Research On Transformer Oil Level Monitoring System Based On Image Recognition

The development of automation technology has brought new innovations to substations,and the State Grid is also following the progress of science and technology towards automation and unmanned operations.Traditional oil-level transformers are monitored by regular inspections by inspectors.This method is inefficient and prone to "false inspections" and "missing inspections".At the same time,the transformer information provided by manual inspections is lagging and inconvenient.Management,unable to understand and grasp the oil level status of the transformer in time.It is not economical to replace the transformer type in a short time.In order to adapt to the development of intelligence,adapt to the oil level gauge type of the existing transformer,and expand the intelligent business on this basis.This paper studies and proposes an oil level monitoring system based on image recognition,using image recognition technology and cloud platform technology,based on the embedded terminal to collect,process,recognize and transmit the image of the oil level gauge,and use communication technology to transfer the collected image information With the oil level information transmitted to the cloud for monitoring,the cloud design provides a visual human-computer interaction interface to realize remote online monitoring of the transformer oil level.The specific research is as follows:First,according to the functional requirements of the transformer oil level monitoring system,the main functional requirements of the oil level monitoring system are analyzed from the four perspectives of equipment early warning,remote monitoring,data statistics and equipment cost performance,and determined from the three perspectives of terminal,cloud and communication layer.The basic functions and framework scheme of the system ensure the cost-effectiveness and robustness of the system.Second,in view of the difference in recognition effect between normal illumination and haze weather,the image recognition algorithm is introduced from three aspects: oil level image preprocessing,oil level image defogging algorithm and oil level feature extraction.Aiming at the problems that the oil level area cannot be identified or the recognition effect is poor under foggy conditions,an oil level image defogging algorithm is proposed.In addition,combined with the problem of the edge halo effect in the restored image of the He algorithm,a new method based on improved guided filtering is proposed.The image defogging algorithm solves the local halo effect by adopting the quad-tree algorithm and introducing the weight factor of the Canny operator.Finally,the oil level area image is extracted through color space segmentation and morphological processing.Third,the cloud platform design selects Apache + My SQL + PHP combined with the Think PHP web framework of MVC architecture for development.Through the monitoring signal port,the received data information is stored in the database,and the deployment of the WEB interface function is completed.Finally,the terminal subsystem and the cloud subsystem were tested separately,and the system was tested for accuracy.The results showed that the expected results were achieved and the feasibility of the system functions were verified.