167 results about "Smart power" patented technology

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention relates to the technical field of smart power grids, in particular to a hybrid game-based virtual power plant optimization operation method under multiple markets, and the method comprises the steps: building an electricity-certificate-carbon multi-market coupling transaction framework, deeply analyzing a master-slave game relationship between a virtual power plant operation platform and a VPP alliance, and building a price guidance-based double-layer interaction mechanism; according to the method, research is carried out aiming at the condition that a virtual power plant belongs to different interest subjects, a resource complementation-based alliance cooperation model is proposed, and a VPP alliance cooperation optimization framework considering demand side load elasticity is established by introducing a refined model of a flexible load; and establishing a virtual power plant operation platform and VPP alliance mixed game optimization model. Solving a master-slave game stage by adopting a double-layer iterative algorithm; and solving a cooperative game stage in combination with an alternating direction multiplier method. The virtual power plant is guided to participate in multi-market cooperative regulation and control through a mixed game mechanism, safe and stable operation of the power system is ensured, and the operation economy and environmental protection property are 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 invention relates to the technical field of smart power grids, in particular to a power distribution network energy storage optimization scheduling method based on dynamic load prediction, which comprises the following steps: acquiring power data, extracting a reversal node, calibrating a disturbance section, identifying a charge trend, screening a scheduling unit, constructing a scheduling link, identifying an intervention node by fusing voltage and current directions, and setting release limitation. And generating an energy storage release freezing instruction set. According to the method, the direction reversal point in the power data of the energy storage access node of the power distribution network is extracted, a trend sequence is constructed in combination with the change trend of the state of charge, a continuous descending unit is eliminated, synchronous matching of the energy storage trend and the disturbance behavior is realized in a time axis, and the power distribution network energy storage access node power data is obtained through combined screening of trend direction consistency and communication path conditions. An energy storage path map is constructed, the structural combination capability of energy storage resources is enhanced, the voltage fluctuation main vector and the current release auxiliary vector are fused, and the response capability of the energy storage resources of the power distribution network and the path regulation and control precision are remarkably 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 discloses an electricity consumption anomaly detection method, system and device based on an environmental perception graph convolutional network and a medium, and belongs to the technical field of smart power grids. The method comprises the steps that historical electricity consumption of a power grid area unit and external environmental factors are acquired to construct a multi-dimensional space-time association graph; a periodic trend component and environment-related residual fluctuation are separated through the multi-dimensional space-time correlation diagram, and a two-channel diagram convolution feature is obtained; in combination with the convolution features of the two-channel graph, feature space decoupling of a periodic channel and a residual channel is realized, and decoupling features are obtained; and extracting a historical residual fluctuation sequence based on the decoupling features, and constructing an environmental state sensitive probability density function. According to the method, the historical electricity consumption and the external environment factors such as temperature, humidity or weather events are closely fused by constructing the multi-dimensional space-time association diagram, so that the space-time dependence of the electricity consumption behavior is effectively captured, and misjudgment caused by ignoring environment dynamics in a traditional method is avoided.

The invention relates to the technical field of power grid power dispatching. The new energy power cooperative scheduling method and system based on the smart power grid are provided, and the method comprises the steps: generating an original data set; based on the original data set, constructing a digital twinborn model of the wind turbine generator, and generating a real-time fatigue damage distribution prediction result of the gearbox and the bearing; the blade tip speed ratio and the variable pitch angle are dynamically optimized, and dynamic control parameters are generated; the dynamic control parameters are issued to a wind turbine generator controller for execution, the power grid dispatchable capacity is calculated based on a real-time fatigue damage distribution prediction result, an adjustable capacity upper limit is generated, a cross-energy compensation strategy is triggered, closed-loop optimization is achieved, the problem that accumulated damage of high-frequency power adjustment to equipment cannot be accurately quantified is solved, and the power grid dispatching capacity is optimized. The efficiency of a multi-objective optimization algorithm is improved, and the phenomena of frequent shutdown, wind curtailment rate rise and peak regulation resource waste are reduced, so that the new energy consumption capability and the power grid economy are improved.

The invention provides a spare power automatic switching risk prediction method and system for a smart power grid, and relates to the technical field of smart power grids, and the method comprises the steps: collecting and screening an initial feature set of the smart power grid, and generating a dynamic feature set; outputting a conventional risk probability based on a time sequence feature through a random forest model, identifying a sudden abnormal mode based on a frequency domain feature through an XGBoost model, and synchronously generating a feature importance matrix; determining a power grid state by combining the time sequence characteristics and the frequency domain characteristics, dynamically fusing output results of the models according to fusion weights of the models determined by the power grid state, and generating a comprehensive risk level; inputting the comprehensive risk level and the dynamic feature set into a reinforcement learning environment model, and solving a multi-objective function generated by the model to obtain a spare power automatic switching execution strategy; and executing the spare power automatic switching execution strategy, and dynamically adjusting the sliding window for feature acquisition until the execution result meets the preset condition, so that the accuracy of spare power automatic switching risk prediction and the adaptability of the strategy are remarkably improved.

The invention discloses a power demand response system which comprises the following steps: S1, data acquisition and monitoring: the system acquires detailed power consumption data such as power consumption, power consumption time, power consumption power and the like of a user in real time through widely distributed intelligent electric meters, and uses sensors installed at key nodes of a power grid to determine the power consumption of the user; operation parameters such as voltage, frequency and load of a power grid are monitored in an omnibearing manner; and S2, data analysis and load prediction: fusing and sorting various types of data from a user side and a power grid side, and eliminating data format differences and repeated data. According to the power demand response system, a power consumption mode is remodeled through two-way interaction, the operation efficiency and economical efficiency of a power grid are improved, key support is provided for energy transformation and carbon neutralization targets, and the power demand response system is an important tool for achieving an intelligent power grid and a sustainable energy system and has wide application prospects. The successful implementation of the method depends on policy support, technical innovation and collaborative optimization of a user participation mechanism.

The invention relates to the technical field of data early warning, in particular to an intelligent power plant safety early warning method based on multi-data coupling. The method comprises the following steps: acquiring multi-source coupling data including power plant production operation data, key equipment state monitoring data, environment monitoring data and historical accident records, and calculating acquisition time intervals and synchronous delays between data sources; defining the ratio of the acquisition time interval to the topological distance between the corresponding nodes of the key equipment as a coupling propagation rate; constructing a time sequence-topology association window based on the coupling propagation rate, and judging the state type of the key equipment according to the time sequence-topology association window; a key equipment risk parameter is obtained by quantifying the state type; according to the method, key equipment risk parameter quantification and potential risk marking are realized through multi-source coupling data and time sequence-topology correlation analysis, so that dynamic grading early warning is supported, and the operation safety and early warning real-time performance of a power plant are improved.

The invention discloses an electric vehicle charging method, device and equipment based on time-of-use electricity price, and a medium, and relates to the field of smart power grids, and the method comprises the steps: generating a target power data set according to the load data of a power grid side, the charging data of a target charging pile, and a preset time-of-use electricity price parameter; performing power grid load curve prediction based on the target power data set through a preset prediction model, and performing peak-valley period division on a predicted target prediction power grid load curve to obtain divided periods; generating a time-of-use electricity price signal according to the divided time periods, and constructing a charging strategy function according to the target power data set and the time-of-use electricity price signal; and solving the charging strategy function to obtain a corresponding target solution set, scoring a plurality of solutions in the target solution set through a preset weight, and generating a charging strategy based on the target solution with the highest score, so that the electric vehicle can be charged based on the charging strategy. Therefore, the electric vehicle charging cost of the user can be reduced while the intra-day load peak-valley difference is reduced.

The invention discloses a power grid regulation and control scene generation method and device, electronic equipment and a storage medium, and relates to the field of smart power grids or other related technical fields, and the method comprises the steps: collecting power grid load data, and carrying out the preprocessing of the power grid load data, and obtaining the preprocessed power grid load data; performing feature extraction and coding on the preprocessed power grid load data to obtain a load feature vector; inputting the load feature vector into an unsupervised generation model, and outputting a power grid regulation and control scene through the unsupervised generation model; and calculating the similarity between the power grid regulation and control scene and a pre-constructed reference regulation and control scene, configuring a type label for the generated power grid regulation and control scene based on the similarity, and generating a target power grid regulation and control scene based on the power grid regulation and control scene and the type label. According to the invention, the technical problem of low efficiency of a power grid regulation and control scene generation method based on historical data and an expert strategy in related technologies is solved.

The invention provides an equipment layout optimization method and system in power transmission and transformation project design, and relates to the technical field of smart power grids, and the method comprises the steps: carrying out the region division of design drawing information, and obtaining N power transmission and transformation project regions; calling a power transmission and transformation project layout data space, performing effect evaluation fitting, and constructing a power transmission and transformation project layout simulation module; carrying out equipment layout optimization on the N power transmission and transformation project areas in sequence, and determining equipment layout schemes of the N power transmission and transformation project areas; carrying out three-dimensional modeling, generating an initial engineering equipment layout model, and carrying out anomaly identification marking; and performing global optimization analysis on the initial engineering equipment layout model based on the equipment layout abnormal feature information, and determining a power transmission and transformation engineering equipment layout scheme. According to the method, the technical problem of low power transmission and transformation engineering equipment layout efficiency caused by unreasonable space utilization is solved, and the power transmission and transformation engineering equipment layout efficiency is improved through region division and global optimization in combination with anomaly recognition.

The invention relates to the technical field of intelligent power distribution network safety detection and fault processing, in particular to a multi-source coupling leakage current accurate traceability and cooperative suppression method and system for a power distribution network, and the method comprises the steps: collecting leakage current, voltage phase and power fluctuation data through a synchronous phasor measurement device; based on the collected data, constructing a four-dimensional feature library, carrying out decoupling processing on leakage current signals, and then realizing accurate traceability positioning of the leakage current according to a matching result of the leakage current signals and the four-dimensional feature library and a time difference between sensors; data are uploaded to a distribution network dispatching center, leakage current is evaluated based on a dynamically adjusted safety threshold value, and then operation states of a photovoltaic inverter, an energy storage converter and a charging pile are cooperatively controlled to realize active suppression of the leakage current; and performing preliminary diagnosis on the leakage current at the edge side, uploading a result to a cloud end, and determining a final diagnosis conclusion. According to the invention, the accuracy of multi-source coupling leakage current tracing and the accuracy of cooperative suppression of the power distribution network are improved.

The invention relates to the technical field of intelligent power plants, in particular to a robot inspection control method and system for an intelligent power plant. Comprising the steps that an inspection equipment library and a power plant monitoring model are established, and the power plant monitoring model is used for generating a risk thermodynamic diagram; generating an inspection demand package according to the risk thermodynamic diagram, and generating a primary inspection strategy according to a preset inspection model and the inspection demand package; obtaining a feedback data packet of each inspection sub-device according to a preset feedback time node, and judging whether to generate an operation correction instruction or not according to the feedback data packet; the method comprises the following steps: periodically predicting power plant equipment, constructing a corresponding risk thermodynamic diagram, screening first-level risk points according to the risk thermodynamic diagram, performing path optimization based on position parameters of each first-level risk point according to an inspection model, generating corresponding inspection sub-paths, and completing an inspection task by matching the optimal inspection sub-equipment. The inspection efficiency and fault early warning efficiency of the power plant are improved, and safe operation of the power plant is ensured.

The invention discloses a block chain enabling smart power grid transaction system based on a game theory, and relates to the technical field of regional chains, and the system comprises a strategy management module which is used for making a buying and selling strategy based on the optimal utility through the game theory according to the electric meter storage space of a user, power utilization historical information and transaction record information, the electric equipment of the whole network is dispatched through the block chain; the transaction contract module is used for creating and updating a transaction contract based on the operation strategy formulated by the strategy management module; and the user management module is used for managing the information of the power users and the historical electricity utilization records. According to the invention, an efficient, fair and transparent power transaction mode is constructed by using the security of the block chain technology and the real-time response characteristic of the smart power grid, and the method aims at fully utilizing the distributed power storage units to achieve power utilization peak regulation so as to reduce the energy loss.

The invention discloses an intelligent power utilization data acquisition control method and system based on edge calculation. The method comprises the following steps: acquiring acquisition task information of a master station; the acquisition task information of the master station is divided into a plurality of sub-acquisition tasks according to regions; sending the sub-collection tasks to collectors and concentrators in the corresponding areas for storage; based on the sub-collection task, the collector collects user-side electric energy meter data and verifies the data to obtain first collection data; based on the concurrent reading command, the concentrator reads the first aggregated data in the collector to obtain second aggregated data; and based on the set requirement of the master station, the master station extracts the corresponding electric energy meter data in the collector. According to the invention, local collection of a user side is completed by reading meter data storage through the collector, minute-level high-frequency collection and power failure active reporting of data such as voltage, current and electric energy of the electric energy meter can be realized, and a data basis is provided for services such as power supply line aging trend analysis, power grid voltage quality monitoring and load monitoring analysis.

The invention relates to an automatic pre-warning system after equipment failure for a smart power plant. The automatic pre-warning system comprises a data acquisition unit, a fault point judgment unit, a fault propagation relationship network establishment unit, a shutdown risk assessment unit and a pre-warning unit. The method has the beneficial effects that through cooperative work of data acquisition, fault point judgment, fault propagation relationship network modeling, shutdown risk assessment and an early warning unit, fault propagation chain reaction is dynamically quantified, and the shutdown risk probability of the whole plant is generated, so that the defects of incomplete path analysis and lagging risk calculation in a multi-equipment fault scene in a traditional method are overcome; the accuracy of fault early warning is improved, so that an effective emergency response basis is provided for operation and maintenance personnel of an intelligent power plant; the propagation probability matrix generation module corrects the final propagation probability according to the actual fault propagation mark value, and the final propagation probability is corrected in each fault propagation event, so that prediction becomes more and more accurate along with system operation.

The invention provides a dynamic cooperative control method and system for a smart power plant with few people on duty, relates to the field of power plant operation control, and solves the technical problem of insufficient adaptive ability of a control strategy in a few people on duty environment in the prior art. The method comprises the following steps: adjusting a weight coefficient of a global optimization objective function by utilizing a meta-cognitive optimization model based on historical and real-time operation data; generating an optimal control strategy in a pre-constructed digital twinborn model through a reinforcement learning algorithm by using the adjusted global optimization objective function; performing distributed negotiation on each sub-region through a potential game mechanism according to the optimal control strategy to obtain a cooperative control strategy of each sub-region; and performing simulation verification on the cooperative control strategy by using a pre-constructed digital twinborn model, and performing risk assessment and strategy decision in combination with a risk assessment model.

The invention relates to the technical field of industrial big data, in particular to an Internet of Things sensing and data monitoring integrated system, which comprises an Internet of Things sensing module used for acquiring a sensing parameter set of a distributed simulation unit of an intelligent power plant and a historical performance evolution trend reflecting a dynamic evolution rule; the twin mapping module is used for realizing signal space-time synchronization by utilizing a time service protocol, calling a multi-physics field evolution model to calculate thermal stress offset so as to execute numerical compensation, and generating a high-fidelity physical feature vector; the collaborative decision-making module shares observation information by using a multi-agent distributed collaborative architecture, identifies an electromagnetic-dynamic coupling response mode through a joint reward function and outputs a collaborative optimization strategy; and the execution module is used for generating an evaluation result by calculating the numerical deviation between the physical feature vector and the design envelope, and adjusting the excitation control variable according to a strategy until the system converges to a target equilibrium value. According to the method, the steady state monitoring of the power system is realized through twin mapping and collaborative decision.

The invention discloses a renewable energy power grid voltage control method, device and system based on machine learning and a medium, and aims to solve the problems of power grid voltage fluctuation and out-of-limit caused by high-permeability renewable energy access. The method comprises the following steps: firstly, collecting power grid operation parameters, denoising, complementing and standardizing through Kalman filtering; constructing and training a GNN model in combination with power grid topology to realize real-time prediction of voltage deviation; then, an MARL decision framework with stable voltage, low consumption and high efficiency as targets is built, and an intelligent agent is trained to complete decentralized collaborative decision; driving a voltage regulating device to execute regulation based on a GNN prediction result and an MARL optimal strategy, and adapting to the dynamic change of the power grid through an interactive feedback mechanism; and finally, continuously monitoring the state of the power grid, and triggering iterative optimization of the model and the strategy. Through multi-module cooperation and dynamic iterative optimization, the power grid voltage stability is effectively maintained, the voltage fluctuation is suppressed, and the operation reliability and the electric energy quality of the smart power grid are improved while the efficient consumption of renewable energy sources is ensured.

The invention provides an automatic power generation dispatching control method and device of a thermal power generating unit, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting multi-source observation data of an electric side and a hearth combustion side, and extracting a hearth negative pressure fluctuation amplitude, a flame centroid jitter amplitude and a sound pressure fluctuation amplitude to construct a sound-combustion coupling stability index; establishing a coal consumption cost function and an emission cost function in combination with historical unit operation data; dividing an acoustic-fuel coupling stability domain according to an acoustic-fuel coupling stability index, monitoring a rising trend, and taking an acoustic-fuel coupling risk as a scheduling constraint triggering condition; a scheduling optimization model is reconstructed in a risk dominant scene, active power output, a coal mill combination and air distribution parameters are used as decision variables, an optimal track is solved with physical limits, power grid side constraints and a stability domain as limiting conditions, and finally the optimal track is issued to a coordination control layer. According to the method, the output curve can be actively deviated in the acoustic-fuel coupling instability risk rising stage to avoid acoustic resonance, and therefore the dynamic dispatching capacity of the unit is guaranteed.

The invention discloses an active power distribution network multi-mode electric vehicle charging and discharging bearing capacity evaluation method, which belongs to the technical field of smart power grids, and comprises the following steps: firstly, aiming at the behavior randomness of an electric vehicle, constructing a time sequence scene containing arrival time, departure time and an initial charge state, and establishing three types of charging and discharging mode constraints; generating a time sequence demand curve through Latin hypercube sampling; establishing each-phase voltage and current steady-state constraint and distributed power supply constraint by taking the maximum bearing capacity as a target and combining the characteristics of the power distribution network, and constructing an evaluation model; and finally, solving by adopting mixed integer linear programming, and linearizing the nonlinear constraint to obtain a linear model capable of being efficiently solved, so as to realize evaluation of the bearing capacity of the electric vehicle. The method focuses on the core demand of large-scale electric vehicle bearing capacity evaluation, overcomes the limitation that a traditional model is difficult to adapt to complex characteristics of a three-phase unbalanced active power distribution network, and realizes collaborative optimization of the electric vehicle bearing capacity and safe and stable operation of the power distribution network.

The application provides an anti-theft intelligent metering box suitable for smart power grids, which comprises a metering box body and a closing door. The inside of the metering box body is an installation cavity. The front of the metering box body is provided with an opening. The closing door is slidably connected with the front of the metering box body in the horizontal direction. The upper and lower sides of the front of the metering box body are both provided with horizontal sliding grooves. The back of the closing door is provided with two sliding assemblies. The two sliding assemblies are slidably connected with the two sliding grooves respectively. A camera is arranged in the installation cavity. The application further comprises a protective cover which is horizontally and rotatably arranged in the installation cavity. A driving assembly is arranged between the installation cavity and the back of the closing door. The driving assembly is used to drive the protective cover to be sleeved on the camera or to drive the protective cover to be away from the camera. The application solves the problems that the camera in the electric energy metering box is easily exposed, the protective cover is not responsive to the opening and cannot timely shoot the facial features of the person who opens the door in the prior art.

The invention relates to the technical field of smart power grid information security and privacy protection, and discloses a smart power grid P2P electric energy transaction differential privacy method comprising the following steps: setting related parameters according to a P2P electric energy transaction platform; constructing an attack evaluation module to obtain a quantified current privacy disclosure risk score; a differential privacy noise adaptive deployment problem is formalized into a Markov decision model; the optimal or near-optimal strategy of the candidate noise injection position and intensity is output in a self-adaptive mode; after noise parameters are generated and a noise injection decision is obtained through a set differential privacy noise mechanism, noise is injected and liquidation is executed at a specified position, a market liquidation result is issued, and meanwhile, corresponding privacy budget is deducted from global privacy budget parameters; and the attack evaluation module is used for carrying out privacy risk re-evaluation on the released market liquidation result, and carrying out online adaptation and rolling updating on the strategy network model. The privacy protection strategy can be dynamically adjusted according to the market state and the privacy risk.

The application relates to the field of data processing, and relates to a power consumption prediction method and system based on a smart electric energy meter. The method comprises the following steps: acquiring an average temperature prediction value of a next date; acquiring a planned running duration of production equipment of the next date; acquiring real power consumption values of a continuous preset number of days by using the smart electric energy meter; inputting the average temperature prediction value of the next date and the planned running duration into a preset power consumption prediction model, so that a power consumption prediction value of the next date of the enterprise is obtained. The method can improve the accuracy of the power consumption prediction result of the enterprise.

This invention discloses a method and system for configuring and restoring flexible resources and mobile energy storage in a power distribution network, belonging to the field of smart power distribution network technology. Based on a typhoon wind field and wind and rain load model, this invention constructs a time-varying failure rate for power lines and uses Latin hypercube sampling to generate a set of failure scenarios. Typical scenarios are obtained through scenario reduction. Based on the failure scenarios, in the pre-disaster stage, the invention identifies the deployment locations of smart soft switches (SOPs) and mobile energy storage systems (MESSs) that minimize the sum of pre-disaster investment costs and post-disaster recovery period weighted load shedding costs. In the post-disaster stage, time-varying accessibility constraints are constructed based on road travel time preprocessing results, establishing a collaborative recovery model that includes SOP operation, network reconfiguration, and MESS time-varying scheduling. This invention, based on the collaborative configuration and post-disaster control of flexible resources and mobile energy storage, effectively reduces the configuration cost of the power distribution network while improving its support and recovery capabilities in the face of extreme disasters such as typhoons.

The invention provides an energy router for an intelligent power distribution area, and relates to the technical field of power systems, and the energy router comprises a three-phase power supply, a high-voltage side main winding, a three-phase load, a low-voltage side main winding, a first voltage source converter, a second voltage source converter and a third voltage source converter. The high-voltage side main winding comprises a first high-voltage winding, a second high-voltage winding and a third high-voltage winding which are connected end to end; the low-voltage side main winding comprises a low-voltage small winding and a low-voltage winding; the low-voltage small windings comprise a first low-voltage small winding, a second low-voltage small winding and a third low-voltage small winding; the first low-voltage small winding, the second low-voltage small winding and the third low-voltage small winding are in star connection; the low-voltage winding comprises a first low-voltage winding, a second low-voltage winding and a third low-voltage winding which are connected end to end, and the low-voltage winding forms a three-phase series loop and is connected to the alternating current output terminal of the second voltage source converter.

The invention relates to the technical field of distribution boxes, and particularly discloses a waterproof comprehensive distribution box special for a smart power grid, which comprises a main box body, a cable chamber is fixedly connected to the bottom of the inner wall of the main box body, a sealing gasket is fixedly connected to the side surface of the main box body, a box door is rotatably connected to the side surface of the main box body, and a connecting groove is formed in the side surface of the box door. The sealing gasket is matched with the connecting groove in shape and size, an inclined rain baffle is fixedly connected to the top of the main box body, an air treatment box is fixedly connected to the part, located in the inclined rain baffle, of the top of the main box body, a drying device is fixedly connected to the top of the air treatment box, and a temperature adjusting chamber is fixedly connected to the top of the inner wall of the main box body; according to the comprehensive distribution box special for the waterproof intelligent power grid, damage caused by frosting of electrical components in winter can be avoided through the arrangement of the temperature adjusting chamber, air entering the distribution box can be dried through the arrangement of the drying device, and the electrical components in the distribution box are prevented from being damaged by moisture in the air.

The application relates to the technical field of smart power grids, and discloses a power distribution cabinet for electrical equipment, which comprises a power distribution cabinet, a zero line row arranged in the power distribution cabinet and electrically connected with the branch line, a conductive pin in sustained conductive contact with the zero line row, a pressing assembly comprising a pressing ring capable of being sleeved on the conductive pin, and a heat-sensitive trigger piece which releases the pressing effect of the pressing ring when the temperature of the conductive pin exceeds a threshold value, so that the contact piece instantaneously releases the zero line row and forms a repair gap therebetween, and then re-closes. When the temperature exceeds the threshold value, the heat-sensitive trigger piece instantaneously releases the pressing force of the pressing ring, so that a controllable repair gap is formed between the contact piece and the zero line row, and under the shearing action, the silver powder and the reducing agent are released by breaking the microcapsules, then the silver powder is sintered by Joule heat to form a dense conductive layer and reduce the oxidation layer, so that the effects of automatically repairing electrochemical corrosion, restoring low contact resistance and avoiding continuous high-temperature fire are achieved.