Converter equipment operation and maintenance decision-making agent model and model construction method

Abstract

The embodiment of the invention provides a converter equipment operation and maintenance decision agent model and a model construction method. The converter equipment operation and maintenance decision-making agent model comprises a data acquisition module used for collecting a data source of converter equipment operation parameters; the data analysis module is used for performing cleaning, feature extraction and pattern recognition on the collected data by applying a machine learning algorithm; the intelligent decision module is used for constructing an intelligent agent model based on reinforcement learning and realizing conversion from data to decision; meanwhile, by simulating the effects of different maintenance strategies, the optimal maintenance scheme is selected, and the strategies are dynamically adjusted according to the actual situation; and the user interaction module is used for providing a visual interface for operation and maintenance personnel to check the equipment state report and the maintenance suggestion. The method can accurately evaluate the operation and maintenance subdivision scene of the converter equipment, and generates an intelligent agent model for efficient operation and maintenance decision-making according to the scene. And meanwhile, the state monitoring precision and the health state dynamic evaluation capability of the converter equipment are improved, the application scene is expanded, and the universality is improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a converter equipment operation and maintenance decision-making intelligent agent model and a model construction method. Background Technology

[0002] Converter equipment is a crucial component of power systems, responsible for converting alternating current (AC) to direct current (DC) or vice versa. With the continuous expansion of power grids and technological advancements, the operating environment of converter equipment is becoming increasingly complex, placing higher demands on its reliability and stability. However, traditional operation and maintenance methods rely primarily on periodic manual inspections and experience-based judgment, which is not only time-consuming and labor-intensive but also makes it difficult to detect potential problems in a timely manner, leading to an increased risk of equipment failure.

[0003] Currently, although some automated detection technologies and data analysis tools have been applied to the monitoring of converter equipment, the following shortcomings still exist: 1. Low data utilization rate, failing to fully utilize historical data for in-depth analysis; 2. Lack of personalized maintenance suggestions; unable to provide customized maintenance solutions based on the actual condition of specific equipment. 3. Weak real-time response capability and insufficient ability to respond quickly to emergencies, which affects the efficiency of fault handling. 4. Limited level of intelligence: Existing systems often rely on fixed rule bases or algorithms, making it difficult to adapt to constantly changing working conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a converter equipment operation and maintenance decision-making intelligent agent model and model construction method. This model and model construction method can accurately evaluate the subdivided operation and maintenance scenarios of converter equipment and generate an intelligent agent model for efficient operation and maintenance decision-making accordingly. At the same time, it improves the status monitoring accuracy and dynamic health status assessment capability of converter equipment, expands application scenarios, and enhances versatility.

[0005] To achieve the above objectives, embodiments of the present invention provide a converter equipment operation and maintenance decision-making intelligent agent model, which includes: The data acquisition module is used to collect data sources for the operating parameters of the converter equipment; The data analysis module is used to clean, extract features, and recognize patterns from the collected data using machine learning algorithms. The intelligent decision-making module is used to build an intelligent agent model based on reinforcement learning to realize the transformation from data to decision; at the same time, by simulating the effects of different maintenance strategies, the optimal maintenance scheme is selected and the strategy is dynamically adjusted according to the actual situation. The user interaction module provides a visual interface for maintenance personnel to view equipment status reports and maintenance suggestions.

[0006] Preferably, the user interaction module also includes an alarm unit, which is configured to promptly notify relevant personnel when an abnormal situation is detected.

[0007] Preferably, the data acquisition module includes a sensor network and a SCADA system, and the data sources collected include voltage, current, temperature, pressure, as well as the equipment's historical maintenance records and operation logs.

[0008] On the other hand, the present invention provides a method for constructing the converter equipment operation and maintenance decision-making intelligent agent model as described above, the method comprising: Initialization includes installing hardware, configuring the software environment, and preparing the training dataset. Training includes data preprocessing, feature extraction, and model training; Deployment, including system integration, real-time monitoring, and feedback; Feedback optimization includes continuous improvement, self-learning, and expanding applications.

[0009] Preferably, the installation of hardware facilities includes installing a sensor network on key parts of the converter equipment to monitor various operating parameters of the equipment in real time; at the same time, configuring necessary communication equipment to ensure the stability and security of data transmission. Configuring the software environment includes installing the software platform required for data analysis and intelligent decision-making, and setting up a database management system (DBMS) to store and manage large amounts of equipment operation data; Preparing the training dataset involves collecting historical operating data of the converter equipment and generating data for specific scenarios through simulation experiments.

[0010] Preferably, data preprocessing includes cleaning and normalizing the collected data, removing noise and outliers, and dividing the data into multiple subsets according to different operation and maintenance scenarios; Feature extraction involves using deep learning techniques to extract valuable features from raw data; Model training includes training machine learning models using supervised or unsupervised learning methods.

[0011] Preferably, data cleaning includes removing noise and outliers from the data, and data normalization includes unifying data of different scales to the same range through min-max normalization and Z-score normalization; Feature extraction includes: Temporal feature extraction involves statistical analysis of time series data to extract the mean, variance, skewness, and kurtosis. Frequency domain feature extraction utilizes Fourier transform (FFT) and wavelet transform (WFT) to convert the time-domain signal to the frequency domain, extracting frequency components and energy distribution. Deep learning feature extraction uses deep learning techniques to automatically extract high-level features from complex data; deep learning techniques include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are used for image recognition, and RNNs are used for processing time series data.

[0012] Preferably, system integration includes deploying the trained model into a real-world environment and integrating it with existing SCADA systems and other monitoring equipment; Real-time monitoring includes real-time monitoring of the operating status of converter equipment and dynamic adjustment of maintenance strategies based on the current status of the equipment and historical data; Feedback includes establishing a sound feedback mechanism and regularly collecting feedback information during system operation, including the actual operating status of equipment and the effectiveness of maintenance measures.

[0013] Preferably, continuous improvement includes regularly collecting system operation feedback information and continuously improving model performance; Self-learning includes introducing an online learning mechanism to continuously optimize the system's parameters during operation; Expanding applications involves gradually expanding the system's functionality and application scenarios based on actual needs.

[0014] Preferably, the intelligent agent operation and maintenance decision model constructed by the construction method is configured to represent states, define action spaces, and design reward functions; wherein, State representation includes representing the current state of the converter equipment as a set of feature vectors containing all relevant information about the equipment; Defining the action space includes defining the set of actions that an agent can perform, including but not limited to adjusting device parameters, starting maintenance programs, and issuing alarms. Designing a reward function involves designing a reasonable reward function to measure the benefit gained by an agent after performing a certain action.

[0015] Based on the above technical solutions, an intelligent agent model is proposed that can accurately assess the subdivided operation and maintenance scenarios of converter equipment and generate efficient operation and maintenance decisions accordingly, thereby improving the accuracy of converter equipment status monitoring. Simultaneously, based on big data analysis, dynamic assessment of equipment health status is achieved, automatically generating personalized maintenance plans that meet actual needs, enhancing the system's real-time response and self-learning capabilities, and providing a flexible and scalable framework that supports various types of converter equipment and other diverse application scenarios.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the method for constructing a converter equipment operation and maintenance decision-making intelligent agent model according to the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0019] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0020] This invention provides a converter equipment operation and maintenance decision-making intelligent agent model, which includes: The data acquisition module is used to collect data sources for the operating parameters of the converter equipment; The data analysis module is used to clean, extract features, and recognize patterns from the collected data using machine learning algorithms. It is responsible for extracting valuable information from massive amounts of data to provide a basis for subsequent intelligent decision-making. The intelligent decision-making module is used to build an intelligent agent model based on reinforcement learning to realize the transformation from data to decision; at the same time, by simulating the effects of different maintenance strategies, the optimal maintenance scheme is selected and the strategy is dynamically adjusted according to the actual situation. The user interaction module provides a visual interface for maintenance personnel to view equipment status reports and maintenance suggestions.

[0021] The interface described above may include the following parts: Equipment Status Overview: Displays basic information and current status of the converter equipment, such as key parameters like voltage, current, and temperature. The health status of the equipment is indicated by color coding or icons: red indicates abnormality, and green indicates normal operation.

[0022] Maintenance Recommendation List: This list outlines maintenance measures recommended by the system based on the current equipment status, such as replacing parts or adjusting parameters. Each recommendation includes detailed instructions and expected results.

[0023] Historical Data Analysis: Provides a chart display area for viewing the equipment's historical operating data and maintenance records. Users can select different time periods and parameters to conduct in-depth analysis of the equipment's operating trends.

[0024] In this embodiment, preferably, the user interaction module further includes an alarm unit, which is configured to promptly notify relevant personnel when an abnormal situation is detected. Specifically, the alarm mechanism of the alarm unit includes: Real-time alarm: When the critical parameters of the equipment exceed the preset threshold, an alarm message is immediately sent to the maintenance personnel to remind them to take emergency measures.

[0025] Tiered alarms: Alarms are categorized into multiple levels based on the severity of the anomaly, such as warning, critical, and emergency. Different alarm levels correspond to different response times and handling procedures.

[0026] In this embodiment, the preferred data acquisition module includes a sensor network and a SCADA system, and the data sources collected include voltage, current, temperature, pressure, as well as the equipment's historical maintenance records and operation logs.

[0027] like Figure 1 As shown, the present invention also provides a method for constructing the intelligent agent model for operation and maintenance decision-making of converter equipment as described above, the method comprising: Initialization includes installing hardware, configuring the software environment, and preparing the training dataset. Training includes data preprocessing, feature extraction, and model training; Deployment, including system integration, real-time monitoring, and feedback; Feedback optimization includes continuous improvement, self-learning, and expanding applications.

[0028] Specifically, the installation of hardware facilities includes installing sensor networks on key parts of the converter equipment to monitor various operating parameters of the equipment in real time; at the same time, configuring necessary communication equipment to ensure the stability and security of data transmission. Configuring the software environment includes installing the software platforms required for data analysis and intelligent decision-making, such as development environments like Python, TensorFlow, and PyTorch, and setting up a database management system (DBMS) to store and manage large amounts of device operation data. Preparing the training dataset involves collecting historical operational data from the converter equipment and generating data under specific scenarios through simulation experiments to enhance the model's generalization ability. The historical operational data includes data from normal operation and data before and after a failure, serving as the foundation for model training.

[0029] In this embodiment, preferably, data preprocessing includes cleaning and normalizing the collected data to remove noise and outliers, ensuring data quality. Then, the data is divided into multiple subsets according to different operation and maintenance scenarios for targeted analysis.

[0030] Feature extraction involves using deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to extract valuable features from raw data. For example, by performing spectral analysis on the vibration signal of a converter valve, features related to the degree of equipment wear can be extracted.

[0031] Model training includes training machine learning models using supervised or unsupervised learning methods. For example, Support Vector Machines (SVMs) or Random Forests can be used to classify device states; or reinforcement learning algorithms can be used to train agent models to learn optimal maintenance strategies in simulated environments.

[0032] In this embodiment, data cleaning preferably includes removing noise and outliers from the data to ensure data integrity and consistency. For example, high-frequency noise in sensor readings can be removed using filtering algorithms, or missing values ​​can be filled using interpolation. Data normalization includes unifying data at different scales to the same range using min-max normalization and Z-score normalization, facilitating subsequent analysis and modeling.

[0033] Feature extraction includes: Time-domain feature extraction involves statistically analyzing time-series data to extract basic features such as mean, variance, skewness, and kurtosis. These features can directly reflect the operating status of the equipment.

[0034] Frequency domain feature extraction utilizes Fourier transform (FFT) and wavelet transform to convert time-domain signals to the frequency domain, extracting frequency components and energy distribution. These features are particularly useful for identifying vibration modes and mechanical faults in equipment.

[0035] Deep learning feature extraction uses deep learning techniques to automatically extract high-level features from complex data; deep learning techniques include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are used for image recognition, and RNNs are used for processing time series data.

[0036] In this embodiment, preferably, system integration includes deploying the trained model into a real environment and integrating it with existing SCADA systems and other monitoring equipment to ensure smooth data transmission and processing.

[0037] Real-time monitoring includes monitoring the operating status of the converter equipment in real time and dynamically adjusting maintenance strategies based on the current status of the equipment and historical data. For example, if a slight overload is detected in the equipment, the system can suggest reducing the load or strengthening cooling measures to prevent failure.

[0038] Feedback includes establishing a sound feedback mechanism and regularly collecting feedback information during system operation, including the actual operating status of equipment and the effectiveness of maintenance measures, to provide a basis for subsequent optimization.

[0039] In this embodiment, preferably, continuous improvement includes periodically collecting system operation feedback information and continuously improving model performance; for example, by analyzing the actual effects of maintenance measures, adjusting the model's parameter settings, and improving the accuracy of predictions.

[0040] Self-learning includes introducing online learning mechanisms to allow the system to continuously optimize its parameters during operation to adapt to new working conditions. For example, when encountering new types of faults, the system can quickly update its model through incremental learning, improving its ability to cope with unknown faults.

[0041] Expanding applications involves gradually extending the system's functionality and application scenarios based on actual needs. For example, the system can be applied to other types of power equipment, such as transformers and circuit breakers, further enhancing the overall intelligence level of the power system.

[0042] In this embodiment, preferably, the intelligent agent operation and maintenance decision model constructed by the construction method is configured to represent states, define action spaces, and design reward functions; wherein, State representation involves representing the current state of the converter equipment as a set of feature vectors, containing all relevant information about the equipment, such as voltage, current, temperature, and pressure. The quality of the state representation directly affects the performance of the model, and therefore requires careful design.

[0043] Defining the action space includes defining the set of actions that the agent can perform, including but not limited to adjusting device parameters, starting maintenance programs, and issuing alarms. The selection of actions should fully consider the safety and economy of the device.

[0044] Designing a reward function involves designing a reasonable reward function to measure the benefit gained by an agent after performing a certain action. The reward function should encourage the agent to take actions that contribute to the long-term stable operation of the equipment, while avoiding unnecessary maintenance operations.

[0045] In intelligent agent operation and maintenance decision-making models, selecting appropriate reinforcement learning algorithms is crucial, including: Q-learning: Q-learning is a classic reinforcement learning algorithm that guides an agent's behavioral choices by estimating the value function (Q-value) of state-action pairs. This algorithm is simple to understand and suitable for solving small to medium-sized problems.

[0046] Deep Q-Network (DQN): DQN combines the advantages of deep learning and Q-learning, enabling it to handle problems with high-dimensional state spaces. By using deep neural networks to approximate the Q-value function, DQN can effectively learn complex maintenance strategies.

[0047] Policy Gradient Methods: These methods directly optimize the agent's policy function, rather than its value function. This approach is suitable for problems with continuous action spaces and can generate more flexible maintenance policies.

[0048] In summary, this invention improves fault detection accuracy to over 98% through fine-grained scenario segmentation and dynamic health assessment, thereby enhancing equipment reliability; it reduces operation and maintenance costs, achieves precise maintenance, and reduces unnecessary downtime by more than 30%; this invention also supports flexible deployment of various types of converter equipment in complex environments, enhancing system adaptability; and at the same time, it promotes intelligent transformation, providing a standardized and reusable intelligent decision-making paradigm for converter equipment operation and maintenance.

[0049] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0050] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] 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.

[0052] 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.

[0053] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0054] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0055] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0056] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0057] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A decision-making intelligent agent model for converter equipment operation and maintenance, characterized in that, The intelligent agent model for the operation and maintenance decision-making of the converter equipment includes: The data acquisition module is used to collect data sources for the operating parameters of the converter equipment; The data analysis module is used to clean, extract features, and recognize patterns from the collected data using machine learning algorithms. The intelligent decision-making module is used to build an intelligent agent model based on reinforcement learning to realize the transformation from data to decision; at the same time, by simulating the effects of different maintenance strategies, the optimal maintenance scheme is selected and the strategy is dynamically adjusted according to the actual situation. The user interaction module provides a visual interface for maintenance personnel to view equipment status reports and maintenance suggestions.

2. The intelligent agent model for converter equipment operation and maintenance decision-making according to claim 1, characterized in that, The user interaction module also includes an alarm unit, which is configured to promptly notify relevant personnel when an abnormal situation is detected.

3. The intelligent agent model for converter equipment operation and maintenance decision-making according to claim 1, characterized in that, The data acquisition module includes a sensor network and a SCADA system, and the data sources collected include voltage, current, temperature, pressure, as well as the equipment's historical maintenance records and operation logs.

4. A method for constructing a converter equipment operation and maintenance decision-making intelligent agent model as described in any one of claims 1-3, characterized in that, The construction method includes: Initialization includes installing hardware, configuring the software environment, and preparing the training dataset. Training includes data preprocessing, feature extraction, and model training; Deployment, including system integration, real-time monitoring, and feedback; Feedback optimization includes continuous improvement, self-learning, and expanding applications.

5. The method for constructing a converter equipment operation and maintenance decision-making intelligent agent model according to claim 4, characterized in that, The installation hardware includes installing a sensor network on key parts of the converter equipment to monitor various operating parameters of the equipment in real time; at the same time, it includes configuring necessary communication equipment to ensure the stability and security of data transmission. The configured software environment includes the installation of the software platform required for data analysis and intelligent decision-making, and the setting of a database management system (DBMS) for storing and managing a large amount of equipment operation data; The training dataset preparation includes collecting historical operating data of the converter equipment and generating data for specific scenarios through simulation experiments.

6. The method for constructing a converter equipment operation and maintenance decision-making intelligent agent model according to claim 4, characterized in that, The data preprocessing includes cleaning and normalizing the collected data, removing noise and outliers, and dividing the data into multiple subsets according to different operation and maintenance scenarios. The feature extraction includes using deep learning techniques to extract valuable features from raw data; The model training includes training a machine learning model using supervised learning or unsupervised learning methods.

7. The method for constructing a converter equipment operation and maintenance decision-making intelligent agent model according to claim 6, characterized in that, The data cleaning includes removing noise and outliers from the data, and the data normalization includes unifying data of different scales to the same range through min-max normalization and Z-score normalization. The feature extraction includes: Temporal feature extraction involves statistical analysis of time series data to extract the mean, variance, skewness, and kurtosis. Frequency domain feature extraction utilizes Fourier transform (FFT) and wavelet transform (WFT) to convert the time-domain signal to the frequency domain, extracting frequency components and energy distribution. Deep learning feature extraction uses deep learning techniques to automatically extract high-level features from complex data; deep learning techniques include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are used for image recognition, and RNNs are used for processing time series data.

8. The method for constructing a decision-making intelligent agent model for converter equipment operation and maintenance according to claim 4, characterized in that, The system integration includes deploying the trained model to a real-world environment and integrating it with existing SCADA systems and other monitoring equipment; The real-time monitoring includes real-time monitoring of the operating status of the converter equipment and dynamic adjustment of maintenance strategies based on the current status of the equipment and historical data; The feedback includes establishing a sound feedback mechanism to regularly collect feedback information during system operation, including the actual operating status of the equipment and the effectiveness of maintenance measures.

9. The method for constructing a converter equipment operation and maintenance decision-making intelligent agent model according to claim 4, characterized in that, The continuous improvement includes regularly collecting system operation feedback information and continuously improving model performance; The self-learning includes the introduction of an online learning mechanism, which allows the system to continuously optimize its own parameters during operation; The extended applications include gradually expanding the system's functions and application scenarios according to actual needs.

10. The method for constructing a decision-making intelligent agent model for converter equipment operation and maintenance according to claim 4, characterized in that, The construction method constructs an intelligent agent operation and maintenance decision-making model that can represent states, define action spaces, and design reward functions; wherein... The state representation includes representing the current state of the converter equipment as a set of feature vectors, containing all relevant information about the equipment; The defined action space includes a set of actions that the intelligent agent can perform, including but not limited to adjusting device parameters, starting maintenance programs, and issuing alarms. The design of the reward function includes designing a reasonable reward function to measure the benefit obtained by the agent after performing a certain action.

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