5122 results about "Safe operation" patented technology

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

The invention belongs to the field of aero-engine bearings, and provides a method and system for predicting the remaining service life of an aero-engine bearing, and the method comprises the steps: carrying out the processing of an original vibration signal collected in the operation process of the engine bearing through a Pearson correlation analysis method, carrying out feature extraction to obtain a plurality of feature sequences of common bearing RUL prediction statistics; smooth processing and cumulative transformation are carried out on the statistical feature sequence, monotonicity and tendency values are calculated, screening is carried out, and a screened cumulative feature sequence is obtained; and inputting the screened accumulated feature sequence into an attention full convolutional network (AFCN) model based on physical information to predict a life ratio, and calculating a residual service life prediction value through the life ratio. According to the method, through the full convolutional network fusing the bearing physical degradation mechanism and the attention mechanism, the key feature capturing capability and prediction precision in the long-time-sequence degradation process are improved, and technical support is provided for safe operation and maintenance of an aero-engine.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention discloses a railway vehicle test data management and analysis system, and the system comprises a multi-modal data collection module which collects the heterogeneous data of tests such as airtightness and weighing in real time; the block chain credible evidence storage module is used for performing time-space stamp marking and hash encryption on the data and verifying the integrity; the space-time atlas analysis module is used for constructing a space-time heterogeneous atlas and generating vehicle-level feature vectors through graph convolutional network fusion data; the predictive maintenance decision module is used for predicting subsystem performance degradation and fault risks and generating a hierarchical maintenance strategy; and the adaptive visual platform integrates a large screen and a mobile terminal to display data and decisions. According to the system, efficient integration and credible evidence storage of multi-source data are realized, the data utilization efficiency and analysis depth are improved, accurate maintenance decision is supported, and safe operation of railway vehicles is guaranteed.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention discloses an urban underground pipe network multi-dimensional state perception and risk assessment method and system, and relates to the field of urban pipe network monitoring. The method comprises the following steps: S1, multi-source data acquisition: acquiring physical, chemical and environmental perception differentiation data of a gas pipe network and a drainage pipe network through a multi-source heterogeneous sensor network; s2, edge side data processing: preprocessing the data and dynamically weighting the data, and executing local anomaly recognition; s3, performing multi-parameter coupling analysis to obtain a coupling risk index; s4, carrying out three-dimensional dynamic evaluation, and carrying out differentiated evaluation from dimensions of probability, consequence and vulnerability; and S5, intelligent decision making is carried out, and grading early warning and linkage control are triggered. The system comprises a sensor layer, an edge calculation layer, a cloud analysis layer and an early warning platform. The problems of single dimension, response lag and the like of traditional monitoring are solved, full-life-cycle intelligent management of a pipe network is realized, and safe operation of a city is guaranteed.

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

The invention belongs to the field of bird repelling and equipment state monitoring, and particularly discloses a bird inhabitation multi-mode repelling and state monitoring device and method. The device is combined with a photosensitive device for day and night environment discrimination, is integrated with an intelligent hardware device of a multi-mode driving unit and a state monitoring module, and is suitable for long-term bird prevention operation in unattended scenes such as power transmission lines and transformer substations; further, accurate detection of bird approaching is realized by fusing various sensors, efficient repelling is performed by combining a multi-channel interference means of touch, hearing and vision, meanwhile, the running state of the anti-bird structure is monitored in real time, and data is remotely transmitted back, so that long-term, stable, protection and monitoring integration in an unattended scene is realized, and the safety and reliability of the system are improved. The threat of bird damage to safe operation of a power system is effectively reduced.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention relates to the technical field of pipeline safety, and discloses a hydrogen-doped pipeline hydrogen damage grading evaluation method based on fracture mechanics. Comprising the following steps: acquiring initial pipeline state data by establishing a hydrogen-doped pipeline digital twin model; then introducing a hydrogen concentration dependency term to correct the J integral expression, and performing stress analysis based on the J integral expression; generating a material toughness attenuation curve based on a stress analysis result; hydrogen diffusion and accumulation rate parameters are obtained based on a material toughness attenuation curve; predicting a damage development track in combination with real-time operation data; establishing a grading standard system of dynamic response based on the model for risk assessment; and finally, real-time monitoring and scientific grading of the hydrogen damage of the hydrogen-doped pipeline are realized. According to the invention, comprehensive dynamic evaluation of hydrogen damage of the hydrogen-doped pipeline is realized, and scientific decision support is provided for safe operation of the pipeline.

The present application relates to the technical field of safety management, in particular to a mine air compressor safe operation management method and system based on deep learning, comprising the following steps: collecting output pressure and input current, calculating slope change rate and direction switching frequency, clustering fluctuation amplitude and frequency by time period, generating working condition fluctuation label, identifying state mutation point and duration, calculating risk level label, analyzing load fluctuation coefficient, judging periodic fatigue early warning, combining synchronous rate to identify abnormal section, judging the need to switch state and generating instruction set. In the present application, by constructing the load risk level sequence on the basis of state duration and mutation point identification, the quantitative identification of multi-period load deviation is realized, the early warning period is marked by combining risk level and fatigue deviation ratio, and the abnormal load trend is judged according to the change direction of pressure and return gas flow, which strengthens the capture ability of abnormal operation section and improves the accuracy of operation safety risk identification and the adaptability of response strategy.

The invention provides an oil and gas equipment leakage acoustic characteristic intelligent identification early warning method and system, and relates to the technical field of oil and gas equipment safety monitoring, and the method comprises the steps: arranging a sonic sensor array to collect signals, calculating a wavefront propagation path, optimizing the wavefront curvature, carrying out the multi-scale decomposition of a sonic signal, and extracting time-frequency characteristics; and reconstructing a sound field by using an acoustic propagation function to determine a leakage point space coordinate. The method can accurately identify the acoustic characteristics of oil and gas equipment leakage, improves the leakage point positioning precision, achieves the early warning of leakage, and guarantees the safe operation of oil and gas equipment.

The invention relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

The present invention belongs to the field of smart power grids and power system automation control. Disclosed is a load emergency control method based on a dynamic event triggering mechanism, which method comprises: on the basis of an indicator of safe operation of a system, performing region division on a distribution network; proposing an adaptive load level division method based on user response willingness, and by taking the minimization of the system operation cost of the distribution network as an objective, establishing a precise load control model of the distribution network; and using a dynamic event triggering mechanism to reduce communication between substations and a master station of the distribution network, so as to realize effective exchange and precise execution of load control policy information. The method of the present invention can precisely realize precise control of a load, and can effectively alleviate communication transmission pressure of a distribution network, thereby ensuring the stability of a power grid and the continuous supply of critical loads.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention discloses a cable insulation aging multi-modal data fusion evaluation system, particularly relates to the field of power equipment state monitoring and evaluation, and comprises a multi-dimensional data acquisition module, a progressive data processing module, a hierarchical analysis module, a multi-stage output module, a data storage module and a self-diagnosis module. The multi-dimensional data acquisition module synchronously acquires electrical, thermal and chemical parameters; the progressive data processing module carries out three-level processing on data, namely preprocessing, feature extraction and fusion, the hierarchical analysis module evaluates the aging level through single-mode identification, multi-mode association verification and a fuzzy comprehensive evaluation model, and the multi-level output module outputs basic snapshots, early warning information and trend reports. The data storage module stores full-process data, and the self-diagnosis module regularly detects and pushes a fault report. The system solves the problems of one-sided evaluation, data asynchronization and the like in the prior art, improves the evaluation accuracy and the operation and maintenance convenience, and guarantees the safe operation of the cable.

The invention belongs to the technical field of power dispatching control, discloses an intelligent dispatching accident plan optimization method based on deep learning, and aims to solve the problems that traditional power accident dispatching depends on artificial experience, the plan generation efficiency is low, and the current situation of wide application of distributed photovoltaic and energy storage is difficult to adapt. According to the core technical path, on the basis of massive historical dispatching accident data, accident processing key features are automatically mined through a deep learning model, distributed photovoltaic output and energy storage charging and discharging features are fused, and a multi-source feature learning framework considering power flow balance in the accident state is constructed; and generating a dynamically optimized structured plan. By applying the method, the generation efficiency and accuracy of the accident plan can be remarkably improved, the accident handling speed is increased, the power failure duration and the economic loss are effectively reduced, meanwhile, the power flow stability of the power system after distributed energy access is guaranteed, and finally the overall reliability and the safe operation level of the power system are improved.

The invention relates to the technical field of chemical safety emergency, in particular to a quick plugging emergency system for leakage of a chemical storage tank. The system comprises a leakage sensing data acquisition module, a leakage feature extraction module, a plugging decision generation module, a leakage trend prediction module, a plugging decision optimization module and a plugging execution control module. The leakage sensing data acquisition module acquires multi-sensor leakage data flow in real time; the leakage feature extraction module combines preset leakage physical constraint information and analyzes data to determine a leakage source and diffusion feature information; the plugging decision generation module generates an initial plugging scheme according to a built-in decision space and a fitness constraint condition; the leakage trend prediction module predicts leakage development to obtain prediction feature information; a plugging decision optimization module optimizes the initial scheme according to the initial scheme to form an optimization scheme; and the plugging execution control module drives the execution device to complete plugging. The system can improve the leakage detection accuracy, decision scientificity and plugging effectiveness, and guarantees the operation safety of the chemical storage tank.

The invention relates to the technical field of power system stability control, in particular to a new energy station transient voltage and frequency coordination stability control method, which comprises the following steps of: analyzing a transient energy evolution rule of an equivalent synchronous motor and stored energy by constructing an alternating current and direct current hybrid dynamic model and fusing with multi-time scale characteristics; based on dynamic weight matrix optimization and a multi-target coordination control strategy, self-adaptive balance of power distribution is realized; a nonlinear feedback algorithm and a real-time compensation mechanism are designed in combination with the Lyapunov theory, and voltage frequency fluctuation in a disturbance scene is suppressed; establishing a multivariable cooperative control architecture, and dynamically adjusting a power adjustment priority and an inertia time constant through coupling iterative optimization of a source storage cooperative strategy to realize adaptive compensation of a transient energy propagation path; according to the method, the dynamic stability and the anti-interference capability of the new energy station under transient impact are remarkably improved, and safe operation of a high-proportion new energy power grid is guaranteed.

The invention discloses a fan blade clearance intelligent monitoring method based on multi-modal fusion. A laser radar and a visual sensor are adopted to collect point cloud data and image data of the fan blade; environment parameters are collected, validity judgment is conducted on the point cloud data and the image data of the fan blade according to the environment parameters, and radar monitoring data and visual monitoring data are obtained; modeling the fan blade according to the radar monitoring data to obtain a three-dimensional model of the fan blade, and further obtaining a radar measurement value of the blade clearance according to the three-dimensional model of the fan blade; performing image segmentation, edge extraction and depth estimation on the visual monitoring data in sequence to obtain a visual measurement value of the blade clearance; and the radar measurement value and the vision measurement value of the blade clearance are combined, and a multi-modal fusion algorithm is adopted for processing to obtain the fusion clearance distance of the fan blade. The accuracy and real-time performance of fan blade clearance monitoring are remarkably improved, blade collision accidents can be effectively prevented, and reliable guarantee is provided for safe operation of a wind turbine generator.

The invention relates to the technical field of wind power equipment detection, in particular to a wind turbine generator blade fracture damage detection method applying an inspection robot, which comprises the following steps of: S1, constructing a blade three-dimensional point cloud model and positioning the position of the robot in real time through a laser radar and a visual SLAM (Simultaneous Localization and Mapping) module carried on a robot body; s2, generating a spiral detection path covering the surface of the blade based on a path planning algorithm optimized by the genetic algorithm; s3, synchronously collecting blade surface data by adopting a multi-mode sensor array, wherein the blade surface data comprise pulse thermal imaging data, ultrasonic guided wave signals and high-resolution visible light images; s4, performing feature fusion on the acquired data through a deep convolutional neural network; through multi-modal sensor cooperative detection, deep learning feature fusion and digital twinborn evaluation technologies, full-process automatic detection from damage identification to life prediction is realized, the detection precision and efficiency are significantly improved, and reliable guarantee is provided for safe operation of the wind turbine generator.

The invention discloses a ship power battery health state intelligent assessment method and system based on multi-feature fusion and a dynamic threshold value, and relates to the technical field of ship power battery management. The method comprises the following steps: synchronously acquiring single voltage, current, temperature, internal resistance and insulation resistance signals in real time through a signal acquisition layer; processing through a feature fusion layer to form a fusion feature vector; inputting the CBP-LSTM model to continuously output SOH and SOC estimation values in real time to form an SOH / SOC estimation sequence; calculating adaptive upper and lower threshold boundaries for the sequence based on an RLS algorithm; health levels are divided through comparison of the health deviation and a threshold value, a control instruction is generated and issued to a BMS / EMS system, and closed-loop protection and energy optimization are achieved. According to the method, the defects of insufficient feature dimension, high model rigidity, lack of adaptive ability and system closed loop deficiency in the prior art are overcome, the method has the advantages of high evaluation precision, strong robustness and good real-time performance, and the operation safety of the ship power battery can be effectively guaranteed.

The invention discloses an engineering equipment operation intelligent monitoring and automatic adjusting system and method, and the method comprises the steps: collecting the multi-dimensional operation data of equipment in real time, constructing a three-dimensional operation state evaluation model through an intelligent algorithm, accurately recognizing a stable operation region, and generating a dynamic control reference and a feedback adjustment loop; a control channel decoupling and selective reconstruction technology is adopted, a beneficial synergistic effect is reserved while harmful coupling is eliminated, potential risk points such as resonance are identified through spectral analysis, and a safe operation boundary for perfecting a is established; the analysis equipment responds to dead zone characteristics, the dead zone characteristics are converted into an energy-saving working zone through energy analysis, an optimized power distribution strategy is made, and a multi-stage emergency reserve system is established; a fault early warning mechanism based on vibration characteristics is established, and rapid detection and power compensation of an abnormal state are realized; through equipment response matrix construction and time delay compensation analysis, an accurate real-time control instruction is generated, and the operation efficiency, the stability and the energy-saving level of engineering equipment are comprehensively improved.

The invention discloses a power equipment identification and anomaly detection method and system based on cross-modal alignment and a storage medium, and relates to the technical field of power inspection, and the method comprises the steps: collecting historical power equipment inspection images, and constructing a historical power equipment inspection image data set; constructing a power equipment identification and anomaly detection model; training a power equipment identification and anomaly detection model by using the historical power equipment inspection image data set to obtain a trained power equipment identification and anomaly detection model; and inputting a to-be-detected power equipment inspection image into the trained power equipment identification and anomaly detection model to obtain a detection result. According to the invention, through the design of a two-stage contrast learning framework, the model realizes effective balance between global semantic understanding and local defect identification. Therefore, multi-level optimization from semantic understanding to abnormal positioning is realized, and powerful technical support can be provided for intelligent inspection and safe operation and maintenance of a power system.

The invention provides a damp heat-oxygen-internal pressure-load coupled non-metal pipeline aging device and a method capable of evaluating the service life of an in-service non-metal pipeline. The non-metal pipeline aging device comprises six parts, namely an inner cavity, a box body temperature control system, a box body humidity control system, a pipeline internal pressure control system, a pipeline external load loading device and an air supply circulating system, and an accelerated aging test of a non-metal pipeline under different working condition combinations of four factors, namely damp heat, oxygen, internal pressure and load can be carried out by virtue of the aging device. Measuring oxidation induction periods before and after aging of the in-service non-metal pipeline and the non-metal pipeline with the same mark through a differential heat scanning method, substituting the oxidation induction periods into a non-metal pipeline aging life prediction formula coupled with damp heat-oxygen-internal pressure-load factors, and calculating to obtain the service aging life. According to the method, damp heat-oxygen-internal pressure-external load multi-factor coupling is realized, so that an actual service environment is accurately simulated, and comprehensive technical support can be provided for safe operation of the non-metal pipeline.

The invention discloses a safe operation system and a safe operation method of a space-ground integrated rescue vehicle, which are used for the rescue vehicle working in a complex disaster environment and ensure stable and reliable two-way communication connection between the rescue vehicle and a command center by establishing an intelligent switching mechanism of a low-orbit satellite main link and a ground network standby link. Real-time transmission of key instructions and field information is guaranteed, and a foundation is laid for safe operation. Advanced risk early warning is achieved through a space-ground cooperation mechanism, dynamic decision making and path planning are carried out by integrating satellite information and local sensor data, and the global adaptive capacity of the rescue vehicle to the complex disaster environment is improved. Satellite pre-sensing information is combined with a local decision, and a pre-control instruction is generated to act on a drive-by-wire chassis, so that the rescue vehicle can smoothly deal with hidden or developmental risks in advance, and the driving safety and stability are improved.

The invention relates to the technical field of valve leakage detection, and particularly discloses an ultrahigh pressure gas valve leakage detection method and system. According to the method, a unified leakage feature vector is obtained through multi-source acquisition and processing; capturing a time sequence dependency relationship, and determining a micro leakage mode; carrying out abnormal peak detection to obtain a preliminary leakage probability; combining the initial leakage probability with real-time environmental parameters, and dynamically adjusting a judgment standard; comparing the unified leakage feature vectors through a judgment standard, fusing position coordinate triangulation positioning, judging whether micro crack leakage occurs or not, and obtaining leakage position estimation; the historical data sequence is fused, the micro crack development trend is predicted, and the early warning level is determined; and generating a corresponding response signal according to the early warning level, and outputting micro crack leakage early warning. Multi-source data fusion, dynamic threshold adjustment and position prediction can be carried out, the accuracy and robustness of micro crack leakage detection are remarkably improved, and efficient guarantee is provided for safe operation of a pipeline.

The invention discloses a system safety assessment method and system for an energy storage power station containing a network-building type energy storage converter, and the method comprises the following steps: S1, collecting converter control parameters, power grid dynamic data and lithium battery mechanism data through multi-source heterogeneous data fusion, and carrying out the preprocessing through noise elimination and a normalization algorithm; s2, constructing a network construction characteristic quantitative evaluation system including indexes such as dynamic stability and voltage supporting capability; s3, establishing a three-layer evaluation system of an equipment layer, a control layer and a system layer, and calculating a total risk value of the system by using a weight distribution algorithm; s4, verifying system adaptability and optimizing parameters through time domain simulation in scenes such as a strong power grid and a weak power grid; and S5, generating an evaluation report containing parameter optimization suggestions, triggering graded early warning based on an index threshold value, and automatically generating a closed-loop control instruction. According to the invention, multi-dimensional safety assessment of the energy storage power station is realized, and systematic technical support is provided for safe operation of the network-forming type energy storage power station.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.