Micro-grid fault reconfiguration method and system based on power flow calculation

Abstract

The present application relates to the technical field of micro-grid control, and particularly relates to a micro-grid fault reconstruction method and system based on power flow calculation. The method comprises: in a plurality of fault reconstruction schemes of a micro-grid, analyzing the similarity between each reconstruction island in each fault reconstruction scheme and a plurality of historical islands to determine a plurality of historical similarity values of each reconstruction island; identifying a plurality of target historical islands associated with each reconstruction island based on the plurality of historical similarity values of each reconstruction island; predicting an abnormal probability value of each reconstruction island according to the operation data of the plurality of target historical islands associated with each reconstruction island; and determining a target fault reconstruction scheme from the plurality of fault reconstruction schemes according to the abnormal probability value of each reconstruction island. The present application can accurately evaluate the implementation risk of each fault reconstruction scheme during reconstruction by analyzing the data fluctuation of each reconstruction island during reconstruction, so as to determine the optimal fault reconstruction scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid control, and in particular to a micro-grid fault reconstruction method and system based on power flow calculation. BACKGROUND

[0002] A micro-grid is a small power system composed of distributed energy sources (such as photovoltaic, wind power, small gas turbines, etc.), loads (user electrical equipment), control and protection systems, that is, a distributed energy source coordination system centered on "user demand". Its essence is to solve the short board of traditional large power grids in "end power supply" and "new energy consumption" through fine management and control of local energy production, storage and consumption. When a micro-grid fails, in order to ensure the safety of the system and the stability of power supply, the micro-grid needs to be reconstructed, that is, by re-adjusting the network topology, optimizing the output of distributed power and the charging and discharging strategy of energy storage, the power supply range is maximized and the power loss is minimized under the premise of ensuring system safety constraints.

[0003] The existing micro-grid fault reconstruction can be realized based on power flow calculation. When the micro-grid fails, the steady-state analysis of the power data of each grid node can be performed, and the nodes are divided according to the operable nodes, so as to plan the reconstruction scheme of the micro-grid. Then, the loss of the reconstruction scheme is quantitatively analyzed to screen out the reconstruction scheme with the minimum loss. However, the power flow calculation does not consider the dynamic power balance in the process of micro-grid fault reconstruction, which leads to a large deviation between the theoretical calculation results and the actual operation results of the power flow calculation. This makes the screened reconstruction scheme not the optimal reconstruction scheme.

[0004] That is, in the case of micro-grid failure, the reconstruction effect of the fault reconstruction scheme output by the existing technology is poor. SUMMARY

[0005] The purpose of the present application is to provide a micro-grid fault reconstruction method and system based on power flow calculation, which solves the technical problem of poor reconstruction effect of the fault reconstruction scheme output by the existing technology in the case of micro-grid failure.

[0006] In a first aspect, an embodiment of the present application provides a micro-grid fault reconstruction method based on power flow calculation, which comprises:

[0007] Among a plurality of fault reconstruction schemes of the micro-grid, the similarity between each reconstruction island in each fault reconstruction scheme and a plurality of historical islands is analyzed to determine a plurality of historical similarity values of each reconstruction island, wherein the plurality of fault reconstruction schemes are obtained based on power flow calculation;

[0008] Based on the plurality of historical similarity values of each reconstruction island, a plurality of target historical islands associated with each reconstruction island are identified;

[0009] predicting an abnormal probability value of each reconstructed island according to operation data of a plurality of target historical islands associated with each reconstructed island;

[0010] determining a target fault reconstruction scheme from the plurality of fault reconstruction schemes according to the abnormal probability value of each reconstructed island.

[0011] In some embodiments, the analyzing the similarity between each reconstructed island in each fault reconstruction scheme and a plurality of historical islands to determine a plurality of historical similarity values of each reconstructed island comprises:

[0012] analyzing the similarity between each reconstructed island in each fault reconstruction scheme and a plurality of historical islands in a power supply dimension to obtain a plurality of historical power supply similarity factors of each reconstruction scheme;

[0013] analyzing the similarity between each reconstructed island in each fault reconstruction scheme and a plurality of historical islands in a node dimension to obtain a plurality of historical node similarity factors of each reconstruction scheme;

[0014] determining a plurality of historical similarity values of each reconstructed island according to the plurality of historical power supply similarity factors and the plurality of historical node similarity factors of each reconstruction scheme.

[0015] In some embodiments, the historical power supply similarity factor and a corresponding first difference feature are in a negative correlation relationship, and the first difference feature is used to represent the difference in the number of power supplies between the corresponding reconstructed island and the corresponding historical island.

[0016] the historical node similarity factor and a corresponding second difference feature are in a negative correlation relationship, and the second difference feature is used to represent the difference in the total number of nodes between the corresponding reconstructed island and the corresponding historical island.

[0017] the historical node similarity factor and a corresponding first similarity feature are in a positive correlation relationship, and the first similarity feature is used to represent the number of common nodes between the corresponding reconstructed island and the corresponding historical island.

[0018] In some embodiments, the predicting an abnormal probability value of each reconstructed island according to operation data of a plurality of target historical islands associated with each reconstructed island comprises:

[0019] obtaining predicted operation information of each reconstructed island according to operation data of a plurality of target historical islands associated with each reconstructed island;

[0020] obtaining a global risk feature of each reconstructed island according to the overall loss of each reconstructed island in the mode switching process according to the predicted operation information of each reconstructed island;

[0021] According to the predicted operation information of each reconstructed island, data fluctuations of each reconstructed island in the mode switching process are analyzed to obtain a local risk feature of each reconstructed island;

[0022] According to the global risk feature and the local risk feature of each reconstructed island, an abnormal probability value of each reconstructed island is predicted.

[0023] In some embodiments, the global risk feature is positively correlated with loss of net data included in the predicted operation information of the corresponding reconstructed island, the global risk feature is positively correlated with switch frequency data included in the predicted operation information of the corresponding reconstructed island, and the global risk feature is negatively correlated with recovery load data included in the predicted operation information of the corresponding reconstructed island.

[0024] In some embodiments, the analysis of data fluctuations of each reconstructed island in the mode switching process according to the predicted operation information of each reconstructed island includes:

[0025] According to the predicted operation information of each reconstructed island, a plurality of predicted values of each reconstructed island in each data dimension are determined;

[0026] The difference between the maximum predicted value of each reconstructed island in each data dimension and the corresponding first reference value is analyzed to obtain a first fluctuation value of each reconstructed island in each data dimension;

[0027] The difference between the minimum predicted value of each reconstructed island in each data dimension and the corresponding second reference value is analyzed to obtain a second fluctuation value of each reconstructed island in each data dimension, wherein the first reference value and the second reference value are determined based on the reconstruction constraint condition of the corresponding data dimension;

[0028] According to the first fluctuation value and the second fluctuation value of each reconstructed island in each data dimension, a fluctuation risk value of each reconstructed island in each data dimension is determined;

[0029] According to the fluctuation risk values of each reconstruction in a plurality of data dimensions, a local risk feature of each reconstructed island is determined.

[0030] In some embodiments, the determination of the target fault reconstruction scheme in the plurality of fault reconstruction schemes according to the abnormal probability value of each reconstructed island includes:

[0031] In the plurality of fault reconstruction schemes, an average value of the abnormal probability values of the plurality of reconstructed islands included in each fault reconstruction scheme is calculated to obtain a scheme risk value of each fault reconstruction scheme;

[0032] In the plurality of fault reconstruction schemes, the fault reconstruction scheme with the minimum scheme risk value is determined as the target fault reconstruction scheme.

[0033] In some embodiments, after determining the target fault reconstruction scheme from the plurality of fault reconstruction schemes based on the anomaly probability value of each reconstructed island, the method further includes:

[0034] Based on the difference between the measured voltage amplitude of the main grid power supply and the predicted voltage amplitude of the power supply of each reconstructed island in the target fault reconstruction scheme, the initial synchronization amount of each reconstructed island in the target fault reconstruction scheme is determined.

[0035] Based on the abnormal probability value of each reconstructed island in the target fault reconstruction scheme, and the difference between the measured voltage amplitude and the predicted voltage amplitude of each reconstructed island in the target fault reconstruction scheme, the synchronization correction amount of each reconstructed island in the target fault reconstruction scheme is determined.

[0036] The initial synchronization amount is corrected based on the synchronization correction amount of each reconstructed island in the target fault reconstruction scheme to obtain the target synchronization amount of each reconstructed island in the target fault reconstruction scheme.

[0037] In some embodiments, after correcting the initial synchronization amount based on the synchronization correction amount of each reconstruction island in the target fault reconstruction scheme to obtain the target synchronization amount of each reconstruction island in the target fault reconstruction scheme, the method further includes:

[0038] Based on the target synchronization amount of each reconstructed island in the target fault reconstruction scheme, and the historical synchronization amount and historical synchronization time of multiple target historical islands associated with each reconstructed island in the target fault reconstruction scheme, the target synchronization time of each reconstructed island in the target fault reconstruction scheme is determined.

[0039] Secondly, another embodiment of the present invention also provides a microgrid fault reconfiguration system based on power flow calculation, the system comprising:

[0040] The similarity analysis module is used to analyze the similarity between each reconfigured island and multiple historical islands in each fault reconfiguration scheme of a microgrid, so as to determine multiple historical similarity values ​​of each reconfigured island, wherein the multiple fault reconfiguration schemes are obtained based on power flow calculation.

[0041] The target identification module is used to identify multiple target historical islands associated with each reconstructed island based on multiple historical similarity values ​​of each reconstructed island;

[0042] The anomaly prediction module is used to predict the anomaly probability value of each reconstructed island based on the operational data of multiple target historical islands associated with each reconstructed island.

[0043] The scheme selection module is used to determine the target fault reconstruction scheme from the multiple fault reconstruction schemes based on the anomaly probability value of each reconstructed island.

[0044] Thirdly, in another embodiment of the present invention, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method described in the first aspect.

[0045] Fourthly, in another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0046] The present invention has the following beneficial effects:

[0047] This invention analyzes the similarity between each reconstructed island and multiple historical islands in a candidate fault reconfiguration scheme to determine multiple historical similarity values ​​for each reconstructed island. Based on this, multiple target historical islands valuable for predicting the operational status of the reconstructed island are identified from historical data. Then, based on the operational data of the multiple target historical islands associated with each reconstructed island, the operational status of each reconstructed island during the reconfiguration process is predicted. The probability value of anomalies in each reconstructed island is dynamically predicted in conjunction with the predicted operational data. By summarizing the fault probability values ​​of the multiple reconstructed islands included in each fault reconfiguration scheme, the implementation risk of each fault reconfiguration scheme is quantified. This allows the fault reconfiguration scheme with the lowest implementation risk to be selected as the final target fault reconfiguration scheme. Since the above measures effectively analyze the data fluctuation of each reconstructed island in the fault reconfiguration scheme during the reconfiguration period, the implementation risks that may arise during the reconfiguration period of each fault reconfiguration scheme can be assessed more accurately, ultimately resulting in the determined target fault reconfiguration scheme having a better reconfiguration effect. Attached Figure Description

[0048] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating a microgrid fault reconfiguration method based on power flow calculation provided in an embodiment of the present invention.

[0050] Figure 2This is a schematic diagram of a microgrid fault reconfiguration system based on power flow calculation provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the microgrid fault reconfiguration method and system based on power flow calculation proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0053] 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 invention pertains.

[0054] The specific scheme of the microgrid fault reconfiguration method and system based on power flow calculation provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0055] like Figure 1 As shown, in one embodiment, the microgrid fault reconfiguration method based on power flow calculation provided by the present invention includes:

[0056] Step S1: In multiple fault reconfiguration schemes of the microgrid, analyze the similarity between each reconfiguration island in each fault reconfiguration scheme and multiple historical islands to determine multiple historical similarity values ​​for each reconfiguration island.

[0057] The aforementioned multiple fault reconfiguration schemes are obtained based on power flow calculations.

[0058] When a microgrid is unable to supply power to the main grid due to a fault, it switches from grid-connected operation to islanded operation by disconnecting its common connection point with the main grid. It then adjusts its internal topology, distributed generation output, and energy storage strategies to ensure continuous power supply to critical loads through a core reconfiguration process. During this process, distributed generation in the microgrid needs to quickly assume voltage / frequency support responsibilities. Based on real-time data from the microgrid (collected from devices along each branch using various sensor modules to monitor the microgrid's status, including node voltage, branch power, distributed generation output (discharge) status, load power, and switch status), multiple fault reconfiguration schemes are generated through steady-state analysis. The specific steps are as follows:

[0059] First, the fault location is located by the action signal of the protection device and the real-time data of the PMU. The switches at both ends of the fault branch are disconnected to form an isolation zone. Then, the remaining nodes in the microgrid are verified and analyzed according to the preset steady-state constraints (such as setting the node voltage ±5% of the rated value, the line current ≤1.2 times the rated value, the DG output ≤ the rated capacity (such as photovoltaic ≤1.1Pn), and the energy storage SOC ≥20% (to avoid over-discharge)). The remaining usable healthy nodes in the grid are screened out. Finally, heuristic search (such as improved BFS) and mixed integer programming (MIP) algorithm are combined to generate multiple candidate schemes (that is, the aforementioned multiple fault reconstruction schemes. The differences between different candidate schemes are reflected in the number of islands formed after mode switching, the number of dominant power sources and nodes included in the islands, etc.).

[0060] It should be understood that each fault reconfiguration scheme includes multiple reconfiguration islands, and each reconfiguration island includes at least one dominant power source capable of maintaining voltage or frequency, and several nodes dependent on the dominant power source. Historical islands can be understood as the islands included in historical reconfiguration schemes implemented in the microgrid.

[0061] Specifically, the analysis of the similarity between each reconstructed island and multiple historical islands in each fault reconstruction scheme to determine multiple historical similarity values ​​for each reconstructed island includes:

[0062] Analyze the similarity between each reconfigured island and multiple historical islands in each fault reconfiguration scheme in terms of power supply dimension to obtain multiple historical power supply similarity factors for each reconfiguration scheme.

[0063] Analyze the similarity between each reconstructed island and multiple historical islands in each fault reconstruction scheme at the node level to obtain multiple historical node similarity factors for each reconstruction scheme.

[0064] Based on the multiple historical power source similarity factors and multiple historical node similarity factors of each reconstruction scheme, multiple historical similarity values ​​are determined for each reconstruction island.

[0065] As mentioned above, since islands are mainly composed of power sources and nodes, by analyzing the similarity between reconstructed islands and historical islands in the power source and node dimensions respectively, and combining the two to determine the similarity between reconstructed islands and historical islands, the determined historical similarity values ​​can be more accurate and reliable.

[0066] It should be understood that each reconstructed island has multiple historical similarity values ​​that correspond one-to-one with multiple historical islands, and each historical similarity value is used to represent the degree of similarity between the corresponding reconstructed island and the corresponding historical island.

[0067] It should be noted that the larger the value of the historical power source similarity factor, the higher the similarity between the corresponding reconstructed island and the corresponding historical island in the power source dimension. Similarly, the larger the value of the historical node similarity factor, the higher the similarity between the corresponding reconstructed island and the corresponding historical island in the node dimension.

[0068] The larger the value of the historical power similarity factor, the higher the similarity between the corresponding reconstruction island and the corresponding historical island. This means that the probability of the accuracy of the data used to predict the operating status of the corresponding reconstruction island during mode switching is higher. Therefore, the corresponding historical island should be used to predict the operating status of the corresponding reconstruction island during mode switching.

[0069] The larger the value of the historical node similarity factor, the higher the similarity between the corresponding reconstruction island and the corresponding historical island. The higher the probability of the accuracy of the data in predicting the operation status of the corresponding reconstruction island during mode switching based on the corresponding historical island, the more likely the corresponding historical island should be used to predict the operation status of the corresponding reconstruction island during mode switching.

[0070] Furthermore, the historical power supply similarity factor is negatively correlated with the corresponding first difference feature, which is used to represent the difference in the number of power supplies between the corresponding reconstructed island and the corresponding historical island;

[0071] The historical node similarity factor is negatively correlated with the corresponding second difference feature, which is used to represent the difference in the total number of nodes between the corresponding reconstructed island and the corresponding historical island.

[0072] The historical node similarity factor is positively correlated with the corresponding first similarity feature, which is used to represent the number of shared nodes between the corresponding reconstructed island and the corresponding historical island.

[0073] Among them, the common nodes between the reconstructed island and the historical island can be understood as nodes that belong to both the reconstructed island and the historical island.

[0074] In practical applications, the consistency of power type between the reconstructed island and the historical island can be further analyzed. If the power type of the dominant power source of the reconstructed island is different from that of the historical island, the first difference feature corresponding to the two is reduced accordingly. If the power type of the dominant power source of the reconstructed island is the same as that of the historical island, the first difference feature corresponding to the two is enhanced accordingly (or the first difference feature corresponding to the two is kept unchanged). By combining the difference in the number of power sources, the similarity between the two in the power source dimension can be analyzed more comprehensively, making the determined historical similarity value more accurate.

[0075] For example, in multiple fault reconfiguration schemes, the first The first fault refactoring scheme The first of a series of reconstructed islands and multiple historical islands Historical similarity between isolated historical islands It can be represented as:

[0076]

[0077] in, Represents the normalization function. Indicates the first The first fault refactoring scheme The reconstructed island and the first Historical power source similarity factors between isolated historical islands Indicates the first The first fault refactoring scheme The reconstructed island and the first The first similarity feature between two isolated historical islands (specifically, the number of nodes they share). Indicates the first The first fault refactoring scheme The reconstructed island and the first The second distinguishing feature between historical islands is the absolute difference in the total number of nodes between them.

[0078] No. The first fault refactoring scheme The reconstructed island and the first Historical power source similarity factors between isolated historical islands Specifically, it is expressed as follows:

[0079]

[0080] in, Indicates the first The first fault refactoring scheme The Reconstruction of Isolated Islands and the First Consistency of power type between historical islands (value is 1 when the dominant power types of the two are the same, and 0 when they are different). Indicates the first The first fault refactoring scheme The reconstructed island and the first The first distinguishing feature between the historical islands is the absolute difference in the number of dominant power sources.

[0081] Step S2: Identify multiple target historical islands associated with each reconstructed island based on multiple historical similarity values ​​of each reconstructed island.

[0082] In this invention, historical islands whose historical similarity value with the reconstructed island is greater than or equal to a set similarity threshold (e.g., 0.8 based on experience) are identified as target historical islands associated with the corresponding reconstructed islands.

[0083] Step S3: Based on the operational data of multiple target historical islands associated with each reconstructed island, predict the anomaly probability value of each reconstructed island.

[0084] Specifically, the step of predicting the anomaly probability value of each reconstructed island based on the operational data of multiple target historical islands associated with each reconstructed island includes:

[0085] Based on the operational data of multiple target historical islands associated with each reconstructed island, predictive operational information for each reconstructed island is obtained.

[0086] Based on the predicted operational information of each reconstructed island, the overall loss during the mode switching process of each reconstructed island is analyzed to obtain the global risk characteristics of each reconstructed island.

[0087] Based on the predicted operational information of each reconstructed island, the data fluctuations during the mode switching process of each reconstructed island are analyzed to obtain the local risk characteristics of each reconstructed island.

[0088] Based on the global and local risk characteristics of each reconstructed island, the anomaly probability value of each reconstructed island is predicted.

[0089] Specifically, based on the operational data of multiple target historical islands associated with each reconstructed island, the predicted operational information for each reconstructed island is obtained as follows:

[0090] Calculate the sum of historical similarity values ​​of multiple target historical islands associated with each reconstructed island to obtain the similarity base value of each reconstructed island;

[0091] Calculate the ratio of the historical similarity value to the similarity base value of the multiple target historical islands associated with each reconstructed island, and obtain the predicted weight value of the multiple target historical islands associated with each reconstructed island.

[0092] Based on the predicted weight values ​​of the multiple target historical islands associated with each reconstructed island, the data of the multiple target historical islands associated with each reconstructed island in each dimension are weighted and calculated to obtain the predicted data of each reconstructed island in each dimension. The predicted operation information of each reconstructed island includes its predicted data in each dimension.

[0093] In this invention, the data dimensions involved in the island can be divided into two main categories: overall dimensions (such as grid loss, switching frequency, and load recovery) and local dimensions (such as frequency, voltage, current, power supply SOC, power supply SOH).

[0094] Based on the above settings, the operational risks of each reconfigured island during mode switching are comprehensively assessed from both the overall and local levels. This involves not only analyzing the apparent risks that each reconfigured island may exhibit through various indicators at the overall level, but also further exploring the data changes of each reconfigured island at the local level to analyze the potential risks that each reconfigured island may have, thereby accurately identifying the abnormal risks of each reconfigured island during mode switching.

[0095] Furthermore, the global risk characteristics are positively correlated with the network loss data included in the predicted operation information of the corresponding reconstructed island, the global risk characteristics are positively correlated with the switching frequency data included in the predicted operation information of the corresponding reconstructed island, and the global risk characteristics are negatively correlated with the recovery load data included in the predicted operation information of the corresponding reconstructed island.

[0096] The network loss data included in the predicted operation information for reconstructing islands is predicted based on the minimum network loss data recorded by multiple target historical islands during mode switching. The switching frequency data included in the predicted operation information for reconstructing islands is predicted based on the minimum number of switching actions recorded by multiple target historical islands during mode switching. The recovery load data included in the predicted operation information for reconstructing islands is predicted based on the maximum recovery load recorded by multiple target historical islands during mode switching.

[0097] The global risk characteristic indicates the reasonableness of not using the corresponding refactoring island for fault refactoring. A higher global risk characteristic indicates that the corresponding refactoring island should not be used for fault refactoring.

[0098] It should be understood that the larger the network loss data, the more severe the grid loss that the reconfiguration island may cause during mode switching. In this case, the risk of abnormal problems occurring in the reconfiguration island is higher, and the corresponding reconfiguration island should not be used for fault reconfiguration.

[0099] The higher the switching frequency data, the higher the frequency of switching control during mode switching of the reconfiguration island. This means that the switching unit may fail due to high-frequency switching control, which in turn increases the risk of abnormal problems in the reconfiguration island. Therefore, the corresponding reconfiguration island should not be used for fault reconfiguration.

[0100] The larger the recovery load data, the stronger the load-bearing capacity of the reconstructed island after mode switching, and the more appropriate it is to use the corresponding reconstructed island for fault reconstruction.

[0101] For example, the first The first fault refactoring scheme Global risk characteristics of reconstructing isolated islands It can be represented as:

[0102]

[0103] in, Represents the normalization function. Indicates the first The first fault refactoring scheme A network loss data point for reconstructing isolated data points. Indicates the first The first fault refactoring scheme The switching frequency data of a reconstructed island. Indicates the first The first fault refactoring scheme Recovery load data for a reconstructed island.

[0104] Furthermore, based on the predicted operational information of each reconstructed island, the data fluctuations during the mode switching process of each reconstructed island are analyzed to obtain the local risk characteristics of each reconstructed island, including:

[0105] Based on the predicted operational information of each reconstructed island, determine multiple predicted values ​​for each reconstructed island in each data dimension;

[0106] By analyzing the difference between the maximum predicted value and the corresponding first reference value of each reconstructed island in each data dimension, the first fluctuation value of each reconstructed island in each data dimension is obtained.

[0107] The difference between the minimum predicted value and the corresponding second reference value of each reconstructed island in each data dimension is analyzed to obtain the second fluctuation value of each reconstructed island in each data dimension, wherein the first reference value and the second reference value are determined based on the reconstruction constraints of the corresponding data dimension.

[0108] Based on the first and second volatility values ​​of each reconstructed island in each data dimension, determine the volatility risk value of each reconstructed island in each data dimension.

[0109] Based on the volatility risk values ​​of each reconstruction across multiple data dimensions, the local risk characteristics of each reconstruction island are determined.

[0110] It should be noted that the refactoring constraints are used to indicate the rated value range (under standard operating conditions) for each data dimension. The first reference value can be understood as the maximum value within the rated value range of the corresponding data dimension, and the second reference value can be understood as the minimum value within the rated value range of the corresponding data dimension.

[0111] The first fluctuation value can be the ratio of the corresponding maximum predicted value to the corresponding first reference value, and the second fluctuation value can be the ratio of the corresponding minimum predicted value to the corresponding second reference value.

[0112] During mode switching, isolated systems exhibit strong data fluctuations across all data dimensions. Specifically, the maximum and minimum predicted values ​​of the reconstructed isolated system in each data dimension typically exceed the corresponding rated range. Based on the aforementioned settings, by calculating the ratio of the maximum predicted value to the corresponding first reference value and the ratio of the minimum predicted value to the corresponding second reference value, the extent to which the predicted value exceeds the rated range can be effectively quantified. In other words, the intensity of data fluctuations that the reconstructed isolated system may exhibit during mode switching can be quantified. This allows for a comprehensive assessment of the degree of instability of the reconstructed isolated system at the local data level by integrating the predicted data of the reconstructed isolated system across all data dimensions, making the final anomaly probability value more accurate and reliable.

[0113] For example, the first The first fault refactoring scheme Anomaly probability value of a reconstructed island It can be represented as:

[0114]

[0115] in, This represents the total number of data dimensions involved in the isolated island. Indicates the first The first fault refactoring scheme The first reconstructed island in the Volatility risk value for each data dimension Indicates the first The first fault refactoring scheme The local risk characteristics of reconstructing isolated islands.

[0116] No. The first fault refactoring scheme The first reconstructed island in the Volatility risk value of each data dimension It can be represented as:

[0117]

[0118] in, Indicates the first The first fault refactoring scheme The first reconstructed island in the The second fluctuation value of each data dimension Indicates the first The first fault refactoring scheme The first reconstructed island in the The first fluctuation value of each data dimension.

[0119] Step S4: Based on the anomaly probability value of each reconstructed island, determine the target fault reconstruction scheme among the multiple fault reconstruction schemes.

[0120] Further, determining the target fault reconstruction scheme from the multiple fault reconstruction schemes based on the anomaly probability value of each reconstructed island includes:

[0121] In the multiple fault reconfiguration schemes, the average of the abnormal probability values ​​of the multiple reconfiguration islands included in each fault reconfiguration scheme is calculated to obtain the scheme risk value of each fault reconfiguration scheme.

[0122] Among the multiple fault reconfiguration schemes, the fault reconfiguration scheme with the lowest risk value is determined as the target fault reconfiguration scheme.

[0123] It should be understood that prediction errors are unavoidable in the data prediction process, and the existence of prediction errors can sometimes lead to extreme values. In order to suppress the interference of extreme values ​​on the assessment of the overall execution risk of the fault reconfiguration scheme, this invention chooses to adopt the mean calculation method to ensure that the execution risk of the selected target fault reconfiguration scheme is at a low level as much as possible, that is, to ensure the smooth execution of subsequent fault reconfiguration operations on the microgrid.

[0124] In summary, this invention analyzes the similarity between each reconstructed island and multiple historical islands in a candidate fault reconfiguration scheme to determine multiple historical similarity values ​​for each reconstructed island. Based on this, multiple target historical islands valuable for predicting the operational status of the reconstructed island are identified from historical data. Then, based on the operational data of the multiple target historical islands associated with each reconstructed island, the operational status of each reconstructed island during the reconfiguration process is predicted. Combined with the predicted operational data, the probability value of anomalies occurring in each reconstructed island is dynamically predicted. By summarizing the fault probability values ​​of the multiple reconstructed islands included in each fault reconfiguration scheme, the implementation risk of each fault reconfiguration scheme is quantified, so as to select the fault reconfiguration scheme with the lowest implementation risk as the final target fault reconfiguration scheme. Since the above measures effectively analyze the data fluctuation of each reconstructed island in the fault reconfiguration scheme during the reconfiguration period, the implementation risks that may arise during the reconfiguration period of each fault reconfiguration scheme can be assessed more accurately, and the final target fault reconfiguration scheme has a better reconfiguration effect.

[0125] In some embodiments, after determining the target fault reconstruction scheme from the plurality of fault reconstruction schemes based on the anomaly probability value of each reconstructed island, the method further includes:

[0126] Based on the difference between the measured voltage amplitude of the main grid power supply and the predicted voltage amplitude of the power supply of each reconstructed island in the target fault reconstruction scheme, the initial synchronization amount of each reconstructed island in the target fault reconstruction scheme is determined.

[0127] Based on the abnormal probability value of each reconstructed island in the target fault reconstruction scheme, and the difference between the measured voltage amplitude and the predicted voltage amplitude of each reconstructed island in the target fault reconstruction scheme, the synchronization correction amount of each reconstructed island in the target fault reconstruction scheme is determined.

[0128] The initial synchronization amount is corrected based on the synchronization correction amount of each reconstructed island in the target fault reconstruction scheme to obtain the target synchronization amount of each reconstructed island in the target fault reconstruction scheme.

[0129] The measured voltage amplitude of the main grid power supply is the voltage amplitude measured at the current moment, or the latest measured voltage amplitude of the main grid power supply. The measured voltage amplitude of the reconstructed island is the voltage amplitude of the island power supply measured at the current moment (when no mode switching has occurred and the island power supply is in grid-connected operation mode). The predicted voltage amplitude of the reconstructed island is the average value obtained by weighting the historical voltage amplitudes of multiple target historical islands associated with the reconstructed island after mode switching (the higher the similarity between the target historical island and the reconstructed island, the greater the weight in the calculation).

[0130] When synchronizing islanded power sources with the main grid power source based on the target fault reconfiguration scheme, the actual synchronization amount of the islanded power source may differ significantly from the predicted synchronization amount due to prediction errors. To avoid these differences affecting the overall voltage stability of the island during synchronization, the abnormal probability value of each reconfigured island in the target fault reconfiguration scheme is introduced as a correction coefficient, and the degree of difference between the measured voltage and the predicted voltage of each reconfigured island is used as a correction reference. The synchronization correction amount of each reconfigured island is determined by combining the two factors, and the initial synchronization amount of each reconfigured island is numerically corrected accordingly to compensate for the numerical deviation introduced by the prediction error. This ensures that the target synchronization amount of each reconfigured island is as close as possible to the actual required synchronization amount, thereby suppressing voltage fluctuations during the transition from grid-connected operation mode to islanded operation mode and ensuring the operational safety of each node within the reconfigured island.

[0131] For example, the first fault reconfiguration scheme in the target fault reconfiguration scheme The target synchronization quantity for reconstructing isolated islands It can be represented as:

[0132]

[0133] in, This represents the measured voltage amplitude of the main grid power supply. Indicating the first fault in the target fault reconfiguration scheme The predicted voltage amplitude of a reconstructed island. Indicating the first fault in the target fault reconfiguration scheme Anomaly probability values ​​for reconstructing isolated islands. Indicating the first fault in the target fault reconfiguration scheme The measured voltage amplitude of a reconstructed island.

[0134] Furthermore, after correcting the initial synchronization amount based on the synchronization correction amount of each reconstruction island in the target fault reconstruction scheme to obtain the target synchronization amount of each reconstruction island in the target fault reconstruction scheme, the method further includes:

[0135] Based on the target synchronization amount of each reconstructed island in the target fault reconstruction scheme, and the historical synchronization amount and historical synchronization time of multiple target historical islands associated with each reconstructed island in the target fault reconstruction scheme, the target synchronization time of each reconstructed island in the target fault reconstruction scheme is determined.

[0136] Through the aforementioned settings, a more accurate target synchronization amount can be determined for each reconfigured island in the target fault reconfiguration scheme to ensure the stability of the power supply voltage of each reconfigured island during mode switching. Based on this, by using the target synchronization amount of each reconfigured island in the target fault reconfiguration scheme, and combining it with the historical synchronization amounts and historical synchronization times of multiple target historical islands associated with each reconfigured island in the target fault reconfiguration scheme, the target synchronization time of each reconfigured island in the target fault reconfiguration scheme can be dynamically determined. This allows for adaptive adjustment of the mode switching time of each reconfigured island in the target fault reconfiguration scheme according to the amount of voltage adjustment required during mode switching, in order to adapt to the voltage adjustment amplitude. This balances the need to suppress current surges with the need for rapid switching, further ensuring the stability of the power supply voltage of each reconfigured island during mode switching while improving the mode switching efficiency of each reconfigured island.

[0137] Historical synchronization amount is the actual voltage amplitude adjusted by the corresponding historical island during mode switching, and historical synchronization time is the execution time of the corresponding historical island's control switch unit during mode switching.

[0138] For example, the first fault reconfiguration scheme in the target fault reconfiguration scheme The synchronization time for reconstructing isolated islands It can be represented as:

[0139]

[0140] in, Indicating the first fault in the target fault reconfiguration scheme The average historical synchronization amount of multiple target historical islands associated with a reconstructed island. Indicating the first fault in the target fault reconfiguration scheme The average historical synchronization time of multiple target historical islands associated with a reconstructed island.

[0141] In some implementations, after determining the target fault reconfiguration scheme and the target synchronization quantity and target synchronization time for each reconfigured island, the operating status of each island's power grid can be monitored. This facilitates timely detection of potential risks and triggers secondary reconfiguration as needed, preventing island power grid collapse. The indicators to be monitored include:

[0142] Static indicators: node voltage deviation (≤±5%), frequency deviation (≤±0.2Hz), grid loss rate (≤5%), energy storage SOC (20%-80%);

[0143] Dynamic indicators: voltage / frequency fluctuation amplitude (e.g., fluctuation within 10s ≤ 0.05pu / 0.1Hz), DG output fluctuation rate (PV ≤ 20% / min), load change response time (≤ 100ms).

[0144] It monitors whether each of the above indicators exceeds the preset warning range, and the degree to which it exceeds the preset warning range, and assesses the operating status of each isolated power grid accordingly and triggers corresponding handling operations.

[0145] For example, if at most two of the above indicators exceed the limit (exceeding the preset warning range), and the exceedance of the exceeding indicators is less than 10%, the corresponding islanded power grid is judged to have a level one risk. At this time, the islanded power grid can be controlled to adjust the output of DG and the charging and discharging power of energy storage. If 3-6 of the above indicators exceed the limit (exceeding the preset warning range) and / or the exceedance of the exceeding indicators is between 11% and 35%, the corresponding islanded power grid is judged to have a level two risk. At this time, the islanded power grid can be controlled to close the backup tie line and transfer some load to other DG areas.

[0146] If seven or more of the above indicators exceed the limit (exceeding the preset warning range) and / or the exceedance of the indicators is greater than 35%, the corresponding islanded power grid is judged to have a level three risk. At this time, the islanded power grid can be controlled to cut off non-critical loads (such as residential air conditioners) according to load priority to ensure power supply to critical loads (such as hospitals).

[0147] like Figure 2 As shown, in one embodiment, the microgrid fault reconfiguration system 200 based on power flow calculation provided by the present invention includes:

[0148] The similarity analysis module 201 is used to analyze the similarity between each reconfiguration island in each fault reconfiguration scheme and multiple historical islands in multiple fault reconfiguration schemes of a microgrid, so as to determine multiple historical similarity values ​​of each reconfiguration island, wherein the multiple fault reconfiguration schemes are obtained based on power flow calculation.

[0149] The target identification module 202 is used to identify multiple target historical islands associated with each reconstructed island based on multiple historical similarity values ​​of each reconstructed island;

[0150] The anomaly prediction module 203 is used to predict the anomaly probability value of each reconstructed island based on the operational data of multiple target historical islands associated with each reconstructed island.

[0151] The scheme selection module 204 is used to determine the target fault reconstruction scheme from the multiple fault reconstruction schemes based on the anomaly probability value of each reconstructed island.

[0152] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the microgrid fault reconfiguration system based on power flow calculation and the microgrid fault reconfiguration method based on power flow calculation provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0153] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 3 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.

[0154] When program 3021 is executed by processor 301, it can achieve the following: Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0155] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0156] This invention also provides a readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described functions. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0157] The computer-readable storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0158] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0159] The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0160] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or terminal. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the microgrid fault reconfiguration method and system based on power flow calculation provided in the above embodiments.

[0162] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0163] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A microgrid fault reconfiguration method based on power flow calculation, characterized in that, The method includes: In multiple fault reconfiguration schemes of a microgrid, the similarity between each reconfigured island in each scheme and multiple historical islands is analyzed to determine multiple historical similarity values ​​of each reconfigured island. The multiple fault reconfiguration schemes are obtained based on power flow calculations. Identify multiple target historical islands associated with each reconstructed island based on multiple historical similarity values ​​of each reconstructed island; Based on the operational data of multiple target historical islands associated with each reconstructed island, predict the anomaly probability value of each reconstructed island; Based on the anomaly probability value of each reconstructed island, a target fault reconstruction scheme is determined among the multiple fault reconstruction schemes. The step of predicting the anomaly probability value of each reconstructed island based on the operational data of multiple target historical islands associated with each reconstructed island includes: Based on the operational data of multiple target historical islands associated with each reconstructed island, predictive operational information for each reconstructed island is obtained. Based on the predicted operational information of each reconstructed island, the overall loss during the mode switching process of each reconstructed island is analyzed to obtain the global risk characteristics of each reconstructed island. Based on the predicted operational information of each reconstructed island, the data fluctuations during the mode switching process of each reconstructed island are analyzed to obtain the local risk characteristics of each reconstructed island. Based on the global and local risk characteristics of each reconstructed island, the anomaly probability value of each reconstructed island is predicted. The global risk characteristics are positively correlated with the network loss data included in the predicted operation information of the corresponding reconstructed island, the global risk characteristics are positively correlated with the switching frequency data included in the predicted operation information of the corresponding reconstructed island, and the global risk characteristics are negatively correlated with the recovery load data included in the predicted operation information of the corresponding reconstructed island. The process of analyzing data fluctuations during mode switching for each reconstructed island based on its predicted operational information is described above, to obtain the local risk characteristics of each reconstructed island, including: Based on the predicted operational information of each reconstructed island, determine multiple predicted values ​​for each reconstructed island in each data dimension; By analyzing the difference between the maximum predicted value and the corresponding first reference value of each reconstructed island in each data dimension, the first fluctuation value of each reconstructed island in each data dimension is obtained. The difference between the minimum predicted value and the corresponding second reference value of each reconstructed island in each data dimension is analyzed to obtain the second fluctuation value of each reconstructed island in each data dimension, wherein the first reference value and the second reference value are determined based on the reconstruction constraints of the corresponding data dimension. Based on the first and second volatility values ​​of each reconstructed island in each data dimension, determine the volatility risk value of each reconstructed island in each data dimension. Based on the volatility risk values ​​of each reconstruction across multiple data dimensions, the local risk characteristics of each reconstruction island are determined.

2. The microgrid fault reconfiguration method based on power flow calculation according to claim 1, characterized in that, The analysis examines the similarity between each reconstructed island and multiple historical islands in each fault reconstruction scheme to determine multiple historical similarity values ​​for each reconstructed island, including: Analyze the similarity between each reconfigured island and multiple historical islands in each fault reconfiguration scheme in terms of power supply dimension to obtain multiple historical power supply similarity factors for each reconfiguration scheme. Analyze the similarity between each reconstructed island and multiple historical islands in each fault reconstruction scheme at the node level to obtain multiple historical node similarity factors for each reconstruction scheme. Based on the multiple historical power source similarity factors and multiple historical node similarity factors of each reconstruction scheme, multiple historical similarity values ​​are determined for each reconstruction island.

3. The microgrid fault reconfiguration method based on power flow calculation according to claim 2, characterized in that, The historical power supply similarity factor is negatively correlated with the corresponding first difference feature, which is used to represent the difference in the number of power supplies between the corresponding reconstructed island and the corresponding historical island. The historical node similarity factor is negatively correlated with the corresponding second difference feature, which is used to represent the difference in the total number of nodes between the corresponding reconstructed island and the corresponding historical island. The historical node similarity factor is positively correlated with the corresponding first similarity feature, which is used to represent the number of shared nodes between the corresponding reconstructed island and the corresponding historical island.

4. The microgrid fault reconfiguration method based on power flow calculation according to claim 1, characterized in that, The step of determining a target fault reconstruction scheme from among the multiple fault reconstruction schemes based on the anomaly probability value of each reconstructed island includes: In the multiple fault reconfiguration schemes, the average of the abnormal probability values ​​of the multiple reconfiguration islands included in each fault reconfiguration scheme is calculated to obtain the scheme risk value of each fault reconfiguration scheme. Among the multiple fault reconfiguration schemes, the fault reconfiguration scheme with the lowest risk value is determined as the target fault reconfiguration scheme.

5. The microgrid fault reconfiguration method based on power flow calculation according to claim 1, characterized in that, After determining the target fault reconstruction scheme from the multiple fault reconstruction schemes based on the anomaly probability value of each reconstructed island, the method further includes: Based on the difference between the measured voltage amplitude of the main grid power supply and the predicted voltage amplitude of the power supply of each reconstructed island in the target fault reconstruction scheme, the initial synchronization amount of each reconstructed island in the target fault reconstruction scheme is determined. Based on the abnormal probability value of each reconstructed island in the target fault reconstruction scheme, and the difference between the measured voltage amplitude and the predicted voltage amplitude of each reconstructed island in the target fault reconstruction scheme, the synchronization correction amount of each reconstructed island in the target fault reconstruction scheme is determined. The initial synchronization amount is corrected based on the synchronization correction amount of each reconstructed island in the target fault reconstruction scheme to obtain the target synchronization amount of each reconstructed island in the target fault reconstruction scheme.

6. The microgrid fault reconfiguration method based on power flow calculation according to claim 5, characterized in that, After correcting the initial synchronization amount based on the synchronization correction amount of each reconstruction island in the target fault reconstruction scheme to obtain the target synchronization amount of each reconstruction island in the target fault reconstruction scheme, the method further includes: Based on the target synchronization amount of each reconstructed island in the target fault reconstruction scheme, and the historical synchronization amount and historical synchronization time of multiple target historical islands associated with each reconstructed island in the target fault reconstruction scheme, the target synchronization time of each reconstructed island in the target fault reconstruction scheme is determined.

7. A microgrid fault reconfiguration system based on power flow calculation, characterized in that, The system for implementing the method as described in any one of claims 1-6 comprises: The similarity analysis module is used to analyze the similarity between each reconfigured island and multiple historical islands in each fault reconfiguration scheme of a microgrid, so as to determine multiple historical similarity values ​​of each reconfigured island, wherein the multiple fault reconfiguration schemes are obtained based on power flow calculation. The target identification module is used to identify multiple target historical islands associated with each reconstructed island based on multiple historical similarity values ​​of each reconstructed island; The anomaly prediction module is used to predict the anomaly probability value of each reconstructed island based on the operational data of multiple target historical islands associated with each reconstructed island. The scheme selection module is used to determine the target fault reconstruction scheme from the multiple fault reconstruction schemes based on the anomaly probability value of each reconstructed island.

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