1129results about "Emergency protection data processing means" patented technology

The invention belongs to the field of power systems, and discloses a power distribution network fault autonomous diagnosis and self-healing control method and system based on deep reinforcement learning, and the method comprises the steps: carrying out the collection and preprocessing of the multi-source heterogeneous data of a power distribution network; key feature vectors are extracted from the multi-source heterogeneous data based on a graph convolutional network, and a state representation model is constructed; based on an asynchronous dominant actor-commentator algorithm, constructing a fault diagnosis agent capable of quickly diagnosing faults; a meta-reinforcement learning algorithm is adopted, a dynamic reconstruction strategy library is generated through pre-training, and a self-healing control agent capable of rapidly adapting to various different fault scenes to generate an optimal reconstruction strategy is constructed; constructing a self-healing control module based on virtual impedance matching, wherein the self-healing control module is used for intelligent reconstruction and self-healing control of the power distribution network; a risk-sensitive reward function and a game equilibrium strategy optimization method are introduced to improve performance and robustness; and finally carrying out system deployment and engineering verification.

The invention relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

The invention discloses a digital twinning-based power distribution network relay protection evaluation test method, system and equipment and a storage medium, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the site selection optimization according to a distribution network topological structure and load distribution, configuring a data collection terminal, and building a data transmission network to collect the operation data of a power distribution network; preprocessing operation data of the power distribution network, constructing a digital twinning model, and realizing twinning synchronous updating through data mapping; simulation and fault test verification are carried out, and analysis and optimization feedback are carried out on verification data. According to the digital twinborn-based power distribution network relay protection evaluation test method provided by the invention, the problems are effectively solved by optimizing data acquisition distribution points, constructing a twinborn model in real time and performing multi-scene simulation verification, and an innovative solution is provided for relay protection strategy evaluation and optimization of a complex power distribution network.

The invention relates to the technical field of power distribution protection cabinet fault diagnosis, and discloses an intelligent fault diagnosis and rapid isolation method for a power distribution protection cabinet, and the method comprises the steps: synchronously collecting electrical parameters, environment states and equipment motion signals in the cabinet as multi-source monitoring data when the power distribution protection cabinet operates; parameter fluctuation, state change and action time sequence features are extracted after preprocessing, cross-data association analysis is carried out through a multi-dimensional association model to generate an association feature set, a fault diagnosis information set is formed based on the identified abnormal types, fault positions and development trends, an isolation strategy is formulated according to severity levels and influence ranges, and a fault diagnosis result is obtained. And generating a rapid isolation instruction and dynamically adjusting along with fault evolution. According to the method, multi-source data fusion analysis is realized, faults can be accurately diagnosed and rapidly isolated, the operation safety and reliability of the power distribution protection cabinet are improved, and the method is suitable for fault processing of the power distribution protection cabinet in a power system.

The invention discloses an intelligent reclosing circuit breaker modular protection system, which relates to the technical field of circuit breaker intelligent management, and comprises an acquisition module, a judgment module, a reclosing control module and an optimization module. Voltage holes caused by fault tripping and non-fault disturbance are effectively distinguished, and false triggering of reclosing operation is avoided; meanwhile, the optimization module introduces a machine learning algorithm to carry out continuous filing and strategy updating on misjudgment events, and self-adaptive discrimination logic optimization is realized, so that reclosing judgment accuracy and power supply stability of the power grid system under a complex load condition are remarkably improved, and system impact and equipment damage are effectively prevented.

The invention belongs to the technical field of fault analysis and monitoring, and particularly relates to an intelligent monitoring and fault isolation method and system for a branch box, and the method comprises the steps: carrying out the multi-physical field data fusion collection of the branch box, and obtaining a spatial-temporal feature matrix; performing dynamic feature extraction on the multi-physical field data of the branch box, and outputting a fused dynamic feature vector of the branch box; obtaining a branch box topological graph structure, and obtaining a branch box node fault probability matrix; building a physically constrained fault propagation model based on the branch box node fault probability matrix, obtaining a pre-isolation set, and outputting a dynamic blocking instruction; taking the pre-isolation set as an initial solution space constraint, outputting a fault type and obtaining an isolation instruction; and setting a branch box topology reconstruction constraint, and performing closed-loop control on each branch box node in combination with the isolation instruction to obtain a switching action sequence of each branch box node and a reconstructed power supply path. Precise monitoring, rapid isolation and intelligent reconstruction of the fault of the branch box can be realized, and the operation and maintenance efficiency of the power distribution network is remarkably improved.

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention relates to the technical field of power grid self-healing control, and discloses a power grid self-healing control intelligent optimization method and system, and the method comprises the steps: obtaining the topological structure information of a power grid, dividing n power grid toughness evaluation regions according to the topological structure information of the power grid, calculating a hierarchical toughness evaluation index, and forming a comprehensive toughness evaluation index according to the hierarchical toughness evaluation index; a self-healing control intelligent optimization model is constructed based on the comprehensive toughness evaluation indexes, a self-healing control optimal solution is obtained by solving the self-healing control intelligent optimization model, the self-healing control optimal solution is dynamically calibrated, and a self-healing control strategy is generated; according to the method, the topological structure, the historical fault data and the real-time operation state of the power grid are comprehensively considered, a layered evaluation and dynamic calibration combined mode is adopted, the intelligent level and adaptability of self-healing control of the intelligent micro-grid are improved, the toughness and recovery capacity of the micro-grid for coping with complex faults are enhanced, and safe, reliable and efficient operation of the power grid is guaranteed.

The invention discloses an autonomous protection control method for abnormal feedback of an LED switching power supply, and the method comprises the steps: obtaining parameter data in a switching period based on power supply operation data in a historical database; the method comprises the following steps: constructing a prediction model of the running state of the LED switching power supply, sampling based on a real-time feature window of the LED switching power supply, outputting a state prediction vector and abnormal probability distribution of a future switching period, constructing a real-time state feature matrix, dynamically matching the real-time state of the LED switching power supply with a prediction abnormal mode, and calculating a feature similarity index. And designing a hierarchical protection mechanism, uploading complete event data to a database, updating a power supply abnormity prediction model, and dynamically adjusting a protection trigger threshold value based on a deviation degree between a prediction result and an actual state. The method has the advantages that accurate pre-judgment and grading protection of the abnormal probability are achieved, model self-optimization and threshold value self-adaptive adjustment are driven through real-time data, and the system reliability and the protection response sensitivity are remarkably improved.

The invention discloses a multi-end line pilot protection method and system based on time domain model identification, and relates to the technical field of electrical engineering, and the method comprises the steps: obtaining a three-phase voltage sampling value, a three-phase current sampling value and a switching value of each end of a multi-end line; judging whether each end of the multi-end line meets a protection starting condition or not; if yes, differential voltage variation and differential current are calculated, and correlation coefficients of the differential voltage variation and the differential current are calculated; judging whether the correlation coefficient is within a preset fault threshold range or not; if the correlation coefficient is not within the preset fault threshold range, determining that the multi-terminal line has an external fault, and enabling the protection device not to act; and if the correlation coefficient is within a preset fault threshold range, determining that the multi-terminal line has an internal fault, and enabling the protection device to act to remove the fault. According to the invention, the influence of capacitive current and a power electronic power supply on pilot protection performance is overcome, the operating characteristics of a relay protection device are improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of protection device testing, in particular to a verification method of an intelligent comprehensive protection device, which comprises the following steps of: extracting starting time and response time in a tripping state transition sequence of the protection device, calculating execution delay and action segment length, and comparing and identifying an abnormal link based on a state number sequence; the method comprises the following steps: realizing analysis and standard reconstruction of a state logic structure, forming an ordered tripping path, introducing a long short-term memory neural network to carry out time alignment on a state change sequence and a current signal segment, identifying a potential mismatch or abnormal change trend in a state chain through linkage analysis of a state-current abrupt change position, and reconstructing a state path. According to the method, path continuity and dynamic adaptability are enhanced, a random forest is adopted to take device numbers, the number of jump segments and response residual errors as multiple inputs for training modeling, device scores are output through an integrated tree voting mechanism, and recognition sorting of abnormal frequency significant devices is achieved.

The invention relates to a power equipment self-repairing surge protection system based on artificial intelligence, and the system is characterized in that a surge detection module is connected between a live wire and a protection ground wire of power equipment, collects voltage and current data, and transmits the data to an artificial intelligence analysis unit; and the artificial intelligence analysis unit extracts the surge characteristic parameters, generates protection strategy parameters through a deep neural network, and controls the trigger thresholds of all levels of protection devices in the programmable protection circuit. The programmable protection circuit comprises a gas discharge tube capable of adjusting trigger voltage, a metal oxide varistor and a transient voltage suppressor diode, and is used for discharging surges with different energies step by step. The resettable fuse cuts off the circuit when the temperature exceeds a threshold value and recovers conduction after the temperature is reduced. And the controller receives and applies the protection strategy parameter output by the artificial intelligence analysis unit, and dynamically adjusts the trigger threshold value of the protection device, so that the response and protection performance of the system to the surge is improved, and the self-repairing and stable operation of the power equipment are realized.

The invention relates to the technical field of leakage protection, and provides a self-adaptive mining low-voltage leakage protection method based on working conditions, which comprises the following steps: detecting leakage resistance in real time, and determining the operation condition of a power grid; according to the power grid operation condition, matching a target detection algorithm in preset detection algorithms; wherein the preset detection algorithm comprises an integrated zero-sequence current break variable method, a zero-sequence current and zero-sequence voltage phase method and a zero-sequence current size phase comparison method; according to a target detection algorithm, establishing a unified criterion to coordinate the action logic of the main switch and each branch switch, and determining the electric leakage degree of the electric leakage resistor; and according to the electric leakage degree, performing switch locking on the electric leakage branches in sequence.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a rapid fault positioning method, system and device based on three-section protection and FA cooperative protection, and a medium, and the method specifically comprises the steps: carrying out the detection and judgment through employing a preset three-section action rule, and obtaining a real-time protection action signal; acquiring action time sequence data of each protection device of the power distribution network in real time, constructing an FA fault feature matrix according to the action time sequence data, the electrical quantity information and the topological structure, analyzing time sequence association of the FA fault feature matrix by using an LSTM neural network, and generating an FA fault probability distribution diagram; based on the real-time protection action signal and the FA fault probability distribution diagram, a fault positioning algorithm is adopted for collaborative analysis, and a fault positioning result is obtained; and dynamically planning a load transfer path of the power distribution network by adopting a reinforcement learning algorithm according to a fault positioning result, and generating an optimal isolation and recovery scheme. The method is suitable for various complex power distribution network structures and operation modes, and can realize rapid and accurate fault positioning and processing under different fault conditions.

The invention discloses a digital calibration system and method for dynamic response parameters of a surge protection circuit, relates to the technical field of power electronic protection and test calibration, and is used for solving the problem that an existing surge protection circuit calibration method cannot reflect device degradation and dynamic response mismatch. Surge event voltage and current waveforms are collected through a synchronous trigger mechanism, non-stationary signal features are extracted through combined time-frequency analysis, and a voltage-current phase trajectory diagram is constructed to generate a response feature matrix; performing phase-space reconstruction on the matrix, calculating Kolmogorov entropy quantification system stability, and establishing a material degradation transfer function in combination with an energy absorption sequence to generate a grain boundary degradation index; key parameters are screened through L1 regularization regression, and a quantitative relation between a grain boundary degradation index and a protection threshold value is established to realize adaptive threshold value calculation; and executing strategy switching from calibration to predictive maintenance according to the degradation trend and threshold fluctuation change, and outputting a maintenance signal to complete dynamic accurate calibration and reliable operation of the surge protection circuit.

The invention provides a power grid fault positioning and intelligent relay protection selective control method and system, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting the multi-point electrical data of a power grid, adjusting a sampling window and a filtering threshold value according to the operation state of the power grid, and carrying out the data processing; after eliminating the end effect, performing empirical mode decomposition to obtain a fault feature component; inputting the characteristic component into a fault diagnosis model, calculating a fault initial probability based on a Bayesian network, generating multiple groups of fault prediction data in combination with a time sequence evolution rule, and taking a position corresponding to a minimum information entropy value as a fault positioning result; determining a fault area according to a positioning result, establishing a local communication network of relay protection devices in the area, obtaining the operation state of each device, calculating a protection sensitivity coefficient, determining a protection range and a tripping time sequence, generating and sending a control instruction, and realizing selective isolation of the fault area.

The invention relates to the technical field of relay protection, in particular to a power distribution network relay protection collaborative optimization method and system considering new energy, and the method comprises the steps: constructing a model architecture of a power distribution network; based on the new energy access characteristics, the topological structure of the power distribution network and the line parameters of the power distribution network, performing simulation and calculation on a short-circuit fault possibly occurring in the power distribution network to obtain a short-circuit current calculation result; determining relay protection equipment of the power distribution network according to the topological structure of the power distribution network, and determining a protection constant value of each protection section of the relay protection equipment according to a short-circuit current calculation result and a protection constant value setting principle; establishing an optimization model to update an iteration decision variable, and obtaining an optimal solution of the decision variable; and adjusting the configuration of the relay protection equipment according to the optimal solution of the decision variable. By comprehensively considering the influence of new energy access on short-circuit current distribution, the reliability and effectiveness of a relay protection system are improved, and the economic operation efficiency of a power distribution network is enhanced.

The invention belongs to the technical field of low-voltage switch cabinet operation control, and particularly discloses an intelligent low-voltage switch cabinet system based on a building intelligent function, which comprises an operation sensing module, a spatial topology module, a fusion diagnosis module, a fault positioning module and a dynamic protection module, current and insulation resistance are synchronously detected at a circuit wiring terminal of the low-voltage switch cabinet, temperature and humidity distribution data of the surrounding environment are collected, and then coupling characteristics between the current and temperature rise are fused. And carrying out visual display of coupling and time sequence correlation in the built three-dimensional heat-electricity correlation topological graph in the switch cabinet by utilizing the time sequence correlation between the insulation resistance and the humidity change, so as to realize comprehensive identification of potential faults. According to the method, the sensitivity and the accuracy of fault identification are effectively improved, early fault symptoms such as poor contact and insulation degradation can be found earlier, the early warning capability of the system is enhanced, and the safety and the stability of operation of the switch cabinet are guaranteed.

The invention provides a remote intelligent control distribution box, which belongs to the technical field of distribution boxes and comprises a control chip, a wireless communication device, a current monitoring device, a voltage monitoring device, a temperature monitoring device, a leakage protection device, a circuit breaker state monitoring device, a grounding state monitoring device, a humidity monitoring device and a power management device. A control module is arranged in the control chip and is used for acquiring output data of the current monitoring device, constructing a linear temperature rise prediction model, identifying potential fault types by using a decision tree algorithm, operating a lightweight power distribution network sensing model to process dimension-reduced time sequence data and realizing low-calculation-amount feature extraction by adopting a sparse attention mechanism; and when the system health state risk score exceeds a threshold value, a protection strategy is adjusted, and fault precursor characteristics are recorded. The technical problem that early warning of the abnormal state of the power distribution system is difficult to realize in a low-computing-resource environment is solved.

The invention belongs to the technical field of power system power distribution network relay protection, and provides an active fault property discrimination method based on injection disturbance current response characteristics, which comprises the following steps: step 1, when an inter-phase fault occurs in a system, three-phase tripping of a fault line outlet circuit breaker occurs, and according to three-phase current and voltage change conditions before and after the fault, determining whether the fault occurs or not; determining two phases with increased line current and reduced voltage as fault phases; 2, delaying for 200ms after the circuit breaker trips, and controlling the action of a split-phase switch; according to the method, disturbance current signals are injected through an external power supply, the disturbance signals have obvious difference in response characteristics during permanent faults and instantaneous faults, the reliability and superiority of the method under different working conditions are verified through simulation, the method makes up for the defects of an active injection type method during fault property recognition processing, and the method has the advantages of high reliability and high reliability. The method is not influenced by injection time and fault duration, fault property discrimination can be realized only through one-time injection, and the method is simple in operation, small in calculation amount and high in reliability.

The invention discloses a power distribution network fault self-healing power restoration method and device, and belongs to the technical field of power distribution networks. Obtaining fault data corresponding to a plurality of fault types, performing fault simulation in the mathematical model of the power distribution network, and determining a self-healing power restoration operation rule; acquiring operation data of the power distribution network in real time, determining a current fault type of the power distribution network, and determining equipment action constant values and equipment action sequences corresponding to the plurality of protection equipment in the self-healing power restoration operation rule; when the operation data of the power distribution network reaches the equipment action constant value, performing isolation processing on each protection equipment according to the equipment action sequence, and collecting the isolated operation data of the power distribution network, thereby determining a fault area and a non-fault area of the power distribution network; and removing the fault area, and forwarding the non-fault area. Therefore, by implementing the method, the problem of low self-healing power restoration efficiency of the power distribution network in the prior art can be solved.

The invention relates to the technical field of electrical fault identification and response, in particular to a relay protection fault identification and response system, which comprises a fault propagation path tracking module, a regional fault monitoring module, a misoperation cumulative effect calculation module, a protection level adjustment module and a fault response module. According to the method, based on the triggering sequence and the triggering interval time of the relay protection device, a malfunction propagation mode is extracted, cascade malfunction associated devices are screened, regional power grid fault risk scores are combined, protection level setting is optimized, the malfunction accumulation trend is calculated through malfunction triggering frequency, and high-malfunction devices are identified. The method comprises the following steps: calculating a protection level adjustment factor, optimizing a relay protection level according to a power grid state, enabling a protection strategy to adapt to different operation conditions, calculating power grid operation state parameters in combination with the adjusted protection level, analyzing stability change, optimizing a relay protection response strategy, enabling a relay protection system to have adaptive adjustment capability, and reducing the sudden fault influence range.

The invention discloses an electrical equipment overload protection method and system based on multistage load monitoring, and relates to the technical field of electrical equipment overload protection, and the method comprises the steps: setting an equipment-level monitoring node, a line-level monitoring node and a system-level monitoring node, and obtaining a three-level load data set; constructing a load characteristic matrix; setting an initial overload threshold conforming to the industrial standard of the switch equipment; a correction coefficient is configured, and the initial overload threshold value is synchronously corrected; drawing up a grading threshold comparison table; meanwhile, a load dynamic constraint mechanism and an equipment health intervention mechanism are set, and electrical equipment overload protection parameter setting is carried out. The technical problems that in the prior art, the overload protection threshold value of the electrical equipment is fixed, dynamic adjustment is lacked, and the adaptability to the health state and load change of the equipment is insufficient, so that protection pertinence and effectiveness are low are solved, accurate overload protection of the electrical equipment is achieved, and the safety of the electrical equipment is improved. And the technical effects of pertinence and effectiveness of overload protection are improved.

The invention relates to the technical field of circuit breakers, in particular to a leakage protection action alarm method based on an intelligent circuit breaker, and the alarm method comprises the following steps: collecting a leakage signal of a main loop through a zero-sequence coil, carrying out the dynamic gain adjustment and noise filtering processing of the leakage signal, and generating a preprocessing signal; performing frequency band screening on the preprocessed signal, extracting an electric leakage characteristic signal in a preset frequency band, and obtaining an electric leakage effective value based on the electric leakage characteristic signal; obtaining load classification through the microcontroller unit based on a power signal of a temporary power utilization scene; based on the load classification result, combined with environmental parameters, a dynamic protection threshold is generated through a multi-objective optimization algorithm, and the dynamic protection threshold is used for adjusting leakage protection sensitivity in real time; and when the electric leakage effective value exceeds the dynamic protection threshold value, executing a hierarchical tripping control strategy.

The invention discloses a primary and secondary collaborative planning method based on a novel power distribution system, and relates to the technical field of power system optimization, and the method comprises the following specific steps: constructing a power distribution network model and fault feature analysis, designing an adaptive protection mechanism, realizing a rapid fault processing mechanism, and performing primary and secondary system collaborative planning. And implementing and verifying: implementing a planning method in the power distribution network, installing and debugging related equipment, carrying out field test, and verifying the effectiveness of an adaptive protection mechanism and a fault rapid processing mechanism, the running state of the power distribution network is monitored in real time, and the protection constant value is automatically adjusted according to the monitored fault characteristic parameters, so that the reliability of the power distribution network is improved. Therefore, the adaptive capacity of the system to a complex and changeable operation environment is remarkably improved, the dynamic adjustment mechanism can quickly respond to dynamic factors of new energy output fluctuation and load change, it is ensured that a protection strategy is always matched with the operation state of the system, and the problem of protection maloperation or operation refusal caused by rigid setting of a fixed value is effectively prevented.

The invention discloses a self-adaptive intelligent current limiting and managing method, and relates to the technical field of current limiting. Comprising the steps of collecting real-time current data, historical current data and a system load state of a node, performing feature extraction on the real-time current data and the historical current data of the node, performing analysis and prediction on the collected current data by using an intelligent algorithm, and predicting current according to a current state analysis result; performing risk assessment on the current state in combination with historical current data of the node and a system load condition, analyzing predicted current data, and judging whether the current state has a short circuit risk or not; according to a short-circuit risk judgment result, determining a short-circuit risk node, selecting a current limit strategy, and adjusting current limit by setting a current limit protection value; and continuously monitoring the current state of the short-circuit risk node, adding the node which frequently causes an abnormal state into a blacklist, and carrying out key monitoring or regular maintenance.

The invention belongs to the field of overvoltage protection, and relates to an overvoltage stabilizing and protecting device which comprises a data acquisition operation module, a control logic module, a display module, an overvoltage protecting assembly and a high-voltage vacuum circuit breaker or an electric isolation handcart structure. The real-time monitoring and protection of the working state of the overvoltage protection assembly are realized; through the arrangement of the high-voltage circuit breaker or the electric isolation handcart, when the overvoltage protection assembly is detected to be abnormal, the connection between the overvoltage protection assembly and the high-voltage bus can be rapidly cut off, so that the system safety is ensured; the device integrates a transient fault recording function and can capture wave map data before and after a power frequency overvoltage fault of the system; the overvoltage protection assembly in the device adopts a geometric center air cooling heat dissipation mode, the fan is quick in starting response, the operating temperature of the overvoltage protection assembly is greatly reduced, the operating time of the overvoltage protection assembly can be effectively prolonged, and the device adopts an integrated modular structural design, so that the operation safety, the maintenance and the management are greatly facilitated.

The invention discloses a microcomputer synchronous automatic closing analysis system, which relates to the technical field of power system automation and comprises a data acquisition module, a data processing and storage module, a machine learning analysis module, a closing control module and a maintenance decision module. Electrical quantity data and equipment state data are monitored in real time through the data acquisition module, and the machine learning analysis module is utilized to perform modeling analysis on key parts and the overall operation state of the system, so that health assessment and fault prediction of the key parts are realized; when a fault possibly influencing closing safety or a health score of a key component is predicted to be lower than a set threshold value, the closing control module can adjust a closing control strategy to ensure the safety and reliability of closing operation, and the intelligent closing control mechanism remarkably improves the operation safety and reliability of the microcomputer synchronous automatic closing system.

The invention discloses a relay protection method and device based on time sequence data prediction, and the method comprises the steps: collecting the operation sequence data of a main network power transformation relay protection device in real time through a wireless sensing node, and transmitting the operation sequence data to a data processing layer through a Zigbee wireless network protocol; historical data features are extracted based on a sliding window method, abnormal data are recognized and removed through a quartile method, and missing data are filled up through a linear interpolation algorithm; constructing a CNN-GRU prediction model fused with the attention mechanism, dynamically weighting key features by the attention mechanism, inputting the preprocessed data into the model for training, and generating an operation state evaluation value based on a prediction result, and when the evaluation value exceeds a preset threshold, triggering a multi-level early warning mechanism. And the prediction precision is verified by using a decision coefficient R2, the R2 is required to be greater than or equal to 0.95, and model parameters are dynamically updated to adapt to the working condition change of the power grid.