This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller’s output, which further inputs the rotor side converter’s speed control loop to control the rotor’s actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller’s performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller’s wide range of input wind speed.
The most prominent and rapidly increasing source of electrical power generation, wind energy conversion systems (WECS), can significantly improve the situation with regard to remote communities’ power supply. The main constituting elements of a WECS are a wind turbine, a mechanical transmission system, a doubly-fed induction generator (DFIG), a rotor side converter (RSC), a common DC-link capacitor, and a grid-side converter. Vector control is center for RSC and GSC control techniques. Because of direct and quadrature components, the active and reactive power can also be controller precisely. This study tracks the maximum power point (MPP) using a maximum power point tracking (MPPT) controller strategy. The MPPT technique provides a voltage reference to control the maximum power conversion at the turbine end. The performance and efficiency of the suggested control strategy are validated by WECS simulation under fluctuating wind speed. The MATLAB/Simulink environment using simpower system toolbox is used to simulate the proposed control strategy. The results reveal the effectiveness of the proposed control strategy under fluctuating wind speed and provides good dynamic performance. The total harmonic distortions are also within the IEEE 519 standard’s permissible limits which is also an advantage of the proposed control approach.
Distribution networks, established over past decade are not enough smart to satisfy the growing demand of the society for reliable power supply. Being the only link between utility and consumers, it is an utmost important to analyze and enhance the reliability of distribution network. Reliability improvement demands investment, which will increase the supply cost. Network reconfiguration can be a way to mitigate the investment issue and to enhance the system reliability. This paper presents the optimal network configuration by changing the open/close sequence of sectionalizing switches and tie switches to reduce the Expected Cost of Interruption (ECOST) and Energy Not Served (ENS), well known reliability indices. The Greedy search algorithm is used in MATLAB environment to determine the optimal result. Further, to get more realistic results, effects of fuse failure probability and load transfer restriction are also included in the proposed approach. The radial distribution network of the Roy Billionton Test System (RBTS), which is connected at BUS-2, is utilized as a case study, where various types of loads (Residential, Commercial, Small users and Govt. and Inst. Buildings) are connected.
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