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Conventionally, the parameters of a fuzzy logic controller (FLC) are obtained by a trial and error method or by human experience. In this paper, the problem of designing a FLC for maximum power point tracking (MPPT) of a photovoltaic system (PV) that consists of a PV generator, a DC-DC boost converter and a lead-acid battery is studied. The normalization gains, the membership functions and the fuzzy rules are automatically adjusted using a particles swarm optimization algorithm (PSO) in order to maximize the criterion based on the integration of the PV module power under standard temperature condition (STC) (T=25ºC and S=1000 W/m2). The robustness test of the optimized fuzzy logic MPPT controller (FLC-MPPT) is carried out under different scenarios. Simulation results of the system clearly show that the proposed optimized FLCMPPT controller outperforms in terms of maximum efficiency the FLC-MPPT controller not optimized, the FLC-MPPT controller with optimized normalization gains and the FLC-MPPT controller with optimized normalization gains and membership functions.

In renewable system control engineering, Maximum Power Point Tracking (MPPT) techniques are considered as one of the key steps in photovoltaic (PV) system development. Indeed, some MPPT techniques provide high performance under uniform irradiation and temperature conditions. However, their performance significantly degrades when the two preceding climatic conditions suddenly change. Therefore, this paper proposes a new voltage regulation based on Fuzzy Logic Control (FLC) strategy and small-signal model of DC–DC booster converter. The proposed control scheme is mainly based on the indirect incremental conductance (Inc) technique, equipped with an optimal integrator controller (OIC). It is implemented in a PV system, consisting of a DC–DC boost converter connected to a resistive load, with the main objective of ensuring a good MPP tracking behavior for variable operating conditions. To achieve this objective, the proposed MPPT strategy is designed around two stages. First, the smooth reference voltage is ensured by a Single-Input Single-Output (SISO) fuzzy logic controller using the Inc technique based on the MPPT approach. Then, the optimal integrator controller stabilizing the closed-loop system is integrated with optimization of its parameter according to Routh’s criterion for resistive load variations. Simulation results confirm the effectiveness of the proposed MPPT technique compared to some commonly used MPPT approaches. The superiority of the proposal is proven by using efficiency ratio, which rises more than 98% in all case studies. These objectives are ensured for both uniform and critical sudden change in weather conditions, regardless of resistive load variations. [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]

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]