Hanene Medhaffar

The increase in load demand has a negative impact on power system quality, reliability, and stability. Transporting reactive power from the generating side to the distribution side reduces the transmission line’s capacity to deliver active power to loads. In conventional power systems, during faults or sudden loads, the load flow changes and causes extra voltage drop. Reactive power compensation at the load side can improve voltage quality at distribution busbars, but the conventional compensation method doesn’t offer the required flexibility. With the development of the power electronics industry, flexible alternating current transmission system (FACTS) devices have been introduced. There are many categories for FACTS controllers, including series, shunt, and hybrid, which can exhibit dynamic operating characteristics. In this paper, two types of FACTS devices (STATCOM and fixed capacitor-thyristor-controlled reactors (FCTCR)) have been used to improve voltage profile in a part of Iraq’s high voltage power system during unusual operating conditions, such as sudden loads and the absence generation. Load flow calculations were conducted to determine the critical bus, and then the performance of the FACTS devices was investigated during heavy loading when the devices were connected separately to the critical bus. The voltage drop in the bus was 1.58%, which reduced to 0.78% with STATCOM and 0.96% with FCTCR.

Fractional order Controllers have been used in several industrial cases to achieve better performance of the systems. This paper proposes a Fractional Order Proportional Integral Derivative (FOPID) controller. It is synthesized using Oustaloup approximation, and its parameters are tuned using the Genetic Algorithm (GA) optimization method. The aim is to minimize the error, the energy and the startup torques using two objective functions to improve the control performances and the robustness. The validity of the proposed controller is shown via simulation by controlling a two-link exoskeleton for children's gait rehabilitation, and the results are compared to an Integer order PID (IOPID) controller. Simulation results clearly indicate the superiority of the optimized FOPID in terms of trajectory tracking and the used torques. Moreover, the FOPID controller is tested with parameter uncertainties. Its robustness is proven against thigh and shank masses variation. Both controllers are simulated under the same frequency conditions using Simulink MATLAB R2018a.

This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output‐error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input‐output data measurements. In this work, the need for the commonly used assumptions including well‐known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy‐type stochastic output‐error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi‐innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi‐innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi‐innovation‐based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.

This paper presents a fuzzy second-order sliding mode controller for a two-cell DC-DC converter. For this aim, a second-order sliding mode controller and a type-2 fuzzy system are combined to achieve an adequate control. For this reason, backgrounds on the type-2 fuzzy sets and on the second-order sliding mode control applied to discrete systems have been presented briefly. A proposed control algorithm is then presented combining these two robust approaches. The asymptotic stability of the overall controlled system has been ensured using the Lyapunov theory. The efficiency and the robustness of the proposed controller have been tested by simulations.