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Smart grids are the electrical grid with intelligent communication systems comprising conventional- and renewable-based power generation units along with storage systems that can monitor the power flow from generation to consumption and manage power flow to match the generation. This paper studies the frequency control of multi-area systems integrated with traditional and renewable power plants taking reliability and sustainability into account. Several nonlinearities like boiler dynamics and generation rate constant are also reflected in the simulation model. Further, a delay is introduced in the plant and controller formulation phase. To maintain stability, fractional calculus-based Proportional-Integral-derivative controller is formulated. The gain parameters of the fractional controller are optimized using the recent optimization algorithm. Furthermore, the chaotic-based function is also included in the existing Harris hawk optimizer to improve the optimizer's effectiveness. System stability is established with the fractional controller through the Bode plot and eigenvector analysis. Additionally, the system response is evaluated with the application of 2% step loading and time-varying random loading. The system's response through the suggested controller is verified using MATLAB/SIMULINK environment with various widely used controllers, and significant performance enhancement of peak overshoot, undershoot, and settling time are also observed. [ABSTRACT FROM AUTHOR]

Three-phase unbalanced system can be represented mathematically in terms of positive, zero, and negative sequence components. These components are really useful to provide information regarding power quality disturbances in an unbalanced system. Thus effective estimation of sequence components using appropriate signal processing models helps to detect short-duration disturbances like sag, swell, etc. The use of fractional-order calculus-based signal processing models has given a new dimension to estimate parameters and track non-stationary power quality events. The input matrix and weight vector are generated using trigonometric expansion considering the voltages across all the three phases. The magnitude and phase of the sequence components can be estimated from the optimal weight vector which is recursively updated using the proposed algorithm. In this study, the strength of fractional-order calculus is exploited using least mean square/fourth (LMS/F) to estimate sequence components in an unbalanced three-phase power system. The proposed fractional LMS/F (FLMS/F)-based model is tested using signals generated from MATLAB 2020 and IEEE 1159 PQE database. All the comparisons of results are made using standard performance measures, and also the convergence analysis of the algorithm is presented. [ABSTRACT FROM AUTHOR]

Summary: Accurate estimation and tracking of power quality (PQ) disturbances requires efficient adaptive model‐based techniques, which should have elegant structures to be applicable in practical systems. Though extended Kalman filter (EKF) has been used as a popular estimator to track the time‐varying PQ events, the performance is limited due to higher‐order nonlinearity exists in system dynamics. Moreover, the computational complexity and sensitivity to the measurement noise affect the estimation accuracy and error convergence properties. If the underlying sparse behavior of adaptive filters can be exploited through norm penalty, then significant improvement can be achieved in terms of convergence speed, stability, and steady state excess mean square error performance. In this paper, a modification to EKF is proposed by using sparse model to estimate PQ disturbances with better estimation accuracy and faster convergence speed. A thorough comparison is presented in terms of simulation results by using a family of existing algorithms like least mean square, recursive least square, and EKF. Adaptive estimation model incorporates real and complex state space representations to represent discrete time PQ disturbances. The convergence of the proposed sparse model–based filter is also mathematically proved considering the mean square error performance. [ABSTRACT FROM AUTHOR]

Correct estimation of harmonics and sequence components is essential to monitor the overall performance of three phase power systems. Least mean square (LMS) based adaptive models have simpler structures to estimate the required parameters for a given power quality (PQ) event. However, these models are not very much effective in case of time-varying and short duration disturbances. Least mean fourth (LMF) is comparatively a better approach than LMS to deal with different PQ problems, but high computational complexity limits the filter performance in case of practical applications. LMS/fourth (LMS/F) filter is a compromise between LMS and LMF to improve the error convergence properties. The proposed model explained in this paper is based on the concept of LMS/F with Volterra expansions of input samples. The proposed Volterrra LMS/F filter is tested in case of distorted power signals to estimate harmonics, decaying dc, and sequence components in presence of white Gaussian noise. The detail comparison results are presented using MATLAB simulations to verify the performance of the adaptive model. [ABSTRACT FROM AUTHOR]