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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.

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]

This paper deals with the modeling and control of a new two-stage photovoltaic conversion cascade composed of a Three-Level Boost (3LB) converter and a three-phase NPC fivelevel inverter (5LI). The main objective is to ensure the continuity of energy transfer from a renewable continuous source to the electrical grid while regulating the voltages of the DC bus capacitors with a new proposed configuration. The 3LB is controlled in MPPT based on fuzzy logic. A PI regulator is introduced into the 3LB regulation loop in order to maintain the equality of the two output voltages. The overall voltage of the DC bus being regulated by the control of the multilevel inverter interconnected to the grid, using synchronous reference frame control. Therefore, in our case there is the problem of voltages imbalance caused by the 5LI. We propose in this work to introduce two Clamping Bridges (CBs) each one is composed by a transistor in series with a resistor. These CBs are introduced in parallel with two capacitors of the DC bus. This solution avoids us to use the control algorithm by redundant configurations in the regulation loop of the 5LI. The results obtained show the effectiveness of the proposed control in stabilizing and maintaining the four DC bus voltages at equal values. With four balanced capacitor voltages, we get symmetrical inverter output voltage waveforms, which directly affects the quality of the current injected to the grid. [ABSTRACT FROM AUTHOR]

finite control set model predictive control (FCS-MPC) based controller has a fast dynamic response and robustness. furthermore, the presence of a cost function gives designers a degree of freedom to include system control targets, constraints and system non-linearities. On the other hand, Multilevel inverter (MI) topologies are becoming a strong alternative in distributed power generation system (DPGS), among these topologies is the three-phase three-level NPC (TTLNPC) inverter. Generally, to properly operate this topology, the applied current control ensures the achievement of two main objectives. First, the output current must be controlled to track its reference. Second, the two dc-link capacitor voltages have to be equal and balanced. In this paper, FCS-MPC is proposed to control the TTLNPC inverter based parallel active power filter (APF) adopted to connect a photovoltaic system (PVS) to the grid and perform a harmonic mitigation. The proposed FCS-MPC exploit the model of the system to predict the future values of the inverter currents by selecting the best voltage vector that aims to minimize a predefined cost function. Instead of using the popular redundant vectors algorithms to balance the twosplit dc-link capacitor voltages, another term will be added to the expression of the cost function to achieve this goal. The PV panel is coupled directly to the inverter without DC/DC converter, the P&O MPPT algorithm is responsible to generate the capacitor reference voltage whatever the climatic conditions are. Simulations using Matlab/Simulink were performed to prove the efficiency of the proposed technique to mitigate the grid current harmonics, and to ensure a continuous power injection and perform a load power sharing. [ABSTRACT FROM AUTHOR]

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]

In the last two decades, the maximum power point tracking (MPPT) methods for PV system is becoming very interesting subject. Among these methods there is Fuzzy- MPPT, which is mainly based on two inputs and at least 25 rules. This structure need more calculation time and not easy to be implemented in hardware. For these reasons and in order to facilitate the MPPT synthesis, this paper proposes an intelligent MPPT controller based on a single-input Takagi-Sugeno fuzzy logic controller (SI-TS-FLC) with three linguistic variables and three rules. The proposed controller is simulated using MATLAB-SIMULINK for a PV system, which consists of a PV generator, DC-DC converter, and resistive load under varied temperature and irradiance levels. An experimental study is conducted using DSPACE 1104 card real-time board under partial shading condition. Simulation and experimental results show that the proposed controller exhibits less settling time and lower overshoot than the commonly used perturb and observe (P&O) algorithm in the transient state and minimum oscillation around the optimal operating point. [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]