Optimal Tuning of Single Input Power System Stabilizer Using Quasi-Oppositional Butterfly Optimization Algorithm

Optimization Techniques for Hybrid Power Systems

1. Introduction

A power plant often has a lot of synchronous generators installed to control the energy at the contact spontaneously. The Power network component, which has the innate potential to increase power system cohesion limits while maintaining high electrical Power Strength (PS), is seen to be a useful tool in many Distributed Generation (DG) applications. The scale and complexity of modern power systems are constantly growing, which increases the need for a reduced oscillations damping in the power network stabiliser. The low magnitude and low frequency often prolong in some scenarios even restricting the power transfer capacity. As a result, low-frequency oscillation or electro-mechanical oscillations (EMO) (Leonard. 2006) conventionally takes place in large power systems and occasionally make the feeding system unstable. Also, the incorporation of AVR used to improve the transient stability during fault also contribute to the reduction in damping of the power system owing to its high gain and fast-acting effect. Power system stabiliser (PSS) is utilised in the auxiliary feedback to add additional braking to the system to dampen these a reduced oscillations on the rotor in order to mitigate the negative impacts of the AVR.

The lead-lag compensators, also known as the PSS or conventional PSS (CPSS), are made up of gain stage K, a high pass filter, and temporal constants T1 through T4. To enhance the system damping, these parameters must be fine-tuned at a specific set of operating circumstances, often nominal. The operational environment is continually changing because of how highly nonlinear the power system is. As a result, it may be necessary to fine-tune the CPSS’s parameters if they do not give an appropriate performance. (Ferber et. al. 1968; Graham, 2012) details the impact of excitation and PSS. Several academics have worked to improve the PSS parameter tweaking methods. Yet, because of the non-linearity and multi-modality features, modelling the PSS is a difficult endeavour.

After changing system operating conditions, the PSS may not provide satisfactory damping in an unstable system. Several academics have worked to improve the PSS parameter tuning methods. These solutions include robust control (Gupta et. al. 2006; Sambariya et al. 2013), optimization strategies (Magzoub et al. 2014; Mariano et al. 2016) artificial intelligence techniques like neuro-fuzzy (Eke et al. 2015; Segal et al. 2004) and fuzzy logic (Bhati et al. 2013; Hiyama et al. 1999). These methods demand a lot of calculation time because they are complicated and use numerous particles in the optimization process.