Power equipment fault diagnosis analysis and early warning system

Abstract

The invention relates to a power equipment fault diagnosis analysis and early warning system. A video monitoring device and an operation data detection device are used for detecting and collecting the operation state and operation data of power equipment respectively; abnormal data and error information can be filtered out by utilizing the data preprocessing module, missing values are filled up, and the data are normalized; a fault diagnosis model can be established by utilizing a diagnosis model establishing unit, and the preprocessed data can be input into the fault diagnosis model by utilizing a fault analysis unit for contrastive analysis, so that an analysis result is formed; the classification unit can be used for classifying the levels of faults according to the analysis result of the fault analysis unit, and the alarm module can be used for starting fault warning lamps corresponding to the fault levels while sending out alarm signals according to different fault levels. The method has the advantages of timely and accurate fault discovery and efficient and convenient use.

Description

Technical Field

[0001] The present invention belongs to the technical field of power fault diagnosis and early warning, and in particular relates to a power equipment fault diagnosis analysis and early warning system. Background Art

[0002] The development of electricity has become an important driving force for social stability and economic development. Among them, power equipment is a key component facility that affects the safe and stable operation of the power system and the material and technical foundation on which power companies rely for survival and development. It is an important component of productivity. However, due to the complex structure of power equipment, it usually involves a large number of hardware and software related to machinery, automation, electricity and communications, and has a high degree of coupling. At the same time, the failure of power equipment is sudden, difficult to troubleshoot, and highly harmful. It is impossible to detect and handle the fault in time only through regular maintenance and overhaul operations. In addition, the traditional power equipment operation status detection and fault monitoring method requires the equipment to be shut down for regular test detection. This method not only has low detection efficiency, time-consuming and labor-intensive, but also seriously affects the normal operation of the power grid. Therefore, in order to solve the above problems, it is necessary to develop a power equipment fault diagnosis analysis and early warning system that can detect faults in a timely and accurate manner and is efficient and convenient to use. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a power equipment fault diagnosis analysis and early warning system which can detect faults promptly and accurately and is efficient and convenient to use.

[0004] The object of the present invention is achieved as follows: A power equipment fault diagnosis analysis and early warning system, comprising an operation monitoring component, a data collection module, a data preprocessing module, a storage module, a diagnosis analysis module and an alarm module, wherein the operation monitoring component is composed of a plurality of video monitoring devices and a plurality of operation data detection devices, wherein the video monitoring device is used to monitor the operation status and equipment temperature of the power equipment, and the operation data detection device is used to detect and collect the data generated during the operation of the power equipment, thereby integrating and forming original data; The data collection module can uniformly collect the raw data detected and collected by the operation monitoring component; The data preprocessing module can perform preprocessing operations on the raw data collected by the data collection module, thereby filtering out abnormal data and error information generated by the transition state when the operation mode changes, filling in missing values ​​and normalizing the data to ensure the accuracy and consistency of the data; The storage module can store and back up the data after preprocessing; The diagnostic analysis module is internally provided with a diagnostic model building unit, a fault analysis unit and a classification unit, wherein the diagnostic model building unit can obtain a standardized matrix through sample data, and use an improved quantum particle swarm algorithm to train and adjust the system parameters of the adaptive fuzzy neural inference system according to the standardized matrix to establish a fault diagnosis model, and the fault analysis unit can input the pre-processed data into the fault diagnosis model for comparative analysis to form an analysis result; the classification unit can classify the level of the fault according to the analysis result of the fault analysis unit; The alarm module can, according to different fault levels, send out an alarm signal and start a fault warning light corresponding to the fault level.

[0005] Furthermore, the operation monitoring component also includes a positioning device, which can be used to obtain location information of the power equipment.

[0006] Furthermore, the storage module is connected to a cloud platform, and data information can be uploaded to the cloud platform for storage and backup.

[0007] Furthermore, a marking unit is provided inside the storage module, and the marking unit can number the processed data according to time segments.

[0008] Beneficial effects of the present invention: The present invention sets a video monitoring device and an operation data detection device, and the video monitoring device can be used to monitor the operation status and equipment temperature of the power equipment, and the operation data detection device can be used to detect and collect the data generated during the operation of the power equipment; by setting a data preprocessing module, it can be used to filter out abnormal data and error information generated by the transition state when the operation mode changes, fill in missing values ​​and normalize the data to ensure the accuracy and consistency of the data; by setting a diagnosis model establishment unit, a fault analysis unit, a classification unit and an alarm module, the diagnosis model establishment unit can be used to obtain a standardized matrix through sample data, and the improved quantum particle swarm algorithm is used to train and adjust the system parameters of the adaptive fuzzy neural reasoning system according to the standardized matrix to establish a fault diagnosis model, and the fault analysis unit can be used to input the preprocessed data into the fault diagnosis model for comparative analysis to form an analysis result; the classification unit can be used to classify the level of the fault according to the analysis result of the fault analysis unit, and the alarm module can be used to send an alarm signal according to the different fault levels, and start the fault warning light corresponding to the fault level; in general, the present invention has the advantages of timely and accurate fault detection and efficient and convenient use. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a structural flow chart of the present invention. DETAILED DESCRIPTION

[0010] The present invention will be further described below in conjunction with the accompanying drawings.

[0011] Example: Figure 1 As shown, a power equipment fault diagnosis analysis and early warning system includes an operation monitoring component, a data collection module, a data preprocessing module, a storage module, a diagnosis analysis module and an alarm module. The operation monitoring component is composed of a plurality of video monitoring devices and a plurality of operation data detection devices, wherein the video monitoring device is used to monitor the operation status and equipment temperature of the power equipment, and the operation data detection device is used to detect and collect the data generated when the power equipment is running, so as to integrate and form the original data; the data collection module can uniformly collect the original data detected, collected and formed by the operation monitoring component; the data preprocessing module can preprocess the original data collected by the data collection module, so as to filter out abnormal data and error information generated by the transition state when the operation mode changes, fill in the missing values ​​and make Data normalization ensures data accuracy and consistency; the storage module can store and back up the data after preprocessing; the diagnosis and analysis module is internally provided with a diagnosis model building unit, a fault analysis unit and a classification unit, wherein the diagnosis model building unit can obtain a standardized matrix through sample data, and use an improved quantum particle swarm algorithm to train and adjust the system parameters of the adaptive fuzzy neural reasoning system according to the standardized matrix to establish a fault diagnosis model; the fault analysis unit can input the preprocessed data into the fault diagnosis model for comparative analysis to form an analysis result; the classification unit can classify the fault level according to the analysis result of the fault analysis unit; the alarm module can, according to different fault levels, send out an alarm signal and start the fault warning light corresponding to the fault level.

[0012] The operation monitoring component also includes a positioning device, which can be used to obtain the location information of the power equipment; the storage module is connected to a cloud platform, and data information can be uploaded to the cloud platform for storage and backup; at the same time, a marking unit is provided inside the storage module, and the marking unit can number the processed data according to time segments. This structure can facilitate data search and review.

[0013] When the present invention is used, first, the operation status and equipment temperature of the power equipment are monitored by the video monitoring device, and the data generated by the operation of the power equipment is detected and collected by the operation data detection device. After that, the monitored information and the detected and collected data are integrated to form the original data. Then, the original data integrated by multiple video monitoring devices and multiple operation data detection devices are collected by the data collection module, and after the collection is completed, the data is transmitted to the data preprocessing module. Then, the data preprocessing module is used to perform preprocessing operations on the original data, so as to filter out abnormal data and error information generated by the transition state when the operation mode changes, fill in missing values ​​and normalize the data to ensure the accuracy and consistency of the data, and then, the marking unit inside the storage module is used according to the time period. The original data is numbered, and after the numbering is completed, it is uploaded to the cloud platform for storage and backup. Finally, the processed data is analyzed and diagnosed through the diagnosis and analysis module. The diagnosis model building unit can obtain the standardized matrix through the sample data, and use the improved quantum particle swarm algorithm to train and adjust the system parameters of the adaptive fuzzy neural reasoning system according to the standardized matrix to establish a fault diagnosis model. The fault analysis unit can input the pre-processed data into the fault diagnosis model for comparative analysis to form an analysis result. The classification unit can classify the level of the fault according to the analysis result of the fault analysis unit. After completing the above operations, the alarm module can be used to send an alarm signal according to the different fault levels, and at the same time, the fault warning light corresponding to the fault level can be started to warn the staff.

[0014] The present invention provides a video monitoring device and an operation data detection device. The video monitoring device can be used to monitor the operation status and equipment temperature of the power equipment. The operation data detection device can be used to detect and collect the data generated during the operation of the power equipment. The data preprocessing module is provided. The data preprocessing module can be used to filter out abnormal data and error information generated by the transition state when the operation mode is changed, fill in missing values ​​and normalize the data to ensure the accuracy and consistency of the data. The diagnostic model establishment unit, the fault analysis unit, the classification unit and the alarm module are provided. The diagnostic model establishment unit can be used to obtain a standardized matrix through sample data, and the improved quantum particle swarm algorithm is used to train and adjust the system parameters of the adaptive fuzzy neural reasoning system according to the standardized matrix to establish a fault diagnosis model. The fault analysis unit can be used to input the preprocessed data into the fault diagnosis model for comparative analysis to form an analysis result. The classification unit can be used to classify the level of the fault according to the analysis result of the fault analysis unit. The alarm module can be used to send an alarm signal according to different fault levels and start a fault warning light corresponding to the fault level. In general, the present invention has the advantages of timely and accurate fault detection and efficient and convenient use.

[0015] 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 above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A power equipment fault diagnosis analysis and early warning system, comprising an operation monitoring component, a data collection module, a data preprocessing module, a storage module, a diagnosis analysis module and an alarm module, characterized in that: The operation monitoring component is composed of a plurality of video monitoring devices and a plurality of operation data detection devices, wherein the video monitoring devices are used to monitor the operation status and equipment temperature of the power equipment, and the operation data detection devices are used to detect and collect the data generated during the operation of the power equipment, so as to integrate and form the original data; The data collection module can uniformly collect the raw data detected and collected by the operation monitoring component; The data preprocessing module can perform preprocessing operations on the raw data collected by the data collection module, thereby filtering out abnormal data and error information generated by the transition state when the operation mode changes, filling in missing values ​​and normalizing the data to ensure the accuracy and consistency of the data; The storage module can store and back up the data after preprocessing; The diagnostic analysis module is internally provided with a diagnostic model building unit, a fault analysis unit and a classification unit, wherein the diagnostic model building unit can obtain a standardized matrix through sample data, and use an improved quantum particle swarm algorithm to train and adjust the system parameters of the adaptive fuzzy neural inference system according to the standardized matrix to establish a fault diagnosis model, and the fault analysis unit can input the pre-processed data into the fault diagnosis model for comparative analysis to form an analysis result; the classification unit can classify the level of the fault according to the analysis result of the fault analysis unit; The alarm module can, according to different fault levels, send out an alarm signal and start a fault warning light corresponding to the fault level.

2. The power equipment fault diagnosis analysis and early warning system according to claim 1, characterized in that: The operation monitoring component also includes a positioning device, which can be used to obtain the location information of the power equipment.

3. The power equipment fault diagnosis analysis and early warning system according to claim 1, characterized in that: The storage module is connected to a cloud platform and can upload data information to the cloud platform for storage and backup.

4. The power equipment fault diagnosis analysis and early warning system according to claim 1, characterized in that: The storage module is internally provided with a marking unit, and the marking unit can number the processed data according to time segments.

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