Power grid monitoring based on Machine Learning and Deep Learning techniques

Background: In this work, some application examples of machine learning and deep learning techniques in the monitoring of electricity distribution services and infrastructures are proposed. Three different fields of application are considered to highlight the use of techniques based on neural networks: detection and classification of power quality disturbances, monitoring of underground cables in medium voltage lines and diagnosis of joints in high voltage overhead power lines. Methods: In the field of power grid monitoring, this work proposes a classification method based on a complex valued neural network to assess working conditions of junction regions in high-voltage overhead lines and insulating materials in medium voltage underground networks. The purpose of this method is to prevent the rupture of joint structures and the abnormal degradation of underground cables via frequency response measurements. This approach allows the direct processing of complex measurements and to reduce the computational effort compared to other methods available in the literature. Results: The results obtained in the monitoring of underground cables and joints of high voltage lines guarantee an overall classification rate higher than 90%. In the field of power quality, several deep learning and machine learning methods are proposed to detect the most common voltage disturbances. Conclusions: In this paper, an innovative use of widespread algorithms such as convolutional neural networks is proposed with excellent results. Furthermore, the use of a complex-valued neural network in electrical infrastructure monitoring is presented, introducing a minimally invasive classification method that could be instrumental in the transition from corrective to predictive maintenance in the near future.