Configuration method, device, equipment, readable storage medium and program product

Abstract

The invention relates to a configuration method and device, equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring target power change data of a power grid corresponding to a plurality of target historical time periods, a target voltage sag observability matrix corresponding to the power grid can be constructed according to the target power change data, so that a target configuration strategy is obtained according to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and a configuration solving model, the target configuration strategy is used for indicating whether the monitoring device needs to be configured for each device in the power grid. By adopting the method, the monitoring accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the field of monitoring technology, and in particular to a configuration method, apparatus, device, readable storage medium and program product. Background Art

[0002] With the increase of equipment in urban distribution networks, it is necessary to configure different monitoring devices for each device in the power grid to monitor voltage sag events.

[0003] Traditional configuration methods usually directly provide a configuration plan for the monitoring devices based on a fixed number of monitoring devices, and then use the configuration plan to configure the monitoring devices.

[0004] However, the above configuration method has the problem of low monitoring accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a configuration method, device, equipment, readable storage medium and program product that can improve monitoring accuracy in response to the above technical problems.

[0006] In a first aspect, the present application provides a configuration method, comprising:

[0007] Obtain target power change data corresponding to the power grid and multiple target historical time periods;

[0008] According to each target power change data, a target voltage sag observability matrix corresponding to the power grid is constructed;

[0009] According to the observability matrix of target voltage sag, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, a target configuration strategy is obtained, and the target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

[0010] In one embodiment, a target voltage sag observability matrix corresponding to the power grid is constructed according to each target power change data, including:

[0011] For each target power change data, the device voltage corresponding to each device is calculated according to the target power change data and the device resistance parameters of each device, where the device resistance parameters include the device identification, impedance matrix and device resistance corresponding to the device;

[0012] According to the voltage of each device and the preset voltage sag parameters, an initial voltage sag observability matrix corresponding to the target power change data is obtained;

[0013] According to each initial voltage sag observability matrix, a target voltage sag observability matrix is ​​determined.

[0014] In one embodiment, the preset voltage sag parameters include voltage thresholds corresponding to multiple voltage sag events. According to the voltage of each device and the preset voltage sag parameters, an initial voltage sag observability matrix corresponding to the target power change data is obtained, including:

[0015] For each device, the device voltage is compared with each voltage threshold to obtain a comparison result corresponding to each voltage sag event;

[0016] Determine voltage sag observability data corresponding to the equipment according to each comparison result, wherein the voltage sag observability data includes voltage sag parameters corresponding to each comparison result;

[0017] The observability data of each voltage sag corresponding to each device is used as a matrix element to obtain an initial voltage sag observability matrix.

[0018] In one embodiment, the initial voltage sag observability matrix includes voltage sag observability data corresponding to each device, and the target voltage sag observability matrix is ​​determined according to each initial voltage sag observability matrix, including:

[0019] For each device, extracting each voltage sag observability data corresponding to the device from each initial voltage sag observability matrix, and obtaining the target voltage sag observability data corresponding to the device according to the extracted voltage sag observability data;

[0020] The observability data of each target voltage sag corresponding to each device is used as a matrix element to obtain a target voltage sag observability matrix.

[0021] In one embodiment, obtaining target voltage sag observability data corresponding to the device according to each extracted voltage sag observability data includes:

[0022] For each voltage sag event, extracting each voltage sag parameter corresponding to each voltage sag event from each voltage sag observability data;

[0023] Perform an AND operation on each voltage sag parameter to obtain a target voltage sag parameter corresponding to the voltage sag event;

[0024] The target voltage sag parameter corresponding to each voltage sag event is used as the observability data of the target voltage sag corresponding to the device.

[0025] In one embodiment, the configuration solution model includes an observability index equation, a severity index equation, and preset constraints. The observability index is used to characterize the number of recorded voltage sag events, and the severity index is used to characterize the recording probability of voltage sag events. According to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid, and the configuration solution model, a target configuration strategy is obtained, including:

[0026] According to different numbers of monitoring devices, the observability index equation and the severity index equation are iteratively solved to obtain the observability index, severity index and configuration strategy corresponding to each number of monitoring devices, and the number of monitoring devices is less than or equal to the reference number of monitoring devices;

[0027] According to preset constraints and observability indicators and severity indicators corresponding to the number of monitoring devices, a target configuration strategy is determined from various configuration strategies.

[0028] In one embodiment, according to preset constraints and observability indicators and severity indicators corresponding to the number of monitoring devices, a target configuration strategy is determined from each configuration strategy, including:

[0029] For each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices;

[0030] According to preset constraints and various statistical values, a target configuration strategy is determined from various configuration strategies.

[0031] In one embodiment, obtaining target power change data corresponding to a plurality of target historical time periods of a power grid includes:

[0032] Obtain power change data corresponding to the power grid in multiple historical time periods;

[0033] The power change data of each power grid are clustered to determine the target power change data corresponding to multiple target historical time periods.

[0034] In a second aspect, the present application also provides a configuration device, including:

[0035] An acquisition module is used to acquire target power change data corresponding to a plurality of target historical time periods of the power grid;

[0036] A construction module is used to construct a target voltage sag observability matrix corresponding to the power grid according to each target power change data;

[0037] The configuration module is used to obtain a target configuration strategy based on a target voltage sag observability matrix, a reference number of monitoring devices corresponding to the power grid, and a configuration solution model. The target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

[0038] In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of the first aspect when executing the computer program.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of the first aspect described above.

[0040] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method in the first aspect are implemented.

[0041] The above configuration method, device, equipment, readable storage medium and program product can obtain the target power change data corresponding to the power grid and multiple target historical time periods, and can construct the target voltage sag observability matrix corresponding to the power grid according to each target power change data, thereby obtaining the target configuration strategy according to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, wherein the target configuration strategy is used to indicate whether it is necessary to configure the monitoring device for each device in the power grid. In this way, the target voltage sag observability matrix corresponding to the power grid is determined based on the target power change data, and the target voltage sag observability matrix and the configuration solution model are used, and the reference number of monitoring devices are comprehensively considered to determine the target configuration strategy for configuring the monitoring device, thereby avoiding the problem of low detection accuracy caused by directly giving a configuration scheme based on a fixed number of monitoring devices in the traditional technology without considering the monitoring effect of the configuration scheme corresponding to different numbers of monitoring devices. The target configuration strategy obtained by comprehensively considering the reference number of monitoring devices, the target voltage sag observability matrix and the configuration solution model in the present application has higher monitoring accuracy after the monitoring device is configured for each device in the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments of the present application or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 An application environment diagram of a configuration method in an embodiment;

[0044] Figure 2 A schematic diagram of a configuration method in an embodiment;

[0045] Figure 3 is a flow chart of step 202 in another embodiment;

[0046] Figure 4 is a flow chart of step 303 in another embodiment;

[0047] Figure 5 is a schematic flow chart of step 203 in another embodiment;

[0048] Figure 6 It is a structural block diagram of a configuration device in one embodiment;

[0049] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] With the increase of equipment in urban distribution networks, the penetration rate of photovoltaic power in new distribution networks continues to increase. Ignoring the volatility of photovoltaic output when conducting observability analysis of voltage sags may lead to blind spots in monitoring. Therefore, different monitoring devices need to be configured for each device in the power grid to monitor voltage sag events.

[0052] Traditional configuration methods usually directly provide a configuration plan for the monitoring devices based on a fixed number of monitoring devices, and then use the configuration plan to configure the monitoring devices.

[0053] However, the number of monitoring devices in the configuration scheme obtained by the above configuration method is fixed, the configuration scheme is single, and the monitoring effects corresponding to different numbers of monitoring devices are not considered. Therefore, there is a problem of low monitoring accuracy.

[0054] In view of this, the present application provides a configuration method, device, equipment, readable storage medium and program product, by obtaining the target power change data corresponding to the power grid and multiple target historical time periods, the target voltage sag observability matrix corresponding to the power grid can be constructed according to each target power change data, thereby obtaining the target configuration strategy according to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, wherein the target configuration strategy is used to indicate whether it is necessary to configure the monitoring device for each device in the power grid. In this way, the target voltage sag observability matrix corresponding to the power grid is determined based on the target power change data, and the target voltage sag observability matrix and the configuration solution model are used, and the reference number of monitoring devices are comprehensively considered to determine the target configuration strategy for configuring the monitoring device, thereby avoiding the problem of low detection accuracy caused by directly giving a configuration scheme based on a fixed number of monitoring devices in the traditional technology without considering the monitoring effect of the configuration scheme corresponding to different numbers of monitoring devices. The target configuration strategy obtained by the present application comprehensively considering the reference number of monitoring devices, the target voltage sag observability matrix and the configuration solution model has a higher monitoring accuracy after the monitoring devices are configured for each device in the power grid.

[0055] The configuration method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the data storage system can store the data that the server 101 needs to process. The data storage system can be integrated on the server 101, or it can be placed on the cloud or other network servers. The server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0056] In an exemplary embodiment, Figure 2 As shown, a configuration method is provided, which is applied to Figure 1 The server 101 in the example is used as an example to illustrate, including the following steps 201 to 203. Among them:

[0057] Step 201: Obtain target power change data corresponding to a plurality of target historical time periods of a power grid.

[0058] The power grid is a network system composed of multiple power supply devices. In an embodiment of the present application, the server can obtain target power change data corresponding to the power grid and multiple target historical time periods. The target power change data is data on the change of photovoltaic output power of the power grid over time in the target historical time period. In an embodiment of the present application, different target power change data can reflect different power change scenarios, that is, the target power change data corresponds to the power change scenario.

[0059] Optionally, the server can directly obtain target power change data corresponding to multiple target historical time periods through an external device; optionally, the server can first obtain power change data corresponding to multiple historical time periods, and then determine the target power change data corresponding to each clustering scenario by clustering the power change data.

[0060] In one possible implementation, the process of obtaining target power change data corresponding to a power grid and multiple target historical time periods may include: obtaining power change data corresponding to the power grid in multiple historical time periods; clustering the power change data of each power grid to determine the target power change data corresponding to multiple target historical time periods.

[0061] The power change data refers to the data on the change of photovoltaic output power over time in the historical time period of the power grid. Optionally, the server can obtain the power change data corresponding to multiple historical time periods through an external input device. Optionally, the server can directly obtain the power change data corresponding to multiple historical time periods from the database.

[0062] After obtaining the power change data corresponding to multiple historical time periods, the server can cluster the power change data corresponding to each historical time period to obtain multiple clustering scenarios, and determine the target power change data corresponding to the target historical time period based on the cluster center corresponding to each clustering scenario.

[0063] Regarding the clustering process, in the embodiment of the present application, the server may first randomly select a power change data as an initial cluster center, and calculate the dissimilarity coefficient between other power change data and the initial cluster center. The calculation formula may refer to formula (1):

[0064] (1)

[0065] Among them, D a,b That is, the dissimilarity coefficient between power change data a and power change data b, P aj and P bj represents the photovoltaic output power corresponding to the power change data a and the power change data b at the jth sampling moment, and n is the total number of samples of the power change data.

[0066] After obtaining the dissimilarity coefficient between the current initial cluster center and other power change data, the other power change data corresponding to the largest dissimilarity coefficient is used as the second initial cluster center, and the above steps are repeated until a preset number k of initial cluster centers are selected.

[0067] Calculate the dissimilarity coefficients between the power change data of the initial cluster centers and other non-cluster centers, and combine the dissimilarity coefficients into a dissimilarity sparse matrix D:

[0068] (2)

[0069] Among them, suppose there are n power change data, then nk represents the power change data of non-cluster center, k represents the cluster scene identifier, D n-k,k Represents the dissimilarity coefficient between the nkth power change data and the initial cluster center corresponding to the kth clustering scenario.

[0070] For each clustering scenario, the corresponding initial clustering center is not accurate enough. At this time, the initial clustering center of each clustering scenario can be updated, and the target clustering center corresponding to each clustering center is determined from the power change data included in each clustering scenario. The target clustering center is the target power change data.

[0071] Regarding the updating process, in an embodiment of the present application, for each clustering scenario, the power change data included therein are used as candidate clustering centers, the dissimilarity coefficients between each candidate clustering center and other power change data are calculated, and the statistical values ​​of each dissimilarity coefficient are calculated. The target clustering center is determined from each power change data based on the statistical values. Optionally, the statistical value may be the sum of the dissimilarity coefficients. Optionally, the statistical value may be the mean of the dissimilarity coefficients.

[0072] After determining the target cluster center corresponding to each clustering scenario, the server may directly use the power change data corresponding to the target cluster center as the target power change data. Accordingly, the historical time period corresponding to the target power change data is the target historical time period.

[0073] Step 202: construct a target voltage sag observability matrix corresponding to the power grid according to each target power change data.

[0074] After determining the target power change data under different power change scenarios, the server can construct a target voltage sag observability matrix based on each target power change data. The target voltage sag observability matrix can reflect the voltage sag observability of different devices under different power change scenarios.

[0075] In a possible implementation, the server can determine the device power corresponding to each device based on the target change power data, and then determine whether a voltage sag occurs for each device under different power change scenarios based on the device power corresponding to each device, thereby determining the observability value of the voltage sag for each device under different power change scenarios, and determine the target voltage sag observability matrix corresponding to the power grid based on the observability value.

[0076] In another possible implementation, the server can determine the actual device voltage corresponding to each device in different power change scenarios based on each target change power data, and then set the corresponding voltage threshold based on the voltage sag event. According to the actual device voltage and the voltage threshold corresponding to the voltage sag event, it can be determined whether each device has a voltage sag in different power change scenarios, thereby determining the target voltage sag observability matrix corresponding to the power grid.

[0077] Step 203, obtaining a target configuration strategy according to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid, and the configuration solution model.

[0078] After determining the target voltage sag observability matrix, the server can obtain the target configuration strategy according to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid, and the configuration solution model.

[0079] In an embodiment of the present application, the reference number of monitoring devices can be a preset value, which can represent the maximum number of configurable monitoring devices, and the configuration solution model can be a preset solution formula, which reflects the correlation between relevant indicators of the observability of voltage sag and the number of different monitoring devices.

[0080] In a possible implementation, the server may set different values ​​of the number of candidate monitoring devices based on a reference number of monitoring devices, where the number of candidate monitoring devices is less than the reference number of monitoring devices, and then obtain the target configuration strategy based on different numbers of candidate monitoring devices, the target voltage sag observability matrix, and the configuration solution model.

[0081] In another possible implementation, the server may iteratively solve the configuration solution model according to the target voltage sag observability matrix based on different numbers of monitoring devices, thereby determining the target configuration strategy.

[0082] After obtaining the target configuration strategy, the server can configure the monitoring device according to the target configuration strategy. In the embodiment of the present application, the target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid. The server can determine the equipment that needs to be configured with a monitoring device according to the target configuration strategy and output it, so that the staff can determine the equipment that needs to be configured with a monitoring device based on the output content and ask the corresponding equipment to configure the monitoring device.

[0083] In the above embodiment, the target voltage sag observability matrix corresponding to the power grid is determined based on the target power change data, and the target voltage sag observability matrix and the configuration solution model are used to determine the target configuration strategy for configuring the monitoring device, and the reference number of monitoring devices is comprehensively considered. This avoids the problem of low detection accuracy caused by directly giving a configuration scheme based on a fixed number of monitoring devices in the traditional technology without considering the monitoring effect of the configuration scheme corresponding to different numbers of monitoring devices. The present application comprehensively considers the reference number of monitoring devices, the target voltage sag observability matrix and the configuration solution model to obtain a target configuration strategy, and after the monitoring devices are configured for each device in the power grid, the monitoring accuracy is higher.

[0084] In one embodiment, based on the above Figure 2 The embodiment shown, see Figure 3 This embodiment involves the process of constructing the target voltage sag observability matrix corresponding to the power grid according to each target power change data. Figure 3 As shown, step 202 may include steps 301 to 303 .

[0085] Step 301 : for each target power variation data, the device voltage corresponding to each device is calculated according to the target power variation data and the device resistance parameter of each device.

[0086] The device resistance parameters include a device identifier, an impedance matrix, and a device resistance corresponding to the device;

[0087] Since different target power change data correspond to different power change scenarios, the server can determine the device voltage of each device under different power change scenarios according to different target power change data. The device voltage is used to represent the actual voltage of each device in the target historical time period.

[0088] In a possible implementation, the server may substitute the target power change data and the device resistance parameters of each device into a preset voltage calculation formula to obtain the device voltage corresponding to each device. The voltage calculation formula may refer to formula (3):

[0089] (3)

[0090] Among them, NV k It represents the device voltage under the kth target power change data, P k is the kth target power change data, x is the device identifier, Z s is the impedance matrix, Z f is the device resistance.

[0091] Among them, P k It can be represented by a matrix:

[0092] (4)

[0093] Where t represents the sampling identifier in the target power change data, that is, there are t sampling points in the target power change data, P k,t Represents the photovoltaic output power corresponding to the tth sampling point in the target power change data.

[0094] Step 302: Obtain an initial voltage sag observability matrix corresponding to the target power variation data according to the voltages of the devices and the preset voltage sag parameters.

[0095] Since the types of voltage sag events are different, their corresponding voltage sag ranges are different. Therefore, different preset voltage sag parameters are set for different voltage sag events. The preset voltage sag parameter can be a numerical range. For each device, the server can determine whether a voltage sag event of the device has been monitored by judging whether the device voltage corresponding to the device is within the numerical range corresponding to the preset voltage sag parameter, thereby determining the voltage sag observability matrix corresponding to each device.

[0096] In a possible implementation, the preset voltage sag parameters include voltage thresholds corresponding to multiple voltage sag events. Then, according to the voltages of each device and the preset voltage sag parameters, obtaining the initial voltage sag observability matrix corresponding to the target power change data may include: for each device, the server compares the device voltage with each voltage threshold respectively to obtain a comparison result corresponding to each voltage sag event, and then determines the voltage sag observability data corresponding to the device according to each comparison result, wherein the voltage sag observability data includes the voltage sag parameters corresponding to each comparison result. In this way, the server can use the voltage sag observability data corresponding to each device as a matrix element to obtain the initial voltage sag observability matrix.

[0097] In an embodiment of the present application, corresponding voltage thresholds can be set for different voltage sag events according to actual needs. For each device, the server can compare the device voltage with the voltage threshold. The comparison result can reflect whether the corresponding voltage sag event has occurred in the device. In a possible implementation, the server can digitize the comparison result into a voltage sag parameter so that the voltage sag parameter corresponding to each voltage sag event voltage can characterize whether the corresponding voltage sag event has occurred in the device.

[0098] For example, the calculation formula of the voltage sag parameter may refer to formula (5):

[0099] (5)

[0100] For the kth target power change data, for the jth voltage sag event, when the voltage corresponding to device i is less than or equal to the voltage threshold When the voltage sag parameter is MRA k (i,j) is 1, indicating that the jth voltage sag event has occurred in device i; when the voltage corresponding to device i is greater than the voltage threshold When the voltage sag parameter is MRA k (i,j) is 0, indicating that no voltage sag event was detected for device i.

[0101] Through the above formula, for each device, the server obtains the voltage sag data composed of various voltage sag parameters, and the voltage sag data corresponding to each device is used as a matrix element to obtain the initial voltage sag observability matrix.

[0102] Step 303: determining a target voltage sag observability matrix according to each initial voltage sag observability matrix.

[0103] In an embodiment of the present application, in order to avoid detection blind spots caused by photovoltaic access, the server needs to take into account the volatility of each target power change data, and conduct a comprehensive analysis of the initial voltage sag observability matrix for multiple target power change data to obtain the target voltage sag observability matrix.

[0104] In a possible implementation, the server may multiply the initial voltage sag observability matrices to obtain a target voltage sag observability matrix.

[0105] In another possible implementation, the server may extract the voltage sag data corresponding to different devices from each initial voltage sag observability matrix, and then comprehensively analyze the voltage sag data corresponding to each device to obtain the target voltage sag data corresponding to each device. Each target voltage sag data may be used as a matrix element to form a target voltage sag observability matrix.

[0106] In one embodiment, based on the above Figure 3 The embodiment shown, see Figure 4 This embodiment relates to a process of determining a target voltage sag observability matrix based on each initial voltage sag observability matrix. In this embodiment, the initial voltage sag observability matrix includes voltage sag observability data corresponding to each device. Figure 4 As shown, step 303 may include step 401 and step 402 .

[0107] Step 401 : for each device, the server extracts each voltage sag observability data corresponding to the device from each initial voltage sag observability matrix, and obtains target voltage sag observability data corresponding to the device according to the extracted voltage sag observability data.

[0108] For each device, in a possible implementation manner, the server may multiply the extracted voltage sag objective data to obtain target voltage sag objective data.

[0109] In another possible implementation, for each voltage sag event, the server may extract each voltage sag parameter corresponding to each voltage sag event from each voltage sag observability data, and then perform an AND operation on each voltage sag parameter to obtain a target voltage sag parameter corresponding to the voltage sag event, and use the target voltage sag parameter corresponding to each voltage sag event as the target voltage sag observability data corresponding to the device.

[0110] The calculation formula for the target voltage sag parameter corresponding to each voltage sag event can refer to the following formula:

[0111] (6)

[0112] Among them, DMRA k (i,j) represents the target voltage sag parameter corresponding to device i in the jth voltage sag event, & is the AND operator, It represents an AND operation on the initial voltage sag parameters corresponding to the device i under the jth voltage sag event.

[0113] Step 402: taking each target voltage sag observability data corresponding to each device as a matrix element to obtain a target voltage sag observability matrix.

[0114] After obtaining each target voltage sag observability data corresponding to each device, the service may use each target voltage sag observability data as a matrix element to obtain a target voltage sag observability matrix.

[0115] In one embodiment, based on the above Figure 2 The embodiment shown, see Figure 5 This embodiment involves a process of obtaining a target configuration strategy based on a target voltage sag observability matrix, a reference number of monitoring devices corresponding to the power grid, and a configuration solution model. In this embodiment, the configuration solution model includes an observability index equation, a severity index equation, and preset constraints. The observability index is used to characterize the number of recorded voltage sag events, and the severity index is used to characterize the probability of recording voltage sag events. Figure 5 As shown, step 203 may include step 501 and step 502 .

[0116] Step 501, according to different numbers of monitoring devices, iteratively solve the observability index equation and the severity index equation to obtain the observability index, severity index and configuration strategy corresponding to each number of monitoring devices.

[0117] Among them, the number of monitoring devices is less than or equal to the reference number of monitoring devices.

[0118] In an embodiment of the present application, the number of monitoring devices set by the server can be from 1 to a reference number of monitoring devices. In this way, the server can iteratively solve the observability index equation and the severity index equation according to different numbers of monitoring devices and the target voltage sag observability matrix, thereby obtaining observability indicators and severity indicators corresponding to different numbers of monitoring devices.

[0119] The observability index equation can refer to the following formula:

[0120] (7)

[0121] Among them, OBSI stands for observability index, which is used to characterize the number of records of voltage sag events. The larger the OBSI, the more records there are and the better the monitoring effect. ft Represents the total number of voltage sag events, N bus represents the reference number of monitoring devices, X(j) is the configuration strategy, which is used to indicate whether to configure a monitoring device on the jth device, that is, the result of solving this equation.

[0122] X(j) can be expressed by the following formula:

[0123] (8)

[0124] The severity index equation can be referred to the following formula:

[0125] (9)

[0126] Among them, NVSI is the severity index, which is used to characterize the recording probability of voltage sag events. The larger the NVSI, the greater the possibility that the monitoring device records voltage sag events, and the better the monitoring effect. k represents the identifier corresponding to different target power change data.

[0127] Based on the different numbers of monitoring devices, the above formulas (7) and (9) are iteratively solved to obtain the configuration strategies corresponding to the different numbers of monitoring devices.

[0128] Step 502: Determine a target configuration strategy from among various configuration strategies according to preset constraints and observability indicators and severity indicators corresponding to the number of monitoring devices.

[0129] The preset constraint condition is a preset boundary condition. In the embodiment of the present application, the preset constraint condition may include that the first device must be equipped with a monitoring device, etc.

[0130] According to the preset constraints and the observability indicators and severity indicators corresponding to the number of monitoring devices, the server can determine the target configuration strategy from each configuration strategy. In a possible implementation, the server can calculate the statistical values ​​of the observability indicators and severity indicators corresponding to the number of monitoring devices for each number of monitoring devices, and determine the target configuration strategy from each configuration strategy according to the preset constraints and the statistical values.

[0131] Optionally, the statistical value may be the sum of the observability index and the severity index, optionally, it may be a mean, optionally, it may be a weighted average.

[0132] It can be understood that the larger the statistical value, the more optimized the corresponding configuration strategy is. The server can determine the target configuration strategy from various configuration strategies based on the preset constraints and the statistical values. For example, the server can first determine the candidate configuration strategy that meets the preset constraints from the configuration strategies based on the preset constraints, and then determine the candidate configuration strategy corresponding to the maximum statistical value as the target configuration strategy based on the statistical value.

[0133] In one embodiment, an exemplary configuration method is provided, which is used for a server and can be applied to Figure 1 In the implementation environment shown.

[0134] Step a, obtaining power change data corresponding to the power grid in multiple historical time periods.

[0135] Step b: clustering the power change data of each power grid to determine target power change data corresponding to multiple target historical time periods.

[0136] Step c: for each target power change data, calculate the device voltage corresponding to each device according to the target power change data and the device resistance parameter of each device.

[0137] The device resistance parameters include a device identifier, an impedance matrix, and a device resistance corresponding to the device.

[0138] Step d: for each device, compare the device voltage with each voltage threshold respectively to obtain a comparison result corresponding to each voltage sag event.

[0139] Step e: determining voltage sag observability data corresponding to the equipment according to each comparison result, wherein the voltage sag observability data includes voltage sag parameters corresponding to each comparison result.

[0140] Step f: using the observability data of each voltage sag corresponding to each device as a matrix element to obtain an initial voltage sag observability matrix.

[0141] The preset voltage sag parameters include voltage thresholds corresponding to a plurality of voltage sag events.

[0142] The initial voltage sag observability matrix includes observability data of each voltage sag corresponding to each device.

[0143] Step g: for each device, extracting the voltage sag observability data corresponding to the device from each initial voltage sag observability matrix.

[0144] Step h: for each voltage sag event, extracting each voltage sag parameter corresponding to each voltage sag event from each voltage sag observability data.

[0145] Step i: performing an AND operation on each voltage sag parameter to obtain a target voltage sag parameter corresponding to the voltage sag event.

[0146] Step j: using the target voltage sag parameter corresponding to each voltage sag event as the target voltage sag observability data corresponding to the device.

[0147] Step k: taking the observability data of each target voltage sag corresponding to each device as a matrix element to obtain a target voltage sag observability matrix.

[0148] Step 1, according to different numbers of monitoring devices, iteratively solve the observability index equation and the severity index equation to obtain the observability index, severity index and configuration strategy corresponding to each number of monitoring devices, and the number of monitoring devices is less than or equal to the reference number of monitoring devices.

[0149] Step m, for each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices.

[0150] Step n: determining a target configuration strategy from among various configuration strategies according to preset constraints and various statistical values.

[0151] The configuration solution model includes an observability index equation, a severity index equation and preset constraints. The observability index is used to characterize the number of recorded voltage sag events, and the severity index is used to characterize the probability of recorded voltage sag events.

[0152] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0153] Based on the same inventive concept, the embodiment of the present application also provides a configuration device for implementing the configuration method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more configuration device embodiments provided below can refer to the limitations on the configuration method above, and will not be repeated here.

[0154] In an exemplary embodiment, Figure 6 As shown, a configuration device is provided, including: an acquisition module 601, a construction module 602 and a configuration module 603, wherein:

[0155] An acquisition module 601 is used to acquire target power change data corresponding to a plurality of target historical time periods of a power grid;

[0156] A construction module 602 is used to construct a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data;

[0157] The configuration module 603 is used to obtain a target configuration strategy based on the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid, and the configuration solution model. The target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

[0158] In one embodiment, the building block 602 includes:

[0159] a voltage acquisition unit, configured to calculate, for each of the target power change data, a device voltage corresponding to each of the devices according to the target power change data and a device resistance parameter of each of the devices, wherein the device resistance parameter includes a device identifier, an impedance matrix, and a device resistance corresponding to the device;

[0160] An initial matrix construction unit, used to obtain an initial voltage sag observability matrix corresponding to the target power change data according to each of the device voltages and a preset voltage sag parameter;

[0161] The target matrix construction unit is used to determine the target voltage sag observability matrix according to each of the initial voltage sag observability matrices.

[0162] In one embodiment, the preset voltage sag parameter includes voltage thresholds corresponding to a plurality of voltage sag events, and according to each of the device voltages and the preset voltage sag parameter, the initial matrix construction unit is specifically configured to perform:

[0163] For each of the devices, the device voltage is compared with each of the voltage thresholds to obtain a comparison result corresponding to each of the voltage sag events;

[0164] Determining voltage sag observability data corresponding to the device according to each of the comparison results, wherein the voltage sag observability data includes voltage sag parameters corresponding to each of the comparison results;

[0165] The voltage sag observability data corresponding to each of the devices are used as matrix elements to obtain the initial voltage sag observability matrix.

[0166] In one embodiment, the initial voltage sag observability matrix includes the voltage sag observability data corresponding to each of the devices, and the target matrix construction unit is specifically used to perform:

[0167] For each of the devices, extracting each of the voltage sag observability data corresponding to the device from each of the initial voltage sag observability matrices, and obtaining the target voltage sag observability data corresponding to the device according to the extracted each of the voltage sag observability data;

[0168] The target voltage sag observability matrix is ​​obtained by using the target voltage sag observability data corresponding to each of the devices as a matrix element.

[0169] In one embodiment, the target matrix construction unit is further configured to perform:

[0170] For each of the voltage sag events, extracting each of the voltage sag parameters corresponding to each of the voltage sag events from each of the voltage sag observability data;

[0171] Performing an AND operation on each of the voltage sag parameters to obtain a target voltage sag parameter corresponding to the voltage sag event;

[0172] The target voltage sag parameter corresponding to each of the voltage sag events is used as the target voltage sag observability data corresponding to the device.

[0173] In one embodiment, the configuration solution model includes an observability index equation, a severity index equation, and preset constraints. The observability index is used to characterize the number of recordings of voltage sag events, and the severity index is used to characterize the probability of recording voltage sag events. The configuration module 603 includes:

[0174] A solving unit, configured to iteratively solve the observability index equation and the severity index equation according to different numbers of monitoring devices, to obtain the observability index, the severity index and the configuration strategy corresponding to each number of monitoring devices, wherein the number of monitoring devices is less than or equal to the reference number of monitoring devices;

[0175] A determination unit is used to determine the target configuration strategy from each of the configuration strategies according to the preset constraint conditions and the observability index and the severity index corresponding to the number of each of the monitoring devices.

[0176] In one embodiment, the determining unit is specifically configured to perform:

[0177] For each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices;

[0178] The target configuration strategy is determined from the configuration strategies according to the preset constraint conditions and the statistical values.

[0179] In one embodiment, the acquisition module 601 includes:

[0180] A power data acquisition unit, used to acquire power change data corresponding to the power grid in multiple historical time periods;

[0181] The clustering unit is used to cluster the power change data of each power grid to determine the target power change data corresponding to multiple target historical time periods.

[0182] Each module in the above configuration device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0183] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store configuration data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a configuration method is implemented.

[0184] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0185] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0186] Obtain target power change data corresponding to the power grid and multiple target historical time periods;

[0187] Constructing a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data;

[0188] According to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, a target configuration strategy is obtained, and the target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

[0189] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0190] For each of the target power change data, calculating the device voltage corresponding to each of the devices according to the target power change data and the device resistance parameters of each of the devices, wherein the device resistance parameters include a device identifier, an impedance matrix, and a device resistance corresponding to the device;

[0191] According to each of the device voltages and the preset voltage sag parameter, an initial voltage sag observability matrix corresponding to the target power change data is obtained;

[0192] The target voltage sag observability matrix is ​​determined according to each of the initial voltage sag observability matrices.

[0193] In one embodiment, the preset voltage sag parameter includes voltage thresholds corresponding to a plurality of voltage sag events, and the processor further implements the following steps when executing the computer program:

[0194] For each of the devices, the device voltage is compared with each of the voltage thresholds to obtain a comparison result corresponding to each of the voltage sag events;

[0195] Determining voltage sag observability data corresponding to the device according to each of the comparison results, wherein the voltage sag observability data includes voltage sag parameters corresponding to each of the comparison results;

[0196] The voltage sag observability data corresponding to each of the devices are used as matrix elements to obtain the initial voltage sag observability matrix.

[0197] In one embodiment, the initial voltage sag observability matrix includes the voltage sag observability data corresponding to each of the devices, and the processor further implements the following steps when executing the computer program:

[0198] For each of the devices, extracting each of the voltage sag observability data corresponding to the device from each of the initial voltage sag observability matrices, and obtaining the target voltage sag observability data corresponding to the device according to the extracted each of the voltage sag observability data;

[0199] The target voltage sag observability matrix is ​​obtained by using the target voltage sag observability data corresponding to each of the devices as a matrix element.

[0200] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0201] For each of the voltage sag events, extracting each of the voltage sag parameters corresponding to each of the voltage sag events from each of the voltage sag observability data;

[0202] Performing an AND operation on each of the voltage sag parameters to obtain a target voltage sag parameter corresponding to the voltage sag event;

[0203] The target voltage sag parameter corresponding to each of the voltage sag events is used as the target voltage sag observability data corresponding to the device.

[0204] In one embodiment, the configuration solution model includes an observability index equation, a severity index equation, and preset constraints, the observability index is used to characterize the number of recorded times of voltage sag events, and the severity index is used to characterize the probability of recording voltage sag events. When the processor executes the computer program, the following steps are also implemented:

[0205] Iteratively solving the observability index equation and the severity index equation according to different numbers of monitoring devices to obtain the observability index, the severity index and the configuration strategy corresponding to each number of monitoring devices, wherein the number of monitoring devices is less than or equal to the reference number of monitoring devices;

[0206] The target configuration strategy is determined from each of the configuration strategies according to the preset constraint conditions and the observability index and the severity index corresponding to the number of each of the monitoring devices.

[0207] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0208] For each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices;

[0209] The target configuration strategy is determined from the configuration strategies according to the preset constraint conditions and the statistical values.

[0210] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0211] Obtaining power change data corresponding to the power grid in multiple historical time periods;

[0212] Clustering is performed on each of the power grid power change data to determine the target power change data corresponding to a plurality of the target historical time periods.

[0213] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0214] Obtain target power change data corresponding to the power grid and multiple target historical time periods;

[0215] Constructing a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data;

[0216] According to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, a target configuration strategy is obtained, and the target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

[0217] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0218] For each of the target power change data, calculating the device voltage corresponding to each of the devices according to the target power change data and the device resistance parameters of each of the devices, wherein the device resistance parameters include a device identifier, an impedance matrix, and a device resistance corresponding to the device;

[0219] According to each of the device voltages and the preset voltage sag parameter, an initial voltage sag observability matrix corresponding to the target power change data is obtained;

[0220] The target voltage sag observability matrix is ​​determined according to each of the initial voltage sag observability matrices.

[0221] In one embodiment, the preset voltage sag parameter includes voltage thresholds corresponding to a plurality of voltage sag events, and when the computer program is executed by the processor, the following steps are further implemented:

[0222] For each of the devices, the device voltage is compared with each of the voltage thresholds to obtain a comparison result corresponding to each of the voltage sag events;

[0223] Determining voltage sag observability data corresponding to the device according to each of the comparison results, wherein the voltage sag observability data includes voltage sag parameters corresponding to each of the comparison results;

[0224] The voltage sag observability data corresponding to each of the devices are used as matrix elements to obtain the initial voltage sag observability matrix.

[0225] In one embodiment, the initial voltage sag observability matrix includes the voltage sag observability data corresponding to each of the devices, and the computer program further implements the following steps when executed by the processor:

[0226] For each of the devices, extracting each of the voltage sag observability data corresponding to the device from each of the initial voltage sag observability matrices, and obtaining the target voltage sag observability data corresponding to the device according to the extracted each of the voltage sag observability data;

[0227] The target voltage sag observability matrix is ​​obtained by using the target voltage sag observability data corresponding to each of the devices as a matrix element.

[0228] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0229] For each of the voltage sag events, extracting each of the voltage sag parameters corresponding to each of the voltage sag events from each of the voltage sag observability data;

[0230] Performing an AND operation on each of the voltage sag parameters to obtain a target voltage sag parameter corresponding to the voltage sag event;

[0231] The target voltage sag parameter corresponding to each of the voltage sag events is used as the target voltage sag observability data corresponding to the device.

[0232] In one embodiment, the configuration solution model includes an observability index equation, a severity index equation, and preset constraints, the observability index is used to characterize the number of recorded voltage sag events, the severity index is used to characterize the probability of recording voltage sag events, and the computer program further implements the following steps when executed by the processor:

[0233] Iteratively solving the observability index equation and the severity index equation according to different numbers of monitoring devices to obtain the observability index, the severity index and the configuration strategy corresponding to each number of monitoring devices, wherein the number of monitoring devices is less than or equal to the reference number of monitoring devices;

[0234] The target configuration strategy is determined from each of the configuration strategies according to the preset constraint conditions and the observability index and the severity index corresponding to the number of each of the monitoring devices.

[0235] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0236] For each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices;

[0237] The target configuration strategy is determined from the configuration strategies according to the preset constraint conditions and the statistical values.

[0238] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0239] Obtaining power change data corresponding to the power grid in multiple historical time periods;

[0240] Clustering is performed on each of the power grid power change data to determine the target power change data corresponding to a plurality of the target historical time periods.

[0241] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0242] Obtain target power change data corresponding to the power grid and multiple target historical time periods;

[0243] Constructing a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data;

[0244] According to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, a target configuration strategy is obtained, and the target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

[0245] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0246] For each of the target power change data, calculating the device voltage corresponding to each of the devices according to the target power change data and the device resistance parameters of each of the devices, wherein the device resistance parameters include a device identifier, an impedance matrix, and a device resistance corresponding to the device;

[0247] According to each of the device voltages and the preset voltage sag parameter, an initial voltage sag observability matrix corresponding to the target power change data is obtained;

[0248] The target voltage sag observability matrix is ​​determined according to each of the initial voltage sag observability matrices.

[0249] In one embodiment, the preset voltage sag parameter includes voltage thresholds corresponding to a plurality of voltage sag events, and when the computer program is executed by the processor, the following steps are further implemented:

[0250] For each of the devices, the device voltage is compared with each of the voltage thresholds to obtain a comparison result corresponding to each of the voltage sag events;

[0251] Determining voltage sag observability data corresponding to the device according to each of the comparison results, wherein the voltage sag observability data includes voltage sag parameters corresponding to each of the comparison results;

[0252] The voltage sag observability data corresponding to each of the devices are used as matrix elements to obtain the initial voltage sag observability matrix.

[0253] In one embodiment, the initial voltage sag observability matrix includes the voltage sag observability data corresponding to each of the devices, and the computer program further implements the following steps when executed by the processor:

[0254] For each of the devices, extracting each of the voltage sag observability data corresponding to the device from each of the initial voltage sag observability matrices, and obtaining the target voltage sag observability data corresponding to the device according to the extracted each of the voltage sag observability data;

[0255] The target voltage sag observability matrix is ​​obtained by using the target voltage sag observability data corresponding to each of the devices as a matrix element.

[0256] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0257] For each of the voltage sag events, extracting each of the voltage sag parameters corresponding to each of the voltage sag events from each of the voltage sag observability data;

[0258] Performing an AND operation on each of the voltage sag parameters to obtain a target voltage sag parameter corresponding to the voltage sag event;

[0259] The target voltage sag parameter corresponding to each of the voltage sag events is used as the target voltage sag observability data corresponding to the device.

[0260] In one embodiment, the configuration solution model includes an observability index equation, a severity index equation, and preset constraints, the observability index is used to characterize the number of recorded voltage sag events, the severity index is used to characterize the probability of recording voltage sag events, and the computer program further implements the following steps when executed by the processor:

[0261] Iteratively solving the observability index equation and the severity index equation according to different numbers of monitoring devices to obtain the observability index, the severity index and the configuration strategy corresponding to each number of monitoring devices, wherein the number of monitoring devices is less than or equal to the reference number of monitoring devices;

[0262] The target configuration strategy is determined from each of the configuration strategies according to the preset constraint conditions and the observability index and the severity index corresponding to the number of each of the monitoring devices.

[0263] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0264] For each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices;

[0265] The target configuration strategy is determined from the configuration strategies according to the preset constraint conditions and the statistical values.

[0266] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0267] Obtaining power change data corresponding to the power grid in multiple historical time periods;

[0268] Clustering is performed on each of the power grid power change data to determine the target power change data corresponding to a plurality of the target historical time periods.

[0269] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0270] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0271] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0272] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A configuration method, characterized in that: The method comprises: Obtain target power change data corresponding to the power grid and multiple target historical time periods; Constructing a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data; According to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, a target configuration strategy is obtained, and the target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

2. The method according to claim 1, characterized in that The step of constructing a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data includes: For each of the target power change data, calculating the device voltage corresponding to each of the devices according to the target power change data and the device resistance parameters of each of the devices, wherein the device resistance parameters include a device identifier, an impedance matrix, and a device resistance corresponding to the device; According to each of the device voltages and the preset voltage sag parameter, an initial voltage sag observability matrix corresponding to the target power change data is obtained; The target voltage sag observability matrix is ​​determined according to each of the initial voltage sag observability matrices.

3. The method according to claim 2, characterized in that The preset voltage sag parameter includes voltage thresholds corresponding to a plurality of voltage sag events. The initial voltage sag observability matrix corresponding to the target power change data is obtained according to each of the device voltages and the preset voltage sag parameter, including: For each of the devices, comparing the device voltage with each of the voltage thresholds respectively, to obtain a comparison result corresponding to each of the voltage sag events; Determining voltage sag observability data corresponding to the device according to each of the comparison results, wherein the voltage sag observability data includes voltage sag parameters corresponding to each of the comparison results; The voltage sag observability data corresponding to each of the devices are used as matrix elements to obtain the initial voltage sag observability matrix.

4. The method according to claim 3, characterized in that The initial voltage sag observability matrix includes the voltage sag observability data corresponding to each of the devices, and determining the target voltage sag observability matrix according to each of the initial voltage sag observability matrices includes: For each of the devices, extracting each of the voltage sag observability data corresponding to the device from each of the initial voltage sag observability matrices, and obtaining the target voltage sag observability data corresponding to the device according to the extracted each of the voltage sag observability data; The target voltage sag observability matrix is ​​obtained by using the target voltage sag observability data corresponding to each of the devices as a matrix element.

5. The method according to claim 4, characterized in that The step of obtaining target voltage sag observability data corresponding to the device according to the extracted voltage sag observability data includes: For each of the voltage sag events, extracting each of the voltage sag parameters corresponding to each of the voltage sag events from each of the voltage sag observability data; Performing an AND operation on each of the voltage sag parameters to obtain a target voltage sag parameter corresponding to the voltage sag event; The target voltage sag parameter corresponding to each of the voltage sag events is used as the target voltage sag observability data corresponding to the device.

6. The method according to claim 1, characterized in that The configuration solution model includes an observability index equation, a severity index equation and preset constraints. The observability index is used to characterize the number of recorded voltage sag events, and the severity index is used to characterize the recording probability of voltage sag events. The target configuration strategy is obtained according to the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid and the configuration solution model, including: Iteratively solving the observability index equation and the severity index equation according to different numbers of monitoring devices to obtain the observability index, the severity index and the configuration strategy corresponding to each number of monitoring devices, wherein the number of monitoring devices is less than or equal to the reference number of monitoring devices; The target configuration strategy is determined from each of the configuration strategies according to the preset constraint conditions and the observability index and the severity index corresponding to the number of each of the monitoring devices.

7. The method according to claim 6, characterized in that The determining the target configuration strategy from each of the configuration strategies according to the preset constraint condition and the observability index and the severity index corresponding to the number of each of the monitoring devices includes: For each number of monitoring devices, calculate the statistical values ​​of the observability index and the severity index corresponding to the number of monitoring devices; The target configuration strategy is determined from the configuration strategies according to the preset constraint conditions and the statistical values.

8. The method according to claim 1, characterized in that The obtaining of target power change data corresponding to the power grid and multiple target historical time periods includes: Obtaining power change data corresponding to the power grid in multiple historical time periods; Clustering is performed on each of the power grid power change data to determine the target power change data corresponding to a plurality of the target historical time periods.

9. A configuration device, characterized in that: The device comprises: An acquisition module is used to acquire target power change data corresponding to a plurality of target historical time periods of the power grid; A construction module, used for constructing a target voltage sag observability matrix corresponding to the power grid according to each of the target power change data; A configuration module is used to obtain a target configuration strategy based on the target voltage sag observability matrix, the reference number of monitoring devices corresponding to the power grid, and a configuration solution model. The target configuration strategy is used to indicate whether it is necessary to configure a monitoring device for each device in the power grid.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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