2145results about "Contigency dealing ac circuit arrangements" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

According to the optical storage direct flexible system scheduling method based on multi-objective optimization and an adaptive scheduling strategy, monitoring devices are installed on a photovoltaic array, an energy storage unit and a load side, a data sensing network covering the whole link of'source-storage-load-network 'is constructed, and key data are collected in real time. A multi-objective optimization model with maximization of economical efficiency, reliability and clean energy consumption rate as objectives is established, and a hybrid optimization mechanism of a genetic algorithm and particle swarm optimization is adopted to generate a day-ahead scheduling reference scheme. And designing an adaptive scheduling algorithm and a dynamic parameter adjustment mechanism based on fuzzy logic, and combining a rolling optimization window to realize real-time optical storage coordination control and power real-time balance. According to the invention, the operation efficiency, stability and flexibility of the optical storage direct-flexible system are effectively improved, the capability of coping with emergencies is enhanced, and the optimization of the overall performance of the system is realized.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The present disclosure relates to the technical field of power systems. Disclosed are a distributed fault self-healing method and system for an active distribution network, and a device and a medium. In the present disclosure, using a two-terminal waveform difference as a faulty section locating principle is applicable to an active distribution network, which contains various types of DGs and different DG permeabilities. The present disclosure has a relatively low requirement for data computation and storage regarding an STU, and topology identification and update can be automatically completed during operation by simply configuring basic static network topology information for the STU, thereby adapting to a change in the operation mode of the network. By means of the present disclosure, a rational fault recovery rule is set, and important load nodes can be recovered as much as possible while meeting a network power constraint. In a recovery solution, the presence of distributed generation is taken into consideration, and different processing methods are designed according to whether the distributed generation can operate in an island manner for a long period of time.

The invention provides a photovoltaic active distribution network voltage out-of-limit problem tracing method comprising the following steps: receiving research data and carrying out multi-scale feature analysis to obtain a standardized multi-dimensional feature matrix and a time-space incidence matrix; constructing an initial causal graph based on basic research data and power grid physical constraints, and constructing a multi-layer causal network by calculating causal intensity; extracting a dynamic feature sequence, analyzing propagation features, and generating a propagation path set and a path weight matrix; performing tensor completion and state estimation on the research data to obtain a state estimation matrix and estimation uncertainty; and outputting an out-of-limit source position, an out-of-limit risk score and early warning information based on pattern matching analysis and risk assessment. According to the invention, through combination of multi-dimensional feature analysis and a multi-layer causal network, accurate positioning and risk early warning of a voltage out-of-limit source are realized, and traceability accuracy and analysis efficiency are improved.

The invention discloses a power distribution network collaborative management method and system based on artificial intelligence, and relates to the technical field of electrochemical detection, and the method comprises the steps: constructing a distributed edge computing node network, deploying nodes at key positions of a power distribution network, achieving the collection and preprocessing of local power data, and reducing the cross-regional data transmission pressure; an AI real-time communication scheduling model is established based on the preprocessed data, communication resources are dynamically allocated according to the operation state of the power distribution network, and fault data transmission is guaranteed preferentially; seamless interaction of multi-protocol equipment is realized through a self-adaptive protocol conversion mechanism containing protocol identification, format conversion and data verification; training a fault diagnosis model by using a federated learning framework, and enabling edge nodes to only upload parameters to a coordination center for aggregation and updating, so as to balance model precision and data privacy; when a fault is detected, a millisecond response mechanism is started, and a processing strategy is generated and executed in combination with edge local decision and central global optimization.

Disclosed is a method and system for quickly identifying and responding weak point-associated power flow based on a sensitivity factor, relating to the technical field of dispatching automation of power system. The method includes: parameter data is acquired, and a transfer factor (SF) matrix is established; consumption and accommodation of new energy of a power distribution network is evaluated, and a line with power flow exceeding a limit is evaluated; and flexible load in the power distribution network is dispatched based on the sensitivity factor, and safety verification is carried out on the result. The present invention identifies a blocking line of the power distribution network, the situation of out of limit of the line is eliminated, so that the safety of the power gird is ensured, and the new energy is consumed and accommodated.

The present application relates to a virtual synchronous control method and apparatus for an energy storage system, a device, and a storage medium. The method comprises: controlling an output voltage of a direct-current energy storage valve on the basis of an output electrical signal measurement value of a direct-current side of a voltage source converter and an output voltage reference value of the direct-current energy storage valve; further, controlling an output signal of an alternating-current side of the voltage source converter on the basis of a grid connection point electrical signal measurement value and a grid connection point electrical signal reference value of the voltage source converter. It can thus be seen that the embodiments of the present application implement a virtual synchronous control method independently controlling a voltage source converter and a direct-current energy storage valve in an energy storage system, so that the method can be applied to virtual synchronous control of a high-voltage direct-current direct-mounted energy storage system. In addition, by means of independently controlling the voltage source converter and the direct-current energy storage valve in the energy storage system, the voltage source converter does not need to be configured to take into account both direct-current side control and alternating-current side control, so that the complexity of a control loop of the voltage source converter can be simplified.

The invention discloses a self-adaptive management method and system for intelligent terminal equipment of a distributed power distribution network. According to the method, a self-organizing peer-to-peer communication network covering the whole network is constructed, and neighbor discovery and information sharing among intelligent terminal devices are achieved; the terminal independently completes control instruction calculation based on the local electrical parameters and a preset multi-objective function and optimizes an area control effect through a cooperation mechanism; introducing a self-adaptive adjustment strategy of a control algorithm; when abnormal operation is detected, the terminal can autonomously complete fault positioning and isolation, and power supply recovery is realized based on standby path judgment or micro-grid switching; the central management unit periodically carries out acceptance capacity evaluation, and terminal model parameters are aggregated in a federated learning mode; the terminal has an edge autonomous capability, and can maintain local control and data retention when communication is interrupted. The system has the characteristics of quick response, flexible structure, intelligent cooperation, high adaptability and the like, and is suitable for intelligent operation and management and control scenes of various types of power distribution systems.

The invention provides a new energy power distribution fault diagnosis system and method based on multi-source data fusion, and relates to the technical field of power system fault diagnosis. The system comprises a data processing module, a fusion reasoning module, a graph structure diagnosis module and a control response module. The data processing module performs unified structured processing on the multi-source operation data to generate a feature tensor; the fusion reasoning module constructs a dynamic fusion weight based on the information entropy of each data source, and outputs a fusion confidence judgment result; the graph structure diagnosis module is combined with the distribution network topology to construct a graph structure model, and node-level fault positioning is realized through a graph neural network; and the control response module generates a control instruction based on the diagnosis result, and acquires and executes feedback to update graph structure attributes, thereby realizing a diagnosis closed loop. The method has the advantages of high diagnosis precision, strong adaptability, timely response and the like, and is suitable for intelligent fault identification and dynamic processing in a new energy power distribution scene.

The invention discloses a transformer substation on-line monitoring system based on big data analysis, and relates to the technical field of power system state monitoring, the system obtains data through a data acquisition module, and after the data is processed by a signal preprocessing and feature window extraction module, a system response entropy calculation module calculates a multi-scale permutation entropy, a multi-scale fuzzy entropy and a transfer entropy; the method comprises the following steps of: constructing a composite entropy feature vector, establishing a working condition self-adaptive health entropy baseline by an entropy feature baseline management module by utilizing a machine learning algorithm, comparing a current entropy feature with the health baseline by a degradation evaluation and critical early warning module, performing analysis by combining various abnormal judgment rules and indexes based on a critical moderation theory, and outputting an evaluation and early warning result; according to the method, the functional out-of-order of the equipment can be sensed in advance, the dynamic interaction health degree is evaluated in a non-intrusive mode, effective early warning is provided for critical transformation such as system instability, and the reliability and safety of operation of the transformer substation are remarkably improved.

The present invention relates to the technical field of power system dispatching automation. Disclosed are a rapid identification and response method and system for a weak point-associated power flow based on a sensitivity factor. The method comprises: acquiring parameter data, and establishing a transfer factor matrix; assessing renewable energy integration of a distribution network and assessing a power flow over-limit line; and dispatching flexible loads in the distribution network on the basis of a sensitivity factor, and carrying out safety verification on a dispatching result. According to the rapid identification and response method for a weak point-associated power flow based on a sensitivity factor provided by the present invention, a weak point in new energy integration of the distribution network is identified, to obtain a blocking line that hinders new energy integration, providing a foundation for subsequent precise dispatching control for flexible loads. Sensitivity analysis is carried out on a weak point-associated power flow and flexible load user nodes on the basis of the sensitivity factor, and the line over-limit condition is eliminated by dispatching flexible loads of corresponding nodes, so that the power grid safety is ensured and new energy integration is implemented.

The invention discloses a virtual power plant-based power distribution area collaborative governance system and method, and relates to the technical field of power system automation and intelligent power distribution networks, and the system comprises a dynamic autonomous partition governance unit which is used for dividing a power distribution area node into a plurality of subunits, correcting abnormal states in the subunits based on a power mutual aid strategy between adjacent subunits; the multi-time-scale collaborative optimization unit is used for constructing a second-level, minute-level and hour-level multi-time-scale collaborative mechanism and coordinating control targets of the power distribution area at different time scales; the edge-cloud collaborative decision-making unit is used for complementing the edge side real-time control strategy and the cloud global strategy; and the elastic resource pool unit is used for calculating the elastic contribution degree of the resources and formulating priorities, and calling the resources according to the priorities to meet load requirements. By integrating the virtual power plant technology, the edge calculation, the digital twinning and the multi-time scale optimization strategy, the intelligent and efficient treatment of the power distribution area is realized.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system based on dynamic optimization control and a multi-mode energy scheduling method. Comprising a renewable energy power generation unit which comprises at least two distributed power supplies with different power generation characteristics; the multi-stage hydrogen production device is configured to receive the electric energy output of the renewable energy power generation unit and perform proton exchange membrane water electrolysis hydrogen production; the hybrid energy storage system comprises a super capacitor bank and a lithium ion battery pack which are connected in parallel to the direct current bus through a bidirectional DC / DC converter; the dynamic optimization controller is internally provided with a multi-time-scale coordination algorithm and is used for acquiring meteorological prediction data, load demand data and an energy storage SOC state in real time; the multi-mode scheduling module comprises three working modes including an island operation mode, a grid-connected operation mode and an emergency standby mode; according to the scheme, hydrogen production economy, equipment durability and power supply reliability can be considered at the same time.

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 relates to the technical field of power grid self-healing control, and discloses a power grid self-healing control intelligent optimization method and system, and the method comprises the steps: obtaining the topological structure information of a power grid, dividing n power grid toughness evaluation regions according to the topological structure information of the power grid, calculating a hierarchical toughness evaluation index, and forming a comprehensive toughness evaluation index according to the hierarchical toughness evaluation index; a self-healing control intelligent optimization model is constructed based on the comprehensive toughness evaluation indexes, a self-healing control optimal solution is obtained by solving the self-healing control intelligent optimization model, the self-healing control optimal solution is dynamically calibrated, and a self-healing control strategy is generated; according to the method, the topological structure, the historical fault data and the real-time operation state of the power grid are comprehensively considered, a layered evaluation and dynamic calibration combined mode is adopted, the intelligent level and adaptability of self-healing control of the intelligent micro-grid are improved, the toughness and recovery capacity of the micro-grid for coping with complex faults are enhanced, and safe, reliable and efficient operation of the power grid is guaranteed.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention provides a transformer substation fault intelligent analysis and diagnosis method and system based on big data, and the method comprises the steps: obtaining the surface and internal temperature data flows of all power equipment in a transformer substation, and the environment temperature and humidity data flow of the transformer substation in the operation environment of the transformer substation; performing cross-time-scale dynamic coupling on the temperature data stream and the environment temperature and humidity data stream to generate a heterogeneous temperature characteristic stream; based on the heterogeneous temperature characteristic flow, dynamically adjusting an association rule of the temperature anomaly of the power equipment and the operation state of the transformer substation; performing cross-device coupling analysis on the heterogeneous temperature characteristic flow through a deep belief network to generate a dynamic conduction topological structure; and generating a transformer substation fault diagnosis result in combination with the adjusted fault association rule and the dynamic conduction topological structure. According to the invention, the early warning accuracy and diagnosis efficiency of the substation fault are improved.

The invention relates to the technical field of power grid monitoring, and discloses a grid operation risk early warning method, system and device based on power grid cloud edge collaboration and a medium, and the method comprises the steps: generating a dynamic grid division adjustment instruction through density peak clustering analysis in combination with edge node calculation resources and communication delay, and adaptively adjusting the size and frequency of a detection window; analyzing the standardized grid data flow, detecting and identifying abnormal data, and forming a local abnormal event set; according to the local abnormal event set, analyzing a grid structure relationship by adopting a graph convolutional network, extracting association features among grid regions, and generating a global operation risk situation map; and generating a risk early warning information list, pushing the risk early warning information list to a scheduling control center and an edge node, and performing real-time early warning and closed-loop management on the gridding operation risk. According to the invention, the sensing capability of sudden electrical abnormality is enhanced, and the dynamic low delay of the early warning process is ensured.

The present invention relates to the technical field of power systems and operations research. Disclosed are a method and system for coordinated source-grid-load-storage dispatch optimization during post-disaster restoration of a power distribution system. The method comprises: acquiring network topology constraint data for a power distribution system, wherein network topology constraints for the power distribution system comprise a virtual network topology constraint for the power distribution system, a geometric network topology constraint for the power distribution system, and an electrical network topology constraint for the power distribution system; on the basis of fault situations of a power source device, a load device and an energy storage device that are caused by a disaster, generating operational characteristic constraint data for devices in the power distribution system, wherein the operational characteristic constraint data for the devices in the power distribution system comprises operational characteristic constraints for the power source device, the load device and the energy storage device; and inputting the network topology constraint data for the power distribution system and the operational characteristic constraint data for the devices in the power distribution system into a pre-established coordinated source-grid-load-storage dispatch optimization model for power distribution systems in a post-disaster restoration scenario, such that a coordinated dispatch optimization result is output.

Disclosed in the present invention are an improved ADMM-based distributed coordinated restoration method and system for a power transmission network and a power distribution network. The method comprises: constructing an objective function to maximize the load restoration benefit of a power transmission network and a power distribution network and, in light of constraint conditions of the power transmission network and the power distribution network, establishing a coordinated restoration optimization model for the power transmission network and the power distribution network; on the basis of the coordinated restoration optimization model for the power transmission network and the power distribution network, introducing an augmented Lagrange function to construct an augmented objective function of the power transmission network and he power distribution network; using an improved alternating direction method of multipliers to solve the augmented objective function of the power transmission network and the power distribution network; using a dual update accelerated iteration policy to update a penalty coefficient and a Lagrange multiplier; and, on the basis of primal residual and dual residual convergence situations or the number of iterations, acquiring a coordinated restoration policy for the power transmission network and the power distribution network. The present invention efficiently solves distributed coordinated restoration of power transmission networks and power distribution networks, and not only increases restoration speed and reduces economic losses, but also reduces calculation complexity and communication burdens.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention relates to the technical field of distribution network fault scheduling decisions, and particularly discloses a distribution network fault scheduling decision generation method fusing a knowledge graph, and the method comprises the steps: constructing and dynamically updating an initial knowledge graph containing power grid topology, historical faults and environment data, and generating a dynamic knowledge graph to integrate multi-source heterogeneous data; reasoning a fault influence range by using a graph neural network, generating a fault influence sub-graph and analyzing the fault influence sub-graph into an initial scheduling strategy; and carrying out balance adjustment on the initial strategy through reinforcement learning simulation optimization in combination with a multi-objective optimization algorithm, and outputting a Pareto optimal solution giving consideration to the recovery efficiency, the operation cost and the load loss as a final decision. According to the method, the problems of data islands, experience dependence and poor adaptability in a traditional scheme are solved, full-process automation from data fusion to intelligent decision making is realized, and the timeliness, accuracy and economical efficiency of distribution network fault processing are remarkably improved.

The invention provides a fault ride-through control method and system for a network-forming converter, and relates to the technical field of power electronics. The fault ride-through control method of the network construction type converter comprises the following steps: determining a real-time virtual impedance value corresponding to a control loop based on a virtual admittance control module; in response to the fact that the real-time virtual impedance value is larger than or equal to the reference virtual impedance value of the control loop, transient current output by the network-building type converter is adjusted based on first-stage current limiting, and steady-state current output by the network-building type converter is adjusted based on second-stage current limiting; calculating an active power proportionality coefficient based on the real-time parameter and the reference parameter; calculating a target active power reference value according to the active power proportionality coefficient and the reference active power reference value; and executing power control based on the target active power reference value, and adjusting the output voltage of the network-forming converter. According to the method, when the power grid breaks down, the grid-forming type converter can have the functions of current limiting and transient synchronization stability improving at the same time.

The invention discloses a direct-current power transmission receiving end and a direct-current power transmission system. The direct-current power transmission receiving end comprises an inverter circuit and a receiving end transformer; the inverter circuit comprises a hybrid power grid commutation converter and a modular multi-level converter; a direct current first end of the hybrid power grid commutation converter is used for connecting a direct current power transmission line, a direct current second end of the hybrid power grid commutation converter is connected with a direct current first end of the modular multilevel converter, and an alternating current end of the hybrid power grid commutation converter is connected with a receiving end alternating current bus through a corresponding receiving end transformer; the direct current second end of the modular multilevel converter is grounded, and the alternating current end of the modular multilevel converter is connected with the receiving end alternating current bus through the corresponding receiving end transformer. In the embodiment of the invention, the hybrid power grid commutation converter and the modular multi-level converter which are connected in series are used at the direct-current transmission receiving end, so that the direct-current transmission receiving end has direct-current side fault ride-through capability and alternating-current side fault ride-through capability.

Embodiments of the present invention provide a method and apparatus for suppressing transient current of a grid-forming converter, a computing device, a computer-readable storage medium, and a computer program product. The suppressing method comprises: calculating first virtual impedance on the basis of an actual voltage instantaneous value of a point of common coupling; calculating second virtual impedance on the basis of an actual current maximum amplitude of the point of common coupling; and suppressing the transient current of the grid-forming converter on the basis of the first virtual impedance and the second virtual impedance. In this way, by combining the actual voltage instantaneous value of the point of common coupling and the actual current maximum amplitude of the point of common coupling, transient current can be suppressed at the moment when a fault occurs in a grid-forming converter, thereby avoiding misoperation triggering of a protection apparatus, and ensuring that the grid-forming converter can restore stable operation once the fault is removed.

The invention relates to the technical field of power generation, power transformation or power distribution, and discloses a control method for preventing overload of an energy storage system transformer, which comprises the following steps: monitoring the three-phase total power and the power of each phase of a power grid side ammeter in real time, and calculating the three-phase unbalance degree based on the three-phase total power when the three-phase unbalance phenomenon occurs; when the three-phase unbalance degree is greater than a preset unbalance degree threshold value, dynamically adjusting the charging power of the energy storage converter according to a starting threshold value, a charging limiting threshold value and a charging forbidding threshold value by adopting a multi-threshold value dynamic adjustment mechanism; the phase with the maximum deviation between the power of each phase and the average power of the three phases is calculated as an overload phase, load reduction is performed on the overload phase based on a load increasing and decreasing strategy, load increasing is performed on a non-overload phase, and the adjusted three-phase power is obtained; a power distribution instruction is calculated in real time in combination with a dynamic response mechanism, and the three-phase unbalance degree does not exceed a safety threshold value in cooperation with total power control. The problems of easy overload, three-phase imbalance and slow response of a transformer in the prior art are solved, and the purposes of accurate load control, high system stability and high response speed are achieved.

The invention relates to the technical field of power distribution networks, and discloses a power distribution network fault self-recovery method and system based on machine learning, and the method comprises the steps: collecting the multi-source fault feature data of a power distribution network in real time, including the voltage and current waveform distortion feature, the load sudden change feature and the equipment temperature anomaly feature; and inputting the multi-source fault feature data into a multi-stage classification model for processing, and by setting a pseudo fault filtering module, establishing a typical interference feature library and setting dynamic filtering thresholds for different working conditions during real-time fault detection of the power distribution network, so that the recognition accuracy of various interference signals is ensured, and the fault detection accuracy of the power distribution network is improved. Meanwhile, similarity comparison is carried out on real-time collected data and pseudo fault features, whether the problems of signal distortion and noise pollution occur at the fault positioning position of the power distribution network or not can be detected in real time, the accuracy of fault type recognition is ensured, the misjudgment risk is reduced, and when strategy adaptation is abnormal, the matching degree of the self-healing strategy and the actual state of the power grid is ensured.

The invention discloses a power grid transient process deduction method based on digital twinning, electronic equipment and a medium, and the method comprises the steps: building a digital twinning simulation model based on a new energy single-machine simulation model and a three-dimensional field station virtual model, and outputting a virtual value by the digital twinning simulation model; collecting core parameters in a monitoring period; calculating the deviation ratio of the physical value and the virtual value of the physical station based on the core parameter; and adjusting the transient process control strategy based on an adjustment object and the deviation rate, wherein the adjustment object comprises a virtual value, a core parameter, a scene parameter and a model control mode. The digital twinborn model can be calibrated in real time through deviation ratio closed-loop correction of a physical value and a virtual value, it can be ensured that simulation of the transient process of the power grid is highly consistent with the physical reality, and the precision and reliability guarantee of a deduction result is improved.