Real-time detection of electrical load anomalies through hyperdimensional computing

Load anomalies in power distribution systems are relatively rare, yielding imbalanced classification datasets. Consequently, traditional artificial intelligence approaches for detecting load anomalies tend to be relatively inaccurate or rely heavily on pre-processing optimization. Reasoning that hyperdimensional computing (HDC) lends itself naturally to classification with imbalanced datasets, we present an HDC-based method for detecting anomalies accurately and in real time using raw meter data without prior optimization. An associative memory is used to classify the hypervectors, providing resilience against data imbalance. Moreover, a novel retraining function is designed to further improve classification accuracy. The proposed method is evaluated using real-world datasets from two sources with significantly different characteristics—a US university campus that relies on state-of-the-art power monitoring and uses mostly non-renewable energy sources, and a rural village in Tanzania that relies on wireless monitoring and uses 100% solar energy. In comparison with traditional AI approaches, it achieves consistently higher accuracy and runtime efficiency on both datasets and under a wide variety of evaluation metrics. To the best of our knowledge, this work is the first investigation of HDC for anomaly detection in smart grids. Our results show that HDC-based methods have considerable potential for accurate real-time detection of load anomalies.