Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution system. However, real-world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data-driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system are not overloaded, which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We present a feature engineering pipeline that combines Singular Value Decomposition (SVD) with an Interquartile Range (IQR)-based nonlinear filter to denoise matrices of voltage magnitude measurements. We empirically show that this pipeline is effective in denoising data that contains either additive Gaussian, Laplacian, Cauchy, or mixed Gaussian/Cauchy noise. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of non-stationary time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.