A method and system for acquiring a non-inductive transfer path

Abstract

The application discloses a kind of inductiveless transfer path acquisition method and system, belong to power system control technical field, the method is: based on the initial topological graph of microgrid to be transferred, real-time operation data and real-time state data are obtained, to obtain real-time topological graph and fault benchmark threshold based on the above data;Based on the above data, the fault benchmark threshold is corrected, and the fault threshold is obtained, if the fault electrical characteristics are all more than the threshold in the preset time period, then determine the fault type and fault location;Based on the fault type, determine the capacity margin coefficient, several qualified standby power supply and total available output capacity, to obtain the corrected capacity margin;Based on the above data and preset constraint, several inductiveless transfer initial paths are screened, and inductiveless transfer path is obtained.The inductiveless transfer path acquisition method provided by the application can quickly plan the inductiveless transfer path of distribution network under fault condition.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, and in particular to a method and system for obtaining a sensorless power transfer path. Background Technology

[0002] In traditional power distribution network operation systems, fault transfer has long employed a passive triggering and step-by-step processing response logic. This mode is suitable for early power distribution network applications with single-source radial power supply and simple load types. However, with the continuous expansion of power distribution network functions and the continuous upgrading of electricity demand, the adaptability of the traditional response mode has gradually decreased, and its inherent response lag problem has gradually become the core bottleneck restricting the improvement of power supply reliability in power distribution networks.

[0003] Under the current technological background, with the continuous increase in the penetration rate of distributed power sources in the distribution network and the diversification of electricity load, the lag problem of the traditional fault transfer power supply mode has become more prominent: the large-scale access of distributed power sources has transformed the distribution network from the original single power supply structure to a multi-power source coexistence power supply structure. The current direction and amplitude at the time of fault occurrence exhibit dynamic change characteristics, while the detection logic and judgment criteria of traditional fault detection devices are relatively fixed and cannot adapt to this dynamic change. It is difficult to quickly and accurately identify the nature of the fault, which can easily cause fault location deviation, thereby delaying the isolation operation of the fault area and the subsequent power transfer process, seriously affecting the power supply stability and continuity of the distribution network. Summary of the Invention

[0004] The present invention aims to provide a method and system for obtaining seamless power transfer paths to solve the above-mentioned technical problems, avoid the lag and poor adaptability of seamless power transfer path planning due to power supply capacity mismatch during microgrid faults, and realize rapid planning of seamless power transfer paths for distribution networks under fault conditions.

[0005] To address the aforementioned technical problems, this invention provides a method for obtaining a seamless power transfer path, comprising: Based on the initial topology map, real-time operation data and real-time status data of the microgrid to be transferred, the fault electrical characteristics, real-time topology map and fault benchmark threshold are obtained based on the real-time operation data, real-time status data and initial topology map; The fault baseline threshold is corrected based on real-time operation data and real-time status data to obtain the fault threshold. If the fault electrical characteristics all exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics. Based on the fault type and the preset backup power access rules, several backup power sources in the microgrid to be supplied are evaluated to determine the capacity margin factor, several qualified backup power sources and the total available output capacity. The corrected capacity margin is obtained based on the capacity margin factor, total available output capacity, and total real-time power supply demand; Based on the real-time topology map, fault location, several qualified backup power supplies, corrected capacity margin, and preset power transfer path library, a path search is performed to obtain several seamless power transfer initial paths. Based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints, several initial paths for seamless power transfer are screened to obtain seamless power transfer paths.

[0006] In the above scheme, by acquiring real-time operational data, real-time status data, real-time topology maps, and fault baseline thresholds, initial fault judgment criteria that align with the actual operating state of the microgrid can be provided for subsequent distribution network fault determination. Simultaneously, a real-time topology foundation is provided for the planning of power transfer paths, avoiding poor path planning adaptability due to lagging topology information. Next, by combining fault electrical characteristics and fault thresholds within a preset time period to determine the fault type and location, microgrid faults can be quickly identified and located, providing core fault information for subsequent seamless power transfer path planning and preventing path planning delays due to missing fault information. Then, by determining the capacity margin coefficient, several qualified backup power sources suitable for the current distribution network fault type are selected, and their total available output capacity is determined, thereby providing accurate power source-side capacity parameters for subsequent capacity margin correction and path planning.

[0007] Subsequently, by obtaining the corrected capacity margin, the actual matching capability of several qualified backup power sources to the microgrid's power supply demand after a fault can be quantified. This avoids the problem of power supply capacity matching imbalance in path planning from a capacity perspective, ensuring the capacity adaptability of path planning. Next, by conducting path search using real-time topology maps, fault locations, several qualified backup power sources, corrected capacity margins, and a pre-set transfer path library, it is possible to avoid the generated seamless transfer initial path being out of sync with the actual operating conditions of the microgrid, thus preventing path planning lag. Finally, by screening several seamless transfer initial paths through four constraints, unqualified seamless transfer initial paths can be eliminated, ensuring that the obtained seamless transfer paths not only meet the power supply capacity matching requirements of the microgrid under fault conditions but also possess safety, feasibility, and operational stability. Ultimately, this achieves rapid planning of seamless transfer paths for the distribution network under fault conditions.

[0008] Furthermore, the step of acquiring an initial topology map, real-time operating data, and real-time status data based on the microgrid to be supplied, and then acquiring fault electrical characteristics, a real-time topology map, and a fault baseline threshold based on the real-time operating data, real-time status data, and initial topology map, includes: Acquire initial topology, real-time operation data, and real-time status data based on the microgrid to be supplied; The initial topology map is updated based on real-time running data and real-time status data to obtain the real-time topology map; The operating conditions of the microgrid are determined based on real-time operational data, real-time status data, and real-time topology diagrams. Both real-time operating data and real-time status data are filtered and denoised, and feature extraction is performed to obtain fault electrical characteristics. The microgrid operating conditions are matched with a preset microgrid operating condition database to obtain the fault baseline threshold.

[0009] In the above scheme, obtaining the initial topology map, real-time operating data, and real-time status data of the microgrid to be supplied provides fundamental data support for subsequent microgrid topology updates, fault condition determination, fault feature extraction, and fault benchmark threshold matching. Next, updating the initial topology map using real-time operating data and real-time status data ensures that the microgrid topology information aligns with the actual operating state of the microgrid, eliminating topology information lag and obtaining a real-time topology map, providing accurate real-time topology basis for subsequent supply path planning. Then, determining the microgrid operating condition using real-time operating data, real-time status data, and the real-time topology map lays the foundation for matching fault benchmark thresholds. Subsequently, by filtering and denoising both the real-time operating data and real-time status data and extracting features, interference signals in the data can be removed, and fault electrical features reflecting the microgrid fault state can be extracted, providing feature basis for subsequent fault determination. Finally, by matching the microgrid operating conditions with the preset microgrid operating condition database to obtain the fault benchmark threshold, the fault benchmark threshold can be adapted to the current operating conditions of the microgrid, avoiding false or missed faults caused by fixed thresholds, and providing an initial judgment standard for subsequent fault threshold correction.

[0010] Furthermore, the fault baseline threshold is corrected based on real-time operating data and real-time status data to obtain a fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics, including: The correction factor is obtained based on real-time operational data and real-time status data; The fault baseline threshold is corrected based on the correction factor to obtain the fault threshold; If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics.

[0011] In the above scheme, the correction factor obtained through real-time operating data and real-time status data provides a basis for adjusting the fault baseline threshold. Then, by correcting the fault baseline threshold using the correction factor, the fault threshold can be made to fit the current operating conditions of the microgrid while offsetting the effects of data fluctuations and harmonic interference, thus improving the accuracy and sensitivity of subsequent fault determination. Next, by comparing the fault electrical characteristics with the fault threshold within a preset time period, if the fault electrical characteristics continuously exceed the fault threshold, the fault type and fault location are determined based on the fault electrical characteristics. This enables accurate fault determination and rapid location of microgrid faults, thereby providing fault information support for the planning of subsequent seamless power transfer paths.

[0012] Furthermore, the step of obtaining the correction factor based on real-time operating data and real-time status data includes: The voltage fluctuation rate, current change rate, and total harmonic distortion rate of the microgrid are obtained based on real-time operation data and real-time status data. The original correction factor is calculated based on the microgrid voltage fluctuation rate, microgrid current change rate, and microgrid total harmonic distortion rate. The original correction factor is corrected based on the preset correction model to obtain the correction factor.

[0013] In the above scheme, by using real-time operational data and real-time status data, it is possible to extract microgrid voltage fluctuation rate, microgrid current change rate, and microgrid total harmonic distortion rate, which reflect the real-time operational stability, dynamic changes in electrical quantities, and power quality of the microgrid. This provides multi-dimensional quantitative foundational data for the subsequent calculation of correction factors. Next, the original correction factor is calculated using the microgrid voltage fluctuation rate, microgrid current change rate, and microgrid total harmonic distortion rate. Then, the original correction factor is corrected using a preset correction model, ensuring that the obtained correction factor value is within a reasonable range and adapts to the actual operating state of the microgrid. This provides reliable adjustment parameters for the accurate correction of subsequent fault baseline thresholds.

[0014] Furthermore, the evaluation of several backup power sources in the microgrid to be supplied, based on fault type and preset backup power access rules, to determine the capacity margin factor, several qualified backup power sources, and total available output capacity, includes: Based on the fault type and the preset capacity margin database, determine the capacity margin coefficient; Based on the fault type and the preset backup power access rules, several backup power sources in the microgrid to be supplied are evaluated to obtain the matching score of each backup power source. Based on the matching score and fault type of each backup power source, several qualified backup power sources are identified. The available output capacity of each qualified backup power source is obtained, and the total available output capacity is calculated based on the available output capacity of each qualified backup power source.

[0015] In the above scheme, the capacity margin coefficient is determined by fault type and a preset capacity margin database. This provides a suitable capacity margin coefficient for subsequent quantification of qualified backup power supply capacity, ensuring that the capacity assessment matches the power supply demand under fault conditions. Next, several backup power supplies are evaluated based on fault type and preset backup power supply access rules. The matching score of each backup power supply quantifies its suitability for the current distribution network fault condition, thus providing a criterion for selecting qualified backup power supplies. Then, by determining several qualified backup power supplies based on their matching scores and fault types, backup power supplies that do not match the fault type or do not meet the preset backup power supply access rules can be eliminated, selecting qualified backup power supplies suitable for the current fault scenario and providing reliable power support for power transfer path planning. Finally, by obtaining the available output capacity of each qualified backup power supply and calculating the total available output capacity, the actual power supply potential of the backup power supply under fault conditions can be obtained, providing accurate power-side capacity parameters for subsequent capacity margin correction.

[0016] Furthermore, the process of filtering several initial non-inductive power transfer paths based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints to obtain non-inductive power transfer paths includes: Based on the preset microgrid constraints, several initial non-sensory power transfer paths are screened to obtain the first set of non-sensory power transfer paths; Based on preset capacity constraints, the first set of seamless power transfer paths is filtered to obtain the second set of seamless power transfer paths; The second set of sensorless power transfer paths is filtered based on the preset node voltage constraints to obtain the third set of sensorless power transfer paths. The seamless power transfer path is determined based on preset impact constraints, a third set of seamless power transfer paths, and preset optimization objectives.

[0017] In the above scheme, by pre-setting microgrid constraints to screen several initial seamless power transfer paths, paths that do not meet basic safety requirements can be eliminated from the perspective of microgrid safe operation, obtaining a first set of seamless power transfer paths, providing a safe and compliant path foundation for subsequent screening. Next, by pre-setting capacity constraints to screen the first set of seamless power transfer paths, paths that cannot adapt to the microgrid's power supply needs after a fault can be eliminated, obtaining a second set of seamless power transfer paths, ensuring that the capacity carrying capacity of the remaining paths matches the actual power supply demand. Then, by pre-setting node voltage constraints to screen the second set of seamless power transfer paths, it can be ensured that the remaining paths can maintain stable node voltages under microgrid faults, meeting the voltage quality requirements for normal distribution network operation, obtaining a third set of seamless power transfer paths. Finally, by pre-setting impulse constraints, the third set of seamless power transfer paths, and pre-setting optimization targets to determine seamless power transfer paths, paths with excessive power impulses can be eliminated, ensuring that the finally determined seamless power transfer paths can achieve smooth switching under faults.

[0018] Furthermore, it also includes: Several microgrid device control commands are obtained based on the aforementioned seamless power transfer path; The control commands for the microgrid devices are sent to the microgrid devices and real-time operating data and real-time status data are reacquired. If the real-time operating data or real-time status data exceeds the preset microgrid performance index, the control commands for the microgrid devices are corrected until the real-time operating data or real-time status data does not exceed the preset microgrid performance index.

[0019] In the above scheme, by acquiring several microgrid device control commands, the planned seamless power transfer path can be transformed into executable microgrid device control commands, providing specific operational guidelines for the actual implementation of seamless power transfer. Next, by issuing these control commands to several microgrid devices and re-acquiring real-time operating and status data, and correcting the control commands when the real-time operating or status data exceeds preset microgrid performance indicators until it returns to within the preset performance range, the execution process of seamless power transfer can be dynamically controlled in a closed loop. This allows for timely correction of command execution deviations, ensuring the microgrid remains in a stable operating state and guaranteeing the effectiveness of seamless power transfer and power supply reliability.

[0020] This invention provides a system for obtaining seamless power transfer paths, including a data acquisition module, a fault location module, a capacity parameter determination module, a capacity margin correction module, a power transfer path search module, and a power transfer path determination module, specifically: The data acquisition module is used to acquire an initial topology map, real-time operating data and real-time status data based on the microgrid to be supplied, and to acquire fault electrical characteristics, real-time topology map and fault benchmark threshold based on the real-time operating data, real-time status data and initial topology map; The fault location module is used to correct the fault baseline threshold based on real-time operating data and real-time status data, and obtain the fault threshold. If the fault electrical characteristics all exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics. The capacity parameter determination module is used to evaluate several backup power sources in the microgrid to be supplied based on the fault type and preset backup power access rules, and to determine the capacity margin coefficient, several qualified backup power sources and the total available output capacity. The capacity margin correction module is used to obtain the corrected capacity margin based on the capacity margin coefficient, the total available output capacity, and the total real-time power supply demand. The power transfer path search module is used to perform path search based on real-time topology map, fault location, several qualified backup power supplies, corrected capacity margin and preset power transfer path library to obtain several seamless power transfer initial paths. The power transfer path determination module is used to filter several initial non-sensory power transfer paths based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints, and to obtain non-sensory power transfer paths.

[0021] This invention provides a system for acquiring seamless power transfer paths. In practical applications, only a data acquisition module is needed. By acquiring real-time operating data, real-time status data, real-time topology maps, and fault baseline thresholds, it can provide initial fault judgment criteria that fit the actual operating state of the microgrid for subsequent distribution network fault determination. Simultaneously, it provides a real-time topology basis for power transfer path planning, avoiding poor path planning adaptability due to lagging topology information. Next, a fault location module is used to determine the fault type and location within a preset time period by combining fault electrical characteristics and fault thresholds. This enables rapid identification and location of microgrid faults, providing core fault information for subsequent seamless power transfer path planning and preventing path planning delays due to missing fault information. Then, a capacity parameter determination module is used to determine the capacity margin coefficient, screen out several qualified backup power sources suitable for the current distribution network fault type, and clarify their total available output capacity. This provides accurate power source-side capacity parameters for subsequent capacity margin correction and path planning.

[0022] Subsequently, a capacity margin correction module is employed. By acquiring the corrected capacity margin, the actual matching capability of several qualified backup power sources to the microgrid's power supply demand after a fault can be quantified. This avoids the problem of power supply capacity matching imbalance in path planning from a capacity perspective, ensuring the capacity adaptability of path planning. Next, a transfer path search module is used. Through real-time topology map, fault location, several qualified backup power sources, corrected capacity margin, and a preset transfer path library, path search is conducted. This avoids the generated seamless transfer initial path being out of sync with the actual operating conditions of the microgrid, preventing path planning lag. Finally, a transfer path determination module is used. Several seamless transfer initial paths are screened through four constraints. Unsuitable seamless transfer initial paths can be eliminated, ensuring that the obtained seamless transfer paths not only meet the power supply capacity matching requirements of the microgrid under fault conditions but also possess safety, feasibility, and operational stability. Ultimately, this achieves rapid planning of seamless transfer paths for the distribution network under fault conditions.

[0023] Furthermore, the data acquisition module is used to acquire an initial topology map, real-time operating data, and real-time status data based on the microgrid to be supplied, and to acquire fault electrical characteristics, a real-time topology map, and a fault baseline threshold based on the real-time operating data, real-time status data, and initial topology map, including: Acquire initial topology, real-time operation data, and real-time status data based on the microgrid to be supplied; The initial topology map is updated based on real-time running data and real-time status data to obtain the real-time topology map; The operating conditions of the microgrid are determined based on real-time operational data, real-time status data, and real-time topology diagrams. Both real-time operating data and real-time status data are filtered and denoised, and feature extraction is performed to obtain fault electrical characteristics. The microgrid operating conditions are matched with a preset microgrid operating condition database to obtain the fault baseline threshold.

[0024] In the above scheme, obtaining the initial topology map, real-time operating data, and real-time status data of the microgrid to be supplied provides fundamental data support for subsequent microgrid topology updates, fault condition determination, fault feature extraction, and fault benchmark threshold matching. Next, updating the initial topology map using real-time operating data and real-time status data ensures that the microgrid topology information aligns with the actual operating state of the microgrid, eliminating topology information lag and obtaining a real-time topology map, providing accurate real-time topology basis for subsequent supply path planning. Then, determining the microgrid operating condition using real-time operating data and real-time status data lays the foundation for matching the fault benchmark threshold. Subsequently, by filtering and denoising both the real-time operating data and real-time status data and extracting features, interference signals in the data can be removed, and fault electrical features reflecting the microgrid fault state can be extracted, providing feature basis for subsequent fault determination. Finally, by matching the microgrid operating conditions with the preset microgrid operating condition database to obtain the fault benchmark threshold, the fault benchmark threshold can be adapted to the current operating conditions of the microgrid, avoiding false or missed faults caused by fixed thresholds, and providing an initial judgment standard for subsequent fault threshold correction.

[0025] Furthermore, the fault location module is used to correct the fault baseline threshold based on real-time operating data and real-time status data to obtain a fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics, including: The correction factor is obtained based on real-time operational data and real-time status data; The fault baseline threshold is corrected based on the correction factor to obtain the fault threshold; If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics.

[0026] In the above scheme, the correction factor obtained through real-time operating data and real-time status data provides a basis for adjusting the fault baseline threshold. Then, by correcting the fault baseline threshold using the correction factor, the fault threshold can be made to fit the current operating conditions of the microgrid while offsetting the effects of data fluctuations and harmonic interference, thus improving the accuracy and sensitivity of subsequent fault determination. Next, by comparing the fault electrical characteristics with the fault threshold within a preset time period, if the fault electrical characteristics continuously exceed the fault threshold, the fault type and fault location are determined based on the fault electrical characteristics. This enables accurate fault determination and rapid location of microgrid faults, thereby providing fault information support for the planning of subsequent seamless power transfer paths. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a method for obtaining a seamless power transfer path according to an embodiment of the present invention; Figure 2 This is an architecture diagram of a system for obtaining a seamless transfer path according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0031] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0034] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0035] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0036] See Figure 1 To avoid delays and poor adaptability in seamless power transfer path planning due to power supply capacity mismatch during microgrid faults, and to achieve rapid planning of seamless power transfer paths in the distribution network under fault conditions, this embodiment provides a method for obtaining seamless power transfer paths. The flowchart of this method can be found in [link to flowchart]. Figure 1 ,include: Step S1: Obtain the initial topology map, real-time operation data and real-time status data based on the microgrid to be transferred, and obtain the fault electrical characteristics, real-time topology map and fault baseline threshold based on the real-time operation data, real-time status data and initial topology map; Step S2: Correct the fault baseline threshold based on real-time operation data and real-time status data to obtain the fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, determine the fault type and fault location based on the fault electrical characteristics. Step S3: Based on the fault type and the preset backup power access rules, evaluate several backup power sources in the microgrid to be supplied, and determine the capacity margin factor, several qualified backup power sources and the total available output capacity. Step S4: Obtain the corrected capacity margin based on the capacity margin factor, total available output capacity, and total real-time power supply demand; Step S5: Based on the real-time topology map, fault location, several qualified backup power supplies, corrected capacity margin, and preset power transfer path library, perform path search to obtain several seamless power transfer initial paths; Step S6: Based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints, several initial non-sensorless power transfer paths are screened to obtain non-sensorless power transfer paths.

[0037] In this embodiment, a microgrid serves as the core entity for power consumption and energy dispatch, supplemented by at least one backup power source. The microgrid encompasses distributed power sources (such as photovoltaics and energy storage devices), various power loads (including industrial production loads and residential power loads), and internal transmission lines and distribution equipment, undertaking the basic functions of energy production, transmission, and consumption within the region. The backup power source can be flexibly configured according to the microgrid's scale and power supply reliability requirements; it can be an independent energy storage power station, a diesel generator, or a backup interconnection line connected to the main grid, used to quickly provide power support in the event of a failure of the microgrid's main power supply. By acquiring real-time operational data, real-time status data, real-time topology maps, and fault baseline thresholds, initial fault judgment criteria that align with the actual operating state of the microgrid can be provided for subsequent distribution network fault determination. Simultaneously, a real-time topology foundation is provided for planning power transfer paths, avoiding poor path planning adaptability due to lagging topology information. Next, by combining fault electrical characteristics and fault thresholds within a preset time period, the fault type and location are determined. This enables rapid identification and location of microgrid faults, providing crucial fault information for subsequent seamless power transfer path planning and preventing path planning delays due to missing fault information. Then, by determining the capacity margin coefficient, several qualified backup power sources suitable for the current distribution network fault type are selected, and their total available output capacity is clarified. This provides accurate power source-side capacity parameters for subsequent capacity margin correction and path planning.

[0038] Subsequently, a corrected capacity margin is obtained through the capacity margin coefficient, total available output capacity, and real-time total power demand. Specifically, the product of the capacity margin coefficient and the real-time total power demand serves as a power supply matching index. The corrected capacity margin, obtained by subtracting the product of the capacity margin coefficient and the real-time total power demand from the total available output capacity, can be used to determine the power supply matching capability. If the corrected capacity margin is greater than or equal to 0, it indicates that the power supply to all loads can be met. If it is between the power supply matching index and 0, it indicates that the power supply to important loads can only be met. If it is less than the power supply matching index, it indicates that the corrected capacity margin is severely insufficient. Finally, by integrating the qualification assessment results of all backup power sources in the microgrid with the corrected capacity margin assessment results, a detailed assessment report can be generated for each backup power source. The assessment report includes the backup power source number, the overall availability status of the backup power source, and the specific level of capacity margin. This provides a precise and clear basis for selecting backup power sources for subsequent planning of seamless power transfer paths. It can quantify the actual matching capability of several qualified backup power sources to the power supply demand of the microgrid after a fault, thereby avoiding the problem of power supply capacity matching imbalance in path planning from the capacity dimension and ensuring the capacity adaptability of path planning.

[0039] Next, path search is conducted using a real-time topology map, fault location, several qualified backup power sources, adjusted capacity margin, and a pre-set transfer path library. Specifically, when the main power supply line of the microgrid is disconnected due to a fault, all qualified backup power supply access nodes are used as the starting point for the path search, and all load nodes in the non-faulty area are used as the ending point. An improved breadth-first search algorithm is used to conduct the initial transfer path search, adding a 60% weighting for power capacity margin matching and a 40% weighting for voltage level matching. This gives higher priority to search directions with more sufficient capacity margin and better matching of voltage level and load node, prioritizing the search of transfer paths in these directions, reducing the number of invalid searches, and automatically selecting non-faulty sections in the faulty area to be connected to the faulty area via tie lines from adjacent qualified backup power sources. Meanwhile, by using real-time topology maps, fault locations, and corrected capacity margins, the system calculates the bus voltage range to be maintained after power transfer, the upper limit of tie-line power transmission, and the allowable frequency fluctuation range. Based on these objectives, it calculates initial control parameters, such as the proportional gain of the power loop controller and the integral time of the frequency synchronization controller. Finally, it searches the preset power transfer path library for several seamless power transfer initial paths that meet the initial control parameters. This provides a preliminary basis for the power transfer operation, avoids the generated seamless power transfer initial paths from being out of sync with the actual operating conditions of the microgrid, and avoids path planning lag.

[0040] Simultaneously, several initial seamless power transfer paths are deduplicated and merged, merging overlapping segments from different starting points to the same endpoint to form several initial seamless power transfer paths containing basic information such as path number, starting and ending points, circuit switches along the route, and topological distance. Finally, four constraints are used to filter these initial seamless power transfer paths, eliminating those that do not meet the requirements. This ensures that the obtained seamless power transfer paths not only meet the power supply capacity matching requirements of the microgrid under fault conditions but also possess safety, feasibility, and operational stability, ultimately achieving rapid planning of seamless power transfer paths for the distribution network under fault conditions.

[0041] Furthermore, the step of acquiring an initial topology map, real-time operating data, and real-time status data based on the microgrid to be supplied, and then acquiring fault electrical characteristics, a real-time topology map, and a fault baseline threshold based on the real-time operating data, real-time status data, and initial topology map, includes: Acquire initial topology, real-time operation data, and real-time status data based on the microgrid to be supplied; The initial topology map is updated based on real-time running data and real-time status data to obtain the real-time topology map; The operating conditions of the microgrid are determined based on real-time operational data, real-time status data, and real-time topology diagrams. Both real-time operating data and real-time status data are filtered and denoised, and feature extraction is performed to obtain fault electrical characteristics. The microgrid operating conditions are matched with a preset microgrid operating condition database to obtain the fault baseline threshold.

[0042] In this embodiment, all power supply nodes, load nodes, tie nodes, feeders, tie lines, circuit breakers, sectionalizing switches, and other switching equipment within the microgrid to be supplied are abstracted into a standardized topology diagram. Each node, line, and switching equipment is assigned a unique identifier, and core attribute parameters such as line impedance, rated current carrying capacity, switch switching response time, and node voltage level are recorded. An initial topology diagram, real-time operating data, and real-time status data are obtained. Once topology adjustments such as switch opening / closing, line faults or maintenance, or changes in node connection relationships are detected, the corresponding information in the initial topology diagram is immediately and synchronously updated to ensure that the topology diagram is completely consistent with the actual operating state of the microgrid. This provides an accurate topology basis for path searching and also provides fundamental data support for subsequent microgrid topology updates, fault condition determination, fault feature extraction, and fault benchmark threshold matching. The real-time operating data includes information such as voltage, current, power, and load changes, while the real-time status data includes data such as the connection status of flexible interconnection interfaces, power transmission status, and microgrid equipment health. Next, by updating the initial topology map with real-time operating data and real-time status data, the microgrid topology information can be made to match the actual operating status of the microgrid, eliminating the problem of topology information lag, obtaining a real-time topology map, and providing accurate real-time topology basis for subsequent power transfer path planning.

[0043] Then, the microgrid's operating conditions are determined through real-time operational data, real-time status data, and real-time topology maps. Specifically, real-time operational and status data determine the microgrid load level and distributed generation output status, while the real-time topology map determines the grid topology. This allows for a focus on the overall load level (e.g., peak load, average load, off-peak load) and the load composition (e.g., intermittent load from heavy industrial equipment, stable load from residential use), as well as load distribution (e.g., centralized load, decentralized load). Regarding distributed generation output status, the system tracks changes in photovoltaic power plant output (daytime fluctuations affected by sunlight intensity), the charging and discharging status of energy storage systems (standby, charging, discharging), and the output stability of small wind power plants, accurately capturing the dynamic characteristics of the power source side. In terms of grid topology, the system records the connection paths between power sources and loads (e.g., radial connections, ring network connections), the commissioning and decommissioning status of lines (e.g., maintenance shutdown of a branch line), and the operating taps of transformers. By combining and analyzing the different states of these three variables, the microgrid's operating conditions are ultimately defined. The obtained microgrid operating conditions can lay the foundation for matching fault baseline thresholds.

[0044] Subsequently, by filtering and denoising both real-time operational and status data and extracting features, interference signals in the data can be removed. Signal processing techniques (such as wavelet transform and Fourier analysis) are used to extract fault electrical features that reflect the microgrid fault state, providing a characteristic basis for subsequent fault determination. These fault electrical features include one or more of the following: voltage sag depth, current surge, frequency change rate, and negative sequence voltage component. Voltage sag depth directly reflects the degree of sudden voltage drop; current surge captures the magnitude of instantaneous current increase or decrease; frequency change rate reflects the rate at which the grid frequency deviates from its rated value; and negative sequence voltage component can be used to identify asymmetrical faults (such as single-phase grounding faults). Finally, fault baseline thresholds are obtained by matching the microgrid operating conditions with a preset microgrid operating condition database. Specifically, this involves: first, referring to the normal operating parameter range of the microgrid under each operating condition, such as the allowable voltage fluctuation range and normal current variation amplitude when the microgrid is operating under high load - full photovoltaic output conditions; second, analyzing historical fault data under these conditions and summarizing the critical values ​​of fault electrical characteristics when a fault occurs as fault baseline thresholds to ensure that the fault baseline thresholds can effectively distinguish between normal fluctuations and fault signals; and third, combining this with power grid safety operation specifications to avoid protection failure or false tripping due to improper fault baseline threshold settings.

[0045] Taking the low-load-energy-storage standby condition as an example, since the overall operating pressure of the microgrid is relatively small and the normal load fluctuation is limited, the fault benchmark threshold for voltage sag depth can be set relatively high (e.g., a sag of less than or equal to 85% of the rated voltage is considered abnormal) to prevent minor voltage fluctuations from being misjudged as faults. For the industrial heavy-load-multiple-power-grid-connected condition, since equipment startup is prone to current surges and fault propagation is rapid, the fault benchmark threshold for current surges can be set lower (e.g., a current surge greater than or equal to 15% triggers an early warning) to ensure rapid identification of potential faults. This allows the fault benchmark threshold for each condition to balance the sensitivity and accuracy of fault identification, adapting the fault benchmark threshold to the current operating condition of the microgrid, avoiding fixed thresholds that could lead to misjudgments and missed faults, and providing an initial judgment standard for subsequent fault threshold correction. For example, if the current load is in the peak range, photovoltaic output reaches 90% of the rated value, and all lines are operating normally, then the current condition is determined to be high load, with full photovoltaic output and a complete topology, and the corresponding fault benchmark threshold is retrieved. The operating conditions of the microgrid include the load level of the microgrid, the output status of distributed power sources, and the grid topology.

[0046] Furthermore, the fault baseline threshold is corrected based on real-time operating data and real-time status data to obtain a fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics, including: The correction factor is obtained based on real-time operational data and real-time status data; The fault baseline threshold is corrected based on the correction factor to obtain the fault threshold; If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics.

[0047] In this embodiment, the correction factor is obtained through real-time operating data and real-time status data. Specifically, the value of the correction factor needs to be adjusted based on the key dynamic parameters of the real-time operating data and real-time status data. Voltage fluctuation rate, current change rate, and total harmonic distortion rate are obtained through the real-time operating data and real-time status data. ,in, As the initial correction factor, , and These are preset weighting coefficients for voltage fluctuation rate, current change rate, and total harmonic distortion rate, respectively, used to characterize the influence of these three parameters on the microgrid fault baseline threshold. The sum of the weighting coefficients is 1, and they can be dynamically allocated according to different operating scenarios or monitoring characteristics of the microgrid. It is the voltage fluctuation rate of the bus voltage at the monitoring node of the microgrid within a specified time window, used to reflect the real-time stability of the voltage. The rate of change of line current at a microgrid monitoring node within a specified time window is used to reflect the degree of dynamic change in current. The total harmonic distortion rate refers to the voltage of the monitoring nodes in a microgrid.

[0048] If the voltage fluctuation rate is large, it indicates that the current voltage is unstable, and there may be a risk that voltage fluctuations mask fault signals. The baseline threshold will be appropriately reduced based on the voltage fluctuation rate to improve fault identification sensitivity. If the current change rate is abnormally slow (e.g., the current rise rate is less than 50% of the normal start-up speed), the fault signal may be weakened. A correction factor will be calculated based on the current change rate to further optimize the threshold. If the harmonic content is high (e.g., the total harmonic distortion rate exceeds 5%), the correction factor will be adjusted based on the harmonic content parameter to counteract the interference of harmonics on the judgment of electrical characteristic quantities. Then, through... The initial correction factor is modified, with K being the correction factor, so that the value range of the correction factor is 0.5≤K≤1.0, thereby providing a basis for adjusting the fault baseline threshold.

[0049] Next, by adjusting the fault baseline threshold using a correction factor, the fault threshold can be made to fit the current operating conditions of the microgrid while offsetting the effects of data fluctuations and harmonic interference, thereby improving the accuracy and sensitivity of subsequent fault determination. One example of adjusting the fault baseline threshold using a correction factor is as follows: If the current operating condition is during a high-load period (such as peak electricity consumption in the summer afternoon), considering that even small current faults can quickly trigger cascading problems, the fault baseline threshold will be appropriately lowered using the correction factor to improve fault identification sensitivity; if the current operating condition is during a low-load period (such as late at night), to avoid misjudging normal load fluctuations as faults, the fault baseline threshold will be appropriately raised using the correction factor to reduce the probability of misjudgment, ultimately calculating a fault threshold that highly matches the current operating state of the microgrid.

[0050] Then, by comparing the fault electrical characteristics with the fault threshold within a preset time period, if the fault electrical characteristics continuously exceed the fault threshold, a fault in the microgrid is determined. Further analysis is then performed based on a preset sensorless fault detection algorithm and the fault electrical characteristics to determine the fault type and location. Specifically: if the current surge at both ends of a microgrid line increases significantly, and the current surge at the monitoring point closer to the power source occurs earlier than at the load end, the fault is determined to occur on that line; if the negative sequence voltage component increases significantly, and the voltage sag of one phase is much greater than that of the other two phases, it is determined to be a single-phase ground fault. This process enables accurate fault determination and rapid location of microgrid faults, thus providing fault information support for the subsequent planning of sensorless power transfer paths.

[0051] Furthermore, after obtaining the fault location, it can also identify all non-faulty areas in the microgrid that are not affected by the fault through the process of elimination. These include load clusters that are not directly electrically connected to the faulty section, areas powered by other healthy lines, etc. It can also record the power demand characteristics of these areas, such as total load capacity and the proportion of important loads, to ensure that subsequent power transfer operations can accurately cover all target areas that need to be restored to power, avoiding omissions or misjudgments.

[0052] Furthermore, the step of obtaining the correction factor based on real-time operating data and real-time status data includes: The voltage fluctuation rate, current change rate, and total harmonic distortion rate of the microgrid are obtained based on real-time operation data and real-time status data. The original correction factor is calculated based on the microgrid voltage fluctuation rate, microgrid current change rate, and microgrid total harmonic distortion rate. The original correction factor is corrected based on the preset correction model to obtain the correction factor.

[0053] In this embodiment, by using real-time operational data and real-time status data, microgrid voltage fluctuation rate, microgrid current change rate, and microgrid total harmonic distortion rate (THD) can be extracted, reflecting the real-time operational stability, dynamic changes in electrical quantities, and power quality of the microgrid. This provides multi-dimensional quantitative foundational data for the subsequent calculation of correction factors. Next, the original correction factor is calculated using the microgrid voltage fluctuation rate, microgrid current change rate, and microgrid THD. Then, the original correction factor is corrected using a preset correction model, ensuring that the obtained correction factor values ​​are within a reasonable range and adapt to the actual operating state of the microgrid. This provides reliable adjustment parameters for the accurate correction of subsequent fault baseline thresholds.

[0054] Furthermore, the evaluation of several backup power sources in the microgrid to be supplied, based on fault type and preset backup power access rules, to determine the capacity margin factor, several qualified backup power sources, and total available output capacity, includes: Based on the fault type and the preset capacity margin database, determine the capacity margin coefficient; Based on the fault type and the preset backup power access rules, several backup power sources in the microgrid to be supplied are evaluated to obtain the matching score of each backup power source. Based on the matching score and fault type of each backup power source, several qualified backup power sources are identified. The available output capacity of each qualified backup power source is obtained, and the total available output capacity is calculated based on the available output capacity of each qualified backup power source.

[0055] In this embodiment, the capacity margin coefficient is determined through fault type and a preset capacity margin database. Specifically, short-circuit faults, due to their high propagation risk, can be assigned a capacity margin coefficient of 1.2-1.3; overload / undervoltage faults, a capacity margin coefficient of 1.1-1.2 can be assigned; and power quality faults, due to their lack of propagation risk, can be assigned a capacity margin coefficient of 1.0-1.1. The obtained capacity margin coefficient can provide an appropriate capacity margin coefficient for subsequent quantification of qualified backup power supply capacity, ensuring that the capacity assessment matches the power supply demand under fault conditions. Next, several backup power supplies are evaluated based on fault type and preset backup power supply access rules. Specifically, a three-level availability quantification assessment is performed by combining fault type, backup power supply operating attributes, and backup power supply capability attributes. An attribute matching degree scoring method (maximum score 100 points) is used. The weights of each evaluation indicator are preset according to the fault type. A score ≥80 indicates availability, 60-79 indicates conditional availability, and <60 indicates unavailability. Evaluation indicators may include the current operating status of the backup power supply, backup power supply switching / response time, and backup power supply voltage and frequency regulation capabilities, etc. The matching score of each backup power source can quantify the degree of matching of each backup power source with the current fault conditions of the distribution network, thus providing a criterion for the selection of qualified backup power sources.

[0056] Then, based on the matching score and fault type of each backup power source, several qualified backup power sources are determined. This process eliminates backup power sources that do not match the fault type or do not meet the preset backup power source access rules, thus selecting qualified backup power sources suitable for the current fault scenario and providing reliable power support for power transfer path planning. The preset backup power source access rules are as follows: For short-circuit faults, backup power sources without short-circuit withstand capability and fault current suppression function (such as ordinary photovoltaic backup units and small-capacity lead-acid energy storage) are deemed unusable, and only backup power sources with short-circuit withstand capability (such as containerized energy storage, main grid interconnection lines, and high-voltage diesel generators) are retained; for overload / undervoltage faults, backup power sources in a fault state and whose real-time output has reached its maximum are deemed unusable, and only backup power sources in standby / light load operation and with voltage regulation capability are retained; for power quality faults, backup power sources without power quality regulation capability (such as ordinary diesel generators) are deemed unusable, and only backup power sources with harmonic suppression and voltage stabilization capabilities (such as energy storage converter PCS and SVG-supported energy storage) are retained.

[0057] Finally, the available output capacity of each qualified backup power source is obtained, and the total available output capacity is calculated. Specifically, for energy storage power sources, the corresponding output capacity is calculated based on the remaining capacity and real-time output, while ensuring that the remaining capacity is not less than 20% to avoid over-discharge; for diesel generators, main grid interconnection lines, and other power sources, the corresponding output capacity is calculated based on the maximum continuous output and real-time output, while also meeting the corresponding minimum stable output requirements. The obtained total available output capacity reflects the actual power supply potential of the backup power sources under fault conditions, providing accurate power-side capacity parameters for subsequent capacity margin adjustments.

[0058] Furthermore, the process of filtering several initial non-inductive power transfer paths based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints to obtain non-inductive power transfer paths includes: Based on the preset microgrid constraints, several initial non-sensory power transfer paths are screened to obtain the first set of non-sensory power transfer paths; Based on preset capacity constraints, the first set of seamless power transfer paths is filtered to obtain the second set of seamless power transfer paths; The second set of sensorless power transfer paths is filtered based on the preset node voltage constraints to obtain the third set of sensorless power transfer paths. The seamless power transfer path is determined based on preset impact constraints, a third set of seamless power transfer paths, and preset optimization objectives.

[0059] In this embodiment, several initial seamless power transfer paths are screened by pre-set microgrid constraints. Paths that do not meet basic safety requirements from the perspective of microgrid safe operation can be eliminated, obtaining a first set of seamless power transfer paths, which provides a safe and compliant path basis for subsequent screening. The pre-set microgrid constraints focus on grid operation safety and can eliminate paths in the initial seamless power transfer paths that pose safety risks, such as insufficient line current carrying capacity to meet transmission power requirements, abnormal switchgear status, switching response time exceeding 100ms, passing through fault or maintenance areas, failing to maintain a safe topological distance from fault areas, or connecting across voltage levels without voltage regulation equipment. Next, the first set of seamless power transfer paths is screened by pre-set capacity constraints, eliminating paths that cannot adapt to the microgrid's power supply needs after a fault, obtaining a second set of seamless power transfer paths, ensuring that the capacity carrying capacity of the remaining paths matches the actual power supply demand. The preset capacity constraints, combined with adjusted capacity margins and path transmission capabilities, screen paths whose maximum transmission power is not less than the available output capacity of a qualified backup power supply. If the backup power supply can only meet the power needs of critical loads, additional paths that can accurately connect to critical load nodes and have load partitioning and shelving capabilities can be screened. Then, the second set of seamless power transfer paths is screened using preset node voltage constraints to ensure that the remaining paths can maintain stable node voltages under microgrid faults, meeting the voltage quality requirements for normal distribution network operation, thus obtaining the third set of seamless power transfer paths. The preset node voltage constraints can be used to eliminate paths with network loss rates exceeding 5%, low topology stability, or node voltage deviations exceeding ±5%, ensuring power quality and economy during path operation. Finally, by using preset impulse constraints and the third set of seamless power transfer paths, combined with preset optimization objectives, seamless power transfer paths are determined, eliminating paths with excessive power impulses, ensuring that the finally determined seamless power transfer paths can achieve smooth switching under fault conditions. The preset optimization objectives include the shortest switching operation time, the lowest network loss, or the highest power supply reliability. The preset impact constraints are used to finally screen the path based on the criteria that the switching operation time does not exceed 200ms, the power impact amount does not exceed 10% of the rated power, and the voltage phase difference between the power supply and the load node does not exceed 5°.

[0060] Furthermore, it also includes: Several microgrid device control commands are obtained based on the aforementioned seamless power transfer path; The control commands for the microgrid devices are sent to the microgrid devices and real-time operating data and real-time status data are reacquired. If the real-time operating data or real-time status data exceeds the preset microgrid performance index, the control commands for the microgrid devices are corrected until the real-time operating data or real-time status data does not exceed the preset microgrid performance index.

[0061] In this embodiment, an initial control strategy for seamless power transfer, including device control parameters, is generated through a seamless power transfer path. Several microgrid device control commands are then obtained based on this strategy, and the power transfer operation is initiated. This transforms the planned seamless power transfer path into executable microgrid device control commands, such as controlling corresponding circuit breaker tripping or closing backup power switches, providing a concrete operational basis for the actual implementation of seamless power transfer. In this embodiment, the real-time operating data and real-time status data include tie-line power, bus voltage, frequency, and phase difference. The preset microgrid performance indicators include voltage fluctuation amplitude, frequency deviation, power surge, and switching transition time. Voltage fluctuation amplitude measures the maximum range of voltage deviation from the rated value; frequency deviation reflects the stability of the system frequency; power surge reflects the intensity of power surges during switching; and switching transition time records the total time from fault occurrence to completion of power transfer.

[0062] The microgrid device control commands include three categories: active power commands, reactive power commands, and voltage / frequency regulation commands. Active power commands specify the active power output or absorption of each microgrid device, such as requiring photovoltaic devices to increase their active power output from 500kW to 600kW to improve renewable energy absorption, or instructing energy storage devices to absorb 100kW of active power to reduce tie-line power. Reactive power commands specify the reactive power regulation of the devices, such as instructing a device to output 300kvar of reactive power to compensate for line losses and maintain voltage stability. Voltage / frequency regulation commands set the control baseline for the devices, such as requiring the frequency to be stable at 50±0.05Hz in islanded mode, or instructing a certain area to control the bus voltage within the range of 380V±1%. These commands are precisely matched with the operating capabilities of each device, avoiding exceeding device limits while achieving coordinated control objectives.

[0063] Next, by issuing control commands to several microgrid devices and reacquiring real-time operating and status data, and when the real-time operating or status data exceeds the preset microgrid performance indicators, the device control parameters in the initial control strategy of the seamless power transfer are corrected online through a parameter adaptive adjustment mechanism. For example, if the power surge is too large, the damping coefficient of the power loop controller will be increased to buffer the surge: after the power transfer is started, if the user-end bus voltage drops from 380V to 350V within 10ms (the fluctuation range reaches 7.9%, exceeding the allowable fluctuation range of 5%), or the tie-line power jumps from 0kW to 800kW instantaneously (the surge exceeds 80% of the rated transmission power of 1000kW, breaking through the preset 50% upper limit), the adjustment parameters of the power loop controller will be adjusted first. If the abnormality is determined to be caused by oscillations during power transmission, such as voltage fluctuations caused by high-frequency power fluctuations between 750kW and 850kW, the damping coefficient of the power loop controller can be increased to enhance the controller's ability to suppress oscillations, reduce the steepness of power changes, and thus reduce the voltage fluctuation amplitude. If voltage fluctuations or power surges are caused by an overly sensitive controller response, such as a small power deviation triggering a large adjustment by the controller, the proportional gain of the power loop controller will be further reduced. For example, the proportional gain can be lowered to reduce the controller's overreaction to power deviations, making power regulation more moderate. This will gradually control the voltage fluctuation range within ±5% and limit the power surge to less than 50% of the rated value, preventing precision equipment at the user end (such as medical monitors and industrial PLCs) from triggering shutdown protection due to sudden changes in electrical parameters.

[0064] If the frequency deviation converges too slowly, the integral gain of the frequency synchronization controller will be increased to accelerate the adjustment. For scenarios where the convergence speed of frequency deviation or phase difference is lower than a preset threshold, first analyze the reasons for the lag in the synchronization process. For example, if the system frequency drops from 50Hz to 49.8Hz due to power switching and fails to recover to above 49.98Hz after 200ms (convergence speed 0.001Hz / ms, lower than the preset threshold of 0.002Hz / ms), or if the phase difference between the main and backup power supplies drops from 15° to 8° in 80ms (convergence speed 0.0875° / ms, lower than the requirement of 0.1° / ms), increasing the integral gain can enhance the cumulative correction effect of the integral stage on the deviation. For example: By increasing the integral gain from 0.1 to 0.3, the controller continuously adds adjustment when frequency deviation exists, thus accelerating the frequency recovery speed. The frequency that originally required 200ms to recover can be shortened to return to the stable range within 100ms. For cases of phase difference convergence lag, increasing the integral gain can improve the synchronization controller's tracking sensitivity to phase changes. For example, increasing the integral gain from 0.05 to 0.12 allows the controller to capture phase difference changes between the primary and backup power supplies more quickly. By dynamically adjusting the phase of the backup power supply, the time for the phase difference to decrease from 15° to 5° can be shortened to less than 50ms, ensuring that the primary and backup power supplies complete the switching within a safe range of phase difference ≤ 5°, and avoiding switching failures caused by untimely frequency or phase synchronization.

[0065] By re-observing the initial control strategy of the seamless power transfer, the revised control commands for several microgrid devices are obtained and reissued to several microgrid devices until the performance of the microgrid is restored to the preset range of microgrid performance indicators. This enables closed-loop dynamic control of the seamless power transfer process, timely correction of command execution deviations, and ensures that the microgrid is always in a stable operating state, thus guaranteeing the implementation effect of seamless power transfer and the reliability of power supply.

[0066] This embodiment provides a system for obtaining a seamless transfer path. Please refer to [link / reference]. Figure 2 It includes a data acquisition module, a fault location module, a capacity parameter determination module, a capacity margin correction module, a transfer path search module, and a transfer path determination module, specifically: The data acquisition module is used to acquire an initial topology map, real-time operating data and real-time status data based on the microgrid to be supplied, and to acquire fault electrical characteristics, real-time topology map and fault benchmark threshold based on the real-time operating data, real-time status data and initial topology map; The fault location module is used to correct the fault baseline threshold based on real-time operating data and real-time status data, and obtain the fault threshold. If the fault electrical characteristics all exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics. The capacity parameter determination module is used to evaluate several backup power sources in the microgrid to be supplied based on the fault type and preset backup power access rules, and to determine the capacity margin coefficient, several qualified backup power sources and the total available output capacity. The capacity margin correction module is used to obtain the corrected capacity margin based on the capacity margin coefficient, the total available output capacity, and the total real-time power supply demand. The power transfer path search module is used to perform path search based on real-time topology map, fault location, several qualified backup power supplies, corrected capacity margin and preset power transfer path library to obtain several seamless power transfer initial paths. The power transfer path determination module is used to filter several initial non-sensory power transfer paths based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints, and to obtain non-sensory power transfer paths.

[0067] This embodiment provides a system for acquiring seamless power transfer paths. In practical applications, only a data acquisition module is needed. By acquiring real-time operating data, real-time status data, real-time topology maps, and fault baseline thresholds, it can provide initial fault judgment criteria that fit the actual operating state of the microgrid for subsequent distribution network fault determination. Simultaneously, it provides a real-time topology foundation for power transfer path planning, avoiding poor path planning adaptability due to lagging topology information. Next, a fault location module is used to determine the fault type and location within a preset time period by combining fault electrical characteristics and fault thresholds. This enables rapid identification and location of microgrid faults, providing core fault information for subsequent seamless power transfer path planning and preventing path planning delays due to missing fault information. Then, a capacity parameter determination module is used to determine the capacity margin coefficient, filter out several qualified backup power sources suitable for the current distribution network fault type, and clarify their total available output capacity. This provides accurate power source-side capacity parameters for subsequent capacity margin correction and path planning.

[0068] Subsequently, a capacity margin correction module is employed. By acquiring the corrected capacity margin, the actual matching capability of several qualified backup power sources to the microgrid's power supply demand after a fault can be quantified. This avoids the problem of power supply capacity matching imbalance in path planning from a capacity perspective, ensuring the capacity adaptability of path planning. Next, a transfer path search module is used. Through real-time topology map, fault location, several qualified backup power sources, corrected capacity margin, and a preset transfer path library, path search is conducted. This avoids the generated seamless transfer initial path being out of sync with the actual operating conditions of the microgrid, preventing path planning lag. Finally, a transfer path determination module is used. Several seamless transfer initial paths are screened through four constraints. Unsuitable seamless transfer initial paths can be eliminated, ensuring that the obtained seamless transfer paths not only meet the power supply capacity matching requirements of the microgrid under fault conditions but also possess safety, feasibility, and operational stability. Ultimately, this achieves rapid planning of seamless transfer paths for the distribution network under fault conditions.

[0069] Furthermore, the data acquisition module is used to acquire an initial topology map, real-time operating data, and real-time status data based on the microgrid to be supplied, and to acquire fault electrical characteristics, a real-time topology map, and a fault baseline threshold based on the real-time operating data, real-time status data, and initial topology map, including: Acquire initial topology, real-time operation data, and real-time status data based on the microgrid to be supplied; The initial topology map is updated based on real-time running data and real-time status data to obtain the real-time topology map; The operating conditions of the microgrid are determined based on real-time operational data, real-time status data, and real-time topology diagrams. Both real-time operating data and real-time status data are filtered and denoised, and feature extraction is performed to obtain fault electrical characteristics. The microgrid operating conditions are matched with a preset microgrid operating condition database to obtain the fault baseline threshold.

[0070] In this embodiment, obtaining the initial topology map, real-time operating data, and real-time status data of the microgrid to be supplied provides fundamental data support for subsequent microgrid topology updates, fault condition determination, fault feature extraction, and fault benchmark threshold matching. Next, updating the initial topology map using real-time operating data and real-time status data ensures the microgrid topology information aligns with the actual operating state of the microgrid, eliminating topology information lag and obtaining a real-time topology map, providing accurate real-time topology basis for subsequent supply path planning. Then, determining the microgrid operating condition using real-time operating data and real-time status data lays the foundation for matching the fault benchmark threshold. Subsequently, filtering and noise reduction processing and feature extraction are performed on both real-time operating data and real-time status data to remove interference signals and extract fault electrical features reflecting the microgrid fault state, providing feature basis for subsequent fault determination. Finally, matching the microgrid operating condition with a preset microgrid operating condition database to obtain the fault benchmark threshold adapts the fault benchmark threshold to the current operating condition of the microgrid, avoiding false or false faults due to fixed thresholds, and providing an initial judgment standard for subsequent fault threshold correction.

[0071] Furthermore, the fault location module is used to correct the fault baseline threshold based on real-time operating data and real-time status data to obtain a fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics, including: The correction factor is obtained based on real-time operational data and real-time status data; The fault baseline threshold is corrected based on the correction factor to obtain the fault threshold; If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics.

[0072] In this embodiment, a correction factor is obtained by acquiring real-time operating data and real-time status data, providing a basis for adjusting the fault baseline threshold. Then, by correcting the fault baseline threshold using the correction factor, the fault threshold can be made to fit the current operating conditions of the microgrid while offsetting the effects of data fluctuations and harmonic interference, improving the accuracy and sensitivity of subsequent fault determination. Next, by comparing the fault electrical characteristics with the fault threshold within a preset time period, if the fault electrical characteristics continuously exceed the fault threshold, the fault type and fault location are determined based on the fault electrical characteristics. This enables accurate fault determination and rapid location of microgrid faults, thereby providing fault information support for the planning of subsequent seamless power transfer paths.

[0073] This embodiment also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the functions of the system as described above.

[0074] It is understood that the above system item embodiments correspond to the method item embodiments of the present invention, and can realize the method for obtaining a seamless transfer path provided by any of the above method item embodiments of the present invention.

[0075] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for obtaining a seamless power transfer path, characterized in that, include: Based on the initial topology map, real-time operation data and real-time status data of the microgrid to be transferred, the fault electrical characteristics, real-time topology map and fault benchmark threshold are obtained based on the real-time operation data, real-time status data and initial topology map; The fault baseline threshold is corrected based on real-time operation data and real-time status data to obtain the fault threshold. If the fault electrical characteristics all exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics. Based on the fault type and the preset backup power access rules, several backup power sources in the microgrid to be supplied are evaluated to determine the capacity margin factor, several qualified backup power sources and the total available output capacity. The corrected capacity margin is obtained based on the capacity margin factor, total available output capacity, and total real-time power supply demand; Based on the real-time topology map, fault location, several qualified backup power supplies, corrected capacity margin, and preset power transfer path library, a path search is performed to obtain several seamless power transfer initial paths. Based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints, several initial paths for seamless power transfer are screened to obtain seamless power transfer paths.

2. The method for obtaining a seamless power transfer path according to claim 1, characterized in that, The process of acquiring initial topology map, real-time operating data, and real-time status data based on the microgrid to be supplied, and acquiring fault electrical characteristics, real-time topology map, and fault baseline threshold based on real-time operating data, real-time status data, and initial topology map, includes: Acquire initial topology, real-time operation data, and real-time status data based on the microgrid to be supplied; The initial topology map is updated based on real-time running data and real-time status data to obtain the real-time topology map; The operating conditions of the microgrid are determined based on real-time operational data, real-time status data, and real-time topology diagrams. Both real-time operating data and real-time status data are filtered and denoised, and feature extraction is performed to obtain fault electrical characteristics. The microgrid operating conditions are matched with a preset microgrid operating condition database to obtain the fault baseline threshold.

3. The method for obtaining a seamless power transfer path according to claim 1, characterized in that, The fault baseline threshold is corrected based on real-time operating data and real-time status data to obtain a fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics, including: The correction factor is obtained based on real-time operational data and real-time status data; The fault baseline threshold is corrected based on the correction factor to obtain the fault threshold; If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics.

4. The method for obtaining a seamless power transfer path according to claim 3, characterized in that, The method of obtaining correction factors based on real-time operating data and real-time status data includes: The voltage fluctuation rate, current change rate, and total harmonic distortion rate of the microgrid are obtained based on real-time operation data and real-time status data. The original correction factor is calculated based on the microgrid voltage fluctuation rate, microgrid current change rate, and microgrid total harmonic distortion rate. The original correction factor is corrected based on the preset correction model to obtain the correction factor.

5. The method for obtaining a seamless power transfer path according to claim 1, characterized in that, The evaluation of several backup power sources in the microgrid to be supplied, based on fault type and preset backup power access rules, determines the capacity margin factor, several qualified backup power sources, and the total available output capacity, including: Based on the fault type and the preset capacity margin database, determine the capacity margin coefficient; Based on the fault type and the preset backup power access rules, several backup power sources in the microgrid to be supplied are evaluated to obtain the matching score of each backup power source. Based on the matching score and fault type of each backup power source, several qualified backup power sources are identified. The available output capacity of each qualified backup power source is obtained, and the total available output capacity is calculated based on the available output capacity of each qualified backup power source.

6. The method for obtaining a seamless power transfer path according to claim 1, characterized in that, The process involves filtering several initial non-sensorless power transfer paths based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints to obtain non-sensorless power transfer paths, including: Based on the preset microgrid constraints, several initial non-sensory power transfer paths are screened to obtain the first set of non-sensory power transfer paths; Based on preset capacity constraints, the first set of seamless power transfer paths is filtered to obtain the second set of seamless power transfer paths; The second set of sensorless power transfer paths is filtered based on the preset node voltage constraints to obtain the third set of sensorless power transfer paths. The seamless power transfer path is determined based on preset impact constraints, a third set of seamless power transfer paths, and preset optimization objectives.

7. The method for obtaining a seamless power transfer path according to claim 1, characterized in that, Also includes: Several microgrid device control commands are obtained based on the aforementioned seamless power transfer path; The control commands for the microgrid devices are sent to the microgrid devices and real-time operating data and real-time status data are reacquired. If the real-time operating data or real-time status data exceeds the preset microgrid performance index, the control commands for the microgrid devices are corrected until the real-time operating data or real-time status data does not exceed the preset microgrid performance index.

8. A system for acquiring a seamless supply path, characterized in that, It includes a data acquisition module, a fault location module, a capacity parameter determination module, a capacity margin correction module, a transfer path search module, and a transfer path determination module, specifically: The data acquisition module is used to acquire an initial topology map, real-time operating data and real-time status data based on the microgrid to be supplied, and to acquire fault electrical characteristics, real-time topology map and fault benchmark threshold based on the real-time operating data, real-time status data and initial topology map; The fault location module is used to correct the fault baseline threshold based on real-time operating data and real-time status data, and obtain the fault threshold. If the fault electrical characteristics all exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics. The capacity parameter determination module is used to evaluate several backup power sources in the microgrid to be supplied based on the fault type and preset backup power access rules, and to determine the capacity margin coefficient, several qualified backup power sources and the total available output capacity. The capacity margin correction module is used to obtain the corrected capacity margin based on the capacity margin coefficient, the total available output capacity, and the total real-time power supply demand. The power transfer path search module is used to perform path search based on real-time topology map, fault location, several qualified backup power supplies, corrected capacity margin and preset power transfer path library to obtain several seamless power transfer initial paths. The power transfer path determination module is used to filter several initial non-sensory power transfer paths based on preset microgrid constraints, preset capacity constraints, preset node voltage constraints, and preset impulse constraints, and to obtain non-sensory power transfer paths.

9. The system for obtaining a seamless transfer path according to claim 8, characterized in that, The data acquisition module is used to acquire an initial topology map, real-time operating data, and real-time status data based on the microgrid to be supplied, and to acquire fault electrical characteristics, a real-time topology map, and a fault baseline threshold based on the real-time operating data, real-time status data, and initial topology map, including: Acquire initial topology, real-time operation data, and real-time status data based on the microgrid to be supplied; The initial topology map is updated based on real-time running data and real-time status data to obtain the real-time topology map; The operating conditions of the microgrid are determined based on real-time operational data, real-time status data, and real-time topology diagrams. Both real-time operating data and real-time status data are filtered and denoised, and feature extraction is performed to obtain fault electrical characteristics. The microgrid operating conditions are matched with a preset microgrid operating condition database to obtain the fault baseline threshold.

10. The system for obtaining a seamless transfer path according to claim 8, characterized in that, The fault location module is used to correct the fault baseline threshold based on real-time operating data and real-time status data to obtain the fault threshold. If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics, including: The correction factor is obtained based on real-time operational data and real-time status data; The fault baseline threshold is corrected based on the correction factor to obtain the fault threshold; If all the fault electrical characteristics exceed the fault threshold within a preset time period, the fault type and fault location are determined based on the fault electrical characteristics.

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