976 results about "Smart grid" patented technology

The invention discloses an energy storage system operation and maintenance strategy optimization method based on digital twinning, and belongs to the technical field of electric energy storage and intelligent power grids. Establishing a digital twinning synchronous model of the energy storage system and calibrating the digital twinning synchronous model; generating prediction data at the current moment based on the digital twinborn model, performing residual analysis on the prediction data and real-time data, and generating a quantitative diagnosis index; according to the quantitative diagnosis index and the fault mode, adjusting parameters of the digital twinning synchronization model, and ensuring that the model and the actual state of the energy storage system are kept synchronous; inputting real-time data into the adjusted digital twinborn model, calculating a future operation index of the energy storage system, and generating simulation operation data; and generating a non-periodic operation and maintenance strategy according to the simulation operation data, the fault mode and the operation and maintenance rule base. According to the method, the adaptive calibration digital twinborn model is adopted, real-time diagnosis and prospective optimization can be fused, and the operation and maintenance efficiency and reliability of the energy storage system are remarkably improved.

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 discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention relates to the technical field of power grid intellectualization, in particular to a digital power grid asset classification and evolution monitoring method, system and device based on self-supervised comparative learning and a storage medium. The method comprises the following steps: acquiring network flow data in a digital power grid, extracting multi-dimensional features such as a time interval, a direction, a data packet length, a protocol type and an address port, and constructing an asset behavior feature matrix; then constructing a self-supervised contrast learning model, taking the communication behavior sequences of the same asset in different time periods as a positive sample pair, taking the communication behavior sequences of different assets as a negative sample pair, and training an asset embedding model through an InfoNCE contrast loss function; then, asset communication behaviors are mapped to a semantic embedding space, and asset classification and identification are carried out based on embedded vectors; and finally, carrying out evolution monitoring on the assets by adopting a sliding time window mechanism, generating an asset evolution trajectory sequence, and carrying out abnormity perception early warning. Autonomous learning without manual annotation, high-precision asset identification and real-time state evolution monitoring are realized.

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The present invention belongs to the field of smart power grids and power system automation control. Disclosed is a load emergency control method based on a dynamic event triggering mechanism, which method comprises: on the basis of an indicator of safe operation of a system, performing region division on a distribution network; proposing an adaptive load level division method based on user response willingness, and by taking the minimization of the system operation cost of the distribution network as an objective, establishing a precise load control model of the distribution network; and using a dynamic event triggering mechanism to reduce communication between substations and a master station of the distribution network, so as to realize effective exchange and precise execution of load control policy information. The method of the present invention can precisely realize precise control of a load, and can effectively alleviate communication transmission pressure of a distribution network, thereby ensuring the stability of a power grid and the continuous supply of critical loads.

The invention relates to the technical field of intelligent power grid operation and maintenance, and discloses a power grid node health index assessment method suitable for remote operation and maintenance, and the method comprises the steps: S1, carrying out the fusion collection of multi-source data; s2, dynamic weight self-adaptive distribution is carried out; s3, dynamically correcting the life reference; step S4, layered health index calculation; s5, health state decision making and visualization are carried out; the data weight is automatically adjusted according to the timeliness difference of high and low frequency data through a dynamic weight adaptive distribution mechanism, so that the problem of health score long-term distortion possibly caused by low frequency data updating lag is solved, key parameters can be monitored in real time through a real-time anomaly detection and response mechanism, a parameter overrun early warning threshold value is set, and the early warning accuracy is improved. When the key parameters continuously exceed the limit, an online diagnosis mode is triggered, the data sampling frequency is increased, it is ensured that abnormal conditions can be quickly found and processed, the ability of the system to deal with sudden faults is improved, and the fault risk is reduced.

The invention provides a coordinated control method and device for flexible interaction of an optical storage and charging micro-grid and a power distribution network, and belongs to the technical field of intelligent power grids and distributed energy management. According to the method, through a day-ahead, intra-day and real-time three-layer coordination control architecture, the system operation cost is optimized, and the power grid stability is improved. The day-ahead layer generates a 24-hour scheduling plan based on photovoltaic output prediction, electric vehicle charging requirements and time-of-use electricity price; the intra-day layer performs rolling optimization in combination with real-time photovoltaic / load fluctuation, and dynamically allocates V2G and energy storage adjustment tasks; the real-time layer adopts adaptive droop control, monitors power grid abnormity and triggers cross-layer cooperation, and also sets photovoltaic fluctuation compensation and island mode switching conditions. Collaborative optimization is realized among the layers through bidirectional data channels. According to the method, the defect of interactive control of the optical storage and charging micro-grid and the power distribution network in the prior art is overcome, the adaptability of the system to distributed energy fluctuation is enhanced, the V2G resource potential is fully played, and the coping capacity under the abnormal condition of the power grid is improved.

The invention, which relates to the technical field of power grid electrical equipment monitoring, discloses an electrical equipment abnormal power state monitoring system comprising a data acquisition module, a data processing module, a monitoring analysis module and a management module. The monitoring analysis module comprises a power reference offset sub-module, a power fluctuation sub-module and a comprehensive abnormal power risk sub-module, the abnormal power state monitoring effect of the electrical equipment can be remarkably improved, the influence of multiple factors on power reference offset is considered by the system, the power reference can be accurately corrected, and power grid interaction factors can be effectively improved. Therefore, the abnormal power risk can be comprehensively and accurately assessed based on the power reference offset, the power fluctuation stability and multiple factors, so that the safe and stable operation of a smart power grid and a large-scale power grid can be powerfully ensured.

The invention relates to the technical field of intelligent power grid and electric vehicle cooperative control, and discloses a plug-and-play and flexible power regulation and control integrated vehicle network interaction cooperative control system, which comprises a protocol adaptive conversion module, a multi-modal data alignment module, a flexible power regulation and control module, a vehicle pile identity authentication module and a power grid state sensing module, and realizing inter-generation vehicle millisecond-level identification and data unified modeling through the protocol middleware. According to the method, the problems of protocol compatibility and data alignment in a plug-and-play scene are solved, and deep coordination between the charging process and the power grid state is realized through flexible power regulation and control and a multi-stage safety mechanism. The system guarantees the non-inductive charging experience of a user, reduces the impact on a power grid to the maximum extent, prolongs the service life of a battery, and improves the energy utilization efficiency.

The invention provides an APF and SVG cooperative control-based electric energy quality comprehensive treatment system, and belongs to the technical field of intelligent power grid industry and electric energy quality comprehensive treatment, and the system comprises the steps of obtaining a power grid current signal and a voltage signal, and detecting a harmonic component and a voltage deviation through a harmonic detection module; a collaborative decision controller is adopted to determine a compensation strategy according to the harmonic component and the voltage deviation, and compensation tasks of an active power filter channel and a static var generator channel are distributed; harmonic compensation current is generated through an active power filter channel, and high-frequency harmonics are suppressed; reactive compensation current is generated through a static var generator channel, and voltage fluctuation is stabilized; and the compensation current is filtered through the filtering module and is output to a power grid. The electric energy quality comprehensive treatment system can effectively inhibit power grid harmonic waves, stabilize voltage fluctuation and improve electric energy quality, and is suitable for a complex power grid environment.

The invention discloses a current and voltage signal synchronous correction and output method and system, and relates to the technical field of power system analysis and control. Compared with a previous current and voltage signal correction method, the method solves the problems that traditional multi-channel signal acquisition depends on conventional clock control, and synchronization precision is insufficient; a conventional sampling circuit is weak in anti-interference capability. The problem that an existing scheme lacks efficient error prediction and dynamic optimization capability is solved. Through a multi-channel FPGA clock synchronization and double-differential sampling technology, complete acquisition of high-frequency transient characteristics of voltage and current signals is ensured; a dynamic calibration algorithm and multi-stage composite filtering are combined, so that common-mode interference and non-linear errors are effectively suppressed; and finally, through optical fiber isolation output and intelligent feedback control, signal full-link synchronous correction is realized, the electric energy metering error can be reduced, the fault positioning precision is improved, the characteristics of high precision, strong anti-interference and low energy consumption are realized, and a reliable data reference is provided for intelligent power grid and new energy grid connection.

The invention discloses an electric power detection method and system for multi-modal enhanced fine tuning, and relates to the technical field of vision-driven intelligent electric power inspection, and the method comprises the steps: 1, obtaining an electric power inspection image, and marking the image; 2, inputting the marked image into the initial vision-language model, and carrying out cold start supervision fine tuning to train the initial vision-language model to obtain a model subjected to supervision fine tuning; 3, performing reinforced fine tuning on the model subjected to supervised fine tuning, and updating model parameters to obtain a PowerGPT-R1 model; and 4, evaluating the performance of the PowerGPT-R1 model based on the test set, and verifying the reliability of power detection. According to the method, the detection precision is remarkably improved under the condition of few samples, meanwhile, the multi-modal reasoning capability which a visual perception model does not have is shown, and the method has important application value in autonomous inspection of the infrastructure of the smart power grid.

The invention relates to the field of intelligent power grids, and discloses an electric power marketization operation optimization method and system based on user-side resource aggregation, and the method comprises the following steps: a), recognizing and classifying user-side resources, specifically including distributed power generation, energy storage equipment and adjustable loads, forming a user-side resource database, and facilitating the subsequent management and scheduling; b) formulating transaction rules of market participants, and determining participation conditions, rights and obligations of household users, small commercial users and large industrial users to ensure fair participation of various users in the market; and c) automatically executing the transaction through the smart contract. Through user side resource aggregation, the method can effectively integrate distributed power generation, an energy storage system and an adjustable load, optimizes supply and demand balance of power, and enables a user to adjust a power utilization strategy in different demand time periods through a real-time monitoring and dynamic pricing mechanism, thereby reducing resource waste and improving the overall efficiency of a power system.

The invention relates to the field of intelligent power grids, and discloses a fault classification and traceability method based on an electric energy meter event log, which comprises the following steps: performing millisecond-level dynamic acquisition on power failure, recovery and abnormal voltage data of a user electric meter and an adjacent electric meter, and performing second-level time alignment based on a transformer area reference clock signal; executing multi-table cross-time sequence correlation analysis on the event priority sequence, and constructing a power grid event multi-dimensional feature tensor; for the multi-dimensional feature tensor of the power grid event, carrying out abnormal aggregation on local modes in the tensor by adopting a hierarchical causal reasoning model; on the basis of the candidate fault propagation path, a time sequence dependence attention mechanism is introduced, and contribution degrees of different electric meter events to potential faults are dynamically evaluated; dividing electric meter nodes and affected branches corresponding to the candidate power failure reason set into fault sensitive areas, and outputting a traceability result; the method has the advantages of judging the power failure reason in real time and automatically generating the solution.

The invention relates to a cable joint resonance real-time diagnosis method and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the following steps: obtaining a high-frequency current signal collected by a high-frequency current sensor and an ultrasonic vibration signal collected by an ultrasonic sensor array; performing signal synchronization on the high-frequency current signal and the ultrasonic vibration signal, and extracting a high-frequency current feature and an ultrasonic array feature; calculating a dominant frequency synchronization index, a normalized phase stability index and a nonlinear coupling index based on the high-frequency current characteristics and the ultrasonic array characteristics, and performing weighted summation to determine a fusion confidence coefficient function; and judging whether resonance exists or not based on the fusion confidence coefficient function, and if so, carrying out resonance source positioning. Compared with the prior art, the method has the advantages of high precision, low false alarm and high adaptability, and is suitable for key power system scenes such as new energy grid connection, urban smart power grids and wind power.

The invention belongs to the technical field of power supply transmission, and relates to a POE-POF power supply transmission method and system, and a storage medium. The method comprises the following steps: collecting electric quantity data of POE-POF power supply transmission network nodes in real time, and extracting a dynamic power feature set; identifying an abnormal power loss event based on the feature set and extracting an abnormal mode feature; abnormal path nodes are positioned by combining a POE-POF power supply transmission network topological structure, voltage and current distribution parameters are optimized, and an optimized power distribution scheme is generated; dynamically adjusting voltage and current values of a power supply path, evaluating an energy consumption trend and generating a trend data set; and adaptively adjusting the electric quantity acquisition parameters according to the energy consumption trend, updating the acquisition configuration and circularly executing the steps. According to the invention, intelligent detection, accurate positioning and dynamic optimization of abnormal loss of the power grid are realized, the stability and energy efficiency of the power supply network are improved, and the method is suitable for fine management and energy-saving optimization of the intelligent power grid.

The invention relates to the technical field of intelligent power grids, in particular to a low-voltage draw-out type switch cabinet which comprises a cabinet body, a power grid arranged on the inner side wall of the cabinet body, a heat dissipation plate arranged on the inner side wall of the cabinet body, a plurality of static contacts arranged on the inner side wall of the cabinet body and a cleaning mechanism arranged on the inner side wall of the cabinet body and used for cleaning the power grid. The cleaning mechanism comprises two first electric sliding rails arranged on the inner side wall of the cabinet body, according to the low-voltage draw-out type switch cabinet, the overturning plate is driven by the first motor to rotate at multiple angles, the spraying head can evenly cover the outer corners of the cabinet body after the insect repellent is atomized, reptiles are dispersed from the source, and the low-voltage draw-out type switch cabinet is convenient to use. The cleaning mechanism can be quickly started, heat dissipation blocking and equipment faults caused by accumulation of dead insects are prevented, and it is guaranteed that the low-voltage draw-out type switch cabinet is always kept in a safe and stable working state in long-term operation.

The invention discloses a power distribution network carbon emission metering and responsibility distribution method and device and a medium, and relates to the technical field of smart power grids. The method comprises the following steps: collecting multi-source carbon emission data in real time, and preprocessing the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain a dynamic carbon factor of the thermal power generating unit, and combining the dynamic carbon factor of the thermal power generating unit with a carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; performing load flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch load flow distribution matrix, and combining the dynamic carbon intensity vector, the node active flux matrix and the branch load flow distribution matrix to obtain the total carbon potential of each node on the load side; and based on the total carbon potential and the real-time load distribution matrix, calculating the carbon flow rate distributed to each node on the load side so as to obtain the carbon emission responsibility of each node on the load side.

The invention discloses an HPLC and HRF dual-mode communication method and system for an intelligent power grid, and the method comprises the steps: enabling a source communication node to transmit power grid service data through an HPLC and HRF dual channel, and forming a dual-mode data package; after receiving the data, the target communication node demodulates and recovers service data from the HPLC and HRF signals, synchronously measures and calculates the two paths of signals, and extracts real-time cable fingerprints reflecting power line medium characteristics and real-time space fingerprints reflecting space wireless characteristics; further, performing similarity comparison on the real-time dual-mode fingerprint and a pre-stored source node reference dual-mode fingerprint; and finally, judging the security state of the communication link and the validity of the data in a combined manner according to whether the comparison result meets a credible threshold value or not and by combining the integrity verification of the service data, and executing subsequent network operation accordingly. According to the invention, the physical layer fingerprints are invisibly extracted in normal service communication, so that real-time link authentication without special detection signals is realized, and man-in-the-middle attack and equipment counterfeit are effectively defended.

The invention relates to a wear-resistant high-voltage power cable for a smart power grid, which is applied to the technical field of cables, and comprises a cable body, a plurality of wear detection early warning mechanisms are arranged on the cable body, and each wear detection early warning mechanism comprises a simulated wear sleeve sleeving the cable body; the simulated grinding sleeve and the wear-resistant outer protective layer are made of the same material, the thickness of the simulated grinding sleeve is smaller than that of the wear-resistant outer protective layer, and the inner sides of the two ends of the simulated grinding sleeve are fixedly connected with supporting connecting rings matched with the simulated grinding sleeve; through the arrangement of the abrasion detection early warning mechanism, the abrasion detection early warning mechanism not only can improve the abrasion resistance of the cable to a certain extent, but also has detection and early warning functions, and can indirectly detect the abrasion condition of the cable body by detecting the abrasion condition of the simulated abrasion sleeve, so that early warning can be carried out in time when the cable body is seriously abraded; related technicians are prompted to carry out corresponding inspection and maintenance on the cable in time, the potential safety hazard of the cable is reduced, and the safety and intelligence of the cable are improved.

The invention relates to the technical field of energy meteorology and intelligent power grids, in particular to a multi-extreme meteorological high-risk scene set generation method, system and equipment based on a joint training generative adversarial network. The method comprises the following steps: constructing a physical information generative adversarial network framework comprising a generator, a discriminator, a predictor and a physical constraint module; designing a multi-objective loss function fusing adversarial loss, prediction loss, physical consistency loss and task performance loss; adopting a training strategy combining meta-learning initialization and incremental learning to jointly optimize parameters of the generator and the discriminator in stages; extreme risk scene data of specified disaster types, seasons and intensity grades are generated through condition vector control, and the extreme risk scene data are stored in a high-risk scene library after physical consistency verification. Through the method, a multi-extreme-weather high-risk scene set with statistical authenticity, physical rationality and task correlation can be directly generated, and the risk identification, scheduling optimization and toughness evaluation capabilities of the clean energy base under extreme weather conditions are remarkably improved; the problems of sample scarcity, model overfitting and lack of physical constraints in scene generation in the prior art are solved, efficient and automatic generation of a high-risk scene is realized, and reliable data support is provided for power grid toughness evaluation and scheduling decision of a clean energy base.

The invention discloses an intelligent power grid dispatching instruction optimization system based on semantic analysis, relates to the technical field of power grid dispatching instruction optimization, realizes the conversion of dispatching instruction generation from black box output to white box reasoning, constructs a new intelligent dispatching normal form with deep integration of knowledge driving and data driving, and improves the intelligent dispatching efficiency. The automation level, decision reliability and operation safety of power grid dispatching are remarkably improved, and the method has outstanding innovativeness and wide engineering application prospects.

The invention discloses a power distribution network cluster dividing and planning method adaptive to distributed power consumption, and belongs to the technical field of intelligent power grid and distributed energy optimization scheduling. The invention aims to solve the current situation that the traditional centralized scheduling mode is difficult to effectively deal with the problems of power fluctuation and local imbalance of the distributed power supply, so that the operation efficiency of a power grid is low and the consumption capability of the distributed power supply is insufficient. The cluster dividing and planning method comprises the following steps: constructing a digital twin model of the power distribution network, and realizing high-precision real-time mapping and dynamic updating; based on the electrical distance and sensitivity characteristics, adaptive cluster division is realized by using a clustering algorithm; the fluctuation characteristics of the distributed power supply are accurately quantified through residual variance and wavelet energy analysis; establishing an operation cost and distributed power consumption dual-objective optimization model, and realizing economic dispatching; and a layered dynamic coordination control mechanism is designed to ensure voltage and frequency stability and overall economy of the power distribution network. According to the invention, the consumption capability of the distributed power supply and the economic operation level of the system are obviously improved.

The invention relates to the technical field of intelligent detection of power equipment, in particular to a complex illumination low-voltage electricity meter frequency sensing detection method, system and equipment and a medium, and aims to solve the problems of high-frequency noise interference, intra-class feature difference, insufficient edge end energy efficiency and the like existing in electricity meter detection in a high-brightness environment. Proposing a dynamic frequency domain compensation mechanism to suppress light noise and recover signal details, and optimizing dense scene feature consistency in combination with adaptive filtering and a multi-scale sampling strategy; the boundary fuzzy problem is solved through dynamic feature alignment and an edge compensation mechanism, and parallel acceleration of frequency domain decomposition and dynamic resampling is realized by adopting a heterogeneous hardware architecture; a multi-modal data training framework and a cross-domain transfer learning strategy are fused to enhance the generalization ability of the model; according to the invention, collaborative optimization of detection precision, real-time performance and low power consumption is realized in a complex illumination scene, and high-robustness technical support is provided for intelligent power grid inspection.

The invention discloses a current detection method and system based on a double-ring TMR nested structure, and belongs to the technical field of current detection. The method comprises the following steps: based on a double-ring TMR system of a double-ring TMR nested structure, performing double-ring detection on the current of a to-be-detected wire in a current measurement scene; synchronously acquiring double-ring data obtained by double-ring detection through AD, and performing wavelet denoising on the double-ring data according to the structure data of the double-ring TMR nested structure; and based on a Kalman filtering algorithm, fusing the double-loop data after wavelet denoising to obtain current information of the to-be-measured wire in the current measurement scene. The method not only has outstanding advantages in the aspects of measurement accuracy and anti-interference performance, but also has a wide measurement range and stable linearity performance, and can be widely applied to complex electromagnetic environments with high requirements on current detection accuracy, such as new energy grid connection, electric vehicle charging piles and intelligent power grids.

The invention relates to the technical field of smart power grids, and provides a multi-field coupling-based overhead conductor vibration test method, which comprises the following steps of: constructing a multi-field coupling test environment, and loading an overhead conductor into the constructed multi-field coupling test environment; applying a multidirectional vibration load to the overhead conductor, and collecting vibration response data of the overhead conductor in real time; and calculating an equivalent stress field and equivalent stress amplitude distribution according to the vibration response data acquired in real time, and evaluating the damage state and residual life of each position of the overhead conductor. According to the overhead conductor vibration test method based on multi-field coupling, complex working conditions can be truly simulated, the test accuracy is improved, high-precision multidirectional vibration load loading is realized, high-resolution data acquisition and synchronization are realized, and the damage state and the residual life are accurately evaluated.

The invention discloses a topology identification method based on intelligent measurement data of a low-voltage transformer area. According to the technical scheme, the topology identification method comprises the steps that 1, a master station issues a timing command to an intelligent measurement terminal and an electric meter; step 2, the master station issues a transformer area electric meter file to the intelligent measurement terminal, configures an acquisition task and issues the acquisition task to the intelligent measurement terminal; step 3, establishing an electric quantity sum linear regression model based on a Lasso algorithm by using daily frozen electric quantity data, and converting the identification problem of the transformer area user-transformer relation into regression coefficient solving; step 4, on the basis of user change relation identification, performing dimension reduction on historical voltage data by using a t-SNE algorithm to improve the identification efficiency of the model; and step 5, clustering the voltage data after dimension reduction by using agglomerated hierarchical clustering, identifying a user phase and a meter box to which the user belongs, and completing identification of a topological structure. According to the method, the power grid topology information of the transformer area can be automatically identified, the efficiency and the accuracy of topology identification are improved, and the method has far-reaching significance for construction of an intelligent power grid.