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Hybrid MPPT Algorithm for PV Systems Under Partially Shaded Conditions Using a Stochastic Evolutionary Search and a Deterministic Hill Climbing

generation , Ren. Sust. En. Rev., 2015, 41, 284–297. [4] K ot R., S tynski S., M alinowski M., Hardware methods for detecting global maximum power point in a PV power plant , Proc. IEEE Int. Conf. Industrial Technology, ICIT, 2015, 2907–2914. [5] L ogeswarana T., S enthilkumarb A., A review of maximum power point tracking algorithms for photovoltaic systems under uniform and non-uniform irradiances , 4th Int. Conf. Advances in Energy Research, ICAER 2013, India, 2013, 228–235. [6] I shaque K., S alam Z., A mjad M., M ekhilef S., An improved

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Novel Fuzzy-Based Self-Adaptive Single Neuron PID Load Frequency Controller for Power System

References Bensenouci, A. and Ghany, A.A., (2010). Performance analysis and comparative study of LMI-based iterative PID load-frequency controllers of a singlearea power system. Power, 4, p.7. Blevins T. and Nixon, M. (2011). Control Loop Foundation Batch and Continuous Processes. North Carolina: International Society of Automation. Duman, S., Yorukeren, N. and Altas, I. H. (2012). Load frequency control of a single area power system using Gravitational Search Algorithm. International Symposium on Innovations

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Multiobjective Improved Particle Swarm Optimisation for Transmission Congestion and Voltage Profile Management using Multilevel UPFC

). Particle Swarm Optimization with an Aging Leader and Challengers Algorithm for the Solution of Optimal Power Flow Problem. Applied Soft Computing Journal, 40, pp. 161–177. Raj, S. and Bhattacharyya, B. (2018). Optimal Placement of TCSC and SVC for Reactive Power Planning Using Whale Optimization Algorithm. Swarm and Evolutionary Computation , 40, pp. 131–143. Singh, K., Yadav, V. K., Padhy, N. P. and Sharma, J. (2014). Congestion Management Considering Optimal Placement of Distributed Generator in Deregulated Power System Networks. Electric Power

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Application of Competitive and Transition Petri Layers in Adaptive Neuro-Fuzzy Controller

-Implemented Adaptive Recurrent Fuzzy Controller Designed by Evolutionary Algorithm, IEEE Trans. Circuits and Systems, 2005, Vol. 52, No. 11, 2376-2384. [9] KABZIŃSKI J., KACERKA J., TSK Fuzzy Modeling with Nonlinear Consequences, Artificial Intelligence Applications and Innovations, 2014, 498-507. [10] KLUSKA J., HAJDUK Z., Digital Implementation of Fuzzy Petri Net Based on Asynchronous Fuzzy RS Flip-Flop, Proc. 7th Int. Conf., Zakopane, Poland, 2004. [11] KNYCHAS S., SZABAT K., Adaptive Recurrent Neuro-Fuzzy Control of the Complex

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