Investigating the significance of special measurement matrices for DMD based frequency estimation

Power systems are prone to failures such as power quality tripping, resonance, and CMV failures. These failures are infinite and dynamic because of the fluctuations in the power system impedance together with its load demand and harmonic dis-tortions. To have stability and control over the power system together with a quality power supply, it is imperative to monitor the fundamental frequency. Motivated by this, a data-driven approach based on dynamic mode decomposition (DMD) is proposed which is essential to observe parameters such as frequency and amplitude. This methodology works well for extracting fundamen-tal frequency under harmonics, sub-harmonics, and inter-harmonics scenarios in the signal which causes considerable damage to end-user equipment if not monitored properly. In this methodology, appended time-shifted power signals stacked into a measure-ment matrix are used for extracting the parameters from the signal. DMD relies on dynamics to capture information and predict frequencies. Measurement matrices play a crucial role in identifying the underlying dynamics of the system. Thus it creates a suitable measurement matrix and gives us input to the data-driven system which is vital for the system to perform well. This study investigates the importance of different special matrices such as Hankel, Toeplitz, and Circulant to create the measurement matrices in DMD to extract the signal parameters. DMD can replace conventional Fourier-based techniques by reducing the estimation error percentage. Various experiments are conducted to confirm the potentiality of the proposed methodology.