events, which determines the nature and level of events through predefined rules. In step S6, the impact scope analysis of the event is used to understand the equipment scope or business scope affected by the event; trend warning is used to judge the development trend of the event in advance and take preventive measures; event handling decisions are based on the handling decision library, providing the best handling plan for the event to ensure timely and effective problem resolution; the handling decision library supports event handling; the processed events are used to further optimize the handling decision library, that is, the judgment and handling rules of the handling decision library are continuously optimized and updated through the processed events to improve the intelligence level and response capabilities.

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