A data-driven technique for harmonics monitoring in emerging power grids using noise-aware dynamic mode decomposition

Abstract

A future power grid should be more robust, efficient, renewable, stable, reconfigurable, resilient, and distributed with more advanced control, protection and security schemes. It will combine a myriad of technologies such as information, communication, and power system engineering with computational intelligence. Because of the high proliferation of renewable energy sources, distributed generation systems and non-linear loads, grids are affected by several distortions and issues related to quality, stability, and control. Harmonics monitoring is a primary task in power grids for their safe and stable operation. It is essential for the protection and control of microgrid systems. Detection of harmonics, inter-harmonics and sub-harmonics improves the quality supply of power and protects the consumer equipments from failures. This paper investigates the effectiveness of a noise-aware dynamic mode decomposition algorithm, namely total-dynamic mode decomposition (TDMD), for harmonics monitoring in power grids. The ability of the TDMD algorithm to extract the hidden dynamic characteristics of time-series data is exploited for harmonics identification and its analysis. In the proposed method, multiple time-shifted copies of measured power signals are appended to create the initial data matrices. A singular value decomposition-based hard-thresholding is performed to avoid the ambiguities in the measured signal. Further, the eigendecomposition is performed using the TDMD algorithm and the corresponding frequencies and amplitudes are estimated. The performance advantage of the proposed method is verified by conducting several experiments using simulated and field measurements. The satisfactory performance certifies the practical applications of the proposed method for harmonics monitoring in emerging power grids.