Abstract
This paper addresses a coordinated wind turbine equipped with a doubly fed induction generator, photovoltaic plant, and a fractional lead-lag stabilizer to enhance the low-frequency oscillation (LFO) in response to their effects on the power system under load conditions. To achieve better stability in the power system, the DFIG wind turbine and PV plant are equipped with a power oscillation damper (POD) in a practical form by single-input signal output. Although power system stabilizers (PSSs) are the first choice offered for multimachine power system stability, they cannot guarantee power system stability with different types of power plants and their interconnections. To get the high-speed response and the least steady-state error, the fractional-order lead-lag stabilizer is suggested. To save the initial price of equipping all generators with the stabilizer, only the best locations are selected using a probabilistic method formulated in the frequency domain. The optimal tuning of FL-L and POD variables is formulated by the optimization problem, and the developed version of particle swarm optimization is used to solve it. Finally, the suggested coordinated controller is implemented in the MATLAB tool. Moreover, the suggested controller is compared to other available controllers by several indices. The simulation results show that the proposed controller can provide acceptable performance in low frequency oscillation in large scale power systems. According to the simulation results, the damping ratio was modified from 0.1343 to 0.5773. It leads to a reduction in the settlement time from 3.65 s to 2.14 s. Also, the proposed modified optimization algorithm makes a faster and better global and local search compared to other optimization algorithms.
•A novel fractional stabilizer for power system stability.•Developed method significantly lowering the investment cost and gain profitability of the system.•A new probabilistic theory based on eigenvalue analysis to select the best locations for the stabilizer.•Implementation of Modified Multi-Objective Particle Swarm Optimization.