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As a result of the exhaustion of fossil energy sources and the corresponding increase of their negative environmental impact, recent research has intensively focused on regions of alternative energy resources and, especially, on solar energy. Slow tracking of the maximum power point (MPP) and fluctuations around the MPP reduce the efficiency of photovoltaic power generation systems (PV). This study offers a novel design for the MPPT controller, which we refer to as the "hybrid IC-LQI approach", which combines the incremental conductance (IC) technique and the linear quadratic integral (LQI) controller based on the boost converter's small signal model. We conduct a comparative study of the proposed hybrid IC-LQI, and the classical one-stage IC technique in order to show the effectiveness of our proposal under three different scenarios of weather conditions and load. According to simulation findings, the proposed hybrid IC-LQI approach has a high tracking efficiency of up to 98.92%, owing to faster tracking of MPP with very large reduction of oscillations. On the other hand, the IC technique provides less efficiency, up to 96.1%, showing very slow tracking and high oscillations. The presented analysis of the results confirms the superior performance of the developed hybrid IC-LQI technique to the classical IC technique. [ABSTRACT FROM AUTHOR]

In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency. [ABSTRACT FROM AUTHOR]

The Incremental Conductance (IncCond) algorithm based on direct control mode is one of the highest Maximum Power Point Tracking (MPPT) algorithms ensuring multiple performances compared to some other classical methods. Unfortunately, these performances can deteriorate, especially in the case of sudden changes in weather conditions and load variation., Preventing these drawbacks will form the basis of a new IncCond-MPPT scheme based on indirect control mode in which a smooth electrical power response generated by photovoltaic (PV) panels is well ensured, regardless of quick changes in solar irradiance, absolute temperature and load variation. The proposed MPPT approach uses two stages of control. The first one is used to provide the reference PV-module voltage. The second control loop is formulated as a transfer function that is then controlled using an appropriate Integral controller. Its integral gain is well tuned by applying the Routh's stability criterion on the characteristic equation of the obtained closed loop transfer function. This allows providing the proposed new guideline, which represents the main contribution of this paper. The proposed IncCond-MPPT scheme is compared with the standard one in terms of MPP tracking accuracy and electrical power ratio, which exceeds 97 %, where quick changes of weather conditions and load variations are considered. [ABSTRACT FROM AUTHOR]