1237 results about "Voltage fluctuation" patented technology

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 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 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 power distribution area topology identification method and system based on an intelligent fusion terminal and correlation coefficients, and the method comprises the steps: collecting the multi-source data of a power distribution area through an edge fusion terminal, and carrying out the data preprocessing and feature extraction at a terminal side; carrying out parallel calculation on a voltage fluctuation Pearson's correlation coefficient and a load change Kendall rank correlation coefficient, and constructing a correlation coefficient matrix in different time periods; calculating an adaptive weight based on the load fluctuation entropy, and fusing a dual-mode correlation coefficient; carrying out topology generation by using a graph neural network, and outputting an edge existence probability and a node hierarchy; performing physical constraint optimization on the initial topology by adopting a genetic algorithm; the system comprises an edge fusion terminal cluster, a cloud analysis platform, a topology verification module and a terminal management platform. According to the method, topology recognition precision and dynamic adaptability are remarkably improved, bimodal correlation coefficients and graph neural network space modeling are creatively fused, and a complex topological structure is precisely restored.

The invention discloses a power grid broadband oscillation suppression method and device based on adaptive robust control, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: performing fuzzy disturbance classification according to an oscillation mode, an oscillation mode characteristic parameter and a state parameter obtained according to a power grid operation parameter to obtain a disturbance type label and a disturbance degree; the disturbance degree serves as a weighting factor, the initial control parameters are corrected according to the power grid operation parameters, the initial control parameters of the controller and the disturbance type label, and a self-adaptive control strategy is generated after correction; when the disturbance degree is smaller than a disturbance threshold value, the state parameters are regulated and controlled according to a self-adaptive control strategy to update power grid operation parameters; and when the disturbance degree is not less than a disturbance threshold value, taking the minimum sum of the system gain, the frequency deviation and the voltage fluctuation as a target, and regulating and controlling the state parameters under the H infinite robust constraint to update the power grid operation parameters. The problem that in the prior art, broadband oscillation suppression is difficult to adapt to sudden change working conditions is solved.

The invention relates to the technical field of droop control, in particular to a voltage droop control optimization method and system based on parameter self-adaption and a medium, and the method comprises the steps: dynamically recognizing a voltage sensitive interval according to voltage fluctuation characteristics, determining critical voltage distribution data, and determining a switching trigger condition of a current power grid control mode according to the critical voltage distribution data; analyzing the correlation degree between the current power grid control mode switching event and the power grid stability based on the switching trigger condition to obtain a switching behavior risk level; reconstructing voltage droop control parameter adjustment direction logic based on the switching behavior risk level, and generating a stable direction vector; calibrating the original droop control output signal according to the stable direction vector, and generating a self-adaptive droop control output instruction; and distributing the adaptive droop control output instruction to each substation node for voltage regulation. The voltage droop control parameters are adaptively optimized through the real-time running state of the power grid, and parameter adaptive optimization of voltage droop control is achieved.

The invention relates to the technical field of intelligent automobiles, in particular to a battery performance test method and system for an intelligent automobile, and the method comprises the steps: obtaining the operation data of each single battery of the intelligent automobile under the current driving working condition and the working condition statistical data of the automobile, and constructing an input feature set, standardizing the input feature set, and inputting the standardized input feature set into a battery performance evaluation model to obtain key performance indexes of the single battery; dynamically adjusting a battery charging and discharging control strategy according to the key performance index and a real-time load demand under the current driving condition, verifying the accuracy of the key performance index by comparing the voltage fluctuation amplitude and the temperature change rate of the battery before and after adjustment, comparing with a preset safety threshold value, and if the threshold value range is exceeded, determining that the battery is in a charging state. If yes, generating corresponding performance early warning information; the method can adapt to complex and changeable driving working conditions of the intelligent automobile, and the precision and the real-time performance of battery performance evaluation are remarkably improved.

The invention discloses an energy storage rapid compensation method and system based on track overhead line system voltage fluctuation feature recognition, and relates to the technical field of track traffic traction power supply. The method comprises the following steps: synchronously acquiring voltage and current signals of the overhead line system, and extracting a composite feature vector containing disturbance root attributes and transient change rate; on the basis of the vector, a transient energy vacancy sequence covering the ultra-short-term future is output in real time through a prediction model fusing dynamic phasor analysis and a feedforward neural network; according to the sequence, a cooperative control strategy fusing overshoot and active damping is generated in combination with a proximity compensation principle and a voltage recovery state so as to drive an along-line energy storage unit; the strategy is executed in advance, compensation energy is injected, and finally double closed-loop correction is conducted based on the voltage residual error. According to the method, the problems of response lag, unclear disturbance identification, poor collaboration and lack of adaptive ability in the prior art are solved, and advanced, accurate, collaborative and self-optimized rapid suppression of the voltage fluctuation of the overhead line system is realized.

The invention discloses a variable frequency driving device energy recovery control system based on direct power supply of a high-voltage power grid, and relates to the technical field of electrical transmission and energy recovery control. Updating the power grid impedance model in real time by using a complex impedance calculation method; load operation state parameters are dynamically adjusted in combination with motor end output power, rotating speed change and torque calculation; according to the method, regenerative energy is accurately calculated, a feedback path is adjusted according to voltage fluctuation and frequency deviation of a power grid, a feedback current fluctuation ratio and a power factor are monitored, a feedback path conduction proportion is optimized in real time, when the current is unstable or the power factor is abnormal, a suboptimal path is automatically switched, and continuous and stable operation of a system is guaranteed; according to the invention, the energy feedback efficiency and the power grid compatibility of the high-voltage power grid variable-frequency driving device are effectively improved, and the method is suitable for energy feedback application of a high-power motor driving system.

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

The invention relates to the technical field of electric energy quality management of an electric power system, and discloses an industrial power grid voltage fluctuation suppression method and system, and the method comprises the steps: firstly building a system-level voltage and current forward transmission model composed of a power unit, a filter circuit and power grid impedance which are cascaded; reversely constructing a feedforward controller based on a voltage and current forward transfer model, and generating a main compensation voltage instruction according to the power grid disturbance quantity detected in real time; state parameters of each power unit are obtained, a differential equation is constructed, and a comprehensive real-time health degree index is obtained through fuzzy reasoning; dynamically correcting an equivalent impedance parameter in the forward transfer model by using the index, and calculating a voltage distribution weight of each unit; and decomposing the total compensation voltage instruction into each unit modulation signal according to the weight. Through systematic modeling, intelligent state evaluation and dynamic fault-tolerant distribution, high-speed, accurate and high-reliability suppression of the voltage fluctuation of the industrial power grid is realized.

This invention discloses a method and system for suppressing capacitor voltage fluctuations in SiC MMC submodules, belonging to the field of electronic power technology. Based on the analysis results of the optimal broadband harmonic circulating current injection boundary and order for SiC MMC submodules, an optimized model for submodule capacitor voltage fluctuations is determined that simultaneously injects 2nd, 4th, and 8th harmonic circulating currents and 3rd harmonic voltages, which can fully utilize the higher control bandwidth characteristic of SiC MMC. By solving the optimized model for submodule capacitor voltage fluctuations, the amplitude and phase of the reference values ​​of the 2nd, 4th, and 8th harmonic circulating currents to be injected, as well as the amplitude and phase of the corresponding 3rd harmonic voltage, are determined. Then, by injecting 2nd, 4th, and 8th harmonic circulating currents and jointly injecting the 3rd harmonic voltage, broadband harmonic joint injection is achieved, reducing the capacitor voltage fluctuations of the submodule and realizing a method for suppressing capacitor voltage fluctuations in SiC MMC submodules with high suppression effect.

The invention relates to the technical field of lithium battery evaluation, and discloses a lithium battery SOC voltage evaluation method and system. The method comprises the following steps: acquiring multiple groups of historical charging and discharging data of voltage, current and temperature of a lithium battery, performing curve alignment through dynamic time warping, and generating a standardized charging and discharging characteristic set; inputting the voltage into a pre-trained deep neural network model to extract features, obtaining a high-order charging and discharging feature matrix, constructing a dynamic voltage evaluation baseline model according to the high-order charging and discharging feature matrix, and adjusting parameters in real time; acquiring real-time charging and discharging data, comparing the real-time charging and discharging data with the dynamic baseline model to generate a voltage deviation degree score set, and detecting an abnormal voltage fluctuation time period according to the voltage deviation degree score set; and performing multi-scale feature decomposition on the data in the abnormal time period to obtain local and global abnormal feature vectors, tracing abnormal root causes to determine key influence factors, and finally generating an SOC voltage calibration strategy. The method can comprehensively capture dynamic changes of the battery, accurately identify abnormities and trace root causes, adapt to complex working conditions, and guarantee the running performance of the lithium battery.

The invention provides a voltage stability control method for a multi-output switching power supply, which comprises the following steps of: sampling a main output channel in real time to obtain a voltage signal; the voltage signal is processed through a wide bandwidth error amplifier, a main channel adjusting signal is generated, and the main channel adjusting signal is used for driving a main power switch device; receiving an input voltage fluctuation frequency band of an auxiliary channel, a load current transient change gradient and a frequency spectrum coupling characteristic of a main channel adjusting signal through a dynamic prediction model, and generating an auxiliary channel adjusting signal with a phase lead compensation characteristic; a cross coupling active offset mechanism is constructed, time domain-frequency domain characteristics of coupling interference are extracted through a high-precision differential detection circuit, amplitude-frequency adjustable reverse correction signals are generated in real time through an adaptive compensation algorithm and injected into control nodes of an auxiliary output channel, and dynamic decoupling and ripple suppression are carried out. According to the method, high-stability control of multi-channel voltage under all working conditions is realized, and the dynamic response speed and the anti-interference capability are improved.

The invention relates to the technical field of energy storage battery simulation, and discloses an energy storage battery module simulation method and system based on machine learning. The method comprises the steps of collecting battery signal circuit data of a BMS system in charging and discharging circulation of an energy storage battery pack, extracting voltage fluctuation characteristics and current ripple characteristics of an energy storage high-voltage box, and constructing a multi-dimensional dynamic parameter matrix reflecting the internal state of a battery according to time sequence alignment; carrying out dimensionality reduction compression on the matrix through an encoder of the generative adversarial network to generate a battery state hidden variable; dynamic evolution modeling is carried out on hidden variables by utilizing a time sequence convolutional network, and hidden variable tracks of three charge and discharge cycles in the future are predicted; and performing reverse decoding to generate a simulation battery signal, and outputting a simulation data flow containing voltage, current and temperature parameters. According to the method, the multi-dimensional features and the time sequence modeling capability are fused, accurate simulation of the running state of the energy storage battery module is achieved, and the method is suitable for diversified application scenes.

The invention discloses an automatic voltage regulating device for a power distribution area and a control method of the automatic voltage regulating device, and belongs to the technical field of power distribution network voltage treatment. The device comprises a three-phase power supply, a power supply side input circuit, a load side output circuit, a current transformer, a single-P air switch, a power supply conversion module, a three-phase independent voltage regulation module, a main path control system, an automatic bypass control system, a gear driving module, a man-machine interaction module, a display module, a monitoring module, a mode switching module, a parameter storage module and an alarm module. The system comprises a manual adjustment execution module, a counting module, a voltage detection module, a current detection module, a 4G communication module and an over-temperature protection module. A main circuit control system depends on input and output voltage data fed back by a voltage detection module, compensation voltage U can be independently calculated for differential voltage fluctuation of L1, L2 and L3 phases, intelligent adjusting switches of all phases are driven to act accurately, and finally three-phase output voltage U2 is stabilized within an allowable range.

The invention discloses a voltage transformer state influence factor analysis method, system and device and a medium, and belongs to the technical field of voltage fault analysis, and the method comprises the steps: collecting state parameters of a voltage transformer, carrying out the preprocessing of the state parameters, and constructing a nonlinear coupling relation between multidimensional feature tensor capture features; analyzing voltage fluctuation characteristics of the voltage transformer, and automatically correcting the insulation state evaluation model; and inputting abnormal feature vectors obtained by monitoring into a recursive attribution decision tree model, carrying out multi-dimensional abnormal clustering and causal analysis, generating a tracing report, and carrying out multi-dimensional evaluation and verification on an analysis result. According to the invention, continuous dynamic full-coverage monitoring of the equipment insulation health condition is realized, the accuracy and robustness of anomaly detection are improved, the phenomena of missing detection and false alarm are reduced, the scientificity of anomaly tracing analysis and the interpretation of early warning decision are improved, and the reliability of the system is improved. And the intelligent level of the monitoring and early warning system is improved by continuously adapting to new abnormal types and complex operation environments.

The invention belongs to the technical field of power supply, and particularly relates to a photovoltaic power generation energy storage system switching system and a switching method, and the method comprises the steps: building an output trend prediction model and issuing an early warning signal at the initial stage of illumination change, starting the pre-charging and state self-inspection of an energy storage battery in advance, and synchronously carrying out the phase locking and voltage matching preparation of a commercial power side, a real-time monitoring network covering the whole system is constructed before switching so as to evaluate the bus stability, and then grading decreasing of photovoltaic output and synchronous power undertaking of an energy storage battery are executed after the condition is confirmed to be met, so that smooth transition between power supplies is realized. According to the method, the passive response of a traditional system after the voltage drops is avoided, the voltage fluctuation and power supply interruption in the switching process are effectively eliminated, the power supply continuity and the electric energy quality are remarkably improved, and the power utilization experience of a user and the equipment operation safety are guaranteed.

The micro-grid multi-time scale voltage optimization method based on deep reinforcement learning comprises the steps of providing a multi-time scale collaborative optimization framework based on deep reinforcement learning for a voltage out-of-limit problem caused by distributed photovoltaic access in a micro-grid; an improved hierarchical DQN (HRL-Dueling DQN) algorithm is adopted to carry out long-time scale optimization control on discrete capacitor equipment, so that the voltage out-of-limit risk is reduced; and performing short-time scale fine adjustment on reactive output of the continuous photovoltaic inverter by using a depth deterministic strategy gradient (DDPG) algorithm to suppress voltage deviation. According to the micro-grid multi-time scale voltage optimization method based on deep reinforcement learning, the real-time performance and robustness of voltage optimization are improved, the voltage out-of-limit risk is effectively restrained, voltage fluctuation is finely adjusted, and reliable guarantee is provided for safe and stable operation of a micro-grid with new energy access.

The invention discloses a voltage regulation control method and system for an electric power regulator, and belongs to the technical field of voltage regulation, and the method specifically comprises the steps: collecting parameters of a power grid side and a load side, including current and voltage, building an operation model of the electric power regulator based on parameter identification, and according to the operation model and a target output voltage, calculating the voltage of the electric power regulator. The method comprises the following steps: calculating to obtain a reference voltage sequence in combination with electric energy quality and efficiency constraints, monitoring the states of a power grid and a load in real time, projecting the reference voltage sequence to a safe operation envelope when voltage fluctuation or abnormity is detected, performing hierarchical derating control, generating a regulation signal of the power regulator based on the corrected reference voltage sequence, and outputting the regulation signal of the power regulator. The operation model of the power regulator is calibrated through real-time feedback; the power quality and efficiency can be considered under the normal working condition, stable voltage control can be kept during power grid fluctuation, frequency deviation or load sudden change, and resource waste caused by single shutdown or extensive derating is avoided.

The invention provides an operation state data processing method and system applied to an electronic circuit board, and the method comprises the steps: obtaining the real-time voltage fluctuation data of each power path of the electronic circuit board in a preset monitoring time, and carrying out the self-adaptive time-frequency decomposition of the real-time voltage fluctuation data; separating to obtain a steady-state substrate component representing stable operation characteristics of the circuit board and an instantaneous pulsation component reflecting an instantaneous interference phenomenon, and constructing an amplitude-time sequence correlation heat map in a two-dimensional data plane based on an amplitude change sequence of the steady-state substrate component and a time interval parameter corresponding to a sampling timestamp; performing pixel-by-pixel morphological fusion on the pulse pattern and a standard pulse pattern stored when the circuit board leaves a factory to generate a fusion difference spectrum containing space distribution characteristics of difference pixels, and calculating a state coupling coefficient of each power supply path according to the space density distribution characteristics of the difference pixels; and integrating the state coupling coefficients of all the power paths to obtain a quantitative condensation index. According to the invention, the reliability and comprehensiveness of operation state evaluation can be improved.

The invention discloses a voltage control method and system considering cooperation of multiple types of reactive compensation equipment, and the method comprises the steps: inputting different types of reactive compensation equipment according to the voltage fluctuation type of a power grid; wherein the power grid voltage fluctuation type comprises steady-state voltage fluctuation and transient-state voltage fluctuation; wherein when the steady-state voltage fluctuates, the discrete reactive power compensation device is firstly put into use, and if the voltage of the power grid is still unstable, the continuous reactive power compensation device is put into use; wherein when the transient voltage fluctuates, the continuous reactive power compensation device is put into use. When the voltage drops, the reactive power demand of the power grid can be quickly responded, the cooperation among the reactive power compensation devices is optimized, the overall efficiency of reactive power compensation and the stability of the power grid are improved, the suppression capability on overvoltage when the voltage transiently changes is remarkably enhanced, and an important technical guarantee is provided for safe and reliable operation of a power system.

The invention relates to the technical field of power distribution network optimization, in particular to a power distribution network voltage dynamic optimization control method, system and device and a storage medium, and the method comprises the steps: judging a mode interval in real time, and judging whether the system operates in a normal state or a fault state according to voltage fluctuation; calculating the photovoltaic residual capacity and the real-time charge state of the energy storage battery; according to the operation state of the system, judging the action state of the energy storage system and the photovoltaic operation mode, and calculating the total reactive power demand of the power distribution network system; on the basis of an improved TOPSIS algorithm, a power instruction is calculated by considering the voltage variation of each node in a power distribution network, the photovoltaic power regulation capability, the charge state of an energy storage system, the available residual capacity of energy storage and the voltage variation of a dominant node caused by unit power, and the power instruction is distributed to an optical storage node. According to the invention, power dynamic distribution under dual modes of a normal state and a fault state is set to ensure stable operation of voltage; and the power distribution network always operates in an optimal state.

The invention discloses a self-adaptive multi-phase parallel power supply management system, and relates to the technical field of intelligent regulation and control of a power system, and the system comprises a feature extraction module which is used for extracting periodicity, tendency and randomness features from historical power utilization data and real-time sensor data, determining a load sudden increase probability through a time sequence decomposition algorithm, and determining a load sudden increase probability; obtaining a load sudden increase probability value; the load prediction module is used for processing real-time load data through a multi-layer perceptron structure if the load sudden increase probability value is higher than a preset threshold value, predicting a load sudden increase peak value and duration in an hour-level or minute-level time window, and obtaining a load sudden increase prediction result; according to the self-adaptive multi-phase parallel power supply management system, multi-algorithm collaboration and real-time dynamic optimization are adopted, the operation stability and power supply continuity of a power grid are remarkably improved, and overload and voltage fluctuation risks are reduced.

The invention relates to the technical field of network data security, in particular to a power fgOTN power low-delay secure transmission method. Obtaining a service bearing network topology and link transmission parameters, and constructing a network connection matrix; simulating a flow redistribution process after a fault of a single node, and calculating a topology betweenness centrality index of the substation node; the method comprises the following steps: analyzing payload contents of power business data frames at a fgOTN mapping preceding stage, extracting continuous monitoring features and discrete control features, and calculating a real-time business criticality index of each data frame in combination with a frequency deviation and a voltage fluctuation state of a current network; establishing a two-dimensional decision matrix, and generating a defense strategy according to mapping positions of the topological betweenness centrality index and the criticality index value in the matrix; processing the service data according to the generated defense strategy, and mapping the service data to a fine granularity time slot of the fgOTN; and embedding a security state signaling in the frame overhead, and sending the security state signaling to opposite-end equipment through an optical transmission unit.

The invention relates to the technical field of micro-grids, and discloses a control method of a distributed power supply in a plant micro-grid, and the method comprises the steps: S1, constructing a multi-source collaborative micro-grid system; s2, executing real-time power dynamic allocation; s3, working condition power balance control; s4, active regulation of bidirectional power flow and the like. Based on a real-time AGC instruction and plant load data, a hierarchical coordination mechanism is adopted, charging and discharging power is dynamically adjusted by means of stored energy to track the AGC instruction, and constant power output of wind power is maintained in combination with a wind power prediction model; when a thermal power generating unit is subjected to deep peak regulation, shutdown or fault, the system absorbs excess wind power through energy storage charging, synergistically improves the hydrogen production load power, guarantees dynamic matching of the load and power generation, and actively adjusts energy storage charging and discharging and hydrogen production loads by monitoring the power flow directions of a thermal power generating unit section and a desulfurization section in real time according to voltage fluctuation caused by bidirectional power flow. And the voltage deviation is dynamically compensated, and the system voltage stability is greatly improved.

The invention discloses a switching control method based on SPWM soft start and a pulse type load LLC resonant converter. The converter mainly comprises an LLC resonant converter, an H-bridge inverter circuit and a load of the H-bridge inverter circuit, and the control method comprises the following steps: setting k-level SPWM modulation in a starting stage to realize smooth soft start of output voltage (k is any positive integer); the load state is accurately judged through an H-bridge switching logic unit, and Burst intermittent control and linear frequency control are adopted for no-load and full-load in a steady state respectively; during load switching, a nonlinear SOTC optimal trajectory controller is adopted to perform two-pulse optimal control, so that the state is transited to a new mode along the shortest trajectory, and a dynamic response optimizer is introduced to compensate the switched frequency according to the state radius error so as to suppress secondary voltage fluctuation. The problems of slow dynamic response, large output voltage fluctuation and the like when traditional linear control faces a pulse type load LLC resonant converter are solved, and the stability and the anti-interference capability of the output voltage of the system are improved.

The invention discloses an intelligent power supply system for an integrated energy cabinet of a communication base station, and the system comprises a power supply parameter collection module which obtains parameters such as an input voltage fluctuation sequence and multipath load current data; the power supply feature extraction module performs fusion analysis on the parameters to generate a power supply feature vector set; the decision space construction module presets a power supply strategy knowledge base and a feature vector set, and establishes a power supply decision space index structure; the power supply decision module matches the optimal power supply strategy combination and outputs a first power supply control instruction set; the power supply execution module drives the power distribution unit to execute load power supply switching and storage battery charging and discharging control. The system comprises an energy pool module, an integrated photovoltaic device, a commercial power device and the like to realize access and dynamic distribution; the anti-countercurrent control unit ensures the energy flow safety; the zero-loss switch array regulates and controls an electric energy path to improve the efficiency; the power supply execution module drives multi-power-supply switching, security isolation and path switching, supports commercial power peak clipping, light stacking and off-peak power utilization, and improves the power supply reliability and energy efficiency of the base station.

The invention discloses an integrated optimization method of a flywheel energy storage system. The corresponding system is formed by connecting a plurality of flywheel monomers in parallel through a direct current bus. The method comprises the steps that the real-time rotating speed, the direct-current bus voltage, the maximum charging and discharging power capacity and a power grid frequency modulation power instruction of each flywheel single body are collected; calculating current stored kinetic energy based on the rotating speed and the rotational inertia of the rotor, and normalizing to obtain an energy state index SoE; a double-layer collaborative architecture is adopted for power scheduling; at the moment of charging and discharging mode switching, the total disturbance of the system is estimated on line through a cascade expansion state observer, and feed-forward compensation is implemented in control output to suppress instantaneous fluctuation of the voltage of the direct-current bus. According to the method, the SoE is obtained by calculating the kinetic energy and normalizing the kinetic energy, so that the accurate representation of the energy storage state is realized; and voltage fluctuation can be effectively suppressed when working conditions suddenly change. The method avoids overcharge and overdischarge of individual units, guarantees high reliability and safety, prolongs the service life of equipment, and is especially suitable for power grid frequency support application in a high-proportion renewable energy access scene.

The invention discloses an active and reactive coordinated optimization scheduling method considering a distributed power distribution network, and relates to the technical field of power distribution networks. Comprising four steps of distributed resource modeling and interaction characteristic analysis, power distribution network linear power flow calculation and active-reactive coupling characteristic analysis, energy storage and reactive compensation cooperative configuration, and sensitivity analysis-based multi-time scale active and reactive coordinated optimization scheduling. Aiming at the problems of voltage fluctuation, network loss increase, wind curtailment, light curtailment and the like caused by access of high-permeability renewable energy sources, a multi-flexibility resource model is constructed, linear power flow calculation is improved, energy storage and reactive compensation configuration is optimized, and a multi-time-scale coordinated scheduling strategy is designed; the method can effectively improve the voltage quality, reduce the network loss, improve the renewable energy consumption rate, provide technical support for the construction of a novel power system, and achieve the safety and economy of the efficient operation of an active power distribution network system.