3059 results about "Electric network" patented technology

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention discloses a photovoltaic inverter insulation resistance intelligent detection system and method, and particularly relates to the technical field of photovoltaic equipment operation safety monitoring, and the method comprises the following steps: generating frozen window shielding interference data through collecting a bus voltage in a state transition stage and constructing a dynamic convergence model; identifying an abnormal trend based on resistance derivative change in a stable state, inputting a dual-path decision process by combining environment and power grid parameters, and dynamically outputting an operation state level; control strategies such as remote prompt, power drop adjustment or output disconnection are executed according to the state grade, and accurate identification and grading response control of the insulation state are achieved; according to the invention, by constructing a voltage disturbance shielding mechanism, a trend derivative identification and delay confirmation strategy and a double-path safety state decision process with dynamic switching capability, high accuracy of insulation abnormity judgment, stability of trend identification and adaptability of a decision strategy are realized, and robustness and application range of a detection system are improved.

A tracking and adaptive compensation method for power fluctuation of a pulse load of a ship is provided. The method includes: based on a microgrid topology model of the ship, a pulse load model of the pulse load, and an interharmonic power and pulse parameter relationship model, performing power fluctuation tracking, frequency selection and a power spectrum analysis to obtain filtering parameters of a low-pass filter and filtering parameters of a band-pass filter; performing waveform adaptation to eliminate a phase delay caused by the low-pass filter and the band-pass filter, and obtaining a control command for a power compensation strategy; and generating, by a power loop, a pulse width modulation (PWM) signal to control a switching tube in a bidirectional direct current (DC) / DC converter of each of an energy storage battery pack and a supercapacitor of a hybrid energy storage system.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

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

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention belongs to the technical field of power load prediction, and particularly relates to a Mama-based endogenous and endogenous variable fusion power load prediction method, which comprises the following steps of: collecting and preprocessing a data set; embedding an endogenous variable, dividing a power load endogenous sequence into N non-overlapping Patch blocks, adding position codes to each block, generating a block token Pen of block fine granularity through linear projection and position codes, and introducing a learnable global token Gen to represent the macroscopic state of the sequence; global embedding of exogenous variables; performing cross-granularity fusion on internally and externally generated tokens; carrying out Mama time sequence dependence modeling; and performing multi-step prediction output, mapping the time sequence characteristics into future multi-step load prediction values through a full connection layer, restoring the original data scale after reverse normalization, and outputting a final prediction result. According to the method, through a block embedding strategy and a dynamic parameter selection mechanism, the adaptability of the model to a complex power scene is remarkably improved, and reliable technical support is provided for efficient scheduling and marketization operation of an intelligent power grid.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention relates to the technical field of intelligent power grid communication, in particular to a dual-mode communication channel switching method and device based on multimode perception and weight decision. The method comprises the following steps: acquiring multimode parameters of two channels; respectively calculating two channel quality indexes, and calculating a comprehensive score based on the two channel quality indexes and the dynamic weight; and determining whether to trigger mode switching according to the relationship between the comprehensive score and the switching threshold value and the change trend of the comprehensive score. According to the method, the channel quality index and the comprehensive score are calculated through the multimode parameters of the channel, whether mode switching, dynamic threshold and hysteresis setting are triggered or not is determined according to the relation between the comprehensive score and the switching threshold and the change trend of the comprehensive score, and the problems of frequent switching, critical fluctuation and the like of the channel can be avoided. And on the basis of judging the comprehensive score and the switching threshold, the change trend of the comprehensive score is considered, so that a better channel can be selected for transmission, the success rate of communication transmission is guaranteed, and the transmission reliability is improved.

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

The invention 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 modular reclosing circuit breaker protection system based on intelligent electrical cloud coordination, particularly relates to the technical field of intelligent power distribution network relay protection, and is used for solving the problems that fault diagnosis is delayed and a protection strategy cannot be generated in a fault clearing time window due to resource competition in a cloud coordination protection system. A dynamic preemption priority is generated based on power grid topology vulnerability and associated circuit breaker collaborative requirements by monitoring the load state of cloud computing node resources in real time; credibility attenuation control is carried out in combination with a fault current waveform distortion rate; when the resource preemption necessity exceeds a threshold value, freezing a periodic task thread and releasing computing resources, and opening up a high-priority communication channel at the same time; sending a fault recording compression instruction to a target circuit breaker terminal to obtain an edge preprocessing feature vector; and protection strategy optimization is executed in the released resources to generate reclosing parameters, and the reclosing parameters are issued and executed through a high-priority channel, so that quick response of a fault diagnosis task is realized.

The invention relates to the technical field of integrated energy micro-grid scheduling optimization, and discloses an improved TD3 algorithm-based electricity-hydrogen-heat integrated energy micro-grid optimization scheduling method, which aims at minimizing the sum of the environmental cost, the equipment life loss cost and the large power grid interaction cost, and uses the improved TD3 algorithm to optimize the power-hydrogen-heat integrated energy micro-grid scheduling optimization. The optimization scheduling strategy of the electricity-hydrogen-heat comprehensive energy microgrid system is researched, a hydrogen energy storage mathematical model is constructed, the electrothermal coupling characteristic between the hydrogen energy storage system and the microgrid is explored, the optimization scheduling process of the microgrid is converted into a Markov decision process, and an experience playback mechanism is introduced to improve the algorithm performance. According to the method, the environmental friendliness and the scheduling strategy solving speed of the micro-grid energy storage system are improved, and the solving speed of the optimal scheduling strategy is accelerated by using the improved priority experience playback reinforcement learning algorithm.

The invention discloses a method for predicting and suppressing harmonic waves generated by photovoltaic grid connection, which relates to the technical field of harmonic wave prediction, and comprises the following steps: detecting a three-phase current signal of a photovoltaic grid connection circuit by using an ip-iq detection method, and decomposing the three-phase current into an active component and a reactive component through Clarke conversion and Park conversion; according to the method, high-precision detection and separation of harmonic waves in the photovoltaic grid-connected system are realized by combining an ip-iq method with Clarke and Park transformation, dynamic modeling and harmonic signal prediction are performed by using the LSTM model, the defect of poor adaptability of a traditional method to nonlinear loads is overcome, and the prediction error is as low as 0.1%. Through combination of LSTM prediction and APF dynamic compensation, the compensation current opposite to the harmonic current is generated in real time, the harmonic suppression efficiency and the power grid stability are significantly improved, the electric energy quality is optimized, and the dynamic change of a complex load is adapted.

The invention discloses a meter reading system and a meter reading method based on dual-mode communication, and relates to the technical field of meter reading systems and methods, the meter reading system comprises a dual-mode communication module, intelligent data demand switching, strong NB-IoT penetration and far coverage; the BLE is short-distance, high-speed, low-power-consumption and fast in switching, the intelligent electric meter module and the high-precision metering chip measure electric energy accurately, historical data is stored in a large-capacity mode, the shell protection is high, the intelligent electric meter module adapts to severe environments, the data processing and storage module and the high-performance processor compress and process data, the SSD stores in a large-capacity mode and backups redundancy, and the user interaction module and the large-screen LCD clearly display power utilization information. And a button or a touch screen is convenient to operate, and a remote management and monitoring module is provided. Innovative technologies such as dual-mode communication are fused, high-precision metering, intelligent switching communication and convenient interaction are achieved, power line carrier standby and fault early warning diagnosis are achieved, meter reading efficiency, precision and reliability are improved, and development of an intelligent power grid is promoted.

The invention relates to the technical field of voltage control, in particular to a time-delay-containing uncertain AC / DC micro-grid DC bus voltage control method, which comprises the following steps: acquiring load change and DC bus voltage fluctuation data, calculating a load current change rate, analyzing the correlation between voltage fluctuation and load change, and performing voltage adjustment. And time delay characteristics in a load state are identified, voltage fluctuation uncertainty factors are identified, and the time delay compensation amount is adjusted to restore the voltage stability. According to the invention, by adopting linear and nonlinear time-delay compensation strategies, the problem of accuracy of time-delay compensation under different load conditions is effectively solved, various load fluctuations and voltage changes can be dynamically adapted, the problem of uncertainty of voltage fluctuations is solved, and the prediction of voltage fluctuations is optimized by dynamically adjusting the weight of uncertainty factors. The over-reaction to sudden voltage change is reduced, the voltage stability is improved, and the operation stability and the voltage regulation precision of the micro-grid are remarkably improved.

The invention provides a distributed photovoltaic access distribution network voltage fluctuation evaluation method, medium and system, and belongs to the technical field of distributed photovoltaic, and the method comprises the steps: firstly collecting the parameters of a photovoltaic system and a distribution network, and building a power grid response equation set considering the parameters; more parameter samples are obtained through data augmentation and expansion, and indexes such as voltage, power and harmonic waves of each node are calculated according to the response equation set. Constructing a voltage fluctuation evaluation index matrix including voltage deviation, fluctuation, harmonic waves, short-circuit capacity change and the like; and decomposing the matrix by adopting singular value decomposition to obtain a voltage stability and fluctuation evaluation matrix, and finally calculating a voltage stability index and a fluctuation index. If the voltage fluctuation evaluation index exceeds a preset threshold value, an alarm is triggered, and decision support is provided for distribution network planning and operation. According to the invention, the technical problems of short evaluation time scale and difficulty in capturing the dynamic characteristics of voltage fluctuation in the evaluation of the distributed photovoltaic access distribution network in the prior art are solved.

The invention discloses a hierarchical coordination control system and method for an AC / DC hybrid micro-grid group, and relates to the technical field of smart grids and micro-grids, the system comprises a dynamic partition aggregation unit, a flexible interconnection control unit and a wide-area coordination control unit. The method is advantaged in that firstly, operation efficiency and reliability of the micro-grid group are substantially improved, stable supply of electric energy is guaranteed, secondly, the system has high flexibility and adaptivity, dynamic partition aggregation and flexible interconnection control can be carried out according to different source load scene characteristic parameters, various complex conditions can be effectively handled, response speed and stability of the system are improved, and the method is suitable for large-scale popularization and application. And finally, the implementation of the system also contributes to reducing the operation cost, realizes double improvement of economy and reliability by optimizing energy scheduling and energy storage management, and lays a solid foundation for sustainable development of the microgrid group.

The invention discloses a non-contact magnetoelectric composite current sensor with magnetism gathering and magnetic shielding. A sensor core is composed of a magnetoelectric sensing element, a magnetofluid layer, a magnetism gathering structure and a magnetic shielding layer. The magnetoelectric sensing element is made of manganese-doped lead magnesium niobate and amorphous alloy film composite materials, the outer side of the sensing element is coated with a magnetofluid layer made of soft magnetic composite materials, the magnetism gathering structure is made of permalloy materials with high magnetic conductivity and is divided into an upper part and a lower part, the upper part accommodates the sensing element and the magnetofluid layer, and the lower part accommodates the magnetic gathering structure. And the lower part is provided with a standardized groove for placing a conductor to be tested. The outermost layer is a magnetic shielding layer composed of multiple layers of nanocrystalline alloy strips, the vertical split type design is also adopted, and it is ensured that the magnetic shielding layer is tightly matched with the magnetic gathering structure through precision machining. The upper magnetic shielding layer and the lower magnetic shielding layer are detachably connected, and field installation is facilitated. The overall structure is compact, a circuit does not need to be disconnected during installation, and the device is especially suitable for on-line current monitoring requirements in the fields of smart grids, industrial automation and the like.

The invention discloses a power grid cross-correlation harmonic suppression method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet packet decomposition of a subnet electric signal, and obtaining the harmonic component of a subnet at each frequency band; calculating the harmonic phase difference between the subnets according to the cross-correlation function between the harmonic components of the subnets and the topological impedance matrix, and calculating the total harmonic distortion rate according to the harmonic components of the subnets in the frequency bands; constructing a harmonic transfer equation according to the harmonic phase difference between the subnets and the equivalent output impedance of the inverter so as to calculate the harmonic responsibility proportion of the subnets; solving the double-layer game model based on the total harmonic distortion rate, the reference value of the total harmonic distortion rate and the harmonic responsibility proportion of the subnet, and obtaining the output of the governance equipment; and inputting the output of the treatment equipment into the fuzzy controller to obtain the impedance adjusting quantity of the subnet, and adjusting the virtual impedance according to the impedance adjusting quantity to update the harmonic backflow path. According to the invention, harmonic suppression among multiple subgrids during high-permeability distributed grid connection is realized, and the operation stability of the power grid is improved.

The invention discloses an offshore wind power low-frequency networking system based on hybrid topology and a control method, in N onshore receiving end nodes of the offshore wind power low-frequency networking system, one node adopts an M3C current converter, N-1 nodes adopt DRU and MMC current converters, an offshore low-frequency power grid voltage is established through the M3C current converter, and the M3C current converter is connected with the M3C current converter; the power flow control of other power control nodes is realized through the DRU and the MMC, and by adopting the technical scheme of the invention, the engineering construction cost can be obviously reduced under the condition of ensuring the reliable operation of the system, and the method has a good application prospect.

The invention discloses a micro-grid electric energy quality global optimization method, which comprises the steps of collecting real-time data, dividing the real-time data into a plurality of segments according to frequency, and calculating harmonic compensation capacity of each frequency band according to the real-time data; according to the harmonic compensation capacity obtained through calculation, priority distribution is carried out on the compensation sequence of the polluted nodes in the micro-grid; the virtual impedance and the injection damping resistance of the inverter are adjusted, and the harmonic current and the resonance phenomenon in the micro-grid are suppressed in different frequency bands; and according to the adjusted virtual impedance and damping resistance, updating a node admittance matrix, monitoring the compensation capacity of the inverter in real time, comparing real-time monitoring data with a threshold value, if the real-time monitoring data exceed the threshold value, correcting the inverter, otherwise, not changing. Through frequency-band-divided control, pertinence and effect of harmonic suppression are improved, frequency-band-divided suppression of micro-grid harmonic and high-frequency resonance is realized, residual capacity of an inverter and harmonic compensation requirements are dynamically matched, and safe and stable operation of a system is ensured.

The invention discloses a distributed photovoltaic access power distribution network relay protection analysis method and system, and belongs to the technical field of photovoltaic grid connection, and the method specifically comprises the steps: injecting a dynamic resonance detection signal at the AC side of a current converter of a photovoltaic grid-connected system, and enabling the frequency to be matched with the natural resonance frequency of a target section; analyzing the energy focusing effect of the dynamic resonance detection signal in the power distribution network, and generating a resonance energy distribution map; when the energy density of a certain section in the resonance energy distribution map exceeds a preset threshold value, the photovoltaic output current waveform is adjusted to be a discontinuous waveform containing periodic intermittent cutoff; based on the reflection delay difference of the waveform in the fault section, constructing a space-time mapping model of a fault point, and analyzing and determining a fault position; according to an output result of the space-time mapping model, controlling a photovoltaic side circuit breaker and a power grid side relay to execute a selective tripping action based on waveform phase synchronization; according to the invention, the sensitivity and reliability of distributed photovoltaic access power distribution network relay protection are improved.

The invention relates to the field of clean energy, and provides a distributed secondary control method based on deep reinforcement learning, and the method comprises the steps: S1, deploying a DDPG intelligent agent in a distributed micro-grid, and enabling the DDPG intelligent agent to be used for secondary control; s2, establishing a communication graph model of the distributed micro-grid based on a graph theory, so that each distributed power supply in the distributed micro-grid only exchanges control signals and voltage information with the adjacent distributed power supplies; and S4, regulating and controlling the output power of the distributed power supply by using the intelligent agent, and dynamically regulating the frequency and voltage secondary control signal according to the decision of the intelligent agent. Therefore, the dynamic adaptability and the system-level reliability of the distributed power system are improved.

The invention discloses an electric vehicle V2G micro-grid energy storage capacity optimization method based on dynamic planning, particularly relates to the technical field of micro-grid energy storage optimization, and aims to solve the problem of energy storage capacity optimization defects caused by spatial dynamic characteristics caused by mobility of an existing electric vehicle. Performing geographic grid division on the node position, generating a space-time matrix by combining with a timestamp, and identifying a high-frequency access node; constructing a state transition equation based on the inter-node impedance matrix, and setting a node-level safety boundary by taking a line capacity change rate as a dynamic constraint; a staged reverse dynamic programming algorithm is adopted, spatial dimension optimization is preferentially executed on the high-frequency access nodes, and charging and discharging constraints fusing voltage deviation and capacity overrun penalty terms are generated; carrying out global optimization by taking the minimization of the total operation cost as a target after the constraints are integrated, and outputting an energy storage capacity configuration strategy of each time period; and finally generating an energy storage deployment scheme.