Stability is the major factor that should be maintained in every power system. To predict and to optimize the nonlinear parameters this research provides a control system transient analysis using Bode plot and Nyquist plot to regulate the stability in wind power generation. The rotor speed should be balanced with respect to the generation of generator power for viable wind power generation. This study introduces a sliding mode controller for controlling wind speed and preserving system stability, and it may be improved using an Artificial Neural Network based Radial Basis Function Neural Network to eliminate nonlinearities induced by changing wind speed. The tip speed ratio approach is utilized in this study to harvest the most power from wind energy. To optimize this TSR method, a PI-RBFN tuned sliding mode controller was utilized to get maximum power while minimizing active power losses. This proposed approach may be used to address nonlinearities in the pitch angle caused by changing wind speed. As a result, the resilience of the redesigned Type-III wind turbine system is investigated using MATLAB simulink in this study. The simulation results are compared to the current DFIG-based modified Type-III wind turbine method.
Buildings consume over 40% of the world’s total energy supply, and their occupancy is increasingly recognized as a major performance indicator due to its effect on a building’s energy costs and occupant satisfaction. In this paper, a hybrid model is created to estimate future loads of a building with high efficiency and accuracy. The proposed model is composed of two - connected in a cascade - artificial neural networks, where the outcomes of the first network are fed into the second one, which in its turn performs the load forecasts. A pre-existing dataset is used to verify the proposed model and to test a variety of training set sizes. Analysis of the results is executed by taking six pair of combinations separately for both open door and closed door fault cases. In this analysis, cascaded back propagation and Elman back propagation method - among the rest of the analyzed methods – is found to give the best accuracy, i.e, 97.2% - 97.9%, which indicates that the suggested hybrid technique is more accurate than the existing non-hybrid methods.
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