Stability Enhancement of Sulselrabar Electricity System Using Mayfly Algorithm Based on Static Var Compensator and Multi-Band Power System Stabilizer PSS2B

Abstract

An additional controller in an electric power system is currently required to increase the system stability, especially when a disturbance occurs. The stability of the multimachine system can be increased by installing a Static Var Compensator (SVC) and Power System Stabilizer (PSS). However, SVC and PSS equipment require precise coordination to determine the optimal location and parameters. This study presents an optimal analysis of SVC coordination with single-band PSS1A and multi-band PSS2B (MB-PSS2B) in the South, Southeast and West Sulawesi (Sulselrabar) electrical systems. An artificial intelligence method based on the Mayfly Optimization Algorithm (MOA) is proposed to optimize the location and parameters of the SVC and PSS. A comparative investigation related to controller parameter optimization from a previous work was used to measure the effectiveness of the MOA based on the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). Performance analysis using the time-domain simulation method to review the speed deviation response, field voltage response, and rotor angle response for each generator, as well as eigenvalue analysis for each control scheme when there is a change in the load disturbance on generators 1 and 13. The results show an increase in bus voltage from critical to marginal conditions and a decrease in network losses after installing SVC on bus 31 of 40 MW capacity. The application of MB-PSS2B based on the MOA provided an increased damping ratio, optimal speed response, rotor angle, field voltage generator, and eigenvalue system after installing 14 PSS.

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

“…PSS has coordinated with the other stability controllers, e.g. Power Oscillation Damper (POD) [12], Static Synchronous Compensator (STATCOM) [13], Static Synchronous Series Compensator (SSSC) [14], Static Var Compensator (SVC) [15], etc. Those work results have significant stability improvements in various power system models.…”

“…Nowadays, metaheuristic algorithms have rapidly developed with countless inspirations in the world and it has been implemented in modern power systems, including for controlling parameters in PSS or VI cases. For example, in [15], [27], the Mayfly Optimizer Algorithm (MOA) has been applied for PSS-SVC and PSS-SSSC. Quantum Artificial Gorilla Troop Optimizer (QGTO), Harris Hawk Optimizer (HHO), Equilibrium Optimizer (EO), and Arithmetic Optimizer (AO) have been implemented in parameter tuning of PSS in Single Machine Infinite Bus (SMIB) and Multi Machine systems [8], [28]- [30].…”

A Hybrid Controlling Parameters of Power System Stabilizer and Virtual Inertia Using Harris Hawk Optimizer in Interconnected Renewable Power Systems

“…Moreover, the RES energy transfer in interconnected power systems worsens this condition. In dynamic stability study, these kinds of disturbances must mitigated quickly with proper approaches [10]. If it is not, then the oscillations can spread throughout the areas and cause blackouts.…”

“…In more complex power systems, PSS is usually combined with other controllers to work complementary. For example, in [10], the controlling parameters of PSS with the Static Var Compensator (SVC) using the Mayfly Optimizer Algorithm (MOA) is implemented for Indonesia's real power system. In [14], PSS is coordinated with SVC and the other supplementary damping controllers using deep and reinforcement learning algorithms in High Voltage Direct Current (HVDC) power systems.…”

“…The next group is intelligent algorithms based on learning algorithms, like Machine Learning, Deep Learning, Reinforcement Learning, etc [14], [26], [35]. Besides that, the most popular group is intelligent optimizers based on metaheuristic algorithms, such as the Whale Optimizer Algorithm (WOA) [21], Mayfly Optimizer Algorithm (MOA) [10], Manta Ray Foraging Algorithm (MRFA) [36], Equilibrium Optimizer Algorithm (EOA) [37], Arithmetic Optimizer Algorithm (AOA) [8], Moth Flame Optimizer (MFO) [38], Rat Swarm Optimizer (RSO) [30], Harris Hawk Optimizer (HHO) [39], etc. Intelligent optimizers based on metaheuristic algorithms offer convenience and flexibility in exploring and exploiting the best results.…”