Adaptive current harmonic estimation under fault conditions for smart grid systems

This paper presents an adaptive harmonic disturbances detection under fault conditions in a smart grid (SG) system. Monitoring and controlling SG networks require real-time measuring. This measuring demands high-speed, fully integrated, and two-way communication technologies.On a fault condition, the online accurate monitoring becomes difficult due to the transient current occurrences requiring adaptive methods to achieve SG requirements. In this work an adaptive technique to reliable monitoring harmonic distortion in the power network has been proposed. Harmonic estimation is implemented by utilizing the least square (LS), Kalman filter (KF), Maximum Likelihood estimation (MLE), and Goertzel Algorithm to obtain the amplitude of the harmonics in the network and its variation under dynamic fault conditions. The tested smart micro-grid (SmG) includes wind turbines (WT) and photovoltaic (PV) panels with time-variable loads at a medium voltage level. Consequently, the total demand distortion (TDD) estimations using the filtering estimators are also compared with the predictions obtained by the conservative power theory (Tenti) accordingly. Finally, a performance-complexity tradeoff analysis demonstrated the (dis)advantages of each filtering method.