Smart meter for the estimation of power quality indices based on second-order generalized integrator

With the increasing integration of renewable energy sources and nonlinear loads in the grid, it is necessary to compute energy and power quality parameters for monitoring as well as analyzing the power consumption indices (PCI) and power quality indices (PQI). Smart meters (SMs) can monitor the PCI and PQI at the consumer end. SM necessitates the measurement of voltage and current, which can be further processed by an algorithm for estimating different parameters through signal processing and arithmetic operations. This paper proposes the combined dual second-order generalized integrator (SOGI) and proportionate least mean square (PLMS) algorithm-based PCI and PQI estimation for smart metering. Dual SOGIs individually process voltage and current signals to extract the respective fundamental in-phase and quadrature components. To account the variations in grid frequency, the SOGI processing the voltage also incorporates a frequency locked loop. This prevents performance degradation on account of frequency variations. The PLMS algorithm then process the output of SOGI processing the current for extracting the fundamental active and reactive components of current. Moreover, the PLMS algorithm results in further attenuation of harmonics. Also, only two separate values of learning rate are required, which is easy to tune for better dynamic performance. With only two quantities to be determined, this results in largely reduced computation. These computations facilitate in PCI and PQI computations. The proposed smart meter calculates peak value, RMS value, and phase angle for the fundamental components of voltage and current for each phase. Additionally, it assesses fundamental power factor, total harmonic distortion (THD), distortion factor, true power factor, active power, reactive power, and apparent power for each phase. The performance validation of the proposed combined dual-SOGI-PLMS algorithm-based PCI and PQI estimation for smart metering is carried out in MATLAB/SIMULINK for fifteen different operating scenarios. Further, the real-time implementation of the proposed methodology is carried out on dSPACE MicroLab Box 1202. Comparative analysis is also presented, which reveals the computational simplicity and other merits of the proposed scheme over the earlier reported scheme.