12results about How to "Avoid fault expansion" patented technology

The invention relates to the technical field of electric power system operation and maintenance, and particularly discloses an intelligent agent construction method and system for electric power inspection, which can automatically trigger an inspection task, schedule edge equipment to execute inspection and identify equipment defects based on a visual large model by collecting multi-source operation data of electric power equipment in real time and performing intelligent analysis. And generating a structured abnormal point location and a disposal suggestion, and finally forming a complete inspection report and pushing the inspection report to the operation and maintenance management platform. By automatically judging the risk and generating the targeted inspection task, manual intervention is not needed, and compared with a traditional mode depending on a fixed period or threshold alarm, task response is more timely, coverage is more accurate, the capacity of capturing sudden hidden dangers is effectively improved, intelligentization and automation of the whole inspection process are achieved, manual intervention is reduced, and the inspection efficiency is improved. The inspection efficiency and accuracy are improved; equipment abnormity and potential risks can be identified in time, and rapid disposal is supported.

This invention discloses an automatic alarm system for abnormal property equipment based on multi-sensor fusion, specifically relating to the field of property equipment management. The invention utilizes a multi-source sensor module to deploy vibration, temperature, current, sound, and infrared sensors at key locations, collecting multi-dimensional data with equipment numbers, type identifiers, and time-series tags. A data fusion processing module employs Kalman filtering to eliminate noise, combines D-S evidence theory to fuse multi-source information, and dynamically filters valid data based on equipment type, operating stage, and aging parameters provided by the information filing unit. An anomaly analysis module constructs a risk assessment model, calculating the stage anomaly risk coefficient and the comprehensive anomaly risk coefficient. An intelligent alarm module sets three-level warning thresholds, triggering differentiated alarms via APP push notifications, SMS messages, and voice calls, and generating a structured log containing risk tracing and handling suggestions.

This application discloses a parallel system control method, electronic device, storage medium, and product, belonging to the field of parallel control technology. The parallel system control method includes: acquiring operating data of the host and slave devices in the parallel system through a host in the parallel system, wherein the host is connected to a designated sensor for monitoring the global operating status of the parallel system; performing fault detection based on the operating data to obtain fault detection results; when the fault detection results indicate that the host has an internal fault, controlling the host's function execution circuit to stop working while maintaining the normal operation of the host's sampling circuit and communication circuit; collecting monitoring data from the designated sensor through the host's sampling circuit; and controlling the parallel system based on the sampling data and monitoring data reported by each slave device and the host's communication circuit. The embodiments of this application can improve the availability and power output continuity of the parallel system.

This invention relates to the field of industrial Internet of Things (IoT) technology, and in particular to an anomaly detection and source tracing analysis system and method based on an information model. The system includes a data acquisition and storage module, an information model construction and management module, an indicator construction and calculation module, an anomaly detection module, a source tracing analysis module, and a result output and visualization module. This invention integrates multi-dimensional data to construct a graph-structured information model, unifying data from different sources into the information model. Through entities, attributes, and relationships within the information model, the dependencies between various indicator data are clarified, achieving comprehensive source tracing of anomalies. Compared to traditional methods that only focus on a single type of data or local indicator correlations, this invention can uncover all potential data correlations related to anomalies from a holistic system perspective, avoiding incomplete source tracing problems caused by missing data dimensions.

The application discloses a kind of based on digital ray technology's transmission line strain clamp nondestructive testing method, it is related to transmission line strain clamp detection field, method includes: collection strain clamp radiographic image and geometric dimension, stress and so on multidimensional parameter, data fusion is carried out by hypergraph structure, multidimensional heterogeneous strain clamp analysis model is calculated and analyzed in edge side using optimized hypergraph neural network and edge computing gateway, to visualize presentation detection result in the form of double screen linkage, while realizing intelligent diagnosis and defect early warning;The application provides an efficient, accurate and reliable solution for the intelligent operation and maintenance of transmission line strain clamp, effectively improves the safe operation level and operation and maintenance efficiency of power grid.

The application discloses a kind of wind turbine fault prediction and health management system based on multi-parameter fusion, belong to wind power equipment intelligent operation and maintenance and fault prediction and health management (PHM) technical field.The present application takes harmonic diagnosis technology as core, the monitoring mode of multi-parameter fusion, optimizes system architecture and carries out function innovation, solves the technical defects such as traditional diagnosis precision deficiency, real-time poor, early warning ability weak.System includes intelligent data acquisition module, enhanced signal hardware processing module, 5G / fiber double communication module, edge computing node, cloud side collaborative server, intelligent diagnosis and health management module, distributed memory, multi-terminal visualization platform and intelligent power supply module;Each module cooperates, through multi-source data acquisition, high-precision signal processing, cloud side collaborative diagnosis, fault grading early warning, health status evaluation and residual life prediction.

The invention discloses a day-ahead and intra-day triggering multi-time-scale scheduling system and method for a micro-energy grid, relates to the technical field of power system scheduling, and mainly aims to solve the problems that in the prior art, due to single-time-scale optimization or power quality single-index management, the robustness of the system to uncertainty is poor, the controllability of power quality is low, and the scheduling efficiency is high. And the multi-agent cooperation efficiency is low. The system comprises a source load data prediction module, a dynamic time-of-use electricity price module, an optimization scheduling module, an electric energy quality monitoring module, a trigger scheduling module and an instruction distribution module. And a multi-time scale framework of'prediction-monitoring-scheduling-execution 'full-chain coordination is constructed, so that the robustness of the system to uncertainty, the controllability of electric energy quality and the high efficiency of multi-agent coordination are improved.

The invention provides a power coil anti-adhesion protection device and method. The power coil anti-adhesion protection device comprises a knife switch, a time relay, an intermediate relay, a coil and a circuit breaker. The time relay is integrated with a normally open contact; the intermediate relay is integrated with a normally closed contact; one end of the knife switch is electrically connected with the direct-current access point, and the other end of the knife switch is electrically connected with the power input end of the time relay, one end of the normally open contact and one end of the normally closed contact; the power supply output end of the time relay is electrically connected with the direct current lead-out point; the other end of the normally open contact is electrically connected with the power input end of the intermediate relay, and the power output end of the intermediate relay is electrically connected with the direct current outgoing point. The other end of the normally-closed contact is electrically connected with one end of the coil, and the other end of the coil is electrically connected with the direct current outgoing point. And the circuit breaker is connected in series between the direct current access point and the knife switch. The circuit breaker is used for preventing the closing coil and the opening coil of the circuit breaker from being burnt due to adhesion caused by continuous electrification.

The application provides a power distribution network stability operation and maintenance method based on LSTM, comprising the following steps: collecting power distribution network related data, filtering abnormal values and filling missing values, and dividing continuous data into time series data; a long short-term memory neural network model is established to predict the probability of failure, and the long short-term memory neural network is optimized and corrected; the stability risk of the power distribution network is evaluated and reported to the background monitoring system in real time; when the risk degree exceeds the set threshold, the system sends a risk alarm to the operation and maintenance personnel; according to the prediction result of the neural network, the real-time monitoring data and the environmental change, the genetic algorithm is used for scheduling optimization management of the power distribution system. The application has the beneficial effects that: the power distribution network data is monitored in real time, the failure cause and position are identified, and the failure elimination efficiency is improved; the load is predicted according to the historical data, the operation efficiency of the power distribution network is optimized, the demand for manual operation is reduced, and the operation and maintenance efficiency and safety are improved.

The invention relates to the technical field of power distribution Internet of Things, and provides resource adaptive power edge computing Internet of Things equipment and a lightweight engine implementation method thereof, the resource adaptive power edge computing Internet of Things equipment comprises an Internet of Things module, an application support module, a computing processing module, a communication interface and a power edge computing module; the Internet of Things module performs unified standardized management on various application scenes accessed to the power edge computing Internet of Things equipment, and supports rich interface types; the application support module is used for supporting the electric power Internet of Things equipment to perform electric power application development; the power edge calculation module realizes an edge calculation function of the power internet-of-things equipment by utilizing hardware resources and data processing capability of the edge calculation internet-of-things equipment; data processing and calculation links are moved forward to the edge side of the network, and the edge Internet of Things device migrates part of calculation tasks to the edge side for calculation through edge calculation, so that network edge intelligence is realized, edge data can be protected, response delay of an application program can be reduced, and business innovation can be assisted.

The invention discloses a magnetic suspension bearing suspension force and zero sequence current decoupling control system, particularly relates to the technical field of magnetic suspension, and comprises a four-bridge-arm inverter module, a double closed-loop control unit, a multi-source sensing acquisition module, a power driving module and a magnetic suspension bearing body. According to the decoupling control system for the suspension force and the zero-sequence current of the magnetic suspension bearing, the fourth bridge arm connected with the neutral point of the three-phase winding is additionally arranged, an independent zero-sequence current loop is constructed, and zero-sequence current closed-loop control based on the alpha-beta O coordinate system is matched, so that the suspension force and the zero-sequence current of the magnetic suspension bearing are decoupled. Zero-sequence current generated by three-phase imbalance, parameter asymmetry and magnetic field distortion in a traditional three-phase inverter architecture can be pertinently dredged and suppressed; and the fourth bridge arm adopts a SiCMOSFET power switch device and fly-wheel diode combined design, so that a compensation instruction can be quickly responded, winding current distortion caused by superposition of zero-sequence current to working current is avoided, and a linear control relationship between suspension force and current is guaranteed.

The invention relates to the field of intelligent operation and maintenance of power equipment, in particular to a predictive maintenance method for a transformer respirator in combination with edge calculation. The method comprises the following steps: S1, deploying a sensor at an edge node, collecting parameters such as temperature and humidity of a respirator and transformer associated data, removing abnormity through Kalman filtering, standardizing, and establishing a structured data set; s2, extracting time domain and frequency domain features, and using an improved SVM model to early warn and mark anomalies; s3, transmitting abnormal data to a cloud end, training an LSTM model, and then optimizing parameter issuing; s4, establishing a life sub-model, calculating the residual life and sorting; and S5, combining a load fixed maintenance priority and a scheme, and performing post-maintenance evaluation to form a closed loop. According to the method, fault early warning and efficient operation and maintenance are realized, and support is provided for equipment operation and maintenance.