Adaptive Power System Stabilizer Using Kernel Extreme Learning Machine

A disturbance such as a small load change in the electrical power grid triggers the system to oscillate and may lead to performance degradation of the control system in the generation source unit. To overcome this problem, a power system stabilizer (PSS) is added as a supplementary control in the power system grid to damp the oscillation caused by the disturbance. This paper proposes a kernel extreme learning machine (K-ELM) method to adjust the parameters of PSS for a wide range of operating conditions. The proposed control scheme in this research work is PSS based on K-ELM called KELM-PSS. To examine the robustness of KELM-PSS, a single machine infinite bus (SMIB) is utilized as a test system. The simulation results showed that the KELM-PSS provided a satisfactory result in both the training and testing phases. In the training process, KELM-PSS has a smaller mean square error (MSE) value, mean absolute error (MAE) value, and sum square error (SSE) value in terms of accuracy criterion compared to PSS based on machine learning such as extreme learning machine (ELM), support vector machine (SVM), and least square support vector machine (LS-SVM). Also, in terms of computation time, KELM-PSS has faster CPU time than other machine learning methods to obtain the parameters of PSS. The parameters of K-ELM including the regularization coefficient Cr and kernel parameter σ obtained from the training phase are utilized in the testing process. In a testing phase, the K-ELM approach will obtain the appropriate parameters of PSS (Kstab, T1, and T2) when there are changes of generator real power output P, reactive power output Q, terminal voltage Vt, and equivalent reactance Xe. In this stage, KELM-PSS could decrease the overshoot and compress the settling time better than other approaches utilized in this study. Moreover, KELM-PSS has the best performance index based on integral time absolute error (ITAE) compared to other techniques utilized in this research work.