In an interconnected power system, short-term hydrothermal scheduling (SHTS) portrays a paramount important task in the economic operation of electric power system. SHTS is discerned as a formidable non-convex constra...
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In an interconnected power system, short-term hydrothermal scheduling (SHTS) portrays a paramount important task in the economic operation of electric power system. SHTS is discerned as a formidable non-convex constrained optimization problem considering the cascade nature of hydro plants, and valve-point loading (VPL) and multi-fuel sources of thermal plants. In the present study, a novel metaheuristic approach, a quasi-oppositional turbulent water flow-based optimization (QOTWFO) is proposed to solve the SHTS problem. Turbulent water flow-based optimization (TWFO) is evolved by the whirlpool prodigy formed in a turbulent flow of water. QOTWFO is an amended version of TWFO, where the quasi-opposition-basedlearning is pioneered for population initialization and iteration vaulting. It generates a quasi-oppositional solution which has the highest probability to elicit the global optimum solution than a randomly generated solution. Thus, the global searching ability and computational efficiency of the algorithm is enhanced. A new heuristic constraints handling mechanism is proposed to fulfil the equality constraints notably active power balance and dynamic water flow balance. The cogency of proposed QOTWFO is examined on standard benchmark functions, and multi-reservoir cascaded hydrothermal test systems with the cogitation of VPL effects and multi-fuel sources of thermal plants. Besides, Newton Raphson load flow approach is employed to determine power losses and line flows in a 16-bus, and 35 transmission lines hydrothermal power system. To model a realistic power system model, the maximum power transfer capability of each transmission line is taken into account. Simulation outcomes substantiate that, compared with state-of-the-art heuristic techniques, QOTWFO offers better feasible solutions as well as guarantees the robustness of the algorithm.(c) 2022 Elsevier Ltd. All rights reserved.
In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorit...
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In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorithm (OWGWA). The OWGWA can choose the control parameters of the grey wolf algorithm utilizing whale parameters. To balance out the randomness of optimization strategies another methodology is implemented called controlled search space. Adaptive filter's noise reduction capability has been tested through adding adaptive white Gaussian noise over contaminated EEG signals at different noise levels. The performance of the proposed OWGWA-CSS algorithm is evaluated by signal to noise ratio in dB, mean value, and the relationship between resultant and input ERP. In this work, ANCs are also implemented by utilizing other optimization techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWGWA-CSS technique provides higher SNR value, significantly lower mean and higher correlation as compared to other techniques.
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