Power supply equipment operation and maintenance management system based on Internet of Things

Abstract

The invention relates to the technical field of power supply operation and maintenance, and particularly discloses a power supply equipment operation and maintenance management system based on the Internet of Things, which comprises a data acquisition unit, a data analysis unit and a decision execution unit, the data acquisition unit is deployed in a power supply equipment body and a surrounding environment and is used for acquiring equipment state data and environmental parameters in real time; the data analysis unit is in data connection with the data acquisition unit and is used for performing fusion analysis on the equipment state data and the environmental parameters; the decision execution unit is in communication connection with the data analysis unit and is used for generating a hierarchical operation and maintenance instruction according to an analysis result. By constructing an intelligent protection system, the technical limitation of a traditional operation and maintenance system is fundamentally broken through, the problem that the traditional system neglects the external environment is solved, and systematic risk prevention is realized based on hidden danger conduction analysis of power grid topology.

Description

Technical Field

[0001] The present invention relates to the field of power supply operation and maintenance technology, and more particularly to an Internet of Things-based power supply equipment operation and maintenance management system. Background Art

[0002] As a core component of ensuring the stable operation of power systems, the technology behind power supply equipment operation and maintenance is undergoing a profound transformation, shifting from experience-driven to data-driven intelligence. Power supply equipment operation and maintenance is crucial for ensuring the safe operation of power grids. Current mainstream systems primarily generate alarms for abnormalities by setting fixed thresholds for internal electrical parameters (such as voltage, current, and temperature).

[0003] In existing technologies, switchgear protection solutions based on temperature thresholds are usually adopted, but this technology has fundamental limitations. For example, when a coastal substation encounters salt spray corrosion, the internal temperature parameters of the equipment may still be within the normal range, while the copper busbar connections have already caused hidden fractures due to hydrogen sulfide corrosion. This type of equipment failure caused by environmental factors also accounts for a relatively high proportion, but traditional operation and maintenance management methods are completely ineffective in the above situations.

[0004] At the same time, existing technologies lack the ability to analyze the correlation between equipment status and environmental factors. While existing books attempt to incorporate ambient temperature and humidity monitoring, they fail to establish a dynamic correlation model between environmental data and equipment degradation. A more prominent problem is that when a substation detects excessive dust levels, the system cannot automatically identify the associated downstream distribution equipment. This lack of topological risk transmission analysis has led to cases where a single device failure has caused cascading tripping of multiple substations, resulting in direct economic losses.

[0005] In terms of early warning mechanisms, traditional systems use manual handling of system warnings, requiring manual confirmation of alarm information before issuing work orders, resulting in a long average response time for key equipment failures and missing the best maintenance opportunities.

[0006] Therefore, how to provide a power supply equipment operation and maintenance management system based on the Internet of Things is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0007] In view of this, the present invention provides a power supply equipment operation and maintenance management system based on the Internet of Things to solve the problems raised in the above background technology section. The present invention solves the problem that traditional systems ignore the external environment, and realizes systematic risk prevention based on the hidden danger transmission analysis of the power grid topology.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] An operation and maintenance management system for power supply equipment based on the Internet of Things, comprising a data acquisition unit, a data analysis unit and a decision execution unit;

[0010] The data acquisition unit is deployed on the power supply equipment and its surrounding environment to collect equipment status data and environmental parameters in real time;

[0011] The data analysis unit is connected to the data acquisition unit for performing fusion analysis on the equipment status data and environmental parameters;

[0012] The decision execution unit is in communication with the data analysis unit and is used to generate hierarchical operation and maintenance instructions based on the analysis results;

[0013] The data acquisition unit includes an equipment status monitoring module and an environmental parameter monitoring module. The equipment status monitoring module collects the insulation resistance value and partial discharge value of the power supply equipment, and the environmental parameter monitoring module collects the corrosive gas concentration, dust content and electromagnetic interference data around the power supply equipment.

[0014] The data analysis unit includes an environmental degradation assessment subunit and an equipment health fusion subunit. The environmental degradation assessment subunit calculates the equipment casing aging coefficient based on the corrosive gas concentration and dust content. The equipment health fusion subunit compares the real-time insulation resistance value with the historical baseline to generate an insulation health score.

[0015] The decision execution unit includes a three-level warning generation module and a topology linkage module. The three-level warning generation module outputs maintenance instructions based on the coupling result of the equipment casing aging coefficient and the insulation health score. The topology linkage module responds to abnormal signals from target equipment, automatically retrieves related equipment in the power grid topology, and triggers a collaborative diagnosis mechanism.

[0016] The present invention realizes closed-loop management from data collection to execution decision through the collaboration of three units, solves the problem of delayed response of traditional systems, and significantly improves operation and maintenance efficiency.

[0017] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the environmental parameter monitoring module includes a gas sensor array, a laser dust monitor and an electromagnetic interference probe; the gas sensor array is arranged at the ventilation opening of the power supply equipment to detect the concentration of SO2 and H2S; the laser dust monitor is installed above the equipment to quantify the content of airborne particulate matter; the electromagnetic interference probe is attached to the surface of the metal casing of the equipment to monitor the strength of the power frequency magnetic field;

[0018] The gas sensor array, laser dust monitor, and electromagnetic interference probe transmit data to a data analysis unit via LoRa wireless networking. The multi-dimensional environmental monitoring system covers key equipment corrosion areas, improves dust detection accuracy, and provides early warning of salt spray corrosion risks.

[0019] Preferably, in the aforementioned IoT-based power supply equipment operation and maintenance management system, the device health fusion subunit establishes a historical insulation resistance baseline database, storing the daily peak insulation resistance values ​​of the equipment after commissioning. It then calculates the rate of decrease of the current insulation resistance value compared to the same time period of the previous day. When the rate of decrease exceeds 15%, the environmental degradation assessment subunit is activated to perform a coupled analysis and generate a comprehensive environmental-equipment risk level. This invention identifies hidden degradation through dynamic baseline comparison, detecting signs of insulation degradation earlier than the threshold alarm mechanism.

[0020] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the decision logic of the three-level warning generation module includes:

[0021] When the aging coefficient of the equipment shell exceeds the threshold and the insulation health score is ≥70 points, a first-level warning instruction is generated and an equipment cleaning task is pushed;

[0022] When the equipment casing aging coefficient exceeds the limit and the insulation health score is between 50 and 70 points, a second-level warning instruction is generated and a spare parts pre-replacement work order is triggered;

[0023] When the insulation health score is lower than 50 points and the partial discharge volume increases by more than 200%, a level 3 warning instruction is generated and the equipment isolation procedure is initiated.

[0024] The present invention adopts a hierarchical response mechanism to improve the efficiency of maintenance resource allocation and avoid excessive maintenance.

[0025] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the topology linkage module pre-stores a connection relationship map of power grid equipment, including the topological association of transformers, circuit breaker cabinets and relay protection cabinets; when the target transformer is marked as a level 2 or higher warning by the three-level warning generation module, the circuit breaker cabinet and relay protection cabinet directly connected to it are automatically retrieved;

[0026] High-frequency monitoring instructions are sent to related devices to shorten the data sampling interval from the default 10 minutes to 30 seconds. Using the above solution, the present invention effectively improves the success rate of risk conduction blocking and avoids single-point failures causing system crashes.

[0027] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the data analysis unit further includes a hidden danger transmission analysis subunit, and when the dust content in the substation environment exceeds the warning value for three consecutive hours, the hidden danger transmission analysis subunit marks the site as a pollution source node;

[0028] Based on the grid topology, the insulation monitoring threshold of the power distribution cabinet downstream of the pollution source node is dynamically lowered. The present invention establishes a pollution diffusion early warning model to greatly reduce the failure rate of related equipment.

[0029] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the data acquisition unit further includes a node self-test protocol:

[0030] The sensor node sends a heartbeat signal to the gateway every two hours. If the gateway fails to receive the heartbeat signal three times in a row, the sensor node automatically switches to a backup relay node for data transmission. The node self-check protocol improves data transmission reliability in complex electromagnetic environments and avoids monitoring blind spots.

[0031] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the instruction distribution logic of the decision execution unit is:

[0032] The first-level warning instruction is pushed to the on-site mobile inspection terminal through the wireless communication module;

[0033] Secondary warning instructions are synchronously transmitted to the spare parts management system and personnel dispatch server;

[0034] The three-level warning instructions are directly written into the emergency operation queue of the power grid SCADA system and activate the sound and light alarm. The invention significantly improves the speed of multi-level instruction distribution, shortening the response time to major faults.

[0035] Preferably, in the aforementioned IoT-based power supply equipment operation and maintenance management system, the data analysis unit includes a meteorological data access interface to obtain real-time rainfall and wind speed data from the Meteorological Bureau API. When the forecast indicates continuous heavy rain, a 1.2x risk weighting factor is applied to the insulation health score of outdoor equipment. This invention can accurately predict extreme weather failures and enable flood control measures to be initiated 24 hours in advance.

[0036] Preferably, in the above-mentioned power supply equipment operation and maintenance management system based on the Internet of Things, the equipment status monitoring module also includes a vibration analysis component, which collects the core vibration spectrum through a vibration sensor attached to the transformer casing; when it is detected that the energy in the 100-150Hz frequency band increases by more than 40% compared with the baseline value, the partial discharge monitoring module is triggered to perform directional detection; the vibration analysis component improves the recognition rate of transformer mechanical faults, avoiding burning accidents caused by loose core.

[0037] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides an IoT-based power supply equipment operation and maintenance management system, which has the following technical effects:

[0038] This invention fundamentally overcomes the technical limitations of traditional operation and maintenance systems by building an intelligent protection system. Its core innovation lies in incorporating environmental hazards such as corrosive gases and dust concentrations into the monitoring system and establishing a dynamic correlation model between these and equipment insulation degradation. This invention addresses the problem of traditional single-dimensional monitoring systems completely failing in salt spray corrosion scenarios, reducing the incidence of such failures.

[0039] To address the significant risk of cascading grid failures, this invention employs a topology linkage mechanism to precisely block risk transmission paths. By using a pre-stored grid connection map, the system automatically searches for associated devices and increases the monitoring level when a target device experiences an anomaly.

[0040] At the early warning response level, the three-level command distribution system improves the operation and maintenance response speed and automatically matches the disposal plan according to the risk level: the first-level early warning pushes the cleaning task to the on-site mobile terminal, and the third-level early warning directly connects to the SCADA system to trigger equipment isolation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0042] Figure 1 The accompanying drawing is a schematic diagram of the framework structure of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0044] The embodiment of the present invention discloses an operation and maintenance management system for power supply equipment based on the Internet of Things, comprising a data acquisition unit, a data analysis unit and a decision execution unit;

[0045] The data acquisition unit is deployed on the power supply equipment and its surrounding environment to collect equipment status data and environmental parameters in real time;

[0046] The data analysis unit is connected to the data acquisition unit for fusion analysis of equipment status data and environmental parameters;

[0047] The decision execution unit is in communication with the data analysis unit and is used to generate hierarchical operation and maintenance instructions based on the analysis results;

[0048] The data acquisition unit includes an equipment status monitoring module and an environmental parameter monitoring module. The equipment status monitoring module collects the insulation resistance value and partial discharge value of the power supply equipment, and the environmental parameter monitoring module collects the corrosive gas concentration, dust content and electromagnetic interference data around the power supply equipment.

[0049] The data analysis unit includes an environmental degradation assessment subunit and an equipment health fusion subunit. The environmental degradation assessment subunit calculates the equipment casing aging coefficient based on the corrosive gas concentration and dust content, while the equipment health fusion subunit compares the real-time insulation resistance value with the historical baseline to generate an insulation health score.

[0050] The decision-making execution unit includes a three-level early warning generation module and a topology linkage module. The three-level early warning generation module outputs maintenance instructions based on the coupling results of the equipment casing aging coefficient and the insulation health score. The topology linkage module responds to abnormal signals from the target equipment, automatically retrieves related equipment in the power grid topology, and triggers a collaborative diagnosis mechanism.

[0051] The present invention realizes closed-loop management from data collection to execution decision through the collaboration of three units, solves the problem of delayed response of traditional systems, and significantly improves operation and maintenance efficiency.

[0052] To further optimize the above technical solution, the environmental parameter monitoring module includes a gas sensor array, a laser dust monitor, and an electromagnetic interference probe. The gas sensor array is deployed at the ventilation opening of the power supply equipment to detect SO2 and H2S concentrations. The laser dust monitor is installed above the equipment to quantify the content of airborne particulate matter. The electromagnetic interference probe is attached to the surface of the equipment's metal casing to monitor the strength of the power frequency magnetic field.

[0053] The gas sensor array, laser dust monitor, and electromagnetic interference probe transmit data to the data analysis unit via LoRa wireless networking. This multi-dimensional environmental monitoring system covers key corrosion areas of equipment, improves dust detection accuracy, and provides early warning of salt spray corrosion risks.

[0054] To further optimize this technical solution, the Equipment Health Fusion subunit establishes a historical insulation resistance baseline database, storing daily peak insulation resistance values ​​since the equipment was commissioned. It then calculates the rate of decrease of the current insulation resistance value compared to the same period of the previous day. When the rate of decrease exceeds 15%, the Environmental Degradation Assessment subunit is activated to perform a coupled analysis and generate a comprehensive environmental-equipment risk rating. This invention identifies hidden degradation through dynamic baseline comparison, detecting signs of insulation degradation earlier than the threshold alarm mechanism.

[0055] To further optimize the above technical solution, the decision logic of the three-level warning generation module includes:

[0056] When the aging coefficient of the equipment shell exceeds the threshold and the insulation health score is ≥70 points, a first-level warning instruction is generated and an equipment cleaning task is pushed;

[0057] When the equipment casing aging coefficient exceeds the limit and the insulation health score is between 50 and 70 points, a second-level warning instruction is generated and a spare parts pre-replacement work order is triggered;

[0058] When the insulation health score is lower than 50 points and the partial discharge volume increases by more than 200%, a level 3 warning instruction is generated and the equipment isolation procedure is initiated.

[0059] The present invention adopts a hierarchical response mechanism to improve the efficiency of maintenance resource allocation and avoid excessive maintenance.

[0060] To further optimize the above technical solution, the topology linkage module pre-stores a connection relationship map of power grid equipment, including the topological associations of transformers, circuit breaker cabinets, and relay protection cabinets. When the target transformer is marked as a level 2 or higher warning by the level 3 warning generation module, the circuit breaker cabinet and relay protection cabinet directly connected to it are automatically retrieved.

[0061] High-frequency monitoring instructions are sent to related devices to shorten the data sampling interval from the default 10 minutes to 30 seconds. Using the above solution, the present invention effectively improves the success rate of risk conduction blocking and avoids single-point failures causing system crashes.

[0062] To further optimize the above technical solution, the data analysis unit also includes a hidden danger transmission analysis sub-unit. When the dust content in the substation environment exceeds the warning value for three consecutive hours, the hidden danger transmission analysis sub-unit marks the station as a pollution source node;

[0063] Based on the grid topology, the insulation monitoring threshold of the distribution cabinet downstream of the pollution source node is dynamically lowered.

[0064] The present invention establishes a pollution diffusion early warning model, which greatly reduces the failure rate of related equipment.

[0065] In order to further optimize the above technical solution, the data acquisition unit also includes a node self-test protocol:

[0066] The sensor node sends a heartbeat signal to the gateway every two hours. If the gateway fails to receive the heartbeat signal three times in a row, the sensor node automatically switches to a backup relay node for data transmission. The node self-check protocol improves data transmission reliability in complex electromagnetic environments and avoids monitoring blind spots.

[0067] In order to further optimize the above technical solution, the instruction distribution logic of the decision execution unit is as follows:

[0068] The first-level warning instruction is pushed to the on-site mobile inspection terminal through the wireless communication module;

[0069] Secondary warning instructions are synchronously transmitted to the spare parts management system and personnel dispatch server;

[0070] The three-level warning instructions are directly written into the emergency operation queue of the power grid SCADA system and activate the sound and light alarm. The invention significantly improves the speed of multi-level instruction distribution, shortening the response time to major faults.

[0071] To further optimize this technical solution, the data analysis unit incorporates a meteorological data access interface to obtain real-time rainfall and wind speed data from the Meteorological Bureau's API. When the forecast indicates continuous heavy rain, a 1.2x risk weighting factor is applied to the insulation health score of outdoor equipment. This approach accurately predicts extreme weather failures and enables flood control measures to be initiated 24 hours in advance.

[0072] To further optimize the above technical solution, the equipment status monitoring module also includes a vibration analysis component. This component collects the core vibration spectrum through a vibration sensor attached to the transformer casing. When the energy in the 100-150Hz frequency band is detected to increase by more than 40% compared to the baseline value, the partial discharge monitoring module is triggered to perform directional detection. The vibration analysis component improves the recognition rate of mechanical faults in the transformer and avoids burning accidents caused by loose core.

[0073] Technical principle:

[0074] The core technical principle of this system lies in establishing a closed-loop analysis model of "environmental hazard factors → equipment degradation response → grid risk transmission." Its operating mechanism begins with the collaborative perception of multi-source heterogeneous data: through insulation resistance sensors and partial discharge probes deployed on the equipment itself, as well as corrosive gas detectors and laser dust monitors placed around the equipment, the internal status of the equipment and external environmental parameters are synchronously collected. When an abnormally high H2S concentration is detected (a typical scenario such as a coastal substation), the system immediately activates a dual verification mechanism—comparing the current insulation resistance value with a historical baseline database to identify the abnormal increase in the conductivity of the insulation material caused by the salt spray environment.

[0075] Dynamic risk coupling analysis constitutes the second layer of core technology. Through environmental degradation assessment, the system converts parameters such as gas concentration and dust content into the aging coefficient of the equipment casing; at the same time, through the equipment health fusion model, it calculates the correlation index between the insulation resistance drop rate and the partial discharge amount. When the two types of data are strongly correlated (such as excessive dust accompanied by an insulation drop rate of more than 15%), it is determined that the equipment is degraded due to environmental factors. In this process, the original topological risk conduction calculation scans the grid connection relationship map in real time. Once a substation is marked as a "pollution source node", the insulation monitoring threshold of the downstream equipment is immediately adaptively lowered to form an intelligent blocking of the conduction path.

[0076] As the final step, graded response execution creatively and precisely matches warning levels with disposal resources. When a Level 1 warning only requires equipment cleaning, instructions are pushed to on-site mobile terminals via the LoRa network (response delay <5 seconds). When the system detects a sudden drop in insulation rating accompanied by a surge in partial discharge (Level 3 warning conditions), an equipment isolation instruction is directly written to the SCADA system, reducing the overall response time to under 20 seconds. Crucially, the weather linkage correction mechanism automatically increases the outdoor equipment monitoring level after obtaining a rainstorm forecast via the API, placing the system in an enhanced protection state 24 hours in advance of severe weather.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0078] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A power supply equipment operation and maintenance management system based on the Internet of Things, characterized in that: It includes data collection unit, data analysis unit and decision execution unit; The data acquisition unit is deployed on the power supply equipment and its surrounding environment to collect equipment status data and environmental parameters in real time; The data analysis unit is connected to the data acquisition unit for fusion analysis of equipment status data and environmental parameters; The decision execution unit is in communication with the data analysis unit and is used to generate hierarchical operation and maintenance instructions based on the analysis results; The data acquisition unit includes an equipment status monitoring module and an environmental parameter monitoring module. The equipment status monitoring module collects the insulation resistance value and partial discharge value of the power supply equipment, and the environmental parameter monitoring module collects the corrosive gas concentration, dust content and electromagnetic interference data around the power supply equipment. The data analysis unit includes an environmental degradation assessment subunit and an equipment health fusion subunit. The environmental degradation assessment subunit calculates the equipment casing aging coefficient based on the corrosive gas concentration and dust content, while the equipment health fusion subunit compares the real-time insulation resistance value with the historical baseline to generate an insulation health score. The decision-making execution unit includes a three-level early warning generation module and a topology linkage module. The three-level early warning generation module outputs maintenance instructions based on the coupling results of the equipment casing aging coefficient and the insulation health score. The topology linkage module responds to abnormal signals from the target equipment, automatically retrieves related equipment in the power grid topology, and triggers a collaborative diagnosis mechanism.

2. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The environmental parameter monitoring module includes a gas sensor array, a laser dust monitor, and an electromagnetic interference probe. The gas sensor array is placed at the ventilation opening of the power supply equipment to detect SO2 and H2S concentrations. The laser dust monitor is installed above the equipment to quantify the content of airborne particulate matter. The electromagnetic interference probe is attached to the surface of the equipment's metal casing to monitor the strength of the power frequency magnetic field. Among them, the gas sensor array, laser dust monitor and electromagnetic interference probe transmit data to the data analysis unit through LoRa wireless networking.

3. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The equipment health fusion subunit establishes a historical insulation resistance baseline database to store the daily peak insulation resistance values ​​after the equipment is put into operation; it then calculates the rate of decrease of the current insulation resistance value compared with the same time period of the previous day; when the rate of decrease exceeds 15%, the environmental degradation assessment subunit is activated to perform coupling analysis and generate a comprehensive environment-equipment risk level.

4. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The decision logic of the three-level warning generation module includes: When the aging coefficient of the equipment shell exceeds the threshold and the insulation health score is ≥70 points, a first-level warning instruction is generated and an equipment cleaning task is pushed; When the equipment casing aging coefficient exceeds the limit and the insulation health score is between 50 and 70 points, a second-level warning instruction is generated and a spare parts pre-replacement work order is triggered; When the insulation health score is lower than 50 points and the partial discharge volume increases by more than 200%, a level 3 warning instruction is generated and the equipment isolation procedure is initiated.

5. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 4, characterized in that: The topology linkage module pre-stores the connection relationship map of power grid equipment, including the topological association of transformers, circuit breaker cabinets, and relay protection cabinets. When the target transformer is marked as a level 2 warning or above by the level 3 warning generation module, the circuit breaker cabinet and relay protection cabinet directly connected to it are automatically retrieved. Send high-frequency monitoring instructions to associated devices to shorten the data sampling interval from the default 10 minutes to 30 seconds.

6. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The data analysis unit also includes a hidden danger transmission analysis subunit. When the dust content in the substation environment exceeds the warning value for three consecutive hours, the hidden danger transmission analysis subunit marks the station as a pollution source node; Based on the grid topology, the insulation monitoring threshold of the distribution cabinet downstream of the pollution source node is dynamically lowered.

7. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The data acquisition unit also includes a node self-test protocol: The sensor node sends a heartbeat signal to the gateway every 2 hours; if the gateway does not receive the heartbeat signal for 3 consecutive times, the sensor node is controlled to automatically switch to the backup relay node to transmit data.

8. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 4, characterized in that: The instruction distribution logic of the decision execution unit is: The first-level warning instruction is pushed to the on-site mobile inspection terminal through the wireless communication module; Secondary warning instructions are synchronously transmitted to the spare parts management system and personnel dispatch server; The third-level warning instructions are directly written into the emergency operation queue of the power grid SCADA system and activate the sound and light alarms.

9. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The data analysis unit sets up a meteorological data access interface to obtain rainfall and wind speed data from the Meteorological Bureau API in real time; when the forecast shows continuous heavy rain, a 1.2 times risk weighting factor is applied to the insulation health score of outdoor equipment.

10. A power supply equipment operation and maintenance management system based on the Internet of Things according to claim 1, characterized in that: The equipment status monitoring module also includes a vibration analysis component, which collects the core vibration spectrum through a vibration sensor attached to the transformer casing. When the energy in the 100-150Hz frequency band is detected to increase by more than 40% compared to the baseline value, the partial discharge monitoring module is triggered to perform directional detection.

Images