12883 results about "Distribution networks" patented technology

The invention relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to a power distribution network, and carrying out structured disassembly and preprocessing to construct a space-time double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the space-time double graph structure, performing multi-scale topological feature extraction, and generating a space-time separated feature vector set; performing deep coupling fusion on the feature vector set subjected to time-space separation to generate corresponding unified topological feature representation containing abnormal topology; and constructing a dynamic topology state prediction model based on the unified topology feature representation to optimize a space-time joint loss function and output a corresponding real-time topology connection relationship and an equipment state change trend, and meanwhile, performing dynamic topology reconstruction to generate a current-moment reliable topological graph corresponding to potential branch disconnection and temporary tripping. The topology analysis accuracy of the power distribution network can be improved.

The invention relates to the technical field of power systems and automation thereof, in particular to a distribution network auxiliary decision-making method and system considering source load fluctuation relevance and a medium. The method comprises the following steps: firstly, collecting related information of a distribution network area, quantifying a synchronization and hysteresis association rule of multi-source heterogeneous data fluctuation, and constructing a composite feature vector and a standardized risk perception data set; defining a state space and an action space of a reinforcement learning algorithm based on the composite feature vector, and realizing auxiliary decision-making optimization of the distribution network; constructing a scene feature library, calculating the fluctuation relevance similarity between a new scene and a historical scene, and multiplexing a deep reinforcement learning model architecture and carrying out transfer learning; building a power grid digital twinborn simulation platform, designing evaluation indexes, generating candidate schemes, deducing the candidate schemes, selecting recommendation strategies and storing the recommendation strategies in a strategy knowledge base.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention discloses an intelligent power distribution network equipment state perception and abnormity diagnosis system, and the system operation process specifically comprises the following steps: collecting the operation state data of power distribution network equipment in real time, carrying out the time-space alignment and feature fusion, and generating an equipment multi-dimensional state vector; inputting a pre-constructed equipment health dynamic baseline model, and outputting a real-time health deviation degree; when the real-time health deviation degree exceeds an early warning deviation threshold value, triggering an abnormal preliminary screening mechanism, and extracting abnormal feature fragments; inputting a multi-stage diagnosis knowledge graph model, and generating an abnormal cause hypothesis set; performing confidence ranking on the abnormal cause hypothesis set, and outputting first # imgabs0 diagnosis results and corresponding confidence weights; and generating an equipment maintenance strategy instruction set according to the diagnosis result. The method has the following advantages and effects: the dynamic baseline is adaptively generated from multi-source data, and a multi-stage diagnosis framework of a physical model, a power grid rule and a historical case is fused, so that the accuracy and timeliness of anomaly diagnosis are finally improved.

The embodiment of the invention provides a power distribution network disaster risk assessment method and system based on multi-source big data, and the method comprises the steps: obtaining a multi-source dynamic data set associated with a power distribution network, carrying out the multi-source feature deep coupling of the multi-source dynamic data set, and generating a power distribution network risk coupling feature set; the power distribution network risk coupling feature set comprises an equipment state coupling feature, an environment interference coupling feature and a topological correlation coupling feature; inputting the power distribution network risk coupling feature set into a preset risk situation coupling deduction model to perform multi-dimensional risk situation coupling deduction, and outputting a power distribution network disaster risk situation map; key node risk traceability coupling analysis is carried out based on the power distribution network disaster risk situation map, and a power distribution network weak link set and a risk evolution dynamic parameter set are determined. According to the method, the weak link in the power distribution network can be accurately positioned, the change rule of the risk along with time can be captured, and the improvement from static recognition to dynamic traceability and evolution prediction is realized.

A method for locating a high-impedance ground fault of a smart distribution network with topology change adaptation includes: acquiring a fault traveling wave sample within a specified time window after a fault occurs, and performing continuous wavelet transform on the fault traveling wave sample to obtain traveling wave full waveform feature information; establishing a graph structure of a power distribution network, obtaining a corresponding adjacency matrix, and obtaining node position and structure encoding information in the graph structure through graph random walk and graph Laplace transform; concatenating the node position, the structure encoding information and the traveling wave full waveform feature information to obtain a node feature, and inputting the node feature and an edge feature into the graph structure to establish a graph sample data set; constructing and training a Graph Transformer model; and calling the trained Graph Transformer model to locate a fault in to-be-detected sample data.

The present application provides a multi-level coordinated voltage control method and system for a power distribution network based on three-tier priority objectives. The method comprises: acquiring operation parameters of a power distribution network; on the basis of the operation parameters of the power distribution network and a pre-constructed three-tier optimization objective planning model, using an affine theory, a duality theory, and power circle linearization and absolute value linearization methods to solve the three-tier optimization objective planning model to obtain maximization of an admissible net-load disturbance domain at each node, minimization of the total operation cost of the power distribution network and minimization of an expected voltage deviation; and performing coordinated control on various reactive devices of the power distribution network by means of the operation parameters of the power distribution network corresponding to the maximization of the admissible net-load disturbance domain at each node, the minimization of the total operation cost of the power distribution network and the minimization of the expected voltage deviation. The present application can more clearly characterize the uncertainty of distributed generation power output and the impact of the distributed generation power output on system reserve capacity, and mitigate the problems of ambiguous characterization of distributed generation power output characteristics, and multi-level voltage violation and voltage fluctuation of the power distribution network caused by large-scale distributed generation integration.

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention belongs to the field of power systems, and discloses a power distribution network fault autonomous diagnosis and self-healing control method and system based on deep reinforcement learning, and the method comprises the steps: carrying out the collection and preprocessing of the multi-source heterogeneous data of a power distribution network; key feature vectors are extracted from the multi-source heterogeneous data based on a graph convolutional network, and a state representation model is constructed; based on an asynchronous dominant actor-commentator algorithm, constructing a fault diagnosis agent capable of quickly diagnosing faults; a meta-reinforcement learning algorithm is adopted, a dynamic reconstruction strategy library is generated through pre-training, and a self-healing control agent capable of rapidly adapting to various different fault scenes to generate an optimal reconstruction strategy is constructed; constructing a self-healing control module based on virtual impedance matching, wherein the self-healing control module is used for intelligent reconstruction and self-healing control of the power distribution network; a risk-sensitive reward function and a game equilibrium strategy optimization method are introduced to improve performance and robustness; and finally carrying out system deployment and engineering verification.

The invention relates to the technical field of fault positioning, and discloses a power distribution network grounding fault positioning method and system. The method comprises the following steps: acquiring a fault initial transient section signal, an arcing continuous section signal and an arc quenching recovery section signal of the power distribution network, and executing spectral analysis to obtain a fault feature matrix; performing zero-sequence transient frequency spectrum reconstruction on the fault feature matrix to obtain reconstructed frequency spectrum features and time-varying frequency spectrum features; calculating a transient characteristic index and a fault type based on the reconstructed spectrum characteristic and the time-varying spectrum characteristic; performing fault positioning calculation on the power distribution network according to the transient characteristic index and the fault type to obtain an initial fault point position; and performing transient fingerprint matching and probability density accumulation compensation on the initial fault point position to obtain a target fault point position. According to the method, high-precision characterization of the rapid change signal in the short time window is realized, interference of the compensation state on fault positioning is eliminated, and the grounding fault positioning accuracy of the power distribution network is improved.

The invention relates to a power distribution network state estimation method based on an adaptive space-time diagram neural network, and the method mainly comprises the following steps: S1, collecting historical and real-time measurement data of a power distribution network, and carrying out the preprocessing of the data, so as to guarantee the integrity of the data, provide high-quality input data for a model, and improve the estimation precision and stability of the model; s2, discrete wavelet transform is carried out on historical measurement data, multi-scale decomposition is achieved, and low-frequency and high-frequency components are extracted; a double-branch time sequence fusion module is constructed, global trend and local fluctuation features are respectively captured through a dynamic attention mechanism and a time convolution network, and the features are efficiently fused by means of adaptive weights. S3, in the real-time data processing process, branch measurement features are extracted through a multi-layer perceptron (MLP) and mapped to nodes of the whole network, dynamic integration of historical data and real-time data is achieved, and therefore the real-time performance and accuracy of state estimation of the power distribution network are improved.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention discloses a power distribution network distributed measurement synchronization method and device based on Beidou satellite time service, and belongs to the technical field of power distribution network measurement, and the method comprises the steps: obtaining a Beidou satellite signal, calibrating a local clock source, and generating a standard time reference; when it is continuously monitored that the Beidou satellite signal quality parameter is lower than a quality threshold value, clock working environment parameters of a local clock source are collected, and a short-term clock offset predicted value is generated in combination with a standard time reference; generating topology correction according to the topology connection relation of the power distribution network; and fusing the short-term clock offset predicted value and the topology correction quantity to generate a dynamic compensation time reference, performing time marking on the acquired electrical quantity measurement data of the power distribution network, and generating synchronous measurement data with a timestamp. According to the technical scheme, when the Beidou signal is lost, the local clock drift predicted value and the adjacent node topology correction amount are fused to generate the dynamic compensation time reference, so that the measurement data synchronization precision and robustness of the power distribution network in a complex environment can be remarkably 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.

The invention discloses a power distribution network feeder terminal FTU intelligent monitoring method based on artificial intelligence. The method comprises the following steps that multi-dimensional operation parameter data of a power distribution network feeder terminal FTU are collected in real time through multiple sensors; carrying out dynamic preprocessing on the multi-dimensional operation parameter data to generate a standardized feature matrix, and matching the dimension of the standardized feature matrix with a preset multi-modal deep learning model input layer; inputting the standardized feature matrix into a pre-trained multi-modal deep learning model to generate a real-time monitoring result; wherein the real-time monitoring result comprises fault probability distribution and an equipment health degree score. Compared with the prior art, the method has the following advantages and effects that the data processing precision can be improved, and fault prediction and equipment health assessment can be optimized through the multi-modal deep learning model, so that more intelligent management of the power distribution network is realized.

The invention discloses an insulator defect detecting and positioning method and system based on cross-scale feature fusion, and belongs to the field of intelligent routing inspection of power distribution network lines, and the method comprises the following steps: S1, obtaining image data containing a plurality of insulator defects of the power distribution network lines in real time, and carrying out the preprocessing of the image data; s2, image enhancement; s3, fusing the enhancement results under multiple scales obtained in the step S2 by adopting a pyramid fusion strategy to obtain a fused image; s4, inputting the fused image into a pre-trained cross-scale feature fused defect detection model, and outputting defect features; and S5, based on the detection time and the real-time speed of the defect features, considering the delay time, and predicting the position coordinates of the defect features. By the adoption of the insulator defect detection positioning method and system based on cross-scale feature fusion, high precision, high robustness and self-adaptive processing of insulator defect detection are achieved, and the method and system have important significance on intelligent detection and maintenance of insulator defects of a power distribution network line.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention provides a power distribution network system fault monitoring method and system based on multi-source information, and the method comprises the steps: obtaining a multi-source monitoring data set of a power distribution network system, carrying out the data feature extraction of the multi-source monitoring data set, and obtaining the time sequence change feature of operation state data and the space correlation feature of environment influence data; calling a pre-constructed fault detection model to carry out cooperative fault analysis on the time sequence change characteristics and the space correlation characteristics, and generating a fault detection result of the power distribution network system; determining the fault type in the power distribution network system and the position characteristic of the fault in the network structure according to the fault detection result; and generating a fault early warning instruction containing the fault positioning coordinate based on the fault type and the position feature. The overall practicability and reliability of power distribution network system fault monitoring are effectively improved.

The invention relates to the technical field of robot control, and discloses a robot control method, system, equipment and medium based on a multi-modal large model, and the method comprises the steps: collecting the multi-source modal data of a scene where an operation task is located, and carrying out the processing through a machine learning model, obtaining a multi-modal feature, and carrying out the position coding and Transform fusion processing, multi-modal fusion features are obtained, the multi-modal fusion features and the constructed job task knowledge base are input into a large language model to decompose a target job task, a human-in-the-loop mechanism is introduced to optimize a decomposition result, and a sub-task sequence is obtained; according to a subtask type in the subtask sequence, processing the subtask sequence through a visual language action model or a reinforcement learning model, and generating a motion instruction to enable the robot to start an execution process of the target operation task; live-line work tasks are processed through the multi-modal large models LLM, VLA and the like, and the work efficiency of the autonomous distribution network live-line work robot is improved.

The invention relates to the technical field of power system scheduling, and discloses a power distribution network simulation scheduling optimization method and system based on artificial intelligence, and the system comprises a data fusion module, a digital twin modeling module, an intelligent prediction module, a strategy optimization module, and a visual scheduling module. The whole scene of the power distribution network is simulated through the digital twin model, the operation state and fault influence of equipment are accurately simulated, a scientific basis is provided for making a maintenance plan, blind maintenance is avoided, and the maintenance and repair cost of the equipment is reduced; meanwhile, by optimizing a load transfer path and distributed power supply output, the network loss rate is reduced, and the utilization efficiency of electric power resources is improved; in addition, the knowledge graph and the LSTM deep learning algorithm are fused, the distribution network topology entity relation network is constructed, and multi-source data are trained, so that the fault prediction accuracy is improved, the power failure risk can be early warned in advance, the conversion from passive first-aid repair to active prevention is realized, and the power failure frequency outside a plan is reduced.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention discloses a power distribution network fault accurate positioning method and system based on a graph convolutional neural network, and relates to the technical field of power systems, and the method comprises the steps: deploying monitoring equipment at a power distribution network node; in response to the distributed power supply switching event, generating a dynamic graph structure based on a pre-stored simulation model; taking the dynamic graph structure as a reference to initialize graph convolution kernel parameters, and generating two types of operation parameters based on a communication delay condition; fusing the new energy output prediction data, the electrical quantity monitoring data and the meteorological data to construct a dynamic causal graph; when a fault feature signal is detected, extracting electrical quantity monitoring data, a topological connection relationship and causal reasoning knowledge of the associated node; and constructing a graph convolutional network taking a dynamic graph structure as a network topology, selecting an operation parameter of a corresponding communication delay region as a convolution kernel weight, processing electrical quantity monitoring data, a topological connection relationship and causal reasoning knowledge of associated nodes, and outputting a fault coordinate.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention provides an anti-islanding protection method based on cloud edge collaboration, and the method comprises the steps: obtaining historical fault data, carrying out the data processing of the historical fault data, obtaining a fault mode discrimination result, judging whether a micro-grid has an islanding operation risk or not through combining the topological structure information of a power grid, real-time operation data, and load demand prediction data, and obtaining an islanding protection result. If so, acquiring power output fluctuation data; the output power of the power supply and the load demand power are monitored in real time, if the output fluctuation of the power supply exceeds a voltage protection constant value or the load demand power exceeds a frequency protection constant value, the edge computing gateway starts a local anti-islanding control strategy, and meanwhile, the charging and discharging power of the power distribution network is adjusted; in the anti-islanding control strategy execution process, the edge computing gateway continuously collects real-time operation state data of the micro-grid, the power supply duration, the electric energy quality and the standby capacity of the micro-grid under islanding operation are evaluated according to the operation state data, and the multi-target optimization model and the operation constraint condition of the micro-grid are dynamically adjusted.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention relates to the technical field of data processing, and discloses an intelligent risk early warning method, device and equipment for a power distribution network, and a medium. Historical risk monitoring data of the power distribution network under multiple dimensions are fused through a graph convolutional network and a variational auto-encoder; a variational recurrent neural network and a long-short term memory network are trained in combination with historical risk fault data of the power distribution network, and an attention mechanism is introduced in the training process to generate a risk assessment model; acquiring real-time risk monitoring data of the power distribution network under multiple dimensions to extract multi-dimensional real-time fusion features and inputting the multi-dimensional real-time fusion features into the risk assessment model for processing to obtain a real-time risk assessment level so as to further process the multi-dimensional real-time fusion features through a multi-criterion decision analysis method and a fuzzy inference system; the target risk assessment level is obtained, the level is compared with the risk early warning threshold value, if the level exceeds the threshold value, early warning is triggered, and the accuracy of power distribution network risk assessment is effectively improved.