Anomaly-detection-based learning for real-time data processing in non-intrusive load monitoring

Abstract

A power system can be regarded as a cyber-physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non-intrusive load monitoring (NILM), an emerging smart-grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection-based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber-attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection-based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real-time assessment of power- loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.