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    Wyświetlanie 1-3 z 3
    Tytuł:
    A novel approach for power system stabilizer control parameter selection: a case-study on two-area four-machine system
    Autorzy:
    Gude, Murali Krishna
    Salma, Umme
    Powiązania:
    https://bibliotekanauki.pl/articles/2086725.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    grey wolf optimizer
    power system stabilizer
    optimization
    stability
    Opis:
    This paper proposes a power system stabilizer (PSS) with optimal controller parameters for damping low-frequency power oscillations in the power system. A novel meta-heuristic, weighted grey wolf optimizer (GWO) has been proposed, it is a variant of the grey wolf optimizer (GWO). The proposed WGWO algorithm has been executed in the selection of controller parameters of a PSS in a multi-area power system. A two-area four- machine test system has been considered for the performance evaluation of an optimally tuned PSS. A multi-objective function based on system eigenvalues has been minimized for obtained optimal controller parameters. The damping characteristics and eigenvalue location in the proposed approach have been compared with the other state-of-the-art- methods, which illustrates the effectiveness of the proposed approach.
    Źródło:
    Archives of Electrical Engineering; 2022, 71, 2; 297--407
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm
    Autorzy:
    Shial, Gyanaranjan
    Saho, Sabita
    Panigrahi, Sibarama
    Powiązania:
    https://bibliotekanauki.pl/articles/27312857.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    grey wolf optimizer
    JAYA algorithm
    article swarm optimization
    ine-cosinealgorithm
    partitional clustering
    Opis:
    This paper presents a hybrid meta-heuristic algorithm that uses the grey wolfoptimization (GWO) and the JAYA algorithm for data clustering. The ideais to use the explorative capability of the JAYA algorithm in the exploitativephase of GWO to form compact clusters. Here, instead of using only one bestand one worst solution for generating offspring, the three best wolves (alpha,beta and delta) and three worst wolves of the population are used. So, the bestand worst wolves assist in moving towards the most feasible solutions and simul-taneously it helps to avoid from worst solutions; this enhances the chances oftrapping at local optimal solutions. The superiority of the proposed algorithmis compared with five promising algorithms; namely, the sine-cosine (SCA),GWO, JAYA, particle swarm optimization (PSO), and k-means algorithms.The performance of the proposed algorithm is evaluated for 23 benchmarkmathematical problems using the Friedman and Nemenyi hypothesis tests. Ad-ditionally, the superiority and robustness of our proposed algorithm is testedfor 15 data clustering problems by using both Duncan's multiple range test andthe Nemenyi hypothesis test.
    Źródło:
    Computer Science; 2023, 24 (3); 361--405
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of Proportional-Integral Controllers of Grid-Connected Wind Energy Conversion System Using Grey Wolf Optimizer Based on Artificial Neural Network for Power Quality Improvement
    Autorzy:
    Alremali, Fathi Abdulmajeed M.
    Yaylacı, Ersagun Kürşat
    Uluer, İhsan
    Powiązania:
    https://bibliotekanauki.pl/articles/2201727.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    artificial neural network
    grey wolf optimizer
    PI controller
    grid connection
    power quality
    wind energy
    Opis:
    This research presents a combination of artificial neural network (ANN) with the grey wolf optimizer (GWO) to improve the power quality of a grid-connected distributed power generation system (DPGS). To assess the effectiveness of the proposed algorithm, a grid-tied of small-scale wind energy conversion system (WECS) is chosen. The term power quality refers to voltage and frequency regulation, and limited harmonics. Power quality improvement is achieved through the cascaded control system's optimal tuning of three proportional-integral (PI) controllers of the grid-side inverter (GSI). However, because the DPGS model is computationally costly, the Artificial Neural Network (ANN) model is utilized as an alternative model for DPGS. Furthermore, the ANN model is employed in conjunction with the GWO to boost the optimization precision and minimize the execution time of GWO. The considered power system was repetitively simulated to obtain the input-output datasets, which validate and train the ANN model. According to the ANN model's performance evaluation, the correlation coefficient (R) is close to one, while the mean squared error (MSE) is near zero. These findings demonstrate the ANN model's great accuracy in approximating the DPGS model. Using MATLAB/Simulink, the system's performance is evaluated using the optimum values obtained using GWO-ANN for various wind speed profiles. It showed the suggested power quality method’s improved stability, convergence behavior, the effectiveness of the control mechanism, and the robustness of the proposed topology.
    Źródło:
    Advances in Science and Technology. Research Journal; 2022, 16, 3; 295--305
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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