A Novel Maximum Power Point Tracking Method for Photovoltaic Application Using Secant Incremental Gradient Based on Newton Raphson

In this chapter, some common methods of maximum power point tracking (MPPT) of the photovoltaic system such as perturb and observe, particle swarm optimization and grey wolf optimizer are described to solve the MPPT problem. Also, a novel method is proposed for MPPT of PV system titled secant incremental gradient based on Newton Raphson (SIGBNR) method. SIGBNR uses the chord slope passing through two points of the function instead of using the explicit derivative of the function, which is equal to tangent line tilt of the function. In addition to high convergence speed, the proposed method requires less computation and also has a higher accuracy in the number of repetitions when it solves MPPT problems. The results arising from the proposed method are compared and analyzed with the other methods to evaluate its performance for solving MPPT problem. The proficiency of the methods is investigated in different scenarios of partial shading condition and compared in view of various features especially efficiency and convergence velocity. The results showed that the proposed method has better performance in achieving to global maximum power point with more tracking efficiency and convergence speed than the other methods. Also, superior capabilities of the proposed method are demonstrated.

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