This study presents the extraction of unknown parameters of various photovoltaic (PV) cells and modules by using the weighted mean of vectors (INFO) algorithm. The parameter estimation of PV cells and modules is one o...
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This study presents the extraction of unknown parameters of various photovoltaic (PV) cells and modules by using the weighted mean of vectors (INFO) algorithm. The parameter estimation of PV cells and modules is one of the most important issues in the design of effective PV power systems. Since the PV parameters are highly nonlinear and complex in nature, the estimation of these parameters also becomes a challenging optimization problem for designers. The main challenge is to obtain the most accurate estimation. In order to solve the problem in a unique way, the state-of-the-art metaheuristic algorithms that have not been tried so far in parameter extraction are chosen. The selected ones are the INFO optimizationalgorithm, the artificial hummingbird algorithm (AHA), the artificialecosystem-basedoptimization (AEO) algorithm, the runge kutta (RUN) optimizer, and lastly the reptile search algorithm (RSA). The motivation here is to test as many algorithms as possible to reach to the most accurate solution. In addition, the gray wolf optimizer (GWO), the frequently used one in literature due to its superiority in parameter extraction applications, is selected to validate the results of the evaluated algorithms through comparison against the GWO. Moreover, the performances of these algorithms are compared with evaluation metrics consisting of minimum, mean, maximum, standard deviation, and statistical tests using Wilcoxon signed-rank test and Friedman test. At the end of the study, it is demonstrated that the INFO, statistically, produces the highest accuracy and reliable results. Due to its statistical success compared to other algorithms, the INFO is used to extract the parameters of a commercially available PV cells and modules. It is clearly shown that the parameters extracted by the INFO closely match the parameters provided by the manufacturer's datasheet, which points out the superiority of the INFO algorithm in PV modeling.
In this paper, the artificialecosystem-basedoptimization (AEO) algorithm is applied to coordinate directional over current relays (DOCRs). Optimal coordination of DOCRs is non-linear and highly constrained problem b...
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ISBN:
(纸本)9781665401272
In this paper, the artificialecosystem-basedoptimization (AEO) algorithm is applied to coordinate directional over current relays (DOCRs). Optimal coordination of DOCRs is non-linear and highly constrained problem but it is important to keep secure and protected power system operation. The AEO algorithm is one of the recent nature-inspired meta-heuristic algorithms. It simulates the flow of energy into an Earth's ecosystem by three living organisms including production, consumption, and decomposition. The AEO is successfully applied to coordinate DOCRs with the aim of minimizing the overall operating time of the relays used in three test systems including 8-bus, 9-bus, and 15-bus systems. The results of the developed algorithm are compared with other well-known algorithms. The simulation results demonstrated the importance and effectiveness of the proposed algorithm in finding the optimal coordination of DOCRs and minimizing the overall operating time of the relays.
In the domain of electric power systems, ensuring stable voltage and efficient transmission is crucial. Optimal reactive power dispatch (ORPD) is a vital step toward improving system stability, enhancing economic feas...
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In the domain of electric power systems, ensuring stable voltage and efficient transmission is crucial. Optimal reactive power dispatch (ORPD) is a vital step toward improving system stability, enhancing economic feasibility, and increasing overall efficiency. Various optimization techniques are employed to minimize real power loss and voltage deviation in the network. This article proposes a hybrid algorithm called Enhanced Jaya and artificialecosystem-basedoptimization (EJAEO) to address the ORPD problem. The algorithm aims to determine the optimal values of variables such as reactive compensation, generators' voltages, and transformers' tape ratio. Two single objective functions are tested on standard IEEE 30-Bus and IEEE 57-Bus systems, and the suggested modification of the artificialecosystem-basedoptimization (AEO) improves population effectiveness in achieving optimal solutions. The simulation findings of the EJAEO algorithm demonstrated its superiority over three recent algorithms (AEO), Turbulent Flow of Water-basedoptimization, and Jaya algorithm), as well as certain previously published ORPD methods used for the same problem. In the case of the IEEE 30-Bus system, the proposed EJAEO algorithm achieved the best results in terms of power losses (4.944805) and voltage deviation (0.121196). Similarly, in the IEEE 57-Bus system, our EJAEO algorithm outperformed others, yielding the lowest power losses (23.33052) and voltage deviation (0.579286). The EJAEO algorithm outperforms other algorithms in terms of accuracy, speed of convergence, and robustness. These findings highlight the potential of EJAEO as a promising technique for solving the ORPD problem in power systems.
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