1561 results about "Electric parameter" patented technology

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 relates to the technical field of intelligent power grids, in particular to an intelligent power distribution room sensing system and method based on data fusion. The multi-source data acquisition module is used for synchronously acquiring electrical parameters, mechanical vibration signals, temperature distribution data and environment monitoring data of power equipment in a power distribution room; the edge computing gateway is connected to the multi-source data acquisition module and is configured to perform time alignment, abnormal value elimination and feature extraction on the original sensing data; the data fusion analysis server is connected to the edge computing gateway through a network and comprises a space-time alignment unit used for unifying monitoring data of different sampling frequencies to the same time reference; the self-adaptive weight fusion unit is used for dynamically adjusting fusion weight according to the reliability of the data of each sensor; the state evaluation unit is used for generating an equipment health degree score and a fault early warning signal based on a fusion result; according to the scheme, monitoring blind areas and misjudgment risks caused by data islands can be fundamentally solved.

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

The invention provides a power grid frequency modulation power energy coordination control method and system based on a super capacitor, and relates to the technical field of power grid frequency modulation, and the method comprises the steps: carrying out the time-frequency analysis of a collected power grid frequency signal, obtaining a frequency disturbance characteristic value, and determining power control data through combining with an electrical parameter value of a super capacitor group; and performing nonlinear programming calculation based on the power control data to obtain a power distribution sequence, dividing the power distribution sequence into a quick response section, a dynamic transition section and a steady state maintenance section according to a preset threshold value, and solving a control value by adopting an augmented Lagrange equation, recursive least square operation and a Lyapunov function respectively. Obtaining a power control instruction; and inputting a power control instruction into the disturbance observer, calculating a dynamic error value, constructing a nonsingular terminal equation, and outputting a power regulation signal in combination with recursive minimum entropy calculation and a superhelix algorithm.

This invention pertains to an intelligent electronic device designed for sophisticated monitoring and analysis of electrical power distribution. The device integrates at least one sensor for detecting electrical parameters from a distribution system to a load, coupled with an analog-to-digital converter to transform sensed analog signals into digital data. A processing module, linked to the converter, employs customized moving average filters to calculate the frequency of electrical power distribution, enhancing measurement accuracy by adjusting for transient fluctuations. It retrieves frequency threshold settings from memory to define operational bounds, logging frequency data around identified deviation events when measurements surpass these thresholds. This system facilitates precise frequency analysis and robust event logging, providing a comprehensive solution for maintaining electrical power distribution within defined standards.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention provides a substation self-adaptive inspection method based on equipment health degree dynamic evaluation. The substation self-adaptive inspection method based on equipment health degree dynamic evaluation comprises the steps of S1, collecting electrical parameters, mechanical vibration parameters and environmental parameters of substation equipment in real time through a multi-source sensor network, and S2, performing data cleaning and time synchronization on the electrical parameters, the mechanical vibration parameters and the environmental parameters, and performing subset dynamic normalization processing. According to the substation self-adaptive inspection method based on equipment health degree dynamic assessment, electrical, mechanical and environmental parameters of the equipment are acquired in real time through the multi-source sensor network, and the real-time performance and accuracy of equipment health degree assessment are remarkably improved by combining dynamic normalization processing and real-time anomaly detection. And the LSTM neural network is used for analyzing the change trend of the equipment health index, so that the residual life of a key component can be predicted, and differentiated inspection and resource optimization allocation can be realized.

The invention relates to the technical field of power equipment state monitoring, in particular to a power equipment operation state evaluation and prediction method based on big data, which comprises a multi-source data acquisition module, a feature engineering processing module, an intelligent evaluation and prediction module and a decision support output module. By setting a multi-source sensing end, when the operation state of the power equipment is evaluated, the definiteness of state evaluation of different types of equipment is ensured by formulating multi-modal data fusion standard parameters and setting different state sensing weights for different types of equipment; and meanwhile, real-time state sensing is performed by fusing electrical parameters, mechanical vibration, thermal distribution and environmental stress data, so that whether a sensing blind area problem caused by a single data dimension occurs in an equipment evaluation process or not can be detected in real time, the comprehensiveness and accuracy of equipment operation state evaluation are ensured, and state sensing fragmentation errors are further reduced.

The invention relates to an inverter IGBT junction temperature prediction method and related equipment, and the method comprises the following steps: generating a loss characteristic spectrum through collecting IGBT electrical parameters and analyzing switching loss; calculating surface heat flux density, and constructing a heat flux distribution matrix. And performing thermal-electric coupling analysis by combining the two to obtain a junction temperature dynamic response sequence. Fourier transform is utilized to extract temperature fluctuation harmonic features, and a feature set is constructed; and extrapolating and predicting a junction temperature change trend based on the feature set, and outputting an IGBT junction temperature early warning signal through threshold judgment. The technical problem that the service life of a device is affected due to the fact that a large amount of loss is generated due to frequent switching actions in the working process of the inverter, the junction temperature of a chip rises and even temperature fluctuation occurs is solved.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention relates to the technical field of fire safety, in particular to an intelligent fire-fighting electrical fire monitoring method, device and equipment and a medium, and the method comprises the steps: obtaining electrical parameters; according to the electrical parameters, a fire hazard identification model is established by using an LSTM algorithm and a CNN algorithm, the operation state of the electrical equipment is predicted according to the fire hazard identification model, an operation state trend prediction result is obtained, and a potential fire hazard type is identified; according to a fire hazard type identification result, in combination with a use environment of electrical equipment and a threshold determination algorithm, performing quantitative evaluation on a fire hazard risk to obtain a corresponding risk level, and when the risk level exceeds a set multi-layer threshold, automatically starting a linkage control mechanism; therefore, the problems of response lagging, high labor cost, high intermittent fault omission ratio and the like in the prior art are solved.

The invention provides a coordination control method for a photovoltaic off-grid hydrogen production system, which comprises the following steps of: acquiring voltage and current time domain signals of a photovoltaic array, constructing an impedance frequency characteristic mapping model through tensor decomposition and a radial basis function network, and calculating converter control parameters; based on converter control parameters and environment data, temperature-sensing-free self-correction photovoltaic characteristic prediction is achieved through electrical parameter indirect temperature estimation, and an MPPT reference track and a power prediction track are generated; multi-time-scale data coordination processing of millisecond-level electrical control, second-level pressure regulation and minute-level temperature optimization is executed, potential conflicts are solved, and an execution instruction set is generated; and generating and issuing a system comprehensive control instruction by adopting a double-layer deep reinforcement learning network. According to the method, the system efficiency and stability are improved, and the performance is excellent especially under the illumination fluctuation working condition.

The invention relates to the technical field of power grids, in particular to a metering box remote monitoring method, system and device based on artificial intelligence and a medium. Acquiring electrical parameter data and environmental data of the metering box; performing time domain and frequency domain feature extraction on the data to obtain key features; inputting the processed data into a preset abnormal state detection model; identifying an abnormal state of the metering box based on a model output result; and when the abnormity is identified, alarm information is generated and sent to a monitoring terminal through a remote communication network. Multi-dimensional monitoring data are collected in real time through multiple sensors, feature extraction and anomaly detection are performed by applying an artificial intelligence technology, a model is continuously optimized in combination with an online learning mechanism, and comprehensive sensing of the running state of the metering box and accurate recognition of the abnormal state are achieved. Compared with a traditional monitoring system, the method improves the detection accuracy, reduces the false alarm rate and the missing report rate, improves the fault processing efficiency through the automatic generation of the maintenance suggestions, and provides a powerful guarantee for the operation of a power system.

The invention relates to the technical field of electrical performance testing, in particular to a water supply equipment electrical performance detection system based on artificial intelligence, which comprises an electrical parameter acquisition module, an energy efficiency analysis module, an insulation performance evaluation module and a comprehensive diagnosis module. According to the invention, a three-phase voltage and current root-mean-square value is dynamically calculated through a broadband current transformer and a sliding window integration method, a 50Hz fundamental component amplitude is extracted to separate power frequency and high frequency harmonic waves, a continuous period energy efficiency ratio standard deviation triggers an insulation detection threshold value, and a parameter fluctuation and insulation degradation response mechanism is established. Harmonic interference correction synchronous processing energy efficiency ratio and leakage current integration, the detection error being lower than 5%, temperature rise rate linear regression and bearing current threshold value cooperative determination, winding aging cross validation, fault misjudgment rate reduction to 26%, closed-loop detection fusion transient capture, dynamic threshold value triggering and multi-parameter diagnosis, early warning success rate reaching 92%, and positioning response being reduced to 3 seconds.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention relates to the technical field of electrical variable measurement, in particular to a control system and a control method of a battery measuring instrument. The method comprises the following steps: acquiring single voltage, total voltage, current and internal resistance data of a battery pack to obtain initial electrical parameters; collecting the surface temperature of the battery pack to obtain an initial thermodynamic parameter; performing synchronous time sequence integration on the initial electrical parameters and the initial thermodynamic parameters, and unifying formats to obtain a battery running state monitoring data set; and performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state identification result. By constructing a complete intelligent monitoring system covering data acquisition, processing, abnormity identification, state evaluation, communication packaging and visualization, high-precision real-time monitoring, fault pre-judgment and remote communication of the running state of the battery are realized, and the synchronism, the accuracy and the adaptability of the system and the intelligence and the practicability of field application are remarkably improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power construction management system which comprises an electrical parameter acquisition module, a node state clustering module, a task instruction generation module and a construction path scheduling module. According to the method, deviation nodes are screened in combination with the resistance steady-state difference value and the power factor change ratio, instantaneous fluctuation interference is reduced, unclosed task marks and path correction instructions are generated through mapping of abnormal node physical coordinates and logic nodes, the positioning precision is improved, a trigger path is reversely deduced through grey correlation analysis, and the positioning accuracy is improved. The method comprises the following steps: establishing a logic association between power mutation and construction intervention, dynamically adjusting priority, performing bidirectional verification on a resistance response vector and a power factor sequence, enhancing detection and correction collaboration, fusing equipment nameplate data to quantify a load deviation degree, improving abnormal node screening efficiency, integrating path correction and construction instruction logic, shortening scheduling delay, and improving the reliability of the system. Physical and logic nodes are converted in real time, and synchronous execution of instructions is guaranteed.

The invention belongs to the technical field of power system control, and discloses an intelligent adjusting system and method for a transformer. The method comprises the following steps: inputting transformer operation data and weather data collected in real time into a pre-trained load prediction model to obtain predicted load data; inputting the acquired physical parameters and operation data and preset calibration parameters into a pre-constructed digital twinborn model to obtain a cooling capacity index; calculating the total heat source power of the copper loss and the iron loss according to the predicted load data and the electrical parameters of the transformer collected in real time, and dynamically quantifying the balance state of the total heat source power and the cooling capacity index through a matching degree evaluation function in combination with the cooling capacity index to obtain a matching degree evaluation index; and the problem of dynamic unbalance between the heat dissipation capability of the transformer and the load demand in an extreme environment is systematically solved.

The invention relates to the technical field of energy storage power station operation and maintenance, and particularly provides an energy storage power station risk assessment method and system, and the method comprises the steps: obtaining an operation state data set of an energy storage power station, the operation state data set comprises a plurality of equipment monitoring sequences, and each equipment monitoring sequence comprises the electrical parameter and environmental parameter monitoring data of energy storage equipment; according to the method, an electrical risk feature set and an environmental risk feature set are accurately extracted from operation state data by adopting risk feature extraction, real-time dynamic assessment is performed on extracted risk features based on a dynamic risk assessment model, and a comprehensive risk assessment result of an equipment monitoring sequence is generated; according to the comprehensive risk assessment result, an optimized regulation and control strategy of the energy storage power station is intelligently generated, and the optimized regulation and control strategy is fed back to an energy storage power station control system in real time so as to trigger targeted risk reduction operation, so that closed-loop management of risk assessment and regulation and control strategies is realized, and the risk assessment efficiency is improved. And the safe operation level and the risk response capability of the energy storage power station are obviously improved.

The invention discloses a power distribution network dynamic multi-target regulation and control method and system based on graph-model driving, and belongs to the technical field of power system automation. The method comprises the following steps: generating a dynamic topological graph model with a time mark correction label based on an acquired power distribution network topological state and preset power grid graph model verification; in combination with the dynamic topological graph model and the electrical parameters, executing state estimation and security risk analysis, and generating a security risk space-time distribution matrix; based on the security risk space-time distribution matrix, generating a multi-target strategy set for simultaneously optimizing a network loss index, a voltage fluctuation index and a load balancing index; and executing the multi-target strategy set, collecting power grid state response data after execution, calculating a control effect index, and judging whether to iteratively update the dynamic topological graph model or not. The method is used for solving the problems that topology modeling is static, state estimation granularity is coarse, risk assessment lacks a spatio-temporal evolution mechanism and strategy closed-loop capacity is weak in a traditional method.

The invention relates to the technical field of power grid equipment management, in particular to a power grid equipment state intelligent evaluation and prediction method based on artificial intelligence. Through data acquisition and processing and a real-time monitoring and fault early warning mechanism, in the data acquisition stage, historical data are called, key electrical parameters and fault information are acquired in real time by means of a sensor, a data and fault association mechanism is established, and in the real-time monitoring link, the similarity between a real-time curve and a standardized curve is calculated, so that the fault early warning accuracy is improved. Once the similarity reaches a threshold value, the system quickly gives an early warning, preliminarily judges the fault position, more accurately detects the specific condition of the fault position, and meanwhile, performs troubleshooting according to the weight of the fault position, can quickly position the fault and deal with the fault in time, reduces the power failure time of the fault, guarantees the stable power supply of a power grid, and reduces the influence on the production and life of a user. The method is of great significance in improving the safe operation level of the whole power grid.

The invention provides an overhead ground wire uninterruptible ice melting automatic segmentation method and system and a storage medium, and the method comprises the following steps: constructing an ice melting evaluation model based on the environment conditions and structure parameters of a power transmission line, and judging whether an ice melting device has enough output capability or not; if the output capability of the ice melting device is not enough to cover the whole ground wire for ice melting, constructing a plurality of segmentation configurations at the sectionable tower, generating a candidate segmentation scheme set, and performing electrical parameter calculation on each candidate segmentation scheme; taking minimization of the number of segments and minimization of ice melting energy consumption as double targets, adopting a multi-target genetic algorithm to carry out iterative evolution on the candidate segment scheme set, and screening out a Pareto optimal solution set meeting the capacity of an ice melting device and line safety constraints; and selecting the optimal segmentation configuration meeting the uninterrupted ice melting requirement from the Pareto optimal solution set, and outputting the optimal segmentation configuration. According to the invention, good lightning protection performance and economical efficiency can be taken into account under the condition of live ice melting without power failure.

The invention discloses an intelligent diagnosis method for electric power secondary equipment based on digital twinning, which relates to the technical field of operation and maintenance of electric power equipment, and comprises the following steps: uniformly accessing real-time data of the electric power secondary equipment and carrying out timestamp standardization to complete cross-channel alignment; constructing an equipment-level digital twinborn body, and calculating a key business volume, a contrast deviation of an output and a field volume and a credible interval; cross-channel transition parameters, key electrical parameters and link state parameters are extracted under the unified time axis and secondary network topology, and a constraint quantity set is generated; and constructing an evidence chain on the topology based on the contrast deviation and the constraint quantity, executing root cause convergence and conflict stripping, and forming a diagnosis conclusion with a confidence level and processing steps. Through unified time reference and multi-domain twinborn contrast, triple constraint quantity weighting and evidence chain reasoning, cooperative constraint of time sequence consistency, physical consistency and safety boundary is realized, positioning precision and closed loop efficiency are improved, false alarm and missing alarm are reduced, strong isolation of simulation and production links is guaranteed, and visual tracing is realized.

The invention discloses a power equipment overheating fault diagnosis method, system, equipment and medium, and relates to the field of fault diagnosis. According to the method, a visible light image and an infrared image are enhanced through a preset double-flow generative adversarial network to obtain a target visible light image and a target infrared image, and a device semantic mask graph is generated according to the target visible light image; projecting temperature data in the target infrared image to each segmented power device according to the device semantic mask map to form a device temperature distribution map; determining static temperature characteristics and dynamic temperature characteristics of each power device according to the device temperature distribution diagram, and generating a space-time temperature evolution diagram according to the static temperature characteristics, the dynamic temperature characteristics and the physical connection relationship among the power devices; and predicting an overheating fault probability, a fault type and a potential fault source of each power device according to the space-time temperature evolution diagram and the electrical parameters. By implementing the technical scheme provided by the invention, the overheating fault prediction accuracy of the power equipment is improved.

The invention discloses a switching method and device of a primary and secondary fusion switch, a medium and a program product, and relates to the field of electric power intelligent dispatching, and the method comprises the steps: obtaining real-time electrical parameters and historical electrical parameters of an operation power supply; determining a system disturbance level based on the change rate of the real-time electrical parameters relative to the historical electrical parameters, and determining a system sampling period according to the system disturbance level; collecting a target electrical parameter of a power supply to be switched in a system sampling period, and calculating a switching evaluation index according to the real-time electrical parameter and the target electrical parameter; according to the switching evaluation index and the system disturbance level, determining a dynamic control parameter of the power supply to be switched in; when the adjustment rate of the dynamic control parameter is greater than a first preset threshold value and the switching evaluation index is smaller than a second preset threshold value, generating a switching enable signal; and controlling the primary and secondary fusion switch to execute switching operation according to the switching enable signal. According to the invention, the transient response performance in the power supply switching process can be improved.

The invention provides a power grid energy conservation and loss reduction system based on dynamic reactive compensation and a reliability optimization method, and belongs to the technical field of power grid energy conservation and loss reduction. Comprising a data acquisition module which is used for acquiring real-time electrical parameters of a power grid; the reactive power demand calculation module is in communication connection with the data acquisition module and is used for receiving the real-time electrical parameters of the power grid transmitted by the data acquisition module, and the reactive power demand calculation module is further used for calculating the real-time electrical parameters of the power grid based on an improved compensation demand prediction algorithm. And according to the real-time electrical parameters of the power grid, dynamically calculating a reactive compensation amount and a reactive compensation type, and generating a compensation instruction by combining the reactive compensation amount, the reactive compensation type and a preset threshold value. The beneficial effects are that the compensation instruction is generated according to the reactive compensation type and the preset threshold value, and the compensation execution module switches the compensation device according to the compensation instruction, thereby realizing reactive dynamic adjustment, and enabling the power grid to achieve the effects of energy saving and loss reduction.

The invention relates to the field of on-line monitoring of electrical equipment, in particular to an on-line monitoring method of the electrical equipment, which comprises the following steps: acquiring multi-dimensional monitoring parameters of the electrical equipment, including an electrical parameter, a temperature parameter and an insulation parameter; performing feature extraction on the collected multi-dimensional monitoring parameters to obtain feature parameters corresponding to the monitoring parameters, and constructing a feature vector set; based on historical normal operation data and real-time acquisition data of the electrical equipment, establishing a dynamic threshold model, and calculating a threshold corresponding to the characteristic parameter through the dynamic threshold model; feature parameters in the feature vector set are compared with corresponding thresholds output by the dynamic threshold model, and whether the electrical equipment is abnormal or not is judged; when the abnormity exists, outputting an abnormity monitoring result; and if not, returning to S1. According to the invention, by collecting multi-dimensional parameters, calibrating, correcting, de-noising and extracting features, and combining historical and real-time data to build a model and verify, data preprocessing, threshold adaptation and anomaly judgment are realized, and the monitoring reliability is improved.

The invention relates to a remote fault diagnosis method and system for an intelligent energy storage power supply, and the method comprises the following steps: carrying out the real-time monitoring of electrical parameters of the energy storage power supply, capturing abnormal fluctuation, and transmitting the abnormal fluctuation to a remote intelligent diagnosis center; and extracting a fault feature vector group through a deep learning algorithm, analyzing a fault propagation path, and generating a fault propagation link diagram. Causal relationship reasoning is carried out based on the graph, and a fault source and a fault type are positioned. And according to the fault source and the type evaluation influence range, obtaining a fault influence degree evaluation report. Finally, an intelligent fault processing suggestion scheme is generated according to the report, rapid and accurate fault diagnosis and processing are realized, and safe and stable operation of the energy storage system is ensured. The technical problems that a traditional fault diagnosis method often depends on the experience of field technicians and simple fault detection equipment, time and labor are consumed, and in the face of complex or hidden faults, the traditional fault diagnosis method is often out of effort are solved.

The invention discloses a power distribution network multi-source load intelligent optimization control method, and relates to the technical field of power system power distribution networks, and the method comprises the following components: S1, building a digital twin model, S2, optimization control strategy preview, S3, strategy verification and evaluation, S4, strategy adjustment and optimization, and S5, practical application and feedback. According to the method, intelligent optimization control over the multi-source loads of the power distribution network is achieved by building the digital twin model and optimizing control strategy rehearsal, the topological structure, the electrical parameters and the real-time operation state of the physical power distribution network can be accurately mapped, the optimization control strategy is formulated according to the load priority and the collaborative weight of distributed energy, and the optimization control over the multi-source loads of the power distribution network is achieved. The operation efficiency and stability of the power distribution network are improved, the reliability and safety of load supply are ensured, and potential problems can be found and solved in time through simulation operation and strategy verification of the digital twin model, so that risks and faults possibly occurring in the operation process of the power distribution network are effectively avoided.