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    Wyświetlanie 1-6 z 6
    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
    Tytuł:
    Fractional order PIλDμ controller with optimal parameters using Modified Grey Wolf Optimizer for AVR system
    Autorzy:
    Verma, Santosh Kumar
    Devarapalli, Ramesh
    Powiązania:
    https://bibliotekanauki.pl/articles/2134890.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    integer order PID controller
    fractional order PID controller
    automatic voltage regulator
    evolutionary optimization
    Grey Wolf Optimizer
    Opis:
    In this paper, an automatic voltage regulator (AVR) embedded with fractional order PID (FOPID) is employed for the alternator terminal voltage control. A novel meta-heuristic technique, a modified version of grey wolf optimizer (mGWO) is proposed to design and optimize the FOPID AVR system. The parameters of FOPID, namely, proportional gain (ΚP), the integral gain ( ΚI), the derivative gain ( ΚD), λ and μ have been optimally tuned with the proposed mGWO technique using a novel fitness function. The initial values of the ΚP, ΚI , and ΚD of the FOPID controller are obtained using Ziegler-Nichols (ZN) method, whereas the initial values of λ and μ have been chosen as arbitrary values. The proposed algorithm offers more benefits such as easy implementation, fast convergence characteristics, and excellent computational ability for the optimization of functions with more than three variables. Additionally, the hasty tuning of FOPID controller parameters gives a high-quality result, and the proposed controller also improves the robustness of the system during uncertainties in the parameters. The quality of the simulated result of the proposed controller has been validatedby other state-of-the-art techniques in the literature.
    Źródło:
    Archives of Control Sciences; 2022, 32, 2; 429--450
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reactive power convex optimization of active distribution network based on Improved GreyWolf Optimizer
    Autorzy:
    Li, Yuancheng
    Yang, Rongyan
    Zhao, Xiaoyu
    Powiązania:
    https://bibliotekanauki.pl/articles/140678.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    active distribution network (ADN)
    Improved Grey Wolf Optimizer (IGWO)
    reactive power optimization
    second-order cone relaxed convex model
    Opis:
    The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.
    Źródło:
    Archives of Electrical Engineering; 2020, 69, 1; 117-131
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of the Grey Wolf Optimizer in the optimization of state space controller for three-mass drive
    Autorzy:
    Żychlewicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1189934.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    GWO
    regulator stanu
    układ trójmasowy
    napęd elektryczny
    Grey Wolf Optimizer
    state space controller
    three-mass system
    electrical drive
    Opis:
    This article presents control structure of complex drive that contains two elastic couplings. In the outer control loop the state space controller was implemented. The main point of described work is optimization of parameters used in this part of the drive using metaheuristic algorithm called GWO (Grey Wolf Optimizer). The control structure, designed using mentioned optimization method, was compared to classic solution, known from control theory. High precision of reference speed tracking was achieved. An analysis of the system in the presence of mechanical parameters changes was also prepared. Theoretical considerations were confirmed in numerical tests.
    Źródło:
    Interdisciplinary Journal of Engineering Sciences; 2018, 6, 1; 12--20
    2300-5874
    Pojawia się w:
    Interdisciplinary Journal of Engineering Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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