1140 results about "Grid fault" patented technology

The present invention relates to the field of power distribution network fault detection, and in particular to a power distribution network fault detection method applicable to a distributed power supply. Potential fault modes and rules are extracted from a large amount of real-time monitoring data by using multiple data models, achieving accurate detection and diagnosis of various fault types; data collected in real time is processed and analyzed, so that fault signals can be found in a timely manner and corresponding alarm and notification mechanisms can be triggered, improving the response speed and processing efficiency of faults; continuous data iteration and model updating are performed, so that the change of a system and the condition of different types of faults can be adapted, improving the robustness and accuracy of detection; and faults are detected and diagnosed in a timely manner, so that a power grid operation strategy is effectively optimized, the energy utilization efficiency is improved, and the energy consumption and the operation and maintenance costs are reduced.

The invention relates to a real-time fault detection and automatic repair system for a power grid. The real-time fault detection and automatic repair system comprises a multi-level sensing module, a data processing module, an automatic repair module and a monitoring and logging module. The multi-level sensing module collects data such as voltage, current, frequency and temperature in operation of a power grid by deploying multiple types of sensors, and sends the data to the data processing module. And the data processing module analyzes the collected data through an embedded intelligent algorithm, identifies potential faults and generates fault diagnosis results. And the automatic repair module executes repair operations of isolating a fault area, reconstructing a power grid topological structure and recovering normal power supply according to a fault diagnosis result. The monitoring and log module records the operation state of the power grid and the fault diagnosis and repair process, and provides fault analysis and performance evaluation functions through a user interface. According to the system, real-time detection, accurate positioning and automatic repair of power grid faults and comprehensive monitoring of operation data are realized, and the safety and reliability of power grid operation are remarkably improved.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention relates to the technical field of fault analysis, in particular to a power grid fault intelligent diagnosis and analysis system which comprises a fault signal detection module, a propagation path analysis module, a fault positioning calculation module, a fault type analysis module and a fault influence analysis module. According to the invention, by monitoring the voltage transient data of the plurality of nodes of the power grid in real time, the instantaneous offset and the time sequence difference of the voltage can be accurately calculated, so that the voltage mutation exceeding the preset threshold value can be quickly identified in the continuous time window, the early identification of the power grid fault is quicker, the risk of false alarm is reduced, and the accuracy of the early identification is improved. By analyzing the propagation path and speed of the sudden change quantity along the power grid, the fault positioning precision is improved, rapid and accurate fault source tracing is realized, and by careful analysis of the current waveform of a fault point and combination of real-time tripping current data, the fault type can be identified more accurately, the fault handling strategy can be optimized, and the fault handling efficiency is improved. And the power grid fault processing efficiency and safety are improved.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

The invention relates to the technical field of smart power grids, in particular to a fine tuning method and system based on a power failure multi-modal model and a medium, and the method comprises the steps: generating a knowledge retention mask according to a pre-training weight matrix of a multi-modal large language model; generating a sparse low-rank matrix according to the space-time sparse characteristic of the structured multi-modal data set; generating a conflict mitigation regularization item according to the knowledge retention mask and the sparse low-rank matrix, constructing a power task joint loss function according to the conflict mitigation regularization item, and performing fine tuning training on the multi-modal large language model to obtain a fine tuning multi-modal large language model; and inputting the real-time operation data of the power grid into the fine-tuning multi-mode large language model for reasoning to obtain a power grid fault diagnosis result, and performing isolation control on a power grid fault section according to the power grid fault diagnosis result. According to the method, efficient fine tuning is performed on the multi-modal large language model by using the power grid data space-time sparsity modeling and the conflict mitigation regularization strategy, and rapid and accurate diagnosis of the power grid fault is realized.

The invention relates to the technical field of power system operation training, and discloses a virtual reality-based power grid fault simulation training system and method, and the system comprises a virtual scene modeling module which is used for constructing a three-dimensional model of power grid equipment and an operation environment thereof, and providing a vivid training environment for subsequent modules; and the interaction and operation module is connected with the virtual scene modeling module and realizes interaction between the trainee and the virtual environment by using a VR head-mounted display, a handle and a motion capture device. By adopting a high-precision three-dimensional modeling technology, PBR material rendering and an LOD optimization strategy, high simulation of power grid equipment and an operation environment is realized, students can experience a vivid visual effect and immersion in a virtual reality scene, and meanwhile, by combining a gesture recognition technology realized by motion capture equipment and a convolutional neural network, the real-time simulation of the power grid equipment and the operation environment is realized. Operation actions of trainees can be accurately captured, a natural and visual interaction mode is provided, and the authenticity and operation experience of the training system are greatly improved.

The invention discloses an operation and maintenance decision driving method and system based on a multi-modal data knowledge graph, and relates to the field of intelligent decision support, and the method comprises the steps: obtaining the multi-source and multi-modal data of a power distribution network, and carrying out the preprocessing, and obtaining a knowledge graph database; constructing a knowledge graph based on the knowledge graph database; constructing a fault decision mode based on the knowledge graph; performing decision analysis and output based on the fault decision mode; according to the method, the utilization efficiency of data is improved, the data quality and relevance are improved, the fault type and the influence range thereof can be quickly identified, fault early warning is realized, personalized decision suggestions are provided, operation and maintenance personnel are helped to deal with power grid faults in time, and the fault processing efficiency and accuracy are improved; risks can be prevented in advance, resource allocation is optimized, and stable operation of a power grid is guaranteed; the automatic execution of the decision is realized, and the overall operation and maintenance efficiency is improved.

The present invention discloses a method and system for smart grid fault self-healing based on generative artificial intelligence, which relates to the field of smart grid fault self-healing. The method includes: obtaining and preprocessing multi-source heterogeneous data; generating a pre-trained language model and performing domain adaptability fine-tuning, adding a task-specific output layer; performing feature fusion using a multimodal data fusion method, and using annotated historical fault data sets for supervised training to obtain fault diagnosis results. Fault self-healing is modeled as a Markov decision process, and a deep reinforcement learning algorithm is used to interactively learn the optimal decision-making strategy with a power grid simulation environment, and an optimal fault self-healing plan is generated and converted into specific operation instructions. The present invention integrates multi-source heterogeneous data, performs accurate positioning and description generation, improves the reliability and self-healing ability of the smart grid, and ensures rapid recovery of normal operation when a power grid fails.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention provides a power transmission network equipment fault diagnosis and life prediction method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining the time sequence electrical characteristic data of a plurality of monitoring nodes, carrying out the window segmentation and statistical characteristic extraction, building a dynamic association graph structure based on a space-time association constraint model, and obtaining the time sequence electrical characteristic data; and calculating the abnormal contribution degree of each node, marking candidate abnormal nodes, determining a fault propagation path through reverse tracing and path analysis, and finally outputting a fault positioning result. According to the invention, abnormal nodes can be accurately identified, a fault propagation path can be accurately tracked, and the accuracy and timeliness of power transmission network fault diagnosis are improved.

The invention discloses a low-voltage ride-through control method of a flywheel energy storage system, which is based on a low-voltage ride-through control strategy of model predictive current control (MPCC) so as to improve the low-voltage ride-through capability of the flywheel energy storage system when a power grid fails. A future current value is predicted through system modeling and a prediction algorithm, so that effective control is realized. The MPCC-based machine side-power grid side coordination control strategy can quickly respond to the voltage sag of the power grid, provide reactive power support, stabilize the voltage of the direct current bus, and ensure the safe and reliable operation of the flywheel energy storage system during the voltage sag period of the power grid. A traditional pulse width modulation (PWM) technology is abandoned, the optimal switching state is directly selected through a limited control set, and hardware implementation of a power grid side converter is simplified. A differential control strategy aiming at a power grid symmetric fault (three-phase voltage sudden drop) and an asymmetric fault (single-phase grounding) is provided, the method is suitable for a complex power grid fault scene, and the system universality is improved.

The invention discloses a power failure and recovery logic-based power supply fault diagnosis method for a low-voltage power distribution network, which belongs to the technical field of power supply fault diagnosis and comprises the following steps of: converting a user unstructured power failure complaint into a standardized event tag by utilizing a natural language processing technology, synchronously aligning an ammeter power failure pulse and a branch switch action record, and performing power failure diagnosis; constructing a multi-dimensional space-time event sequence; a fault propagation logic network is established through power supply unit topology division, a fault diffusion path is deduced according to a power failure time sequence difference of adjacent units and a failure state of a protection device, and an initial fault point is reversely positioned. And meanwhile, the fault association probability between the units is dynamically corrected, and a weighted candidate fault chain is generated. And through multiple verification mechanisms, the fault propagation tree is reversely corrected. And finally, calculating the confidence coefficient, and screening out the fault chain with the optimal time-space consistency. The problems of multi-source information splitting, space-time correlation weakening and insufficient verification reliability in low-voltage power distribution network fault positioning are solved.

The invention relates to the technical field of data processing, and discloses a power grid fault early warning system based on machine learning and a related device, and the system is characterized in that an initial monitoring module identifies and identifies abnormal data points through monitoring the voltage, current and load of a power grid, and obtains an abnormal mode identification result; the load fluctuation analysis module positions the abnormal node according to the abnormal mode identification result to obtain abnormal node positioning information; the trend analysis and prediction module evaluates and predicts fault nodes and time according to the abnormal node positioning information to obtain a risk prediction result; the strategy formulation and response module formulates an operation instruction based on the risk prediction result to obtain a fault prevention measure; the fault simulation and test module performs virtual fault simulation by adopting fault prevention measures, and performs iterative optimization on a strategy to obtain a test evaluation result; and the fault early warning and optimization module optimizes a fault prevention strategy according to a test evaluation result, and a more accurate target fault early warning and a faster and more effective target response strategy can be obtained.

A method, system, and readable storage medium for fault line selection in distribution networks is provided. The method includes: obtaining the zero-sequence current of each feeder and the zero-sequence voltage of the busbar within a preset time window after a fault occurs; using these to process the feeder's short-time window zero-sequence instantaneous power curve cluster in the distribution network through KPCA (Kernel Principal Component Analysis) for dimensionality reduction, determining the principal component scores; and performing BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering based on these scores to identify whether a feeder is faulted. This clustering process allows for precise and rapid identification of the faulted feeder, even when the current is small, improving detection accuracy. This solves the problem of quickly identifying the faulted feeder in a small current grounding distribution network during single-phase grounding faults.

The invention discloses a power grid lightning stroke fault risk assessment method and system, and relates to the technical field of risk assessment, and the method comprises the following steps: obtaining the operation parameters of a power grid region with dense lightning activities, and constructing a dynamic power grid electrical model considering the load level and reactive compensation influence; obtaining thunder and lightning data and power grid fault data of the current thunder and lightning activity dense power grid area; constructing a lightning stroke fault risk assessment model based on a bp neural network according to the lightning related data and the power grid fault related data; and combining output results of the lightning stroke fault risk assessment model and the dynamic power grid electrical model, simulating power grid operation states and fault risks under different lightning protection strategies, and obtaining a lightning stroke fault risk level. According to the invention, the dynamic power grid electrical model and the lightning stroke fault risk assessment model are constructed to predict and assess the future operation state and the lightning stroke fault risk of the power grid.

The invention discloses a coal mine power supply system multi-scene modeling method, system, device and medium, and belongs to the technical field of power system scene modeling, and the method comprises the steps: building a main circuit topology, and constructing a simulation model containing a user-defined module; configuring equipment parameters, and importing field survey data to construct a dynamic load matrix; setting a fault scene, and accurately simulating various power grid faults through a circuit breaker time sequence; parallel simulation is started, and bus and key node data are collected in the simulation process; performing multi-dimensional index analysis on the collected data, and extracting key indexes; a verification report is automatically generated, and evaluation is completed in combination with a manual checking mechanism; and visually outputting a simulation result. According to the method, real-time adaptation of load impedance characteristics is realized, historical load curves can be imported in batches, a dynamic load matrix can be generated, and time-varying characteristics of coal mine loads can be accurately reproduced. On the fault simulation level, circuit breaker opening and closing logic is adopted, superposition simulation of bilateral voltage loss and multi-source faults is supported, and the seamless switching performance of the energy storage system is verified.

The invention relates to the technical field of digital twinning, in particular to a digital twinning fused power grid fault prediction system and method, and the system comprises a state sensing module, a path deduction module, a twinning mapping module and a prediction execution module. According to the method, multi-source heterogeneous sensing data are fused, a space-time alignment algorithm is executed to construct a structural state tensor, Laplacian operation is combined with a stress wave propagation model to analyze non-rigid deformation characteristics, a load transfer path atlas is generated to reveal recessive mechanical association, Young modulus is dynamically corrected through a finite element grid to form a structural degradation coefficient matrix, and the structural degradation coefficient matrix is obtained. The time sequence difference and the long short-term memory network are combined to capture the equipment degradation trend, the Kolmogov test is used to quantify the fault space-time distribution probability, the power grid equipment jumps from single threshold early warning to multi-physics coupling prediction, the fault prediction space-time resolution and dynamic adaptability are improved, the artificial experience dependence is reduced, and the fault prediction efficiency is improved. And the robustness of the complex working condition system is enhanced.

The invention discloses a digital intelligent management early warning system based on power grid construction, and relates to the technical field of power grid early warning management. The intelligent management early warning center is in communication connection with a data acquisition module, a fault feature analysis module, a fault monitoring module, a fault evaluation module, an early warning grading module, an early warning generation and release module and an emergency response module. According to the method, the electrical parameters and the environmental parameters in the operation of the power grid are monitored in real time, abnormal conditions in the power grid are rapidly identified, fault features are extracted from a large number of historical fault cases and real-time data, the design parameters and the operation specifications of the power grid are combined, the fault evaluation standard is formulated, the coping capacity for the power grid faults is improved, and the fault diagnosis accuracy is improved. When a fault occurs, a response is quickly made, and through timely issuing of early warning information, it is ensured that related personnel can take actions quickly, so that the power failure time is shortened, and the reliability of a power grid and the satisfaction degree of users are improved.

The invention discloses a transient optimization control method based on a parallel system of a virtual synchronous machine and a grid-following converter. The method comprises the following steps: 1, constructing a topological structure of the parallel system of the virtual synchronous machine and the grid-following converter; 2, based on the topological structure, establishing a mathematical model of a parallel system, including an active loop equation and a reactive loop equation of a virtual synchronous machine and a phase-locked loop equation of a grid-following converter; 3, constructing an equivalent circuit, a network equation and a transient analysis model of the parallel system based on the mathematical model; 4, on the basis of the equivalent circuit, the network equation and the transient analysis model, determining conditions met by respective balance points of the virtual synchronous machine and the grid-following converter by using a balance point method; and 5, when the power grid has a fault, constructing a transient optimization strategy according to a condition satisfied by the balance point so as to realize transient optimization control of the parallel system. According to the method, the control modes of a virtual synchronous machine power loop and a grid-following type converter phase-locked loop can be improved, so that the transient stability of a parallel system converter is improved.

The invention discloses an intelligent power grid fault deduction method and device based on a knowledge graph and electronic equipment, and relates to the technical field of power grid fault diagnosis, and the method comprises the steps: receiving protection action alarm information and circuit breaker tripping alarm information when a target intelligent power grid breaks down; based on the protection action alarm information and the circuit breaker tripping alarm information, performing hypothesis reasoning by using a preset power grid topology knowledge graph to obtain a suspected fault equipment set; performing deductive reasoning on each suspected fault device in the suspected fault device set by using a preset protection configuration knowledge graph based on the protection action alarm information and the circuit breaker tripping alarm information to obtain observation event confidence corresponding to each suspected fault device; and evidence fusion reasoning is carried out on each suspected fault device based on each expected observation event confidence, and actual fault information of the smart power grid is obtained. The method can improve the accuracy of the fault diagnosis result of the smart power grid.

The invention provides a power grid fault positioning and intelligent relay protection selective control method and system, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting the multi-point electrical data of a power grid, adjusting a sampling window and a filtering threshold value according to the operation state of the power grid, and carrying out the data processing; after eliminating the end effect, performing empirical mode decomposition to obtain a fault feature component; inputting the characteristic component into a fault diagnosis model, calculating a fault initial probability based on a Bayesian network, generating multiple groups of fault prediction data in combination with a time sequence evolution rule, and taking a position corresponding to a minimum information entropy value as a fault positioning result; determining a fault area according to a positioning result, establishing a local communication network of relay protection devices in the area, obtaining the operation state of each device, calculating a protection sensitivity coefficient, determining a protection range and a tripping time sequence, generating and sending a control instruction, and realizing selective isolation of the fault area.

The invention relates to a power electronics and energy storage technology, and aims to provide a medium-voltage uninterruptible power supply system based on cascade super-capacitance energy storage and a control method. According to the system, a cascade super-capacitance energy storage module with a three-phase structure is connected between an isolation reactor and a load side switch through a filter reactor and is used for stabilizing the voltage of a load bus; according to the invention, operations of adaptive power reference calculation, voltage phase control and voltage amplitude stable control are carried out; according to the method, the over-capacitance wide voltage operation characteristic is fully considered, and the control on the over-capacitance charging and discharging process is adaptively adjusted according to the power grid fault state and the over-capacitance voltage deviation, so that the system can provide the required power support when the fault occurs, stable energy supplementation is performed after recovery, the risks of over-capacitance voltage out-of-limit and the like are favorably avoided, and the system reliability is improved. And the effectiveness of energy management and the stability of the system are improved. And during a power grid fault period, the load voltage is recovered to be close to a rated value in a short time, voltage drop and overshoot are reduced, and the response speed and the voltage supporting capability of the system are enhanced.

The invention discloses a multi-target cooperative fault ride-through method suitable for a network-forming converter, and the method comprises the steps: carrying out the precise amplitude limiting of a fault current through an annular current limiter, and adjusting a power angle to a lower boundary of a transient stability domain during a fault period; and multi-target collaborative optimization of transient synchronous stability, power grid reactive power support and fault current suppression in the fault ride-through process is realized. An optimal power angle set meeting fault ride-through multi-target cooperation in a multi-element fault scene is constructed by performing off-line beforehand rehearsal training on a system physical model; when the power grid breaks down, the network construction type converter carries out online table look-up search based on the trained set, an active power instruction meeting the transient stability and the optimal reactive power support in the current fault scene is calculated, and the supporting capacity of the network construction type converter to the power grid is mined to the maximum extent while the network construction type converter operates safely and stably. According to the method provided by the invention, the network construction operation characteristics of the converter can be maintained under the power grid fault, and the multi-target collaborative fault ride-through capability of equipment is enhanced.

The invention relates to the technical field of power grid fault positioning, in particular to a rapid power grid fault positioning system based on digital twinning and AI, which obtains multi-dimensional operation data of each node of a power distribution network, constructs a structured data set, reflects the state of the system, and performs joint modeling on node features and a topological structure through a graph neural network. And reasoning to generate candidate fault nodes. And further constructing a digital twinborn model containing load flow calculation and disturbance injection, performing analogue simulation on candidate nodes, extracting an electrical quantity change rate, performing fuzzy similarity matching with operation data in a fault window, and accurately positioning a fault node. Through cooperation of three stages of prediction, simulation and matching, the problems of weak complex topology perception, poor perturbation identification, positioning offset and the like of the existing method are effectively solved, and the fault positioning precision and robustness are improved.

The invention relates to a reconfigurable battery energy storage system grid-connected and off-grid seamless switching control method and device. The method comprises the following steps of: sensing a real-time power grid state, pre-judging a system operation mode, and acquiring a millisecond-level panoramic information flow of the power grid state through a high-speed sampling voltage sensor, a high-speed sampling frequency sensor, a high-speed sampling phase sensor and a high-speed sampling communication interface. And pre-judging the stability and disturbance trend of the power grid through a deep learning algorithm, executing a power distribution and voltage support algorithm in grid-connected and off-grid modes, and calculating an optimal switching opportunity point and a system reconstruction target topology scheme. Meanwhile, the harmonic distortion rate and the voltage sag depth are calculated in real time through discrete Fourier transform, so that a mode pre-judgment mechanism is automatically triggered. After switching, key parameters such as voltage, frequency and power are automatically corrected through real-time sampling and observation, and zero-sensing switching is achieved. The method can effectively improve the response speed and stability of the battery energy storage system when the power grid fails, and guarantees the safe operation of the power grid.

The invention discloses a power grid fault positioning system and method based on traveling wave distance measurement, relates to the technical field of fault analysis, and solves the technical problems that traveling wave feature analysis, temperature compensation and high-precision synchronization technologies are not effectively integrated, and rapid and accurate fault positioning of a smart power grid is difficult to meet. Through the technical combination of dynamic temperature compensation, high-precision time synchronization and intelligent fault classification, the bottlenecks of traditional traveling wave distance measurement in the aspects of precision, reliability and intelligence are systematically solved, temperature sensors are deployed at key nodes of a cable, the temperature is monitored in real time, the wave speed is dynamically corrected through a formula, the overall error is reduced, and the fault location accuracy is improved. Meanwhile, an optical fiber two-way time transmission method is combined, transmission delay errors are reduced, synchronization reliability is improved, finally, a mapping library is constructed through laboratory simulation and field data, a Euclidean distance matching algorithm is combined, fault reasons are automatically recognized, and rapid positioning of the faults and accurate recognition of the fault reasons are achieved.

The invention provides a method for setting virtual internal potential control parameters of a network-building type converter, and relates to the technical field of network-building type converters, and the method comprises the steps: S1, determining the virtual internal potential control parameters of the network-building type converter; s2, using an equivalent circuit method to generate a network-forming converter equivalent circuit; constructing an equivalent voltage source model of the network-forming converter through the virtual internal potential control parameters and the equivalent circuit; s3, calculating a constraint condition II of control parameters of the model in different operation states according to a constraint condition I of grid-connected operation of the converter; s4, performing parameter scanning on the value range of the control parameters based on a constraint condition II, and generating a solution space meeting the constraint condition II; and S5, according to the functional characteristics of the converter, obtaining the optimal parameter combination of the virtual internal potential control parameters in the solution space. The method further optimizes the virtual internal potential control algorithm, improves the adaptability and safety of the virtual internal potential control algorithm under various power grid fault conditions, and improves the stability and reliability of a new energy grid-connected system.

The invention discloses a new energy voltage ride-through parameter optimization method considering polymorphic voltage stability constraint, a storage medium and equipment, and belongs to the technical field of power system safety and stability improvement. In order to solve the problem that an existing new energy voltage ride-through parameter optimization method is only suitable for a single fault scene and only aims at a voltage stable form to optimize a new energy voltage ride-through parameter, the invention provides a new energy voltage ride-through parameter optimization method. Based on the dynamic characteristics of the new energy during the power grid fault period, the new energy general control model is established so as to determine the dominant control parameters influencing the dynamic characteristics of the new energy during the high / low voltage penetration period, the influence of the dominant control parameters on the voltage stability under different faults is considered, and the voltage regulation capability and the power regulation cost of the new energy are considered. A new energy voltage ride-through parameter optimization model considering polymorphic voltage stability constraints is established, and the optimization model is solved by adopting a particle swarm optimization algorithm. The method is used for voltage ride-through parameter optimization of new energy.