This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensure stable and reliable voltage output. A crucial innovation in this work is the introduction of the random walk aided artificial rabbits optimizer (RW-ARO). This novel optimization strategy incorporates a random walk approach, enhancing the efficiency of AVR control schemes. The proposed cascaded RPIDD2-PI controller, fine-tuned using the RW-ARO, stands out as a pioneering approach in the AVR domain. It demonstrates superior stability, faster response times, enhanced robustness, and improved efficiency compared to existing methods. Comparative analyses with established controller approaches reaffirm the exceptional performance of the proposed method. The new approach results in shorter rise times, quicker settling times, and minimal overshoot, highlighting its effectiveness and speed in achieving desired system responses. Moreover, the novel approach attains higher phase and gain margins, showcasing its superior performance in the frequency domain. The disturbance rejection and harmonic analysis are performed in order to demonstrate the efficacy of the proposed approach for potential real-world applications. The latter analyses further cement the superior capability of the proposed approach for the automatic voltage regulation.
This study aims to present a novel hybrid metaheuristic algorithm through improving the performance of the arithmetic optimization algorithm (AOA). A modified version of opposition-based learning mechanism (mOBL) has been used to provide the improvement. The greater performance of the improved version of the arithmetic optimization algorithm (mOBL-AOA) has been demonstrated through statistical and non-parametric tests by using benchmark functions of Schwefel 2.22, Rosenbrock, Step, Schwefel, Ackley and Penalized. The results were demonstrated comparatively by using sine cosine, Lévy flight distribution and the original arithmetic optimization algorithms. The performed comparative analyses have confirmed the highly competitive performance of the mOBL-AOA algorithm in terms of tackling with the the optimization problems.
In this paper, Harris hawks optimization (HHO) algorithm has been proposed as an up-to-date meta-heuristic algorithm for training multi-layer perceptron (MLP). The performance of the HHO-based MLP trainer was tested by employing five standard data sets (XOR, Balloon, Iris, Breast Cancer and Heart). The results were compared with those obtained with the sine cosine algorithm (SCA). Comparative statistical results showed that using HHO algorithm as a trainer is more effective and has a higher rate of classification ability.
To enhance controller performance, the optimization of control parameters has emerged as a critical research area. Among the array of optimization algorithms, the modified elite opposition-based artificial hummingbird algorithm (m-AHA) stands out for its ability to emulate behavioral strategies of hummingbirds and elite opposition-based technique. This paper, therefore, proposes m-AHA optimizer as a novel approach to optimize control parameters in a three-tanks liquid level system. By fine-tuning the parameters of proportional-integral-derivative (PID) controller, superior performance is achieved. Comparative evaluations with competitive algorithms, including the arithmetic optimization algorithm with Harris hawks optimization and covariance matrix adaptation evolution strategy, assess the m-AHA optimizer-based approach for three-tank liquid level system control. The ITAE (integral of time multiplied absolute error) performance index analyzes time domain and frequency metrics, revealing the outstanding performance of the m-AHA optimizer-based approach.
The imperative shift towards renewable energy sources, driven by environmental concerns and climate change, has cast a spotlight on solar energy as a clean, abundant, and cost-effective solution. To harness its potential, accurate modeling of photovoltaic (PV) systems is crucial. However, this relies on estimating elusive parameters concealed within PV models. This study addresses these challenges through innovative parameter estimation by introducing the logarithmic spiral search and selective mechanism-based arithmetic optimization algorithm (Ls-AOA). Ls-AOA is an improved version of the arithmetic optimization algorithm (AOA). It combines logarithmic search behavior and a selective mechanism to improve exploration capabilities. This makes it easier to obtain accurate parameter extraction. The RTC France solar cell is employed as a benchmark case study in order to ensure consistency and impartiality. A standardized experimental framework integrates Ls-AOA into the parameter tuning process for three PV models: single-diode, double-diode, and three-diode models. The choice of RTC France solar cell underscores its significance in the field, providing a robust evaluation platform for Ls-AOA. Statistical and convergence analyses enable rigorous assessment. Ls-AOA consistently attains low RMSE values, indicating accurate current-voltage characteristic estimation. Smooth convergence behavior reinforces its efficacy. Comparing Ls-AOA to other methods strengthens its superiority in optimizing solar PV model parameters, showing that it has the potential to improve the use of solar energy.
Direct current (DC) motors that convert electrical energy into mechanical energy are used in almost every field of industry. Therefore, speed control of DC motor is very important and for this purpose generally proportional + integral + derivative (PID) controllers are preferred. In this study, it is aimed to improve the speed response of DC motor by designing a PID controller tuned by improved sine cosine algorithm (ISCA), namely the ISCA-PID controller. Unlike the original SCA and other meta-heuristic algorithms, the ISCA technique has balanced exploration and exploitation processes. The performance of the proposed ISCA-PID controller was compared with two current approaches in the literature in terms of transient response, frequency response and disturbance load response analyzes. The results of these analyzes confirmed the stability of the proposed ISCA-PID controller and its success in suppressing the disturbance loads.
Son yıllarda evrim, fizik, matematik ve sürü ilhamlı çok sayıdaki sezgisel-üstü optimizasyon teknikleri, bilim ve mühendislik alanlarına önerildi. Atom arama optimizasyonu (ASO), temel moleküler dinamiklerden esinlenen popülasyon tabanlı yeni bir optimizasyon algoritmasıdır. ASO, basitliği ve az sayıda kontrol parametresi sayesinde optimizasyon problemlerine kolaylıkla uygulanabilir. ASO, en çok bilinen sekiz test fonksiyonuna (Sphere, Rosenbrock, Step, Schwefel, Rastrigin, Ackley, Griewank ve Egg Crate) uygulandı. Ayrıca, her test fonksiyonu için ASO ile elde edilen istatistiksel sonuçlar (ortalama, standart sapma ve en iyi değer) literatürdeki diğer algoritmalarla elde edilen sonuçlarla karşılaştırıldı. Parçacık sürüsü optimizasyonu (PSO), yapay arı kolonisi (ABC) ve sinüs kosinüs algoritması (SCA) karşılaştırma için seçilen diğer metotlardır. Tüm test fonksiyonları için elde edilen istatistiksel sonuçlar ve yakınsama hızlarına bakıldığında, ASO algoritmasının kısıtlı optimizasyon problemlerini çözmedeki üstün performansı göze çarpmaktadır.
Automatic voltage regulator (AVR) is an important device that regulates and controls the terminal voltage of the synchronous generator in power systems. Proportional-integral-derivative (PID) controllers are generally preferred to improve the stability performance and robustness profile of the AVR system. This paper presents a new parameter tuning approach based on Harris hawks optimization (HHO) to determine PID controller gains in the AVR system. The effectiveness of the proposed HHO tuned PID controller has been confirmed by extensive simulation studies. Compared to bio-geography-based optimization (BBO), the new HHO tuning method has better control system performance and robustness.
This study presents an enhanced reptile search algorithm (ImRSA) optimized tilt-integral-derivative (TID) controller for load frequency control (LFC) in a two-area power system consisting of photovoltaic (PV) and thermal power units. The ImRSA integrates Lévy flight and logarithmic spiral search mechanisms to improve the balance between exploration and exploitation, resulting in more efficient optimization performance. The proposed controller is tested against the original reptile search algorithm (RSA) and other state-of-the-art optimization methods, such as modified grey wolf optimization with cuckoo search, black widow optimization, and gorilla troops optimization. Simulation results show that the ImRSA-optimized TID controller outperforms these approaches in terms of undershoot, overshoot, settling time, and the integral of time-weighted absolute error metric. Additionally, the ImRSA demonstrates robustness in managing frequency deviations caused by solar radiation fluctuations in PV systems. The results highlight the superior efficiency and reliability of the proposed method, especially for renewable energy integration in modern power systems.
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