Real-time analysis method for state monitoring and intelligent interaction of power distribution network equipment

Abstract

The invention relates to the technical field of power distribution network equipment state monitoring and intelligent interaction, in particular to a real-time analysis method for power distribution network equipment state monitoring and intelligent interaction. Comprising the following steps: data acquisition; preprocessing the data; extracting features; state evaluation; performing real-time analysis; and intelligent interaction. According to the real-time analysis method for state monitoring and intelligent interaction of the power distribution network equipment, a comprehensive sensor network is constructed for data acquisition, and advanced data preprocessing, feature extraction and state evaluation algorithms are adopted for processing and analysis; equipment state information, early warning prompts and maintenance suggestions are provided for operation and maintenance personnel through the intelligent interaction interface, and comprehensive, accurate and timely monitoring and management of the power distribution network equipment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network equipment status monitoring and intelligent interaction, and in particular to a real-time analysis method for distribution network equipment status monitoring and intelligent interaction. Background Art

[0002] With the rapid development of the electric power industry, the safe, stable, and efficient operation of distribution networks, a key link in the power system directly connected to users, is crucial for ensuring socioeconomic development and people's living standards. However, distribution network equipment is numerous, widely distributed, and operates in a complex environment. Traditional manual inspections and regular maintenance methods are no longer sufficient to meet the modern power grid's demand for real-time monitoring and intelligent management of equipment status. Therefore, developing a real-time analysis method for distribution network equipment status monitoring and intelligent interaction is crucial for improving distribution network operation and maintenance, reducing failure rates, and enhancing power supply reliability and economic efficiency.

[0003] In the power distribution sector, traditional equipment monitoring relies primarily on manual inspections and periodic testing. This approach is not only inefficient but also struggles to achieve real-time, comprehensive monitoring of equipment status. Furthermore, due to the wide variety of equipment and complex operating environments, traditional monitoring methods often struggle to accurately determine equipment status, leading to delayed fault detection and resolution, posing safety risks to power grid operations. Therefore, with the rapid development of sensor technology, the Internet of Things (IoT), big data technology, and artificial intelligence (AI), distribution network equipment status monitoring and intelligent interaction technology have gradually become a research hotspot and trend. Summary of the Invention

[0004] The present invention proposes a real-time analysis method for distribution network equipment status monitoring and intelligent interaction. By building a comprehensive sensor network for data collection, advanced data preprocessing, feature extraction and status assessment algorithms are used for processing and analysis. Through an intelligent interactive interface, equipment status information, early warning prompts and maintenance suggestions are provided to operation and maintenance personnel, thus realizing comprehensive, accurate and timely monitoring and management of distribution network equipment.

[0005] The technical solution adopted by the present invention is: a real-time analysis method for distribution network equipment status monitoring and intelligent interaction, comprising the following steps:

[0006] Step 1: Data collection: The sensor network collects real-time operating status data of each device in the distribution network, including current, voltage, temperature, humidity, and vibration parameters;

[0007] Step 2: Data preprocessing: Clean, denoise, and standardize the collected raw data to improve the accuracy of subsequent analysis;

[0008] Step 3: Feature extraction: Extract key features reflecting the device status from the preprocessed data, including abnormal current fluctuations and temperature mutations.

[0009] Step 4: Status Assessment: Based on the extracted features, machine learning and deep learning algorithms are used to assess the device status to determine whether the device is in a normal, abnormal, or faulty state.

[0010] Step 5: Real-time analysis: Analyze the assessment results in real time, combine historical data with expert knowledge base, and predict future equipment operating trends and possible failure types.

[0011] Step 6, Intelligent Interaction: Based on the analysis results, equipment status information, early warning prompts and maintenance suggestions are provided to operation and maintenance personnel through the human-computer interaction interface to achieve intelligent interaction and decision support.

[0012] As a further improvement of the present invention, the sensor network includes current sensors, voltage sensors, temperature sensors, humidity sensors and vibration sensors, and is distributed on key nodes and equipment in the distribution network to achieve comprehensive monitoring of the operating status of distribution network equipment.

[0013] As a further improvement of the present invention, the data cleaning process includes processing outliers, missing values ​​and duplicate values, and ensures the integrity and accuracy of the data through interpolation, filling and deletion methods; the data denoising process uses filters and smoothing algorithms to reduce random noise and interference in the signal to improve the signal-to-noise ratio and reliability of the signal; the data standardization process converts the data to the same scale and range to eliminate dimensional differences between different parameters, facilitating subsequent unified analysis and processing.

[0014] As a further improvement of the present invention, the feature extraction adopts principal component analysis (PCA) and linear discriminant analysis (LDA) dimensionality reduction techniques to extract the most representative low-dimensional feature vectors from high-dimensional preprocessed data to reflect the operating status of the equipment; at the same time, deep learning models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), are introduced to perform feature learning on time series data to capture the dynamic characteristics of the equipment status changing over time.

[0015] As a further improvement of the present invention, the state assessment adopts a method that combines a multi-classification support vector machine (SVM) and a deep belief network (DBN). First, a multi-classification support vector machine (SVM) is used to preliminarily classify the equipment state into three categories: normal, abnormal, and faulty. Then, for the abnormal and faulty states, a deep belief network (DBN) is further used to perform a refined state assessment to identify the specific fault type and fault degree.

[0016] As a further improvement of the present invention, the real-time analysis is combined with time series analysis and trend prediction algorithm to track and predict the operating status of the equipment in real time.

[0017] As a further improvement of the present invention, the time series analysis continuously monitors the equipment operation status data and utilizes a time series analysis method to identify the trend and periodicity of equipment status changes.

[0018] As a further improvement of the present invention, the trend prediction algorithm is based on historical data and current status, combined with an expert knowledge base and a machine learning model to predict the future operating status of the equipment and provide corresponding early warnings and maintenance suggestions.

[0019] As a further improvement of the present invention, the human-computer interaction interface adopts a graphical design to intuitively display equipment status information, early warning prompts and maintenance suggestions, and supports multiple interaction methods, including touch, voice and gesture recognition, to improve the operating efficiency and convenience of operation and maintenance personnel.

[0020] As a further improvement of the present invention, the human-computer interaction interface also has remote monitoring and remote control functions. Operation and maintenance personnel can view the equipment status in real time through the network at any location, and remotely control the equipment to perform fault diagnosis and repair, thereby realizing remote intelligent management of distribution network equipment.

[0021] Beneficial effects of the present invention:

[0022] (1) Improving the operation and maintenance level of the distribution network: Through real-time monitoring and intelligent analysis, the present invention can promptly detect potential faults in distribution network equipment and provide accurate early warnings and maintenance suggestions to operation and maintenance personnel. This not only improves the response speed of operation and maintenance, but also reduces the workload of manual inspections and regular maintenance, thereby significantly improving the overall operation and maintenance level of the distribution network.

[0023] (2) Reduce the failure rate and improve the reliability of power supply: With the help of advanced sensor networks and intelligent analysis algorithms, the present invention can achieve comprehensive and accurate monitoring of equipment status, effectively preventing power outages caused by equipment failures. At the same time, through real-time analysis and prediction, operation and maintenance personnel can take measures in advance to avoid the occurrence of failures, thereby greatly reducing the failure rate and improving the reliability and stability of power supply.

[0024] (3) Improved economy and energy-saving effects: The application of the present invention reduces power outage losses and maintenance costs caused by equipment failures, and improves the economic benefits of the power grid. In addition, by monitoring the operating status of the equipment in real time, operation and maintenance personnel can more reasonably arrange the operation and maintenance plans of the equipment, avoid unnecessary energy waste, and thus achieve the effect of energy saving and consumption reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to the present invention. DETAILED DESCRIPTION

[0026] In order to make the technical problems, technical solutions and beneficial effects to be solved by this application more clearly understood, this application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to explain this application and are not intended to limit this application.

[0027] The present invention provides a real-time analysis method for distribution network equipment status monitoring and intelligent interaction, comprising the following steps:

[0028] Step 1: Data collection: Real-time operating status data of each device in the distribution network, including current, voltage, temperature, humidity, and vibration parameters, is collected through a sensor network. The sensor network includes current sensors, voltage sensors, temperature sensors, humidity sensors, and vibration sensors, and is distributed on key nodes and devices in the distribution network to achieve comprehensive monitoring of the operating status of distribution network equipment.

[0029] Step 2: Data preprocessing: The collected raw data is cleaned, denoised, and standardized to improve the accuracy of subsequent analysis. The data cleaning process includes processing outliers, missing values, and duplicate values ​​through interpolation, filling, and deletion methods to ensure the integrity and accuracy of the data. The data denoising process uses filters and smoothing algorithms to reduce random noise and interference in the signal to improve the signal-to-noise ratio and reliability of the signal. The data standardization process converts the data to the same scale and range to eliminate dimensional differences between different parameters, facilitating subsequent unified analysis and processing.

[0030] Step 3: Feature extraction: Extract key features reflecting the device status from the preprocessed data, including abnormal current fluctuations and temperature changes. This feature extraction uses principal component analysis (PCA) and linear discriminant analysis (LDA) dimensionality reduction techniques to extract the most representative low-dimensional feature vectors from the high-dimensional preprocessed data to reflect the device's operating status. Deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are also introduced to perform feature learning on time series data to capture the dynamic characteristics of device status over time.

[0031] Step 4: Status Assessment: Based on the extracted features, machine learning and deep learning algorithms are used to assess the device status and determine whether the device is in a normal, abnormal, or faulty state. This status assessment uses a combination of a multi-class support vector machine (SVM) and a deep belief network (DBN). First, a multi-class support vector machine (SVM) is used to perform a preliminary classification of the device status into three categories: normal, abnormal, and faulty. Then, for abnormal and faulty states, a deep belief network (DBN) is used to perform a refined status assessment to identify the specific fault type and severity.

[0032] Step 5: Real-time analysis: The assessment results are analyzed in real time, combined with historical data and expert knowledge bases to predict the future operating trends of the equipment and possible types of failures. The real-time analysis combines time series analysis and trend prediction algorithms to track and predict the operating status of the equipment in real time. The time series analysis continuously monitors the equipment operating status data and uses time series analysis methods to identify trends and periodic patterns in equipment status changes. The trend prediction algorithm, based on historical data and current status, combines expert knowledge bases and machine learning models to predict the future operating status of the equipment and provide corresponding early warnings and maintenance recommendations.

[0033] Step 6, Intelligent Interaction: Based on the analysis results, equipment status information, early warning prompts and maintenance suggestions are provided to operation and maintenance personnel through the human-computer interaction interface to achieve intelligent interaction and decision support. The human-computer interaction interface adopts a graphical design to intuitively display equipment status information, early warning prompts and maintenance suggestions, and supports multiple interaction methods, including touch, voice and gesture recognition, to improve the operating efficiency and convenience of operation and maintenance personnel. The human-computer interaction interface also has remote monitoring and remote control functions. Operation and maintenance personnel can view the equipment status in real time through the network at any location, and remotely control the equipment for fault diagnosis and maintenance, realizing remote intelligent management of distribution network equipment.

[0034] Example 1:

[0035] In the application scenario of this embodiment, a city's distribution network faces the problem of a large number of outdated equipment and frequent failures, which seriously affects the reliability of power supply and the quality of life of residents. To improve this situation, the city decided to introduce the real-time analysis method of distribution network equipment status monitoring and intelligent interaction of the present invention.

[0036] First, based on the layout of the distribution network and the distribution of equipment, a comprehensive sensor network was installed, including current sensors, voltage sensors, temperature sensors, humidity sensors, and vibration sensors, to ensure real-time data collection on the operating status of each device in the distribution network. These sensors were carefully placed at key nodes and equipment in the distribution network, forming a dense and precise monitoring network.

[0037] During the data acquisition phase, a large amount of operating status data, including current, voltage, temperature, humidity, and vibration parameters, is collected in real time through the sensor network. This data is then transmitted to the data center for subsequent preprocessing and analysis.

[0038] During the data preprocessing phase, the collected raw data is first cleaned, denoised, and standardized. Through a series of complex algorithms and models, the system successfully removes outliers, missing values, and duplicate values ​​from the data. Filters and smoothing algorithms are also used to reduce random noise and interference in the signal. The preprocessed data becomes cleaner, more accurate, and more reliable, laying a solid foundation for subsequent analysis and evaluation.

[0039] During the feature extraction phase, dimensionality reduction techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract key features reflecting device status from preprocessed data. The system also incorporates deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN) to perform feature learning on time series data to capture the dynamic nature of device status over time. These features are not only representative but also accurately reflect the device's operating status and potential issues.

[0040] During the status assessment phase, machine learning and deep learning algorithms are used to analyze and evaluate the extracted features. By combining a multi-class support vector machine (SVM) with a deep belief network (DBN), the system can accurately determine whether a device is in a normal, abnormal, or faulty state. For devices in abnormal or faulty states, the system can further identify the specific fault type and severity, providing operators with precise warnings and repair recommendations.

[0041] During the real-time analysis phase, historical data and expert knowledge are combined to track and predict the operating status of equipment in real time. Through time series analysis and trend prediction algorithms, the system can identify trends and cyclical patterns in equipment status changes, and predict future operating trends and potential failure types. These predictions not only help operations and maintenance personnel take preventive measures, but also provide strong support for grid scheduling and planning.

[0042] Finally, during the intelligent interaction phase, a human-computer interface provides O&M personnel with device status information, early warning alerts, and repair recommendations. This interface utilizes a graphical design to intuitively display device status information, early warning alerts, and repair recommendations, and supports multiple interaction methods, such as touch, voice, and gesture recognition. O&M personnel can use this interface to view device status in real time and remotely control equipment for fault diagnosis and repair. This remote intelligent management approach not only improves O&M response speed and efficiency, but also reduces the workload of manual inspections and scheduled maintenance.

[0043] By implementing the present invention's real-time analysis method for distribution network equipment status monitoring and intelligent interaction, the city's distribution network operation and maintenance capabilities have been significantly improved. Equipment failure rates have been significantly reduced, power supply reliability has been significantly improved, and economic benefits and energy savings have been significantly enhanced. This successful case not only validates the effectiveness and practicality of the present invention but also provides a valuable reference for distribution network operation and maintenance management in other cities.

[0044] Example 2:

[0045] This embodiment is applied to a more complex and diverse distribution network environment, encompassing not only legacy equipment but also emerging smart grid technologies such as distributed energy access and microgrid management. Facing such challenges, the present invention's real-time analysis method for distribution network equipment status monitoring and intelligent interaction has once again demonstrated its strong adaptability and application value.

[0046] First, to meet the diverse needs of smart grids, the sensor network has been upgraded, with the introduction of more types of sensors, such as light sensors, power factor sensors, and specialized sensors for monitoring the access status of distributed energy resources. These sensors can more comprehensively capture the operating status and energy flow of various devices in the distribution network, providing a richer source of information for subsequent data analysis and decision-making.

[0047] During the data collection phase, we adopted more efficient data transmission protocols and compression algorithms to ensure the rapid and accurate transmission of large amounts of real-time data. At the same time, we established a more comprehensive data center, using distributed storage and cloud computing technologies to improve the efficiency and security of data processing and storage.

[0048] During the data preprocessing phase, algorithms for data cleaning, denoising, and standardization were optimized to address the unique characteristics of smart grids. Adaptive filters and intelligent denoising technologies were introduced to better address the complex noise and interference issues inherent in smart grids. Furthermore, data mining techniques were used to uncover hidden correlations and regularities within massive amounts of data, providing strong support for subsequent feature extraction and status assessment.

[0049] During the feature extraction phase, a hybrid feature extraction framework was constructed by combining the strengths of traditional dimensionality reduction techniques and deep learning models. This framework can simultaneously process both static and dynamic features, comprehensively reflecting the device's operating status and potential issues from multiple dimensions. Advanced technologies such as the attention mechanism were also introduced to improve the accuracy and efficiency of feature extraction.

[0050] During the state assessment phase, the team further optimized the evaluation method combining a multi-class support vector machine (SVM) and a deep belief network (DBN). By introducing more training data and optimizing algorithm parameters, the accuracy and robustness of the evaluation model were improved. Furthermore, they conducted in-depth research on specific fault types in smart grids and developed corresponding fault identification algorithms and early warning mechanisms.

[0051] During the real-time analysis phase, we fully leverage the big data and artificial intelligence technologies of smart grids. By building a time series analysis model and a trend prediction algorithm library, we can track and predict the operating status of equipment and energy flow in real time. Furthermore, by combining the strengths of expert knowledge bases and machine learning models, we conduct in-depth analysis and interpretation of prediction results, providing more accurate and comprehensive decision support for operations and maintenance personnel.

[0052] In the intelligent interaction phase, the user-friendliness and intelligence of the human-machine interface have been further enhanced. By introducing natural language processing and augmented reality technologies, operators can now engage with the system through natural dialogue and intuitive interaction. Operators can interact with the system through voice or gestures, and view 3D models and operational status information of the equipment in real time. Furthermore, an intelligent recommendation system has been developed to recommend appropriate maintenance solutions and tools based on operators' operating habits and historical data.

[0053] By implementing the real-time analysis method for distribution network equipment status monitoring and intelligent interaction presented in this embodiment, the operation and maintenance of the smart grid has been significantly improved. Equipment failure rates have been significantly reduced, and power supply reliability and stability have been significantly improved. Furthermore, the intelligent management of the smart grid has been significantly enhanced, providing strong support for sustainable urban development and energy transition.

[0054] In summary, the real-time analysis method for distribution network equipment status monitoring and intelligent interaction of the present invention has successfully coped with the complex and changeable distribution network environment by virtue of its comprehensive monitoring, precise analysis and intelligent interactive functions, and significantly improved the operation and maintenance efficiency and power supply reliability. Whether it is traditional old equipment or emerging smart grid technology, this method can effectively integrate resources to achieve efficient and accurate monitoring and management. By continuously optimizing algorithms and introducing advanced technologies, this method not only improves the accuracy of fault warning and identification, but also realizes remote intelligent management, reduces manual intervention, and reduces operation and maintenance costs.

[0055] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction, characterized in that: The following steps are involved: Step 1: Data collection: The operating status data of each device in the distribution network is collected in real time through the sensor network, including current, voltage, temperature, humidity, and vibration parameters; Step 2: Data preprocessing: Clean, denoise, and standardize the collected raw data to improve the accuracy of subsequent analysis; Step 3, feature extraction: extract key features reflecting the device status from the preprocessed data, including abnormal current fluctuations and temperature mutations; Step 4: Status assessment: Based on the extracted features, use machine learning and deep learning algorithms to assess the status of the equipment and determine whether the equipment is in a normal, abnormal, or faulty state. Step 5: Real-time analysis: Analyze the evaluation results in real time, combine historical data and expert knowledge base to predict the future operation trend of the equipment and the possible failure types; Step 6: Intelligent interaction: Based on the analysis results, equipment status information, early warning prompts and maintenance suggestions are provided to operation and maintenance personnel through the human-computer interaction interface to achieve intelligent interaction and decision support.

2. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 1, characterized in that: The sensor network includes current sensors, voltage sensors, temperature sensors, humidity sensors and vibration sensors, and is distributed on key nodes and equipment in the distribution network to achieve comprehensive monitoring of the operating status of the distribution network equipment.

3. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 1, characterized in that: The data cleaning process includes processing abnormal values, missing values ​​and duplicate values, and ensures the integrity and accuracy of the data through interpolation, filling and deletion methods; the data denoising process uses filters and smoothing algorithms to reduce random noise and interference in the signal to improve the signal-to-noise ratio and reliability of the signal; the data standardization process converts the data to the same scale and range to eliminate the dimensional differences between different parameters, which is convenient for subsequent unified analysis and processing.

4. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 1, characterized in that: The feature extraction adopts principal component analysis (PCA) and linear discriminant analysis (LDA) dimensionality reduction technology to extract the most representative low-dimensional feature vector from high-dimensional preprocessed data to reflect the operating status of the equipment; at the same time, deep learning models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), are introduced to perform feature learning on time series data to capture the dynamic characteristics of the equipment status changing over time.

5. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 1, characterized in that: The state assessment adopts a method combining multi-classification support vector machine (SVM) and deep belief network (DBN). First, the multi-classification support vector machine (SVM) is used to preliminarily classify the equipment state and divide the equipment state into three categories: normal, abnormal and faulty. Then, for abnormal and fault conditions, a deep belief network (DBN) is further used to perform refined state assessment to identify the specific fault type and fault degree.

6. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 1, characterized in that: The real-time analysis combines time series analysis and trend prediction algorithms to track and predict the operating status of the equipment in real time.

7. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 6, characterized in that: The time series analysis continuously monitors the equipment operation status data and uses the time series analysis method to identify the trend and periodicity of equipment status changes.

8. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 6, characterized in that: The trend prediction algorithm is based on historical data and current status, combined with expert knowledge base and machine learning model, to predict the future operating status of the equipment and give corresponding early warnings and maintenance suggestions.

9. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 1, characterized in that: The human-computer interaction interface adopts a graphical design to intuitively display equipment status information, early warning prompts and maintenance suggestions, and supports multiple interaction methods, including touch, voice and gesture recognition, to improve the operating efficiency and convenience of operation and maintenance personnel.

10. A real-time analysis method for distribution network equipment status monitoring and intelligent interaction according to claim 9, characterized in that: The human-computer interaction interface also has remote monitoring and remote control functions. Operation and maintenance personnel can view the equipment status in real time through the network at any location, and remotely control the equipment to perform fault diagnosis and repair, thereby realizing remote intelligent management of distribution network equipment.

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