Power failure event prediction and evaluation method and device based on power failure study and judgment model

Abstract

The invention discloses a power failure event prediction and evaluation method and device based on a power failure study and judgment model. The method comprises the following steps: acquiring multi-data-source data of a target power system; integrating, verifying and cleaning the multi-data-source data to obtain processed multi-data-source data; constructing and training a power failure study and judgment model based on the processed multi-data-source data; the power failure study and judgment model is a neural network model based on deep learning; and based on the power failure study and judgment model, predicting and evaluating a power failure event of the target power system. According to the invention, the technical problems of low prediction accuracy and insufficient emergency response of the existing power failure prediction method in extreme disaster weather are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system safety, and in particular to a method and device for predicting and evaluating power outage events based on a power outage analysis and judgment model. Background Art

[0002] In the event of a natural disaster, the power system is easily affected by the disaster and causes widespread power outages. If the power outage information cannot be accurately evaluated, it will lead to the problem of being unable to accurately lock the power outage area and the progress of emergency repairs. Therefore, it is urgent to strengthen the research and application of related technologies to improve the efficiency and accuracy of disaster emergency response to cope with possible disaster challenges in the future. Existing methods for power outage prediction have technical problems such as low prediction accuracy and insufficient emergency response in extreme disaster weather. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for predicting and evaluating power outage events based on a power outage analysis model in order to solve at least one of the above technical problems.

[0004] In the first aspect, an embodiment of the present invention provides a method for predicting and evaluating power outage events based on a power outage analysis model, comprising: acquiring multi-data source data of a target power system; integrating, verifying and cleaning the multi-data source data to obtain processed multi-data source data; constructing and training a power outage analysis model based on the processed multi-data source data; the power outage analysis model is a neural network model based on deep learning; based on the power outage analysis model, predicting and evaluating power outage events of the target power system.

[0005] Furthermore, the multi-data source data includes fault repair work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data and 10kV line fault signal data.

[0006] Furthermore, based on the power outage assessment model, a power outage event of the target power system is predicted and evaluated, including: based on the power outage assessment model, the power outage scope, power outage duration and power outage cause of the target power system are analyzed and evaluated respectively.

[0007] Furthermore, the power outage analysis model includes: a k-Shape-GRU model.

[0008] In the second aspect, an embodiment of the present invention also provides a power outage event prediction and evaluation device based on a power outage analysis model, comprising: an acquisition module, a processing module, a construction module and an evaluation module; wherein the acquisition module is used to acquire multi-data source data of a target power system; the processing module is used to integrate, verify and clean the multi-data source data to obtain processed multi-data source data; the construction module is used to construct and train a power outage analysis model based on the processed multi-data source data; the power outage analysis model is a neural network model based on deep learning; the evaluation module is used to predict and evaluate power outage events of the target power system based on the power outage analysis model.

[0009] Furthermore, the multi-data source data includes fault repair work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data and 10kV line fault signal data.

[0010] Furthermore, the evaluation module is also used to analyze and evaluate the power outage scope, power outage duration and power outage cause of the target power system based on the power outage analysis model.

[0011] Furthermore, the power outage analysis model includes: a k-Shape-GRU model.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method described in the first aspect is implemented.

[0014] The present invention provides a method and device for predicting and evaluating power outage events based on a power outage analysis and judgment model, which has the following technical effects: by establishing a power outage analysis and judgment model, the power outage situation can be understood with higher accuracy, and corresponding emergency response measures can be taken in a timely manner; key information such as the power outage scope, power outage duration, and power outage cause can be accurately analyzed and judged, the degree and scope of the power outage impact can be better understood, and the conditions of the affected substations and users can be accurately evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flowchart of a method for predicting and evaluating power outage events based on a power outage analysis model provided by an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of a power outage event prediction and evaluation device based on a power outage analysis model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0019] Embodiment 1

[0020] Figure 1 FIG. 1 is a flow chart of a method for predicting and evaluating power outage events based on a power outage assessment model according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0021] Step S102: acquiring data from multiple sources of the target power system.

[0022] Specifically, the data from multiple data sources include fault reporting work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data and 10kV line fault signal data.

[0023] Step S104: Integrate, verify and clean the data from multiple data sources to obtain processed data from multiple data sources.

[0024] Step S106, constructing and training a power outage analysis model based on the processed data from multiple data sources; the power outage analysis model is a neural network model based on deep learning.

[0025] Preferably, in an embodiment of the present invention, the power outage analysis model includes: a k-Shape-GRU model.

[0026] Step S108: Based on the power outage assessment model, a power outage event of the target power system is predicted and assessed. Specifically, based on the power outage assessment model, the power outage scope, power outage duration and power outage cause of the target power system are analyzed and assessed respectively.

[0027] Specifically, multiple factors such as weather factors, equipment conditions, and load conditions of the power network are used as inputs of the power outage assessment model. The power outage scope and duration of the target power system are predicted through the power outage assessment model, and the causes of the power outage are analyzed and evaluated.

[0028] In the embodiment of the present invention, a multi-dimensional power outage assessment model is constructed to improve the prediction and assessment accuracy of power outage events by integrating information from multiple data sources. In order to ensure the reliability and effectiveness of the model, the following methods and data verification steps are also included:

[0029] (1) Multi-source data verification: First, verify the data from different data sources. These data sources include fault repair work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data, and 10kV line fault signal data. By verifying these data, the accuracy and consistency of the data can be ensured.

[0030] (2) Cross-validation: Cross-validation is performed to verify the consistency and complementarity between different data sources. By comparing and analyzing the same or related information provided by different data sources, potential data inconsistencies or omissions can be discovered and handled accordingly.

[0031] (3) Remove duplicate data: In multiple data sources, there may be duplicate records or duplicate data. These duplicate data may affect the accuracy and performance of the model. Therefore, before building a power outage assessment model, it is necessary to deduplicate the data to ensure the uniqueness of each record.

[0032] (4) Collecting the online status information of the central control equipment: The central control equipment is a key component for monitoring and controlling the power network. By obtaining the online status information of the central control equipment, it is possible to determine whether the equipment is operating normally and to detect potential faults or problems in a timely manner.

[0033] (5) Low-voltage meter active call information: Low-voltage meter active call information means that the meter equipment actively sends data to the system to provide information about power consumption, voltage, current, etc. By obtaining low-voltage meter active call information, more accurate power consumption data and power load information can be obtained, thereby improving the accuracy of the power outage model.

[0034] (6) RPA Intelligent Outbound Calling: RPA (Robotic Process Automation) intelligent outbound calling is a technology that uses automated robots to make phone calls. Through RPA intelligent outbound calling, users can be proactively contacted to obtain real-time information and feedback about power outages, thereby supplementing and verifying information provided by other data sources.

[0035] (7) Operator equipment online data: Operators cooperating with power companies may provide online data of relevant equipment. This data can be used to verify and supplement the data collected by the power supply service command platform and improve the reliability of the model.

[0036] By integrating and validating these different data sources, the power outage assessment model can more accurately predict and assess power outage events. The verification and cross-validation of these data ensures the accuracy and consistency of the data, while the use of online status information of the equipment, active call information of low-voltage meters, RPA intelligent outbound calls, and online data of operator equipment can increase the reliability and comprehensiveness of the data. Ultimately, the combination of these steps will help build a more accurate and reliable power outage assessment model.

[0037] The present invention provides a power outage event prediction and evaluation method based on a power outage analysis and judgment model, which has the following technical effects: by establishing a power outage analysis and judgment model, the power outage situation can be understood with higher accuracy, and corresponding emergency response measures can be taken in a timely manner; key information such as the power outage scope, power outage duration, and power outage cause can be accurately analyzed and judged, the degree and scope of the power outage impact can be better understood, and the conditions of the affected substations and users can be accurately evaluated.

[0038] Embodiment 2

[0039] Figure 2 Schematic diagram of a power outage event prediction and evaluation device based on a power outage judgment model provided according to an embodiment of the present invention. Figure 2 As shown, the device includes: an acquisition module 10, a processing module 20, a construction module 30 and an evaluation module 40.

[0040] Specifically, the acquisition module 10 is used to acquire data from multiple data sources of the target power system, including fault repair work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data, and 10kV line fault signal data.

[0041] The processing module 20 is used to integrate, verify and cleanse the data from multiple data sources to obtain processed data from multiple data sources.

[0042] The construction module 30 is used to construct and train a power outage assessment model based on the processed multi-data source data; the power outage assessment model is a neural network model based on deep learning. Preferably, the power outage assessment model includes: a k-Shape-GRU model.

[0043] The evaluation module 40 is used to predict and evaluate the power outage event of the target power system based on the power outage analysis model.

[0044] Specifically, the evaluation module 40 is also used to analyze and evaluate the power outage scope, power outage duration and power outage cause of the target power system based on the power outage analysis model.

[0045] An embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method in the first embodiment when executing the computer program.

[0046] An embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method in the first embodiment is implemented.

[0047] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0048] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A power outage event prediction and evaluation method based on a power outage analysis model, characterized in that: include: Acquire data from multiple sources of the target power system; Integrating, verifying and cleaning the data from multiple data sources to obtain processed data from multiple data sources; Based on the processed multi-data source data, a power outage analysis model is constructed and trained; the power outage analysis model is a neural network model based on deep learning; Based on the power outage analysis model, a power outage event of the target power system is predicted and evaluated.

2. The method according to claim 1, characterized in that: The multi-data source data includes fault repair work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data and 10kV line fault signal data.

3. The method according to claim 1, characterized in that: Based on the power outage assessment model, a power outage event of the target power system is predicted and evaluated, including: Based on the power outage analysis model, the power outage scope, power outage duration and power outage cause of the target power system are analyzed and evaluated respectively.

4. The method according to claim 1, characterized in that: The power outage analysis model includes: a k-Shape-GRU model.

5. A power outage event prediction and evaluation device based on a power outage analysis model, characterized in that: include: Acquisition module, processing module, construction module and evaluation module; among them, The acquisition module is used to acquire data from multiple data sources of the target power system; The processing module is used to integrate, verify and clean the data from multiple data sources to obtain processed data from multiple data sources; The construction module is used to construct and train a power outage analysis model based on the processed multi-data source data; the power outage analysis model is a neural network model based on deep learning; The evaluation module is used to predict and evaluate the power outage event of the target power system based on the power outage analysis model.

6. The device according to claim 5, characterized in that: The multi-data source data includes fault repair work orders, manually released power outage information, distribution transformer terminal power outage event data, terminal offline data, line branch switch data, current cross-section curve data and 10kV line fault signal data.

7. The device according to claim 5, characterized in that: The evaluation module is also used to: Based on the power outage analysis model, the power outage scope, power outage duration and power outage cause of the target power system are analyzed and evaluated respectively.

8. The device according to claim 5, characterized in that: The power outage analysis model includes: a k-Shape-GRU model.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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