Transformer substation comprehensive monitoring management platform and control method

Abstract

The invention discloses a transformer substation comprehensive monitoring management platform and a control method, and the method comprises the steps: collecting equipment operation data and environment parameters in a transformer substation in real time, and carrying out the preprocessing of the collected data; environmental parameters inside and around the transformer substation are monitored in real time, and the environmental parameters are adjusted to optimize equipment operation conditions; analyzing real-time data, identifying potential abnormity and automatically responding; according to the real-time data of the equipment, running an adaptive optimization algorithm to obtain an adjustment signal to optimize the running state of the equipment; comprehensively analyzing operation data and historical data of the transformer substation, and generating decision support suggestions and operation strategies; collecting equipment energy efficiency data in real time, analyzing the energy efficiency data through a dynamic energy efficiency optimization algorithm, and generating an energy efficiency optimization strategy; and combining positioning data of the RFID technology and the UWB technology, analyzing behavior patterns of personnel in the transformer substation, identifying potential safety hazards according to the behavior patterns of the personnel, and giving an alarm. According to the fault management mechanism, the reliability of the substation equipment is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of substation monitoring, and in particular to a substation comprehensive monitoring management platform and a control method. Background Art

[0002] Smart substations are an important foundation and support for strong smart grids. They are the source of data collection and command execution units for grid operation. They are closely linked to other links and are an important guarantee for the safe, high-quality and economical operation of a unified strong smart grid. They are also the main embodiment of the realization of the automation characteristics of smart grids. Therefore, strengthening the construction of smart substations is crucial for State Grid Corporation to strengthen the construction of smart grids. According to the definition of the "Technical Guidelines for Smart Substations", smart substations are substations that use advanced, reliable, integrated, low-carbon and environmentally friendly smart equipment, with full station information digitization, communication platform networking and information sharing standardization as basic requirements, automatically completing basic functions such as information collection, measurement, control, protection, metering and monitoring, and can support advanced functions such as real-time automatic control of the power grid, intelligent regulation, online analysis and decision-making, and collaborative interaction as needed.

[0003] Traditional substation monitoring systems usually rely on static fault detection mechanisms, such as alarm systems based on fixed thresholds, which are often unable to effectively cope with complex and dynamic equipment failure situations.

[0004] Existing energy efficiency management systems usually use static methods to monitor and analyze energy efficiency and are unable to adjust the operating status of equipment in real time to cope with changing load and environmental conditions.

[0005] The prior art document (CN117578723A) discloses an intelligent monitoring system for a substation. The system in the document mainly relies on real-time data from sensors to trigger early warning or automatic control. There are limitations in data monitoring and anomaly detection. It lacks the ability to comprehensively analyze multi-dimensional data and is difficult to effectively identify complex or potential hidden dangers. For example, for situations such as fire, gas leakage, or abnormal temperature and humidity, although an alarm can be triggered by a single sensor, the lack of intelligent prediction function based on multi-dimensional data may lead to untimely or missed warnings. The intelligent control subsystem of the document is insufficient in dynamic optimization and energy efficiency management. It only supports equipment control based on a single rule, such as temperature adjustment, humidity control, and fan start and stop. It lacks dynamic optimization capabilities and cannot adaptively adjust the operation strategy according to the real-time operating status of the equipment and environmental changes, making it difficult to achieve optimal management of energy efficiency, resulting in high operating costs. Moreover, the document only relies on access control systems and infrared cameras to restrict personnel permissions and monitor areas, and lacks real-time tracking and personnel behavior analysis functions. In complex substation operation scenarios, it is difficult to identify abnormal personnel behavior or potential risks by relying solely on regional authority judgment, which may lead to safety hazards that fail to be warned in time. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a comprehensive monitoring and management platform for substations, which solves the problem that traditional substation monitoring systems usually rely on static fault detection mechanisms, such as alarm systems based on fixed thresholds, which are often unable to effectively deal with complex and dynamic equipment failure situations.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention discloses a control method for a substation integrated monitoring and management platform, comprising the following steps: Collect equipment operation data and environmental parameters in the substation in real time and pre-process the collected data; Real-time monitoring of environmental parameters inside and around the substation, and adjustment of environmental parameters to optimize equipment operating conditions; Analyze real-time data using deep learning-based anomaly detection algorithms to identify potential anomalies and automatically respond; According to the real-time data of the equipment, the adaptive optimization algorithm is run to obtain the adjustment signal to optimize the operating status of the equipment; Comprehensively analyze substation operation data and historical data to generate decision support recommendations and operation strategies; Real-time monitoring of equipment energy efficiency, collection of energy efficiency data, analysis of energy efficiency data through dynamic energy efficiency optimization algorithms, and generation of energy efficiency improvement suggestions and optimization strategies; Through the use of RFID technology and UWB technology to locate personnel, combined with the positioning data of RFID technology and UWB technology, the behavior patterns of personnel in the substation are analyzed, and safety hazards are identified and alarms are issued based on the personnel behavior patterns.

[0008] Preferably, adjusting the environmental parameters to optimize the equipment operating conditions is specifically as follows: The temperature control system automatically adjusts the temperature inside the substation according to the real-time environmental data collected by the high-precision temperature sensor to keep it within the preset temperature range. The humidity control system automatically adjusts the humidity level inside the substation according to the data of the humidity sensor to keep the ambient humidity within the set range to avoid excessive humidity causing reduced insulation or corrosion of equipment. The air purification system automatically purifies harmful gases in the substation environment according to the data of the air quality sensor to maintain air quality.

[0009] Preferably, identifying potential anomalies through an anomaly detection algorithm based on deep learning and automatically responding is specifically: Use multi-layer neural networks to train and learn equipment operation data, build a model of normal operation status, and detect abnormal situations by comparing real-time data with model predictions; Use data mining and pattern recognition technology to analyze the root causes of abnormal situations, generate analysis reports, and output corresponding solutions or repair measures based on the analysis reports; Preferably, the operation state of the device is adjusted using the adaptive optimization algorithm as follows: The adaptive optimization algorithm is realized by the collaboration of the adaptive adjustment algorithm and the real-time feedback mechanism. The adaptive adjustment algorithm continuously monitors the operating parameters and performance indicators of the equipment, calculates the operating efficiency and optimization requirements of the equipment in real time, and uses the calculation results as adjustment signals. The real-time feedback mechanism collects the operating data and performance feedback of the equipment after running the adaptive adjustment algorithm, and corrects the adaptive adjustment algorithm. Preferably, the dynamic energy efficiency optimization algorithm can be expressed by formula (1), as follows: (1) in, is the learning rate, is the energy efficiency optimization objective function, is the gradient of the energy efficiency optimization objective function with respect to the equipment load, For the Devices at time Output power; The energy efficiency optimization objective function can be expressed by formula (2), as follows: (2) Among them, λ is a weight parameter used to balance the relationship between energy efficiency maximization and power consumption optimization. For the Devices at time of input power.

[0010] Preferably, the potential safety hazards and alarms are: RFID technology is used to track personnel locations in real time, and UWB technology is used to accurately calculate personnel locations, providing millimeter-level positioning accuracy. The behavior pattern recognition algorithm is used to analyze personnel activity patterns in the substation. The algorithm combines RFID and UWB positioning data to model and analyze personnel movement trajectories and activity patterns, identify personnel routine activity patterns and abnormal behaviors, identify potential safety hazards based on personnel behavior patterns, and issue alarms.

[0011] The second aspect of the present invention discloses a substation integrated monitoring and management platform, which runs the above control method, including: an intelligent environment monitoring and regulation module, an abnormality detection and response module, an adaptive dynamic optimization and control module, a comprehensive decision support and analysis module, an energy efficiency monitoring and dynamic optimization module, and a personnel automatic tracking and positioning module; Intelligent environmental monitoring and regulation module, used to monitor and adjust environmental parameters in the substation in real time to optimize equipment operating conditions; The anomaly detection and response module is connected to the intelligent environment monitoring and adjustment module to detect abnormal conditions of equipment based on deep learning and intelligent algorithms and automatically respond; The adaptive dynamic optimization and control module is connected to the anomaly detection and response module to dynamically optimize the operating status of the equipment based on real-time data and prediction results; The comprehensive decision support and analysis module is connected with the adaptive dynamic optimization and control module to comprehensively analyze the substation operation data and generate decision support suggestions; Energy efficiency monitoring and dynamic optimization module, connected to the comprehensive decision support and analysis module, is used to monitor the energy efficiency of equipment in real time and generate optimization strategies; The personnel automatic tracking and positioning module is connected to the energy efficiency monitoring and dynamic optimization module to track and locate the personnel in the substation in real time to improve safety management and work efficiency.

[0012] Preferably, the environment monitoring unit of the intelligent environment monitoring and regulation module comprises: an environment monitoring unit and an environment regulation control unit; Among them, the environmental monitoring unit includes: high-precision temperature sensor, humidity sensor and air quality sensor; the environmental adjustment control unit includes: temperature control system, humidity control system, air purification system; High-precision temperature sensors for monitoring the temperature inside and around the substation; Humidity sensors to monitor humidity levels inside the substation; Air quality sensors to monitor the concentration of harmful gases in the substation environment; Temperature control system, used to automatically adjust the temperature inside the substation based on real-time environmental data; Humidity control system, used to automatically adjust the humidity inside the substation based on real-time data; Air purification system, used to automatically purify harmful gases to maintain air quality; The high-precision temperature sensor is connected to the temperature control system, the humidity sensor is connected to the humidity control system, and the air quality sensor is connected to the air purification system.

[0013] Preferably, the anomaly detection and response module comprises: an anomaly detection unit and an automatic response unit; Among them, the abnormality detection unit includes: an abnormality detection system and an abnormal event analysis system; the automatic response unit includes: an automatic alarm generation system and an automatic response control system; Anomaly detection systems, which analyze real-time data to identify unusual patterns; Abnormal event analysis system, used to deeply analyze detected abnormalities and generate corresponding reports; Automatic alert generation system for automatically generating alerts when anomalies are detected; Automated response control system to automatically initiate repair procedures based on abnormal conditions; The anomaly detection system is connected to the abnormal event analysis system, the automatic alarm generation system, and the automated response control system.

[0014] Preferably, the adaptive dynamic optimization and control module is internally provided with a dynamic control interface including: Real-time data interface, used to receive and process real-time data; An optimization control signal generating unit, used for sending an adjustment signal to the device to optimize the operating state; The real-time data interface is connected to the optimization control signal generating unit.

[0015] The present invention provides a substation integrated monitoring and management platform, which has the following beneficial effects: 1. Through the integrated intelligent fault prediction and self-repair module, the platform can predict potential equipment failures in real time based on deep learning algorithms, and automatically generate and implement repair strategies. This advanced fault management mechanism significantly improves the reliability of substation equipment, reduces downtime and the need for manual intervention, thereby improving the overall operating efficiency and safety of the substation.

[0016] 2. The present invention adopts a dynamic optimization and control module and an energy efficiency management and optimization module. The platform can analyze the equipment operating status and energy efficiency data in real time, and automatically adjust the equipment operating parameters to achieve the optimal operating state. This dynamic optimization not only improves the equipment's operating efficiency, but also optimizes energy consumption, reduces operating costs, and promotes efficient use of resources.

[0017] 3. The decision support system of the present invention combines multi-level decision models and historical data analysis, and can provide managers with accurate decision-making suggestions and operation strategies. This comprehensive decision support capability helps to improve the scientific nature and pertinence of decisions, enhance the safety management and emergency response capabilities of substations, and at the same time, the automatic personnel tracking and safety management functions further strengthen the real-time monitoring of personnel safety and reduce potential safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 This is a flow chart of the control method of the substation integrated monitoring and management platform of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1:

[0021] The embodiment of the present invention provides a control method for a substation integrated monitoring and management platform, such as Figure 2 As shown, the following steps are included: Real-time collection of equipment operation data and environmental parameters in the substation, including voltage, temperature, humidity, and vibration; Use the advanced data perception and fusion module to pre-process the collected data, including data cleaning, standardization, and multi-dimensional data fusion, to generate a unified data view; use the generated data view to uniformly format and fuse the collected multi-source data, so that subsequent steps can use the data more efficiently and accurately, avoiding problems caused by different data sources, formats, and dimensions; Real-time monitoring of environmental parameters (such as temperature, humidity, and air quality) inside and around the substation, and adjustment of environmental parameters to optimize equipment operating conditions; Specifically, the temperature control system automatically adjusts the temperature inside the substation according to the real-time ambient temperature data to keep it within the preset temperature range, which is generally between -20°C and 50°C, to ensure that the equipment operates under optimal temperature conditions and prevent the equipment from overheating or overcooling. The humidity control system automatically adjusts the humidity level inside the substation according to the humidity data. The system is equipped with a dehumidifier and humidifier, which monitors the humidity changes in real time and automatically adjusts the operation to keep the ambient humidity within the set range to avoid excessive humidity causing reduced insulation or corrosion of the equipment. The humidity setting range is generally between 30% and 70%. The air purification system automatically purifies harmful gases in the substation environment according to the air quality data to maintain air quality, remove harmful gases in the air, such as carbon dioxide, carbon monoxide, hydrogen sulfide, etc., and release fresh air at the same time.

[0022] Analyze real-time data using deep learning-based anomaly detection algorithms to identify potential anomalies and automatically respond; Specifically, a multi-layer neural network is used to train and learn the equipment operation data to establish a model of normal operating status. By comparing real-time data with the model prediction values, possible abnormal behaviors are detected. These abnormal patterns include abnormal fluctuations in equipment operating parameters and deviations from expected operating status.

[0023] Data mining and pattern recognition technology are used to analyze the root causes of abnormal situations and generate analysis reports. The report content covers information such as abnormality type, occurrence time, abnormal impact analysis and possible failure causes. According to the analysis report, the corresponding solutions or repair measures are output, and the preset repair program is automatically started to execute the output repair measures.

[0024] As one of the outstanding substantive features of the present invention and a significant improvement over the prior art, the present invention can comprehensively analyze multi-dimensional equipment and environmental data, identify potential risks using anomaly detection algorithms, and process abnormal situations in real time through automated response mechanisms, significantly improving the predictability and response efficiency of the system. Moreover, the present invention can comprehensively analyze equipment operation data and environmental data, use deep learning technology to identify complex abnormal patterns and generate targeted response measures, avoiding the limitations of the prior art of a single sensor triggering an early warning, and improving the predictability and accuracy of the system.

[0025] According to the real-time data of the equipment, the adaptive optimization algorithm is run to obtain the adjustment signal to optimize the operating status of the equipment; Specifically, the adaptive adjustment algorithm is used to dynamically adjust the optimization strategy according to the real-time status of the device. The algorithm calculates the operating efficiency and optimization requirements of the device in real time by continuously monitoring the operating parameters and performance indicators of the device, such as load, temperature, and power consumption. Based on this data, the algorithm automatically adjusts the optimization strategy to adapt to the current operating status of the device and changes in the external environment. For example, when the load of the device increases, the algorithm automatically adjusts the operating parameters to optimize energy efficiency, and vice versa. The real-time feedback mechanism further optimizes the algorithm adjustment process by closely integrating with the real-time feedback information of the device's operating status. The real-time feedback mechanism collects the operating data and performance feedback of the device after running the adjustment algorithm in real time, and adjusts the optimization strategy based on this feedback information. The feedback data is not only used to correct the prediction and adjustment of the optimization algorithm, but also to update the optimization strategy library in real time to ensure that the optimization measures always adapt to the actual operation of the device.

[0026] As a prominent substantive feature of the present invention and one of the significant advances it brings to the prior art, the present invention realizes dynamic optimization based on real-time data and prediction results, and utilizes an adaptive optimization algorithm combined with a real-time feedback mechanism to dynamically adjust the operating status of the equipment to achieve the goals of efficient equipment operation and energy consumption optimization.

[0027] Comprehensively analyze substation operation data and historical data to generate decision support recommendations and operation strategies; Monitor the energy efficiency of equipment in real time, collect energy efficiency data, solve the energy efficiency target optimization function through dynamic energy efficiency optimization algorithm based on the energy efficiency data, and generate energy efficiency improvement suggestions and optimization strategies; Specifically, the energy efficiency objective function is defined as: (1) in, For the Energy efficiency of equipment, For the Devices at time The output power is in watts (W). For the Devices at time The input power is in watts (W). For the Devices at time The effective energy output contribution value is For all devices at the time Total energy consumption; is the total number of equipment in the substation.

[0028] The energy efficiency optimization objective function is defined as: (2) Among them, λ is a weight parameter, which is used to balance the relationship between energy efficiency maximization and power consumption optimization.

[0029] Dynamic optimization algorithm for adjusting equipment operating parameters: (3) in, is the learning rate, It is the gradient of the energy efficiency optimization objective function with respect to the equipment load. This gradient represents the energy efficiency objective function Total_Efficiency with respect to the equipment output power. The rate of change is used to guide the dynamic optimization algorithm to adjust the operating parameters of the equipment to maximize the overall energy efficiency.

[0030] The dynamic optimization algorithm is an adjustment method based on the energy efficiency optimization objective function. The algorithm calculates the gradient of the objective function and combines the learning rate to dynamically adjust the power of the device, so that the value of the objective function gradually approaches the optimal solution, thereby realizing dynamic energy efficiency optimization of the entire system. The energy efficiency objective function Total_Efficiency is the energy efficiency evaluation function of the entire system, which is used to describe the comprehensive energy efficiency of all devices at a certain moment. The energy efficiency optimization objective function is the objective function used in the dynamic optimization algorithm. It is usually based on the energy efficiency objective function and is optimized to improve the energy efficiency of the entire system. That is, the energy efficiency objective function is a specific form of the energy efficiency optimization objective function, and the energy efficiency optimization objective function is the optimization goal of the algorithm. The two are essentially the same, but dynamic elements such as constraints or weight adjustments may be added to the optimization algorithm.

[0031] RFID technology and UWB technology are used to locate personnel. The positioning data of RFID technology and UWB technology are combined to analyze the behavior patterns of personnel in the substation, identify safety hazards based on the personnel behavior patterns, and issue alarms.

[0032] Specifically, RFID technology is used to track the location of personnel in real time, and UWB technology is used to accurately calculate the location of personnel, providing millimeter-level positioning accuracy. A behavioral pattern recognition algorithm is used in combination with RFID and UWB positioning data to model and analyze the movement trajectory and activity patterns of personnel, identify personnel's regular activity patterns and abnormal behaviors, identify safety hazards based on personnel behavior patterns, and issue alarms.

[0033] As a prominent substantive feature of the present invention and one of the significant advances it brings to the prior art, the present invention achieves high-precision personnel positioning by integrating RFID and UWB technologies, and combines behavioral pattern recognition algorithms to conduct real-time analysis and safety warnings on abnormal personnel behaviors (such as entering restricted areas or staying in dangerous areas for a long time), effectively improving the personnel management level and safety of substations.

[0034] Embodiment 2: As attached Figure 1 As shown, an embodiment of the present invention provides a substation comprehensive monitoring and management platform. The platform runs the control method described in Example 1. The intelligent environment monitoring and adjustment module monitors the temperature, humidity and other environmental parameters in the substation in real time, and automatically adjusts these parameters through the environment adjustment control unit to optimize the operating conditions of the equipment. The abnormal detection and response module uses deep learning and intelligent algorithms to analyze the equipment operation data in real time, automatically detects and responds to abnormal situations, and ensures the stable operation of the equipment. The adaptive dynamic optimization and control module uses an adaptive optimization algorithm based on real-time data and prediction results to dynamically adjust the equipment operation status, and applies the optimization strategy to the equipment in real time through the dynamic control interface. The comprehensive decision support and analysis system is integrated Substation operation data and historical data, using multi-level decision-making models and intelligent analysis engines to generate decision suggestions, improve the scientificity and accuracy of decision-making, energy efficiency monitoring and dynamic optimization module real-time monitoring of equipment energy efficiency, through the dynamic energy efficiency optimization algorithm to generate improvement strategies, optimize energy use, reduce operating costs, personnel automatic tracking and positioning module through the high-precision positioning system to track the location of staff in real time, and through the personnel behavior analysis unit to evaluate their behavior safety, thereby improving the level of safety management and work efficiency, the overall platform through the integration and collaboration of these modules to achieve comprehensive monitoring and intelligent management of the substation operation status, significantly improve the substation operation efficiency, equipment reliability and safety, the platform specifically includes: An intelligent environment monitoring and regulation module, which is used to monitor and regulate the environmental parameters in the substation in real time to optimize the equipment operating conditions. The module includes an environment monitoring unit and an environment regulation control unit; The environment monitoring unit of the intelligent environment monitoring and regulation module includes: High-precision temperature sensors are used to monitor and collect the temperature inside and around the substation; Humidity sensors to monitor humidity levels inside the substation; Air quality sensors are used to monitor the concentration of harmful gases in the substation environment.

[0035] Specifically, the environmental monitoring unit of the intelligent environmental monitoring and regulation module includes multiple high-precision sensors for comprehensively monitoring and optimizing environmental parameters inside and outside the substation. Specifically, the environmental monitoring unit includes high-precision temperature sensors, humidity sensors and air quality sensors. The high-precision temperature sensor is used to monitor the temperature inside the substation and its surrounding areas in real time to ensure that the equipment operates within a preset temperature range, thereby preventing overheating or insufficient cooling from having a negative impact on equipment performance. The humidity sensor is responsible for monitoring the humidity level inside the substation to prevent excessive humidity from causing degradation or corrosion of the insulation performance of electrical equipment. The air quality sensor is used to detect the concentration of harmful gases in the substation environment, such as carbon dioxide, carbon monoxide and hydrogen sulfide, to prevent potential damage to equipment and personnel by harmful gases. The environmental monitoring unit transmits the real-time collected data to the environmental regulation control unit, and automatically adjusts the operation of the environmental regulation control unit through data analysis and processing to maintain the optimal operating conditions of the environment inside the substation, ensuring efficient and stable operation of the equipment and the safety of the staff.

[0036] The environment regulation control unit of the intelligent environment monitoring and regulation module includes: Temperature control system, used to automatically adjust the temperature inside the substation based on real-time environmental data; Humidity control system, used to automatically adjust the humidity inside the substation based on real-time data; Air purification system, used to automatically purify harmful gases to maintain air quality.

[0037] The high-precision temperature sensor is connected to the temperature control system, the humidity sensor is connected to the humidity control system, and the air quality sensor is connected to the air purification system.

[0038] Specifically, the temperature control system automatically adjusts the temperature inside the substation according to the real-time environmental data collected by the high-precision temperature sensor to keep it within the preset temperature range. This system includes efficient air-conditioning equipment and heating devices, which can start or adjust the cooling or heating operation in real time according to the data provided by the high-precision temperature sensor, thereby ensuring that the equipment operates under the optimal temperature conditions and preventing the equipment from overheating or overcooling. The humidity control system automatically adjusts the humidity level inside the substation according to the data of the humidity sensor. The system is equipped with a dehumidifier and humidifier. By monitoring the humidity changes in real time, it automatically adjusts the operation to keep the ambient humidity within the set range to avoid the insulation of the equipment or corrosion problems caused by excessive humidity. The air purification system automatically purifies the harmful gases in the substation environment according to the data of the air quality sensor. The environmental adjustment control unit ensures that the internal environment of the substation is always in the optimal state through close cooperation with the environmental monitoring unit, thereby improving the operating efficiency of the equipment and the safety of the staff.

[0039] An anomaly detection and response module, used to detect abnormal conditions of equipment based on deep learning and intelligent algorithms and automatically respond, the module includes an anomaly detection unit and an automatic response unit; The anomaly detection unit of the anomaly detection and response module includes: Anomaly detection systems, which analyze real-time data to identify unusual patterns; Abnormal event analysis system, used to deeply analyze detected anomalies and generate corresponding reports.

[0040] Specifically, the anomaly detection system adopts an anomaly detection algorithm based on deep learning, which can identify potential abnormal patterns from the real-time data of substation equipment. The abnormal event analysis unit is responsible for in-depth analysis of the identified abnormal situations. The unit uses data mining and pattern recognition technology to analyze the root causes of abnormal data and generate detailed analysis reports to provide a reliable basis for subsequent response measures. In addition, the abnormal event analysis unit can also recommend possible solutions or repair measures based on the analysis results, thereby providing effective decision-making support for operation and maintenance personnel. Through this comprehensive anomaly detection and analysis mechanism, the substation can achieve fast and accurate anomaly identification and response, and improve the reliability of equipment operation and the safety of the system.

[0041] The automatic response unit of the abnormal detection and response module includes: Automatic alert generation system for automatically generating alerts when anomalies are detected; Automated response control system to automatically initiate repair procedures based on abnormal conditions.

[0042] The anomaly detection system is connected to the abnormal event analysis system, the automatic alarm generation system, and the automated response control system.

[0043] Specifically, the automatic alarm generation system is used to automatically generate and send alarms when an abnormality in equipment or system is detected. Based on the output information of the abnormality detection system, the system triggers sound and light alarms and electronic notifications in real time through the integrated alarm system to quickly convey the abnormal situation to the operation and maintenance personnel and the management system. The alarm content includes the abnormality type, occurrence time, impact range and preliminary fault diagnosis information to ensure that relevant personnel can be informed of the abnormal event in time and take appropriate actions. The automated response control system automatically starts the preset repair program based on the output information of the abnormality detection system, including but not limited to the abnormality type, nature and severity of the abnormality. The system includes an automated execution module and a control strategy library, which can select and execute corresponding repair operations according to the nature of the abnormal event, such as adjusting equipment parameters, starting backup equipment, restarting the system or performing fault isolation, etc. The selection and execution process of the repair program is automatically completed by the system according to pre-defined strategies and rules without human intervention, thereby improving the response speed and repair efficiency. Through this automatic alarm generation and response control mechanism, the substation can achieve rapid response and effective handling of abnormal situations, significantly improving the reliability of equipment and the safety of the system.

[0044] The adaptive dynamic optimization and control module has a dynamic control interface inside, which is used to dynamically optimize the operating status of the equipment based on the adaptive optimization algorithm according to real-time data and prediction results; The adaptive optimization algorithm of the adaptive dynamic optimization and control module includes: Adaptive adjustment algorithm, used to dynamically adjust the optimization strategy according to the real-time status of the device; Real-time feedback mechanism, used to adjust optimization strategies based on device feedback information.

[0045] Specifically, the adaptive adjustment algorithm is used to dynamically adjust the optimization strategy according to the real-time status of the equipment. The algorithm continuously monitors the operating parameters and performance indicators of the equipment, such as load, temperature and power consumption, and calculates the operating efficiency and optimization requirements of the equipment in real time. Based on these data, the algorithm automatically adjusts the optimization strategy to adapt to the current operating status of the equipment and changes in the external environment. For example, when the load of the equipment increases, the algorithm automatically adjusts the operating parameters to optimize energy efficiency, and vice versa. The real-time feedback mechanism further optimizes the adjustment process of the algorithm by closely integrating with the real-time feedback information of the equipment's operating status. The mechanism includes a feedback data acquisition unit and a feedback adjustment unit. The former collects the operating data and performance feedback of the equipment after running the adjustment algorithm in real time, and the latter adjusts the optimization strategy based on this feedback information. The feedback data is not only used to correct the prediction and adjustment of the optimization algorithm, but also to update the optimization strategy library in real time to ensure that the optimization measures always adapt to the actual operating conditions of the equipment. Through this comprehensive mechanism of adaptive adjustment and real-time feedback, the substation can achieve dynamic optimization control of the equipment's operating status and significantly improve the operating efficiency and stability of the equipment.

[0046] The dynamic control interface of the adaptive dynamic optimization and control module includes: Real-time data interface, used to receive and process real-time data; The optimization control signal generating unit is used to send an adjustment signal to the device to optimize the operating state.

[0047] Specifically, the optimization control signal generating unit is used to receive real-time data, run an adaptive optimization algorithm based on the real-time data, obtain an adjustment signal, and send the adjustment signal to the device through the optimization control signal generating unit to optimize its operating state. The unit includes a signal generating algorithm and a control signal transmitting module. The signal generating algorithm calculates the adjustment parameters suitable for the device, such as power regulation, load adjustment and operating speed, based on the analysis results and optimization strategy of the adaptive optimization algorithm. The control signal transmitting module sends the generated adjustment signal to the device through the control network to achieve precise control of the device. The optimization control signal generating unit can dynamically adjust the sent control signal according to the real-time feedback of the device to ensure that the equipment always operates in the best state. Through this dynamic control interface, the substation can achieve real-time equipment status adjustment and optimization to improve the overall operating efficiency and stability of the equipment.

[0048] As a prominent substantive feature of the present invention and one of the significant advances it brings to the prior art, the present invention dynamically adjusts the equipment operation strategy through an adaptive dynamic optimization and control module in combination with real-time data and feedback mechanism. It can maintain the equipment operation efficiency and stability when the equipment load changes or the environmental conditions fluctuate, while optimizing energy usage, thus solving the shortcomings of the prior art of single equipment control strategy and lack of energy efficiency optimization.

[0049] A comprehensive decision support and analysis module, which is used to comprehensively analyze the operation data of the substation and generate decision support suggestions. The system includes a multi-level decision model and an intelligent analysis engine; The energy efficiency monitoring and dynamic optimization module is used to monitor the energy efficiency of the equipment in real time and generate optimization strategies through dynamic energy efficiency optimization algorithms to optimize energy usage. The module includes an energy efficiency monitoring unit and a dynamic energy efficiency optimization unit. The energy efficiency monitoring unit is connected to the dynamic energy efficiency optimization unit, and the energy efficiency data of the equipment is monitored and collected in real time through the energy efficiency monitoring unit. The dynamic energy efficiency optimization unit runs the dynamic energy efficiency optimization algorithm based on the energy efficiency data to obtain the optimization strategy.

[0050] The personnel automatic tracking and positioning module is used to track and locate the personnel in the substation in real time to improve safety management and work efficiency. The module includes a positioning unit and a personnel behavior analysis unit.

[0051] The positioning unit of the personnel automatic tracking and positioning module includes: A positioning system based on RFID (Radio Frequency Identification) technology is used to track the location of personnel in the substation in real time; A positioning system based on UWB (Ultra-Wideband) technology to improve positioning accuracy; A safety warning system is provided inside the personnel behavior analysis unit of the personnel automatic tracking and positioning module. The safety warning system adopts a behavior pattern recognition algorithm to analyze the activity patterns of personnel in the substation, identify potential safety hazards based on the personnel behavior patterns, and issue an alarm.

[0052] The positioning system based on RFID technology and the positioning system based on UWB technology are connected to the personnel analysis unit.

[0053] Specifically, the positioning unit based on UWB technology is used to improve positioning accuracy. UWB technology can achieve high-precision indoor positioning by transmitting signals within an ultra-wide frequency band. The unit deploys UWB base stations in the substation and accurately calculates the position of personnel by measuring the arrival time difference of signals, providing millimeter-level positioning accuracy. This high-precision positioning function enables the substation to accurately monitor the specific positions of personnel in complex environments and reduce positioning errors. The safety warning system integrates real-time behavior monitoring and pattern recognition technology. It analyzes the activity patterns of personnel in the substation through behavioral pattern recognition algorithms and can detect abnormal behavior patterns, such as entering restricted areas or staying in dangerous areas. Once a potential safety hazard is detected, the system will automatically generate an alarm and remind relevant personnel and management systems through sound and light warnings or electronic notifications to ensure that safety measures are taken in a timely manner. Through this comprehensive positioning and behavior analysis system, the substation can effectively improve the level of safety management, ensure personnel safety and optimize work efficiency.

[0054] As one of the outstanding essential features of the present invention and the significant progress it brings to the prior art, the personnel automatic tracking and positioning module of the present invention, combined with high-precision positioning and behavior analysis technology, can monitor the position of personnel in real time and identify their behavior patterns, and actively warn of abnormal behavior, effectively making up for the shortcomings of the prior art that only relies on access control and cameras for personnel management, and significantly improving operational safety and management efficiency.

[0055] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for a substation integrated monitoring and management platform, characterized in that: The following steps are involved: Collect equipment operation data and environmental parameters in the substation in real time and pre-process the collected data; Real-time monitoring of environmental parameters inside and around the substation, and adjustment of environmental parameters to optimize equipment operating conditions; Analyze real-time data using deep learning-based anomaly detection algorithms to identify potential anomalies and automatically respond; According to the real-time data of the equipment, the adaptive optimization algorithm is run to obtain the adjustment signal to optimize the operating status of the equipment; Comprehensively analyze substation operation data and historical data to generate decision support recommendations and operation strategies; Real-time monitoring of equipment energy efficiency, collection of energy efficiency data, analysis of energy efficiency data through dynamic energy efficiency optimization algorithms, and generation of energy efficiency improvement suggestions and optimization strategies; Through the use of RFID technology and UWB technology to locate personnel, combined with the positioning data of RFID technology and UWB technology, the behavior patterns of personnel in the substation are analyzed, and safety hazards are identified and alarms are issued based on the personnel behavior patterns.

2. A control method for a substation integrated monitoring and management platform according to claim 1, characterized in that: Adjust environmental parameters to optimize equipment operating conditions: The temperature control system automatically adjusts the temperature inside the substation according to the real-time environmental data collected by the high-precision temperature sensor to keep it within the preset temperature range. The humidity control system automatically adjusts the humidity level inside the substation according to the data of the humidity sensor to keep the ambient humidity within the set range to avoid excessive humidity causing reduced insulation or corrosion of equipment. The air purification system automatically purifies harmful gases in the substation environment according to the data of the air quality sensor to maintain air quality.

3. A control method for a substation integrated monitoring and management platform according to claim 1, characterized in that: Identify potential anomalies and automatically respond through deep learning-based anomaly detection algorithms: Use multi-layer neural networks to train and learn equipment operation data, build a model of normal operation status, and detect abnormal situations by comparing real-time data with model predictions; Use data mining and pattern recognition technology to analyze the root causes of abnormal situations, generate analysis reports, and output corresponding solutions or repair measures based on the analysis reports.

4. A control method for a substation integrated monitoring and management platform according to claim 1, characterized in that: The adaptive optimization algorithm is used to adjust the operating status of the equipment as follows: The adaptive optimization algorithm is implemented through the collaborative efforts of the adaptive adjustment algorithm and the real-time feedback mechanism. The adaptive adjustment algorithm continuously monitors the operating parameters and performance indicators of the equipment, calculates the operating efficiency and optimization requirements of the equipment in real time, and uses the calculation results as adjustment signals. The real-time feedback mechanism collects the operating data and performance feedback of the equipment after running the adaptive adjustment algorithm, and corrects the adaptive adjustment algorithm.

5. A control method for a substation integrated monitoring and management platform according to claim 1, characterized in that: The dynamic energy efficiency optimization algorithm can be expressed by formula (1), as follows: (1) in, is the learning rate, is the energy efficiency optimization objective function, is the gradient of the energy efficiency optimization objective function with respect to the equipment load, For the Devices at time Output power; The energy efficiency optimization objective function can be expressed by formula (2), as follows: (2) Among them, λ is a weight parameter used to balance the relationship between energy efficiency maximization and power consumption optimization. For the Devices at time of input power.

6. A control method for a substation integrated monitoring and management platform according to claim 1, characterized in that: Identify safety risks and issue alerts: RFID technology is used to track the location of personnel in real time, and UWB technology is used to accurately calculate the location of personnel, providing millimeter-level positioning accuracy. A behavioral pattern recognition algorithm is used in combination with RFID and UWB positioning data to model and analyze the movement trajectory and activity pattern of personnel, identify personnel's regular activity patterns and abnormal behaviors, identify safety hazards based on personnel behavior patterns, and issue alarms.

7. A substation integrated monitoring and management platform, running a substation integrated monitoring and management platform control method according to any one of claims 1 to 6, characterized in that: include: Intelligent environment monitoring and regulation module, anomaly detection and response module, adaptive dynamic optimization and control module, comprehensive decision support and analysis module, energy efficiency monitoring and dynamic optimization module, and personnel automatic tracking and positioning module; Intelligent environmental monitoring and regulation module, used to monitor and adjust environmental parameters in the substation in real time to optimize equipment operating conditions; The anomaly detection and response module is connected to the intelligent environment monitoring and adjustment module to detect abnormal conditions of equipment based on deep learning and intelligent algorithms and automatically respond; The adaptive dynamic optimization and control module is connected to the abnormal detection and response module and is used to dynamically optimize the operating status of the equipment based on the adaptive optimization algorithm according to the real-time data and prediction results; The comprehensive decision support and analysis module is connected with the adaptive dynamic optimization and control module to comprehensively analyze the substation operation data and generate decision support suggestions; Energy efficiency monitoring and dynamic optimization module, connected to the comprehensive decision support and analysis module, is used to monitor the energy efficiency of equipment in real time and generate optimization strategies; The personnel automatic tracking and positioning module is connected to the energy efficiency monitoring and dynamic optimization module to track and locate the personnel in the substation in real time to improve safety management and work efficiency.

8. A substation integrated monitoring and management platform according to claim 7, characterized in that: The intelligent environment monitoring and regulation module includes: an environment monitoring unit and an environment regulation control unit; Among them, the environmental monitoring unit includes: high-precision temperature sensor, humidity sensor and air quality sensor; the environmental adjustment control unit includes: temperature control system, humidity control system, air purification system; High-precision temperature sensors for monitoring the temperature inside and around the substation; Humidity sensors to monitor humidity levels inside the substation; Air quality sensors to monitor the concentration of harmful gases in the substation environment; Temperature control system, used to automatically adjust the temperature inside the substation based on real-time environmental data; Humidity control system, used to automatically adjust the humidity inside the substation based on real-time data; Air purification system, used to automatically purify harmful gases to maintain air quality; The high-precision temperature sensor is connected to the temperature control system, the humidity sensor is connected to the humidity control system, and the air quality sensor is connected to the air purification system.

9. A substation integrated monitoring and management platform according to claim 7, characterized in that: The anomaly detection and response module includes: an anomaly detection unit and an automatic response unit; Among them, the abnormal detection unit includes: an abnormal detection system and an abnormal event analysis system; the automatic response unit includes: an automatic alarm generation system and an automatic response control system; Anomaly detection systems, which analyze real-time data to identify unusual patterns; Abnormal event analysis system, used to deeply analyze detected abnormalities and generate corresponding reports; Automatic alert generation system for automatically generating alerts when anomalies are detected; Automated response control system to automatically initiate repair procedures based on abnormal conditions; The anomaly detection system is connected to the abnormal event analysis system, the automatic alarm generation system, and the automated response control system.

10. A substation integrated monitoring and management platform according to claim 7, characterized in that: The adaptive dynamic optimization and control module is internally provided with a dynamic control interface, which includes: Real-time data interface, used to receive and process real-time data; An optimization control signal generating unit, used for sending an adjustment signal to the device to optimize the operating state; The real-time data interface is connected to the optimization control signal generating unit.

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