부산대학교 도서관

Phase identification is the problem of determining the phase connection of loads in a power distribution system. In modern times, utility operators will generally accomplish this using smart meter data that requires some form of feature engineering to achieve practical phase identification using data-driven methods. Feature engineering is essential for voltage magnitude data containing noise, seasonality, and trend. We present crucial components of a feature engineering pipeline to perform linear denoising with Singular Value Decomposition, filtering of the denoised data to remove the seasonality and trend, and fuse multiple meter channels. We use the results of the feature engineering to perform phase label correction, a subproblem of phase identification. To evaluate techniques, the authors generate a synthetic dataset from the meshed IEEE 342-Node test feeder circuit with the 2021 Electric Reliability Council of Texas load profiles. Our results show that denoising is quite effective for improving phase identification accuracy in the presence of measurement noise. We present new insight into filtering voltage measurement data to improve accuracy and eliminate the need to determine salient frequencies. We also present the application of a data channel fusion technique that is novel to the phase identification literature. This technique enhances phase identification in cases where both wye and delta-connected loads are present.