1974 results about "Voltage fluctuation" patented technology

The present application provides a multi-level coordinated voltage control method and system for a power distribution network based on three-tier priority objectives. The method comprises: acquiring operation parameters of a power distribution network; on the basis of the operation parameters of the power distribution network and a pre-constructed three-tier optimization objective planning model, using an affine theory, a duality theory, and power circle linearization and absolute value linearization methods to solve the three-tier optimization objective planning model to obtain maximization of an admissible net-load disturbance domain at each node, minimization of the total operation cost of the power distribution network and minimization of an expected voltage deviation; and performing coordinated control on various reactive devices of the power distribution network by means of the operation parameters of the power distribution network corresponding to the maximization of the admissible net-load disturbance domain at each node, the minimization of the total operation cost of the power distribution network and the minimization of the expected voltage deviation. The present application can more clearly characterize the uncertainty of distributed generation power output and the impact of the distributed generation power output on system reserve capacity, and mitigate the problems of ambiguous characterization of distributed generation power output characteristics, and multi-level voltage violation and voltage fluctuation of the power distribution network caused by large-scale distributed generation integration.

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention relates to the technical field of Internet of Things, in particular to an electric energy meter with a wireless communication module, which comprises a parameter acquisition module, a communication optimization module, an interference analysis module, a fluctuation evaluation response module and a load classification module. According to the invention, a mutual inductor captures a single-phase circuit signal, analog-to-digital conversion is combined to realize parameter quantization, interference is eliminated based on an active power identification algorithm, a structured parameter set is integrated, a wavelet packet decomposition parameter and a quantization threshold are dynamically adjusted according to a load rate, and spread spectrum factor configuration is adopted to improve channel efficiency. The method comprises the following steps: extracting key features by using wavelet packet characteristics; continuously monitoring voltage fluctuation by a sliding window; detecting and identifying an instantaneous interference event by a break variable; inhibiting noise by Kalman filtering and predicting a trend; and the performances of the system in the aspects of acquisition precision, transmission efficiency, interference immunity and detection sensitivity are enhanced.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention provides a multi-dimensional grid-connected test system for a photovoltaic string inverter, and the system comprises a power grid simulation module which is used for constructing a power grid simulation environment based on a multi-dimensional coupling mechanism of voltage fluctuation, frequency deviation and harmonic distortion; the photovoltaic array simulation module is used for constructing IV characteristic dynamic deduction models of different irradiance and temperature gradient effects based on a photovoltaic array equivalent circuit physical model to realize a natural simulation environment; the loading module is used for loading the power grid simulation environment and the natural simulation environment to the photovoltaic string inverter to be tested; the data acquisition module is used for monitoring running state data of the photovoltaic string type inverter in real time and constructing a running state data set containing multi-physical-quantity coupling characteristics; and the diagnosis module is used for inputting the running state data set into the trained LSTM neural network model to obtain a test result of the photovoltaic string inverter. According to the invention, the efficiency and convenience of the grid-connected test of the inverter can be improved, and the accuracy and reliability of the grid-connected performance of the inverter are ensured.

The invention relates to a dual three-phase permanent magnet synchronous generator rectification system and a direct current voltage stabilization control method thereof. Comprising a dual three-phase permanent magnet synchronous generator, a VSD decoupling converter, a load current observer, a dynamic load feed-forward compensator, a voltage closed-loop controller, a dead-beat predictive current controller, a maximum four-vector SVPWM modulation module and a six-phase rectifier. The double-three-phase topological structure adopted by the invention has the advantages of multiple control degrees of freedom, high reliability and the like. A dynamic load feed-forward compensator is constructed to perform dynamic feed-forward compensation on quadrature-axis current of the motor, so that the recovery time of bus voltage is shortened, and the voltage fluctuation amplitude is reduced; the designed dead-beat predictive current controller optimizes the dynamic characteristics of a current loop, improves the torque tracking performance of the motor, and further improves the voltage stabilization regulation capability of the system. The designed load current observer realizes the analysis redundancy of current detection and improves the reliability of the system.

The invention relates to the technical field of power grid monitoring, and discloses a voltage sensor multi-parameter real-time monitoring method and device in an Internet of Things environment. According to the method, real-time voltage fluctuation data of a target area is acquired by deploying a multi-channel voltage sensor array and is transmitted to an edge computing node; dynamic feature extraction is executed in the edge nodes, and a multi-dimensional feature matrix containing voltage fluctuation ratio, harmonic distortion and phase deviation features is generated; identifying voltage sag, overvoltage and harmonic resonance event characteristics based on an anomaly detection model; spatial correlation modeling is carried out in combination with power grid topological structure parameters, the coupling strength of adjacent sensor node parameters is calculated, and a multi-parameter correlation map containing event propagation paths and influence ranges is generated; inputting the voltage into a diagnosis model to generate a power grid state diagnosis report, and dynamically adjusting the sampling frequency and filtering parameters of the voltage sensor according to the power grid state diagnosis report. According to the method, real-time monitoring and analysis of multiple parameters of the power grid can be realized, and comprehensive support is provided for power grid state evaluation.

The invention provides a split type direct current charging multi-split group charging and group control control system, and belongs to the technical field of split type direct current charging piles. The split type direct current charging system comprising a main control cabinet, a power distribution unit, a charging pile terminal and other hardware architectures is constructed; a voltage monitoring unit, a current sensor module, a harmonic filter and other multi-dimensional sensor networks are integrated to acquire power grid state data in real time, and a sliding time window method is used to carry out segmented analysis on the data to extract power grid characteristic parameters; establishing a multi-objective optimization model comprising a power balance equation and a power grid stability equation to calculate an optimal power distribution matrix, adopting a neural network to realize intelligent distribution and dynamic balance adjustment of the power of each charging pile terminal, and starting an emergency power redistribution mechanism to ensure stable operation of the system when the power grid is detected to be abnormal. The technical problem of unbalanced power distribution of the multi-pile charging system caused by power grid voltage fluctuation and harmonic pollution is solved.

The invention relates to a dynamic power regulation and control method and system for an electric vehicle charging pile, and the method comprises the following steps: S1, based on the real-time operation data of a charging pile cluster, employing a multi-source information fusion technology, collecting a power grid voltage fluctuation signal through a Hall sensor disposed at a charging pile end, and combining with the battery charge state data transmitted by a vehicle-mounted BMS, and eliminating noise interference by using an improved Kalman filtering algorithm, and realizing multi-dimensional data fusion by using a D-S evidence theory to generate a multi-source working condition feature set. The method has the advantages that the power grid voltage fluctuation signal and the battery charge state data are integrated through the multi-source information fusion technology, the improved Kalman filtering algorithm is adopted to eliminate noise interference, multi-dimensional data fusion is achieved in combination with the D-S evidence theory, the integrity and reliability of working condition feature extraction are remarkably improved, and the working condition feature extraction efficiency is improved. And the LSTM-ARIMA hybrid prediction model is utilized to synchronously process the nonlinear features and the periodic rules.

The invention discloses a substation remote fault early warning system and method based on deep learning. The method comprises the following steps: obtaining a standardized substation operation data set; constructing model data of the improved variational auto-encoder; an optimized improved variational auto-encoder model is obtained; performing multi-round training on the operation data set of the standardized transformer substation by using the optimized improved variational auto-encoder model to form a high-sensitivity and high-accuracy anomaly detection model; acquiring operation data of the transformer substation in real time, inputting the operation data into the anomaly detection model, analyzing the operation data of the transformer substation in real time, and detecting an abnormal mode having significant deviation from the normal operation state of the transformer substation; and when the abnormal detection model detects an abnormal mode, the system automatically triggers remote fault early warning. According to the method, typical modes of temperature rise abnormity, voltage fluctuation abnormity, equipment decline trend abnormity and sudden composite abnormity are further identified according to the deviation degree of the reconstruction error, and the interpretability and guidance of early warning are enhanced.

The invention provides a power network state recognition method and system based on data feature analysis, which are used for continuously improving the power network state recognition precision through a data-driven optimization mechanism, providing full-process intelligent decision support for power grid operation and maintenance, and effectively promoting the power network to be converted from a passive operation and maintenance mode to an active prevention and control mode. The method comprises the following steps: acquiring a multi-dimensional power data set of a target power network, performing dynamic feature extraction processing on the multi-dimensional power data set to generate a power network operation feature set, and inputting the power network operation feature set into a debugged power state recognition model, and carrying out conjoint analysis on the voltage fluctuation characteristics, the current phase deviation characteristics and the equipment health degree characteristics through a power state identification model to generate a power network state classification result, and generating a network operation and maintenance strategy according to the power network state classification result. And the network operation and maintenance strategy is sent to the power dispatching terminal so as to activate corresponding equipment maintenance operation or load adjustment operation.

The invention provides a medical equipment fault detection method and system based on machine learning, and relates to the technical field of fault detection. The method comprises the steps of monitoring motor start-stop division operation cycles, synchronously collecting and processing vibration, frequency difference and voltage signals, calculating characteristic difference to generate a dynamic sequence, extracting a non-convergence trend and phase deviation, fusing voltage and frequency fluctuation characteristics, constructing a threshold rule to judge abnormity, and outputting a light fault early warning signal. According to the invention, through precise monitoring of motor start-stop nodes, period division, collection of vibration, frequency and voltage signals, combination of time alignment and noise filtering, improvement of signal quality, cross-period calculation of characteristic difference, and extraction of non-convergence trend and phase offset, the ability of capturing tiny anomalies is enhanced; according to the method, multi-dimensional indexes such as voltage fluctuation and frequency deviation are fused, a threshold judgment mechanism in a continuous window is constructed, stable identification and early warning of equipment light faults are achieved, and the detection accuracy and response timeliness are 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 electric energy meter data analysis, and discloses a power consumption data analysis operation and maintenance method and system of an electric energy meter. The method comprises the following steps of: acquiring and preprocessing electricity utilization state data, namely synchronously acquiring voltage, current and power data, and performing timestamp alignment, data cleaning and resampling; classifying according to preset peak, flat and valley time intervals, and extracting time-frequency domain characteristics such as voltage fluctuation amplitude and current change rate in each interval to generate a set; detecting time period switching frequency, and marking an abnormal interval if the time period switching frequency exceeds a threshold value; generating an abnormal feature subset according to similarity decomposition of historical anomalies and current features; correcting the abnormal feature subset based on the electrical and time sequence parameter association relationship; and reversely backtracking a potential fault source along the fault association map, determining an operation and maintenance priority according to the number of abnormal parameters, position depth and the like, and generating an operation and maintenance instruction. The system comprises corresponding modules such as data acquisition and processing. According to the scheme, data can be accurately analyzed, a fault source is positioned, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: acquiring cable operation log data and cable internal structure design data, extracting a cable structure distribution condition in combination with an operation log, and evaluating cable operation state data; electric power sudden demand characteristics are identified, and then a non-uniform thermal coupling unbalance state is determined; based on the thermal coupling unbalance and the operation state data, detecting the coupling failure condition of the cable multilayer structure, and further judging the structural integrity damage condition; deducing a load stability disintegration situation according to a structure damage and insulation strength reduction trend; further detecting the decline of the quality of transmitted electric energy, analyzing the increase degree of abnormal voltage fluctuation, and identifying the fatigue trend of the metal material of the cable; through the above cable aging diagnosis, the cable aging diagnosis data is output. According to the cable aging diagnosis method and device, cable aging diagnosis is diagnosed, so that cable aging diagnosis is more accurate and efficient.

The invention provides a modularized multi-level converter current control method based on feedforward analysis. The deviation between the direct-current side voltage and the reference value is stably monitored in real time based on the direct-current bus voltage, a first waveform signal is dynamically generated, and the direct-current side voltage fluctuation range is limited. According to the circulating current interphase voltage sharing, the dynamic deviation between the actual value and the rated value is calculated by collecting the capacitor voltage actual value of the bridge arm sub-module, and a second waveform signal is generated to balance the circulating current distribution of each phase of bridge arm. The loop current suppression based on feedforward analysis generates a third waveform signal to compensate for high-frequency loop current interference through real-time comparison of theoretical modeling and actual loop current components. In the modulation wave generation process, information in all aspects is integrated to accurately control the on-off state of the sub-module, so that the operation effect of the system is improved under the complex working condition, and the real-time performance and predictability of control are considered through combination of closed-loop adjustment and feed-forward compensation, so that the effectiveness of control is improved.

The invention relates to the technical field of distributed energy storage monitoring, and discloses a distributed energy storage monitoring system based on an Internet of Things data platform. The system comprises a multi-dimensional state evaluation module, a load response strategy generator, a collaborative optimization decision module and a dynamic control instruction compiler. The multi-dimensional state evaluation module obtains real-time Internet of Things monitoring data of the energy storage unit and extracts dynamic operation characteristics, and the data comprises a voltage fluctuation curve, temperature gradient distribution and the number of charge and discharge cycles; the load response strategy generator identifies the operation mode through a charging and discharging mode classification algorithm, and generates a state vector set containing operation mode identification and load capacity score mapping; the collaborative optimization decision-making module generates a fusion result containing node energy interaction characteristics and capacity balance coefficients based on the set; and the dynamic control instruction compiler inputs the fusion result into a strategy optimization model, and outputs a control instruction element containing a load priority time sequence instruction and a parameter self-correction coefficient.

The invention relates to the technical field of electric power operation and maintenance, in particular to a distributed optical storage unit cooperative scheduling control method and system, the method is applied to a distributed optical storage system comprising a plurality of optical storage units, each optical storage unit comprises a photovoltaic unit, an energy storage unit and a load unit, and is configured with a local unit controller. Comprising the following steps: collecting real-time operation data of an optical storage unit, and extracting local collaborative scheduling characteristics and a local DC bus voltage instantaneous fluctuation ratio from the real-time operation data; each unit controller inputs the corresponding local cooperative scheduling feature into a preset local scheduling strategy generation model, and outputs an initial scheduling action instruction set; and performing global voltage stability evaluation on the distributed optical storage system based on the local direct current bus voltage instantaneous fluctuation rates corresponding to all the optical storage units, and calculating to obtain a system voltage fluctuation index. The distributed local control capability and the global collaborative demand are organically fused, and the global voltage stability and the collaborative scheduling consistency are ensured.

The invention relates to the technical field of electric energy identification, in particular to an energy consumption identification system based on a power transformation aliasing scene, which comprises a disturbance boundary extraction module, a boundary stability judgment module, an aliasing state identification module, a periodic power calculation module and an energy consumption attribution judgment module. According to the method, a voltage sag record with a phase offset characteristic is formed through continuous tracking of the periodic voltage fluctuation starting and ending point indexes, a stable fluctuation judgment mechanism is constructed in combination with offset statistics, and accurate identification of aliasing sections is realized by means of disturbance amplitude and frequency extraction. A power sequence is constructed on the basis of voltage and current synchronous sampling values, period integration is completed, a high-precision calculation result of energy consumption is formed, fine division of cross interference period energy consumption under the condition of no physical isolation is achieved, dependence of external metering equipment is avoided, independent quantization of overlapping load energy consumption is achieved, and the calculation efficiency is improved. And a feasible means is provided for energy efficiency evaluation in a non-intrusive load identification and high-coupling scene.

The invention relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The invention discloses a battery pack short circuit early warning and protection method based on multi-sensor fusion, and the method comprises the steps: S1, collecting the multi-dimensional sensor data, including temperature distribution data, voltage fluctuation data, gas component data, deformation data and current ripple data, of a battery pack in real time; s2, performing space-time alignment and feature extraction on the multi-source sensor data, and constructing a fusion feature vector; s3, based on the fusion feature vector, performing short circuit type identification and risk level evaluation through a two-stage short circuit risk judgment model; and S4, triggering a grading response mechanism according to the risk grade, wherein the grading response mechanism comprises an early warning signal, current limiting, fuse action or battery cluster isolation. By fusing five-dimensional data of temperature, voltage, gas, deformation and current, a two-stage short circuit risk judgment model is constructed, and accurate classification and risk grading evaluation of short circuit types are realized. A four-level response mechanism is triggered based on an evaluation result, and protection is strengthened step by step from early warning to physical isolation.

The invention relates to the technical field of on-load voltage regulation, in particular to a transformer on-load voltage regulation control method. According to the technical scheme, the transformer on-load voltage regulation control method comprises the following steps that S1, a transformer cluster running in parallel is decomposed into distributed intelligent nodes, and each node executes local voltage prediction and constraint condition management; s2, adopting a distributed model prediction control framework, and cooperatively solving a voltage regulation optimization target through an edge calculation unit; s3, a harmonic disturbance signal with a preset amplitude is injected into the transformer, equivalent impedance is dynamically identified based on response data, and virtual impedance compensation is triggered; and S4, establishing a hierarchical cooperative control architecture, and realizing clock synchronization, dynamic weight distribution and multi-objective optimization decision among nodes. Through a cooperative mechanism of distributed predictive control and dynamic impedance compensation, the response speed and the regulation precision of the on-load voltage regulation system to the power grid voltage fluctuation are remarkably improved, and the voltage out-of-limit problem under the complex working conditions of new energy grid connection, load sudden change and the like is effectively solved.

The invention relates to the technical field of unit power failure detection, in particular to an AC power supply input power failure detection method and system. The AC power supply input power failure detection method comprises the following steps: comparing a characteristic voltage value with a preset threshold value; triggering a sampling frequency adjustment mechanism according to the preliminary detection signal, increasing the sampling rate according to the voltage fluctuation frequency by adopting an adaptive algorithm, and obtaining high-precision sampling data through real-time calculation; for the optimized power consumption control scheme, acquiring current system state data, coordinating quick response capability and low power consumption optimization through a state machine algorithm, and outputting a multi-stage interaction strategy signal; adjusting a dynamic threshold value of capacitance voltage division design according to the multi-stage interaction strategy signal, and predicting voltage fluctuation by adopting a Kalman filtering algorithm to obtain smoothed detection sensitivity data; and analyzing the misjudgment rate reduction degree through a threshold fine tuning algorithm in combination with the abnormal fluctuation characteristics and the partial pressure state, and outputting a final power failure detection result.

The invention discloses a power grid voltage fluctuation suppression method and system based on dynamic voltage regulation, and the method comprises the steps: synchronously collecting a three-phase voltage signal through a sensor array, generating a multi-dimensional feature vector through Park transformation, and extracting a time-frequency joint feature through a mixed multi-scale decomposition algorithm, and inputting a model fusing fuzzy reasoning and a space-time diagram neural network, generating a cooperative adjustment strategy in combination with power grid topology and equipment constraints, and optimizing model parameters through a closed-loop correction mechanism driven by a dynamic forgetting factor. The system comprises a signal acquisition module, a characteristic decomposition module, an intelligent decision module and a closed loop correction module. According to the method, high-precision suppression of power grid voltage fluctuation is realized, the multi-node cooperative adjustment capability is improved, the method can be adaptive to power grid operation state changes, technical feasibility and economic rationality are considered, and an effective scheme is provided for modern power grid voltage stable control.

The invention discloses an improved DBSCAN abnormal node identification method based on space-time fusion features in the technical field of power systems, and the method comprises the steps: extracting five types of spatial topology features, including node proximity centrality, betweenness centrality and hierarchy depth, through constructing a power distribution area adjacency matrix; according to the method, 16 time sequence features such as a voltage fluctuation index, a current peak-valley difference and power integration are combined to form space-time dual feature representation, principal component analysis (PCA) is adopted to carry out dimension reduction processing on fusion features, and more than 90% of variance information is reserved to reduce calculation complexity. According to the method, the propagation rule of electricity stealing disturbance in a power grid is effectively described through spatio-temporal feature fusion, the problem of dimension disasters is solved through PCA dimension reduction, adaptation of DBSCAN to a complex topology scene is improved by means of dynamic parameter optimization, compared with a traditional detection technology, the false detection rate is reduced by 50% or above, the calculation efficiency is improved by 80%, and the method is suitable for large-scale popularization and application. The method can be widely applied to the fields of high-loss transformer area electricity larceny screening, abnormal electricity consumption behavior monitoring and the like, and provides technical support for safe operation of an electric power system.

An active-reactive voltage cooperative control method for multiple reactive compensation devices considering energy storage comprises the steps that 1, simulation analysis is conducted on time sequence voltage fluctuation characteristics of a high-permeability photovoltaic grid-connected system, and dynamic influences of photovoltaic output fluctuation in different time periods on system node voltage are quantitatively evaluated; 2, solving a non-dominated solution set of the model by adopting a multi-target particle swarm optimization algorithm, realizing collaborative optimization and trade-off analysis of multi-dimensional performance indexes, and establishing a reactive power regulation model and an energy storage active power support model; and organically coupling the reactive power regulation model and the energy storage active power support model. By means of the method, high photovoltaic area voltage treatment can be effectively carried out in real time, and the method has high engineering application value and wide popularization prospects.

The invention relates to the technical field of charging pile management, in particular to a new energy charging pile management method and system. The method comprises the following steps: obtaining a charging log of a charging module through a control terminal of the new energy charging pile, and carrying out the abnormal analysis of the AC voltage fluctuation, and obtaining the AC voltage abnormal fluctuation data. On the basis, analyzing the thermal stability imbalance of the power device, and further quantifying the power conversion performance loss stage number; thirdly, giving an over-limit voltage stage risk level according to the power conversion performance loss level, and constructing a charging risk prediction model according to the risk level; and finally, the charging risk prediction model is sent to a control terminal for management and optimization of the new energy charging pile. According to the invention, the charging pile management technology is optimized, so that the charging pile management technology is more perfect.