Optimal tuning of fractional‐order proportional, integral, derivative and tilt‐integral‐derivative based power system stabilizers using Runge Kutta optimizer

Abstract

Low-frequency power system oscillation is of great concern as it may lead to power system instability. Moreover, this action will lead to the abate capability of electric power transfer. By introducing a stabilizing signal into the excitation system, it was possible to improve the damping in the system. The power system stabilizer (PSS) provides this signal. This manuscript aims to find the optimal tuning of three different PSS controllers using a recent optimization algorithm called Runge Kutta optimizer (RUN). Based on the obtained results, the RUN shows superiority and fast convergence over competitive algorithms. In addition, the fractional-order proportional, integral, derivative (FOPID) and tilt-integral-derivative (TID) based PSS shows great damping capability over the lead-lag, PI, and FOPID-based PSS. Also, the TID-based PSS achieves better performance indices in terms of the lowest maximum overshoot and minimum settling time. Hence, it is recommended for PSS based controller.

Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

“…Overcoming these shortcomings as well as getting along with the merits of using the metaheuristic optimization algorithms like escaping from local optima, the PSS can be optimally tuned efficiently. Recently, a significant number of such algorithms published like, Chaotic Particle Swarm Optimization [6], whale optimization algorithm [7], enhanced whale optimization algorithm [8], improved Moth flame optimization [9], An antlion optimization [10], Slime Mould Algorithm [11], Coyote Optimization Algorithm [12], Henry Gas Solubility Algorithm [13], collective decision algorithm [14], Particle Swarm Optimization [15], Cuckoo Search Algorithm [16], Salp Swarm Algorithm [17], hybrid dynamic GA-PSO algorithm [18], atom search algorithm [19], Runge Kutta optimizer [20], Genetic Algorithms [21], kidney-inspired algorithm [22], modified harmonic search algorithm [23], sine cosine algorithm [24], Harmony Search [25], farmland fertility algorithm [26]- [29], Bat Algorithm [30], Honey Bee Mating Optimization [31], Jaya Algorithm [32], [33], Grey Wolf Optimizer [34], Backtracking Search Algorithm [35], Grasshopper Optimization Approach [36], Rat Swarm Optimization [37]. Harris Hawk Optimizer [38].…”

“…Assessment of the effectiveness of QGTO to optimally tune investigated PSS parameters will be achieved with the help of SMIB embedded with PSS. The SMIB model data can be attained from [20]. The system response will be studied while there is a change of 0.1 p.u in 𝑇 𝑚 .…”

Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study

“…The mechanism of the RUN is depicted in Figure 7. The RUN has been employed in many applications, such as (a) reconfiguring the partially shaded PV array to maximize its generated power [53], (b) enhancing the reliability of the power system and increasing renewables penetration [54], and (c) finding the optimal tuning of different power system stabilizer controllers [55].…”

A Complete Affordable Control System for Remote Astronomical Observing Accompanied by an Intelligent Controller

“…FO concept is based on the expansion of integer order integration and differentiation to a fractional order operator. FO calculus-based controllers such as TID are utilized frequently due to their improved capacity to reject disturbances and higher sensitivity to changes in model parameters [58] [59]. TID controller is similar to the PID structure; however, the proportional component is replaced by a tilted component 1/s 1/n to produce a transfer function described in Eq.…”