Machine Learning for Anomaly Detection in Electric Transportation Networks

¹ Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russian Federation
² Lovely Professional University, Phagwara, Punjab, India
³ Department of EEE, GRIET, Bachupally, Hyderabad, Telangana, India
⁴ Uttaranchal University, Dehradun 248007, India
⁵ Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh 174103 India
⁶ Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India

^* Corresponding author: plml@mail.ru
sandhyaranister@gmail.com
babitarawat@uumail.in
savinder.kaur.orp@chitkara.edu.in
jaspreet.sidhu.orp@chitkara.edu.in

Abstract

This study introduces a sophisticated anomaly detection system based on machine learning. The system is specifically developed to enhance the dependability and safeguard the security of electric transportation networks, with a particular emphasis on the charging infrastructure for electric vehicles (EVs). Utilizing extensive datasets, the research examines several facets of charging stations, charging records, identified abnormalities, and following maintenance measures. The examination of the charging station demonstrates the system’s versatility in accommodating many charging circumstances, as seen by the range of power ratings, consumption patterns, and energy provided. Further examination of charging records provides comprehensive understanding of individual charging sessions, enabling the detection of irregularities such as atypical energy surges and extended charging durations. The machine learning system, having been trained and verified using this data, has a commendable degree of precision in identifying anomalies, as shown by the congruence between anticipated abnormalities and real results. The maintenance and repair measures carried out in reaction to identified abnormalities highlight the practical ramifications of the system, with proactive tactics utilized to reduce downtime and enhance charging station operations. The performance measures, including accuracy, recall, and F1 score, unequivocally validate the resilience of the anomaly detection system, guaranteeing precise identification while mitigating the occurrence of false positives and negatives. The seamless incorporation of machine learning into electric transportation networks, as shown by the results, not only amplifies the dependability and safeguarding of EV charging infrastructure but also establishes the system as an invaluable instrument for practical implementations. The research, in addition to offering a thorough examination of the system’s performance, elucidates forthcoming avenues for scalability, real-time monitoring, and interpretability, thereby making a valuable contribution to the wider discussion on the revolutionary capabilities of machine learning in the ever-changing realm of electric transportation.