653 results about "Line loss" patented technology

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 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 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 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 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 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 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 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.

The invention provides an electricity stealing detection method based on line loss, non-intrusive monitoring and dynamic time warping, and the method comprises the following steps: analyzing the electricity consumption of a user in a power distribution area with an abnormal daily line loss rate, and generating the line loss evidence of a suspected user; comparing the load data of the suspected user obtained based on the non-intrusive load monitoring with the power consumption behavior of the suspected user, and generating a contradictory evidence that the power consumption behavior has contradictions; based on the current waveform library of the electricity stealing behaviors, analyzing the similarity between the user current waveform of the suspected user and an electricity stealing waveform template recorded in the current waveform library, and generating waveform matching evidence; and based on the line loss evidence, the contradiction evidence and the waveform matching evidence, weighting and fusing to obtain the comprehensive score of the suspected user, and screening the target electricity stealing user. The electricity larceny detection method based on line loss, non-intrusive monitoring and dynamic time warping has the advantages of high efficiency, high precision, low cost and strong interference capability, and can effectively identify electricity larceny users.

The invention provides an intelligent power grid multi-sensor fusion line loss analysis system and method, and relates to the technical field of intelligent power grids, and the system comprises a line loss analysis matrix drawing module which draws a line loss analysis matrix and sets a line loss deviation value; the dynamic analysis module is used for determining line loss abnormal offset, and when the line loss abnormal offset exceeds a normal line loss preset threshold range corresponding to the power transmission line, a dynamic analysis mechanism is triggered: differential data calibration is carried out based on a line loss analysis matrix and the line loss offset; the dynamic distribution module is used for dynamically distributing line loss regulation gain factors of each monitoring section; and the line loss calibration module is used for performing line loss precision enhancement control on the intelligent power grid through each edge calibration execution unit deployed in each monitoring section. According to the method and the device, the technical problem of inaccurate line loss analysis caused by difficulty in accurately positioning line loss abnormal sections and causes in the prior art is solved, and the line loss problem of the power grid is accurately identified through multi-sensor monitoring and data fusion.

The invention discloses a line loss analysis method and system based on transformer area topology identification and terminal equipment. The line loss analysis method comprises the steps of obtaining first power data and load data of a plurality of ammeters and second power data of a plurality of transformers in a transformer area; determining a target transformer to which each electricity meter belongs based on the first power data and the second power data, if each electricity meter belongs to the same target transformer, determining a phase group to which each electricity meter belongs based on the first current data and the second voltage data, and determining a branch box group to which each electricity meter belongs based on the first voltage data between the electricity meters, determining an initial topology result based on the target transformer, the phase group and the branch box group; and checking the initial topology result based on the load data to determine a target topology result, and performing line loss analysis based on the target topology result to obtain a transformer area line loss result. According to the invention, the accuracy of topology identification and line loss analysis can be improved.

The invention discloses a power distribution area line loss regulation and control method and system based on light storage cooperation and multi-time scale optimization. Comprising the following steps: data acquisition: acquiring photovoltaic output, energy storage SOC, node voltage and line current data in real time; prediction calculation: predicting photovoltaic and load, and generating regulation and control boundary conditions; optimization decision making: running day-ahead optimization regulation and control and intra-day multi-time scale regulation and control strategies, and generating a regulation and control instruction; and performing control: realizing rapid power adjustment through an energy storage converter and a photovoltaic inverter. A multi-factor coupling line loss model is established, an improved particle swarm algorithm is designed to solve an optical storage scheduling strategy, and data driving and a physical mechanism model are fused to improve prediction precision. And a second-minute-hour collaborative optimization architecture is further constructed, so that rapid voltage stabilization and energy space-time transfer are realized. The method can effectively reduce the line loss rate of the transformer area, and is suitable for the energy-saving and loss-reducing scene of the power distribution network with high-proportion new energy access.

The invention relates to a light driving control system and method based on a 48V system and electronic equipment, and relates to the field of vehicle control. The system comprises a power supply module, an information acquisition module, a central processing module, a communication module and an execution module, the power supply module is used for reducing 48V voltage input by a whole vehicle to obtain preset voltage, and the information acquisition module is used for receiving a light control instruction of the whole vehicle and acquiring a working state of a lamp; the central processing module processes collected data and issues an instruction to the execution module, the communication module achieves information interaction with the whole vehicle and the load lamp, and the execution module provides power supply and drive control for the external load lamp. The system can solve the challenges of a traditional 12V system in the aspects of power output, energy loss, line load and the like, reduces the current intensity, line loss and heat productivity through a 48V system, prolongs the service life of a lamp and a controller, improves the autonomous controllability and the supply chain stability through a domestic chip scheme, and reduces the system cost.

The invention belongs to the technical field of cable equivalent length measurement and calculation, and particularly relates to a transformer area cable equivalent length measurement and calculation method combining topology and line loss constraint, which comprises the following steps: S1, data aggregation and preprocessing; s2, carrying out topology analysis on the power grid of the transformer area; s3, determining a line loss constraint condition; s4, constructing an equivalent length measuring and calculating model; and S5, model verification and application. According to the transformer area cable equivalent length measuring and calculating method combining the topology and the line loss constraint, data aggregation and preprocessing are carried out by using an RPA technology, multi-source data can be automatically captured, analyzed and integrated, cross-system data integration is realized, the data collection efficiency is greatly improved, and the labor burden is reduced; and meanwhile, the data is subjected to cleaning, denoising, missing value filling and other operations, so that the accuracy and integrity of the data are ensured, high-quality data support is provided for subsequent analysis and model training, measurement and calculation errors caused by data problems are avoided, measurement and calculation results are more reliable, and the overall efficiency and quality of data processing are effectively improved.

The invention discloses a CAEGRU-based intelligent power grid electric quantity abnormity detection method, system and device and a storage medium, and belongs to the field of electric quantity abnormity detection, and the method comprises the steps: obtaining the electric quantity data of a target station area user from a power grid metering center database, and carrying out the cleaning and preprocessing; on the basis of the preprocessed data, according to a transformer area topological structure, the length of a transformer area line loss selection sequence is calculated through main table data and sub-table data, and a feature set of power utilization characteristics is constructed; and constructing a CAEGRU model based on a convolutional automatic encoder and a gating cycle unit, training the feature set, and outputting an electric quantity anomaly detection result. According to the method, the CAE and the GRU are combined to construct the features, the features and rules of the power data are comprehensively reflected, and the anomaly detection precision is improved. Compared with a traditional method, the method has higher robustness when processing complex power data. The sliding window technology realizes real-time detection of electric quantity data of the smart power grid, and potential abnormal problems are found in time.

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 discloses a power distribution network line loss intelligent sensing and tracing decision-making method and system, and belongs to the technical field of power distribution network operation and management.The power distribution network line loss intelligent sensing and tracing decision-making method comprises the steps that multi-source heterogeneous data are connected, timestamp interpolation is carried out, and data are obtained; associating the position and GIS coordinates of the electrical equipment in the electrical network through the unique identifier of the electrical equipment, and outputting a standardized data mart; a dynamic line loss separation engine calculates total loss according to the standardized data mart and a photovoltaic back-feeding compensation item, dynamically corrects technical line loss through a photovoltaic output coefficient, subdivides a line loss component by using a K-means algorithm, and outputs a line loss component matrix; and calculating a feature vector and inputting the feature vector into the traceability decision tree to trace the line loss abnormal root cause. Time sequence and space dislocation of multi-source heterogeneous data is eliminated through timestamp intelligent interpolation and equipment ID two-dimensional mapping fusion data, accurate decoupling of management line loss and technical line loss is achieved through a corrected dynamic line loss separation engine, and the misjudgment rate of a new energy scene is reduced through a traceability decision tree.

The invention provides a metering box electricity larceny detection method, system, equipment and medium under carrier communication, and relates to the field of metering box electricity larceny prevention, and the method comprises the following steps: collecting the three-phase current ICCO, voltage UCCO and freezing electric quantity QCCO of a split-phase protection intelligent circuit breaker, and the current Ij, voltage Uj and freezing electric quantity Qj of an electric energy meter in real time; calculating a correlation coefficient and an average distance between the voltage sequence {Uj} of the electric energy meter and the three-phase voltage sequence {UA, UB, UC} of the circuit breaker, and determining a connection phase of each electric energy meter; the deviation value delta I of the sum of the current ICCO of the circuit breaker and the current Ij of the connected electric energy meter under the same phase is calculated, a line loss model is constructed by combining the freezing electric quantity Qj and the QCCO, and the line loss rate p is solved; and performing three-level electricity stealing judgment, positioning an electricity stealing point and generating an electricity stealing probability Ptheft. Through multi-source data fusion, edge-side autonomous efficient electricity stealing closed-loop judgment and split-phase protection and electricity stealing prevention collaborative optimization, the accuracy of electricity stealing identification and the reliability of power supply are remarkably improved.