3099 results about "Voltage fluctuation" patented technology

A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

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 provides a power battery charging and discharging management optimization method and system based on deep learning, and the method comprises the steps: firstly obtaining real-time battery state data including a battery temperature sequence, a voltage fluctuation sequence and a current change sequence during the charging and discharging of a target power battery, and inputting the real-time battery state data into a pre-trained deep learning model; a battery temperature time-varying characteristic, a voltage fluctuation periodic mode and a current change abnormal fluctuation interval are analyzed through different branches of a deep learning model, and a dynamic optimization strategy which is matched with a current charging and discharging stage and contains a charging and discharging rate adjustment threshold value and the like is generated based on a fusion analysis result of the characteristics. And finally, charging and discharging parameters are adjusted in real time according to the dynamic optimization strategy, so that the charging and discharging parameters meet preset battery health degree constraint conditions and energy efficiency maximization is realized, thereby intelligently optimizing charging and discharging management of the power battery, improving the performance of the battery and prolonging the service life of the battery.

The invention discloses a power distribution network intelligent optimization scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power distribution network operation optimization, and the method comprises the steps: recognizing the voltage fluctuation and stability problems of a power distribution network, and setting an optimization target; designing a multi-agent system, and defining agents to establish a load flow calculation model; optimizing the joint strategy by adopting a reinforcement learning method based on an anti-fact multi-agent strategy gradient; designing an optimization objective function to ensure physical constraint and equipment operation limitation; historical data are collected and preprocessed, and a reinforcement learning algorithm is utilized to train an intelligent agent; deploying the trained intelligent agent, collecting power grid data and adjusting a control strategy; and multi-time-scale optimization control is designed in combination with response characteristics of different devices. The method can effectively reduce voltage fluctuation, improve the power grid dispatching efficiency, enhance the flexibility and adaptability of the power grid, is suitable for intelligent power distribution network optimization dispatching of large-scale distributed power supplies, and improves the stability and the intelligent level of power grid operation.

The invention discloses an energy storage control and adjustment method and system for distributed photovoltaic consumption, and relates to the technical field of renewable energy sources, and the method comprises the steps: obtaining illumination intensity, temperature data and historical load data, and predicting the future photovoltaic power generation amount and load demand changes; analyzing the voltage fluctuation of the power grid and the charging demand of the energy storage system based on the prediction results of the photovoltaic generating capacity and the load demand; according to the health state of the energy storage battery, optimizing the charging and discharging depth, and outputting an energy storage adjustment plan; dynamically optimizing the charging and discharging plan and reactive power regulation of the energy storage system by using a multi-objective optimization algorithm, and generating an optimal reactive power regulation instruction; according to the reactive power adjustment instruction, performing transformer area self-adaptive cooperative control to obtain a cooperative control result; according to the invention, through an area adaptive cooperative control mechanism, the reactive power regulation capability of the adjacent areas can be automatically coordinated when the regulation capability of the local energy storage system is insufficient, and the stability of the power grid and the utilization efficiency of the energy storage system are further enhanced.

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 motor train unit battery pack evaluation, and provides a motor train unit battery health state evaluation and life prediction method and system, and the method comprises the steps: building a database through voltage fluctuation and temperature data in a floating charge state, and analyzing a correlation mechanism between target parameters, such as a battery capacity retention ratio and active lithium stock, and internal chemical substance loss; respectively extracting driving behavior characteristics, temperature parameters and electrochemical impedance spectroscopy by adopting second-level, minute-level and hour-level time granularity, and aligning multi-frequency data by utilizing a dynamic time warping algorithm; constructing a graph structure model taking the electrochemical parameters as nodes and cross-level association as edges, and extracting a battery aging characteristic topological relation through a graph neural network; and finally, establishing a prediction model fused with multi-dimensional features, and realizing accurate prediction of the residual life and the capacity recession inflection point of the battery. And the multi-scale characteristics are combined with an electrochemical mechanism, so that the accuracy of battery health state evaluation under a complex working condition is improved.

The invention relates to a self-adaptive intelligent power control method for an electric energy router in a photovoltaic power generation system. According to the method, key parameters of photovoltaic, energy storage and load are monitored in real time, a virtual power pool is constructed to quantify a multi-source power margin and dynamically distribute a priority sequence, energy storage output is dynamically adjusted in combination with a feed-forward compensation control strategy, frequency modulation deviation is periodically checked, weight optimization redistribution is triggered, and energy storage output is dynamically adjusted. Cooperative control of photovoltaic load reduction frequency modulation and DC bus voltage stability is realized, the multi-source cooperative frequency modulation response capability and the energy utilization rate are effectively improved, voltage fluctuation caused by power abrupt change is suppressed, and both the system operation economy and the power supply reliability are considered.

The invention relates to the technical field of power safety early warning, in particular to a power transmission line power safety early warning method and system, and the method comprises the following steps: collecting current and voltage data in real time, recognizing the trend of current and voltage fluctuation and frequency change, carrying out the load change analysis, and analyzing and comparing the fluctuation amplitude of different time periods. And generating an electrical characteristic fluctuation analysis result. According to the invention, through electrical characteristic fluctuation analysis, unstable factors in a power system are identified in time, the aging degree of a power transmission line is deeply analyzed, the aging process is calculated, the influence of a load and an external environment on the line temperature is synchronously monitored, and the influence of load fluctuation and environment change on the line temperature rise is comprehensively analyzed; whether the line is close to the upper temperature limit or not is judged in time, electrical characteristic fluctuation is combined with line aging and temperature risk prediction, a potential fault area is positioned, the fault type and the risk level are recognized in advance, early warning accuracy and response speed are improved, and power supply safety and stability are enhanced.

The invention discloses an intelligent regulation and control method and system for full-amount resource collaborative power grid power flow, and relates to the technical field of resource collaborative power grid power flow, and the method comprises the steps: obtaining a plurality of partition groups, and combining the partition groups with a power grid topology; obtaining a plurality of voltage grades, and randomly combining the partition groups with the voltage grades to obtain a plurality of efficiency optimization groups; voltage fluctuation data and power flow burden data in the power flow regulation and control process are obtained, and a power flow evaluation model is constructed based on a long-short-term neural network; and matching and constructing a point cloud grid model according to the output of the power flow evaluation model and the efficiency optimization group, performing surface analysis on the point cloud grid model to obtain the selected efficiency optimization group, and applying the selected efficiency optimization group to power grid power flow regulation and control, thereby solving the problems of contradiction between power grid power flow calculation precision and regulation and control efficiency and insufficient dynamic adaptability.

The invention relates to a multi-source data electric energy quality energy-saving operation monitoring method and device based on AI Internet of Things, and the method comprises the steps: obtaining a transient electromagnetic signal of a permanent magnet operating mechanism circuit breaker, and extracting a time sequence characteristic parameter of the transient electromagnetic signal; judging whether a harmonic problem caused by an inverter exists or not and whether voltage suddenly rises or drops or not, and determining whether the time sequence characteristic parameters are associated with power quality disturbance or not; acquiring a voltage jump amplitude and a voltage swell rate to obtain a voltage fluctuation degree, and outputting a discharge strategy of the energy storage system through a pre-established discharge optimization model; respectively calculating to obtain a voltage distortion rate and a three-phase voltage unbalance degree, and selecting a charging strategy of the energy storage system through a pre-established charging optimization model; predicting a sudden rise rate and a sudden drop rate of the sample to obtain a prediction result, and forming an energy-saving optimization scheme of an energy storage charging and discharging strategy according to the prediction result; the charging and discharging power of the energy storage pool is automatically adjusted, an energy-saving optimization scheme is formed, and the stability of a power grid is effectively improved.

The invention relates to the field of high-voltage direct-current power transmission, and aims to provide an MMC converter valve and method for realizing alternating-current and direct-current fault ride-through based on an energy consumption sub-module. On the basis of a topological structure of a conventional MMC converter valve, M energy consumption sub-modules which are sequentially connected in series and are the same in structure are arranged between a bridge arm inductor of each bridge arm and the midpoint of an upper bridge arm and the midpoint of a lower bridge arm, and each energy consumption sub-module comprises a main through-flow branch, an energy consumption branch and a bypass loop which are connected in parallel; in the main through-current branch, the half-controlled switching device T1 is reversely connected in parallel with a fly-wheel diode D1 of the half-controlled switching device T1, and then is reversely connected in series with the full-controlled switching device T2 and a reverse fly-wheel diode D2 of the full-controlled switching device T2; the energy consumption branch comprises an energy consumption resistor Rd, and the bypass branch comprises a bypass switch K. According to the invention, alternating current fault ride-through and direct current fault ride-through are integrated into the topology of the MMC converter valve, so that the integration level of the protection scheme is improved; voltage fluctuation during alternating-current fault ride-through can be effectively smoothed, and the stability during alternating-current fault ride-through is improved.

The invention provides a data transmission rapid verification method of a non-standard Type-C interface, which comprises the following steps: firstly, obtaining a verification request triggered when target equipment accesses the non-standard Type-C interface, extracting interface real-time signal characteristics including a voltage fluctuation sequence and the like based on the physical connection state identifier, and sending the extracted interface real-time signal characteristics to the non-standard Type-C interface; and then dynamically matching the real-time signal characteristics with a standard transmission parameter library, generating a test instruction set containing a priority ranking verification action and a trigger condition threshold value, calling the test instruction set to perform staged signal injection and feedback capture on the interface, recording an execution result and marking a time sequence, and finally generating a verification report according to the result. And the abnormal nodes which do not meet the transmission stability index are mapped to the interface physical structure defect description, so that rapid, comprehensive and accurate data transmission verification of the non-standard Type-C interface is realized.

The invention discloses a flywheel energy storage system control method based on data center photovoltaic power supply, and belongs to the technical field of power system and new energy grid-connected control, and the method comprises the steps: obtaining the output prediction data of photovoltaic power generation, the real-time load power, the voltage fluctuation parameter of a commercial power grid, and the real-time rotating speed state of flywheel energy storage; generating a power gap predicted value by taking the real-time rotating speed state of flywheel energy storage as a dynamic constraint variable; generating a flywheel commercial power cooperative control strategy according to the commercial power voltage fluctuation parameter; the temperature change rate of the flywheel motor is detected in real time, and a control instruction is generated; and synchronously sending the control instruction to the photovoltaic inverter and the flywheel motor, adjusting the parameters of the photovoltaic inverter and the flywheel rotating speed response data, and dynamically updating the control parameter set. According to the invention, a dual adjustment mechanism of flywheel rotating speed dynamic constraint and voltage fluctuation ratio weight distribution is adopted, and a temperature gradient harmonic collaborative suppression strategy is combined, so that millisecond-level power compensation response can be realized, the power grid dependence degree is reduced, and the service life of an energy storage system is prolonged.

The invention discloses a cable aging test system and method based on the Internet of Things, and relates to the technical field of cable testing, and the method comprises the steps: collecting cable operation parameters in real time through a circuit breaker node disposed in a cable line, and synchronously monitoring the environment temperature and humidity; wherein the cable operation parameters comprise conductor temperature, leakage current, voltage fluctuation value and load power; the collected cable operation parameters are transmitted to a cloud platform through an Internet of Things communication module; analyzing the aging degree value of the cable insulation layer based on an electrothermal two-factor synergistic aging model; dynamically calculating an ignition risk value of the cable through a fire risk assessment algorithm in combination with the aging degree value; and when the aging degree value or the ignition risk value exceeds a threshold value, generating an early warning report, pushing the early warning report to the terminal equipment, and marking the position of the hidden danger at the same time.

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 electric energy meter error prediction method and system based on big data analysis, and relates to the technical field of electric power measurement, and the method comprises the steps: firstly reading the device attribute, the voltage fluctuation threshold value and the power utilization load threshold value of a target electric energy meter, dividing parameters into a plurality of voltage fluctuation intervals and load proportion intervals, and constructing a plurality of power utilization scenes; thirdly, obtaining a sample training set based on the power big data by taking device attributes and power consumption scenes as constraints, and training through a BP neural network to obtain a plurality of error predictors; and finally, by establishing an error prediction model, inputting a real-time voltage fluctuation coefficient, an electricity load proportion and an environment factor, and outputting a prediction error coefficient of the electric energy meter. The technical problem that an existing electric energy meter is inaccurate in error prediction in a complex environment is solved, and the technical effect that metering of the electric energy meter is more accurate under different voltage fluctuation, environment conditions and load conditions is achieved.

The invention discloses an electric energy meter fault detection method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining the voltage data, current data and temperature and humidity data of an electric energy meter in a current sampling time period, obtaining the historical feedback layer gain of the electric energy meter in a previous sampling time period, and determining a final gain parameter; multiplying the final gain parameter by the current bandwidth and the current cut-off frequency of the anti-interference filter to obtain the updated bandwidth and cut-off frequency of the anti-interference filter; when the anti-interference filter works based on the updated bandwidth and cut-off frequency, acquiring a voltage fluctuation value and a current fluctuation value filtered by the anti-interference filter; and comparing the voltage fluctuation value with a voltage fluctuation threshold value, comparing the current fluctuation value with a current fluctuation threshold value, if the voltage fluctuation value is greater than the voltage fluctuation threshold value and the current fluctuation value is greater than the current fluctuation threshold value, judging that the electric energy meter has a high-interference fault, otherwise, judging that the electric energy meter does not have the high-interference fault.

The invention relates to the technical field of charging piles, and provides a DC charging pile voltage stability optimization method and device and a storage medium, and the method comprises the steps: obtaining voltage sequence data of a DC output end of a charging pile, carrying out the segmentation statistics of the voltage sequence data, and obtaining an optimization adjustment matrix and a voltage compensation factor matrix; and performing dynamic adjustment on a preset direct current output control strategy of the charging pile based on the optimization adjustment matrix to obtain an adjusted control strategy parameter, performing compensation calculation on the adjusted control strategy parameter by using the voltage compensation factor matrix to obtain an optimization control instruction, and generating a dynamic voltage stabilization control signal according to the optimization control instruction. An output control strategy is dynamically adjusted by optimizing an adjustment matrix and a voltage compensation factor matrix, an efficient dynamic voltage stabilization control signal is generated, and the problems of response lag and insufficient prediction precision existing in the prior art when a complex voltage fluctuation mode is processed and a future voltage change trend is predicted are solved.

The invention provides a power management chip pin state intelligent detection method and system. The method comprises the steps that temperature difference quantity is input into a gradient heat conduction compensation structure, and compensation current opposite to a temperature gradient vector is generated based on temperature field distribution in the copper-based composite material; superposing and coupling the voltage fluctuation signal and the compensation current to generate a reference voltage signal; performing frequency component separation processing on the reference voltage signal, and extracting electromagnetic interference characteristic data in a frequency component; performing time sequence correlation on the electromagnetic interference characteristic data and the temperature distribution data, dynamically adjusting an interference suppression parameter through closed-loop control, and acting the adjusted interference suppression parameter on an interference suppression process of a reference voltage signal to generate a pin state detection signal; and detecting the pin state of the power management chip according to the pin state detection signal. According to the invention, the pin voltage stability and the state detection precision of the power management chip are improved.

The invention provides a voltage out-of-limit control system based on multi-agent deep reinforcement learning, and the system comprises a power grid region division module which is used for dividing a power grid into a plurality of regions, and each region at least comprises one piece of photovoltaic equipment; the photovoltaic equipment control strategy model building module is used for carrying out modeling by adopting a partially observable Markov decision process aiming at photovoltaic equipment in each region of the power grid to obtain a photovoltaic equipment control strategy model; and the photovoltaic equipment control strategy output module is used for inputting the current operation parameters of the power grid in each region into the photovoltaic equipment control strategy model, and solving the photovoltaic equipment control strategy model by using a double-delay depth deterministic strategy gradient algorithm. And obtaining reactive power of each photovoltaic inverter and outputting the reactive power to a power grid of a corresponding area to control the voltage of each node to be within a preset range. According to the method, the problems of voltage fluctuation and out-of-limit caused by distributed photovoltaic access to the power grid are effectively solved, and the control efficiency and the control effect 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 relates to the technical field of electric power regulation and control, in particular to a method and system for controlling an electric power energy storage system, and the method comprises the following steps: obtaining the voltage and current when energy storage equipment is charged, analyzing the voltage fluctuation and current change trend, recognizing a fluctuation point, and calculating the fluctuation amplitude and frequency. And judging the deviation degree between the fluctuation data and the normal operation data of the equipment to obtain an equipment stability index. According to the invention, by monitoring the voltage and current of the energy storage device during the charging period and deeply analyzing the fluctuation characteristics of the electrical parameters, the evaluation accuracy of the stability of the device, the accurate fluctuation point identification and the calculation of the fluctuation amplitude and frequency are significantly improved, and the behaviors of the device in different charging and discharging periods are allowed to be more accurately judged. And the residual capacity of the energy storage equipment is matched with the actual power demand, so that the power distribution efficiency is effectively improved.

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 the technical field of connector fault monitoring, in particular to a method and a system for monitoring a connector fault in real time. The method comprises the following steps of performing operation state data acquisition on a connector, and performing timestamp marking to obtain operation state characteristic time sequence marking data; performing abnormal state identification on the operation state characteristic time sequence mark data to obtain current / voltage abnormal fluctuation data; according to the current / voltage abnormal fluctuation data, carrying out fault triggering pre-risk factor identification to obtain a fault triggering pre-risk factor; performing fault lag severity level prediction according to the fault triggering pre-risk factor to obtain fault lag severity level prediction data; and performing connector fault identification model construction on the fault lagging severity level prediction data and the fault triggering pre-risk factors to obtain a connector fault identification model. According to the invention, the connector fault monitoring technology is optimized, so that the connector fault monitoring technology is more perfect.

The invention provides a converter station in-station equipment energy consumption analysis model and method. Belongs to the field of converter station equipment energy consumption analysis. According to the method, a three-dimensional data acquisition system covering equipment in the whole station is established, electrical measurement data of the equipment is obtained in real time through a monitoring system, and meanwhile, a converter valve trigger pulse, a cooling water pump rotating speed adjusting signal and a circuit breaker opening and closing state are captured through an equipment digital twin interface. And carrying out real-time feature extraction by means of an edge computing node, and triggering high-speed data recording for a commutation failure event. And correcting abnormal data by combining a sliding window dynamic cleaning mechanism with a wavelet threshold denoising algorithm, and extracting a current harmonic distortion rate, a voltage fluctuation coefficient and equipment vibration spectrum characteristics. And further establishing a multi-dimensional characteristic matrix containing equipment operation conditions, environmental parameters and power grid interaction characteristics, performing energy consumption prediction by using a hybrid model, and generating a dynamic energy efficiency evaluation index which is applied to energy saving strategy optimization.

The invention discloses a power supply and distribution method and system based on multi-modal data fusion and adaptive control, and the method comprises the steps: collecting real-time power supply and distribution data in a power supply and distribution network node, and obtaining power supply and distribution environment data; fusing the real-time power supply and distribution data and the power supply and distribution environment data by using a quantum heuristic multi-modal fusion algorithm to obtain fused power supply and distribution data; establishing an LSTM-CNN machine learning model based on the LSTM long and short term memory network and the CNN; inputting the fused power supply and distribution data into an LSTM-CNN machine learning model for training to obtain electric load prediction data; and based on the adaptive control strategy, sending a regulation and control instruction to the electric load equipment according to the electric load prediction data. And voltage fluctuation, flicker and harmonic distortion are effectively suppressed through real-time dynamic regulation and control, and voltage deviation and power factors of a user terminal are guaranteed to be optimal.