940 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 monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

The invention relates to the technical field of power system regulation and control, in particular to a mutual aid regulation and control method and system for multiple power distribution transformer areas considering capacity margin, and the multiple power distribution transformer areas comprise N low-load power distribution transformer areas and M high-load power distribution transformer areas which are connected through N + M interconnection devices; the load rate of the low-load power distribution area is less than or equal to a first load rate threshold, and the load rate of the high-load power distribution area is greater than a second load rate threshold; the regulation and control method comprises the following steps: constructing a comprehensive constraint condition based on an economic operation constraint condition and a mutual aid capacity margin constraint condition of a power distribution area; constructing a multi-objective function based on the power distribution area loss objective function and the economic benefit objective function; building a target optimization model through a comprehensive constraint condition and a multi-target function; and obtaining an optimal power distribution scheme based on the target optimization model, and performing hierarchical cooperative control execution. Through the regulation and control method and system, the problem of insufficient load sensitivity can be effectively solved.

The invention relates to the technical field of power distribution network risk early warning, in particular to a power distribution network abnormal risk early warning method based on a multi-mode space-time large model. According to the method, a mapping relation between multi-dimensional influence factors such as a source load space-time matching characteristic, voltage sensitivity, a net load peak value, a power distribution network topological structure and meteorological data and a new energy power distribution network abnormal risk is deeply mined based on a Lama large model; and respectively constructing independent optimization branches of power distribution network node voltage out-of-limit prediction and transformer area distribution transformer weight and overload prediction, and balancing a synchronous optimization target of prediction of different abnormal risk events through a multi-task weighted loss function. The method comprises the following steps: aligning text information and spatio-temporal information of historical marking abnormal risks of a new energy power distribution network by adopting a spatio-temporal instruction fine tuning model, dynamically adjusting model parameters, and generating an abnormal risk auxiliary prediction threshold in combination with a risk judgment threshold; therefore, occurrence moments and positions of abnormal risks such as node voltage out-of-limit of the new energy power distribution network and heavy and overload of the distribution transformer in the transformer area can be accurately predicted.

The invention relates to the related technical field of distribution transformer insulation aging, in particular to a frequency domain diagnosis method, system and device for distribution transformer insulation aging and a medium, and the method comprises the steps: carrying out the frequency response test of a to-be-tested transformer, and obtaining a first frequency response curve; performing a frequency response simulation test on a transformer equivalent circuit model pre-constructed based on the to-be-tested transformer to obtain a second frequency response curve; constructing a target function based on the deviation of the first frequency response curve and the second frequency response curve; taking element parameters in the equivalent circuit model of the transformer as position vectors, taking the objective function as a fitness function, and adopting a multi-objective particle swarm algorithm to solve the element parameters to obtain optimal values of the element parameters; respectively comparing the optimal values of the element parameters with corresponding normal values to obtain insulation aging degrees; according to the method, the insulation aging degree can be obtained by comparing the element parameters obtained through optimization with the normal unaged element parameters.

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 relates to a distributed photovoltaic transformer area metering data abnormity tracing method, belongs to the field of intelligent power distribution networks, and solves the problem of low metering abnormity user positioning efficiency caused by topological information loss of a low-voltage photovoltaic transformer area. Comprising the following steps: acquiring electrical data of a distribution transformer and each user when a distributed photovoltaic transformer area has no photovoltaic output at night, and constructing a transformer area trunk line approximate topology; constructing an inter-node final adjacency matrix between nodes on the basis of the acquired voltage data of each user in the afternoon photovoltaic high output time period of the transformer area; clustering based on the final adjacency matrix among the nodes to divide the users into a plurality of user clusters; constructing a cluster power curve for each user cluster, and calculating the fluctuation consistency between the voltage of each user in the cluster and the cluster power curve of the cluster to which the user belongs based on the approximate topology of the main line of the transformer area so as to identify a suspected abnormal cluster; and performing node-level identification on the suspected abnormal cluster to obtain an abnormal metering user. Automatic traceability and accurate positioning of abnormal measurement data are realized.

The invention relates to a self-adaptive data acquisition method and system for an intelligent distribution transformer monitoring unit, and the method comprises the steps: monitoring a plurality of operation parameters in a power distribution network in real time through the intelligent distribution transformer monitoring unit; classifying various monitored operation parameters into conventional monitoring data and abnormal monitoring data according to the operation state of the power distribution network, analyzing the current state of the power distribution network based on the real-time monitoring data, and judging whether the power distribution network is in a normal, load or abnormal state; constructing a distribution transformer monitoring prediction model for identifying the operation state of the power distribution network, predicting potential faults or abnormal conditions, and determining the frequency and type of data acquisition according to the prediction result; under the normal operation state of the power distribution network, various operation parameters in the power distribution network are acquired by adopting low-frequency data; when the load change of the power distribution network is large or a fault is about to occur, the data acquisition frequency is improved; when a fault or abnormity occurs, the highest-frequency data acquisition mode is started immediately, and detailed power distribution network data are collected in real time.

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 relates to the technical field of power grid bearing capacity evaluation, in particular to a method and system for evaluating the bearing capacity of a power distribution network. The method comprises the following steps: obtaining a noise reduction power distribution network operation data set; performing three-phase imbalance index calculation based on the noise reduction power distribution network operation data set to obtain three-phase imbalance characteristic data; performing wavelet transform decomposition on the noise-reduced operation data set of the power distribution network to obtain photovoltaic output fluctuation characteristic data; associating the photovoltaic output fluctuation characteristic data with the three-phase imbalance characteristic data to obtain a photovoltaic-imbalance mapping relation model; performing electric-thermal coupling modeling on the distribution transformer winding based on the photovoltaic-unbalanced mapping relation model to obtain a thermodynamic response model of the distribution transformer; and performing finite element thermal field simulation on the thermodynamic response model of the distribution transformer to obtain winding temperature distribution data. By accurately quantifying the influence of the intermittent output of the photovoltaic inverter on the three-phase imbalance of the distribution transformer, the defect that the traditional method is difficult to accurately evaluate is overcome.

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 relates to a distribution network topology analysis method and system, and the method comprises the steps: carrying out the metadata analysis of a distribution line SVG file, and constructing an undirected graph, a subgraph and a connected subgraph; traversing the sub-graph by using a breadth-first search algorithm with a specified root node as a starting point, and marking a hierarchy for each node according to the distance from the root node to the current node; carrying out edge pruning based on the marked hierarchy, and converting the undirected graph into a directed graph according to a hierarchical relationship; on this basis, distribution transformer-switch relation analysis is carried out to form a basic information table of a switch to which a distribution transformer belongs, and connection line analysis is carried out to obtain a simple line connection path. According to the method, it is ensured that the sub-graphs conform to the radial structure of the power distribution network through hierarchical pruning, connection of the same hierarchy is removed, formation of an annular structure is prevented, detailed line connection paths and simple line connection paths are formed, and contact points between lines can be checked quickly and conveniently.

The invention relates to the technical field of transformer internal defect diagnosis, in particular to a distribution transformer internal defect diagnosis method and device based on virtual differential current. Comprising the following steps: synchronously acquiring data; calculating an actually measured differential current; establishing a differential current model; according to a differential current model, calculating a deviation value between the actually measured differential current of each phase and a model prediction value, and taking the deviation value as the virtual differential current; carrying out filtering processing on the virtual differential current; and judging defects. According to the method, a differential current model associated with winding impedance and excitation branch parameters is constructed based on operation data when the transformer has no defects, so that the model is bound with physical topological characteristics of the transformer, and virtual differential current (deviation between a measured value and a model predicted value) can reflect actual state change of the winding in the transformer; and the physical relevance and reliability of defect diagnosis are enhanced.

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 distribution transformer on-line health diagnosis system and method. The distribution transformer on-line health diagnosis system comprises a low-voltage transformer area, a cloud platform and a big data application layer. The on-line health diagnosis of the distribution transformer comprises the steps of data acquisition, analysis, storage, statistical model establishment for prediction and artificial intelligence deep learning for prediction. Various data in the operation process of the transformer are collected in real time through the terminal data collector, meteorological indexes such as the temperature and the humidity of the operation environment of the transformer are combined, processing and analysis are carried out through combination of statistical model analysis and an artificial intelligence algorithm, transformer fault diagnosis is carried out, and the operation condition of the transformer is monitored. And once it is found that the operation state of the transformer has a deterioration trend, early warning is given out in advance, and early warning of the sub-health state of the transformer is achieved.

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 discloses a high-overload distribution transformer design method based on geometric dimension reduction and multi-objective optimization, and the method comprises the steps: simplifying the three-dimensional structure of a transformer based on the axial symmetry characteristics of the transformer, building a two-dimensional axial symmetry model, and reserving the key geometric characteristics of an iron core, a winding and transformer oil; based on the two-dimensional axisymmetric model, constructing an electromagnetic-flow-heat multi-physics field coupling simulation model, calculating electromagnetic field distribution, and simulating oil flow and temperature field distribution in the transformer; based on the finite element simulation data and an improved four-vector multi-objective optimization algorithm, establishing a multi-objective optimization model, carrying out collaborative optimization on transformer structure parameters, and outputting a Pareto optimal solution set; and determining an optimal design scheme of the electromagnetic structure of the transformer according to the Pareto optimal solution set. Through the improved four-vector multi-objective optimization algorithm, the local development and global exploration capabilities are comprehensively improved, and the accuracy and diversity of the optimization scheme are improved.

The invention discloses a distribution transformer heavy load rate prediction method and system based on multi-source data fusion, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining transformer operation data of a target region, and constructing a knowledge graph; entities and features having causal paths with the heavy load event of the target transformer are extracted from the knowledge graph, and a feature tensor is generated; performing graph embedding on the knowledge graph through a graph neural network, and inputting the graph embedding and feature tensor into a time sequence prediction model to construct a joint prediction network; performing simulation intervention on the input variables based on a causal reasoning method, and evaluating the influence intensity on a prediction result; according to the influence intensity of simulation intervention, outputting a heavy load rate prediction value of the target transformer in a future time period, and generating a main causal path causing the prediction value to change; according to the method, accurate and explainable prediction is realized through multi-source data fusion and the causal knowledge graph.

The invention provides an online monitoring and early warning method for an abnormal state of a power distribution area, which comprises the following steps of: firstly, acquiring voltage and current sequence data of the power distribution area from a historical database, and acquiring a three-phase voltage value and a current value in real time; calculating a Pearson's correlation coefficient between the voltage data and the current data, and judging the connection group of the distribution transformer according to a preset threshold value and a judgment condition; feature extraction is carried out on the collected three-phase voltage and current values to obtain voltage features and current features, and a voltage feature strengthening value is obtained by combining the connection group; and finally, constructing a total scoring function, scoring the voltage features, the current features and the voltage feature strengthening values, comparing with a preset threshold value, judging whether the power distribution area has a fault or not, and realizing online monitoring and early warning of an abnormal state. The method is deeply focused on how to improve the diagnosis accuracy of the high-voltage side open-phase fault, and the false alarm rate and the missing report rate of online detection of the power distribution area are effectively reduced through low-frequency measurement data.

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 provides a power distribution area three-phase impedance matrix identification method based on a self-supervised graph attention network, and relates to the technical field of power distribution network impedance matrix identification. According to the method, a node set including a transformer area distribution total secondary side and at least one user electric meter of a to-be-identified power distribution transformer area can be constructed, and a three-phase impedance matrix of the to-be-identified power distribution transformer area is identified based on a trained power distribution transformer area three-phase impedance matrix identification model by using feature vector data, collected in real time, of each node, so that the identification accuracy of the three-phase impedance matrix of the to-be-identified power distribution transformer area is improved. The problems that an existing method depends on known network topology, physical law fitting is insufficient and the like are solved, the method is suitable for a power distribution area with an unknown topological structure or dynamic change, and the requirements for refined operation and control of a power distribution network are met.

The invention discloses a transformer area flexible load robust optimization method and device, electronic equipment and a storage medium, relates to the field of low-voltage transformer area flexible load scheduling, and realizes gradient utilization of flexible loads and improves the distribution transformer capacity utilization rate through a multi-time scale two-stage robust optimization model and considering distributed photovoltaic uncertainty. The method comprises the steps that an air conditioner load model, an electric vehicle charge state model and an energy storage charge state model are adopted to construct a transformer area flexible load model; establishing a multi-time-scale two-stage robust optimization model based on a robust optimization algorithm and the transformer area flexible load model; solving the day-ahead stage of the robust optimization model by adopting a column constraint generation algorithm to obtain an air conditioning load, energy storage station and charging station scheduling plan of the day-ahead stage; and based on the scheduling plan of the air conditioner load, the energy storage station and the charging station in the day-ahead stage, solving the intra-day stage of the robust optimization model by adopting a solver to obtain a real-time charging and discharging power scheduling plan of the energy storage equipment and the electric vehicle.

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.