A novelty detection method for efficient data storage in smart grids.

This work presents a new novelty detection method for efficiently storing electrical power system data. The proposed method makes innovative use of the Cycle-by-Cycle Difference (CCD) and the Mathematical Morphology (MM) techniques to identify novelties in the voltage of an electrical power system. Once the novelties are identified, the frames where they occur are extracted from the signal, and the other frames are discarded. Among the extracted frames, those containing power quality (PQ) disturbances are fully stored, while the frames that represent stationary states have only their main parameters stored, contributing to efficient storage. The proposed method exhibited low computational complexity in the operational phase, which was evaluated via a field-programmable gate array (FPGA) platform implementation using a customized embedded processor. The method was evaluated with real and simulated signals. The results indicate that the developed method attained high detection probabilities alongside low false alarm probabilities, resulting in a competitive Area Under the Curve (AUC) exceeding 0.976 (except for interharmonics). Comparison with other methods revealed that our approach yielded competitive results with the lowest computational cost. We concluded that the proposed method may be useful for monitoring PQ disturbances in smart grids, where the data volumes are too large. • A new novelty detection method for Power Quality disturbances was proposed. • It may assist compression methods by revealing the novelty portions of each signal. • Unlike the methods based on machine learning, it does not require training stage. • It presents low computational complexity in both design and operational phases. • It is able to capture unknown deviations in the signals caused by the complex smart grids. [ABSTRACT FROM AUTHOR]