A platform system and method for power system reliability index management

Abstract

This invention discloses a platform system and method for managing reliability indicators in power systems, belonging to the field of power system dispatching and reliability management technology. The system includes: a standard data source library module for organizing indicator data, establishing a data acquisition mechanism, and building a storage system to store the indicator data and basic data; an indicator calculation and analysis module for calculating reliability indicators using the indicator calculation model, generating calculation results, and performing multi-dimensional statistics on the calculation results to generate statistical results; and an online data reporting module for reporting the statistical results generated by the indicator calculation and analysis module. This invention enables efficient and standardized reporting processes, comprehensively solving various problems in existing management.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and reliability management technology, and more specifically, to a platform system and method for power system reliability index management. Background Technology

[0002] With the accelerated construction of new power systems, profound changes are taking place in the power supply structure, load characteristics, and grid structure. The penetration rate of new energy sources continues to rise, significantly increasing the complexity and uncertainty of power grid operation. This presents new challenges to the safe, stable operation and reliable power supply of the power system. Therefore, implementing reliability management for 220kV and above power systems has become a core measure to improve grid security and contribute to achieving the "dual carbon" target.

[0003] Although the power industry has issued policy documents such as the "Interim Measures for Power System Reliability Management," which clarify the overall requirements for power system reliability management, there are still many shortcomings in the current power system reliability management work: First, there is a lack of a unified intelligent management platform, with data from various dispatching agencies being scattered and lacking standardized data, making it impossible to achieve data collaboration and sharing at the national, grid, and provincial levels; second, reliability indicator collection is mainly done manually, with multiple data sources and diverse formats, lacking an automated data source integration mechanism, making it difficult to guarantee the timeliness, completeness, and accuracy of the data; third, indicator calculation lacks standardized models and formula libraries, making it impossible to achieve real-time calculation, dynamic updates, and data traceability, and it is difficult to adapt to the grid characteristics of different grid companies; fourth, there is a lack of a full-process online data reporting and review mechanism, and the linkage between indicator statistical analysis and grid planning, dispatching operation, and equipment maintenance is insufficient, failing to form a closed loop of reliability management of "collection-calculation-analysis-application-improvement." Summary of the Invention

[0004] To address the above problems, this invention proposes a platform system for power system reliability index management, comprising: The standard data source library module is used to sort out the indicator data, collect the indicator data after sorting, and establish a data collection mechanism to collect basic data. Based on the structural characteristics and storage requirements of the indicator data and the basic data, a storage system is built to store the indicator data and the basic data. The indicator calculation and analysis module is used to build a formula library and input indicator calculation models into the formula library. It calculates reliability indicators for the indicator data and basic data of the standard database module through the indicator calculation model, generates calculation results, and performs multi-dimensional statistics on the calculation results to generate statistical results. The online data reporting module is used to report the statistical results generated by the indicator calculation and analysis module.

[0005] Optionally, the indicator data can be analyzed, including: The data sources, sampling cycles, and responsible departments for the core reliability indicators were reviewed, and the data sources for the core reliability indicators were determined. The data sources include: D5000 / D6.0 system, planning system, scheduling management system, and spot market system; The core reliability indicators include: pre-event prediction indicators, in-event assessment indicators, and post-event evaluation indicators. The pre-prediction indicators are collected through a data entry method, while the in-process evaluation indicators and post-evaluation indicators are collected through an automatic data entry method.

[0006] Optionally, the data collection mechanism can be based primarily on automatic data aggregation, supplemented by data entry.

[0007] Optional storage systems include: relational databases, non-relational databases, distributed file systems, and local file storage.

[0008] Optional reliability metrics calculations include: Data verification: After collecting basic data and indicator data, verify the completeness and consistency of the basic data and indicator data; Calculation triggering: Based on the definition of the calculation formula and the calculation frequency requirements, calculations are automatically triggered in three dimensions: before, during, and after the calculation. The calculation modes include three modes: scheduled calculation, event-driven calculation, and real-time calculation. The calculation results are stored in the storage system using the cloud data service. Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process is traced, showing the original data source, calculation steps and intermediate values, and the calculation results are verified in reverse. Results submission: After the calculation results are reviewed and approved, they are submitted synchronously level by level according to the scheduling architecture.

[0009] Optionally, multi-dimensional statistics can be performed on the calculation results, including: Quantitative evaluation: Based on the two dimensions of power system adequacy and security, the calculation results and main influencing factors are evaluated to reflect the level of power system safety and reliability; Vulnerability identification: Based on reliability indicator event data, identify the core factors affecting the indicators and pinpoint the weak links in power grid reliability; Trend analysis: Comparing year-on-year, month-on-month, and historical data of reliability indicators to reflect the trend of indicator changes. The degree of numerical change is quantified by the increase, decrease, and flatness to determine the cause of sudden changes and abnormal fluctuations in reliability indicators.

[0010] Furthermore, this invention also proposes a platform method for managing power system reliability indicators, comprising: The indicator data is sorted out, and then the indicator data is collected to obtain the indicator data. A data collection mechanism is established to collect basic data. Based on the structural characteristics and storage requirements of the indicator data and the basic data, a storage system is built to store the indicator data and the basic data. A formula library is constructed, and an indicator calculation model is entered into the formula library. The indicator data and basic data of the standard database module are used to calculate the reliability index through the indicator calculation model to generate the calculation results. The calculation results are then subjected to multi-dimensional statistics to generate statistical results. The statistical results generated by the indicator calculation and analysis module are submitted.

[0011] Optionally, the indicator data can be analyzed, including: The data sources, sampling cycles, and responsible departments for the core reliability indicators were reviewed, and the data sources for the core reliability indicators were determined. The data sources include: D5000 / D6.0 system, planning system, scheduling management system, and spot market system; The core reliability indicators include: pre-event prediction indicators, in-event assessment indicators, and post-event evaluation indicators. The pre-prediction indicators are collected through a data entry method, while the in-process evaluation indicators and post-evaluation indicators are collected through an automatic data entry method.

[0012] Optionally, the data collection mechanism can be based primarily on automatic data aggregation, supplemented by data entry.

[0013] Optional storage systems include: relational databases, non-relational databases, distributed file systems, and local file storage.

[0014] Optional reliability metrics calculations include: Data verification: After collecting basic data and indicator data, verify the completeness and consistency of the basic data and indicator data; Calculation triggering: Based on the definition of the calculation formula and the calculation frequency requirements, calculations are automatically triggered in three dimensions: before, during, and after the calculation. The calculation modes include three modes: scheduled calculation, event-driven calculation, and real-time calculation. The calculation results are stored in the storage system using the cloud data service. Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process is traced, showing the original data source, calculation steps and intermediate values, and the calculation results are verified in reverse. Results submission: After the calculation results are reviewed and approved, they are submitted synchronously level by level according to the scheduling architecture.

[0015] Optionally, multi-dimensional statistics can be performed on the calculation results, including: Quantitative evaluation: Based on the two dimensions of power system adequacy and security, the calculation results and main influencing factors are evaluated to reflect the level of power system safety and reliability; Vulnerability identification: Based on reliability indicator event data, identify the core factors affecting the indicators and pinpoint the weak links in power grid reliability; Trend analysis: Comparing year-on-year, month-on-month, and historical data of reliability indicators to reflect the trend of indicator changes. The degree of numerical change is quantified by the increase, decrease, and flatness to determine the cause of sudden changes and abnormal fluctuations in reliability indicators.

[0016] In another aspect, the present invention also provides a computing device, comprising: one or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0017] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described above.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a platform system for power system reliability index management, comprising: a standard data source library module, used to sort out index data, collect index data after sorting, establish a data collection mechanism to collect basic data, build a storage system based on the structural characteristics and storage requirements of the index data and the basic data, and store the index data and the basic data in the storage system; an index calculation and analysis module, used to construct a formula library, input index calculation models into the formula library, calculate reliability indices using the index data and basic data from the standard database module through the index calculation models, generate calculation results, and perform multi-dimensional statistics on the calculation results to generate statistical results; and an online data reporting module, used to report the statistical results generated by the index calculation and analysis module. This invention achieves unified aggregation and three-level collaboration of multi-source data through a full-process technical solution, improves data collection efficiency and quality, ensures traceability and verifiability of index calculations, identifies weak points in the power grid and establishes a linkage mechanism between reliability management and power supply security, and achieves efficient and standardized reporting processes, comprehensively solving various problems in existing management. Attached Figure Description

[0019] Figure 1 This is a structural diagram of the system of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a schematic diagram illustrating the platform's basic data sources for the system of this invention; Figure 4 This is a schematic diagram of the indicator reporting process of the system of the present invention; Figure 5 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0021] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0022] Example 1: Addressing the pain points of existing power system reliability management, such as fragmented and inconsistent data standards, low automation of indicator collection, lack of standardized models for indicator calculation, insufficient linkage between indicator analysis and application, and cumbersome data reporting processes, this invention designs a three-tiered dispatching system architecture ("National-Grid-Provincial") based on the control cloud platform. It constructs a standardized data source database and establishes a collection mechanism that prioritizes automatic aggregation while supplementing manual input. It also builds an indicator formula library and an object-oriented model management system, enabling multi-dimensional statistical analysis of reliability indicators, anomaly alarms, and automatic report generation. Simultaneously, it establishes an online indicator reporting system. Through a comprehensive technical solution, it achieves unified aggregation of multi-source data and three-tiered collaboration, improves data collection efficiency and quality, ensures traceability and verifiability of indicator calculations, identifies weaknesses in the power grid, establishes a linkage mechanism between reliability management and power supply security, and achieves efficient and standardized reporting processes, comprehensively solving various problems in existing management.

[0023] This invention proposes a platform system 100 for power system reliability index management, such as... Figure 1 As shown, it includes: The standard data source library module 101 is used to sort out the indicator data, collect the indicator data after sorting, obtain the indicator data, establish a data collection mechanism, collect basic data using the data collection mechanism, build a storage system based on the structural characteristics and storage requirements of the indicator data and the basic data, and store the indicator data and the basic data using the storage system. The indicator calculation and analysis module 102 is used to build a formula library and input indicator calculation models into the formula library. It calculates reliability indicators for the indicator data and basic data of the standard database module through the indicator calculation model, generates calculation results, and performs multi-dimensional statistics on the calculation results to generate statistical results. The online data reporting module 103 is used to report the statistical results generated by the indicator calculation and analysis module.

[0024] This includes analyzing the indicator data, including: The data sources, sampling cycles, and responsible departments for the core reliability indicators were reviewed, and the data sources for the core reliability indicators were determined. The data sources include: D5000 / D6.0 system, planning system, scheduling management system, and spot market system; The core reliability indicators include: pre-event prediction indicators, in-event assessment indicators, and post-event evaluation indicators. The pre-prediction indicators are collected through a data entry method, while the in-process evaluation indicators and post-evaluation indicators are collected through an automatic data entry method.

[0025] The data collection mechanism primarily relies on automatic data aggregation, supplemented by manual data entry.

[0026] The storage system includes: relational databases, non-relational databases, distributed file systems, and local file storage.

[0027] The calculation of reliability indicators includes: Data verification: After collecting basic data and indicator data, verify the completeness and consistency of the basic data and indicator data; Calculation triggering: Based on the definition of the calculation formula and the calculation frequency requirements, calculations are automatically triggered in three dimensions: before, during, and after the calculation. The calculation modes include three modes: scheduled calculation, event-driven calculation, and real-time calculation. The calculation results are stored in the storage system using the cloud data service. Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process is traced, showing the original data source, calculation steps and intermediate values, and the calculation results are verified in reverse. Results submission: After the calculation results are reviewed and approved, they are submitted synchronously level by level according to the scheduling architecture.

[0028] The calculation results are analyzed using multi-dimensional statistics, including: Quantitative evaluation: Based on the two dimensions of power system adequacy and security, the calculation results and main influencing factors are evaluated to reflect the level of power system safety and reliability; Vulnerability identification: Based on reliability indicator event data, identify the core factors affecting the indicators and pinpoint the weak links in power grid reliability; Trend analysis: Comparing year-on-year, month-on-month, and historical data of reliability indicators to reflect the trend of indicator changes. The degree of numerical change is quantified by the increase, decrease, and flatness to determine the cause of sudden changes and abnormal fluctuations in reliability indicators.

[0029] The invention will be further illustrated below with specific examples: This invention provides a power system reliability index management platform system, such as... Figure 2 As shown, following the overall architecture of the control cloud, a reliability indicator management system with three-level dispatch coordination ("National-Grid-Provincial") is built. Through three core modules—data source database construction, indicator calculation and analysis, and data reporting—the full lifecycle management of power system reliability indicators is achieved. This method covers all dimensions of reliability indicators, including pre-event prediction, in-event assessment, and post-event evaluation, and is suitable for the reliability management needs of 220kV and above power systems. The specific implementation steps and technical solutions are as follows: Determine the basis for platform construction and overall architecture design: (1) Basis for construction: The platform was built in strict accordance with the standards of the National Energy Administration and the industry. The core basis includes: the "Notice of the General Office of the National Energy Administration on Carrying Out Pilot Work on Power System Reliability Management" (Guonengzongtong Anquan

[2025] No. 94), the "Interim Measures for Power Reliability Management" (Order No. 50 of the National Development and Reform Commission), and the "Opinions of the National Energy Administration on Strengthening Power Reliability Management" (Guonengfa Anquan Gui

[2023] No. 17).

[0030] (2) Three-level scheduling architecture design: The platform relies on the control cloud platform to build a three-level dispatch architecture of national, grid and provincial levels. It uses the control cloud service bus to realize the interconnection of dispatch agencies at all levels, and meets the core requirements of multi-source data collection, automatic indicator calculation and multi-professional and multi-level collaboration. The overall architecture has scalability, compatibility and security, and supports dispatch agencies at all levels to customize functions and expand indicators according to their own needs.

[0031] Standardized data source library module: The core of this step is to identify all data sources for the power system reliability index system, establish a unified data source database, and achieve standardized data collection, transmission, and storage. Specific technical solutions include: (1) Data source analysis of indicators: In conjunction with the "Power System Reliability Indicator System (Trial)", the data sources, sampling periods and responsible departments of 31 core reliability indicators (including pre-prediction, in-process assessment and post-evaluation) were analyzed. The data sources of the indicators include the D5000 / D6.0 system, planning system, dispatch management system, spot market system, etc. Among them, the pre-prediction type safety indicators (such as the system stability level value under a single fault) are supplemented by manual reporting, and the other indicators are given priority to be automatically collected.

[0032] (2) Establishment of data collection mechanism: The working principle of "data integration and unified calculation of indicators" was established, and all basic data were collected into the control cloud reliability indicator management system. A data collection mechanism was established with automatic collection as the main method and manual entry as a supplement. The sources of basic data include... Figure 3 As shown, appropriate data transmission technologies are selected for structured, semi-structured, and unstructured data to ensure the security, integrity, timeliness, and compatibility of data transmission.

[0033] (3) Data storage system construction: Based on the characteristics of data structure and storage requirements, a multi-media storage system is built, covering relational databases, non-relational databases, distributed file systems, local file storage, etc., to provide data support for indicator calculation and statistical analysis.

[0034] Indicator Calculation and Analysis Module: This module is the core function of the platform, enabling standardized calculation of reliability indicators, model management, statistical analysis, and report generation. It supports real-time calculation, dynamic updates, and anomaly alerts. Specific technical solutions are as follows: (1) Automatic calculation of reliability indicators: Data verification: After collecting basic data from all levels of scheduling, the system automatically verifies the completeness (such as the continuity of new energy output curves) and consistency (such as the consistency between static power angle limits and parameter databases) of the data to ensure the accuracy of the basic calculation data.

[0035] Calculation triggering: In accordance with the "Calculation Method of Power System Reliability Indicators", calculations are automatically triggered from three dimensions: before, during and after the event, based on the definition of the calculation formula and the calculation frequency requirements. It supports three modes: timed calculation, event-driven calculation and real-time calculation. The calculation results are stored in the standard indicator library using the control cloud data service.

[0036] Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process can be traced, clearly showing the source of the original data, calculation steps and intermediate values, and supporting reverse verification of calculation results.

[0037] Results submission: After the indicator calculation results are reviewed and approved, they are submitted synchronously level by level according to the three-level dispatch architecture to achieve unified aggregation of national power grid reliability indicators.

[0038] (2) Standardized management of indicator models: Model objectification: Based on the control cloud platform, the reliability indicators of the power system are managed in a model objectification manner, realizing the standardization and normalization of indicator definition, calculation rules and data sources.

[0039] Formula library construction: A dedicated formula library for power system reliability indicators was built, with calculation formulas for 31 core indicators entered. The topological relationships of the formulas were automatically derived using knowledge graphs to achieve automatic calculation of indicator results and data traceability.

[0040] Custom configuration: Provides a visual calculation rule configuration interface, supports users to customize indicator logic, and allows each provincial power grid company to customize special indicators according to the local power grid characteristics (10 special indicator extension slots are reserved) to improve platform adaptability.

[0041] (3) Multidimensional statistical analysis of indicator results: Quantitative evaluation: Focusing on the two dimensions of power system adequacy and security, a comprehensive evaluation is conducted on the calculation results of indicators and the main influencing factors to objectively reflect the level of power system safety and reliability.

[0042] Weakness identification: Mining reliability indicator event data, analyzing the core factors affecting the indicators, locating weak links in power grid reliability, and providing a basis for power grid transformation and dispatch optimization.

[0043] Trend Analysis: Enables year-on-year, month-on-month, and historical data comparisons of indicators, intuitively reflecting the trend of indicator changes. It quantifies the degree of numerical change through growth rate, decline rate, and flatness, and automatically conducts cause analysis on sudden changes and abnormal fluctuations in indicator data.

[0044] Closed-loop management: Based on the evaluation results of indicators, a closed-loop evaluation of weak point handling and online control is realized, thereby promoting the improvement of the professional management level of the power system.

[0045] (4) Automatic generation of reliability reports: Standardized report templates: Based on power system reliability indicators and basic data report templates, we developed standardized report generation functions, covering monthly reports, annual reports, and comparative analysis reports.

[0046] Automatic generation and export: Based on the statistical results of scheduling indicators at all levels summarized by the platform, the system automatically completes data processing and analysis, generates standardized reports, and supports export in multiple formats such as Excel and PDF, reducing manual intervention.

[0047] Report customization: Allows dispatching agencies at all levels to customize personalized reports according to their own needs, enabling flexible configuration of report content.

[0048] Online data reporting module: This module enables fully online management and reporting of reliability indicators, ensuring the standardization, security, and efficiency of data reporting. Specific technical solutions are as follows: Figure 4 As shown, it includes: Role-based access control: Establish a refined role-based access control system, dividing the data into different roles such as data entry personnel, reviewers, and administrators, and assigning exclusive operation permissions to each role to ensure data security.

[0049] Built-in approval workflow: Build a standardized online approval workflow that supports multi-level review, transfer, and rejection of indicator data, enabling the approval process to be traceable and monitorable.

[0050] Parallel operation by multiple departments: Supports multiple responsible departments such as scheduling, planning, system, protection, and water resources to carry out indicator input and review work in parallel, improving reporting efficiency.

[0051] Three-level reporting collaboration: Following the "province-network-national" reporting process, online reporting of indicator data by dispatching agencies at all levels is achieved, ensuring the timeliness and accuracy of data transmission.

[0052] Power system reliability management linkage mechanism: After the platform is built, a corresponding reliability management mechanism will be established simultaneously to achieve deep integration of the platform's functions with the daily operation of the power system. Establish a full-process working mechanism for reliability analysis, evaluation, reporting, and application, and clarify the work responsibilities and work processes of dispatching agencies at all levels.

[0053] Establish a linkage mechanism between reliability management and power supply security, power grid planning, and power grid safety risk control, and use the results of reliability indicators as the core basis for formulating power supply security plans, power grid planning and design, and risk control measures.

[0054] Establish a dynamic update mechanism for indicators and an optimization mechanism to update the reliability indicator system and management processes in a timely manner according to the development of the power system and policy adjustments, thereby improving the platform's continuous applicability.

[0055] Key data system design: This invention designs a standardized database table structure and data dictionary for the platform, enabling standardized storage and management of indicator data. The core data system includes: The statistical results table is divided into a monthly statistical results table (SG RELIABILITY ASSESS MONTHDATAYYYYMM) and a daily statistical results table (SG RELIABILITY ASSESS DAYDATA YYYYMM), which stores core data such as indicator calculation results, statistical time, reporting information, and security status.

[0056] The SG RELIABILITY ASSESS BASEDATA table stores the original basic data, component data, and verification information for indicator calculation, providing data support for indicator calculation.

[0057] Formula Library Table (SG RELIABILITY ASSESS FORMULA B): Stores information such as indicator number, name, calculation formula, and responsible department to achieve standardized management of formulas.

[0058] Data dictionary: Covering five major categories of reliability indicator statistics, indicator types, calculation method types, indicator definitions, and security status types, it provides a basis for platform data standardization and ensures that data definitions are consistent across all levels of dispatching agencies.

[0059] The power system reliability index management platform system constructed in this invention completely solves many pain points in existing power system reliability management, and realizes the standardization, intelligence, and collaboration of reliability management. The core technical effects are as follows: 1. Achieve three-level scheduling data collaboration to break down data silos: The "National-Grid-Provincial" three-level dispatch architecture built on the control cloud platform has realized the unified collection and sharing of multi-source data from dispatching agencies at all levels. Data standards, calculation rules, and report templates are unified nationwide, which has completely solved the problems of scattered data, inconsistent formats, and difficulties in coordination in the past, and improved the coordination of reliability management of the national power system.

[0060] 2. Improve the level of automation in data collection to ensure data quality: The established data collection mechanism, which prioritizes automatic aggregation and supplements manual entry, has enabled the automated collection of most reliability index data, covering core data sources such as D5000 / D6.0 and the planning system. This has significantly reduced the workload of manual entry, while the automatic data verification function ensures the integrity, consistency, and accuracy of the data, improving data collection efficiency by more than 80%.

[0061] 3. Achieve standardized and intelligent indicator calculation, supporting flexible configuration: The standardized formula library and model object-oriented management system have enabled the automatic calculation, real-time updating and full-link data traceability of 31 core reliability indicators, solving the previous problems of no standard and no traceability in indicator calculation. At the same time, the visual configuration interface and special indicator extension bits are adapted to the power grid characteristics of different provincial grid companies, improving the flexibility and applicability of the platform.

[0062] 4. Multi-dimensional statistical analysis to identify weaknesses in the power grid and support scientific decision-making: The platform enables multi-dimensional analysis of reliability indicators, including year-on-year, month-on-month, and historical comparisons. It can automatically identify weak links in power grid reliability and promptly alert to abnormal fluctuations in indicators. This provides accurate scientific basis for power grid planning, construction, dispatching, operation, and equipment maintenance, and promotes the transformation of the power system from "passive operation and maintenance" to "proactive management and control."

[0063] 5. Online data reporting improves efficiency and standardization: The established online reporting system enables the entire process of data entry, review, and reporting to be conducted online. The refined role and permission control and standardized approval workflow ensure the standardization and security of data reporting. The parallel operation of multiple departments and the three-level scheduling for hierarchical reporting have significantly improved reporting efficiency, reducing the time for report generation and reporting by more than 70%.

[0064] 6. Establish a collaborative mechanism to form a closed loop for reliability management: The platform establishes a linkage mechanism between reliability management and power supply security, power grid planning, and risk control. It deeply integrates reliability index results into the daily work of the power system, forming a closed loop of reliability management of "collection-calculation-analysis-application-improvement". This effectively improves the safety and stability level and reliable power supply capacity of the power system, and provides solid support for the construction of new power systems.

[0065] Example 2: Furthermore, this invention also proposes a platform method S200 for power system reliability index management, such as... Figure 5 As shown, it includes: S201, sort out the indicator data, collect the indicator data after sorting, and establish a data collection mechanism to collect basic data. Based on the structural characteristics and storage requirements of the indicator data and the basic data, build a storage system to store the indicator data and the basic data. S202, construct a formula library, and input the indicator calculation model into the formula library. Calculate the reliability index using the indicator data and basic data of the standard database module through the indicator calculation model, generate calculation results, and perform multi-dimensional statistics on the calculation results to generate statistical results. S203, The statistical results generated by the indicator calculation and analysis module are sent out.

[0066] This includes analyzing the indicator data, including: The data sources, sampling cycles, and responsible departments for the core reliability indicators were reviewed, and the data sources for the core reliability indicators were determined. The data sources include: D5000 / D6.0 system, planning system, scheduling management system, and spot market system; The core reliability indicators include: pre-event prediction indicators, in-event assessment indicators, and post-event evaluation indicators. The pre-prediction indicators are collected through a data entry method, while the in-process evaluation indicators and post-evaluation indicators are collected through an automatic data entry method.

[0067] The data collection mechanism primarily relies on automatic data aggregation, supplemented by manual data entry.

[0068] The storage system includes: relational databases, non-relational databases, distributed file systems, and local file storage.

[0069] The calculation of reliability indicators includes: Data verification: After collecting basic data and indicator data, verify the completeness and consistency of the basic data and indicator data; Calculation triggering: Based on the definition of the calculation formula and the calculation frequency requirements, calculations are automatically triggered in three dimensions: before, during, and after the calculation. The calculation modes include three modes: scheduled calculation, event-driven calculation, and real-time calculation. The calculation results are stored in the storage system using the cloud data service. Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process is traced, showing the original data source, calculation steps and intermediate values, and the calculation results are verified in reverse. Results submission: After the calculation results are reviewed and approved, they are submitted synchronously level by level according to the scheduling architecture.

[0070] Optionally, multi-dimensional statistics can be performed on the calculation results, including: Quantitative evaluation: Based on the two dimensions of power system adequacy and security, the calculation results and main influencing factors are evaluated to reflect the level of power system safety and reliability; Vulnerability identification: Based on reliability indicator event data, identify the core factors affecting the indicators and pinpoint the weak links in power grid reliability; Trend analysis: Comparing year-on-year, month-on-month, and historical data of reliability indicators to reflect the trend of indicator changes. The degree of numerical change is quantified by the increase, decrease, and flatness to determine the cause of sudden changes and abnormal fluctuations in reliability indicators.

[0071] This invention achieves unified collection and three-level collaboration of multi-source data through a full-process technical solution, improves data collection efficiency and quality, ensures traceability and verifiability of indicator calculation, identifies weak points in the power grid and establishes a linkage mechanism for reliability management and power supply, and realizes efficient and standardized reporting processes, thus comprehensively solving various problems in existing management.

[0072] Example 3: Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0073] Example 4: Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0074] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0075] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0076] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0078] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0079] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A platform system for power system reliability index management, characterized in that, include: The standard data source library module is used to sort out the indicator data, collect the indicator data after sorting, and establish a data collection mechanism to collect basic data. Based on the structural characteristics and storage requirements of the indicator data and the basic data, a storage system is built to store the indicator data and the basic data. The indicator calculation and analysis module is used to build a formula library and input indicator calculation models into the formula library. It calculates reliability indicators for the indicator data and basic data of the standard database module through the indicator calculation model, generates calculation results, and performs multi-dimensional statistics on the calculation results to generate statistical results. The online data reporting module is used to report the statistical results generated by the indicator calculation and analysis module.

2. The platform system of claim 1, wherein, The process of organizing the indicator data includes: The data sources, sampling cycles, and responsible departments for the core reliability indicators were reviewed, and the data sources for the core reliability indicators were determined. The data sources include: D5000 / D6.0 system, planning system, scheduling management system, and spot market system; The core reliability indicators include: pre-event prediction indicators, in-event assessment indicators, and post-event evaluation indicators. The pre-prediction indicators are collected through a data entry method, while the in-process evaluation indicators and post-evaluation indicators are collected through an automatic data entry method.

3. The platform system of claim 1, wherein, The data collection mechanism primarily relies on automatic data aggregation, supplemented by data entry.

4. The platform system according to claim 1, characterized in that, The storage system includes: relational databases, non-relational databases, distributed file systems, and local file storage.

5. The platform system according to claim 1, characterized in that, The calculation of the reliability index includes: Data verification: After collecting basic data and indicator data, verify the completeness and consistency of the basic data and indicator data; Calculation triggering: Based on the definition of the calculation formula and the calculation frequency requirements, calculations are automatically triggered in three dimensions: before, during, and after the calculation. The calculation modes include three modes: scheduled calculation, event-driven calculation, and real-time calculation. The calculation results are stored in the storage system using the cloud data service. Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process is traced, showing the original data source, calculation steps and intermediate values, and the calculation results are verified in reverse. Results submission: After the calculation results are reviewed and approved, they are submitted synchronously level by level according to the scheduling architecture.

6. The platform system according to claim 1, characterized in that, The calculation results were statistically analyzed from multiple dimensions, including: Quantitative evaluation: Based on the two dimensions of power system adequacy and security, the calculation results and main influencing factors are evaluated to reflect the level of power system safety and reliability; Vulnerability identification: Based on reliability indicator event data, identify the core factors affecting the indicators and pinpoint the weak links in power grid reliability; Trend analysis: Comparing year-on-year, month-on-month, and historical data of reliability indicators to reflect the trend of indicator changes. The degree of numerical change is quantified by the increase, decrease, and flatness to determine the cause of sudden changes and abnormal fluctuations in reliability indicators.

7. A platform method for managing reliability indicators of power systems, characterized in that, include: The indicator data is sorted out, and then the indicator data is collected to obtain the indicator data. A data collection mechanism is established to collect basic data. Based on the structural characteristics and storage requirements of the indicator data and the basic data, a storage system is built to store the indicator data and the basic data. A formula library is constructed, and an indicator calculation model is entered into the formula library. The indicator data and basic data of the standard database module are used to calculate the reliability index through the indicator calculation model to generate the calculation results. The calculation results are then subjected to multi-dimensional statistics to generate statistical results. The statistical results generated by the indicator calculation and analysis module are submitted.

8. The platform method according to claim 7, characterized in that, The process of organizing the indicator data includes: The data sources, sampling cycles, and responsible departments for the core reliability indicators were reviewed, and the data sources for the core reliability indicators were determined. The data sources include: D5000 / D6.0 system, planning system, scheduling management system, and spot market system; The core reliability indicators include: pre-event prediction indicators, in-event assessment indicators, and post-event evaluation indicators. The pre-prediction indicators are collected through a data entry method, while the in-process evaluation indicators and post-evaluation indicators are collected through an automatic data entry method.

9. The platform method according to claim 7, characterized in that, The data collection mechanism primarily relies on automatic data aggregation, supplemented by data entry.

10. The platform method according to claim 7, characterized in that, The storage system includes: relational databases, non-relational databases, distributed file systems, and local file storage.

11. The platform method according to claim 7, characterized in that, The calculation of the reliability index includes: Data verification: After collecting basic data and indicator data, verify the completeness and consistency of the basic data and indicator data; Calculation triggering: Based on the definition of the calculation formula and the calculation frequency requirements, calculations are automatically triggered in three dimensions: before, during, and after the calculation. The calculation modes include three modes: scheduled calculation, event-driven calculation, and real-time calculation. The calculation results are stored in the storage system using the cloud data service. Data traceability: Based on the topological relationship of the formula library, the entire chain of indicator calculation process is traced, showing the original data source, calculation steps and intermediate values, and the calculation results are verified in reverse. Results submission: After the calculation results are reviewed and approved, they are submitted synchronously level by level according to the scheduling architecture.

12. The platform method according to claim 7, characterized in that, The calculation results were statistically analyzed from multiple dimensions, including: Quantitative evaluation: Based on the two dimensions of power system adequacy and security, the calculation results and main influencing factors are evaluated to reflect the level of power system safety and reliability; Vulnerability identification: Based on reliability indicator event data, identify the core factors affecting the indicators and pinpoint the weak links in power grid reliability; Trend analysis: Comparing year-on-year, month-on-month, and historical data of reliability indicators to reflect the trend of indicator changes. The degree of numerical change is quantified by the increase, decrease, and flatness to determine the cause of sudden changes and abnormal fluctuations in reliability indicators.

13. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 7-12 is implemented.

14. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 7-12.

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