1835 results about "Electric parameter" patented technology

The invention provides a power cable fault detection method, which comprises the following steps of: obtaining a cable material, a laying environment and an insulation aging degree attribute parameter of each monitoring segment according to a pre-established power cable segment model, determining a correction coefficient of the segment model according to load current magnitude at different operating temperatures, and determining a power cable fault detection result according to the correction coefficient; the correction coefficient is used for adjusting the electrical parameter of each segment to adapt to the actual operation condition; in the fault positioning process, a time sequence analysis method is introduced, the dynamic change trend of current and voltage data before and after a fault occurs is considered, the fault occurrence moment is recognized through an anomaly detection algorithm, and the position coordinates of a fault point are corrected according to the difference between the cable material and the laying environment in combination with a judged fault area.

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

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 discloses a cable fault monitoring method and device based on multivariate time series data fusion, and relates to the technical field of cable multi-parameter fault monitoring, and the method comprises the steps: obtaining key time series characteristics representing the health state of a cable; dividing health state grades of the cable according to the electrical parameters and the environmental parameters, performing model training by taking the key time sequence characteristics and the health state grades as input parameters based on a multivariate neural network algorithm, and constructing a health assessment model; predicting the real-time electrical parameters and the environment time sequence parameters of the to-be-tested cable based on the health assessment model, and outputting a health state grade result of the to-be-tested cable; responding to the health state grade result and sending out a corresponding fault monitoring alarm instruction; the problem that the fault judgment condition is unreliable due to the fact that the time sequence dynamic characteristics of monitoring data are not considered in a monitoring method is solved, and the reliability, accuracy and detection efficiency of cable fault monitoring are remarkably improved.

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.

The invention discloses a photovoltaic system abnormal data detection method and device, and relates to an abnormal detection technology based on a multi-head self-attention mechanism fusion variational auto-encoder and a bidirectional long short-term memory network (BiLSTM). Abnormal detection of electrical parameters (including DC voltage and current and AC voltage and current), environment parameters (relative humidity, wind direction and wind speed) and system parameters (including inverter error codes, inverter environment temperature difference, module environment temperature difference and the like) in a photovoltaic system is realized. The invention discloses a photovoltaic system abnormal data detection method. The method comprises the following steps: (1) photovoltaic system data acquisition and data processing; (2) encoding time sequence data of the photovoltaic system; (3) data sampling and data feature enhancement; (4) data decoding operation; and (5) detecting an abnormal value of the photovoltaic system.

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 fault detection, and discloses a fault detection method and device for a power adapter, equipment and a storage medium. The method comprises the steps of obtaining original power supply electrical data of preset multi-dimensional electrical parameters of a target power adapter, and performing feature decoupling on the original power supply electrical data based on electrical features corresponding to the electrical parameters to obtain basic electrical feature data; performing time-frequency characteristic decomposition and characteristic reconstruction of the operation state on the basic electrical characteristic data to obtain power state characteristic data; performing operation characteristic association of multilayer electrical parameters and evaluation of associated operation states on the power supply state characteristic data to obtain a power supply performance evaluation result, and performing state parameter mapping of fault characteristics on the power supply performance evaluation result to obtain power supply state fault characteristics; and carrying out fault feature counting and fault space positioning on the power supply state fault features to generate a fault detection result. According to the invention, the accuracy of the fault detection result of the power adapter 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 discloses a fault monitoring method and system for an analog integrated circuit, and relates to the technical field of integrated circuits, and the method comprises the steps: collecting and recording the electrical parameter data of the analog integrated circuit; preprocessing the electrical parameter data, calculating the resource utilization rate of each subunit and extracting fault feature information; constructing a digital twinborn model corresponding to the analog integrated circuit, and simulating and predicting a fault based on the digital twinborn model; according to the electrical parameter data, the resource utilization rate and the fault feature information, a reinforcement learning algorithm is introduced, and a fault early warning strategy is optimized based on a simulated predicted fault; and according to the fault early warning strategy, early warning thresholds and early warning levels of different fault types are dynamically adjusted, and fault monitoring is realized. According to the invention, abnormal conditions can be found in time, greater damage to the system caused by potential faults is avoided, the real-time performance and accuracy of the monitoring system are ensured, fault information is rapidly provided for maintenance personnel, and the maintenance efficiency is improved.

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 PCBs (printed circuit boards), in particular to an intelligent PCB detection and repair method, which comprises the following steps: S1, initializing module configuration: automatically identifying the type and structure information of a PCB; s2, a dynamic detection stage: collecting electrical parameters of the PCB, and generating fault position information; s3, circuit dynamic characteristic compensation: analyzing the nonlinear distortion characteristic of the circuit, and calculating and executing dynamic compensation; s4, an automatic repair stage: generating a repair scheme, including solder joint repair, line reconstruction or signal compensation; and S5, an intelligent recheck stage: performing comprehensive retest on the repaired PCB, verifying the performance of the repaired area, and generating a repair evaluation report. According to the invention, the repair precision and efficiency are improved, the whole repair process is intelligent and automatic, the risk of manual intervention is reduced, and the overall repair efficiency and the long-term reliability of the PCB circuit are improved.

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 discloses a special transformer state management method and system based on intelligent monitoring and remote diagnosis. The method comprises the steps that electrical parameters of a special transformer user are collected through monitoring equipment; the collected data parameters are analyzed in real time, and the operation state and the abnormal condition of the equipment are judged; monitoring data are transmitted through a communication network, and remote monitoring is achieved; and when an abnormality is detected, triggering an alarm mechanism, and providing fault diagnosis information. According to the invention, the safety and operation efficiency of the power system are improved. Key parameters such as voltage, current, power and frequency can be collected in real time, abnormal conditions such as overload and undervoltage can be rapidly detected through an intelligent algorithm, and an alarm is triggered in time. The system adopts AES encryption to guarantee data transmission security, supports 4G / 5G network to realize remote monitoring, and ensures that operation and maintenance personnel can quickly obtain fault diagnosis information and personalized processing suggestions. The fault response speed and the system stability are obviously improved, and the operation and maintenance cost is reduced.

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 field effect transistor performance prediction, and particularly relates to a reliability prediction method, device and system for a field effect transistor, and the method comprises the steps: determining the correlation between the time sequence data of environment parameters, electrical parameters and performance parameters in historical operation records and the change of the performance parameters through a correlation analysis method when the time sequence data changes; key performance parameters are screened out, then a virtual operation model of the field effect transistor is constructed before life prediction is conducted through the screened key performance parameters, and historical data are used for conducting validity verification on the virtual operation model; finally, dynamic changes of environment parameters and electrical parameters are simulated through historical data when the successfully verified virtual operation model is utilized, the degradation trend of key performance parameters is recorded, residual life prediction is carried out based on the degradation trend, prediction efficiency is improved, life prediction can more accurately reflect actual working conditions, and the service life prediction efficiency is improved. And the accuracy of residual life prediction is improved.

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.