Power automation system pre-detection system and method

Abstract

The present invention belongs to the field of electric power automation technology, and discloses a pre-detection system and method for an electric power automation system; the system includes: a data model detection cloud platform; the data model detection cloud platform includes a data model detection module and a data model training module; the data model detection module is deployed with energy storage equipment models, wind power equipment models, photovoltaic equipment models, hydropower station models, electrochemical equipment models, and microgrid equipment models; the data model detection module is used to perform model detection on the electric power automation system to be detected; the data model training module is used to perform data model training on models with model detection problems in the data model detection module. The present invention fully utilizes cloud platforms and big data detection technologies, has the advantages of localized detection, and is applicable to the detection requirements of new substation automation systems, saving manpower and material costs and effectively reducing carbon emissions.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power automation, and in particular relates to a pre-detection system and method for an electric power automation system. Background Art

[0002] To address climate change, the energy industry has built a new power system based on new energy, which has clarified the action program and methodological path for energy and power development and is the fundamental guideline for future work in the energy field.

[0003] The energy supply and demand landscape is expected to undergo significant changes in the future. Regarding energy supply, fossil fuels such as coal, oil, and natural gas will reach their peaks by 2030. Non-fossil energy sources, represented by solar and wind power, will experience rapid growth, with their share of primary energy rising to over 25% by 2030 and gradually becoming the primary source of primary energy. Regarding energy consumption, the proportion of electricity in final energy consumption will increase significantly, shifting more carbon emissions from sectors like transportation, construction, and industry to the power sector. Consequently, the structural form and technical characteristics of the power system will undergo profound changes, and a new type of power system will emerge.

[0004] The new power system is characterized by a high proportion of new energy and power electronics. Traditional monitoring means, monitoring items and processing methods cannot meet the needs of new power system operation and control:

[0005] First, the power system's real-time operating status perception is insufficient, and a large number of new intelligent sensors and small business systems need to be connected;

[0006] Second, the station is a node for collecting and controlling power grid operation signals. It has numerous siloed systems for various services, requiring analysis and processing of various structured and unstructured data. Furthermore, various secondary collection devices must meet the safety requirements of autonomous control.

[0007] Third, to adapt to the development trend of coordinated control of power generation, grid, load and storage, as well as integrated energy applications, breakthroughs are needed in distributed comprehensive monitoring technologies for the new energy sector and multi-station integrated monitoring technologies for transformation, storage and charging.

[0008] Fourth, when researching new technologies and new products, relevant testing technology research must also be carried out simultaneously to ensure the reliability and stability of new technologies and new products, and support various practical applications of new power system automation business.

[0009] In order to support the smooth operation of the power automation system in the new power system, a pre-detection system for the power automation system is needed to monitor potential problems and deficiencies that may arise during the operation of the power automation system in order to fully eliminate them. However, the existing technology does not have such a mature pre-detection technology for the power automation system. Summary of the Invention

[0010] The object of the present invention is to provide a power automation system pre-detection system and method to solve the above technical problems.

[0011] In order to achieve the above object, the present invention adopts the following technical solutions:

[0012] In a first aspect, the present invention provides a power automation system pre-detection system, comprising:

[0013] Data model detection cloud platform; the data model detection cloud platform includes a data model detection module and a data model training module; the data model detection module is deployed with energy storage equipment models, wind energy equipment models, photovoltaic equipment models, hydropower station models, electrochemical equipment models and microgrid equipment models;

[0014] The data model detection module is used to perform model detection on the power automation system to be detected;

[0015] The data model training module is used to perform data model training on the model with problems in the model detection by the data model detection module, and return the trained and corrected model to the data model detection module.

[0016] A further improvement of the present invention is that the data model detection module performs model detection on the power automation system to be detected, specifically including: the data model detection module performs type matching according to different types of models in the power automation system, and performs general model detection and specific model detection on the matched models; models with problems after detection are sent to the data model training module for data model training;

[0017] The data model training module performs model detection on the data model detection module and performs data model training on the problematic model. Specifically, the data model training module performs model training on the problematic model through metadata management, data flow monitoring, offline data processing or streaming data processing, and the trained and corrected model is returned to the data model detection module for re-detection until the data model is error-free.

[0018] A further improvement of the present invention is that it also includes a big data analysis application module;

[0019] The big data analysis application module is used to perform big data analysis on the model in the power automation system after detection by the data model detection cloud platform; the big data analysis includes: model version comparison, model defect analysis, equipment failure analysis, model level evaluation, and model construction display. One or more.

[0020] A further improvement of the present invention is that it also includes an application function detection cloud platform;

[0021] The application function detection cloud platform is provided with an application function training module;

[0022] The application function training module is used to train the application functions of the power automation system through existing detection cases and detection scripts. The application function training includes operation and control detection, data flow detection, analysis function detection, intelligent alarm detection and advanced application function detection.

[0023] A further improvement of the present invention is that the data model detection module is also used to replace the corresponding model in the application function training module according to the model change characteristics after detection.

[0024] A further improvement of the present invention is that it also includes a storage and calculation module;

[0025] The storage and computing module is used to store data models that have passed data model detection, big data analysis applications, and application function training.

[0026] A further improvement of the present invention is that: the storage and calculation module includes a real-time database and a historical database;

[0027] The real-time database is used to store the latest standard models that have passed data model detection, big data analysis applications, and application function training;

[0028] The historical database is used to store historical data models.

[0029] In a second aspect, the present invention provides a method for pre-detection of an electric power automation system, comprising the following steps:

[0030] The power automation system to be tested is uploaded to the data model detection cloud platform. The data model detection module in the data model detection cloud platform performs type matching according to the different types of models in the power automation system, and performs general model detection and special model detection on the matched models; any models that have problems after detection are sent to the data model training module for data model training, and the corrected models are returned to the data model detection module for re-detection until the data model is error-free.

[0031] A further improvement of the present invention is that it also includes the following steps:

[0032] Perform big data analysis on the models in the power automation system after detection by the data model detection cloud platform; the big data analysis includes: model version comparison, model defect analysis, equipment failure analysis, model level evaluation, and model construction display.

[0033] A further improvement of the present invention is that it also includes the following steps:

[0034] The application functions of the power automation system are trained through existing detection cases and detection scripts. The application function training includes operation and control detection, data flow detection, analysis function detection, intelligent alarm detection and advanced application function detection.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] Compared with traditional detection methods, the detection method of the present invention fully utilizes cloud platforms and big data detection technology, has the advantages of localized detection, and is applicable to the detection requirements of new substation automation systems, saving manpower and material costs, effectively reducing carbon emissions, and maximally meeting the new power system's detection needs for the stability and robustness of the equipment under test, which are all not available in traditional detection methods.

[0037] This invention combines big data cloud platform technology to carry out research on new power system power automation systems and equipment intelligent automatic detection technology and product quality evaluation methods. It has very important research significance for improving reliability and stability, supporting new power system automation business and various applications in other business fields, improving product quality, and ensuring the safe and stable operation of power automation systems and equipment, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 This is a structural block diagram of the power automation system pre-detection system of the present invention;

[0040] Figure 2 Detection cloud platform structure diagram for data model;

[0041] Figure 3 This is a schematic diagram of the structure of a big data analysis application;

[0042] Figure 4 Schematic diagram of the process of big data analysis application. DETAILED DESCRIPTION

[0043] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0044] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0045] The rapid construction of new smart grids is leading to increasingly close interconnectivity between systems and devices, and the continued advancement and deepening of the application of big data, blockchain, the Internet of Things, cloud computing, situational awareness, and artificial intelligence. This new power system, designed for a high proportion of renewable energy and a high proportion of power electronics, features broadband oscillation and wide-area monitoring and control technology for high-proportion renewable energy power systems, holographic data perception, and flexible access for new power systems. Furthermore, the new power system utilizes high-performance measurement and processing platform support technology, station-side heterogeneous data platform technology, and features a unified interface, unified protocol, an autonomous and controllable high-performance hardware platform, and technical features that support the storage, retrieval, and extraction of massive heterogeneous, structured, and unstructured data.

[0046] While the new power automation system possesses new technical features, it also introduces a large number of new power automation systems and equipment, placing higher demands on product quality and its safe and reliable operation. There is an urgent need to conduct in-depth research on intelligent automatic detection technologies based on wide-area measurement, massive data access, wireless sensing, and standardized communication protocols for the new power system, effectively verifying the application of new products and technologies. Furthermore, research should be conducted on high-performance, autonomous, and controllable hardware reliability detection technologies, new service security detection technologies based on existing protection capabilities combined with new services and new technologies, and product quality data analysis models and evaluation methods to provide strong guarantees for improving the reliability and safety of product operations.

[0047] Combined with big data cloud platform technology, research on intelligent automatic detection technology and product quality evaluation methods for new power system power automation systems and equipment is carried out. This has very important research significance for improving reliability and stability, supporting new power system automation business and various applications in other business fields, improving product quality, and ensuring the safe and stable operation of power automation systems and equipment. It has good application prospects.

[0048] Example 1

[0049] The present invention fully utilizes the technical advantages of big data and cloud platforms for on-site use, on-site uploading, and on-site result generation, effectively reducing the manpower, transportation, and operation and maintenance costs in the detection of new power systems and effectively reducing carbon emissions.

[0050] The present invention provides a power automation system pre-detection system, which includes a data model detection cloud platform, a big data analysis application module, an application function detection cloud platform and a storage and calculation module.

[0051] The present invention establishes a data model detection cloud platform, in which the data model detection module performs data model detection on energy storage equipment, wind energy equipment, photovoltaic equipment, hydropower station models, electrochemical equipment models, and microgrid equipment models in the new power system.

[0052] See also Figure 1 and Figure 2 The data model detection cloud platform includes a data model detection module and a data model training module; the data model detection module is deployed with energy storage equipment models, wind power equipment models, photovoltaic equipment models, hydropower station models, electrochemical equipment models and microgrid equipment models. The power automation system to be detected is uploaded to the data model detection cloud platform, and the data model detection module performs type matching based on the different types of models in the power automation system. The matched models are subjected to general model detection and model-specific detection. For models with problems after detection, the data model detection cloud platform sends the models with problems to the data model training module for data model training. Model training is performed through metadata management, data flow monitoring, offline data processing, and streaming data processing. The corrected model is returned to the data model detection module for re-detection until the data model is error-free.

[0053] After the data model passes the test, the big data analysis application module conducts big data analysis on the energy storage equipment models, wind power equipment models, photovoltaic equipment models, hydropower station models, electrochemical equipment models, and microgrid equipment models in the new power system. This big data analysis includes model version comparison, model defect analysis, equipment failure analysis, and model rating assessment. Model construction and presentation can also be performed. The models generated after big data analysis can be applied to various aspects of the new power system, including scientific research, product application, and engineering testing, based on actual engineering and testing needs. The big data analysis application module can also be used to provide a three-dimensional display of the model construction.

[0054] The application function detection cloud platform is equipped with an application function training module; the application function training module trains the application functions of the new power automation system through existing detection cases and detection scripts. The application function training includes operation and control detection, data flow detection, analysis function detection, intelligent alarm detection and advanced application function detection. The detection results are used for experimental verification and problem research in scientific research, product application, and engineering debugging.

[0055] At the same time, the model in the data model detection module can directly replace the model in the application function training module based on the detected model change characteristics. The replaced model can then be retrained for application functions. After application function training, the data model is rectified based on the training issues and training opinions returned after application function training. The rectified data model is then retested in the data model detection module through the association channel. The tested model is then used for big data analysis and application in the big data analysis application module.

[0056] After the data model has passed data model detection, big data analysis application, and application function training and has been tested to have no technical problems, it can enter the storage and calculation module; the latest standard model is stored in the real-time database, and the historical standard model is stored in the historical database; new power automation equipment and systems can obtain the latest standard model for use in the real-time database, and can also obtain historical data models in the historical database according to the actual application scenario needs.

[0057] By organically combining the respective advantages of big data and cloud platforms, the present invention realizes cloud detection and big data analysis for automated detection in new power systems, reduces the work of transportation, personnel flow, and paper report publishing for equipment and system detection, and effectively reduces carbon emissions.

[0058] See also Figure 3 and Figure 4 As shown, the core detection functions of the big data analysis application module of the present invention include model version comparison, model defect analysis, equipment fault analysis, model level assessment, and model construction display. Problems and faults arising from the data model and application function testing process of new power automation systems and equipment are handled through the equipment fault analysis and management platform. Faults in the operation and control, data flow, intelligent alarms, and advanced functions of the new power automation system are analyzed, classified, and analyzed using existing test cases, and corrective action suggestions are provided.

[0059] Example 2

[0060] The present invention also provides a power automation system pre-detection method, comprising the following steps:

[0061] The power automation system to be tested is uploaded to the data model testing cloud platform. The data model testing module performs type matching based on the different types of models in the power automation system. The matched models undergo general model testing and specific model testing. Models with problems after testing are sent to the data model training module for data model training. Model training is carried out through metadata management, data flow monitoring, offline data processing, and streaming data processing. The corrected model is returned to the data model testing module for retesting until the data model is error-free.

[0062] After the data model passes the test, the big data analysis application module conducts big data analysis on the energy storage equipment model, wind power equipment model, photovoltaic equipment model, hydropower station model, electrochemical equipment model, and microgrid equipment model in the new power system. The big data analysis includes: model version comparison, model defect analysis, equipment failure analysis, and model level evaluation. It can also perform model construction and display.

[0063] The application function training module trains the application functions of the new power automation system through existing test cases and test scripts. The application function training includes operation and control testing, data flow testing, analysis function testing, intelligent alarm testing, and advanced application function testing.

[0064] After the data model has passed data model detection, big data analysis application, and application function training and has been tested to have no technical problems, it enters the storage and computing module; the latest standard model is stored in the real-time database, and the historical standard model is stored in the historical database.

[0065] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

[0066] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. The power automation system pre-detection system is characterized by: include: Data model detection cloud platform; the data model detection cloud platform includes a data model detection module and a data model training module; The data model detection module is deployed with energy storage equipment model, wind energy equipment model, photovoltaic equipment model, hydropower station model, electrochemical equipment model and microgrid equipment model; The data model detection module is used to perform model detection on the power automation system to be detected; The data model training module is used to perform data model training on the model with problems detected by the data model detection module, and return the trained and corrected model to the data model detection module; The data model detection module performs model detection on the power automation system to be detected. Specifically, the data model detection module performs type matching according to different types of models in the power automation system, and performs general model detection and specific model detection on the matched models; models with problems after detection are sent to the data model training module for data model training; The data model training module performs data model training on the problematic model through model detection by the data model detection module. Specifically, the data model training module performs model training on the problematic model through metadata management, data flow monitoring, offline data processing or streaming data processing, and returns the trained and corrected model to the data model detection module for re-detection until the data model is error-free. It also includes big data analysis application modules; The big data analysis application module is used to perform big data analysis on the model in the power automation system after detection by the data model detection cloud platform; the big data analysis includes: model version comparison, model defect analysis, equipment failure analysis, model level evaluation, and model construction display. One or more.

2. The electric power automation system pre-detection system according to claim 1, characterized in that: It also includes an application function detection cloud platform; The application function detection cloud platform is provided with an application function training module; The application function training module is used to train the application functions of the power automation system through existing detection cases and detection scripts. The application function training includes operation and control detection, data flow detection, analysis function detection, intelligent alarm detection and advanced application function detection.

3. The electric power automation system pre-detection system according to claim 2, characterized in that: The data model detection module is also used to replace the corresponding model in the application function training module according to the model change characteristics after detection.

4. The electric power automation system pre-detection system according to claim 2, characterized in that: Also includes a storage computing module; The storage and computing module is used to store data models that have passed data model detection, big data analysis applications, and application function training.

5. The electric power automation system pre-detection system according to claim 4, characterized in that: The storage and calculation module includes a real-time database and a historical database; The real-time database is used to store the latest standard models that have passed data model detection, big data analysis applications, and application function training; The historical database is used to store historical data models.

6. A method for pre-detection of an electric power automation system, characterized in that: The electric power automation system pre-detection system according to any one of claims 1 to 5 comprises the following steps: The power automation system to be tested is uploaded to the data model detection cloud platform. The data model detection module in the data model detection cloud platform performs type matching according to the different types of models in the power automation system, and performs general model detection and special model detection on the matched models; any models that have problems after detection are sent to the data model training module for data model training, and the corrected models are returned to the data model detection module for re-detection until the data model is error-free.

7. The electric power automation system pre-detection method according to claim 6, characterized in that: The following steps are also included: Perform big data analysis on the models in the power automation system after detection by the data model detection cloud platform; the big data analysis includes: model version comparison, model defect analysis, equipment failure analysis, model level evaluation, and model construction display.

8. The electric power automation system pre-detection method according to claim 6, characterized in that: The following steps are also included: The application functions of the power automation system are trained through existing detection cases and detection scripts. The application function training includes operation and control detection, data flow detection, analysis function detection, intelligent alarm detection and advanced application function detection.

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