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The widespread integration of new technologies in low-voltage distributionnetworks on the consumer side creates the need for distribution systemoperators to perform advanced real-time calculations to estimate networkconditions. In recent years, data-driven models based on machine learning andbig data analysis have emerged for calculation purposes, leveraging theinformation available in large datasets obtained from smart meters and otheradvanced measurement infrastructure. However, existing data-driven algorithmsdo not take into account the quality of data collected from smart meters. Theylack built-in anomaly detection mechanisms and fail to differentiate anomaliesbased on whether the value or context of anomalous data instances deviates fromthe norm. This paper focuses on methods for detecting and mitigating the impactof anomalies on the consumption of active and reactive power datasets. Itproposes an anomaly detection framework based on the Isolation Forest machinelearning algorithm and Fast Fourier Transform filtering that works in both thetime and frequency domain and is unaffected by point anomalies or contextualanomalies of the power consumption data. The importance of integrating anomalydetection methods is demonstrated in the analysis important for distributionnetworks with a high share of smart meters.

Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies