731 results about "Distribution transformer" patented technology

The invention discloses a precise positioning method for a leakage fault source of a low-voltage distribution network, which belongs to the technical field of power system fault diagnosis and comprises the following steps: acquiring and calculating the voltage unbalance degree and the zero-sequence current value at a total outgoing line of a distribution transformer, generating a primary leakage trigger signal based on a voltage safety threshold and a current safety threshold, and determining the leakage fault source of the low-voltage distribution network. Injecting a characteristic signal with a preset frequency into the line, monitoring the response in the zero sequence loop, and calculating and generating a reference fault path impedance spectrum; and according to the switch topology of the line, sequential opening operation and closing operation are executed to calculate a real-time fault path impedance spectrum, whether impedance spectrum jump exists is judged, a fault branch indication signal is identified and generated, multi-source synchronous data is used for verification, and final fault positioning information is output. Impedance spectrum analysis is carried out by injecting characteristic signals, differential comparison is carried out by combining intelligent switch sequential operation, multi-source synchronous data is utilized to carry out multi-evidence chain verification, and high-precision and high-reliability positioning of an electric leakage fault source can be realized.

The invention discloses a distribution transformer state evaluation method, system and device and a storage medium, and the method comprises the steps: obtaining current and voltage long sequence data through high-frequency sampling, and forming a high-quality data set through expert labeling; signal preprocessing is realized by adopting dual-channel adaptive wavelet packet denoising and cross-correlation sinchinger interpolation phase correction; constructing an asymmetric convolution pyramid to extract current high-frequency and voltage low-frequency multi-scale features, and performing cross-modal fusion by using compressed multi-head attention and a channel-space double-gating mechanism; dynamic random depth regularization is introduced, and a time domain loss function in a fault sensitive period is focused, so that high-accuracy and low-delay real-time state evaluation is realized; the capacity of capturing and recognizing early weak fault features of the distribution transformer in a complex operation environment is remarkably enhanced, and therefore more sensitive and more accurate state early warning is achieved.

The invention discloses a column type distribution transformer winding abnormity detection system and detection method. The detection system comprises a frequency response detection instrument, a signal coupling assembly, an impedance matching module and a data processing module. The frequency response detection instrument is used for outputting a frequency sweep excitation signal and collecting a winding response signal; the signal coupling assembly is used for isolating power frequency voltage and filtering low-frequency interference; the impedance matching module is used for adjusting output impedance and input impedance to realize matching with electrical characteristics of different windings; and the data processing module is used for constructing an equivalent detection loop model, completing frequency response simulation calculation and carrying out frequency domain analysis and anomaly identification on an actually measured response curve. According to the invention, electrified state diagnosis of distribution transformer winding structure abnormity (such as deformation and loosening) can be realized.

The invention discloses a 10kV distribution transformer on-load voltage regulation control method and system, and relates to the technical field of transformer on-load voltage regulation, and the method comprises the steps: collecting the three-phase voltage and current of a low-voltage side of a distribution transformer and a tap switch gear signal, and loading a preset voltage regulation control mode and an oscillation region parameter; based on the acquired three-phase voltage and current of the low-voltage side of the distribution transformer and the tap switch gear signal, calculating a feeder comprehensive impedance parameter and a load reactive power parameter, and outputting a transformer operation state index; according to the transformer operation state index, the voltage regulation control mode and the oscillation area rule, current voltage out-of-limit and reactive power deviation are judged, and a voltage regulation strategy triggering instruction is generated according to the judgment result; and calculating upper and lower limit values of feed line correction voltage based on the voltage regulation strategy trigger instruction, and controlling the tap switch to execute voltage regulation action. According to the method, a combined calculation scheme of feeder comprehensive impedance and load reactive power is fused, and dynamic optimization of the triggering condition of the voltage regulation strategy is achieved.

The utility model discloses a low-voltage outgoing line flexible connection device of a distribution transformer, which belongs to the technical field of transformer wiring and comprises two connectors, a plurality of connecting holes and a plurality of connecting terminals. The connector comprises two connectors, a plurality of flexible copper strips, the flexible copper strips are located between the two connectors, the ends of the flexible copper strips are connected with the connectors in an inserted mode, and the relative positions between the ends of the flexible copper strips and the centers of the connectors change along with changes of stress. The deformation quantity is compensated through sliding of the limiting frame, under the action of the elastic piece, the soft copper strip can be always kept in a tightened state, and meanwhile, due to the arrangement of the elastic frame, the soft copper strip can be prevented from being scattered, and the situation that the soft copper strip makes contact with other external components when deformed, and danger is generated can be avoided.

The invention discloses a transformer area topological structure estimation method, system, equipment and medium based on multi-dimensional power utilization characteristics, and belongs to the technical field of power distribution transformer area topologies, and the method comprises the steps: collecting multi-source data, carrying out dynamic weight distribution and abnormal value correction, carrying out the characteristic extraction according to the collected multi-source data, and generating a high-dimensional characteristic vector; clustering user nodes, dividing cluster labels, performing topological modeling according to a cluster division result, generating a topological graph, and performing anomaly verification through multi-dimensional anomaly scoring and abnormal power utilization detection; and performing incremental model parameter correction and multi-objective optimization according to anomaly verification feedback, and generating dynamic topological graph rendering and multi-dimensional decision suggestions by integrating topological modeling, anomaly verification and optimization results. According to the method, accurate estimation of a topological structure is realized through dynamic clustering and hidden node recognition, real-time diagnosis of abnormal nodes and self-correction of a model are realized by means of a multi-dimensional abnormal scoring and self-adaptive optimization mechanism, and the accuracy and operation and maintenance efficiency of transformer area management are improved.

The invention relates to the technical field of source network load storage cooperative control, in particular to an area source network load storage cooperative optimization control system based on an intelligent fusion terminal, and the system comprises an acquisition module which acquires energy characteristic parameters and cooperative response characteristic parameters of a target area in a historical period; analyzing an energy characteristic characterization value based on the energy characteristic parameter through an analysis module, and calculating a collaborative response characteristic characterization value based on the collaborative response characteristic parameter; determining a corresponding control strategy based on the energy feature representation value through a control module; through a verification module, whether the stability of the transformer area source network load storage meets the standard is judged based on the collaborative response characteristic representation value; and through a feedback module, based on a difference value between the energy feature representation value and a predetermined energy feature representation threshold, determining an adjustment amplitude of the cooperative response feature representation threshold, and performing collection and verification again. According to the invention, through the verification module and the feedback module, the response rate of cooperative control of each link of source network load storage is improved.

The invention discloses an automatic voltage regulating device for a power distribution area and a control method of the automatic voltage regulating device, and belongs to the technical field of power distribution network voltage treatment. The device comprises a three-phase power supply, a power supply side input circuit, a load side output circuit, a current transformer, a single-P air switch, a power supply conversion module, a three-phase independent voltage regulation module, a main path control system, an automatic bypass control system, a gear driving module, a man-machine interaction module, a display module, a monitoring module, a mode switching module, a parameter storage module and an alarm module. The system comprises a manual adjustment execution module, a counting module, a voltage detection module, a current detection module, a 4G communication module and an over-temperature protection module. A main circuit control system depends on input and output voltage data fed back by a voltage detection module, compensation voltage U can be independently calculated for differential voltage fluctuation of L1, L2 and L3 phases, intelligent adjusting switches of all phases are driven to act accurately, and finally three-phase output voltage U2 is stabilized within an allowable range.

The invention discloses a data anomaly detection method and device for a low-voltage power distribution network, a medium and equipment, and belongs to the field of anomaly detection. Inputting the data into a preset LSTMAE-GRU knowledge distillation model to carry out anomaly detection, and outputting an anomaly detection result of each time step; the model is obtained by taking normal measurement time sequence data covering load characteristics of multiple courts as a training sample, extracting high-dimensional time sequence characteristics based on an initial LSTMAE teacher model, aligning teacher model characteristic distribution through latent variable projection by an initial GRU student model, and combining distillation loss training formed by KL divergence and mean square error. The problem that the abnormal data of the low-voltage power distribution network cannot be accurately and efficiently detected in the prior art is effectively solved.

The invention relates to a low-voltage transformer area meter box burning loss fault detection method and system, and the method comprises the following steps: obtaining the time sequence data of a low-voltage meter box, detecting an abnormal behavior through a trained PidanGAE-U model, recognizing the state of the low-voltage transformer area meter box, and generating a preliminary risk assessment result; obtaining multi-dimensional historical and real-time data, carrying out classification division and preliminary evaluation on the identified faults, and constructing a risk factor evaluation system; and based on a risk factor evaluation system, carrying out risk factor correction on a preliminary risk evaluation result, introducing a comprehensive weight evaluation algorithm to carry out quantitative calculation on the influence degree of each corrected factor, and finally generating a risk level of the burning loss fault. Potential faults can be identified and positioned in advance, and the stability of a power system and the power utilization safety of a user are ensured.

The invention discloses a transformer area distribution transformer reverse heavy overload prediction method and system, and the method comprises the steps: obtaining data which comprises the historical electrical quantity characteristics of a photovoltaic user, the numerical weather forecast characteristics of the geographic position of a transformer area, and a prior graph structure formed according to the topological graph of the transformer area; preprocessing the data to construct a data set; the method comprises the steps of establishing a space-time diagram prediction model, inputting historical electrical quantity characteristics and numerical weather forecast characteristics of N photovoltaic users and a prior diagram structure into the model to obtain a future K-step area distribution transformer load prediction result, performing heavy overload judgment based on a prediction value, and finally obtaining a future K-step area distribution transformer reverse heavy overload prediction result. According to the method, the topological structure of the power distribution network, the electrical characteristic data and the numerical weather forecast characteristics are comprehensively utilized, high-precision prediction of the multi-time-step power of the photovoltaic grid-connected system is achieved through the space-time diagram neural network model, and the method is suitable for application scenes such as operation optimization and early warning management of the power distribution network with distributed photovoltaic access.

The invention belongs to the technical field of power grid control, and relates to a transformer area flexible interconnection system three-phase imbalance control method and device and a medium. Taking the transformer area converter controlled by the PQ control algorithm as a target transformer area converter; collecting a first control parameter output by an active power PI controller of the target transformer area converter; based on the upper and lower limits of the voltage target value of the DC bus, performing amplitude limiting setting on the first control parameter of the target transformer area converter to obtain the voltage target value; subtracting the voltage target value from the actual voltage value of the direct-current bus, inputting the difference value into the PI controller, and outputting a second control parameter of the target transformer area converter; taking the second control parameter as a direct-axis current target value, subtracting the direct-axis current target value from a direct-axis current actual value, and taking the difference value as the input of a direct-axis current PI controller; and controlling the target transformer area converter based on the voltage modulation signal output by the PQ control framework. The scheme can effectively improve the power supply reliability of the power distribution area and stabilize the DC bus voltage.

Disclosed in the present invention are an operation and maintenance method and apparatus for severe overload and three-phase imbalance of a distribution transformer, and a medium. The method comprises: by quantifying the degree of difference between devices as the distance between two points, performing clustering analysis on an operation and maintenance state of a distribution transformer, so as to obtain a classification result; and on the basis of a short-term single-phase severe overload category and a short-term three-phase severe overload category in the classification result, establishing a multi-objective optimization model and solving same, so as to obtain a multi-objective optimization result, and on this basis, formulating a day-ahead source-load distribution transformer operation and maintenance plan and performing operation and maintenance control on a distribution network. The present invention provides an operation and maintenance method and apparatus for severe overload and three-phase imbalance of a distribution transformer, and a medium. Solving a multi-objective optimization model to obtain a multi-objective optimization result can ensure that operational risks caused by three-phase imbalance are reduced while balancing three-phase loads, such that differentiated operation and maintenance can be achieved by means of a day-ahead source-load distribution transformer operation and maintenance plan, thereby solving the problem of difficulty in managing severe overload and three-phase imbalance of a distribution transformer to formulate a targeted operation and maintenance plan.

The invention discloses a power grid meteorological disaster damage grade prediction method and system based on a LightGBM model, and belongs to the field of power system disaster early warning. The method comprises the following steps: preprocessing meteorological elements, power grid distribution transformer faults, equipment and regional data; calculating the exposure degree and the vulnerability to obtain a grid-level disaster-bearing body index, aggregating data, and screening key features through correlation analysis; defining disaster damage labels according to the number of power failure users, establishing a two-stage classification data set, configuring and training LightGBM two-stage and three-stage classification models, and optimizing a two-stage classification threshold value; and deploying the model, processing real-time meteorological data, and sequentially reasoning and outputting disaster damage levels. The method solves the problems of low recall, scene separation and poor precision of traditional prediction of serious disaster damage, improves the recognition and prediction precision, fits the operation and maintenance of the power grid, supports emergency decision, and improves the early warning and emergency efficiency of power grid disasters.

The invention belongs to the field of transformer pile head recognition, and particularly relates to a distribution transformer pile head automatic recognition system which comprises a data integration module, a data processing module, a system control module, a waveform parameter calculation module, an excitation circuit analysis module, an equivalent inductance conversion module and a looseness recognition algorithm module. According to the scheme, the high-cost limitation of a traditional vibration sensor or 3D modeling is broken through, and remote monitoring of looseness of the transformer pile head can be achieved through equivalent excitation inductance conversion and waveform similarity analysis without additional hardware transformation based on voltage and current data collected by an existing digital platform (a resource middle platform, a marketing middle platform and the like) of a power grid. Compared with the traditional scheme in the industry, the method has the advantages that the hardware transformation cost is reduced by more than 70%, the problem of missing inspection of manual inspection is avoided, the average recognition time of the pile head loosening fault is shortened to be within 15 minutes from 4 hours of traditional manual inspection, the monitoring efficiency is remarkably improved, and non-intrusive and low-cost accurate sensing of the state of the transformer is realized.

The invention provides a measurement completion method, device and equipment serving panoramic transparency of a power distribution network, and the method comprises the steps: constructing a power distribution network graph model integrating a power grid topological structure, electrical parameters and measurement configuration information, carrying out bad data identification and cleaning on the original measurement data uploaded by the acquisition terminal to obtain a real-time measurement data set; for lines, buses, distribution transformers and circuit breakers in a power distribution network, missing electrical measurement values are obtained through classification certainty deduction calculation by using electrical network topology constraints, conservation laws and forward-back substitution load flow calculation so as to realize measurement completion; and performing quality evaluation on the complemented measurement data, and performing closed-loop feedback optimization based on an evaluation result. According to the scheme, high-precision complementation of the missing electrical quantity can be realized by depending on the measurement data acquired in real time, and the data storage and computing resource overhead is reduced.

A distribution transformer low-voltage prediction method based on spectral clustering and SDTCNet comprises the following steps: step 1, taking historical voltage data of a low-voltage side of a pole-mounted transformer in a power distribution area and meteorological data of the area, checking whether abnormal values and missing values exist in the data or not, and performing corresponding processing; 2, extracting a daily minimum voltage characteristic according to the historical voltage data of each distribution transformer, carrying out spectral clustering analysis based on the daily minimum voltage characteristic, dividing the distribution transformers into k types, and obtaining distribution transformers with different daily minimum voltage characteristics; 3, performing normalization processing on historical voltage data and meteorological characteristic data of different types of distribution transformers, constructing a characteristic data set, and dividing the characteristic data set into a training set, a verification set and a test set; 4, constructing an SDTCNet neural network model, and inputting data of the training set and the verification set into the model for training to obtain a trained model; and 5, inputting test set data into the trained SDTCNet network model for prediction to obtain prediction results of low voltages of different types of distribution transformers.

The invention discloses a low-voltage transformer area multi-level line loss linkage transformer area anomaly diagnosis method, which comprises the following steps of: acquiring archive information, power supply quantity, power sale quantity and statistical line loss rate of a transformer area in a certain day, and installing shunt monitoring units in a transformer area distribution transformer, each level branch and a meter box to acquire current data and voltage data of each node in real time; calculating the theoretical line loss rate of the transformer area; screening the transformer area with the abnormal line loss, calculating the difference between the theoretical line loss rate of the transformer area and the statistical line loss rate, and when the deviation between the theoretical line loss rate and the statistical line loss rate is greater than 1%, judging that the transformer area is the transformer area with the abnormal line loss; and calling the topological relation of the original archives of the transformer area, generating a real-time topological graph, and performing line loss abnormity reason diagnosis. According to the invention, topological relation self-error correction and abnormal type accurate classification are realized, automatic diagnosis is convenient to realize, and the problems of difficult topological file error correction, inaccurate abnormal type positioning, dependence on manpower and low efficiency in existing low-voltage transformer area line loss abnormity diagnosis are solved.

The invention relates to the technical field of distribution transformers, in particular to a distribution transformer excitation surge current identification method and system based on Sym5 wavelet transform and an XGBoost model and a storage medium, and the method comprises the steps: carrying out the three-layer multi-resolution decomposition of an original current signal I (t) through employing a Sym5 wavelet basis, obtaining a D3-layer detail component, and extracting the time-frequency features of the D3-layer detail component; extracting a time domain feature and a frequency domain feature of the original current signal I (t); constructing a multi-dimensional feature vector set based on the time-frequency features, the time-domain features and the frequency-domain features; and normalizing the multi-dimensional feature vector set, and inputting the normalized multi-dimensional feature vector set into an XGBoost model for result prediction. According to the method, through collaborative optimization of Sym5 wavelets and XGBoost, feature positioning deviation caused by asymmetry of wavelet bases in the prior art is overcome, and meanwhile, the adaptability to complex harmonic scenes is enhanced.

A power failure anomaly recognition method and apparatus. The method comprises: acquiring cluster data from a target cluster (S1); on the basis of the cluster data, acquiring distribution transformer data of each distribution transformer at each moment, the distribution transformer data comprising three-phase voltage imbalance, a three-phase voltage, the fluctuation amplitude of a phase current corresponding to voltage sag, a three-phase current, the sag amplitude of a three-phase instantaneous total active power, and the sag amplitude of each phase current (S2); on the basis of the distribution transformer data, screening out a corresponding distribution transformer that meets a first preset anomaly condition, so as to achieve preliminary anomaly recognition (S3); and, on the basis of the distribution transformer data, performing in-depth anomaly recognition on the distribution transformer that meets the first preset anomaly condition, so as to screen out a corresponding distribution transformer that meets a second preset anomaly condition and obtain a distribution transformer having power failure anomaly (S4). On the basis of the three-phase voltage imbalance, the three-phase voltage, the three-phase current and other distribution transformer data, preliminary anomaly recognition and in-depth anomaly recognition are performed; compared with manual inspection, the present method reduces manual workload and eliminates factors that affects power failure anomaly recognition such as limited human energy, thereby improving the timeliness and accuracy in power failure anomaly recognition.

The invention discloses a flexible interconnection and energy storage collaborative planning method for a low-voltage distribution transformer area under high-proportion photovoltaic access, and the method comprises the steps: building a structure-function two-dimensional cluster division model, dividing a distribution transformer area cluster through an electrical distance, a voltage balance degree and an active power balance degree, and reducing the dimensionality of coordinated planning; carrying out connectivity analysis on the transformer areas in the cluster, judging interconnection requirements by taking the maximum net load rate of the transformer and the minimum net load of the transformer areas as feasibility indexes, and generating a candidate scheme set; establishing a power distribution area interconnection device-energy storage double-layer planning model, minimizing equipment capacity configuration at an upper layer according to annual comprehensive investment cost, and formulating a collaborative operation strategy at a lower layer according to minimization of operation cost and maximization of new energy consumption; a hierarchical solution strategy and an improved genetic algorithm for cluster division are adopted to improve the convergence speed and precision, it is ensured that an optimal planning scheme meeting economical efficiency and operation adaptability is obtained, the problem that the influence of factors cannot be comprehensively considered in a single planning dimension is solved, and the actual operation requirement is met.

The invention relates to the technical field of power system monitoring, and discloses an intelligent monitoring method and system for a distribution transformer district, and the method comprises the steps: collecting the operation parameter data of the distribution transformer district; network connection with a monitoring center is established through 4G communication, and a reliable data set is obtained by adopting an adaptive transmission mechanism; performing data quality evaluation and cleaning, and calculating derivative parameters; constructing a load curve, carrying out transformer area classification by adopting a clustering algorithm, and extracting load characteristic parameters by adopting an adaptive window method; performing weighted summation on the evaluation indexes after normalization processing; predicting a load rate and a voltage deviation, carrying out early warning judgment by adopting a self-adaptive threshold value, and generating a disposal suggestion by adopting case matching; the early warning severity and priority are calculated, a multi-channel pushing mechanism is adopted to send early warning information, and a closed-loop tracking mechanism is adopted to evaluate the disposal effect; according to the invention, the problem of distribution transformer district monitoring blind areas is solved, and the power supply reliability and the operation and maintenance efficiency are improved.

The invention provides a source-load collaborative load prediction method and system based on dynamic clustering and trust management, and belongs to the technical field of power distribution network optimization operation, and the method comprises the steps: carrying out the seasonal scene division based on the historical load data of each distribution transformer device in a region, carrying out the clustering of the devices based on a dynamic time warping distance algorithm, and carrying out the clustering of the devices; outputting an equipment cluster type and a corresponding standardized load form curve; according to the cluster type to which the equipment belongs, combining with the real-time output data of the new energy, dynamically correcting the equipment demand coefficient and generating an equipment-level load prediction value; overlapping the load prediction values of the same cluster equipment to generate a cluster-level load value, and aggregating all clusters to obtain a regional total load prediction value; calculating a source load matching degree quantitative index and a new energy output prediction volatility, fusing the two to generate a time-varying trust value, dynamically adjusting a new energy output prediction weight based on the time-varying trust value, and outputting a regional net load prediction value; and according to the regional net load prediction error closed-loop optimization demand coefficient correction parameter.

This system monitors and controls power receiving and transforming equipment installed on the electrical pathways from the power supplier to multiple loads. It prevents electrical accidents that could lead to fires by monitoring and controlling the step-down and distribution transformers installed in the power receiving and transforming equipment, i.e., the high-voltage transformers, and electrical equipment such as distribution boards and panelboards installed on the electrical pathways after the voltage has been stepped down by the high-voltage transformers in the power receiving and transforming equipment. The system constantly monitors the temperature of the high-voltage transformers and the instantaneous current flowing through the electrical pathways of electrical equipment such as distribution boards and panelboards installed on the electrical pathways after the voltage has been stepped down by the high-voltage transformers, and cuts off the power supply to the loads if the temperature of the high-voltage transformer exceeds a predetermined value, or if the instantaneous current flowing through the electrical pathways of the electrical equipment exceeds a predetermined value.

The invention relates to the technical field of controllable series compensation, in particular to a power distribution area voltage rapid compensation device and compensation method based on controllable series compensation, and the method comprises the steps: collecting various monitoring data of each phase of each key node of a power distribution area in real time; obtaining each sub-time period of each monitoring data of each phase; acquiring a change correlation value and a deviation change characteristic value of various monitoring data of each phase in each sub-time period; comparing the deviation change characteristic values of various monitoring data between each phase and each of the other phases of the key node where the phase is located so as to obtain a dynamic change factor of the various monitoring data of each phase at each moment and obtain an influence weight and a voltage characteristic value of each phase of each key node at each moment; and compensating the voltage of the power distribution area based on a controllable series compensation technology. According to the method, the trigger angle in the voltage compensation process is accurately determined by accurately evaluating the voltage deviation of each key node, and the accuracy of voltage compensation of the power distribution area is improved.

The invention relates to the technical field of transformers, in particular to a high-stability three-phase oil-immersed three-dimensional wound core distribution transformer which comprises a transformer body, the transformer body comprises a shell, insulating oil is contained in the shell, and a set of three-dimensional wound cores arranged in an equilateral triangle three-dimensional mode are arranged in the shell. The conveying mechanism is located in the center of the three three-dimensional wound iron cores, low-temperature insulating oil is driven by a spiral twisted blade arranged in a shaft pipe of the conveying mechanism to achieve directional conveying, the low-temperature insulating oil makes full contact with high-temperature insulating oil gathered in the middle of the iron cores to complete heat exchange, and accumulated heat in the centers of the iron cores is efficiently taken away; the lifting device dissipates heat, a dispersing mechanism is further arranged in a matched mode, thermal insulation oil gathered around the three-dimensional wound core can be screwed out, continuous retention of the thermal insulation oil around the three-dimensional wound core is avoided, the heat dissipation efficiency of the three-dimensional wound core is further improved, and directional accurate heat dissipation is achieved through cooperation of the conveying mechanism and the driving mechanism.

The invention discloses a transformer area intelligent interconnection device with a fast charging function and a control method thereof. The device mainly comprises a first current transformation module, a second current transformation module, an all-solid-state energy storage battery, a direct current charging module and a collaborative energy manager (EMS). The DC sides of the two converter modules, the energy storage battery and the input end of the charging module are all connected to a common DC bus, and the AC sides are respectively connected to the low-voltage sides of different distribution transformers. Through uniform control of the EMS, three core functions are realized: 1, during normal operation, the two converter modules are controlled to carry out power mutual assistance and reactive power regulation, the transformer load is balanced, and the voltage of the transformer area is optimized; secondly, the direct current charging module is used for rapidly charging the electric vehicle, and an energy storage battery is used for stabilizing charging power impact; and 3, when the power grid fails, switching the converter module to a grid construction mode, and establishing an island micro-grid in cooperation with the energy storage battery to guarantee power supply of a key load. According to the invention, the integration level is high, and intelligent cooperation and autonomous operation of source-network-load-storage-charging of the transformer area are realized.

The invention provides a park-level flexible micro-grid system and a control method, and the system is based on an AC-DC hybrid power grid architecture, and comprises an electric energy router, a flexible interconnection device, a full-DC building and a DC bus. Wherein the system is connected with a plurality of power distribution areas through the electric energy router and the flexible interconnection device; the direct current bus is directly connected with a photovoltaic module, an energy storage unit and a direct current load of a power distribution area, and at least part of the direct current load is configured in a full direct current building; the system is also provided with an energy management system and a virtual power plant. According to the method, a system comprising cross-transformer-area flexible direct current interconnection and energy routing is established, dynamic routing and real-time sharing of power between transformer areas are achieved in combination with a corresponding control method, output is achieved through an energy storage reverse support main bus in the load peak period, power utilization continuity and reliability are ensured, and further, the power utilization efficiency is improved. The interconnection structure breaks through the island operation limitation of a traditional micro-grid, improves the flexibility and redundancy of the system, and helps to alleviate the overload problem of a distribution transformer.

The invention relates to a visual on-load voltage regulation tap switch control system, device and medium, which comprises a single chip microcomputer control circuit, the single chip microcomputer control circuit is connected with a signal processing circuit and a power supply circuit, and the signal processing circuit is connected with a voltage zero cross detection circuit, a voltage sensor and a data acquisition unit. The voltage zero-cross detection circuit, the voltage sensor, the data acquisition unit and the power supply circuit are all connected with the isolation reduction transformer, and the isolation reduction transformer is connected with the low-voltage side voltage of the distribution transformer; the single-chip microcomputer control circuit is further connected with a switch position sensor, a communication circuit, a switch drive circuit and a thyristor trigger circuit. The communication circuit is in communication connection with a remote computer through an RS485. The method has the advantages of realizing automatic voltage regulation conversion, stabilizing voltage, accurately setting visual parameters and realizing non-impact voltage regulation.

The invention discloses an electric vehicle charging station safety scheduling method and device based on PID-Lagrange deep reinforcement learning, and a medium, and relates to the technical field of intelligent power grid and artificial intelligence crossing. The method comprises the following steps: firstly, constructing a constrained Markov decision process model of a charging station, and defining a reward function containing a power grid tracking error or economic profit and a cost function based on distribution transformer physical capacity limitation and user satisfaction; in a deep reinforcement learning training process, a PID control mechanism is introduced to dynamically update a Lagrange multiplier, and a penalty weight is adjusted by using a proportion, an integral and a differential term of a security constraint violation quantity. The method solves the problems that when a traditional Lagrange relaxation method is used for processing hard constraints, multiplier oscillation is violent, and the convergence speed is low. Experiments show that the method can strictly ensure that the transformer is not overloaded while maximizing the operating benefit of the charging station, effectively considers the charging demand of a user, and has the advantages of stable convergence, high safety, strong adaptability and the like.