Transient stability enhancement using optimized PI tuning of static synchronous series compensator in wind power conversion system

Abstract

Power systems are expanding comprehensively with the increase in load demand from both residential and industrial usage. Renewable energy is penetrating the power system to satisfy the power needs of the load demand. With its potential to generate power and compensate for a large portion of the load demand, wind generators make a major renewable power contribution. Power oscillations inherent in wind generator integration with the grid are a power quality issue to be addressed. Oscillation damping using flexible AC transmission system (FACTS) devices is a relevant solution for the power quality issue. There are multiple reasons for power oscillation. Mainly, power systems encounter fault conditions. The faults can be cleared, and the power system tries to retain stability. Sometimes, the system fails due to a longer settling time. A series-connected FACTS device utilized as a series compensator is referred to as a static synchronous series compensator (SSSC). Controlling the flow of electricity over a transmission line using this method is incredibly efficient. The capacity to switch between a capacitive and an inductive reactance characteristic is necessary. The SSSC regulates the flow of power in transmission lines to which it is linked by adjusting both the magnitude of the injected voltage and the phase angle of the injected voltage in series with the transmission line. This allows the SSSC to manage the power flow in the transmission lines. It does it by inserting a voltage that can be controlled into a transmission line in series with the fundamental frequency. This paper develops the optimally tuned SSSC in the wind-integrated grid system to dampen the oscillation. Teacher–learner-based optimization (TLBO) and gray wolf optimization (GWO) algorithms are used to tune the PI controller to improve the damping response. The obtained results show that the damping performance of the proposed controller is better than that of the other traditional controllers.

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

“…13, No. 2, June 2024: 453-464 454 (MPPT) for different wind speeds, thus ensuring maximum power extraction [5], [6]. However, variations in wind speed can cause fluctuations in turbine rotor speed, necessitating effective MPPT algorithms to find the optimal power point and enhance overall system performance [7].…”