713 results about "Electric distribution network" patented technology

The invention discloses a power distribution operation and maintenance inspection track intelligent planning and optimizing method, and relates to the technical field of power distribution network intelligent operation and maintenance, and the method comprises the steps: collecting the topological data and equipment state information of a power distribution network, and constructing a power equipment association relationship model through an intelligent analysis model; on the basis of the incidence relation model, the inspection importance degree of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance degree; and in combination with the initial inspection sequence and the real-time road condition information, constructing a dynamic inspection track network, and generating a final inspection path through an intelligent optimization algorithm. The problems of inaccurate equipment state evaluation, unreasonable routing inspection task planning, insufficient routing inspection path optimization and the like in the prior art are solved. The method has the main beneficial effects that the utilization efficiency of inspection resources is improved, the adaptability of an inspection strategy is enhanced, and the scientificity of an inspection path is optimized.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

Disclosed a decision-dependent chance-constrained optimal model for enhancing resilience of power distribution system under renewable generation uncertainty through strategically setting-up and activating dispatchable diesel generators, renewable distributed generations, battery energy storage systems, and switchable devices. By incorporating the information of decision variables, a moment-based ambiguity set is employed to depict the uncertainty arising from renewable distributed generators. By leveraging convex approximations to handle the considered joint chance constraints, the disclosed model is transformed into a tractable mixed-integer second-order conic programming problem to be solved.

The invention provides a feeder terminal fault detection method, system and device, a medium and a program product, and the method comprises the steps: obtaining multi-source heterogeneous data comprising feeder terminal operation data, a topological graph structure of a power distribution network and external sensing data, the multi-source heterogeneous data comprises at least one kind of structured or unstructured time sequence data, and the external sensing data comprises at least one kind of structured or unstructured time sequence data; the external sensing data comprises meteorological data, geographic space information and historical fault records; and preprocessing the multi-source heterogeneous data, inputting the preprocessed time-aligned multi-source heterogeneous data into the trained time-space diagram neural network model to extract features and perform joint modeling, and outputting a fault type classification result and a fault probability distribution result corresponding to each feeder terminal node in the power distribution network. According to the method, a multi-source heterogeneous data fusion and time-space diagram neural network modeling mechanism is introduced, so that the model has a dynamic response capability to complex environment changes, and the identification precision of a potential fault mode is effectively enhanced.

The invention discloses a power supply and distribution method and system based on multi-modal data fusion and adaptive control, and the method comprises the steps: collecting real-time power supply and distribution data in a power supply and distribution network node, and obtaining power supply and distribution environment data; fusing the real-time power supply and distribution data and the power supply and distribution environment data by using a quantum heuristic multi-modal fusion algorithm to obtain fused power supply and distribution data; establishing an LSTM-CNN machine learning model based on the LSTM long and short term memory network and the CNN; inputting the fused power supply and distribution data into an LSTM-CNN machine learning model for training to obtain electric load prediction data; and based on the adaptive control strategy, sending a regulation and control instruction to the electric load equipment according to the electric load prediction data. And voltage fluctuation, flicker and harmonic distortion are effectively suppressed through real-time dynamic regulation and control, and voltage deviation and power factors of a user terminal are guaranteed to be optimal.

The invention relates to the technical field of power grids, and discloses an intelligent automatic reclosing and protection system for a power distribution network, and the system comprises a power grid topological structure construction module which is used for constructing a power grid topological model; the signal acquisition and processing module is used for obtaining a high-quality signal; the feature fusion and fault identification module is used for extracting multi-dimensional features and obtaining a preliminary classification result based on the high-quality signal; the fault property judgment module is used for judging the fault property; the reclosing strategy making module is used for combining reclosing strategies; the fault isolation module is used for determining an automatic reclosing node and executing a reclosing strategy; through multi-dimensional feature fusion and a deep learning model, the accuracy of fault type identification is improved, temporary faults and permanent faults can be accurately distinguished, the optimal reclosing strategy is dynamically formulated, intelligent adjustment of reclosing time and reclosing times is included, and the power supply reliability and safety of a power distribution network are improved.

The invention discloses an electricity utilization information acquisition intelligent configuration method based on a pattern recognition algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting a directional data stream, inputting a federal map neural network to construct a power distribution network physical connection relation, and generating a spatio-temporal topological feature vector; extracting a current effective value component of the directional data flow as an electrical load sequence, extracting an equipment state code to identify a voltage sag event, injecting voltage sag event associated disturbance into the electrical load sequence in combination with an event propagation path weight of a spatio-temporal topological feature vector, and generating an anti-fact sample set; and compressing the new configuration strategy through a knowledge distillation engine, and outputting an event response logic and a parameter adjustment instruction to form an executable configuration strategy. According to the method, through anti-fact sample generation and reinforcement learning optimization under spatial-temporal topological feature vector constraint, a physical rule deep embedding decision is realized.

A semiconductor device is provided. The semiconductor device includes a back-end-of-line (BEOL) layer, a backside power distribution network (BSPDN), a stacked field effect transistor (stacked FET) and a backside contact. The stacked FET is vertically interposed between the BEOL layer and the BSPDN and includes a top FET with top source / drain (S / D) epitaxy and a bottom FET with bottom S / D epitaxy. The stacked FET is characterized as having at least partial vertical alignment of the top FET and the bottom FET. A backside contact electrically connects the BSPDN and the top S / D epitaxy and cuts through the bottom S / D epitaxy with isolation between the backside contact and the bottom S / D epitaxy.

The invention relates to the technical field of power distribution network fault early warning systems and methods, in particular to an intelligent power distribution network fault early warning system and method based on the Internet of Things, and the system comprises a sensing unit, a data processing unit, a data analysis unit, a fault early warning unit, a monitoring management unit, and a personnel inspection subsystem unit. The sensing unit is used for installing a current sensor, a voltage sensor, a temperature sensor and a humidity sensor at key node positions of a transformer, a switch cabinet and a line of the power distribution network, the current sensor is used for monitoring the current in the line in real time, the voltage sensor is used for monitoring the voltage value, and the temperature sensor is used for monitoring the operating temperature of equipment; faults are analyzed and judged from multiple angles, misjudgment and missed judgment are reduced, whether fault hidden dangers exist or not is judged more accurately, the operation state of equipment is evaluated in real time through monitoring of the Internet of Things, the frequency of manual inspection is appropriately reduced, the labor cost is saved, and the resource utilization efficiency is improved.

The invention relates to the technical field of battery energy management, in particular to an energy management system and method for a hydrogen fuel cell module, and the system comprises a monitoring collection module which collects and contrasts the readings of a sensor based on the voltage parameter, the current parameter and the temperature parameter of the hydrogen fuel cell module to obtain original monitoring data, and performing numerical comparison on the load demand of the power distribution network to generate supply and demand matching information. In the invention, the difference statistics and the difference operation improve the accuracy of energy distribution, so that the matching between the energy distribution and the actual load demand is more accurate, and the overall energy efficiency is improved while the energy waste is reduced. And frequency band extraction and rate recording can capture subtle changes output by the battery, and the response speed is increased. In addition, the analysis and adjustment of the phase feature group provide adaptive ability, and the stability and reliability of the load change of the power grid are enhanced.

Disclosed are a single-phase-to-ground protection method and system for an active power distribution network. The method includes: acquiring current and voltage data of a line in real time by means of a power distribution terminal, and calculating active power of the line; recording and storing the active power of the line to form an active power curve; performing data statistics according to the active power curve; calculating a direction compensation coefficient and a zero-sequence current compensation coefficient on the basis of statistical data; and detecting whether a single-phase-to-ground fault occurs to the line, where when the single-phase-to-ground fault occurs to the line, a transient-state zero-sequence direction and a steady-state zero-sequence direction are compensated, or a zero-sequence current is compensated, it is determined that the single-phase-to-ground fault occurs to the line when compensation results satisfy action conditions, and tripping is performed or an alarm is given.

The invention provides a multi-path street lamp data acquisition and analysis method based on a centralized controller, relates to the field of intelligent measurement, and realizes intelligent monitoring and fault detection of running states of street lamps through group control, centralized data acquisition and dynamic analysis of the street lamps. According to the method, grouping is carried out based on a distribution network connection relation of street lamps, switching operation of each street lamp group is controlled through a centralized controller, and operation data such as current, voltage and load are collected. And for the street lamp which operates abnormally, through multiple times of data acquisition and trend analysis, an accurate fault detection result is generated. The invention further provides a method for dynamically adjusting the grouping strategy and the delay time, and the accuracy and the detection efficiency of data acquisition are improved. According to the invention, comprehensive monitoring of the operation state of the street lamp in a complex environment can be realized under the condition that the hardware cost is limited, and the method is especially suitable for management and maintenance of an urban intelligent lighting system.

The invention provides a reliability evaluation method and system considering that a micro-grid supports a power distribution network, and the method comprises the steps: employing a Monte Carlo simulation framework, building a probability reliability model through the development of a modeling process of a self-control grid-connected micro-grid, describing the micro-grid suitable for various standard scenes, and evaluating the reliability of a multi-MG grid-connected power distribution network. Scheduling and resource management are optimized by using a mixed integer linear programming technology; a micro-grid component is simplified into an equivalent distributed power generation unit, and the equivalent distributed power generation unit is integrated into an overall analysis framework, so that the calculation complexity is effectively reduced, the reliability evaluation efficiency of the power distribution system is remarkably improved, and the function of performing reliability evaluation on the power distribution network is realized. The framework and the model provided by the invention are adaptive to economic and technical research, and the reliability of the multi-MG power distribution system is enhanced; the method is an accurate and simple reliability evaluation method, reduces the operation burden, and improves the reliability evaluation efficiency of the power distribution network.

The invention discloses a power distribution network line fault positioning system based on multi-source information fusion, which relates to the technical field of power system operation and maintenance and comprises a data acquisition module, a data preprocessing module, an electric network topology management module, a fault prediction module, a fault judgment module and a man-machine interaction and alarm module. The method comprises the following steps: acquiring running state information from a power distribution network in real time, generating a structured multi-dimensional information database according to the running state information acquired by a data acquisition module, and performing time alignment, format standardization and feature vector construction on the acquired running state information to generate the structured multi-dimensional information database; according to the method, a fault section is preliminarily judged according to fault features, a fault state prediction model is constructed, a multi-dimensional information database is used for estimating state variables of the model, and the method aims at overcoming the defects that in the prior art, fault positioning fuzziness and reliability are insufficient due to the fact that a single information source is relied on.

The invention discloses an optimal scheduling method for an AC / DC flexible interconnection power distribution network. The method comprises the following steps: firstly, deeply analyzing consideration factors such as uncertainty of a distributed power supply, dynamic change of a load and characteristics of flexible DC equipment; then, targets of minimizing the operation cost, maximizing the energy utilization efficiency, optimizing the electric energy quality and achieving the highest reliability are set; in the aspect of a scheduling strategy, intra-regional balance and cross-regional power exchange are realized by adopting layered and partitioned scheduling, intelligent cooperation of all units is realized by applying multi-agent cooperative scheduling, and a scheme is adjusted according to real-time data through real-time optimization; then, a hybrid intelligent algorithm combining a genetic algorithm and a deep learning algorithm is constructed in algorithm selection, and optimization and improvement are continuously carried out; and finally, establishing a coordination control mechanism to ensure effective cooperation of the flexible DC system and the traditional AC system. According to the invention, new requirements and new challenges faced by the AC / DC flexible interconnection power distribution network can be more effectively handled, so that safe, stable and efficient operation of the AC / DC flexible interconnection power distribution network is realized.

The invention discloses a power distribution system control strategy optimization method and system. The method comprises the steps of obtaining a graph structure of a power distribution network of a power distribution system; wherein the graph structure comprises nodes and edges; constructing an action space and a reward function of the power distribution network according to the power characteristics of the power distribution network; according to the action space and the reward function, training the GNN and reinforcement learning network by adopting the graph structure to obtain the trained GNN and reinforcement learning network; and optimizing a control strategy of the power distribution system according to the trained GNN and the reinforcement learning network. According to the method, the GNN and reinforcement learning network method can be combined, more intelligent decision and strategy optimization can be obtained through the graph structure of the power distribution network, the source network load storage distributed optimal coordination and management in the power distribution system can be realized, the coordination and accuracy of the control strategy can be improved, the overall strategy optimization effect can be improved, and the method is suitable for the complex power system optimization problem.

The invention discloses a new energy station management system and method based on an intelligent power grid, and the system comprises a real-time monitoring module which is used for predicting the charging load fluctuation of an electric vehicle based on real-time monitoring data, a scheduling module which is used for adjusting the output of new energy power generation equipment according to the predicted load fluctuation, and an optimization module. The power distribution network management module is used for optimizing charging scheduling in a charging station and avoiding overload caused by peak charging at the same time, the dynamic adjustment module is used for dynamically adjusting the voltage and frequency of a power distribution network, and the real-time monitoring module is used for monitoring the state of the power distribution network in real time and dynamically updating a management strategy; according to the new energy station management system and method based on the smart power grid, the problems that in the prior art, due to instability and rapid change characteristics of electric vehicle charging loads, severe fluctuation of the loads can bring extra pressure to a power distribution network, the overload problem of the power distribution network is caused, and then the reliability and safety of the power grid are affected are solved.

The invention relates to the technical field of electric energy distribution, in particular to an intelligent power distribution system and a power distribution method.According to the intelligent power distribution system and method, power flow paths and load density distribution are defined by collecting load distribution values of nodes of a power distribution network and calculating power flow, line transmission loss and load value density of load transmission paths among the nodes; according to the method, a data foundation is laid for subsequent dynamic adjustment, partition node power requirements and transmission path characteristics are integrated by adopting an alternating direction multiplier method, a dynamic partition model is established, a global problem is decomposed into a local optimization sub-problem in a partition optimization process, partition boundary adjustment and power balanced distribution are realized through iterative calculation, and a partition node is optimized. A greedy algorithm is adopted to screen load priorities and demanded quantities, the electric energy guarantee capability of high-priority loads is improved, through adjustment of a voltage offset range and dynamic adjustment of reactive power compensation equipment, the influence of voltage fluctuation on power balance between partitions is effectively reduced, the voltage stability is improved, and the energy consumption and the power loss are reduced.

The invention relates to a power distribution network optimal configuration method for a high-proportion distributed new energy access scene, and the method comprises the steps: obtaining the basic data of a power distribution network, and constructing a bearing capability evaluation index system; a robust optimization evaluation model considering the uncertainty of the source load is constructed, and the critical bearing capacity is solved in combination with a bearing capacity evaluation index system; based on the critical bearing capacity, energy storage, SOP intelligent soft switching and DR demand side response are sequentially configured, an updated power distribution network state is obtained, whether convergence conditions are met or not is judged, and if yes, an optimal configuration scheme including current energy storage, SOP and DR configuration and the current critical bearing capacity is output; otherwise, returning to re-optimize. Compared with the prior art, the power distribution network can safely and efficiently consume the high-proportion distributed new energy, and the bearing capacity of the power distribution network is effectively improved.

The present invention discloses a multi-clustering method for large-scale multi-source heterogeneous data, which relates to the technical field of data processing, including: pre-processing heterogeneous data from different sources through an ETL tool and converting them into a unified target data format; then classifying them according to voltage level, equipment type, and acquisition measurement type; constructing a topology analysis engine for the classified multi-source heterogeneous data sets according to the mutual correlation between the distribution network and network elements; eliminating unsatisfactory data sets based on topological analysis to obtain data sets to be fused; performing observation coefficient analysis on the data sets to be fused, and allocating a corresponding number of processing terminals to fuse the data sets to be fused, thereby improving data fusion efficiency and realizing cross-composite in-depth analysis of distribution network historical data and quasi-real-time data; outputting data fusion results for distribution network staff to study and analyze, providing guidance for refined energy management and user services, and timely achieving fault warnings and improving power safety.

The invention discloses a power distribution network low-voltage adjusting method, an adjusting device and electronic equipment. The method comprises the following steps: acquiring real-time load data of each load node and real-time energy storage data of each energy storage node based on a shared network; determining load demand prediction data of the load node according to the load demand prediction model and the real-time load data; energy storage sharing information between the energy storage nodes is obtained, and a cooperative adjustment coefficient is determined according to the energy storage sharing information; determining a power distribution strategy of each energy storage node according to the real-time load data, the load demand prediction data, the real-time energy storage data and the cooperative adjustment coefficient; and performing voltage regulation according to the power distribution strategy or the optimization strategy of the power distribution strategy. By designing a data sharing, load demand analysis, energy storage equipment multi-node cooperative adjustment and distributed energy storage management method, the load demand of each load node can be analyzed in real time, the output power of the energy storage equipment is dynamically adjusted, and the voltage of the power grid is effectively adjusted.

The invention discloses a parallel operation data interaction system and method for hybrid energy storage inverters, and relates to the technical field of power systems, and the system comprises a parallel operation data interaction center, a data collection and monitoring module which is used for collecting load data in a power distribution network and monitoring the operation state of each hybrid energy storage inverter, performing feature analysis to extract load operation features to obtain a load operation feature sequence table; and the load prediction module is used for obtaining a load trend index by using the historical data and the load operation characteristics in the load operation characteristic sequence table in combination with a pre-trained load prediction model, and analyzing the change trend of the load. By collecting load data and monitoring the operation state of the inverter, the operation condition of the system can be comprehensively mastered, abnormity can be found and processed in time, the output of the inverter is adjusted in advance and the operation mode is switched in combination with the load prediction model, load fluctuation and power grid faults are effectively handled, and the stability and reliability of the power system are remarkably improved.

The invention discloses a rental house electricity consumption abnormity early warning system and method based on data fusion, and relates to the technical field of electricity consumption abnormity early warning. Non-intrusive load decomposers, distributed temperature sensors and high-frequency current transformers are deployed at distribution boxes, sockets and cable nodes, and space-time alignment of current, temperature and load data is realized through a sliding time window algorithm; lSTM model parameters are improved by adopting an optimization algorithm, and the recognition accuracy of abnormal electric appliances is improved; constructing a digital twinborn model, mapping the power distribution network topology into a graph structure, dynamically calculating an abnormal propagation probability and visualizing a risk path; graded early warning is realized by fusing the dynamic weight model of the current deviation degree, the temperature rise gradient and the load sudden change. The recognition accuracy of the complex abnormal mode is improved; the visual pre-judgment of the risk propagation path is realized; and a complete closed loop from risk early warning to active protection is realized.

The invention relates to a surface material uniformity analysis method and system for an insulator of a power distribution network, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining an optical image, a thermal imaging image and ultrasonic echo data of the surface of the insulator; s2, performing image processing on the acquired optical image, extracting characteristic parameters of surface roughness, texture direction and texture consistency, and constructing a surface texture uniformity index; s3, thermal diffusivity and thermal resistance of different areas are calculated from the thermal imaging image, thermal parameter differences of the areas are compared, and a thermal performance uniformity evaluation value is formed; s4, aiming at the ultrasonic echo data, establishing a correlation model of the ultrasonic characteristics and the internal uniformity of the material; s5, the fusion analysis model comprehensively evaluates the uniformity of the surface material of the insulator; the method has the beneficial effects that the fusion analysis model realizes comprehensive evaluation of the uniformity of the surface material of the insulator from multi-dimensional data.

The invention discloses a DTW algorithm-based distribution line current differential protection method, relates to the technical field of power distribution network relay protection, and aims to solve the problems that the III-section type overcurrent protection performance is reduced and the optical fiber communication cost is high after a distributed power supply is connected. According to the method, a 5G communication technology, a five-point numerical differential algorithm, amplitude limiting average filtering and a DTW similarity distance algorithm are fused, and current differential protection is achieved; the method comprises the steps of collecting three-phase current on two sides of a line in real time, starting a criterion through a fault current change rate, suppressing noise through amplitude-limiting average filtering, and normalizing data; the core is that the current sequence similarity is calculated by using a DTW algorithm, and a fault is accurately judged; according to the method, the influence of 5G communication time delay is eliminated, and the reliability of distribution network protection and control is improved; meanwhile, optical fibers are replaced by 5G, so that the cost is greatly reduced, the economical efficiency of distribution network protection is improved, and popularization and application are facilitated; the innovative technology provides powerful support for intelligent upgrading and sustainable development of a power distribution network.

The invention relates to the technical field of electrical safety, and discloses a multistage cooperative fault detection method and system based on an intelligent air switch, and the method comprises the steps: intelligent air switch terminals disposed at all levels of a power distribution network are used for synchronously collecting a multi-physical-quantity time sequence signal; the intelligent air switch terminal calculates the dynamic correlation entropy of the collected current signal and executes local fault diagnosis according to the dynamic correlation entropy; the terminals carry out cooperative communication, and dynamic correlation entropy is collected to construct an entropy migration spectrum so as to carry out cooperative traceability on faults; and finally, integrating local diagnosis and collaborative traceability results, and executing corresponding hierarchical collaborative protection actions. The system comprises an acquisition module; an entropy calculation module; a local diagnosis module; a communication module; a collaborative traceability module; and a protection execution module. According to the invention, through multi-stage cooperative fault detection of the intelligent air switch, accurate identification, rapid response and state early warning of hidden faults are realized, and the safety and reliability of a power distribution system are improved.

The invention relates to a power distribution network intelligent monitoring method based on a data middle station and edge calculation, and the method comprises the following steps: S1, arranging a sensor in a power distribution network, collecting key data in real time, and carrying out the denoising processing and normalization processing of the collected data; s2, performing preliminary anomaly detection on the processed data at the edge end by using a lightweight GMM model; s3, storing the preprocessed data from the edge nodes in the data center set, and performing secondary detection by adopting a variational auto-encoder based on a preliminary anomaly detection result to obtain a final detection result; s4, constructing a causal relationship graph of the power distribution network, and performing anomaly analysis based on a final detection result; and S5, generating a maintenance report according to the final detection result and the abnormal analysis result. According to the invention, by integrating the edge calculation and the data center, and through multi-stage detection and comprehensive analysis, the abnormity in the network is found and predicted in time, and a reliable guarantee is provided for intelligent management and optimization of the power distribution network.

The invention relates to an alternating-current trunk line or an alternating-current power distribution network, in particular to a method for improving the stability of a wind power system through combined control of network construction type energy storage and a nine-switch UPQC. The method comprises the following steps: firstly, proposing an active regulation and inertial support control method based on a virtual synchronous machine mechanism for network construction type energy storage; and secondly, by constructing a coordination control framework, dynamic response complementation of the energy storage system and the UPQC under disturbance is realized, so that the overall frequency stability and voltage supporting capability of the system are improved. And finally, building a system model in Matlab / Simulink, setting three typical working conditions of wind power fluctuation, frequency disturbance and serious voltage drop, and carrying out scheme validity verification. Simulation results show that the method can effectively stabilize the wind power output fluctuation, enhance the frequency and voltage support capability, and significantly improve the stability of a wind power system.

The invention discloses a power distribution network self-healing method for improving power supply reliability, and the method comprises the following steps: S1, collecting fault information and an operation state, constructing a fault description matrix P, and carrying out the fault positioning based on the fault description matrix P; s2, after fault positioning, judging the position of the node relative to a fault position by taking whether fault current is detected at the node as a criterion, and recording the distance x between the node and a fault branch; s3, after a fault branch and a fault node are determined, fault recovery of a non-fault area is carried out based on the distance x between the node and the fault point; s4, constructing multiple objective functions and constraint conditions, determining the weight of each objective function by adopting an analytic hierarchy process to obtain a total objective function F, and optimizing the objective functions based on an ant colony algorithm to obtain an optimal power distribution network reconstruction scheme; the power failure time can be shortened, the power supply reliability is improved, and the power grid operation reliability is improved.