541 results about "Electric distribution network" patented technology

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention provides a feeder terminal fault detection method, system and device, a medium and a program product, and the method comprises the steps: obtaining multi-source heterogeneous data comprising feeder terminal operation data, a topological graph structure of a power distribution network and external sensing data, the multi-source heterogeneous data comprises at least one kind of structured or unstructured time sequence data, and the external sensing data comprises at least one kind of structured or unstructured time sequence data; the external sensing data comprises meteorological data, geographic space information and historical fault records; and preprocessing the multi-source heterogeneous data, inputting the preprocessed time-aligned multi-source heterogeneous data into the trained time-space diagram neural network model to extract features and perform joint modeling, and outputting a fault type classification result and a fault probability distribution result corresponding to each feeder terminal node in the power distribution network. According to the method, a multi-source heterogeneous data fusion and time-space diagram neural network modeling mechanism is introduced, so that the model has a dynamic response capability to complex environment changes, and the identification precision of a potential fault mode is effectively enhanced.

The invention discloses a power supply and distribution method and system based on multi-modal data fusion and adaptive control, and the method comprises the steps: collecting real-time power supply and distribution data in a power supply and distribution network node, and obtaining power supply and distribution environment data; fusing the real-time power supply and distribution data and the power supply and distribution environment data by using a quantum heuristic multi-modal fusion algorithm to obtain fused power supply and distribution data; establishing an LSTM-CNN machine learning model based on the LSTM long and short term memory network and the CNN; inputting the fused power supply and distribution data into an LSTM-CNN machine learning model for training to obtain electric load prediction data; and based on the adaptive control strategy, sending a regulation and control instruction to the electric load equipment according to the electric load prediction data. And voltage fluctuation, flicker and harmonic distortion are effectively suppressed through real-time dynamic regulation and control, and voltage deviation and power factors of a user terminal are guaranteed to be optimal.

The invention relates to the technical field of power grids, and discloses an intelligent automatic reclosing and protection system for a power distribution network, and the system comprises a power grid topological structure construction module which is used for constructing a power grid topological model; the signal acquisition and processing module is used for obtaining a high-quality signal; the feature fusion and fault identification module is used for extracting multi-dimensional features and obtaining a preliminary classification result based on the high-quality signal; the fault property judgment module is used for judging the fault property; the reclosing strategy making module is used for combining reclosing strategies; the fault isolation module is used for determining an automatic reclosing node and executing a reclosing strategy; through multi-dimensional feature fusion and a deep learning model, the accuracy of fault type identification is improved, temporary faults and permanent faults can be accurately distinguished, the optimal reclosing strategy is dynamically formulated, intelligent adjustment of reclosing time and reclosing times is included, and the power supply reliability and safety of a power distribution network are improved.

The invention discloses an electricity utilization information acquisition intelligent configuration method based on a pattern recognition algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting a directional data stream, inputting a federal map neural network to construct a power distribution network physical connection relation, and generating a spatio-temporal topological feature vector; extracting a current effective value component of the directional data flow as an electrical load sequence, extracting an equipment state code to identify a voltage sag event, injecting voltage sag event associated disturbance into the electrical load sequence in combination with an event propagation path weight of a spatio-temporal topological feature vector, and generating an anti-fact sample set; and compressing the new configuration strategy through a knowledge distillation engine, and outputting an event response logic and a parameter adjustment instruction to form an executable configuration strategy. According to the method, through anti-fact sample generation and reinforcement learning optimization under spatial-temporal topological feature vector constraint, a physical rule deep embedding decision is realized.

A semiconductor device is provided. The semiconductor device includes a back-end-of-line (BEOL) layer, a backside power distribution network (BSPDN), a stacked field effect transistor (stacked FET) and a backside contact. The stacked FET is vertically interposed between the BEOL layer and the BSPDN and includes a top FET with top source / drain (S / D) epitaxy and a bottom FET with bottom S / D epitaxy. The stacked FET is characterized as having at least partial vertical alignment of the top FET and the bottom FET. A backside contact electrically connects the BSPDN and the top S / D epitaxy and cuts through the bottom S / D epitaxy with isolation between the backside contact and the bottom S / D epitaxy.

The invention discloses a power distribution network line fault positioning system based on multi-source information fusion, which relates to the technical field of power system operation and maintenance and comprises a data acquisition module, a data preprocessing module, an electric network topology management module, a fault prediction module, a fault judgment module and a man-machine interaction and alarm module. The method comprises the following steps: acquiring running state information from a power distribution network in real time, generating a structured multi-dimensional information database according to the running state information acquired by a data acquisition module, and performing time alignment, format standardization and feature vector construction on the acquired running state information to generate the structured multi-dimensional information database; according to the method, a fault section is preliminarily judged according to fault features, a fault state prediction model is constructed, a multi-dimensional information database is used for estimating state variables of the model, and the method aims at overcoming the defects that in the prior art, fault positioning fuzziness and reliability are insufficient due to the fact that a single information source is relied on.

The invention relates to the technical field of electric energy distribution, in particular to an intelligent power distribution system and a power distribution method.According to the intelligent power distribution system and method, power flow paths and load density distribution are defined by collecting load distribution values of nodes of a power distribution network and calculating power flow, line transmission loss and load value density of load transmission paths among the nodes; according to the method, a data foundation is laid for subsequent dynamic adjustment, partition node power requirements and transmission path characteristics are integrated by adopting an alternating direction multiplier method, a dynamic partition model is established, a global problem is decomposed into a local optimization sub-problem in a partition optimization process, partition boundary adjustment and power balanced distribution are realized through iterative calculation, and a partition node is optimized. A greedy algorithm is adopted to screen load priorities and demanded quantities, the electric energy guarantee capability of high-priority loads is improved, through adjustment of a voltage offset range and dynamic adjustment of reactive power compensation equipment, the influence of voltage fluctuation on power balance between partitions is effectively reduced, the voltage stability is improved, and the energy consumption and the power loss are reduced.

The invention relates to a power distribution network optimal configuration method for a high-proportion distributed new energy access scene, and the method comprises the steps: obtaining the basic data of a power distribution network, and constructing a bearing capability evaluation index system; a robust optimization evaluation model considering the uncertainty of the source load is constructed, and the critical bearing capacity is solved in combination with a bearing capacity evaluation index system; based on the critical bearing capacity, energy storage, SOP intelligent soft switching and DR demand side response are sequentially configured, an updated power distribution network state is obtained, whether convergence conditions are met or not is judged, and if yes, an optimal configuration scheme including current energy storage, SOP and DR configuration and the current critical bearing capacity is output; otherwise, returning to re-optimize. Compared with the prior art, the power distribution network can safely and efficiently consume the high-proportion distributed new energy, and the bearing capacity of the power distribution network is effectively improved.

The invention discloses a parallel operation data interaction system and method for hybrid energy storage inverters, and relates to the technical field of power systems, and the system comprises a parallel operation data interaction center, a data collection and monitoring module which is used for collecting load data in a power distribution network and monitoring the operation state of each hybrid energy storage inverter, performing feature analysis to extract load operation features to obtain a load operation feature sequence table; and the load prediction module is used for obtaining a load trend index by using the historical data and the load operation characteristics in the load operation characteristic sequence table in combination with a pre-trained load prediction model, and analyzing the change trend of the load. By collecting load data and monitoring the operation state of the inverter, the operation condition of the system can be comprehensively mastered, abnormity can be found and processed in time, the output of the inverter is adjusted in advance and the operation mode is switched in combination with the load prediction model, load fluctuation and power grid faults are effectively handled, and the stability and reliability of the power system are remarkably improved.

The invention discloses a rental house electricity consumption abnormity early warning system and method based on data fusion, and relates to the technical field of electricity consumption abnormity early warning. Non-intrusive load decomposers, distributed temperature sensors and high-frequency current transformers are deployed at distribution boxes, sockets and cable nodes, and space-time alignment of current, temperature and load data is realized through a sliding time window algorithm; lSTM model parameters are improved by adopting an optimization algorithm, and the recognition accuracy of abnormal electric appliances is improved; constructing a digital twinborn model, mapping the power distribution network topology into a graph structure, dynamically calculating an abnormal propagation probability and visualizing a risk path; graded early warning is realized by fusing the dynamic weight model of the current deviation degree, the temperature rise gradient and the load sudden change. The recognition accuracy of the complex abnormal mode is improved; the visual pre-judgment of the risk propagation path is realized; and a complete closed loop from risk early warning to active protection is realized.

The invention relates to a surface material uniformity analysis method and system for an insulator of a power distribution network, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining an optical image, a thermal imaging image and ultrasonic echo data of the surface of the insulator; s2, performing image processing on the acquired optical image, extracting characteristic parameters of surface roughness, texture direction and texture consistency, and constructing a surface texture uniformity index; s3, thermal diffusivity and thermal resistance of different areas are calculated from the thermal imaging image, thermal parameter differences of the areas are compared, and a thermal performance uniformity evaluation value is formed; s4, aiming at the ultrasonic echo data, establishing a correlation model of the ultrasonic characteristics and the internal uniformity of the material; s5, the fusion analysis model comprehensively evaluates the uniformity of the surface material of the insulator; the method has the beneficial effects that the fusion analysis model realizes comprehensive evaluation of the uniformity of the surface material of the insulator from multi-dimensional data.

The invention relates to the technical field of electrical safety, and discloses a multistage cooperative fault detection method and system based on an intelligent air switch, and the method comprises the steps: intelligent air switch terminals disposed at all levels of a power distribution network are used for synchronously collecting a multi-physical-quantity time sequence signal; the intelligent air switch terminal calculates the dynamic correlation entropy of the collected current signal and executes local fault diagnosis according to the dynamic correlation entropy; the terminals carry out cooperative communication, and dynamic correlation entropy is collected to construct an entropy migration spectrum so as to carry out cooperative traceability on faults; and finally, integrating local diagnosis and collaborative traceability results, and executing corresponding hierarchical collaborative protection actions. The system comprises an acquisition module; an entropy calculation module; a local diagnosis module; a communication module; a collaborative traceability module; and a protection execution module. According to the invention, through multi-stage cooperative fault detection of the intelligent air switch, accurate identification, rapid response and state early warning of hidden faults are realized, and the safety and reliability of a power distribution system are improved.

The invention relates to an alternating-current trunk line or an alternating-current power distribution network, in particular to a method for improving the stability of a wind power system through combined control of network construction type energy storage and a nine-switch UPQC. The method comprises the following steps: firstly, proposing an active regulation and inertial support control method based on a virtual synchronous machine mechanism for network construction type energy storage; and secondly, by constructing a coordination control framework, dynamic response complementation of the energy storage system and the UPQC under disturbance is realized, so that the overall frequency stability and voltage supporting capability of the system are improved. And finally, building a system model in Matlab / Simulink, setting three typical working conditions of wind power fluctuation, frequency disturbance and serious voltage drop, and carrying out scheme validity verification. Simulation results show that the method can effectively stabilize the wind power output fluctuation, enhance the frequency and voltage support capability, and significantly improve the stability of a wind power system.

The invention discloses a power distribution network self-healing method for improving power supply reliability, and the method comprises the following steps: S1, collecting fault information and an operation state, constructing a fault description matrix P, and carrying out the fault positioning based on the fault description matrix P; s2, after fault positioning, judging the position of the node relative to a fault position by taking whether fault current is detected at the node as a criterion, and recording the distance x between the node and a fault branch; s3, after a fault branch and a fault node are determined, fault recovery of a non-fault area is carried out based on the distance x between the node and the fault point; s4, constructing multiple objective functions and constraint conditions, determining the weight of each objective function by adopting an analytic hierarchy process to obtain a total objective function F, and optimizing the objective functions based on an ant colony algorithm to obtain an optimal power distribution network reconstruction scheme; the power failure time can be shortened, the power supply reliability is improved, and the power grid operation reliability is improved.

The invention discloses a circuit breaker rigid regulation and control method and system based on a load monitoring terminal, and relates to the technical field of electric power automation, and the method comprises the following steps: S1, obtaining a unified nanosecond time base line, continuously collecting current signals of a plurality of loops, reconstructing a complete phase track of each loop current, generating a corresponding low-frequency lag fingerprint base line, and carrying out the collection of the low-frequency lag fingerprint base line; the reference basis is used for dynamic response correction; and S2, constructing a causal playback chain based on the low-frequency hysteresis fingerprint baseline, replaying a current transition behavior in the rigid regulation and control process of the circuit breaker, identifying a nonlinear hysteresis section, and extracting a threshold drift core parameter. According to the method, the current phase is reconstructed through the nanosecond-level time base, the lagging characteristics are extracted, the causal playback and energy coupling model is constructed, threshold double-track generation and time sequence correction are achieved, lagging energy is dissipated in combination with a phase conjugate instruction, the regulation and control precision and stability of a circuit breaker are improved, and the safety and the intelligent level of a power distribution network are enhanced.

The invention discloses a regional power distribution method and system for distributed new energy consumption, and relates to the technical field of power systems, and the method comprises the steps: obtaining regional power distribution network data, and constructing a power distribution network analysis model; based on the power distribution network analysis model, dividing the regional power distribution network into a plurality of energy autonomous units by calculating the electrical distance and the power sensitivity; calculating an energy complementation index and a load elastic coefficient of each energy autonomous unit, and constructing a unit feature model; dynamically adjusting the active power and reactive power output of the distributed new energy in each energy autonomous unit according to the unit characteristic model; and monitoring the operation state of the energy autonomous units, implementing a cooperative control strategy, and adjusting power exchange between the energy autonomous units. According to the energy autonomous unit division method provided by the invention, the tightly coupled node group can be accurately identified, the nearby consumption and efficient utilization of new energy are realized, and the power transmission and distribution loss is reduced.

The invention provides a power distribution network feeder power failure event analysis method, system and device and a medium, and belongs to the field of power system monitoring and automation. The method comprises the following steps: acquiring historical line data, equipment archives and geographic information data of a power distribution network, and initializing a power grid topology model according to the line data, the equipment archives and the geographic information data; acquiring real-time operation state information of the transformer, and verifying and processing the real-time operation state information to determine abnormal data; correcting the abnormal data, and updating the power grid topology model based on the corrected data; and identifying a power failure area and a power recovery area based on the updated power grid topology model. Through integration of multi-source data, real-time monitoring, rapid positioning and intelligent processing of a power failure event of a power distribution network are realized, the fault response speed is significantly improved, power supply is rapidly recovered, the power failure time and the influence of power interruption are reduced, the fault position is accurately positioned through fusion of line topology, and reasons are analyzed.

The invention discloses a stability evaluation method, system and device of a power system and a medium, and the method comprises the steps: obtaining the load parameters of each node in a power distribution network, and determining the voltage amplitude, first active power and first reactive power of a transformer substation bus based on the load parameters; based on the voltage amplitude, the first active power and the first reactive power, inputting a preset power distribution system model to obtain a first sensitivity matrix, a second sensitivity matrix and a plurality of sensitivity vectors, and based on the first sensitivity matrix, the second sensitivity matrix and each sensitivity vector, obtaining a plurality of sensitivity vectors; the power distribution system model is obtained by coupling a power supply system model and a power distribution network model at a substation bus; and evaluating the stability of the power system based on the power flow distribution. According to the invention, the accuracy and comprehensiveness of stability evaluation of the power system can be improved.

Methods, systems, and media for feedback control systems for impedance matching are described. A computer program product for an impedance matching and power distribution network is disclosed. The computer program product may comprise computer-executable instructions which may cause obtaining, at a present time, present values of variable reactances associated with a station of a process chamber, wherein the variable reactances are associated with a first feedback control system for performing impedance matching for the process chamber, and wherein frequency tuning is being performed on an RF generator of the process chamber in association with a second feedback control system for performing impedance matching for the process chamber. The instructions may cause determining updated values of the variable reactances for the station to be utilized in connection with the first feedback control system based at least in part on an error associated with the second feedback control system.

A method and system for deep learning-based automated detection of vegetation encroachment in overhead power distribution networks. A deep learning model can be used to detect vegetation encroachment in preprocessed images and frames and deep learning explainability tools can be used to identify irrelevant objects in the images that contribute to misclassification.

A computing system includes a chassis, a water circulation network coupled to the chassis, a power distribution network coupled to the chassis, and one or more sleds removably coupled to the chassis. The water circulation network includes a cold water distribution network and a hot water collection network. Each sled of the one or more sleds includes a corresponding cold plate. Each sled is configured to slide along the chassis in two slide directions including a slide-close direction to reach a closed position and a slide-open direction to vacate the closed position. When in the closed position, each sled is configured to (a) bridge the hot water collection network and the cold water distribution network via the corresponding cold plate and (b) couple to the power distribution network.

The invention relates to the technical field of electric power communication network emergency disposal, and discloses an electric power communication network emergency disposal method based on artificial intelligence. The method comprises the steps of simultaneously collecting operation data of a communication network and a power distribution network of electric power to construct a linkage fault detection model, and simultaneously detecting whether the communication network and the power distribution network have faults to formulate a communication network fault processing strategy and formulate a communication channel circuitous strategy. According to the application, the linkage fault detection model is constructed by using the operation data to realize accurate detection and correlation analysis of communication network and power distribution network faults, so that whether the fault is a single fault or a linkage fault is effectively judged, and a basis is provided for subsequent targeted processing; when a communication network fault is detected, a corresponding processing strategy is formulated according to whether an associated power distribution network fault exists or not, and a roundabout strategy is formulated in combination with communication network information, so that efficient emergency disposal of the power communication network fault is realized, and emergency disposal of the power communication network in combination with the power distribution network is realized.

The invention relates to the technical field of transformer substation monitoring, and discloses a power supply and distribution safety monitoring method and system for a transformer substation. The method comprises the following steps: acquiring various data in a transformer substation, and performing time synchronization calibration on the three types of data; dividing a power supply and distribution operation mode into a steady-state mode, a transient mode and an overload mode based on the calibrated data, extracting a harmonic distortion rate, a dielectric loss angle tangent value and a vibration spectrum entropy value of a current waveform in each mode, and generating an operation characteristic matrix; detecting an operation mode switching event, and calculating a characteristic offset between modes; decomposing a degradation characteristic component in the operation characteristic matrix according to a matching degree between an insulation aging stage and current dielectric loss data in a historical fault case library; coupling compensation correction is performed on the degradation characteristic component in combination with the cross influence relationship between the electrical parameters and the mechanical parameters of the equipment; and reversely tracing the abnormal signal source equipment node along the topological structure of the power supply and distribution network based on the corrected degradation characteristic component, and outputting an equipment maintenance sequence.

The invention discloses a high-voltage power distribution network flexible operation and risk control method and device and a medium, and belongs to the technical field of high-voltage power distribution networks, and the method comprises the steps: carrying out the simplification of an urban power grid topological structure through employing a load transfer unit; based on the simplified urban power grid topological structure, establishing an output system model, a high-voltage power distribution network flexible reconstruction model and an electric vehicle cluster flexible scheduling model; based on the output system model, the high-voltage power distribution network flexible reconstruction model and the electric vehicle cluster flexible scheduling model, establishing an objective function of a cooperative scheduling strategy; and solving the objective function to obtain an optimal collaborative scheduling strategy. Through a dual-dimension flexible operation mechanism cooperating with electric vehicle charging and discharging flexibility and HVDN flexible reconstruction, the equipment overload risk is solved, and the operation safety of a power distribution network is improved.

The invention provides a power distribution network voltage control method based on multi-agent deep reinforcement learning, and relates to the technical field of power distribution networks, and the method comprises the steps: dividing a power distribution network into a plurality of regions, and carrying out the centralized management and optimization of the voltage of each region; a cooperative voltage control problem between regions is converted into a Markov game so as to realize effective cooperation between intelligent agents; a centralized training and decentralized execution framework is adopted, so that the intelligent agent fully utilizes global information in a training stage and independently makes a decision according to local observation in an execution stage; a self-attention mechanism is integrated, so that the agents are more focused on information related to self rewards, a multi-agent reinforcement learning technology can be combined with the self-attention mechanism, the problem of voltage fluctuation in a power distribution network is effectively solved, stable control and optimal management of voltage are realized, and the power distribution network quality is improved. The voltage regulation precision and response speed of the power distribution network are improved, and the stability and reliability of a power system are ensured.

One or more embodiments of the invention relate to the technical field of power Internet of Things application, in particular to a low-voltage line management and control method and system with a self-healing function. The system comprises a state sensing module which is used for obtaining power state parameters of a power distribution network comprising two paths of incoming lines and two sections of buses; the logic judgment module is used for sending a spare power automatic switching execution instruction to the control execution module based on a preset spare power automatic switching starting condition when it is detected that any one of the two sections of buses loses voltage and the dynamic parameters of the power distribution network meet the spare power automatic switching starting condition, so as to execute a spare power automatic switching transfer flow; and the control execution module executes a spare power automatic switching transfer flow according to the spare power automatic switching execution instruction sent by the logic judgment module, and the spare power automatic switching transfer flow comprises the step of sending an operation instruction to each circuit breaker according to a preset sequential control mode when the power distribution network meets the preset sequential control mode so as to automatically switch the operation mode of power supply.

The invention provides a power distribution network monitoring method and system under robot inspection, and relates to the technical field of power systems, and the method comprises the steps: configuring a sensor group; uploading the network node data set to a central cloud platform; after the central cloud platform calls the network structure of the power distribution network, performing global anomaly identification according to the uploaded data; carrying out anomaly clustering according to global abnormal nodes by taking the communication range of the robot as a constraint; after the robot is controlled to move to a relay center point, a local area communication network is built, and window collection data of network nodes are read; the robot is used for executing power distribution network data collection; and performing abnormity verification, and reporting monitoring abnormity. According to the method and the device, the technical problem that in the prior art, due to the fact that abnormal distribution is uneven and the false alarm rate is high in a large-scale power distribution network, abnormal recognition credibility is insufficient is solved, and the credibility of abnormal recognition is improved by introducing robot inspection and cloud global analysis.

Embodiments described herein provide for power distribution control systems and methods. The power distribution control systems and methods estimate one or more signal measurements of one or more nodes of a power distribution network using state estimation, identify a location of an outage in the power distribution network, identify a status of one or more switches that form a configuration to restore power to one or more cells of the power distribution network based on the one or more signal measurements estimated using state estimation, and output control signaling to control the one or more switches based on the identified status.