854 results about "Line loss" patented technology

The invention relates to the technical field of data processing, in particular to an intelligent analysis system for electric energy quality and read data, which comprises a data fusion module, a modeling module, an analysis module, a detection module, an optimization module and a control module. The data fusion module aligns data of an electric meter terminal, power grid monitoring equipment and an environment sensor through a sliding time window, and the analysis module calculates a power grid node loss transfer coefficient based on a graph neural network and fuses transformer no-load loss and line contact resistance parameters to generate a dynamic line loss evaluation matrix. And the detection module identifies the power consumption characteristic deviation degree through a random forest classifier, and generates a priority management strategy in combination with a multi-dimensional abnormal scoring model. The control module adaptively selects a communication protocol to execute a regulation and control instruction according to a network state, a heartbeat detection mechanism feeds back operation data of a governance device in real time, model parameters are driven to be iteratively updated, and dynamic cooperation of power supply quality optimization and line loss governance is achieved.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

The invention relates to the technical field of charging pile group load intelligent regulation and control, and particularly discloses a charging pile group load intelligent regulation and control method and system based on dynamic power equalization, and the method comprises the steps: collecting the oil temperature of a transformer in real time, calculating a total output power threshold value, and correcting the actual output power of a charging pile through combining the line loss and the equipment aging degree; a multi-dimensional load regulation and control decision matrix including user priority, equipment health degree and power regulation sensitivity is established, a dynamic power strategy is allocated through matrix weight, power allocation is inclined to high-priority users and health equipment, emergency power allocation and progressive readjustment during newly-added demands are supported, and the power regulation and control efficiency is improved. Impact of sudden power change on a power grid is avoided, system stability is ensured when newly added loads are accessed, refined intelligent regulation and control of loads of the charging pile group are finally realized, transformer safety, charging efficiency and user experience are improved, and power grid stability and resource optimization configuration are ensured.

The invention discloses a system for monitoring line loss rate abnormity of a power distribution network station area, and relates to the technical field of power distribution network monitoring. The line loss rate abnormity monitoring system for the power distribution network station area comprises a data acquisition module, a data processing module, a line loss rate calculation module, an abnormity identification and alarm module and an on-site troubleshooting and diagnosis module. Through integrating functions of intelligent electric meter data acquisition, power grid data collection and area division, data processing, line loss rate calculation, abnormity identification and alarm, on-site troubleshooting and diagnosis and the like, comprehensive and real-time monitoring of the line loss rate of the power distribution network station area is realized. And a regional line loss calculation model is established according to the characteristics of the power distribution network of each region, and the line loss rate can be monitored in real time, dynamically calculated and predicted, so that the efficiency and accuracy of line loss management are greatly improved. And the abnormity identification and alarm module can timely discover and give an alarm of line loss abnormity, provides probability analysis of abnormity reasons, and is helpful for operation and maintenance personnel to quickly respond and process.

The invention discloses a line loss treatment method based on low-voltage power grid multi-source data fusion, and belongs to the technical field of information, and the method comprises the steps: reconstructing a power supply path based on a power grid topological structure and a changed power access point; extracting reverse power flow characteristics of the frequency points for correcting a node impedance matrix of the current power supply path to obtain a power supply access point impedance parameter set; updating a power transmission path through the reverse power flow characteristic to obtain a power distribution characteristic; and a power grid topological structure, a current power supply path, a power supply access point impedance parameter set and power distribution characteristics are fused to construct a line loss distribution prediction model and identify a line loss abnormal region, and the line loss abnormal region is treated. Through implementation of the method and the device, the problem that the line loss treatment effect is poor due to the fact that the influence of factors such as transformer area topological structure change, power access point change and load fluctuation on the line loss is not considered in the prior art, and the predicted line loss distribution has a large error compared with the actual situation can be solved.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

The invention relates to the technical field of electric power fault diagnosis, and discloses a transformer area line loss abnormity diagnosis method and system based on a large model, and the method comprises the steps: collecting multi-dimensional data used for supporting transformer area line loss abnormity diagnosis, carrying out the cleaning, correlation fusion and standardization processing of the multi-dimensional data, and obtaining a comprehensive data set; a multi-layer diagnosis system based on a rule model, a random forest model and a large model is constructed, and the rule model identifies the simple and conventional anomalies of the transformer area according to a preset anomaly diagnosis rule based on the basic attribute data and the power operation state data in the comprehensive data set; the random forest model locates complex anomalies and novel anomalies by mining a coupling relationship among energy access condition data, external environment influence data and line loss fluctuation in the comprehensive data set; the big model carries out fusion verification on diagnosis results of the rule model and the random forest model, and outputs a final transformer area abnormity diagnosis result; and based on the diagnosis result, a differential loss reduction strategy adaptive to the actual data characteristics of the transformer area is recommended. The working efficiency is improved.

The invention discloses a high-low voltage linkage line loss comprehensive intelligent diagnosis system and method. The system comprises a distributed storage and high-performance calculation module, a data fusion and processing module, a multi-dimensional feature construction module, a high-low voltage linkage analysis module and a line loss intelligent diagnosis module. The method is used for carrying out line loss comprehensive intelligent diagnosis based on the system, and comprises the following steps: processing multi-source data in real time through the distributed storage and high-performance calculation module; bus-line-user archive data are fused, and a line loss index is calculated; extracting time, space and electrical three-dimensional characteristic indexes; performing high-low voltage linkage analysis based on the Pearson's correlation coefficient and the DTW distance; and fusing the system state model and the isolated forest anomaly detection model to output an anomaly diagnosis result. The method is suitable for accurate analysis and treatment of line loss in an intelligent power grid environment, and the efficiency and quality of line loss management can be improved.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention discloses an optimized operation method and system for a network-constructed converter. The minimum voltage amplitude deviation of a common coupling point, the maximum new energy power generation power, the maximum network-side power factor and the minimum line loss serve as nonlinear multiple targets in optimized operation of the network-constructed converter. The non-linear multi-target constraints comprise a common coupling point voltage constraint, a network side minimum power factor constraint, a converter output current constraint, a converter apparent power constraint, a photovoltaic output constraint and an energy storage state constraint; according to the priority of each target, determining a target function and a constraint condition of each priority target; according to the priority order, the optimal solutions of the target functions of the priority targets under the constraint condition are solved in sequence, and the optimal solutions serve as input data of the proxy model; and according to the output data of the agent model, optimizing the operation of the network construction type converter. Cooperative control of convergence speed, global optimization capability and dynamic adaptability is considered, and power supply quality and operation efficiency of an active power distribution area are improved.

The invention discloses a power distribution network switching station on-line comprehensive monitoring method based on the Internet of Things, and aims to solve the problems that a traditional switching station is poor in monitoring real-time performance, insufficient in fault early warning capability and the like. The method is realized based on an intelligent monitoring and coping system of an Internet of Things architecture, and through cooperation of multiple modules of equipment operation state monitoring, environment monitoring, security monitoring, intelligent linkage control, alarm management, historical data analysis and the like, an alarm linkage intelligent control closed-loop cooperation algorithm and a time sequence analysis algorithm are combined. And comprehensive perception and intelligent analysis of the operation state, environmental parameters and the like of the switching station equipment are realized. The system can monitor key parameters such as cable head temperature and partial discharge in real time, supports intelligent linkage processing of abnormal scenes such as harmful gas exceeding and water immersion, and predicts the equipment trend through historical data mining. The technology improves the equipment operation safety and operation and maintenance efficiency, reduces the line loss, enhances the power supply reliability, lays a foundation for constructing a novel power system, and promotes the digital transformation of a power distribution network.

The invention discloses a low-voltage power grid voltage fluctuation control method and system based on artificial intelligence. The method comprises the steps of obtaining a multi-source data set from a power grid; if the line loss estimation deviation in the multi-source data set exceeds a preset deviation range, iteratively optimizing a preset initial resource allocation scheme according to a multi-objective optimization algorithm to obtain a computing resource allocation scheme; obtaining final line loss distribution characteristics through task grading and parallel decoupling calculation according to a calculation resource allocation scheme; and generating a generator set output adjustment scheme according to the final line loss distribution characteristics, and regulating and controlling the power grid. According to the artificial intelligence-based low-voltage power grid voltage fluctuation control method and system provided by the invention, power grid data are integrated, line loss distribution is analyzed, and a voltage fluctuation trend is predicted, so that the output of a generator set is regulated and controlled, and voltage stabilization and line loss control are realized; therefore, the problem that effective control and line loss treatment are difficult to carry out on the power grid voltage fluctuation can be solved.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention is suitable for the technical field of integrated energy microgrid scheduling, and provides a power distribution network random distributed robust optimization scheduling method of an integrated energy microgrid group, comprising the following steps: constructing a DN structure comprising a plurality of microgrids; the DN day-ahead scheduling optimization model containing the IEMGs pays attention to operation cost and constraint conditions; for the DN, the problem of non-convex nonlinear optimization including line loss is solved; for the IEMG, the model is used for processing a convex optimization problem considering WT and PV output power uncertainty; power exchange is carried out between the DN and the IEMG through a connecting line, so that a double-layer model framework is constructed to analyze the system; the solving process is a double-layer optimization iteration process, an outer layer obtains an interaction power value between the DN and the IEMG by using an ATC method, and an inner layer uses Camp; the CG method solves the problem of random distribution robust optimization of IEMG. According to the method, the carbon emission and the operation cost of the system can be effectively reduced, meanwhile, stable operation of the DN is ensured, and a theoretical basis is provided for efficient utilization of ammonia energy in the DN.

The invention discloses a distributed intelligent power distribution method and system for a power network, and belongs to the technical field of power distribution control, and the method comprises the steps: collecting multi-dimensional electrical parameters of a power generation node in real time, and carrying out the dynamic power monitoring at a feeder terminal of a transformer substation through an edge calculation node; executing line loss calculation and load prediction in the power distribution unit, generating a demand feedback instruction to a feeder terminal node, and triggering an adaptive power distribution strategy; and on the basis of a digital twin system of a regional dispatching center, credibility verification is carried out on operation decisions of all edge nodes, and global optimization compensation control is executed. The method has the remarkable advantages in the aspects of optimizing power distribution, reducing line loss, improving system stability and safety and achieving efficient energy utilization, and intelligent management of a power network becomes possible.

The invention provides a line loss reason analysis and positioning method and system based on artificial intelligence and a medium, and relates to the technical field of power system management.The method comprises the steps that when the bus loss rate of a target station area exceeds a preset line loss threshold value, an excitation sequence is issued to a general meter, a circuit breaker and a user electricity meter of the target station area, respectively obtaining corresponding transient response data, and further constructing a dynamic topological graph of the target transformer area; and a topological structure is optimized by combining a graph neural network and an attention mechanism, and abnormal connection points are detected. And determining downstream associated user electric meters, and calculating the power utilization abnormity score based on the load data. And injecting a steep front traveling wave signal into the electric meter with the highest score to obtain a reflection characteristic parameter. And performing iterative analysis by using a Double DQN reinforcement learning algorithm, determining a fault point, a line loss reason type and confidence, and realizing efficient and accurate line loss reason positioning and analysis. According to the invention, efficient and accurate troubleshooting of line loss reasons is realized.

The invention relates to the technical field of power data analysis, in particular to a line loss abnormity associated mutual inductor insulation fault positioning method. Collecting multi-source operation data of a target station area, and obtaining a datum line loss rate sequence, a residual component sequence and an auxiliary feature vector based on the multi-source operation data; according to the reference line loss rate sequence, obtaining a comprehensive sensitivity vector representing the influence degree of the mutual inductor on the line loss; obtaining a preliminary diagnosis result according to the comprehensive sensitivity vector, the residual component sequence and the real-time load rate; performing spatial logic verification according to the preliminary diagnosis result and the electrical topological graph of the transformer area to obtain a fault positioning list; generating a correction amount according to the fault positioning list and the corresponding comprehensive sensitivity vector, and correcting the line loss rate according to the correction amount; and meanwhile, feedback optimization is performed on the machine learning model according to complete data of the diagnosis. According to the invention, accurate and efficient diagnosis and correction of the line loss abnormity of the target station area can be realized.

The invention relates to a source network load storage system operation control method, system and device and a medium. The operation control method comprises the steps of generating a predicted load curve of a preset time period based on historical load data; according to the predicted load curve, each power generation operation characteristic and each power generation cost, determining a power supply power generation plan; constructing an energy storage charging and discharging model according to the energy storage loss, the charging and discharging efficiency and the electricity price curve; constructing a power grid power flow optimization model according to the power grid line loss, the line safety constraint and the line balance constraint; and obtaining a power generation scheme of each power supply, a charging and discharging scheme of the energy storage system and a power flow direction control scheme in a power grid which meet preset target conditions, and controlling the operation of the source grid load storage system. According to the scheme, deep coordination of load source network storage is realized, the energy utilization efficiency is improved, the system stability is enhanced, the power generation cost is reduced, and the reliability, economy and flexibility of a power system are improved.

The invention discloses a power transmission line intelligent management and control method and system based on power distribution data feedback, and relates to the technical field of power transmission line management and control, and the method comprises the steps: integrating multi-source data, constructing a historical environment-electrical coupling feature matrix, extracting time sequence features through a long and short term memory network, and capturing a spatial distribution rule through combining a convolutional neural network; and establishing a dynamic line loss prediction model to simulate minimum line loss values in different environment scenes. And detecting an abnormal time period through an isolated forest algorithm, screening key damage factors in combination with Pearson correlation analysis and SHAP value analysis, dynamically generating and executing a loss reduction strategy priority event, and optimizing model parameters by using error back propagation and reinforcement learning. And finally, generating a typical environment scene loss reduction case library based on the optimization model, and simulating strategy effectiveness under an extreme condition through a digital twin technology to form a reusable decision rule set, thereby improving the operation efficiency of a power transmission line and the reliability of power supply.

The invention relates to the technical field of electric power, in particular to a line loss abnormity diagnosis method based on electric quantity fluctuation analysis. The method comprises the following steps: acquiring an electricity user and electricity data; calculating a power supply quantity change rate and a power consumption change rate according to the daily power supply quantity of the transformer area and the fluctuation degree of the daily power consumption of the transformer area, and further judging a line loss abnormity type; if the line loss abnormity type is user side abnormity, screening the electricity users, and obtaining a user side abnormity type according to the screened electricity users; if the line loss abnormity type is power supply side abnormity, calculating a power supply quantity multiple, and performing fault attribution to obtain a power supply side abnormity type; and explaining the two exception types through the SHAP so as to generate a line loss exception diagnosis report. In this way, the adaptability and robustness of the diagnosis method in a novel scene containing a distributed power supply and the like can be enhanced, the interpretability of a diagnosis result is further improved, and a clear and credible feature basis is provided for field check.

The invention relates to the technical field of power distribution lines, in particular to a power distribution line abnormal line loss analysis and treatment method and system, and the method comprises the following steps: synchronously extracting real-time monitoring data and historical data based on a power distribution line network, carrying out the data alignment and data cleaning, screening features associated with current leakage and voltage drop, and carrying out the analysis and treatment of the abnormal line loss of the power distribution line. Comprising load change rate and temperature fluctuation. According to the invention, through application of real-time and historical data analysis, the detection and management efficiency of the abnormal line loss of the distribution line is improved, the accuracy of data processing is improved through synchronous extraction and data alignment, early recognition of abnormal modes is allowed, the possibility of energy loss and unplanned power failure is reduced, and through adoption of Fourier transform and wavelet transform, the detection efficiency of the abnormal line loss of the distribution line is improved. According to the method, abnormal signals are accurately recognized, the analysis capability of the signals is enhanced, the accuracy of abnormal detection is improved, fault positioning and maintenance plan making are effectively optimized through combination of the GIS system, and the pertinence and efficiency of maintenance work are remarkably improved.

The invention discloses a power line loss assessment method and system based on load change, relates to the field of theoretical line loss analysis, and provides the following scheme that the method comprises the steps that intraday time sequence load data of a power distribution network line are acquired, and the load data comprise active power, current and timestamp information; and dividing the load data into a plurality of power utilization periods, including a deep valley period, an early peak period, a flat peak period, a late peak period and a low valley period, based on historical power utilization rule analysis and in combination with active power fluctuation characteristics. Through effective peak valley point determination and fluctuation frequency energy coefficient construction, current effective value and line loss secondary correction in a high fluctuation period is executed, line loss assessment sensitivity in complex periods such as an early peak and a late peak is improved, load power curve integral and material electromagnetic thermal response parameters are combined, extra thermal effect of fluctuation energy on line loss is quantified, and the line loss assessment accuracy is improved. The capacity of capturing hidden loss caused by load fluctuation is improved, and the line loss estimation deviation in a high fluctuation period is reduced.

The invention relates to a line loss positioning method and system based on artificial intelligence and big data analysis, and the method comprises the following steps: S1, collecting voltage, current, power and timestamp data from an intelligent ammeter, a sensor and historical records, and expanding a data set through employing a data enhancement and generative adversarial network; s2, features are extracted from the expanded data set obtained from the generator by fusing GNN and Transform, the features are fused into a comprehensive feature vector, and a training data set is constructed; s3, constructing a line loss prediction model; s4, on the basis of the line loss prediction model, data is obtained in real time for prediction and analysis, and line loss abnormal points are obtained; s5, based on the abnormal points, identifying potential reasons of the line loss abnormal points in combination with statistical analysis and frequency analysis; and S6, marking the position and time of an abnormal point based on the power grid topological graph, thereby facilitating field inspection and processing by operation and maintenance personnel. According to the invention, artificial intelligence and big data analysis are combined to realize line loss positioning, and the line loss positioning capability and accuracy are effectively improved.

The invention discloses a low-voltage power distribution network line loss prediction method, a model training method and a related device, and the model training method comprises the steps: (1) employing a neural network structure combining a time sequence feature extraction module and a multi-scale feature extraction module, and capturing the long-term trend of photovoltaic output and load fluctuation, line loss change modes under different time scales are effectively modeled; (2) a deep feature extraction module introduces a deep feature extraction mechanism with mutually different architectures, and the expression ability and generalization performance of the model to a complex nonlinear relationship are improved; (3) a crown porcupine optimization algorithm is adopted to perform joint optimization on model parameters and structures, efficient search can be realized in a high-dimensional non-convex space, local optimum is avoided, and prediction precision and stability are improved; based on the line loss prediction model, the line loss prediction method provided by the invention can be directly used for power distribution network operation situation evaluation and energy efficiency analysis, and has good engineering application value under the background of wide distributed photovoltaic access.

The invention relates to the technical field of three-phase electric energy meters, and discloses an equivalent apparent power implementation method, device and equipment of a three-phase electric energy meter and a medium. Comprising the following steps: acquiring cyclic wave voltage sampling data and cyclic wave current sampling data of each phase of the three-phase electric energy meter in at least one cyclic wave; calculating the equivalent voltage of the three-phase electric energy meter according to the cyclic wave voltage sampling data; calculating the equivalent current of the three-phase electric energy meter according to the cyclic current sampling data; and calculating the equivalent apparent power of the three-phase electric energy meter based on the equivalent voltage and the equivalent current. According to the invention, the accuracy of apparent electric energy charging and line loss calculation can be improved.

The invention relates to a low-voltage transformer area line loss abnormity cause diagnosis method and device, computer equipment and a readable storage medium, and relates to the technical field of power systems. The method comprises the following steps: acquiring file parameters, topological data and current operation data of a to-be-diagnosed transformer area; the to-be-diagnosed transformer area clustering algorithm is determined from all types of transformer areas; inputting the archive parameters, the topological data and the current operation data into the trained line loss anomaly cause prediction model to obtain a line loss anomaly cause prediction result of the to-be-diagnosed transformer area; wherein the trained line loss anomaly cause prediction model is obtained by training a pre-constructed neural network based on archive parameters, topological data and historical operation data of the plurality of transformer areas and line loss anomaly cause labels corresponding to the historical operation data. By adopting the method, the accuracy of line loss abnormal cause diagnosis can be improved.

The invention provides a power system electric quantity compensation method and system based on abnormal gateway analysis, relates to the technical field of abnormal gateway analysis, and aims to solve the problems that the prior art is not suitable for complex scene coverage and poor in dynamic applicability, real-time performance and robustness, and system data is directly called and analyzed, so that the power consumption is low. Data relevance is insufficient, reliability and accuracy of analysis results are not high, and efficiency is low. The method comprises the following steps: acquiring electricity meter bottom data of a related gateway from a constructed power system model in real time, and inputting the electricity meter bottom data and telemetering data into an analysis model for correlation analysis; according to a correlation analysis result, a suspected abnormal gateway is judged; acquiring equipment elements with abnormal line loss rates of the corresponding gateways from the power system model, and further judging the equipment elements as abnormal gateways; and calculating the compensation electric quantity according to the error ratio of the two data and the line loss rate of the corresponding equipment. According to the invention, problems in the prior art are solved, effective electric quantity compensation is realized, and the operation efficiency, accuracy and transparency of a power system are improved.

The invention relates to the technical field of visualization, in particular to a visualization method and system for an energy storage cabinet and a storage medium. The method comprises the following steps: acquiring data of the energy storage cabinet, acquiring image information based on the data, and determining an operation visualization state of the energy storage cabinet; analyzing the stability gradient attenuation trend of the energy storage cabinet by using the visual state data, predicting the abnormal interaction influence state of the battery of the energy storage cabinet, and evaluating the degradation condition of the battery; estimating the health degree of the battery, detecting the unbalance condition of power supply, and evaluating the overload degree of the dynamic load of the transmission line; predicting the loss increase trend of the power transmission line; the heat effect accumulation condition of the energy storage cabinet is estimated based on the line loss condition, the deterioration trend of the energy storage cabinet is predicted by combining the heat effect and the stress abnormal condition, and the service life of the energy storage cabinet is estimated. According to the method, the energy storage cabinet is visually optimized, so that the fault identification of the energy storage cabinet is more accurate.

The invention discloses an electric energy quality loss test method, system and device and a medium. The method comprises the following steps: acquiring three-phase voltage data and three-phase current data; calculating the equivalent total loss power in the power distribution network line through the apparent power value, the voltage and the resistance based on the collected voltage and current data; and based on the equivalent total loss power, in combination with the power factors of different line losses, carrying out line loss subentry decoupling to obtain additional line losses caused by different electric energy quality problems. According to the invention, extra loss of the line caused by electric energy quality can be accurately measured, and line resistance does not need to be measured.

The invention provides a non-technical line loss state evaluation method based on multi-scale spatial-temporal feature fusion in a low-voltage distribution network environment, and relates to the technical field of electric power big data analysis and intelligent operation and maintenance of a distribution network. According to the invention, a fusion architecture based on a multi-scale time convolution network and a long short-term memory network is constructed; extracting multi-scale spatio-temporal characteristics of a user from instantaneous electricity utilization abrupt change to a periodic load rule through an MSTBlock unit; designing a cluster balance constraint mechanism to ensure that rare and key non-technical line loss abnormal early warning signals are not covered by mass normal power utilization data; according to the data scale, adaptively selecting a graph segmentation or spectral clustering integration strategy to output a clustering label, and mapping the clustering label into a user power consumption behavior evolution track; according to the method, the power utilization abnormal level can be identified from the original load signal with random fluctuation interference, and the troubleshooting priority is calculated in combination with the transformer area correlation analysis, so that the accuracy and interpretability of the non-technical line loss unsupervised evaluation decision of the power distribution network are remarkably improved.