Artificial Intelligence-Based Power System Stabilizers for Frequency Stability Enhancement in Multi-Machine Power Systems

Abstract

Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superior damping. To address this issue, a Farmland Fertility Algorithm (FFA-PSSs controller) was used to solve an optimization problem for optimal design of PSSs system parameters, and its performance efficiency was compared to GA and PSO-based PSSs controllers. In addition to PSS, flexible current transmission (FACTS) devices are widely used. PSSs controllers and FACTS devices are frequently constructed in tandem to improve the dampening efficiency of the system. In this study, an Interline Power Flow Controller (IPFC) FACTS device will be added to the PSSs controller to improve the power system's oscillatory stability. PSSs optimal design and supplemental controller of power fluctuations for IPFC were conducted out on WSCC multi-machine test systems using a linear system model. Using time-domain simulations and quantitative analysis, the proposed IPFC model was compared to the FFA-PSSs controller in terms of performance and efficiency. The main disadvantage of this technique is the difficulty in designing a dynamic IPFC model in test systems, as well as the burden of IPFC coordinated PSSs optimization. In both PSSs design using FFA method and FFA-optimized PSS with IPFC cases, rise in the computational and simulation costs was found unavoidable. To compensate for these flaws and obtain the research contribution, this paper proposes a Neuro-Fuzzy Controller (NFC) developed as a damping controller that can take the place of the two controllers (research objectives three). The application of the NFC substitutes the computational and simulation cost involved in designing multi-machine PSS and IPFC-FACTS systems simultaneously. With the availability of NFC in SIMULINK, a dynamic model of the WSCC three-machine system was developed under a variety of operating situations. Quantitative analysis results from the WSCC test system simulation show that when comparing the proposed NFC model to the IPFC model for the WSCC test system, the proposed NFC model was found to be 149 percent and 0 percent efficient in terms of the time to settle of rotor angle respond for G2 and G3, respectively, but 394 percent efficient when compared to the uncontrolled model. The decreased settling time values ensured the proposed NFC model's efficacy in damping down the LFO and achieving superior stability over the two controllers.

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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].…”

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