10891results about "Ac network circuit arrangements" patented technology

The invention relates to the technical field of power equipment early warning, in particular to a power equipment anomaly detection early warning system and method. The method specifically comprises the following steps: acquiring multi-source sensing data of target power equipment; performing cross-modal collaborative denoising based on semantic graph modeling and multi-scale residual feedback; fusing the space-time similarity and abnormal diffusion characteristics of the equipment, constructing a weighted relation graph, carrying out abnormal sensitive clustering by using a graph neural network, and identifying a potential abnormal group; abstracting historical anomalies into fault gene vectors, matching an evolution path, and predicting a new anomaly propagation trend; an expert knowledge graph and a graph neural network are introduced, double-domain decisions are dynamically fused through an objection-driven game, and the early warning credibility is improved; grading early warning is triggered according to propagation influence evaluation, abnormal features and evolution paths are written back to a knowledge base in a structured mode, collaborative optimization of the model and a rule base is driven, and a detection-early warning-optimization self-closed-loop system is formed. According to the method, the problems of dynamic relevance modeling, new anomaly prediction and decision credibility are solved.

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

Disclosed in the present invention is a three-dimensional scene point cloud data extraction and recognition system based on a power distribution network. The system comprises: a data collection module used for collecting data for multiple times and fusing same to acquire a complete power distribution network scene; a data processing module used for using the least square method to calibrate closest point matching and perform point cloud registration; a data extraction and segmentation module used for using principal component analysis to calculate a normal vector of the points in a neighborhood of each point cloud data point, measuring the curvature change near each point of a curved surface of a three-dimensional scene of a power distribution network by means of Gaussian curvature, and segmenting and classifying point cloud data by means of a random sampling fitting method and a clustering method; a data recognition and monitoring module used for using PointNet to perform local and global feature learning on the features of each point cloud data point, and acquiring global features by means of a maximum pooling operation for coding and recognition; and a data visual analysis module used for connecting to an open-source point cloud data processing library PCL and a hardware device for visual interaction. The efficiency and sustainability of the power supply system are improved.

The invention discloses a virtual power plant peak regulation optimization scheduling method and system, electronic equipment and a medium, and the method comprises the steps: obtaining multi-source data of distributed energy, energy storage equipment and a load side, and carrying out the cleaning and normalization processing; utilizing an LSTM neural network and an XGBoost model to carry out short-term and medium-and-long-term load prediction respectively; based on the prediction result and the resource state, generating a scheduling optimization strategy through a deep Q network (DQN) algorithm; according to an optimization strategy, distributed energy power generation, energy storage charging and discharging and load side response are dynamically managed, and multi-resource cooperative control is achieved; and through a feedback optimization module, DQN model parameters and strategy updating are automatically adjusted according to monitoring data, and the scheduling effect is optimized. The system comprises a data acquisition module, a data preprocessing module, a load prediction module, a scheduling decision module, a resource control module, and a monitoring and feedback optimization module. The method has the beneficial effects that the load prediction precision is improved, the resource scheduling is optimized, the operation cost is reduced, and the method is suitable for scheduling management of a multi-source energy system.

The invention provides a power prediction precision improvement method and system for a wind and light station, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the historical power, weather and equipment operation data of the wind and light station through a data preprocessing module, and carrying out the time sequence decomposition, spatial interpolation, abnormal value detection and correction, and obtaining the fusion feature data. And constructing a deep learning prediction model based on the fused feature data, extracting spatial features by using a convolutional neural network, processing the spatial features by using a long-short-term memory network to obtain an initial prediction result, introducing an attention mechanism to perform weight allocation to obtain weighted features, and combining to obtain power prediction data. And judging the weather type according to the real-time meteorological condition, and correcting by adopting the matched correction model when the extreme weather is identified. And finally, an error compensation model is constructed based on historical prediction errors for compensation, a final power prediction result is obtained, error correction is performed according to actual power data, and compensation model parameters are updated.

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 provides a multi-machine collaborative distribution network inspection method and system based on digital twinning, and belongs to the technical field of digital twinning. According to the technical scheme, an electric power distribution network framework and base station distribution are mapped to a GIS remote sensing map, a comprehensive digital twinning model is constructed, and an inspection area is delimited based on the comprehensive digital twinning model. By analyzing base station distribution and topographic features in the area, the comprehensive inspection difficulty is determined, and then the inspection area is divided into a plurality of task blocks. And selecting an optimal target nest from the deployed unmanned aerial vehicle nests by adopting a contract net mechanism to execute an inspection task, and generating an optimized inspection route according to the task block and the nest position. And finally, the unmanned aerial vehicle controlled by the target nest completes all inspection tasks according to the planned route. According to the scheme, intelligent scheduling of inspection resources and accurate distribution of tasks are realized, the inspection efficiency of the power distribution network is remarkably improved, the inspection cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power grid.

The invention discloses a multi-modal data driven power distribution network equipment state evaluation and optimization method, and relates to the technical field of power distribution network equipment, multi-modal data is collected, the data is from multiple equipment and operating environments in a power distribution network, and sensor data comprises real-time monitoring data of the equipment; and performing time sequence correction and synchronization processing on the acquired multi-modal data, and ensuring time alignment between different data sources through an advanced time sequence synchronization algorithm. According to the method, the dynamic time warping algorithm and deep learning are combined, the problem of multi-modal data synchronization is solved, the state of the power distribution network equipment is evaluated in real time, faults are recognized in advance, and early warning is provided. The reinforcement learning algorithm optimizes load distribution and self-healing scheduling, and improves the operation efficiency and fault recovery capability of the power distribution network. The system also generates intelligent early warning and maintenance decisions through the combination of deep learning and reinforcement learning, predicts faults, provides maintenance suggestions, optimizes operation and maintenance, prolongs the service life of equipment, and improves the overall stability of the power distribution network.

The invention discloses a guaranteed power supply quality monitoring and optimization method and system, and relates to the technical field of guaranteed power supply quality monitoring, and the method comprises the steps: obtaining electrical and environmental parameters in real time, modeling a power grid topology into graph data, extracting space correlation and time sequence evolution characteristics, and dynamically adjusting the attention weight of the space and time sequence characteristics, generating a joint feature vector, adaptively setting a dynamic threshold value, comparing real-time parameters in multiple dimensions, marking abnormal nodes, reversely tracing an upstream path of the abnormal nodes, calculating a proportion of impedance attenuation and current-carrying capacity reduction by combining environmental parameters, judging whether a fault point is a fault point, and considering an environmental cumulative effect of the fault point and a load sudden change frequency. And establishing an insulation aging and residual current-carrying capacity model, quantifying a safe operation interval, and implementing a dynamic compensation strategy. Through real-time monitoring, dynamic analysis and self-adaptive compensation, the stability, safety and efficiency of power grid operation are remarkably improved, faults are effectively prevented, and the service life of equipment is prolonged.

The invention relates to the technical field of power system topology identification, in particular to a power distribution network frame topology identification method based on an improved graph neural network, and the method comprises the steps: collecting the real-time electric quantity data of nodes and edges of a power distribution network, and carrying out the modeling of a power distribution network graph structure; the method comprises the following steps: constructing a node, edge and hyperedge feature matrix by using real-time collected data, inputting an improved graph neural network topology identification model, dynamically weighting and adjusting an edge weight through a graph attention network, splicing and fusing local topological features and global topological features, and generating a prediction adjacency matrix; when the power distribution network is dynamically changed, a change area is positioned through adjacency matrix difference, and a sub-graph is extracted for incremental updating; and constructing a topological structure of the power distribution network based on the final adjacent matrix, and outputting a connection relationship between the physical positions of the nodes and the edges. Compared with the prior art, the real-time performance, generalization ability and applicability of the model are remarkably improved, and the problems of accuracy and efficiency of power distribution network topology identification in the dynamic environment are solved.

The invention relates to the technical field of power distribution, in particular to a construction method and system for a digital twinborn model of a power distribution network. Establishing a digital element of the multi-physical field coupling characteristic of the power distribution network equipment based on quantum state space mapping, and simulating a collaborative degradation process of insulation aging and electromagnetic interference of the equipment; constructing a dynamic energy flow entanglement relationship among the nodes of the power distribution network according to the equipment degradation characteristic tensor; based on the quantum entanglement relationship between the device degradation feature tensor and the topology connection hypergraph, establishing a time-space coupling eigenmode of the device multi-physical field state and network energy flow distribution, and generating a quantum topology covariant tensor of a twin model; and carrying out dynamic fusion on real-time characteristic data and the quantum topology covariant tensor to form the digital twin model of the power distribution network. According to the method, the digital twin model capable of accurately reflecting the power distribution network equipment aging and energy transmission spatio-temporal evolution law is constructed, and a high-fidelity simulation platform is provided for power distribution network state evaluation, fault early warning and optimization regulation and control.

The invention relates to the technical field of power distribution network source website collaborative planning, and particularly discloses a power distribution network source website collaborative planning method and system considering a V2G mode. The method comprises the following steps: acquiring an existing topological structure of a power distribution network, traffic road network structure data, electric vehicle number space-time distribution data, photovoltaic resource distribution data and a historical daily load power curve set; predicting to obtain a node load power set, a V2G charging and discharging station maximum schedulable power set and a PVG station maximum output power set in various typical day scenes in the future; establishing an upper-layer planning model by taking the minimum total economic cost of the system as a target, and establishing a lower-layer planning model by taking the minimum annual comprehensive operation cost as a target; and solving the bilayer planning model by adopting a nested iterative algorithm, and outputting a power distribution network source website collaborative planning scheme. According to the method, the economical efficiency and reliability of collaborative planning of the power distribution network, the PVG station and the V2G charging and discharging station are improved, the resource utilization efficiency is remarkably improved, and the total economic cost is reduced.

The invention discloses an intelligent electric meter abnormal electricity utilization detection method and system based on data analysis, and belongs to the technical field of electricity utilization abnormal detection, and the method specifically comprises the steps: obtaining the historical electricity utilization data of a user according to an intelligent electric meter, and carrying out the preprocessing of the obtained historical electricity utilization data of the user, extracting features of the preprocessed historical power consumption data of the user, carrying out three-layer clustering analysis on the extracted features, analyzing historical power consumption behaviors of the user, identifying abnormal behaviors obviously deviating from a normal power consumption mode according to the historical power consumption behaviors of the user, and adaptively adjusting model parameters through a feedback mechanism. The abnormal electricity utilization of the intelligent electric meter is monitored in real time, the method can adapt to the electricity utilization mode change of the user, the dynamic threshold value adjustment can be adaptively adjusted according to the electricity utilization behavior change of the user, and therefore more accurate anomaly detection is achieved.

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

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.

A source-grid-load-hydrogen storage multi-stage planning method and system considering flexible resources, relating to the technical field of source-grid-load-hydrogen storage system planning. The method comprises: collecting source-grid-load-hydrogen storage data for data preprocessing; constructing a flexibility supply and demand characteristic model and a source-grid-load-hydrogen storage multi-stage dynamic planning model; calling a solver to solve the source-grid-load-hydrogen storage multi-stage dynamic planning model to obtain an optimal solution; and outputting a multi-stage source-grid-load-hydrogen storage investment result, a multi-stage source-grid-load-hydrogen storage operation policy, and a multi-stage flexibility supply evaluation result within a planning period. Using the minimization of investment costs, operation costs, and insufficient flexibility penalty costs within the whole planning period as target functions, various constraints such as a new energy permeability constraint and a load loss rate constraint are introduced, a dynamic planning method is proposed, and a source-grid-load-hydrogen storage multi-stage planning solution that has sufficiently economical planning operation and is sufficiently flexible is obtained. According to a processing method based on piecewise linearization, the model is simplified, and the computation speed is increased.

The invention discloses a charging pile intelligent control management platform, relates to the technical field of charging pile intelligent control, and can identify the peak and valley periods of the power grid load in advance by collecting and analyzing the power grid load data and the operation state of a charging pile in real time and combining a regression model of a load prediction and demand analysis module. The power output of each charging pile is dynamically adjusted through the charging pile scheduling and power adjusting module, the power grid resource distribution is optimized, the overload phenomenon is avoided, and the stability of power grid operation is improved. By monitoring and feeding back the charging behavior data of the user, the platform can reasonably plan the working state of the charging pile. Therefore, too fast loss of some charging piles due to continuous overload is avoided, resource waste of idle charging piles is reduced, the utilization efficiency of equipment is improved on the whole, and the service life of the charging piles is prolonged. By using the load prediction and demand analysis module, the platform can pre-judge the charging demand peak of the user and optimize the scheduling strategy in advance, thereby shortening the waiting time of the user.

The invention relates to the technical field of electrical fault monitoring, in particular to an intelligent mobile substation electrical fault monitoring method, which comprises the following steps of: synchronously acquiring an electrical monitoring signal, a multi-dimensional environment noise signal and a dynamic working condition parameter of a mobile substation through a multi-source sensor; the method comprises the following steps: constructing an environmental noise floor vector group by adopting multi-scale spectrum mode decomposition, filtering an environmental noise interference component from an electrical monitoring signal through orthogonal projection filtering, and outputting a baseline correction signal; a working condition disturbance response field matrix is constructed based on time domain and frequency domain correlation analysis of dynamic working condition parameters, gradient sensitivity coefficients are calculated, and components strongly related to working condition disturbance and residual components weakly related to equipment faults are separated out; reconstructing the residual component into a pure fault feature vector; and finally, fault type diagnosis and risk early warning are carried out on the basis. The method effectively solves the problem of fault feature annihilation caused by noise pollution in a complex environment and the problem of false alarm and missing alarm caused by confusion of working condition disturbance and real fault signals.

The present invention relates to the technical field of new energy vehicles, and specifically to a load balancing and scheduling method for charging piles of new energy vehicles based on intelligent algorithms, comprising the following steps: S1, data collection: real-time collection of operating data of charging piles; S2, charging demand prediction: prediction of the charging demand of each charging pile within a predetermined time in the future through a graph convolutional network model; S3, charging pile spatial scheduling optimization: spatial optimization of the scheduling of charging piles according to the geographical distribution of charging piles, the flow of surrounding vehicles and the results of charging demand prediction; S4, load scheduling and resource allocation: based on the results of charging demand prediction and charging pile spatial scheduling optimization, combined with the load of the power grid, resource allocation is performed. The present invention improves the utilization efficiency of charging pile resources, optimizes the layout of the overall charging network, and ensures the fairness of charging services and the balance of power grid load.

This application presents an adaptive arc suppression method for ground faults in distribution networks, merging active and passive relay protection strategies. The method proceeds as follows: based on collected ground parameters, the fault phase is identified; compensation current is calculated from the fault's electromotive force, ground leakage resistance, and ground capacitance; current is injected at the neutral point according to the compensation current; finally, the arc suppression scheme is executed based on the zero-sequence voltage of the network's busbars. This approach incorporates both voltage- and current-based methods, addressing the limitation of fixed operation scenarios in traditional arc suppression methods. By quickly adapting to various fault situations, this method ensures an efficient and prompt response tailored to specific scenario requirements.

The invention relates to the field of ring main units, in particular to an event-driven intelligent ring main unit and a control method, and the event-driven intelligent ring main unit comprises a cabinet body, a cabinet body built-in circuit breaker, an isolation grounding switch and a fusion sensing array. An array synchronously acquires mechanical and electrical quantities, an edge decision-making system takes a voltage zero crossing point as an absolute time mark nanosecond alignment event, a closed-loop control core generates a protection and topology reconstruction instruction in situ according to the event, and primary equipment is driven in a millisecond level; the intelligent communication interface dynamically schedules bandwidth according to the data priority, and key instructions are ensured to be issued in real time. According to the invention, the problems of fault misjudgment and local control delay caused by lack of a unified time sequence reference in a mechanical state and an electrical transient state of a traditional ring main unit are solved.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

The invention discloses a regional energy internet hierarchical control method considering flexible loads, which comprises the following steps of: constructing a global layer, dividing a regional energy internet into a plurality of electrically strongly coupled sub-regions by adopting a dynamic partitioning algorithm, aggregating equivalent adjustable capacities of the flexible loads in the sub-regions, and performing rolling optimization in an hour-level time scale, an improved Benders decomposition algorithm is adopted to accelerate solving and generate a partition-level power regulation instruction; constructing a regional layer, designing a thermodynamic-economic hybrid constraint model according to flexible load physical characteristics and user behavior elasticity, introducing a user comfort recovery time constraint and elastic electricity price response function, and decomposing an adjustment instruction issued by the global layer to each flexible load cluster in a minute-level time scale by adopting a parallel ADMM optimization algorithm; and constructing a load layer, constructing a millisecond-level adjustable load priority switching mechanism based on FPGA hardware acceleration and finite-state machine logic, calculating a load adjustable power boundary in real time, and dynamically adjusting a response queue in combination with system frequency deviation.

The invention discloses an anomaly detection and intelligent diagnosis method, system and device based on digital power grid multi-source data and a medium, and belongs to the technical field of anomaly detection, and the method comprises the steps: obtaining multi-source operation data from a power grid operation process, carrying out the preprocessing, and generating a standardized data set; time sequence features are extracted based on historical data, a power grid state reference model is constructed, and normal operation states in different load scenes are represented; on the basis of deviation calculation of the standardized data set and the power grid state reference model, abnormal candidate signals are detected, and high-confidence-coefficient abnormal signals are screened and generated; determining an abnormal source based on the high-confidence abnormal signal in combination with a power grid topological structure, and performing analysis to obtain fault type information; and generating a control instruction according to the fault type information and issuing the control instruction to a power grid control system. According to the method, a complete technical scheme of multi-dimensional data fusion, dynamic deviation detection, high-confidence anomaly screening, anomaly source accurate positioning and fault type rapid diagnosis is realized.

The invention discloses a system for monitoring line loss rate abnormity of a power distribution network station area, and relates to the technical field of power distribution network monitoring. The line loss rate abnormity monitoring system for the power distribution network station area comprises a data acquisition module, a data processing module, a line loss rate calculation module, an abnormity identification and alarm module and an on-site troubleshooting and diagnosis module. Through integrating functions of intelligent electric meter data acquisition, power grid data collection and area division, data processing, line loss rate calculation, abnormity identification and alarm, on-site troubleshooting and diagnosis and the like, comprehensive and real-time monitoring of the line loss rate of the power distribution network station area is realized. And a regional line loss calculation model is established according to the characteristics of the power distribution network of each region, and the line loss rate can be monitored in real time, dynamically calculated and predicted, so that the efficiency and accuracy of line loss management are greatly improved. And the abnormity identification and alarm module can timely discover and give an alarm of line loss abnormity, provides probability analysis of abnormity reasons, and is helpful for operation and maintenance personnel to quickly respond and process.

The invention discloses a power grid dynamic modeling method based on a physical information neural network and a related device, and the method comprises the steps: obtaining power grid observation data, inputting the power grid observation data into a neural network for power grid dynamic behavior prediction, and obtaining a power grid state prediction value; calculating data item loss through a power grid state prediction value and a power grid state actual measurement value, and calculating physical residual item loss through power grid observation data; the physical residual item loss comprises current conservation constraint loss, voltage closed-loop constraint loss and generator dynamic response loss; and network parameters of the neural network are updated through the data item loss and the physical residual item loss until the neural network converges, and a power grid state model is obtained. According to the method, the mapping relation between the state variable and the system input is established by using the neural network, and the physical rule of the power grid is introduced into the loss function as a hard constraint term, so that physical consistency control during dynamic behavior modeling of the power system is realized, and the accuracy of dynamic behavior modeling of the power grid is improved.

The invention discloses a dynamic power distribution network deduction method based on graph neural network perception and a privacy protection type Transform model. The method comprises the steps of collecting source load storage historical data in a power distribution network and performing standardization processing; performing time sequence segmentation; establishing a dynamic topological structure model of the power distribution network; extracting spatial features of the dynamic topological structure of the power distribution network by using a graph neural network; the spatial features are converted into time sequence data, the time sequence data are combined with time sequence data obtained after time sequence segmentation, and a source network load storage time sequence data set is established; differential privacy processing is carried out; training a Transform model, in the training process, using a differential privacy optimization technology, adding noise to the gradient when the gradient is updated each time, and finally obtaining a trained DP-Transform model; and inputting a specific scene, and deducing the state of the power distribution network by using a DP-Transform model. According to the method, the interpretability of deducing the state of the power distribution network by using the Transform is improved, so that the Transform model can fully receive the graph structure input of the network topology structure of the power distribution network, and privacy protection on user information in the deducing process is considered.

The invention discloses a distribution transformer load prediction method and system based on multi-scale fluctuation mode adaptive decomposition, and relates to the technical field of power system load prediction and power grid safe operation. According to the method, efficient prediction of the load of the power distribution network is realized through technologies of improved maximum overlapping discrete wavelet packet transformation, adaptive density estimation, a multi-level clustering strategy, dynamic time warping, anomaly detection optimization and the like. According to the method, distribution of load signal energy is optimized, a fluctuation mode is finely represented, an efficient index structure is constructed, and prediction accuracy and robustness are improved through an online correction mechanism. According to the whole process, the precision and efficiency of load prediction are remarkably improved, the method adapts to complex changes of load data, the beneficial effects of reducing the calculation amount, improving the prediction stability and capturing nonlinear correlation characteristics are achieved, and reliable support is provided for optimized operation of the power distribution network.

The invention discloses a photovoltaic power station intelligent remote operation and maintenance method and system based on relay protection, and the method comprises the steps: collecting the multi-mode characteristic quantity of a photovoltaic array region relay protection device in real time through a distributed optical fiber sensing network, so as to construct a dynamic protection characteristic matrix; performing feature decoupling on the dynamic protection feature matrix by adopting a space-time convolution adversarial encoder to generate a fault feature vector set; inputting the fault feature vector set into a physically constrained LSTM-GAN hybrid prediction model, and predicting a multi-dimensional failure probability tensor of the key relay protection node in a future preset duration; generating an optimal operation and maintenance strategy decision tree based on the multi-dimensional failure probability tensor; and analyzing the operation and maintenance strategy decision tree through a remote execution gateway driven by the digital twinborn, and synchronously updating the topological connection relationship of the digital twinborn bodies of the power station. According to the embodiment of the invention, the operation and maintenance efficiency and reliability of photovoltaic power station protection can be improved.