399 results about "Line loss" patented technology

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 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 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 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 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 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 discloses a rolling time domain reactive power optimization method and system containing a distributed photovoltaic power distribution network, and the method comprises the steps: firstly, employing a probabilistic prediction model to carry out the prediction of photovoltaic active power, and obtaining a future output expected value and an uncertainty interval; secondly, the prediction result is substituted into a multi-objective optimization problem with line loss, voltage deviation and reactive compensation as optimization objectives, and opportunity constraints are established by using the uncertainty interval so as to ensure the safety margin of the power grid voltage; and finally, performing rolling solution on the problem by adopting a multi-objective evolutionary algorithm guided by a power grid sensitivity index to obtain an optimal reactive compensation scheme. According to the method, through a closed-loop rolling mechanism of prediction, modeling and solving, prospective, collaborative and robust control of reactive power resources is realized, and the stability and economical efficiency of operation of the power distribution network under high-proportion photovoltaic access are improved.

The invention relates to the technical field of wiring terminal boxes, in particular to an intelligent analysis type wiring terminal box and a control system thereof.The intelligent analysis type wiring terminal box comprises the intelligent analysis type wiring terminal box and the control system used for controlling the wiring terminal box, and the intelligent analysis type wiring terminal box comprises a box body and a transparent protective cover; the box body is composed of a first main body, a second main body and a third main body which are sequentially connected, the first main body is provided with a wiring communication assembly, the second main body is an insulation board, and the third main body is provided with a monitoring acquisition assembly; the control system comprises a sampling unit, a comparison unit, a processing unit, a communication unit, an acquisition terminal and an acquisition background, and all the units cooperate to realize data sampling, comparison, processing and transmission. According to the invention, the accuracy of the metering device can be guaranteed, and the working pressure of line loss index governance improvement and electric quantity and electricity charge recovery by basic units can be reduced, so that the purpose of intelligent monitoring of the metering device is achieved.

The invention provides a Bayesian network-based charging pile load prediction method and system, and belongs to the technical field of electric vehicles and load prediction. The method comprises the steps of obtaining real-time state data of a target charging pile, the real-time state data comprises current occupation state information of the target charging pile, real-time voltage data of a power grid node where the target charging pile is located and line loss data of the power grid node where the target charging pile is located; inputting the real-time state data into a preset charging pile load prediction model, so that the charging pile load prediction model takes the real-time state data as an input node of a Bayesian network, and calculating a posterior probability of a target node of the Bayesian network through a variable elimination method or a sampling approximate reasoning method, therefore, by implementing the method and the device, the problem that the accuracy and the efficiency of predicting the load of the charging pile are not high in the prior art can be solved.

The invention provides a photovoltaic power station layered reactive power coordination optimization method and device considering SVG access, equipment and a storage medium, and relates to the technical field of reactive power compensation. According to the method, a three-layer progressive control system is constructed based on a photovoltaic power station topological structure and reactive power transmission characteristics: reactive power centralized compensation is completed preferentially through a main transformer low-voltage side SVG, so that the loss of a current collection line is reduced to the maximum extent; and when the SVG compensation capacity reaches the upper limit, the residual reactive power is reasonably distributed to each photovoltaic power generation unit. Simulation verification results show that the strategy can strictly control the voltage deviation of the grid-connected point within a stable interval, and the voltage stability and economical efficiency of grid-connected operation of the photovoltaic power station are remarkably enhanced.

The invention relates to the field of line loss treatment, in particular to a dynamic treatment method and system for line loss of an electric power station area, and the method comprises the following three core steps: multi-source data cooperative processing: collecting the total meter electric quantity of the station area, branch phase data and a GIS topological relation in parallel, and generating a dynamic monitoring matrix through time sequence alignment and abnormality cleaning; time-space coupling abnormity diagnosis: fusing the line loss rate deviation degree and a hierarchical node transmission efficiency threshold value, and identifying three types of nodes of transformer abnormity, phase imbalance and electricity stealing suspicion; and reinforcement learning closed-loop optimization: outputting a primary instruction based on the pre-training rule base, driving a reinforcement learning engine to dynamically update a rule weight through feedback data, and iteratively optimizing the instruction until the line loss rate continuously reaches the standard. According to the method, the technical bottlenecks that multi-source data are asynchronous, the static threshold adaptability is poor, and the governance instruction lacks dynamic optimization are solved, and full-process dynamic control from data acquisition to governance closed loop is realized.

The invention discloses a power distribution network line loss node screening method and system, relates to the technical field of power distribution network safety, and is used for solving the technical problem that a real physical loss node cannot be accurately identified due to line loss calculation deviation, and the method comprises the steps: monitoring the change condition of the line loss rate of each line segment in a power distribution network in a continuous time period, when the change amplitude of the line loss rate exceeds a preset threshold value and the change trend does not accord with the actual load change trend, preliminarily marking the line section as calculation fluctuation; extracting the line loss data of the line section which is preliminarily marked as calculation fluctuation under a plurality of time scales, and determining a continuous high line loss line section according to the line loss data under the plurality of time scales; aiming at the continuous high-line-loss line section, carrying out line loss abnormity analysis to obtain a line loss abnormity result; and according to the line loss abnormity result, classifying the line loss abnormity condition of each line section, and generating different types of troubleshooting suggestions.

The invention provides a dynamic reactive compensation ad hoc network control system and method for a power transmission line. The control method comprises the steps that a self-repairing network is constructed through a scoring algorithm after compensation nodes are self-initialized; acquiring data such as voltage and current in real time, and preprocessing and transmitting; the central control unit generates a dynamic compensation strategy based on an NSGA-II multi-objective optimization algorithm in combination with voltage stabilization, a power factor, active loss, load balancing and renewable energy consumption targets, and edge calculation realizes local reactive rapid adjustment through fuzzy logic; faults are monitored in real time and rapidly isolated, network topology is reconstructed based on residual nodes, a compensation strategy is recalculated, and system self-healing is achieved. The communication reliability and flexibility are improved through a distributed ad hoc network architecture, the single compensation defect is solved through multi-target collaborative optimization, the method is suitable for a complex power grid containing a distributed power supply, the voltage stability is remarkably improved, the line loss is reduced, and an efficient and reliable reactive compensation solution is provided for an intelligent power grid.

The invention discloses a power distribution network voltage active control method and device based on energy storage converter regulation, and the method comprises the steps: building a multi-objective optimization function with system line loss, converter regulation power quantity and voltage fluctuation risk indexes as optimization objectives, and calculating energy storage converter regulation reactive power quantity according to an action voltage threshold; solving the multi-objective optimization function by taking the action voltage threshold as a decision variable to obtain an optimal action voltage threshold of the energy storage converter; and monitoring the actual voltage of the target grid-connected node in real time, comparing the actual voltage with the optimal action voltage threshold corresponding to the target energy storage converter, triggering the target energy storage converter to carry out reactive power regulation according to the comparison result, and calculating the reactive power regulation quantity required by the target energy storage converter according to the deviation value and the reactive power-voltage sensitivity matrix. The grid-connected point voltage of the power distribution network can be adaptively optimized and adjusted, and the utilization rate, the scheduling performance and the flexibility of scheduling resources are improved.

The invention discloses a line loss positioning method based on phase feature clustering, belongs to the technical field of power system line loss treatment, and constructs a three-dimensional feature space through high-frequency collection of HPLC carrier signal phase offset, current ripple frequency spectrum features and intelligent electric meter load rate to comprehensively characterize the equipment operation state. An improved DBSCAN clustering algorithm is utilized, the neighborhood radius is dynamically calculated according to the feature space density, the minimum sample number MinPts is adaptively set according to the transformer area equipment scale, and clustering parameter adaptation is achieved; meanwhile, a noise filtering mechanism is introduced, secondary verification is carried out on initial clustering noise points, effective abnormal points are screened out by calculating local density and overall average density, and the misjudgment rate is reduced. The method can quickly and accurately position the position of the line loss abnormal equipment, greatly improves the line loss abnormal processing efficiency, can be widely applied to power distribution network line loss management, effectively improves the line loss analysis accuracy and treatment efficiency, and provides powerful technical support for reducing the power grid loss and improving the power supply reliability.

The invention provides a transformer area two-stage distribution robust line loss optimization method considering wind and light uncertainty, and belongs to the technical field of new energy. Firstly, a day-ahead-real-time two-stage line loss optimization method is constructed, and in the day-ahead stage, a K-means clustering algorithm and a conditional generative adversarial network are combined to generate a multi-scene sample set capable of reflecting the uncertainty of wind and light output; in a real-time stage, based on a day-ahead optimization result and updated output information, a multi-scene distribution robust optimization model is utilized to realize joint correction of line loss and economic dispatching; in the optimization solution process, a forward-backward substitution method is adopted to embed the load flow calculation model so as to accurately reflect the line transmission loss of the transformer area power distribution network; secondly, a distributed robust optimization method based on scene generation is constructed, historical wind and light data are utilized, a diversified scene set is generated through K-means clustering and a conditional generative adversarial network, and distributed robust optimization planning and correction are carried out in different scenes.

The invention relates to the technical field of line loss assessment, in particular to a power system line loss assessment method and system based on big data processing, and the method comprises the following steps: collecting voltage, current and phase angle signals of a power supply section, carrying out the frequency domain transformation, extracting the amplitude and phase of a frequency component, and generating a frequency spectrum feature set; energy spectrum characteristics and a migration sequence are calculated to analyze spectrum form stability, a risk section is identified by combining a power flow direction and an energy migration included angle difference, and a line loss assessment result is generated by combining a gravity center drift trend and an energy change sequence. Energy spectrum features and migration sequences are extracted to describe power grid energy distribution changes, an included angle difference between a power flow direction vector and an energy migration direction is calculated to screen an abnormal section, an energy drift trend is analyzed in combination with a frequency spectrum gravity center track, line loss real-time quantification and area positioning are achieved, and abnormal loss identification precision and operation evaluation stability and reliability are improved.

The invention provides a dynamic three-phase imbalance treatment system and method with an intelligent phase-change switch, and relates to the technical field of power system distribution network operation control. The system comprises a load monitoring unit, a central control unit and an intelligent phase-change switch array. According to the treatment method, the load monitoring unit collects load current and voltage data of each phase in real time and transmits the data to the central control unit; the central control unit adopts a load balance decision model based on an improved particle swarm algorithm to analyze and process the acquired data so as to calculate an optimal commutation scheme; and the intelligent phase-change switch array completes load phase sequence switching within millisecond-level time through a solid-state phase-change switch with rapid on-off capability according to the control instruction, so that three-phase load dynamic balance adjustment is realized. By adopting the scheme of the invention, the real-time performance and the adjustment precision of three-phase imbalance treatment can be obviously improved, the method adapts to complex and changeable distribution network load working conditions, the line loss is effectively reduced, and the utilization rate of the transformer and the operation stability of the distribution network are improved.

The invention discloses a low-voltage transformer area topology identification method and system based on node mutual impedance characteristic identification, and relates to the technical field of power supply and distribution. The identification method comprises the following steps: firstly, synchronously acquiring voltage and current time sequence data of the head end of a transformer area and each user node; constructing and solving a mathematical model based on a circuit superposition principle to obtain a user node mutual impedance coefficient matrix; the impedance feature vector of each user node is extracted, the difference value between the self-impedance and the mutual impedance is calculated, and the difference value is compared with a preset judgment threshold value based on physical parameters, so that the relationship between the upstream and the downstream of the user nodes and the same access point is accurately judged; and finally, reconstructing a complete tree-shaped physical topology of the transformer area. According to the method, only existing measurement data is utilized, extra hardware is not depended on, efficient and accurate automatic identification of the topological structure of the low-voltage transformer area is achieved, and reliable support is provided for refined operation and maintenance such as power distribution network line loss treatment and fault positioning.

The invention discloses a transformer area line loss adaptive scene perception and dynamic optimization method and system, and relates to the technical field of power system line loss analysis and governance, and the method comprises the steps: collecting voltage and current data of a transformer area distribution transformer, carrying out the time sequence decomposition of the data, obtaining a trend term and a period term, calculating a scene discrimination index, and carrying out the operation scene division; constructing a line loss estimation equation according to the operation scene and the scene discrimination index, dividing power distribution branches and calculating a theoretical line loss value; when the deviation between the theoretical line loss value and the actually measured line loss value exceeds a threshold value, calculating a line loss contribution degree and determining an abnormal line loss branch; calculating the ratio of the compensation cost to the line loss improvement value for the abnormal line loss branch as a target function, and solving a compensation strategy through a gradient descent method; and executing the compensation strategy and dynamically adjusting the constraint condition until a preset line loss rate requirement is met. According to the invention, self-adaptive perception of the operation scene of the transformer area and dynamic optimization compensation of the line loss can be realized, and the line loss treatment effect is improved.

The invention provides a power distribution network line loss optimization method and system, and relates to the technical field of power distribution network line loss optimization, and the method comprises the steps: receiving local operation data collected by a measurement unit in real time, and generating the approximate resistance characteristics of a line segment based on the data and the operation information of an adjacent measurement unit, according to the method, approximate resistance characteristics and local operation data are fused to generate more reliable measurement data, an instantaneous power loss integral method is adopted to calculate instantaneous power loss aiming at bidirectional power flow and harmonic problems caused by distributed power supply access, and energy loss is generated through sliding time window integration. And according to the fused measurement data and energy loss, determining the global line loss of the power distribution network and generating an optimization control instruction, so that challenges such as complex operation environment, dynamic change of line parameters and bidirectional power flow of the power distribution network can be effectively handled, and the accuracy of line loss evaluation and the effectiveness of optimization control are remarkably improved. Accurate evaluation and efficient optimization of the line loss of the power distribution network are realized.

The invention relates to the field of clean energy electrolysis hydrogen production, in particular to a three-section electrolytic bath hydrogen production power supply system and a power supply control method thereof. According to the system, an alternating current bus module is connected with three-phase alternating current and provides a three-phase alternating current power supply; the power supply conversion module comprises three power supply conversion units, the three power supply conversion units are jointly connected to the alternating current bus module in parallel, and each power supply conversion unit converts the three-phase alternating current power supply into a working direct current power supply; the three-section type electrolytic bath comprises three sub-electrolytic baths which are connected in series, and each sub-electrolytic bath is correspondingly connected with one power supply conversion unit to obtain the working direct-current power supply; the power supply conversion unit comprises a rectification circuit, a three-level BUCK circuit and a resonance LLC cascade circuit which are connected in sequence. According to the invention, accurate power distribution and independent decoupling control of a load section are realized through the independent modular power supply; meanwhile, harmonic interference is restrained, the power factor is improved, and device stress and line loss are reduced.

The invention discloses a line loss detection device, and particularly relates to the technical field of line loss detection, the line loss detection device comprises a base, the two ends of the top of the base are symmetrically provided with fixing frames, the tops of the fixing frames are connected with a fixing plate, and the top of the fixing plate is provided with a line guiding mechanism; and the supporting plate is movably arranged at the top of the base and located between the two fixing frames, a current transformer is fixed to the top of the supporting plate, and a moving assembly is arranged on the base and used for driving the supporting plate to move. According to the invention, by arranging the wire guiding mechanism, the wire guiding mechanism can drive the cable to move intermittently, so that the current transformer can detect the line in a segmented manner, and by arranging the supporting plate and the moving assembly, when each segment of line is detected, the supporting plate can drive the current transformer to linearly move along the penetrated line, so that the relative movement of the current transformer can be realized; therefore, the circuit can be detected efficiently and stably, and the circuit is prevented from deviating and shaking.

The invention discloses an electricity larceny identification method, system and device based on multi-source electricity utilization characteristic fusion and a medium, and belongs to the technical field of electricity larceny diagnos.The method comprises the steps that a sample set containing electricity larceny samples and normal electricity utilization samples is obtained, the characteristic judgment capacity is calculated through adjacent sample difference analysis, and electricity utilization behavior characteristics are screened according to the judgment capacity; constructing an electricity consumption trend decrease index based on the electricity consumption change trend, calculating a user theoretical line loss contribution factor according to the correlation between the user electricity consumption ratio and the voltage sequence, and constructing an electricity larceny suspicion index based on the theoretical line loss contribution factor; analyzing the relevance change characteristics of the user electric quantity and the court line loss rate through a superposition verification method, and calculating the electricity stealing suspicion degree according to the relevance change characteristics; and constructing a logistic regression model, and judging an electricity stealing user in combination with the electricity stealing suspicion degree. According to the method, the feature quality is improved through adjacent sample difference analysis, the electricity larceny suspicion degree is accurately quantified through Monte Carlo multi-round iteration verification, and the electricity larceny judgment precision is optimized and improved through the quasi-Newton method.