Substation operation and maintenance data prediction method and system

Abstract

The invention relates to the technical field of power operation and maintenance, in particular to a substation operation and maintenance data prediction method and system. According to the method, firstly, historical operation and maintenance data are selected and preprocessed, correction coefficients of all kinds of cost data are calculated, and the correction coefficients are used for correcting current operation and maintenance data, so that future operation and maintenance data of a transformer substation are predicted. The system realizes automation and intellectualization of the method, and has the functions of data input, processing, prediction calculation, output, operation and maintenance plan making and the like. According to the invention, the operation and maintenance data of the transformer substation can be accurately predicted, a targeted operation and maintenance plan is formulated, the operation and maintenance efficiency and reliability are improved, and the operation and maintenance cost is reduced. Meanwhile, the self-learning and optimization capabilities of the system enable the system to continuously adapt to changing environments and requirements, and the prediction accuracy is kept. The user customization function meets the individual requirements of different users, and the flexibility and applicability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and maintenance management, and in particular to a substation operation and maintenance data prediction method and system. Background Art

[0002] With the rapid development of the power industry, substations, as an important part of the power system, are receiving increasing attention for their operation and maintenance management. Accurate operation and maintenance cost forecasting is of great significance for formulating reasonable operation and maintenance plans, optimizing resource allocation, and reducing operation and maintenance costs. Traditional operation and maintenance cost forecasting methods are often based on empirical estimates or simple statistical analysis, which lack scientificity and accuracy. Therefore, developing a substation operation and maintenance cost forecasting method and system based on historical operation and maintenance data has important practical application value. Summary of the invention

[0003] The purpose of the present invention is to provide a substation operation and maintenance data prediction method and system, which can calculate various cost data correction coefficients by analyzing historical operation and maintenance data, and use these correction coefficients to correct current operation and maintenance data, thereby predicting future substation operation and maintenance data.

[0004] The embodiment of the present invention is achieved as follows: In a first aspect, the present invention provides a method for predicting substation operation and maintenance data, comprising the following steps: Step s100: select historical operation and maintenance data as basic data for comparison, wherein the historical operation and maintenance data includes equipment maintenance records, operating parameters, environmental factor records, and operation records, and these data should be pre-processed, including data cleaning, outlier removal, and standardization, to ensure the accuracy and reliability of the data; Step s200: Calculate various cost data correction coefficients based on historical operation and maintenance data, including labor cost data correction coefficients, material cost data correction coefficients, parts purchase cost data correction coefficients, and other cost data correction coefficients; during the calculation process, use statistical analysis methods to determine the correlation between various factors and costs, and generate corresponding correction coefficients based on these correlations; Step s300: using the calculated correction coefficients of various cost data, correct the current substation operation and maintenance data, so as to predict the future substation operation and maintenance data; during the prediction process, the influence of equipment aging and operating condition changes on the operation and maintenance costs is considered, and the predicted value is adjusted according to the correction coefficient; Step s400: formulate a targeted operation and maintenance plan based on the predicted operation and maintenance data, including equipment maintenance cycle, maintenance content, and personnel allocation, so as to improve the operation efficiency and reliability of the substation.

[0005] In an optional implementation manner, the preprocessing step of the historical operation and maintenance data includes: Data cleaning: remove duplicate, erroneous or invalid data to ensure data integrity and accuracy; Outlier processing: Use statistical methods to detect and process outliers to reduce the impact on prediction results; Data standardization: Convert data of different dimensions and units into a unified standard form for easy analysis and calculation.

[0006] Based on the above solution, the preprocessing steps of historical operation and maintenance data are refined, including data cleaning, outlier processing and data standardization. These preprocessing steps help ensure the accuracy and reliability of the data, thereby improving the accuracy of the prediction results.

[0007] In an optional implementation manner, the calculation process of the cost data correction coefficient further includes: Factor correlation analysis: Use regression analysis and correlation analysis methods to determine the correlation between each factor and the cost; Correction coefficient generation: Based on the results of factor correlation analysis, generate corresponding correction coefficients to reflect the impact of each factor on operation and maintenance costs.

[0008] Based on the above scheme, we focus on the calculation process of the cost data correction coefficient, and especially emphasize the factor correlation analysis and the generation of the correction coefficient. This helps to more accurately reflect the impact of various factors on operation and maintenance costs, thereby improving the accuracy of the prediction results.

[0009] In an optional embodiment, the method further includes the steps of verifying and optimizing the prediction results, evaluating the accuracy and reliability of the prediction method by comparing with actual operation and maintenance data, and adjusting the correction coefficient or optimizing the prediction model as needed.

[0010] Based on the above scheme, steps to verify and optimize the prediction results are proposed, and the accuracy and reliability of the prediction method are evaluated by comparing with the actual operation and maintenance data. This helps to continuously improve the prediction method and enhance its effectiveness in practical applications.

[0011] In a second aspect, the present invention provides a system for predicting substation operation and maintenance data based on any one of the above-mentioned embodiments, the system comprising: Data input module: used to obtain historical data related to substation operation and maintenance, and supports the import of multiple data formats; Data processing module: used to clean historical data, process outliers and standardize data to generate standardized operation and maintenance data sets; Prediction calculation module: Calculates various cost data correction coefficients based on the processed data, and uses these correction coefficients to correct the current operation and maintenance data to obtain forecast data; Output module: used to display the forecast results, including the forecasted operation and maintenance costs, equipment maintenance cycle information, and support the generation of output in the form of reports or charts; Operation and maintenance plan formulation module: Based on the prediction results, the operation and maintenance personnel are automatically or assisted in formulating targeted operation and maintenance plans.

[0012] In an optional embodiment, the system also includes a data monitoring module for real-time monitoring of the operating status of the substation, including equipment status, operating parameter information, and comparing and analyzing the monitoring data with the predicted data to promptly detect and handle abnormal situations.

[0013] Based on the above technical solution, the operating status of the substation can be monitored in real time and compared with the predicted data. This function helps to detect and handle abnormal situations in a timely manner, and improve the efficiency and safety of operation and maintenance management.

[0014] In an optional implementation, the system supports integration with other enterprise information systems to achieve data sharing and interoperability, including integration with ERP systems and equipment management systems, to improve the efficiency and convenience of operation and maintenance management.

[0015] Based on the above technical solutions, the system's integration capabilities with other enterprise information systems enable data sharing and interoperability. This function helps break down information silos and improve the overall efficiency and convenience of operation and maintenance management.

[0016] In an optional implementation, the system also has the ability of self-learning and optimization, and automatically adjusts the correction coefficient or optimizes the prediction model by continuously accumulating new operation and maintenance data and experience to improve the accuracy and reliability of the prediction.

[0017] Based on the above technical solutions, the system's self-learning and optimization capabilities improve the prediction model by continuously accumulating new operation and maintenance data and experience. This function helps the system to continuously adapt to changing environments and needs and improve the long-term accuracy of predictions.

[0018] In an optional embodiment, the system also includes a feedback mechanism for collecting actual data after the execution of the operation and maintenance plan, and comparing these data with the predicted data, so as to further verify the accuracy of the prediction method and provide feedback and adjustment basis for subsequent predictions.

[0019] Based on the above scheme, the feedback mechanism is designed to collect actual data after the operation and maintenance plan is executed and compare it with the predicted data. This mechanism helps to verify the accuracy of the prediction method and provide feedback and adjustment basis for subsequent predictions, thereby continuously improving and optimizing the prediction system.

[0020] In an optional embodiment, the system also provides user customization functions, allowing users to adjust prediction parameters, optimize model settings or add preset prediction factors according to their own needs and preset scenarios to meet the needs of different substation operation and maintenance data predictions.

[0021] Based on the above technical solution, users are allowed to adjust prediction parameters and model settings according to their own needs and preset scenarios. This function helps to meet the personalized needs of different users and improve the flexibility and applicability of the system.

[0022] The beneficial effects of the embodiments of the present invention are: 1. Forecasting based on historical operation and maintenance data improves the accuracy and reliability of forecasting.

[0023] By calculating the cost data correction coefficient, the impact of various factors on operation and maintenance costs is taken into account, making the prediction results closer to the actual situation.

[0024] 2. The system has a high degree of automation and can automatically process data, calculate correction coefficients, make predictions, and formulate operation and maintenance plans, which improves the efficiency and convenience of operation and maintenance management.

[0025] 3. Support real-time monitoring and feedback mechanism, which can detect problems and make adjustments in time, further improving the effectiveness of operation and maintenance management.

[0026] 4. Provide user customization functions to meet the preset requirements of different substations.

[0027] In summary, the substation operation and maintenance data prediction method and system of the present invention have broad application prospects and important practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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.

[0029] Figure 1 The present invention is a flowchart of a method for predicting substation operation and maintenance data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0032] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0033] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a preset position, be constructed and operated in a preset position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0034] In addition, the terms "horizontal", "vertical" and the like do not mean that the components are required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0035] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. Example

[0036] Please combine Figure 1The substation operation and maintenance data prediction method provided in this embodiment includes the following steps: Step s100: select historical operation and maintenance data as basic data for comparison, wherein the historical operation and maintenance data includes equipment maintenance records, operating parameters, environmental factor records, and operation records, and these data should be pre-processed, including data cleaning, outlier removal, and standardization, to ensure the accuracy and reliability of the data; Step s200: Calculate various cost data correction coefficients based on historical operation and maintenance data, including labor cost data correction coefficients, material cost data correction coefficients, parts purchase cost data correction coefficients, and other cost data correction coefficients; during the calculation process, use statistical analysis methods to determine the correlation between various factors and costs, and generate corresponding correction coefficients based on these correlations; Step s300: using the calculated correction coefficients of various cost data, correct the current substation operation and maintenance data, so as to predict the future substation operation and maintenance data; during the prediction process, the influence of equipment aging and operating condition changes on the operation and maintenance costs is considered, and the predicted value is adjusted according to the correction coefficient; Step s400: formulate a targeted operation and maintenance plan based on the predicted operation and maintenance data, including equipment maintenance cycle, maintenance content, and personnel allocation, so as to improve the operation efficiency and reliability of the substation.

[0037] In this embodiment, in the step of data selection and preprocessing, the historical operation and maintenance data of the substation is selected as the prediction basis, including equipment maintenance records, operating parameters, environmental factor records, operation records, etc. The data is cleaned to remove duplicate, erroneous or invalid data. Outlier processing is performed to ensure the integrity and accuracy of the data. The data is standardized and the dimensions and units are unified to facilitate subsequent analysis.

[0038] In the step of calculating the correction coefficient, the statistical analysis method is used to determine the correlation between each factor and the operation and maintenance cost. According to the correlation, the data correction coefficients of labor costs, material costs, parts purchase costs and other costs are calculated.

[0039] In the step of correcting and predicting the current data, the current substation operation and maintenance data is corrected using the calculated correction coefficient. The corrected data and the prediction model are combined to calculate the predicted substation operation and maintenance data.

[0040] In the step of formulating the operation and maintenance plan, a targeted operation and maintenance plan is formulated based on the forecast data, including equipment maintenance cycle, maintenance content, staffing, etc.

[0041] Among them, the substation operation and maintenance data prediction method of this embodiment includes selecting historical operation and maintenance data, calculating cost data correction coefficients, using correction coefficients to predict future operation and maintenance data, and formulating an operation and maintenance plan, which provides a clear framework for the method, making the method more systematic and operational.

[0042] In this embodiment, optionally, the preprocessing step of the historical operation and maintenance data includes: Data cleaning: remove duplicate, erroneous or invalid data to ensure data integrity and accuracy; Outlier processing: Use statistical methods to detect and process outliers to reduce the impact on prediction results; Data standardization: Convert data of different dimensions and units into a unified standard form for easy analysis and calculation.

[0043] It should be understood that by refining the pre-processing steps of historical operation and maintenance data, including data cleaning, outlier processing and data standardization, these pre-processing steps help ensure the accuracy and reliability of the data, thereby improving the accuracy of the prediction results.

[0044] In this embodiment, optionally, the calculation process of the cost data correction coefficient further includes: Factor correlation analysis: Use regression analysis and correlation analysis methods to determine the correlation between each factor and the cost; Correction coefficient generation: Based on the results of factor correlation analysis, generate corresponding correction coefficients to reflect the impact of each factor on operation and maintenance costs.

[0045] In this step, we focus on the calculation process of the cost data correction coefficient, and especially emphasize the factor correlation analysis and the generation of the correction coefficient. This helps to more accurately reflect the impact of various factors on operation and maintenance costs, thereby improving the accuracy of the forecast results.

[0046] In this embodiment, optionally, the method also includes the steps of verifying and optimizing the prediction results, evaluating the accuracy and reliability of the prediction method by comparing with actual operation and maintenance data, and adjusting the correction coefficient or optimizing the prediction model as needed.

[0047] It should be noted that in this step, steps for verifying and optimizing the prediction results are proposed, and the accuracy and reliability of the prediction method are evaluated by comparing with the actual operation and maintenance data. This helps to continuously improve the prediction method and enhance its effectiveness in practical applications.

[0048] In this embodiment, the system of the substation operation and maintenance data prediction method includes: Data input module: used to obtain historical data related to substation operation and maintenance, and supports the import of multiple data formats; Data processing module: used to clean historical data, process outliers and standardize data to generate standardized operation and maintenance data sets; able to obtain historical data and real-time data related to substation operation and maintenance costs.

[0049] Prediction calculation module: Calculates various cost data correction coefficients based on the processed data, and uses these correction coefficients to correct the current operation and maintenance data to obtain forecast data; Output module: used to display the forecast results, including the forecasted operation and maintenance costs, equipment maintenance cycle information, and support the generation of output in the form of reports or charts; Operation and maintenance plan formulation module: Based on the prediction results, the operation and maintenance personnel are automatically or assisted in formulating targeted operation and maintenance plans.

[0050] The design of the system includes modules such as data input, processing, prediction calculation, output and operation and maintenance plan formulation, which provides a specific technical solution for the implementation of the system and helps to realize the automation and intelligence of the method.

[0051] In this embodiment, optionally, the system also includes a data monitoring module for real-time monitoring of the operating status of the substation, including equipment status, operating parameter information, and comparing and analyzing the monitoring data with the predicted data to promptly detect and handle abnormal situations.

[0052] This design enables real-time monitoring of the substation's operating status and comparison and analysis with forecast data. This function helps to detect and handle abnormal situations in a timely manner, improving the efficiency and safety of operation and maintenance management.

[0053] In this embodiment, the system optionally supports integration with other enterprise information systems to achieve data sharing and intercommunication, including integration with ERP systems and equipment management systems, to improve the efficiency and convenience of operation and maintenance management. The system's integration capabilities with other enterprise information systems enable data sharing and intercommunication. This function helps break down information silos and improve the overall efficiency and convenience of operation and maintenance management.

[0054] In this embodiment, the system optionally also has the ability to self-learn and optimize, and automatically adjusts the correction coefficient or optimizes the prediction model by continuously accumulating new operation and maintenance data and experience to improve the accuracy and reliability of the prediction. In this way, the system's self-learning and optimization capabilities improve the prediction model by continuously accumulating new operation and maintenance data and experience. This function helps the system to continuously adapt to changing environments and needs and improve the long-term accuracy of predictions.

[0055] In this embodiment, optionally, the system also includes a feedback mechanism for collecting actual data after the operation and maintenance plan is executed, and comparing these data with the predicted data, so as to further verify the accuracy of the prediction method and provide feedback and adjustment basis for subsequent predictions. The feedback mechanism is designed to collect actual data after the operation and maintenance plan is executed and compare it with the predicted data. This mechanism helps to verify the accuracy of the prediction method and provide feedback and adjustment basis for subsequent predictions, so as to continuously improve and optimize the prediction system.

[0056] In this embodiment, the system optionally also provides a user customization function, allowing the user to adjust the prediction parameters, optimize the model settings, or add preset prediction factors according to their own needs and preset scenarios to meet the needs of different substation operation and maintenance data prediction. Allow users to adjust the prediction parameters and model settings according to their own needs and preset scenarios. This function helps to meet the personalized needs of different users and improve the flexibility and applicability of the system.

[0057] The substation operation and maintenance data prediction method and system according to the embodiment of the present invention have at least the following advantages, for example: 1. Forecasting based on historical operation and maintenance data improves the accuracy and reliability of forecasting.

[0058] By calculating the cost data correction coefficient, the impact of various factors on operation and maintenance costs is taken into account, making the prediction results closer to the actual situation.

[0059] 2. The system has a high degree of automation and can automatically process data, calculate correction coefficients, make predictions, and formulate operation and maintenance plans, which improves the efficiency and convenience of operation and maintenance management.

[0060] 3. Support real-time monitoring and feedback mechanism, which can detect problems and make adjustments in time, further improving the effectiveness of operation and maintenance management.

[0061] 4. Provide user customization functions to meet the preset requirements of different substations.

[0062] In summary, the substation operation and maintenance data prediction method and system of the present invention have broad application prospects and important practical value.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting substation operation and maintenance data, characterized in that: The following steps are involved: Step s100: select historical operation and maintenance data as basic data for comparison, wherein the historical operation and maintenance data includes equipment maintenance records, operating parameters, environmental factor records, and operation records, and these data should be pre-processed, including data cleaning, outlier removal, and standardization, to ensure the accuracy and reliability of the data; Step s200: Calculate various cost data correction coefficients based on historical operation and maintenance data, including labor cost data correction coefficients, material cost data correction coefficients, and parts purchase cost data correction coefficients; During the calculation process, statistical analysis methods are used to determine the correlation between various factors and costs, and corresponding correction coefficients are generated based on these correlations; Step s300: using the calculated correction coefficients of various cost data, correct the current substation operation and maintenance data, so as to predict the future substation operation and maintenance data; During the forecasting process, the impact of equipment aging and changes in operating conditions on operation and maintenance costs is considered, and the forecast value is adjusted according to the correction factor; Step s400: formulate a targeted operation and maintenance plan based on the predicted operation and maintenance data, including equipment maintenance cycle, maintenance content, and personnel allocation, so as to improve the operation efficiency and reliability of the substation.

2. The substation operation and maintenance data prediction method according to claim 1, characterized in that: The preprocessing steps of the historical operation and maintenance data include: Data cleaning: remove duplicate, erroneous or invalid data to ensure data integrity and accuracy; Outlier processing: Use statistical methods to detect and process outliers to reduce the impact on prediction results; Data standardization: Convert data of different dimensions and units into a unified standard form for easy analysis and calculation.

3. The substation operation and maintenance data prediction method according to claim 1, characterized in that: The calculation process of the cost data correction coefficient also includes: Factor correlation analysis: Use regression analysis and correlation analysis methods to determine the correlation between each factor and the cost; Correction coefficient generation: Based on the results of factor correlation analysis, generate corresponding correction coefficients to reflect the impact of each factor on operation and maintenance costs.

4. The substation operation and maintenance data prediction method according to claim 1, characterized in that: The method also includes the steps of verifying and optimizing the prediction results. By comparing with actual operation and maintenance data, the accuracy and reliability of the prediction method are evaluated, and the correction coefficient is adjusted or the prediction model is optimized as needed.

5. A substation operation and maintenance data prediction system, characterized in that: Based on the substation operation and maintenance data prediction method described in any one of claims 1 to 4 above, the system comprises: Data input module: used to obtain historical data related to substation operation and maintenance, and supports the import of multiple data formats; Data processing module: used to clean historical data, process outliers, and standardize data to generate standardized operation and maintenance data sets; Prediction calculation module: Calculates various cost data correction coefficients based on the processed data, and uses these correction coefficients to correct the current operation and maintenance data to obtain forecast data; Output module: used to display the forecast results, including the forecasted operation and maintenance costs, equipment maintenance cycle information, and support the generation of output in the form of reports or charts; Operation and maintenance plan formulation module: Based on the prediction results, the operation and maintenance personnel are automatically or assisted in formulating targeted operation and maintenance plans.

6. The substation operation and maintenance data prediction system according to claim 5, characterized in that: The system also includes a data monitoring module, which is used to monitor the operating status of the substation in real time, including equipment status and operating parameter information, and compare and analyze the monitoring data with the predicted data to promptly detect and handle abnormal situations.

7. The substation operation and maintenance data prediction system according to claim 5 or 6, characterized in that: The system supports integration with enterprise information systems to achieve data sharing and interoperability, including integration with ERP systems and equipment management systems, to improve the efficiency and convenience of operation and maintenance management.

8. The substation operation and maintenance data prediction system according to claim 5, characterized in that: The system also has the ability to self-learn and optimize. By continuously accumulating new operation and maintenance data and experience, it can automatically adjust the correction coefficient or optimize the prediction model to improve the accuracy and reliability of the prediction.

9. The substation operation and maintenance data prediction system according to claim 5, characterized in that: The system also includes a feedback mechanism to collect actual data after the operation and maintenance plan is executed and compare this data with the predicted data to verify the accuracy of the prediction method and provide feedback and adjustment basis for subsequent predictions.

10. The substation operation and maintenance data prediction system according to claim 5, characterized in that: The system also provides user customization functions, allowing users to adjust prediction parameters, optimize model settings or add preset prediction factors according to their own needs and preset scenarios to meet the needs of different substation operation and maintenance data prediction.

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