Abstract
In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO.
Highlights
Energy demand is increasing gradually due to the growing population
It is already mentioned that this paper proposed the particle swarm optimization (PSO)-adaptive neuro-fuzzy inference system (ANFIS) models that were applied to two different electric networks to estimate the power system stabilizer (PSS) key parameters in real-time
This paper proposed the PSO optimized ANFIS models for real-time tuning of the PSS
Summary
Energy demand is increasing gradually due to the growing population. To meet this increased energy demand, modern power systems are typically operated at their highest capacity. Several artificial intelligence techniques were implemented previously to optimize the PSS parameters coordinated with/without FACTS devices, which include the water cycle algorithm (WCA) [21], genetic algorithm (GA) [22], backtracking search algorithm (BSA) [23,24], particle swarm optimization (PSO) [25], and differential evolution (DE) [26]. Real-time based parameter settings can be achieved by employing another highly efficient machine learning approach, PSO tuned adaptive neuro-fuzzy inference system (ANFIS) to improve the overall system stability. Two versions of SMIB electric networks were considered to demonstrate the proposed approach of LFO mitigation For both the networks, optimized PSS parameters were found offline for a large number of operating conditions using a heuristic optimization technique.
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