1086 results about "Smart grid" patented technology

The invention discloses a power grid equipment intelligent operation and maintenance method based on a large language model and a knowledge graph, and relates to the technical field of intelligent power grid operation and maintenance, and the method comprises the steps: carrying out the operation and maintenance of power grid equipment through a power grid operation and maintenance knowledge graph constructed through a large language model and a knowledge federation technology, the power grid equipment operation and maintenance comprises one or more of health state evaluation, fault risk prediction and early warning, intelligent operation and maintenance strategy generation and health degree dialogue query of the power grid equipment. According to the invention, a power grid operation and maintenance mode can be effectively promoted to be transformed and upgraded from a traditional manual experience type and a passive maintenance type to a data-driven, intelligent and active preventive maintenance mode. Key intelligent operation and maintenance technical support is provided for building a novel electric power system with new energy as a main body, the novel electric power system is assisted to achieve the development goals of being safer, more efficient, cleaner and lower in carbon, and important industry strategic significance and social contribution are achieved.

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 an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention relates to the technical field of intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

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 discloses a fan inertia support evaluation and control method considering power grid inertia space-time distribution. Based on wide area measurement system data, a power grid inertia space-time distribution model is constructed, inertia contribution characteristics of different areas are analyzed, and the influence of extra-high voltage direct current transmission on inertia distribution is considered. And calculating the inertia response power of the fan, quantifying the support contribution of the virtual inertia of the fan based on the frequency change rate and the regional equivalent inertia, and establishing an inertia support efficiency evaluation index system. A dynamic feedback control strategy is put forward, a reinforcement learning or adaptive gain adjustment method is adopted in combination with the real-time distribution condition of the inertia of the power grid, the virtual inertia control parameters of the fan are optimized, and the response accuracy is improved. Based on a wide-area coordination optimization method, a fan inertia support scheduling strategy is optimized, and the system frequency stability is improved. The method can effectively improve the frequency supporting capability of the new energy power grid, improves the new energy consumption level, enhances the operation safety of the power grid, and is suitable for a high-proportion new energy grid-connected 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 invention discloses an intelligent power grid fault deduction method and device based on a knowledge graph and electronic equipment, and relates to the technical field of power grid fault diagnosis, and the method comprises the steps: receiving protection action alarm information and circuit breaker tripping alarm information when a target intelligent power grid breaks down; based on the protection action alarm information and the circuit breaker tripping alarm information, performing hypothesis reasoning by using a preset power grid topology knowledge graph to obtain a suspected fault equipment set; performing deductive reasoning on each suspected fault device in the suspected fault device set by using a preset protection configuration knowledge graph based on the protection action alarm information and the circuit breaker tripping alarm information to obtain observation event confidence corresponding to each suspected fault device; and evidence fusion reasoning is carried out on each suspected fault device based on each expected observation event confidence, and actual fault information of the smart power grid is obtained. The method can improve the accuracy of the fault diagnosis result of the smart power grid.

The embodiment of the invention discloses a multi-microgrid regulation and control method and device based on federal hierarchical reinforcement learning, and a medium, belongs to the technical field of smart grids, and solves the problem of low microgrid dispatching precision. Each microgrid uploads a model gradient parameter corresponding to the local scheduling model to a central server; the central server determines an aggregation weight according to the similarity between the micro-grid data distribution characteristics so as to update a global model; the central server dynamically divides each micro-grid into a plurality of micro-grid clusters according to model gradient similarity, and constructs a leader-follower game model in each micro-grid cluster; the central server outputs a first scheduling strategy corresponding to each micro-grid based on the updated global model, the plurality of micro-grid clusters and the leader-follower game model; and each micro-grid periodically performs secondary optimization on the first scheduling strategy based on the local feature data to obtain a second scheduling strategy corresponding to the current micro-grid.

The invention belongs to the technical field of intelligent power grids, and discloses an intelligent power grid load balance control method and system. The method comprises the steps of dividing a power grid into a plurality of regions based on a predetermined rule; based on the regional power supply quantity and the regional power consumption quantity, identifying an overload region with insufficient power supply quantity; formulating and executing a load balancing strategy for the overload area by adopting a natural heuristic optimization algorithm, wherein the load balancing strategy is a load scheduling strategy taking scheduling time minimization as a target; calculating the priority coefficient of the electric equipment in the overload area, and supplying power to each electric equipment according to the priority coefficient; effective balance and optimal allocation of intelligent power grid loads are achieved, emergency power supply is guaranteed to the maximum extent in natural disaster time, and therefore the power grid operation level and livelihood guarantee capacity are improved, and the power supply reliability and the power utilization efficiency of the power grid are 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 grids, in particular to a micro-grid energy storage optimization method and system based on big data analysis, and the method comprises the steps: obtaining the comprehensive prediction power generation through the prediction segmentation time, a wind power generator set and a solar power generator set, and obtaining the analysis power based on the prediction segmentation time and the comprehensive prediction power generation, an energy storage battery parameter set is obtained, if the analysis electric quantity is larger than or equal to a storage electric quantity threshold value, a target storage battery pack is retrieved from the energy storage battery packs through the energy storage battery parameter set, active equalizing charging is conducted on the target storage battery pack, and an updated storage battery pack is obtained; and after it is confirmed that a preset target energy supply battery pack is retrieved in the energy storage battery packs by using the analysis electric quantity and the energy storage battery parameter set, power is supplied by using the target energy supply battery pack, and an updated power supply battery pack is obtained. According to the method, the energy storage of the micro-grid can be accurately and intelligently optimized.

A tool for hanging and disassembling a grounding wire of a power transmission line tower through an unmanned aerial vehicle comprises the unmanned aerial vehicle, a detachable connecting rod frame is installed on the unmanned aerial vehicle, and two hanging rings are connected to the bottom end of the connecting rod frame. And the high-altitude falling risk and the induced electric shock hidden danger caused by manual tower climbing are thoroughly eliminated. An operator can perform remote control on the ground and is prevented from being directly exposed to a high-voltage electric field and a severe tower environment, so that the life safety of the operator is fundamentally guaranteed, and the system conforms to the development trend of'few-person and unmanned 'operation and maintenance of an intelligent power grid. The unmanned aerial vehicle can quickly pass through complex terrains to approach an operation point, is particularly suitable for special tower positions such as mountainous areas and cross rivers, greatly improves the operation efficiency, and remarkably improves the operation safety. A traditional manual grounding wire hanging and dismounting mode is changed, the device is not influenced by the terrain and the tower type, the operation process is convenient and fast, the maintenance operation time is shortened, the labor intensity is reduced, and the personal safety is ensured.

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 provides a personnel safety dynamic early warning method and system for distribution network line operation, and relates to the technical field of intelligent power grid management, and the method comprises the steps: calling an operation work ticket which comprises a work task, operation personnel information, safety measure requirements, grounding wire installation information, and a regulation and control instruction; text semantic structuring preprocessing is executed, and three-dimensional security features are extracted; security statement extraction is carried out, and a structured task template is established; when the positioning information of the target operating personnel is identified to accord with the area range of the target area, activating the target area; establishing a time sequence working data set based on ground wire intelligent sensing and operation tool intelligent sensing; and performing security verification, and generating a security dynamic early warning. According to the invention, the technical problem that the response speed and accuracy of safety early warning are reduced because the safety assessment of the operation task is not accurate enough due to the lack of an intelligent information extraction and task verification mechanism in the distribution network line operation in the prior art is solved.

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.