Intelligent research and judgment and power recovery method and system based on low-voltage distribution network

Abstract

The invention discloses an intelligent research and judgment and power recovery method and system based on a low-voltage power distribution network, and relates to the technical field of intelligent power grid and power system automation, and the method comprises the steps: collecting monitoring data, carrying out information fusion check based on geography and power management, dynamically generating a topological file, and constructing a hierarchical visual power grid model. Analyzing the states of a circuit breaker and protection equipment, calculating main and backup protection deviation, analyzing electrical quantity change in combination with wavelet transform and singular value decomposition, and identifying a fault section; a whole-network sensing system is constructed, three-phase imbalance and zero-sequence current are monitored in real time, and rapid power restoration and active research and judgment are carried out by combining a dichotomy and an intelligent switch optimization power restoration strategy. According to the intelligent research and judgment and power recovery method based on the low-voltage power distribution network, the reliability and safety of the power distribution network are improved by constructing an environment monitoring system, the accuracy of fault analysis of a low-voltage transformer area is improved through topology accurate checking and intelligent fusion, and the fault analysis accuracy of the low-voltage transformer area is improved through a dichotomy fault isolation and intelligent power recovery strategy. The fault recovery efficiency is improved, and better effects are achieved in the aspects of safety, accuracy and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid and power system automation, and specifically to an intelligent judgment and power restoration method and system based on a low-voltage distribution network. Background Art

[0002] The distribution network is an important link in the power system responsible for power distribution. Its stability and reliability are directly related to the power consumption experience of users and the normal operation of the economy and society. In recent years, with the development of advanced technologies such as smart grid, Internet of Things, and artificial intelligence, the monitoring and fault handling capabilities of the distribution network have been gradually improved. Especially in the field of 10 - 35 kV medium-voltage distribution network, relatively mature fault monitoring and intelligent management means have been available. However, in the low-voltage distribution network below 10 kV, due to its complex network structure and low automation level, traditional monitoring and operation and maintenance means are still relatively lagging. In recent years, the development direction of the low-voltage distribution network mainly focuses on improving the state perception ability, optimizing the topology structure recognition, strengthening the intelligent fault diagnosis, and enhancing the power restoration efficiency. The application of new technologies such as smart meters, low-voltage monitoring terminals, and distributed energy management provides new opportunities for the optimization management of the low-voltage distribution network, making the intelligent operation and maintenance of the low-voltage distribution network a research hotspot.

[0003] Currently, the perception ability of the low-voltage distribution network is weak, and it is difficult to detect local faults in a timely manner. Due to the lack of effective low-voltage monitoring means, power outages of low-voltage users often cannot be reported in real time, resulting in maintenance personnel usually learning about fault information only after user complaints, which affects the service level of power supply enterprises. Secondly, the ability of fault judgment and location is limited. Traditional fault diagnosis means mainly rely on the experience of maintenance personnel and cannot accurately identify the fault area, resulting in a long time for fault investigation and repair. In addition, the complex topology structure of the low-voltage distribution network increases the operation and maintenance difficulty. Currently, it is difficult to accurately verify the topology information of low-voltage substations, which affects the accuracy and efficiency of fault handling. With the large-scale access of new types of loads such as distributed power sources and electric vehicle charging and swapping facilities, the operating environment of the low-voltage distribution network becomes more complex, the load volatility increases, further exacerbating the uncertainty of fault occurrence. At the same time, due to the lack of precise location means for low-voltage substation faults, existing emergency repair strategies usually require dispatching multiple maintenance personnel to check equipment such as lines, communications, and meters respectively, resulting in a high ineffective attendance rate and low emergency repair efficiency, ultimately affecting the power supply reliability of the distribution network. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing operation and maintenance methods of low-voltage distribution networks have insufficient perception ability, inaccurate fault location, low power restoration efficiency, and the optimization problem of how to achieve intelligent judgment and rapid power restoration.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent judgment and power restoration method based on a low-voltage distribution network, including collecting monitoring data, dynamically generating a topology file based on the fusion verification of geographical and power management information, and constructing a hierarchical visual power grid model; analyzing the states of circuit breakers and protection devices, calculating the deviation between main and backup protections, analyzing the changes in electrical quantities by combining wavelet transform and singular value decomposition to identify the fault section; constructing a whole-network perception system, monitoring the three-phase imbalance and zero-sequence current in real time, and combining the dichotomy method with intelligent switches to optimize the power restoration strategy for rapid power restoration and active judgment.

[0007] As a preferred embodiment of the intelligent judgment and power restoration method based on a low-voltage distribution network according to the present invention, wherein: the collection of monitoring data includes setting up an intelligent monitoring background for the low-voltage distribution network to store and process the status information of all distribution equipment and the environmental monitoring quantities of the distribution substation, and sending control instructions to the intelligent distribution terminal. The intelligent monitoring background of the low-voltage distribution network remotely monitors the overall operation status of the distribution substation, issues danger warnings and abnormal alarms; the data transmission system connects the on-site monitoring data with the background monitoring platform, transmits the data in real time, and notifies authorized users. The intelligent monitoring system is responsible for collecting monitoring information and transmitting it to the intelligent main control terminal. The intelligent execution system controls the data, starts the intelligent execution element according to the information collected by the intelligent monitoring system, controls the environmental parameters of the distribution substation within the target range, dynamically generates an operation and distribution topology file based on IP-based broadband carrier and signal injection technology, and performs autonomous verification with the PMS low-voltage account information. Based on the distribution Internet of Things technology, the intelligent distribution terminal after topology verification interacts with the energy consumption information of the intelligent electricity meter locally, extending the distribution network perception layer to the low-voltage distribution network.

[0008] As a preferred embodiment of the intelligent judgment and power restoration method based on a low-voltage distribution network according to the present invention, wherein: the construction of the hierarchical visual power grid model includes constructing a full-voltage-level visual model covering substations, lines, transformers, meter boxes, and users, and constructing an operation, distribution, and communication integration power grid model covering substations, lines, distribution transformers, meter boxes, meters, concentrators, and communication equipment according to the principles of zoning, voltage division, component division, and substation area by integrating the dispatching main network model, GIS distribution network model, marketing customer files, and metering collection relationship data.

[0009] As a preferred embodiment of the intelligent judgment and power restoration method based on a low-voltage distribution network according to the present invention, wherein: the analysis of the states of circuit breakers and protection devices, the calculation of the deviation between main and backup protections, the analysis of the changes in electrical quantities by combining wavelet transform and singular value decomposition to identify the fault section includes judging the fault section of the low-voltage distribution network based on the digital input information. The fault judgment model of the low-voltage distribution network with digital input information is expressed as:

[0010]

[0011] where r k,m and are the actual and desired states of the main protection of the device respectively, r k,s and are the actual and desired states of the near backup protection of the device respectively, r k,l and are the actual and desired states of the remote backup protection of the device respectively, C i and are the actual and desired operating states of the circuit breaker respectively. k is the device number, m is the main protection of the device, s is the near backup protection of the device, l is the remote backup protection of the device, i is the circuit breaker element number, and incorporating the action status information of the automatic reclosing device of the circuit breaker, the objective function is improved and expressed as:

[0012]

[0013] where r i,c and are the actual and desired states of the breaker failure protection respectively, r i,a and are the actual and desired states of the automatic closing of the circuit breaker respectively. c is the breaker failure protection, and a is the automatic closing of the circuit breaker.

[0014] As a preferred solution of the intelligent judgment and power restoration method based on the low - voltage distribution network described in the present invention, wherein: analyzing the states of the circuit breaker and protection equipment, calculating the deviation of the main and backup protections, combining wavelet transform and singular value decomposition to analyze the change of electrical quantities, and identifying the fault section further includes judging the fault section of the distribution network based on electrical quantity information. After a fault occurs in the distribution network, the change in the current amplitude of the fault line is greater than that of the non - fault line. By defining the wavelet fault degree to measure the change degree of the electrical quantity amplitude before and after the component fault, it is expressed as:

[0015] F if = max{D i1 , D i2 ,…D iβ}

[0016] F ib = max{D iβ+1 , D iβ+2 ,…D iβ+γ}

[0017]

[0018] where F if is the maximum amplitude wavelet transform of the signal before the fault, F ib is the maximum amplitude wavelet transform of the signal after the fault, D i1 , Di2 ,…D iβ is the wavelet sampling result of each signal sampling point before the fault, D iβ+1 ,D iβ+2 ,…D iβ+γ is the wavelet transform result of each signal sampling point after the fault, γ is the sampling point before and after the wavelet transform, the wavelet transform results of all sampling points are the same before the fault, β is the number of values required for a group of wavelet transforms, V i is the amplitude change degree of the signal before and after the fault. In the case of a fault, the processing of the amplitude change degree of the signal before and after the fault is expressed as:

[0019]

[0020] Among them, x i is the wavelet fault degree of the i-th component after the fault occurs, i = 1,…, N, N is the component number. The high-frequency transient component caused by the system fault is transformed by wavelet and decomposed by singular value to obtain the characteristic matrix. In the characteristic matrix, the singular eigenvalue of the fault component is greater than that of the non-fault component. The wavelet singularity theory is introduced for fault signal analysis and expressed as:

[0021]

[0022] Among them, S i is the average value of the singular values of the singular value characteristic matrix of the i-th component, λ i is the singular value of the i-th component. The processing of the average value of the singular values of the singular value characteristic matrix of the component is expressed as:

[0023]

[0024] Among them, m i is the wavelet singularity degree of the i-th component after the fault. The wavelet transform of the fault signal is expressed as:

[0025]

[0026] Among them, E j is the wavelet energy distribution of the signal at the j-th scale, D j is the wavelet sampling result at different scales j and sampling points, W i is the average wavelet energy at all scales, δ is the signal scale. The strength degree of the signal energy is expressed as:

[0027]

[0028] Among them, d i is the wavelet energy degree of the signal.

[0029] As a preferred solution of the intelligent judgment and power restoration method based on the low-voltage distribution network of the present invention, wherein: the construction of the whole-network perception system includes, based on the Internet of Things technology, combining the real-time perception technology of the states of power consumption end nodes, communication technology and two-way power source tracing of the integrated power grid model, using metering meters to construct a real-time state perception system for the distribution network, adjusting the concentrator and the data uploading mechanism. The adjustment strategy for the concentrator is to obtain the state of the meters quasi-real-time through software upgrade. The adjustment strategy for the data uploading mechanism is to adopt a one-sending-two-receiving mechanism. The data distribution system receives the terminal original data messages of the acquisition system front-end machine, and after being processed by the one-sending-two-receiving data distribution system, distributes the data to the application master station.

[0030] As a preferred solution of the intelligent judgment and power restoration method based on the low-voltage distribution network of the present invention, wherein: the combination of the dichotomy method and the intelligent switch to optimize the power restoration strategy for rapid power restoration and active judgment includes analyzing the faults of the low-voltage distribution network lines, quickly restoring power through the dichotomy method based on the intelligent substation area. After the low-voltage distribution network line trips, if no obvious fault information is received, the line is strongly powered on to judge whether it is a permanent fault. When the outgoing line circuit breaker trips, the strong power-on method is adopted. If the strong power-on is successful, the outgoing line is an instantaneous fault. If the strong power-on is not successful, the outgoing line is a permanent fault. Through the dichotomy method and the distribution automation switch in the intelligent substation area, rapid power restoration operations are carried out. For the switch that disconnects the dichotomy point, a trial power-on operation is performed. If the trial power-on is successful, the dichotomy point switch is set permanently, and the system power supply is restored through sectional inspection and trial power-on. If the trial power-on is not successful, for a single-radiation line, the fault point of the previous section of the line is searched, and the power supply is restored by means of sectional inspection and trial power-on. For a ring network connection line, the switch is closed to transfer the load, and the system power supply is restored by means of sectional inspection and trial power-on. Based on the operation of the integrated operation and dispatching power grid model, with the high-frequency acquisition data of intelligent meters as the criterion, and the optical fiber to the substation area as the high-speed information channel, the distribution network state monitoring system, through the comprehensive application of two-way power source tracing, load balancing, state estimation, node conflict algorithm, combined with real-time in-memory computing technology, quasi-real-time perceives the state of the distribution network and completes low-voltage judgment.

[0031] Another object of the present invention is to provide an intelligent judgment and power restoration system based on a low-voltage distribution network, which can judge the fault section of the low-voltage distribution network based on the switch quantity and electrical quantity information through a fault judgment module, and solves the problems of insufficient fault monitoring ability and low fault location accuracy of the current low-voltage distribution network.

[0032] As a preferred solution of the intelligent judgment and power restoration system based on the low-voltage distribution network of the present invention, it includes: a voltage visualization module, a fault judgment module, and an analysis and power restoration module; the voltage visualization module is used to collect monitoring data, dynamically generate a topology file based on the fusion verification of geographical and power management information, and construct a hierarchical visualization power grid model; the fault judgment module is used to analyze the states of circuit breakers and protection devices, calculate the deviation between main and backup protections, analyze the changes in electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault section; the analysis and power restoration module is used to construct a whole-network perception system, monitor the three-phase imbalance and zero-sequence current in real time, and optimize the power restoration strategy by combining the dichotomy method and intelligent switches for rapid power restoration and active judgment.

[0033] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of the intelligent judgment and power restoration method based on the low-voltage distribution network are realized.

[0034] A computer-readable storage medium stores a computer program on it. It is characterized in that when the computer program is executed by a processor, the steps of the intelligent judgment and power restoration method based on the low-voltage distribution network are realized.

[0035] The beneficial effects of the present invention: The intelligent judgment and power restoration method based on the low-voltage distribution network provided by the present invention realizes the visualization of the operation state of the low-voltage distribution network by constructing an environmental monitoring system, improves the reliability and security of the distribution network, improves the accuracy of fault analysis in low-voltage areas through accurate topology verification and intelligent fusion, realizes the rapid identification and positioning of faults through intelligent fault judgment, improves the repair efficiency, provides a clear and intuitive operation situation of the distribution network based on the visualization modeling of the full voltage level, improves the operation and maintenance decision-making efficiency, reduces the number of power restoration trial and error times through the dichotomy method for fault isolation and intelligent power restoration strategy, and improves the fault recovery efficiency. The present invention has achieved better effects in terms of safety, accuracy, and efficiency. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 It is the overall flowchart of an intelligent judgment and power restoration method based on the low-voltage distribution network provided by the first embodiment of the present invention.

[0038] Figure 2Schematic diagram of distribution network status and environment monitoring for an intelligent judgment and power restoration method based on a low-voltage distribution network provided by the first embodiment of the present invention.

[0039] Figure 3 Low-voltage topology identification diagram of a distribution network for an intelligent judgment and power restoration method based on a low-voltage distribution network provided by the first embodiment of the present invention.

[0040] Figure 4 Data analysis diagram of fault judgment for an intelligent judgment and power restoration method based on a low-voltage distribution network provided by the first embodiment of the present invention.

[0041] Figure 5 Flowchart of rapid power restoration for low-voltage faults in a low-voltage intelligent substation area for an intelligent judgment and power restoration method based on a low-voltage distribution network provided by the first embodiment of the present invention.

[0042] Figure 6 Overall module diagram of an intelligent judgment and power restoration system based on a low-voltage distribution network provided by the third embodiment of the present invention. Detailed implementation manners

[0043] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention is provided in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0044] Embodiment 1

[0045] Refer to Figures 1 - 5 , which is an embodiment of the present invention, and provides an intelligent judgment and power restoration method based on a low-voltage distribution network, including:

[0046] S1: Collect monitoring data, dynamically generate a topology file based on the fusion and verification of geographical and power management information, and construct a hierarchical and visual power grid model.

[0047] Furthermore, collecting monitoring data includes setting up an intelligent monitoring background for the distribution network to store and process the status information of all distribution equipment and the environmental monitoring quantities of the distribution substation.

[0048] It should be noted that control instructions are sent to the intelligent distribution terminal, and the intelligent monitoring background of the distribution network remotely monitors the overall operation status of the distribution room, issues danger warnings and abnormal alarms; the data transmission system connects the on-site monitoring data with the background monitoring platform, transmits the data in real time, and notifies authorized users. The intelligent monitoring system is responsible for collecting monitoring information and transmitting it to the intelligent main control terminal. The intelligent execution system controls the data according to the information collected by the intelligent monitoring system, starts the intelligent execution components, and controls the environmental parameters of the distribution room within the target range. Based on IP broadband carrier and signal injection technology, a business-distribution topology file is dynamically generated and independently verified with the PMS low-voltage account information. Based on the distribution Internet of Things technology, the intelligent distribution terminal after topology verification interacts with the energy consumption information of the intelligent electricity meter locally, extending the distribution network perception layer to the low-voltage distribution network.

[0049] Furthermore, constructing a hierarchical visual power grid model includes constructing a visual model covering all voltage levels of stations, lines, transformers, meter boxes, and users.

[0050] It should be noted that by integrating the dispatching main network model, GIS distribution network model, marketing customer files, and metering acquisition relationship data, a business-distribution-regulation integrated power grid model covering stations, lines, distribution transformers, meter boxes, meters, concentrators, and communication equipment is constructed according to the principles of zoning, voltage division, component division, and substation area division.

[0051] It should also be noted that the topological data of the power grid of concern is separated from the full-scale topological data and decomposed into independent distribution network data files according to the concept of feeder loop. Useless equipment in the topological data is removed, the topology is reorganized, and the topological data files are decomposed into medium-voltage topology, substation topology, and low-voltage line topology according to the research objectives and input into the topological data model for fault judgment. Since the objects described in the topological file are not completely consistent with the objects in the attribute account, new objects in the topological data are automatically added and maintained in the account.

[0052] S2: Analyze the status of circuit breakers and protection equipment, calculate the deviation between main and backup protection, analyze the change of electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault section.

[0053] Furthermore, analyzing the status of circuit breakers and protection equipment, calculating the deviation between main and backup protection, analyzing the change of electrical quantities by combining wavelet transform and singular value decomposition, and identifying the fault section include judging the fault section of the distribution network based on switch quantity information.

[0054] It should be noted that the fault judgment model of the distribution network with switch quantity information is expressed as:

[0055]

[0056] where r k,m and They are the actual state and the expected state of the main protection of the device, respectively, r k,s and They are the actual state and the expected state of the near backup protection of the device, respectively, r k,l and They are the actual state and the expected state of the remote backup protection of the device, respectively, C i and They are the actual operating state and the expected operating state of the circuit breaker, respectively. k is the device number, m is the main protection of the device, s is the near backup protection of the device, l is the remote backup protection of the device, i is the circuit breaker element number. Incorporating the action state information of the automatic reclosing device of the circuit breaker, the objective function is improved and expressed as:

[0057]

[0058] Among them, r i,c and They are the actual state and the expected state of the breaker failure protection, respectively, r i,a and They are the actual state and the expected state of the automatic closing of the circuit breaker, respectively. c is the breaker failure protection, and a is the automatic closing of the circuit breaker.

[0059] It should also be noted that for the expected value of the protection, when the protection device operates, the expected value r of all protection operations * is 1, and the expected operation C of the circuit breaker * is 1, otherwise r * is 0, and the expected operation C of the circuit breaker * is 0. In the relay protection configuration, the expected state of the main protection is the state of the protected component. The expected state of the near backup protection = component state × (1 - protection state). The expected state of the remote backup protection = 1 - [1 - state of the associated component × (1 - state of the circuit breaker on the associated path)]. The expected state of the circuit breaker operation = max{action expectation of the circuit breaker-related protection (including breaker failure protection and automatic reclosing) × the actual state of this protection}.

[0060] Furthermore, analyzing the states of the circuit breaker and protection equipment, calculating the deviation of the main backup protection, combining wavelet transform and singular value decomposition to analyze the change of electrical quantities, and identifying the fault section also include judging the fault section of the distribution network based on electrical quantity information.

[0061] It should be noted that after a fault occurs in the distribution network, the change in the current amplitude of the fault line is greater than that of the non-fault line. By defining the wavelet fault degree to measure the change degree of the electrical quantity amplitude before and after the component fault, it is expressed as:

[0062] F if = max{D i1 , D i2,…D iβ}

[0063] F ib =max{D iβ+1 ,D iβ+2 ,…D iβ+γ}

[0064]

[0065] Among them, F if is the maximum amplitude wavelet transform of the signal before the fault, and F ib is the maximum amplitude wavelet transform of the signal after the fault. D i1 , D i2 ,…D iβ are the wavelet sampling results of each signal sampling point before the fault. D iβ+1 , D iβ+2 ,…D iβ+γ are the wavelet transform results of each signal sampling point after the fault. γ is the sampling point before and after the wavelet transform. The wavelet transform results of all sampling points before the fault are the same. β is the number of values required for a group of wavelet transforms. V i is the degree of amplitude change of the signal before and after the fault. Processing the degree of amplitude change of the signal before and after the fault in the case of a fault is expressed as:

[0066]

[0067] Among them, x i is the wavelet fault degree of the i-th component after the fault occurs, i = 1,…, N, where N is the component number. The high-frequency transient component caused by the system fault is obtained through wavelet transform and singular value decomposition to obtain the characteristic matrix. The singular eigenvalue of the faulty component in the characteristic matrix is greater than that of the non-faulty component. Introducing the wavelet singularity theory for fault signal analysis is expressed as:

[0068]

[0069] Among them, S i is the average value of the singular values of the singular value characteristic matrix of the i-th component, and λ i is the singular value of the i-th component. Processing the average value of the singular values of the singular value characteristic matrix of the component is expressed as:

[0070]

[0071] Among them, m i is the wavelet singularity degree of the i-th component after the fault. Performing wavelet transform on the fault signal is expressed as:

[0072]

[0073] Among them, Ej is the wavelet energy distribution at the j-th scale of the signal, D j is the wavelet sampling result at different scales j and the number of sampling points, W i is the average wavelet energy at all scales, δ is the signal scale, and the strength of the signal energy is expressed as:

[0074]

[0075] where d i is the wavelet energy degree of the signal.

[0076] It should also be noted that, as Figure 4 shown, using the equipment operation and maintenance lean management system, a closed-loop management system covering production, dispatching, emergency repair and other specialties, its essence is to take the power grid basic data as the soul and the business requirements of each specialty as the driving force, and finally realize the homogenization, leanization and high efficiency of professional management. The construction of the data model for the integration of operation and distribution takes the user access point as the bridge to construct a full-topology model covering the user meters in low-voltage substations. The integration of the power grid model at all voltage levels and the marketing user model provides the data core for the further lean management of the power grid, especially the medium and low voltage distribution network, and provides an opportunity for breaking the black box of low-voltage power consumption services and constructing an integrated service system for power distribution, communication and marketing. Through the visualization research on the structure analysis of the distribution network, the distribution network, distribution transformer, concentrator, low-voltage outgoing line, user communities, buildings, meters, etc. are integrated on one screen, so as to clearly express the relationship between power consumption customers, concentrators and the distribution network, providing a basis for the lean management and control of the distribution network structure and the efficient service of customers. The visualization technology research for the integration of operation and distribution needs to follow the principle of hierarchical display, and the power grid is divided into an integrated visualization system including the display of medium-voltage feeder loops, the display of low-voltage distribution rooms, the display of marketing customer models, the display of substation environments, and the display of geographical layout maps with real-time status.

[0077] S3: Build a whole-network perception system, monitor the three-phase imbalance and zero-sequence current in real time, and combine the dichotomy method with intelligent switches to optimize the power restoration strategy for rapid power restoration and active judgment.

[0078] Furthermore, building a whole-network perception system includes using Internet of Things technology.

[0079] It should be noted that, combining the real-time perception technology of the power consumption end-node state, communication technology and the two-way traceability of the power source of the integrated power grid model, using metering meters to build a real-time state perception system for the distribution network, adjusting the concentrator and the data upload mechanism. The concentrator adjustment strategy is to obtain the meter status quasi-real-time through software upgrade, and the data upload mechanism adjustment strategy is to use the one-send-two-receive mechanism. The data distribution system receives the terminal original data messages from the front-end machine of the acquisition system, and after being processed by the one-send-two-receive data distribution system, distributes the data to the application master station.

[0080] It should also be noted that by taking advantage of the high real-time rate of the downlink network between the concentrator and the meters, the function of full-network perception is realized. For example, it can be stipulated that within 1 minute, the real-time online status information of all meter management nodes under the concentrator is completed. If a certain node has a power failure or other fault conditions, the concentrator can detect this node within 1 minute. To reduce the network traffic pressure, when all meters are online, a flag needs to be reported to indicate this state. To save network traffic and reduce the pressure on the concentrator and the master station, the message protocol is streamlined. Considering that the number of power-off meters is small, the concentrator only reports the offline meters. If all are online, only the total number needs to be reported.

[0081] It should also be noted that when a single-phase disconnection fault occurs in the system and there is a grounding situation on the load side, since the grounding occurs on the load side, there is no direct electrical connection with the power supply side. And because there is no current generated on the load side, the positive, negative, and zero-sequence currents during the fault will all have sudden changes, and the current amplitude in the fault line will also drop suddenly. When a single-phase disconnection without grounding or a load-side grounding fault occurs, the positive-sequence current in the line will decrease, and the negative-sequence and zero-sequence currents will increase. When a single-phase disconnection fault occurs and there is a grounding fault on the power supply side, the changes in the positive, negative, and zero-sequence currents of the system are the same as those of a single-phase disconnection without grounding fault. Among the changing positive, negative, and zero-sequence current components, since the zero-sequence current is easy to extract, it can be used as the judgment basis for single-phase disconnection faults. Since the judgment criterion for fault startup needs to be fast and be able to accurately identify the fault at the first time when the fault occurs, it is considered to define the periodic ratio of the zero-sequence current component as the sampling ratio of the zero-sequence current in adjacent periods, which is expressed as:

[0082]

[0083] where, i x0 is the periodic ratio of the zero-sequence current component, m is the sampling point sequence, M is the number of periodic sampling points. Since the zero-sequence current of the line hardly changes under normal conditions, so i x0 ≈1. When a single-phase disconnection fault occurs, i x0 >1. The startup criterion for a single-phase disconnection fault in the system based on the periodic ratio of the zero-sequence current component is expressed as:

[0084]

[0085] where, i set0is the zero-sequence current gradient threshold, generally taken as 1.1 - 1.2, P is the accumulation coefficient, generally taken as 0.6 - 0.8. If the single-phase disconnection fault startup criterion is based only on the periodic ratio of the zero-sequence current component, when the load fluctuates frequently, the system may misjudge. Therefore, it is necessary to further judge the mutation degree of the zero-sequence current. When the three-phase load in the system is unbalanced, the effective value of the zero-sequence current component is generally less than 5% of the effective value of the maximum phase current. Since a large zero-sequence current component will appear in the line after a single-phase disconnection fault occurs, the setting value is expressed as:

[0086] I set0 = P * I x

[0087] Among them, I x is 5% of the maximum phase current of the line, I set0 is the setting value of the zero-sequence current component, P * is the reliability coefficient, generally taken as 1 - 2. To further increase the discrimination reliability of the fault criterion and exclude the interference of sudden increase in the zero-sequence current component caused by other situations, the fault criterion is expressed as:

[0088]

[0089] Among them, ΔI rms is the difference value of the effective value of the line phase current at an interval of 0.02 s, I rms is the phase current during the operation of the section line, Q is the differential setting coefficient, generally taken as 0.15 - 0.2. When a single-phase disconnection fault occurs in the system, the no-load condition of the line will not cause obvious changes to the line current, which may lead to the failure of the criterion. Therefore, an auxiliary criterion based on the downstream line voltage is added for the no-load line, which is expressed as:

[0090] U brms < ε

[0091] Among them, U brms is the line voltage of any downstream section, ε is a positive constant close to 0. When an abnormal situation occurs in the zero-sequence current of a certain line section in the system, the startup criterion is detected. If the criterion holds, the electrical quantities of the downstream sections of the distribution line are traversed. When the current or voltage of the downstream line section satisfies any criterion, it can be determined that a disconnection fault has occurred in this section.

[0092] Furthermore, combining the bisection method with the intelligent switch to optimize the power restoration strategy for fast power restoration and active judgment includes analyzing the faults of low-voltage distribution network lines.

[0093] It should be noted that through the rapid power restoration by the dichotomy method based on the intelligent substation area, after the low-voltage distribution network line trips, if no obvious fault information is received, the line is strongly powered on to determine whether it is a permanent fault. When the outgoing line circuit breaker trips, a strong power-on method is adopted. If the strong power-on is successful, the outgoing line is an instantaneous fault. If the strong power-on is unsuccessful, the outgoing line is a permanent fault. Through the dichotomy method and the distribution automation switch in the intelligent substation area, rapid power restoration operations are carried out. For the switch that disconnects the dichotomy point, a trial power-on operation is performed. If the trial power-on is successful, the dichotomy point switch is set up permanently, and the system power supply is restored through sectional inspection and trial power-on. If the trial power-on is unsuccessful, for a single-radiation line, the fault point of the previous section of the line is searched, and the power supply is restored by means of sectional inspection and trial power-on. For a ring network connection line, the switch is closed to transfer the load, and the system power supply is restored by means of sectional inspection and trial power-on. A distribution network state monitoring system with the integrated operation and distribution and adjustment power grid model as the operation basis, the high-frequency collected data of intelligent meters as the criterion, and the optical fiber to the substation area as the high-speed information channel, through the comprehensive application of power bidirectional traceability, load balancing, state estimation, node conflict algorithm, combined with real-time in-memory computing technology, can quasi-real-time perceive the state of the distribution network and complete low-voltage judgment.

[0094] It should also be noted that in the actual implementation process, the circuit breaker can be remotely controlled to achieve strong power-on, improve work efficiency, shorten the power restoration time, and thus achieve rapid power restoration. The dichotomy method means that when the fault occurrence location is not clear, the dichotomy point switch is used to isolate the fault, and this method has the effect of restoring power to 50% of the line segment at one time, and the power restoration is carried out based on the power restoration process of the dichotomy method.

[0095] It should also be noted that the application scenarios of the ubiquitous power Internet of Things in the low-voltage distribution system are summarized, including full monitoring of the low-voltage distribution network environment system, identification of the low-voltage topology of the distribution network, deep integration of operation and distribution in the intelligent substation area, active repair of low-voltage faults, and a low-voltage fault judgment scheme based on the intelligent substation area and the construction principle of the integrated model for low-voltage fault judgment in the distribution network and the architecture of the low-voltage fault judgment platform based on the intelligent substation area are proposed. The low-voltage fault judgment platform can accurately judge the power outage fault at the meter box level and realize the active judgment of power grid faults.

[0096] Embodiment 2

[0097] An embodiment of the present invention provides an intelligent judgment and power restoration method based on a low-voltage distribution network. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0098] Set up an experimental environment to compare and analyze the differences between the traditional low-voltage distribution network and the method of the present invention in terms of fault detection, fault location, emergency repair response, and power restoration efficiency. A low-voltage distribution substation with a typical distribution network structure was selected as the experimental area. The substation includes a substation, distribution lines, and user terminal equipment, and has distributed power access. The experimental design includes two experimental groups. One group uses the traditional operation and maintenance method of the low-voltage distribution network, and the other group uses the method of the present invention for fault monitoring and power restoration.

[0099] Before the experiment, artificial simulated faults were set in the distribution network, including line short circuits, transformer overloads, voltage fluctuations caused by distributed power access, etc. During the experiment, the traditional low-voltage distribution network used a method combining manual inspections, remote data analysis, and manual judgment for fault handling, while the method of the present invention used real-time monitoring data, topology identification, intelligent analysis, and remote power restoration strategies for fault diagnosis and repair.

[0100] As shown in Table 1, the traditional low-voltage distribution network relies on user-reported faults and regular inspections, resulting in a relatively long average fault discovery time. The method of the present invention actively detects faults through the low-voltage distribution substation environment monitoring system using real-time data analysis algorithms, significantly shortening the fault detection time, indicating that the real-time monitoring and intelligent analysis capabilities of the present invention can effectively improve the fault response speed. The operation and maintenance of the traditional distribution network mainly rely on experience for troubleshooting, resulting in a large error in fault location. The method of the present invention combines topology identification and electrical quantity analysis technologies to achieve accurate fault point location, reducing misjudgment and unnecessary inspection work, and improving the accuracy of fault location, enabling emergency repair personnel to find the fault point faster. The traditional method usually requires dispatching multiple professional personnel for troubleshooting, resulting in a high ineffective attendance rate. The method of the present invention automatically generates a fault report when a fault occurs and conducts a preliminary diagnosis through a remote control system, only dispatching necessary personnel for emergency repair, improving the utilization rate of human resources. After the fault troubleshooting of the traditional distribution network is completed, the power restoration operation is judged manually, which takes a long time and may have misjudgments. The method of the present invention isolates the fault point through the dichotomy method and uses automated control equipment to achieve rapid power restoration and improve the power supply restoration efficiency.

[0101] Table 1 Experimental data table

[0102]

[0103] Example 3

[0104] Referring to Figure 6 , as an embodiment of the present invention, an intelligent judgment and power restoration system based on a low-voltage distribution network is provided, including: a voltage visualization module, a fault judgment module, and an analysis and power restoration module.

[0105] Among them, the voltage visualization module is used to collect monitoring data, dynamically generate a topology file based on the fusion verification of geographical and power management information, and construct a hierarchical visualization power grid model; the fault judgment module is used to analyze the states of circuit breakers and protection devices, calculate the deviation between the main and backup protections, analyze the changes in electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault section; the power restoration analysis module is used to construct a whole-network perception system, monitor the three-phase imbalance and zero-sequence current in real time, and optimize the power restoration strategy by combining the dichotomy method and intelligent switches for rapid power restoration and active judgment.

[0106] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0107] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0108] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent judgment and power restoration method based on a low-voltage distribution network, characterized in that, Including: Collect monitoring data, dynamically generate a topology file based on the fusion verification of geographical and power management information, and construct a hierarchical visual power grid model; Analyze the status of circuit breakers and protection devices, calculate the deviation between main and backup protections, analyze the change of electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault section; Construct a whole-network perception system, monitor three-phase unbalance and zero-sequence current in real time, and combine the dichotomy method with intelligent switches to optimize the power restoration strategy for rapid power restoration and active judgment; Analyze the status of circuit breakers and protection devices, calculate the deviation between main and backup protections, analyze the change of electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault section, including judging the fault section of the distribution network based on switch quantity information. The distribution network fault judgment model based on switch quantity information is expressed as: Among them, r k,m and are the actual state and the desired state of the main protection of the device respectively. r k,s and are the actual state and the desired state of the near backup protection of the device respectively. r k,l and are the actual state and the desired state of the remote backup protection of the device respectively. C i and are the actual operating state and the desired operating state of the circuit breaker respectively. k is the device number, m is the main protection of the device, s is the near backup protection of the device, l is the remote backup protection of the device, i is the circuit breaker element number, and by incorporating the action state information of the automatic reclosing device of the circuit breaker, the improvement of the objective function is expressed as: where r i,c and are the actual state and the desired state of the breaker failure protection respectively, r i,a and are the actual state and the desired state of the breaker auto-reclosing respectively, c is the breaker failure protection, and a is the breaker auto-reclosing; Analyze the status of circuit breakers and protection devices, calculate the deviation between main and backup protections, analyze the change of electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault section also includes judging the fault section of the distribution network based on electrical quantity information. After a fault occurs in the distribution network, the change in the current amplitude of the fault line is greater than that of the non-fault line. By defining the wavelet fault degree to measure the change degree of the electrical quantity amplitude before and after the component fault, it is expressed as: F if = max{D i1 , D i2 , … D iβ} F ib = max{D iβ+1 , D iβ+2 , … D iβ+γ} Among them, F if is the maximum amplitude wavelet transform of the pre-fault signal, F ib is the maximum amplitude wavelet transform of the post-fault signal, D i1 , D i2 , … D iβ are the wavelet sampling results of each signal sampling point before the fault, D iβ+1 , D iβ+2 , … D iβ+γ are the wavelet transform results of each signal sampling point after the fault, γ is the sampling point before and after the wavelet transform, the wavelet transform results of all sampling points before the fault are the same, β is the number of values required for a group of wavelet transforms, V i is the degree of amplitude change of the signal before and after the fault. Processing the degree of amplitude change of the signal before and after the fault in the case of a fault is expressed as: where x i is the wavelet fault degree of the i-th component after the fault occurs, i = 1, …, N, N is the component number. The high-frequency transient component caused by the system fault is subjected to wavelet transform and singular value decomposition to obtain the characteristic matrix. In the characteristic matrix, the singular eigenvalue of the fault component is greater than that of the non-fault component. The wavelet singularity theory is introduced for fault signal analysis and is expressed as: Among them, S i is the average value of the singular values of the singular value feature matrix of the i-th component, and λ i is the singular value of the i-th component. The processing of the average value of the singular values of the singular value feature matrix of the component is expressed as: where m i is the wavelet singularity degree of the i-th component after the fault, and the wavelet transform of the fault signal is expressed as: Among them, E j is the wavelet energy distribution at the j-th scale of the signal, D j is the wavelet sampling result at different scales j and the number of sampling points, W i is the average wavelet energy at all scales, δ is the signal scale, and the strength of the signal energy is expressed as: Among them, d i is the wavelet energy measure of the signal.

2. The intelligent judgment and power restoration method based on a low-voltage distribution network according to claim 1, wherein: The collected monitoring data includes setting up an intelligent monitoring background for the distribution network to store and process the status information of all distribution equipment and the environmental monitoring quantities of the distribution substation, and sending control instructions to the intelligent distribution terminal. The intelligent monitoring background of the distribution network remotely monitors the overall operation status of the distribution substation, issues danger warnings and abnormal alarms; the data transmission system connects the on-site monitoring data with the background monitoring platform, transmits the data in real time, and notifies authorized users. The intelligent monitoring system is responsible for collecting monitoring information and transmitting it to the intelligent main control terminal. Control the data through the intelligent execution system. According to the information collected by the intelligent monitoring system, start the intelligent execution component to control the distribution substation environmental parameters within the target range. Based on IP broadband carrier and signal injection technology, dynamically generate the operation and distribution topology file, and perform independent verification with the PMS low-voltage account information. Based on the distribution Internet of Things technology, interact the energy consumption information between the topology-verified intelligent distribution terminal and the intelligent meter locally, so that the distribution network perception layer extends to the low-voltage distribution network.

3. The intelligent judgment and power restoration method based on a low-voltage distribution network according to claim 2, characterized in that: The construction of the hierarchical visual power grid model includes constructing a full-voltage-level visual model covering substations, lines, transformers, meter boxes, and users, and constructing an operation, distribution, and dispatching integrated power grid model covering substations, lines, distribution transformers, meter boxes, meters, concentrators, and communication equipment according to the principles of zoning, voltage division, component division, and substation area by integrating the dispatching main network model, GIS distribution network model, marketing customer files, and metering collection relationship data.

4. The intelligent judgment and power restoration method based on a low-voltage distribution network according to claim 3, wherein: The construction of the whole-network perception system includes, based on Internet of Things technology, combining real-time perception technology of the states of power consumption end nodes, communication technology, and two-way power source tracing of the integrated power grid model, using metering devices to construct a real-time state perception system for the distribution network, adjusting the concentrator and the data uploading mechanism. The adjustment strategy for the concentrator is to obtain the state of the meters quasi-real-time through software upgrade. The adjustment strategy for the data uploading mechanism is to adopt a one-sending-two-receiving mechanism. The data distribution system receives the terminal original data messages from the front-end machine of the acquisition system. After being processed by the one-sending-two-receiving data distribution system, the data is distributed to the application master station.

5. The intelligent judgment and power restoration method based on a low-voltage distribution network according to claim 4, characterized in that: The combination of the dichotomy method and intelligent switches to optimize the power restoration strategy for rapid power restoration and active judgment includes analyzing the faults in the low-voltage distribution network lines, quickly restoring power through the dichotomy method based on intelligent substations. After the low-voltage distribution network line trips, if no obvious fault information is received, the line is strongly powered on to determine whether it is a permanent fault. When the outgoing circuit breaker trips, a strong power-on method is adopted. If the strong power-on is successful, the outgoing line is an instantaneous fault. If the strong power-on is not successful, the outgoing line is a permanent fault. Quick power restoration operations are carried out through the dichotomy method and the distribution automation switches in the intelligent substation. For the switches at the dichotomy points that are disconnected, trial power-on operations are carried out. If the trial power-on is successful, the dichotomy point switches are set permanently, and the system power supply is restored through sectional inspection and trial power-on. If the trial power-on is not successful, for single-radiation lines, the fault points in the front-section lines are searched, and the power supply is restored by means of sectional inspection and trial power-on. For ring network tie lines, the switches are closed to transfer the load, and the system power supply is restored by means of sectional inspection and trial power-on. Based on the operation of the integrated operation and dispatching power grid model, with the high-frequency acquisition data of intelligent meters as the criterion and the optical fiber to the substation as the high-speed information channel, the distribution network state monitoring system accurately perceives the state of the distribution network quasi-real-time through comprehensive application of two-way power source tracing, load balancing, state estimation, and node conflict algorithms, combined with real-time in-memory computing technology, and completes low-voltage judgment.

6. A system adopting the intelligent judgment and power restoration method based on a low-voltage distribution network as described in any one of claims 1 to 5, characterized in that: It includes a voltage visualization module, a fault judgment module, and an analysis and power restoration module; The voltage visualization module is used to collect monitoring data, dynamically generate a topology file based on the fusion and verification of geographical and power management information, and construct a hierarchical visualization power grid model; The fault judgment module is used to analyze the states of circuit breakers and protection devices, calculate the deviation between the main and backup protections, analyze the changes in electrical quantities by combining wavelet transform and singular value decomposition, and identify the fault sections; The analysis and power restoration module is used to construct a whole-network perception system, monitor the three-phase imbalance and zero-sequence current in real time, and perform rapid power restoration and active judgment by combining the dichotomy method and the intelligent switch to optimize the power restoration strategy.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent judgment and power restoration method based on the low-voltage distribution network according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent judgment and power restoration method based on the low-voltage distribution network according to any one of claims 1 to 5.

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