RECOGNIZING A TRANSFORMER OIL GAUGE IN AN IMAGE USING A PRE-TRAINED MOBILENETV2 FPN LITE MODEL
Authors: Petrov Kirill A. , Ivanova Nadezhda N. , Solovev Sergei A. 
Published: December 2024

Oil gauges allow you to monitor the amount of oil in transformers, which is necessary to prevent its overheating and related negative consequences, such as deterioration of the insulating and cooling properties of the oil, increase in pressure inside the device and possible accidents. Most of the oil pointers do not have an electric output to indicate the measurement results, which complicates the process of reading the readings of the device. One of the options for solving this problem may be the use of intelligent methods of image analysis, which have recently been actively used in many scientific and practical fields.

The purpose of the study was to develop and evaluate the effectiveness of an intelligent system for determining the presence of an arrow transformer oil gauge in an image using a pre-trained MobileNetV2 FPN Lite model.

Materials and methods. In the course of the study, theoretical and empirical methods were used. The study considers the pointer oil gauges of the MS-1 and MS-2 models. To develop a machine learning model for image recognition, the Python language was chosen, the open-source TensorFlow Object Detection API platform, which is part of TensorFlow, was used, which implements the creation, training, and testing of neural network object detection models.

Research results. The process of generating a training set using augmentation methods and the process of data labeling are described. The process of selecting hyperparameters for model training was considered and analyzed. The hyperparameter “number of neurons” was used with the same value as in the pre-trained model. ReLU was chosen as the activation function, and the momentum optimization method was used. The batch size was 4, and the number of epochs was assumed to be 2000. The Accuracy, Recall, Precision, and F1-score metrics were calculated, and the quality and efficiency of the resulting model were evaluated based on them. The model did a good job of avoiding false positives, which confirms the high value of the Precision = 1.0 metric. However, it incorrectly recognized a significant number of non-oil gauge objects, as reflected by the low Recall = 0.687 value. The reason for such indicators of the Recall and F1-score metrics is the imbalance of data for training. To eliminate this drawback in further research, it is necessary to prepare a larger number of unique images of this device, displaying it at different angles of inclination, in different weather conditions, in different lighting.

Conclusions. A model of an intelligent system for determining the presence of a transformer oil gauge in an image using a pre-trained MobileNetV2 FPN Lite model has been built. The model was trained to determine the device and its boundaries on the image, but the accuracy of the model is insufficient for its use in real conditions. Increasing the training, validation and test sample size can solve this problem. In general, the proposed method of recognizing transformer oil indicator on the image has shown its performance.