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All Journal Bulletin of Electrical Engineering and Informatics Control Systems and Optimization Letters
Jasmine, Senit Araminta
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Enhancement of frequency transient response using fuzzy-PID controller considering high penetration of doubly fed induction generators Abdillah, Muhammad; Solehan, Alfi; Pertiwi, Nita Indriani; Setiadi, Herlambang; Jasmine, Senit Araminta; Afif, Yusrizal; Delfianti, Rezi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6481

Abstract

In modern power systems, renewable-based power plant such as wind power system is integrated significantly. Among numerous types of wind power systems doubly fed induction generators (DFIG) is becoming favorable in the last few years. However, adding a wind power plant could give a new challenge to the power system, especially in frequency stability. Hence, it is important to control the frequency of the power system to be able to find its initial condition in every condition. Generally, the frequency of the power system can be controlled by using automatic generation control (AGC). AGC is used to maintain the balance between generating capacity and the load by adding integral control to the governor. However, with more and more wind power systems in the grid conventional AGC is unsuitable. Hence, it is important to have an advanced AGC based on the artificial intelligence method. This paper proposed the application of fuzzy-proportional integrator derivative (fuzzy-PID) for AGC in power systems considering the high penetration of wind power systems. From the simulation results, it is found that the proposed method can reduce the overshoot and accelerate the settling time of frequency better than using conventional AGC.
Quantum-Behaved Particle Swarm Optimization-Tuned PI Controller of a SEPIC Converter Perkasa, Sigit Dani; Megantoro, Prisma; Jasmine, Senit Araminta
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.186

Abstract

The Single-Ended Primary Inductor Converter (SEPIC) is vital for voltage regulation in dynamic systems like renewable energy and electric vehicles. Traditional PI controllers struggle with tuning complexity and oscillations. This study introduces Quantum-Behaved Particle Swarm Optimization (QPSO) to optimize PI gains (Kp, Ki) for SEPIC converters. QPSO improves global search by using quantum-inspired probabilistic motion, overcoming issues of premature convergence seen in traditional PSO. Four objective functions—ISE, ITAE, IAE, and MSE—were evaluated to balance transient and steady-state performance. ITAE and IAE outperformed others, minimizing overshoot to 1.26% in boost mode and achieving the fastest settling time of 1,872 s. Sensitivity analysis revealed that Ki 2.0 destabilizes the system, while Kp 1.5 increases voltage ripples. The framework is computationally efficient, ideal for embedded applications. Future work should include hardware-in-loop testing to confirm robustness.
Refined Velocity–Position Dynamics in Particle Swarm Optimization: A Survey of Recent Mathematical Innovations Perkasa, Sigit Dani; Jasmine, Senit Araminta
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.203

Abstract

Particle Swarm Optimization (PSO) remains a pivotal metaheuristic for complex optimization, yet its canonical form faces persistent challenges, including premature convergence and inefficacy in dynamic or high-dimensional landscapes. This survey examines recent advancements in refining PSO’s velocity-position dynamics, emphasizing adaptive mechanisms that enhance exploration-exploitation balance, ensure stability in noisy measurement environments, preserve swarm diversity in discrete search spaces, and maintain robustness under changing problem conditions. Evaluation results on standardized benchmark functions and targeted applications—such as crack detection in bridge structural health monitoring, real-time photovoltaic panel solar tracking, and high-dimensional gene-expression feature selection—demonstrate convergence speeds up to 4-times faster, reliable scaling to over 150 dimensions, and task success rates exceeding 98%. However, these refinements incur moderate runtime overhead and require more intensive hyperparameter tuning, posing challenges for large-scale or real-time deployments. Building on the limitations of static parameter settings and theoretical gaps in dynamic adaptation, the study advocates for future research into hybrid metaheuristic frameworks, automated self-tuning strategies, and rigorous theoretical convergence guarantees. This synthesis bridges mathematical innovation with practical insights, guiding researchers in developing next-generation, self-adaptive PSO variants for contemporary optimization demands.
Gaussian-Process-Augmented Particle Swarm Optimization (GP-PSO): Taxonomy, Survey, and Practitioner Guidance Perkasa, Sigit Dani; Jasmine, Senit Araminta; Fikri, Fachrizal
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.217

Abstract

High‐fidelity engineering simulations-Computational Fluid Dynamics, Finite‐Element Analysis and system‐dynamics models-impose prohibitive costs on optimization via traditional metaheuristics. Particle Swarm Optimization (PSO) and Gaussian Processes (GPs) have each shown promise, but canonical PSO suffers from premature convergence and excessive iterations when evaluations are noisy or expensive, and GP‐PSO integrations lack a unifying framework. In this review, we introduce a three‐axis taxonomy (1) surrogate‐integration strategy (e.g. fitness‐function replacement vs. augmentation), (2) acquisition (infill) function (e.g. Expected Improvement vs. Upper Confidence Bound), and (3) fidelity paradigm (e.g. single‐ vs. multi‐fidelity)-to classify and compare recent methods. We survey advances in deep‐kernel and neural‐augmented GPs, sparse‐GP approximations, adaptive retraining mechanisms, and hybrid/transfer‐learning extensions. Benchmark results on synthetic test suites and three real‐world applications (aerodynamic shape design, structural health monitoring, chemical‐process tuning) demonstrate 30–70 % fewer costly evaluations and 20–50 % faster convergence compared to PSO baselines, while maintaining or improving solution quality. From these studies, we distill practitioner guidelines for kernel and acquisition‐function selection, fidelity‐level choices, and reproducibility best practices-emphasizing shared code repositories and hyperparameter logs. Finally, we outline future directions in online surrogate updates, convergence theory under uncertainty, physics‐informed kernels, and standardized community benchmarks.
Co-Authors Fikri, Fachrizal Herlambang Setiadi Muhammad Abdillah Perkasa, Sigit Dani Pertiwi, Nita Indriani Prisma Megantoro Rezi Delfianti Solehan, Alfi Yusrizal Afif, Yusrizal