In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms.INDEX TERMS Coyote, PSO, Jaya, single-phase transformer, transformer equivalent circuit.

In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.

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The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.

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