Time and Frequency Domain-based Anomaly Detection in Smart Meter Data for Distribution Network Studies
This addresses the need for reliable data-driven models for distribution system operators dealing with high shares of smart meters, though it is incremental as it builds on existing methods.
The paper tackled the problem of data quality in smart meter data for distribution networks by proposing an anomaly detection framework combining Isolation Forest and Fast Fourier Transform filtering, which effectively handles both point and contextual anomalies without being affected by them.
The widespread integration of new technologies in low-voltage distribution networks on the consumer side creates the need for distribution system operators to perform advanced real-time calculations to estimate network conditions. In recent years, data-driven models based on machine learning and big data analysis have emerged for calculation purposes, leveraging the information available in large datasets obtained from smart meters and other advanced measurement infrastructure. However, existing data-driven algorithms do not take into account the quality of data collected from smart meters. They lack built-in anomaly detection mechanisms and fail to differentiate anomalies based on whether the value or context of anomalous data instances deviates from the norm. This paper focuses on methods for detecting and mitigating the impact of anomalies on the consumption of active and reactive power datasets. It proposes an anomaly detection framework based on the Isolation Forest machine learning algorithm and Fast Fourier Transform filtering that works in both the time and frequency domain and is unaffected by point anomalies or contextual anomalies of the power consumption data. The importance of integrating anomaly detection methods is demonstrated in the analysis important for distribution networks with a high share of smart meters.