Method, device and equipment for determining abnormal data of power distribution network, medium and product

Abstract

The invention discloses a power distribution network abnormal data determination method, device and equipment, a medium and a product. The method comprises the following steps: acquiring current operation data acquired by each data acquisition device in the power distribution network; performing time sequence checking on the current operation data to obtain a first abnormal data judgment result; performing spatial distribution checking on the current operation data to obtain a second abnormal data judgment result; and determining abnormal data according to the first abnormal data judgment result and the second abnormal data judgment result. According to the technical scheme provided by the invention, the accuracy and reliability of abnormal data detection can be improved through bidirectional data checking and by utilizing complementarity and consistency of multi-source data.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of power system data processing, and in particular to a method, device, equipment, medium and product for determining abnormal data of a distribution network. Background Art

[0002] As an important part of the power system, the accuracy and reliability of distribution network data are crucial. However, due to the failure of data acquisition equipment, communication problems and other factors, the data of the distribution network may be abnormal. If these abnormal data are not discovered and processed in time, they will affect the normal operation and decision support of the distribution network. Existing abnormal data detection methods often rely on one-way data verification and cannot fully utilize the complementarity of multi-source data, resulting in insufficient accuracy and timeliness of abnormal data marking. Summary of the invention

[0003] Embodiments of the present invention provide a method, device, equipment, medium and product for determining abnormal data in a power distribution network, so as to improve the accuracy and reliability of abnormal data detection.

[0004] According to one aspect of the present invention, a method for determining abnormal data of a power distribution network is provided, comprising:

[0005] Obtain the current operating data collected by each data acquisition device in the distribution network;

[0006] Performing a time series check on the current operating data to obtain a first abnormal data judgment result;

[0007] Performing spatial distribution verification on the current operating data to obtain a second abnormal data judgment result;

[0008] Abnormal data is determined according to the first abnormal data determination result and the second abnormal data determination result.

[0009] According to another aspect of the present invention, there is provided a device for determining abnormal data of a power distribution network, the device comprising:

[0010] An acquisition module is used to acquire the current operation data collected by each data acquisition device in the distribution network;

[0011] A first verification module, used for performing a time series verification on the current operation data to obtain a first abnormal data judgment result;

[0012] A second checking module is used to perform spatial distribution checking on the current operation data to obtain a second abnormal data judgment result;

[0013] A determination module is used to determine abnormal data according to the first abnormal data determination result and the second abnormal data determination result.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining abnormal data of a distribution network according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining abnormal data of a distribution network according to any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, an embodiment of the present invention further provides a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, the method for determining abnormal data of a distribution network described in any embodiment of the present invention is implemented.

[0020] The embodiment of the present invention obtains the current operation data collected by each data collection device in the distribution network, performs time series verification on the current operation data, obtains a first abnormal data judgment result, performs spatial distribution verification on the current operation data, obtains a second abnormal data judgment result, and determines the abnormal data according to the first abnormal data judgment result and the second abnormal data judgment result. The technical solution of the present invention can improve the accuracy and reliability of abnormal data detection through two-way data verification and the complementarity and consistency of multi-source data.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for determining abnormal data of a distribution network in an embodiment of the present invention;

[0024] Figure 2 is a structural schematic diagram of a device for determining abnormal data of a distribution network in an embodiment of the present invention;

[0025] Figure 3 It is a structural schematic diagram of an electronic device for implementing the method for determining abnormal data of a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and their meanings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0029] Embodiment 1

[0030] Figure 1 is a flow chart of a method for determining abnormal data of a distribution network in an embodiment of the present invention. This embodiment is applicable to the case of determining abnormal data of a distribution network. The method can be executed by a device for determining abnormal data of a distribution network in an embodiment of the present invention. The device can be implemented in software and / or hardware, such as Figure 1 As shown, the method specifically comprises the following steps:

[0031] S101. Acquire current operation data collected by each data collection device in the distribution network.

[0032] In this embodiment, the data acquisition device may be a device used to collect and measure various types of data in the distribution network. Exemplarily, the data acquisition device may be, for example, various sensors, smart meters, etc. Among them, the current operation data may be various operation data in the distribution network detected at the current moment, for example, parameter data such as voltage, current, power, and frequency.

[0033] Specifically, real-time operation data is obtained from various data acquisition devices in the distribution network (such as sensors, smart meters, etc.), and the collected current operation data is preprocessed, including data cleaning, denoising and completion, to ensure the accuracy and completeness of the data.

[0034] S102: Perform time series verification on current operating data to obtain a first abnormal data judgment result.

[0035] It should be noted that the time series check can be a data check from the time series dimension of the data. Among them, the first abnormal data judgment result can be a judgment result of whether the data is abnormal after the data is checked from the time series dimension of the current running data. Exemplarily, the first abnormal data judgment result can be the existence of abnormal data. In other embodiments, the first abnormal data judgment result can also be the absence of abnormal data.

[0036] In actual operation, the main purpose of time series verification is to detect anomalies or unreasonable changes by comparing historical data with current data. The core of this process is to use the time series model to predict or smooth the current data and compare it with the actual observed data to identify possible anomalies. Specifically, the operation of data verification from the time series dimension of the current operating data can be further refined as follows: using the historical operating data corresponding to the current operating data to perform time series analysis, detect the deviation between the current operating data and the historical operating data, and determine whether the data is abnormal.

[0037] S103: Perform spatial distribution verification on the current operating data to obtain a second abnormal data judgment result.

[0038] It should be noted that the spatial distribution check may be a data check from the spatial distribution dimension of the data. The second abnormal data judgment result may be a judgment result of whether the data is abnormal after the data is checked from the spatial distribution dimension of the current running data. Exemplarily, the second abnormal data judgment result may be the existence of abnormal data. In other embodiments, the second abnormal data judgment result may also be the absence of abnormal data.

[0039] In actual operation, data verification from the spatial distribution dimension of current operating data can be further refined as follows: using the current operating data of each node in the distribution network, analyzing the consistency of the current operating data between nodes, and detecting whether there is abnormal data.

[0040] S104. Determine abnormal data according to the first abnormal data judgment result and the second abnormal data judgment result.

[0041] The abnormal data may be the abnormal data obtained after data verification from the time series and spatial distribution of the current running data. For example, the abnormal data may be caused by the failure of the data acquisition equipment, communication problems and other factors.

[0042] Specifically, abnormal data is determined based on the bidirectional verification results and marked. The marking information may include the abnormal type, abnormal degree, occurrence time, etc. The marked abnormal data can then be stored in a preset database to support subsequent query and analysis. For example, further processing can be performed on the marked abnormal data, including alarm, data repair, and abnormal report generation.

[0043] The embodiment of the present invention obtains the current operation data collected by each data collection device in the distribution network, performs time series verification on the current operation data, obtains a first abnormal data judgment result, performs spatial distribution verification on the current operation data, obtains a second abnormal data judgment result, and determines the abnormal data according to the first abnormal data judgment result and the second abnormal data judgment result. The technical solution of the present invention can improve the accuracy and reliability of abnormal data detection through two-way data verification and the complementarity and consistency of multi-source data.

[0044] Optionally, performing a time series check on the current operating data to obtain a first abnormal data judgment result includes:

[0045] Obtain the historical operation data collected by each data acquisition device in the distribution network.

[0046] The historical operation data may be various operation data in the distribution network detected at historical moments, such as parameter data such as voltage, current, power, and frequency. In this embodiment, the historical moment is not limited and can be set according to actual needs. For example, the historical moment may be the previous day, the previous week, or the previous month, etc., and correspondingly, the current moment may be the current day, the current week, or the current month, etc.

[0047] Specifically, the historical operation data collected by various data collection devices (such as sensors, smart meters, etc.) of the distribution network are obtained. In the actual operation process, the collected historical operation data can be pre-processed, including data cleaning, denoising and completion, to ensure the accuracy and completeness of the data.

[0048] The initial time series model is trained based on historical operation data to obtain the target prediction model.

[0049] In actual operation, traditional time series verification methods may be based on simple smoothing, regression or moving average methods, but these methods may not be ideal when processing nonlinear and non-stationary data. Therefore, the embodiments of the present invention introduce more advanced time series modeling and prediction algorithms, such as: ARIMA model (Auto Regressive Integrated Moving Average Model) and SARIMA model (Seasonal Auto Regressive Integrated Moving Average Model) perform well in processing non-stationary time series data and are suitable for distribution network operation data with trend and seasonal characteristics. By introducing these advanced modeling and prediction methods, the detection accuracy of abnormal data in time series verification can be improved.

[0050] The initial time series model may be an initial untrained ARIMA model or SARIMA model, and the target prediction model may be a trained ARIMA model or SARIMA model.

[0051] Specifically, by analyzing the trend of historical operation data, a prediction model for current operation data is established, such as time series models such as ARIMA model and SARIMA model. The time series model is trained based on historical operation data to obtain a target prediction model.

[0052] Get the forecast running data output by the target forecast model.

[0053] It should be noted that the predicted operation data may be current operation data predicted by the target prediction model based on historical operation data.

[0054] Specifically, a time series model is trained based on historical operation data to obtain predicted operation data output by a target prediction model.

[0055] If the residual between the current running data and the predicted running data is greater than or equal to a preset residual threshold, it is determined that abnormal data exists.

[0056] The preset residual threshold may be a preset difference threshold between an actual value (i.e., current operation data) and a predicted value (i.e., predicted operation data). This embodiment does not limit the specific value of the preset residual threshold. In practical applications, the preset residual threshold may be set by statistical analysis (e.g., 3 times the standard deviation method) or empirical value.

[0057] Specifically, if the residual between the current operating data (actual value) and the predicted operating data (predicted value) is less than the preset residual threshold, it is determined that there is no abnormal data; if the residual between the current operating data (actual value) and the predicted operating data (predicted value) is greater than or equal to the preset residual threshold, it is determined that there is abnormal data.

[0058] Optionally, determining the first abnormal data judgment result according to the time series data includes:

[0059] Generate time series data based on current running data.

[0060] The time series data may be data generated by sorting the actually observed current operating data in time series.

[0061] Identify breakpoints in time series data.

[0062] It should be explained that a mutation point can be understood as data in which the value in the time series data changes significantly. For example, the voltage values ​​in most values ​​in the time series data are between 0 and 100V, but there is a data whose voltage value is 150V, then this data can be considered as a mutation point.

[0063] Specifically, the change detection method of CUSUM (Cumulative SUM Control Chart) can be used to identify the mutation points in the time series data.

[0064] Get the mutation amplitude corresponding to the mutation point.

[0065] It should be noted that the mutation amplitude may be the difference amplitude between the mutation point and the data before and after the mutation point in the time series data.

[0066] Specifically, after the mutation point in the time series data is identified, the data before and after the mutation point in the time series data can be obtained respectively, and then the mutation amplitude corresponding to the mutation point can be calculated.

[0067] If the mutation amplitude is greater than or equal to the preset amplitude threshold, it is determined that abnormal data exists.

[0068] The preset amplitude threshold may be a threshold of the amplitude of the data value difference in the time series data that is preset according to actual needs. This embodiment does not limit the specific value of the preset amplitude threshold.

[0069] In the implementation process, the operation data of the distribution network should usually be continuous and gradual. Any sudden and large changes may indicate potential anomalies. By detecting the mutation point in the time series, further analyzing the data before and after the mutation point, analyzing the mutation amplitude and frequency of the data, and judging whether there is an abnormal situation. Determine whether the mutation is caused by an actual event (such as equipment switching, failure). If it is not an actual event, further investigation of the data source or system status is required.

[0070] Optionally, determining the first abnormal data judgment result according to the time series data includes:

[0071] Determine the target frequency fluctuation range based on historical operating data.

[0072] The target frequency fluctuation range may be a normal fluctuation range of the voltage frequency in the distribution network determined after statistical analysis of historical operating data.

[0073] Specifically, after the historical operation data is obtained, the historical voltage and frequency data in the historical operation data are statistically analyzed to determine the normal voltage and frequency fluctuation range.

[0074] Determine the current frequency fluctuation range based on the current operating data.

[0075] The current frequency fluctuation range may be an actual fluctuation range of the voltage frequency in the distribution network determined after statistical analysis of current operating data.

[0076] Specifically, after the current operation data is acquired, the current voltage and frequency data in the current operation data are statistically analyzed to determine the actual voltage and frequency fluctuation range.

[0077] If the frequency fluctuation tolerance between the current frequency fluctuation range and the target frequency fluctuation range is greater than or equal to a preset fluctuation tolerance threshold, it is determined that abnormal data exists.

[0078] The preset fluctuation tolerance threshold may be a voltage and frequency fluctuation tolerance threshold preset according to actual requirements. This embodiment does not limit the specific value of the preset fluctuation tolerance threshold.

[0079] In the distribution network, the stability of voltage and frequency is one of the key parameters. If the frequency fluctuates violently in a short period of time, it may indicate data anomalies or system failures. Therefore, the fluctuation of voltage and frequency must be within a certain range, otherwise it is considered abnormal. Through statistical analysis of historical frequency data, the normal fluctuation range is determined, the fluctuation tolerance threshold is set, and the frequency data is monitored in real time. If it exceeds the range, an alarm is triggered. Combined analysis is performed with other time series data (such as voltage and current) to eliminate false alarms caused by data collection or transmission errors.

[0080] In distribution network data, anomalies may occur at different time scales. Therefore, multi-scale analysis methods, such as wavelet analysis or HHT (Hilbert-Huang Transform), can be used to decompose and analyze time series data at multiple scales to detect anomalies at different scales.

[0081] Distribution network data often comes from multiple sensors or different monitoring points. By fusing data from different sources, the accuracy of time series verification can be improved. The following methods can be used for data fusion: Kalman filter: Applicable to state estimation of dynamic systems. By fusing time series data from multiple sensors, more accurate anomaly detection results can be provided. Bayesian method: Time series verification method based on Bayesian statistics can introduce prior knowledge in data fusion, thereby improving the accuracy of anomaly detection.

[0082] In actual operation, spatial distribution verification mainly judges the correctness of data by analyzing the data consistency and spatial rationality between nodes in the distribution network. Due to the complex topological structure of the distribution network, the electrical and spatial connections between nodes need to meet specific physical and logical rules.

[0083] Optionally, performing a spatial distribution check on the current running data to obtain a second abnormal data judgment result includes:

[0084] Build a topology diagram based on the current running data.

[0085] In this embodiment, the topology diagram may be constructed by corresponding the current operation data to the position of each node in the distribution network according to the physical connection topology between the nodes in the distribution network.

[0086] Specifically, after the operation data of each node is obtained, a topology diagram can be constructed from the current operation data according to the physical topology structure between the nodes in the distribution network.

[0087] Nodes in a distribution network usually represent physical or logical connection points in the power system. These nodes can be substations, distribution transformers, feeder segments (distribution lines), switchgear, etc. In a distribution network, node data is usually obtained through direct measurement and calculation. In spatial distribution verification, the current operating data can be electrical parameters or other related information collected at these nodes, such as voltage, current, power, frequency, etc. In some cases, the node data may need to be comprehensively calculated based on data from multiple measurement points.

[0088] If there are nodes in the topology diagram that are not connected, it is determined that there is abnormal data.

[0089] The topological structure of the distribution network requires that all electrical nodes must be physically connected. If the status data (such as voltage and current) of a node shows that the node is inconsistent with other nodes, it may indicate data anomalies or system failures at the node. By constructing a topological diagram of the distribution network, the connectivity algorithms based on graph theory, such as DFS (Depth-First Search) and BFS (Breadth-First Search), can be used to detect whether all nodes in the network are connected. Check the electrical status data of each node to ensure that the voltage and current data of all connected nodes are consistent with each other within a reasonable range. For the detected inconsistencies, further analyze the working status and geographical location of the distribution equipment to confirm whether it is caused by equipment failure, sensor failure or data transmission error.

[0090] Optionally, performing a spatial distribution check on the current running data to obtain a second abnormal data judgment result includes:

[0091] Get the location information corresponding to each data collection device.

[0092] In this embodiment, the location information may be the geographical location information where the data acquisition device is actually installed.

[0093] Specifically, the location information of each data collection device is collected in real time. Furthermore, the location information of each data collection device may specifically be the actual geographic coordinate information of each data collection device.

[0094] Query the database to obtain the geographic coordinate information corresponding to each data collection device.

[0095] The geographic coordinate information may be the geographic coordinate information stored in the database where each data acquisition device should be installed according to the design specifications.

[0096] Specifically, when the equipment is installed, its geographic coordinate information is obtained and recorded to form a standard database.

[0097] The location information and geographic coordinate information corresponding to each data acquisition device are compared. If the deviation value between the location information and the geographic coordinate information is greater than or equal to a preset deviation range, it is determined that there is an abnormality in the data acquisition device.

[0098] The preset deviation range may be a coordinate deviation range threshold value pre-set according to actual needs, and this embodiment does not limit the specific value of the preset deviation threshold value.

[0099] Specifically, equipment in the distribution network (such as transformers and switches) should be installed at specific geographic coordinates according to design specifications. If the equipment location data deviates significantly from its expected location, it may indicate that the data is wrong or the actual location of the equipment is inappropriate. When the equipment is installed, its geographic coordinate information is obtained and recorded to form a standard database. The location information of the equipment is collected in real time and compared with the coordinates in the standard database. If the deviation exceeds the allowable range, it is considered abnormal. In the case of deviation, further check whether it is caused by equipment movement, installation error or GPS positioning error, and conduct on-site inspection if necessary.

[0100] Optionally, performing a spatial distribution check on the current running data to obtain a second abnormal data judgment result includes:

[0101] A set of adjacent node pairs is determined according to the topological structure graph.

[0102] In this embodiment, the adjacent node pair set includes several adjacent node pairs. It should be noted that the adjacent node pair may be a pair of nodes at adjacent positions in a topological structure diagram.

[0103] Specifically, every two adjacent nodes in the topological structure graph are regarded as an adjacent node pair, and the adjacent node pair set is determined according to the topological structure graph.

[0104] Get the running data difference for each adjacent node pair.

[0105] It should be noted that the operating data difference may be the difference between each type of operating data between two adjacent nodes in an adjacent node pair, for example, it may be a voltage difference, a current difference, etc.

[0106] Specifically, the running data difference between every two adjacent nodes in each adjacent node pair is calculated.

[0107] If the running data difference is greater than or equal to the preset tolerance range, it is determined that abnormal data exists.

[0108] The preset tolerance range may be a range of operating data differences between two adjacent nodes that is preset according to actual needs. This embodiment does not limit the specific value of the preset tolerance range.

[0109] In actual operation, the data between adjacent nodes (such as voltage, current, and power) should have a high degree of consistency, especially in the branch network of the distribution network. If there are significant differences in the data of adjacent nodes, it may indicate that a node or data link is abnormal. Analyze the topological structure of the distribution network and determine the set of adjacent node pairs. Compare the key parameters between adjacent nodes (such as voltage difference and current difference) and set a reasonable tolerance range. Any node pair that exceeds the tolerance range is marked as abnormal, and the power equipment status and data acquisition system in the area are further checked.

[0110] Optionally, performing a spatial distribution check on the current running data to obtain a second abnormal data judgment result includes:

[0111] Determine the power flow distribution data based on the topology diagram and equipment rated parameters.

[0112] In this embodiment, the device rated parameters may be the rated parameters of each data acquisition device, such as the rated voltage, rated current, etc.

[0113] The power flow distribution data may be theoretical power flow distribution data calculated using the topological structure of the distribution network and rated parameters of equipment.

[0114] Specifically, theoretical power flow distribution data is calculated based on the topological structure diagram and equipment rated parameters.

[0115] Get the power flow data for each node in the topology diagram.

[0116] The power flow data may be power flow data flowing through each node.

[0117] Specifically, real-time power flow data of each node.

[0118] The power flow data of each branch is determined according to the power flow data of each node.

[0119] It should be noted that a branch can be formed by connecting every two nodes, that is, the connection relationship between two nodes can be called a branch.

[0120] Specifically, the power flow data of each node is monitored in real time, and the actual power flow data of a branch consisting of every two nodes is calculated based on the power flow data of every two nodes.

[0121] The power flow data of each branch is compared with the power flow distribution data. If the difference between the power flow data of each branch and the power flow distribution data is greater than or equal to a preset power flow threshold, it is determined that abnormal data exists.

[0122] Among them, the preset power flow threshold can be a difference threshold between the actual power flow (power flow data) and the theoretical value (power flow distribution data) pre-set according to actual needs. This embodiment does not limit the specific value of the preset power flow threshold.

[0123] In the distribution network, the power flow must satisfy the law of conservation of power, and the difference between the input power and the output power should be within a reasonable range (considering losses). If the distribution of power flow is unreasonable, it may indicate data anomalies or system configuration errors. Use the topology of the distribution network and the rated parameters of the equipment to calculate the theoretical power flow distribution. Monitor the power flow data of each node in real time and calculate the actual power flow of each branch. Compare the actual power flow with the theoretical value. If the difference exceeds the allowable range, an abnormal alarm is triggered. Further analyze the cause of the difference and troubleshoot possible equipment failures, data errors, or illegal access.

[0124] The spatial distribution verification of the distribution network not only involves the consistency of the data of each node, but also needs to consider the topological structure of the entire network. It can be optimized in the following ways: Graph theory and network analysis: Using the concepts of shortest path, connectivity, network flow, etc. in graph theory, the topological structure of the distribution network can be analyzed, and anomaly detection can be performed in combination with node data. Spectral graph theory: By analyzing the Laplace matrix of the distribution network, abnormal behaviors in the network can be detected, such as abnormal fluctuations of certain nodes or abnormal distribution of power flow.

[0125] In spatial distribution verification, the data between nodes often have spatial autocorrelation. The following methods can be used to optimize spatial distribution verification: Spatial clustering analysis: Spatial clustering algorithms, such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise, a density-based clustering algorithm that can divide areas with sufficiently high density into clusters and treat low-density areas as noise or outliers) and K-means, can be used to identify areas with abnormal data in the distribution network. Through clustering analysis, abnormal points that appear concentrated in space can be found, so that they can be processed in a targeted manner.

[0126] The spatial distribution verification of the distribution network needs to consider the multi-scale characteristics of the data. The verification algorithm can be optimized through multi-scale analysis: Multi-scale decomposition methods: such as wavelet transform or multi-scale pyramid method, can perform multi-scale decomposition of distribution network data, analyze spatial distribution characteristics from different scales to improve the accuracy of anomaly detection. Multi-resolution analysis: Combining GIS data and distribution network operation data, spatial verification can be performed at different resolutions. For example, through fine-grained spatial analysis, abnormal areas can be located more accurately.

[0127] In spatial distribution verification, different nodes may collect different types of data (such as voltage, current, frequency, etc.). By fusing these multi-source data, the spatial verification effect can be optimized: Bayesian data fusion: Using the Bayesian inference method, spatial data from different nodes can be fused and combined with prior knowledge for anomaly detection.

[0128] Time series verification and spatial distribution verification verify data from different dimensions respectively, but in practical applications, these two verification methods can complement each other to achieve more accurate and robust anomaly detection. 1. Time-space joint modeling: Dynamic Bayesian network: By combining the dynamic model in time series verification with the spatial dependency in spatial distribution verification, a dynamic Bayesian network model can be constructed for time-space joint verification. 2. Iterative verification mechanism: Multiple rounds of verification and feedback mechanism: Time series verification can be performed first, and after preliminary anomalies are found, they can be verified by spatial distribution verification, and vice versa. Through iteration and feedback mechanisms, false detection and missed detection can be reduced, and the reliability of the overall verification can be improved. In the bidirectional data verification of distribution network operation data, time series verification and spatial distribution verification each have their own unique advantages. Through algorithm optimization and improvement, the accuracy and real-time performance of anomaly detection can be significantly improved. Future research and applications can further explore the coordinated optimization of the two, and realize a more accurate and intelligent distribution network operation data verification system through time-space joint modeling, iterative verification, data fusion and other means. This will not only help improve the stability and reliability of the distribution network, but also provide a solid data foundation for the development of smart grids.

[0129] The technical solution of the embodiment of the present invention, through bidirectional verification of time series and spatial distribution, fully utilizes the complementarity of multi-source data, timely discovers and marks abnormal data of the distribution network, effectively improves the accuracy and reliability of abnormal data detection, and ensures the safe and stable operation of the distribution network; at the same time, by processing abnormal data in real time, it is possible to respond to and repair abnormal situations in a timely manner, providing strong support for the intelligent management of the distribution network.

[0130] Embodiment 2

[0131] Figure 2 is a schematic diagram of the structure of a device for determining abnormal data of a power distribution network in an embodiment of the present invention. This embodiment is applicable to the case of determining abnormal data of a power distribution network. The device can be implemented in software and / or hardware. The device can be integrated in any device that provides the function of determining abnormal data of a power distribution network, such as Figure 2 As shown, the distribution network abnormal data determination device specifically includes: an acquisition module 201 , a first verification module 202 , a second verification module 203 and a determination module 204 .

[0132] Wherein, the acquisition module 201 is used to acquire the current operation data collected by each data acquisition device in the distribution network;

[0133] A first checking module 202 is used to perform a time series check on the current operation data to obtain a first abnormal data judgment result;

[0134] A second checking module 203 is used to perform a spatial distribution check on the current operation data to obtain a second abnormal data judgment result;

[0135] The determination module 204 is configured to determine abnormal data according to the first abnormal data determination result and the second abnormal data determination result.

[0136] Optionally, the first checking module 202 is specifically used for:

[0137] Acquire historical operation data collected by each data acquisition device in the distribution network;

[0138] Training an initial time series model based on the historical operation data to obtain a target prediction model;

[0139] Obtaining forecast operation data output by the target forecast model;

[0140] If the residual between the current operating data and the predicted operating data is greater than or equal to a preset residual threshold, it is determined that abnormal data exists.

[0141] Optionally, the first checking module 202 is specifically used for:

[0142] Generate time series data according to the current operation data;

[0143] Identifying a breakpoint in the time series data;

[0144] Obtaining the mutation amplitude corresponding to the mutation point;

[0145] If the mutation amplitude is greater than or equal to a preset amplitude threshold, it is determined that abnormal data exists.

[0146] Optionally, the first checking module 202 is specifically used for:

[0147] Determine a target frequency fluctuation range according to the historical operation data;

[0148] Determine the current frequency fluctuation range according to the current operating data;

[0149] If the frequency fluctuation tolerance between the current frequency fluctuation range and the target frequency fluctuation range is greater than or equal to a preset fluctuation tolerance threshold, it is determined that abnormal data exists.

[0150] Optionally, the second checking module 203 is specifically used for:

[0151] Constructing a topology diagram according to the current operation data;

[0152] If there are nodes in the topology diagram that are not connected, it is determined that abnormal data exists.

[0153] Optionally, the second checking module 203 is specifically used for:

[0154] Obtaining location information corresponding to each data collection device;

[0155] Query the database to obtain the geographic coordinate information corresponding to each data collection device;

[0156] The location information and geographic coordinate information corresponding to each of the data acquisition devices are compared. If the deviation value between the location information and the geographic coordinate information is greater than or equal to a preset deviation range, it is determined that the data acquisition device is abnormal.

[0157] Optionally, the second checking module 203 is specifically used for:

[0158] Determine a set of adjacent node pairs according to the topological structure diagram;

[0159] Get the running data difference of each adjacent node pair;

[0160] If the operating data difference is greater than or equal to a preset tolerance range, it is determined that abnormal data exists.

[0161] Optionally, the second checking module 203 is specifically used for:

[0162] Determining power flow distribution data according to the topological structure diagram and equipment rated parameters;

[0163] Obtaining power flow data of each node in the topological structure diagram;

[0164] Determine the power flow data of each branch according to the power flow data of each node;

[0165] The power flow data of each branch is compared with the power flow distribution data. If the difference between the power flow data of each branch and the power flow distribution data is greater than or equal to a preset power flow threshold, it is determined that abnormal data exists.

[0166] The above-mentioned product can execute the distribution network abnormal data determination method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0167] Embodiment 3

[0168] Figure 3A schematic diagram of the structure of an electronic device 30 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0169] like Figure 3 As shown, the electronic device 30 includes at least one processor 31, and a memory connected to the at least one processor 31, such as a read-only memory (ROM) 32, a random access memory (RAM) 33, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 31 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 32 or the computer program loaded from the storage unit 38 to the random access memory (RAM) 33. In the RAM 33, various programs and data required for the operation of the electronic device 30 can also be stored. The processor 31, the ROM 32, and the RAM 33 are connected to each other through a bus 34. An input / output (I / O) interface 35 is also connected to the bus 34.

[0170] A number of components in the electronic device 30 are connected to the I / O interface 35, including: an input unit 36, such as a keyboard, a mouse, etc.; an output unit 37, such as various types of displays, speakers, etc.; a storage unit 38, such as a disk, an optical disk, etc.; and a communication unit 39, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 39 allows the electronic device 30 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0171] The processor 31 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 31 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 31 executes the various methods and processes described above, such as the distribution network abnormal data determination method:

[0172] Obtain the current operating data collected by each data acquisition device in the distribution network;

[0173] Performing a time series check on the current operating data to obtain a first abnormal data judgment result;

[0174] Performing spatial distribution verification on the current operating data to obtain a second abnormal data judgment result;

[0175] Abnormal data is determined according to the first abnormal data determination result and the second abnormal data determination result.

[0176] In some embodiments, the distribution network abnormal data determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 38. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 30 via the ROM 32 and / or the communication unit 39. When the computer program is loaded into the RAM 33 and executed by the processor 31, one or more steps of the distribution network abnormal data determination method described above may be performed. Alternatively, in other embodiments, the processor 31 may be configured to perform the distribution network abnormal data determination method in any other appropriate manner (e.g., by means of firmware).

[0177] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0178] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0179] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0180] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0181] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0182] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0183] In one embodiment, the embodiment of the present invention further includes a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, the method for determining abnormal data of a distribution network according to any embodiment of the present invention is implemented.

[0184] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0185] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0186] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining abnormal data of a distribution network, characterized in that: include: Obtain the current operating data collected by each data acquisition device in the distribution network; Performing a time series check on the current operating data to obtain a first abnormal data judgment result; Performing spatial distribution verification on the current operating data to obtain a second abnormal data judgment result; Abnormal data is determined according to the first abnormal data determination result and the second abnormal data determination result.

2. The method according to claim 1, characterized in that Performing a time series check on the current operating data to obtain a first abnormal data judgment result includes: Acquire historical operation data collected by each data acquisition device in the distribution network; Training an initial time series model based on the historical operation data to obtain a target prediction model; Obtaining forecast operation data output by the target forecast model; If the residual between the current operating data and the predicted operating data is greater than or equal to a preset residual threshold, it is determined that abnormal data exists.

3. The method according to claim 1, characterized in that Determining a first abnormal data judgment result according to the time series data includes: Generate time series data according to the current operation data; Identifying a breakpoint in the time series data; Obtaining the mutation amplitude corresponding to the mutation point; If the mutation amplitude is greater than or equal to a preset amplitude threshold, it is determined that abnormal data exists.

4. The method according to claim 2, characterized in that: Determining a first abnormal data judgment result according to the time series data includes: Determine a target frequency fluctuation range according to the historical operation data; Determine the current frequency fluctuation range according to the current operating data; If the frequency fluctuation tolerance between the current frequency fluctuation range and the target frequency fluctuation range is greater than or equal to a preset fluctuation tolerance threshold, it is determined that abnormal data exists.

5. The method according to claim 1, characterized in that Performing a spatial distribution check on the current operating data to obtain a second abnormal data judgment result includes: Constructing a topology diagram according to the current operation data; If there are nodes in the topology diagram that are not connected, it is determined that abnormal data exists.

6. The method according to claim 1, characterized in that Performing a spatial distribution check on the current operating data to obtain a second abnormal data judgment result includes: Obtaining location information corresponding to each data collection device; Query the database to obtain the geographic coordinate information corresponding to each data collection device; The location information and geographic coordinate information corresponding to each of the data acquisition devices are compared. If the deviation value between the location information and the geographic coordinate information is greater than or equal to a preset deviation range, it is determined that the data acquisition device is abnormal.

7. The method according to claim 5, characterized in that Performing a spatial distribution check on the current operating data to obtain a second abnormal data judgment result includes: Determine a set of adjacent node pairs according to the topological structure diagram; Get the running data difference of each adjacent node pair; If the operating data difference is greater than or equal to a preset tolerance range, it is determined that abnormal data exists.

8. The method according to claim 5, characterized in that Performing a spatial distribution check on the current operating data to obtain a second abnormal data judgment result includes: Determining power flow distribution data according to the topological structure diagram and the equipment rated parameters; Obtaining power flow data of each node in the topological structure diagram; Determine the power flow data of each branch according to the power flow data of each node; The power flow data of each branch is compared with the power flow distribution data. If the difference between the power flow data of each branch and the power flow distribution data is greater than or equal to a preset power flow threshold, it is determined that abnormal data exists.

9. A device for determining abnormal data of a distribution network, characterized in that: include: An acquisition module is used to acquire the current operation data collected by each data acquisition device in the distribution network; A first verification module, used for performing a time series verification on the current operation data to obtain a first abnormal data judgment result; A second checking module is used to perform spatial distribution checking on the current operation data to obtain a second abnormal data judgment result; A determination module is used to determine abnormal data according to the first abnormal data determination result and the second abnormal data determination result.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining abnormal data of a power distribution network according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining abnormal data of a power distribution network according to any one of claims 1 to 8 when executed.

12. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for determining abnormal data of a power distribution network according to any one of claims 1 to 8.

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