Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Snåšel, Václav; Rizk‐Allah, Rizk M.; İzci, Davut; Ekinci, Serdar

doi:10.1016/j.asoc.2023.110085

Cited by 40 publications

(16 citation statements)

References 41 publications

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“…These functions provide a rigorous evaluation platform, enabling algorithms to be tested on practical problem domains. By incorporating functions such as the shifted and rotated bent cigar, Schwefel's, Lunacek bi-Rastrigin, hybrid, expanded Rosenbrock's plus Griewangk's, and composition functions [72] in evaluation, we demonstrate the GOANM algorithm's competence in solving complex, real-world optimization problems.…”

Section: Initialize the Gazelle Populationmentioning

confidence: 99%

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Ekinci,

Izci,

Hussien

2024

IET Renewable Power Gen

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The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.

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Section: Initialize the Gazelle Populationmentioning

confidence: 99%

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Ekinci,

Izci,

Hussien

2024

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

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“…By supporting stable voltage levels, it indirectly contributes to frequency control and enhances overall network stability. Proper reactive power compensation ensures efficient power flow, reduces losses, and supports the integration of renewable energy sources [33].…”

Section: Electrical System Operation (Eso)mentioning

confidence: 99%

“…Reactive power compensation devices and control strategies can actively regulate voltage, thereby influencing the frequency response [32]. Voltage control schemes, such as Automatic Voltage Regulators (AVRs) and SVCs [33]- [35], work in conjunction with frequency control mechanisms to maintain grid stability [36]. These devices provide reactive power support to manage voltage fluctuations [37] and contribute to frequency control as a part of ancillary services.…”

Section: Voltage Control and Ancillary Servicesmentioning

confidence: 99%

VRE Integrating in PIAT grid with aFRR using PSS, MPPT, and PSO-based Techniques: A Case Study Kabertene

Tadjeddine,

Bendelhoum,

Bendjillali

et al. 2023

EAI Endorsed Trans Energy Web

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The Fluctuations in demand and weather conditions have a significant impact on the frequency and the voltage of Algeria's isolated PIAT power grid. To maintain stability and reliable power supply, it is crucial to keep these quantities close to their expected levels. An automatic (FRR) is employed to regulate real-time frequency deviations caused by integrating variable renewable energy (VRE), specifically wind and solar power in the Kabertene region. In order to mitigate wind power fluctuations, a power system stabilizer is implemented, which helps dampen oscillations. The use of Maximum Power Point Tracking (MPPT) techniques optimizes the extraction of power from solar panels under varying conditions. For efficient scheduling and dispatch of VRE generation, particle swarm optimization (PSO)-based algorithms are used. These algorithms ensure optimal utilization of renewable energy sources by considering their intermittent nature. This study proves the effectiveness of these techniques in enhancing grid stability, reducing frequency deviations, and improving VRE integration. Valuable insights are provided on their practical implementation, playing a crucial role in transitioning to a cleaner and more sustainable energy system.

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“…ℎ represents the height and 𝐴 is the crosssectional area of the related tank. The transfer function in (12) is used in this study for a three-tanks liquid level system [26], [27]. The system with the PID controller then can be represented with the model in ( 13…”

Section: Liquid Level System With Pid Controllermentioning

confidence: 99%

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

Izci,

Ekinci

2023

IJRCS

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The management of chemical process liquid levels poses a significant challenge in industrial process control, affecting the efficiency and stability of various sectors such as food processing, nuclear power generation, and pharmaceutical industries. While Proportional-Integral-Derivative (PID) control is a widely-used technique for maintaining liquid levels in tanks, its efficacy in optimizing complex and nonlinear systems has limitations. To overcome this, researchers are exploring the potential of metaheuristic algorithms, which offer robust optimization capabilities. This study introduces a novel approach to liquid level control using the Prairie Dog Optimization (PDO) algorithm, a metaheuristic algorithm inspired by prairie dog behavior. The primary objective is to design and implement a PID-controlled three-tank liquid level system that leverages PDO to regulate liquid levels effectively, ensuring enhanced stability and performance. The performance of the proposed system is evaluated using the ZLG criterion, a time domain metric-based objective function that quantifies the system's efficiency in maintaining desired liquid levels. Several analysis techniques are employed to understand the behavior of the system. Convergence curve analysis assesses the PDO-controlled system's convergence characteristics, providing insights into its efficiency and stability. Statistical analysis determines the algorithm's reliability and robustness across multiple runs. Stability analysis from both time and frequency response perspectives further validates the system's performance. A comprehensive comparison study with state-of-the-art metaheuristic algorithms, including AOA-HHO, CMA-ES, PSO, and ALC-PSODE, is conducted to benchmark the performance of PDO. The results highlight PDO's superior convergence, stability, and optimization capabilities, establishing its efficacy in real-world industrial applications. The research findings underscore the potential of PDO in PID control applications for three-tank liquid level systems. By outperforming benchmark algorithms, PDO demonstrates its value in industrial control scenarios, contributing to the advancement of metaheuristic-based control techniques and process optimization. This study opens avenues for engineers and practitioners to harness advanced control solutions, thereby enhancing industrial processes and automation.

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Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Cited by 40 publications

References 41 publications

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

VRE Integrating in PIAT grid with aFRR using PSS, MPPT, and PSO-based Techniques: A Case Study Kabertene

Optimizing Three-Tank Liquid Level Control: Insights from Prairie Dog Optimization

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