Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle

This paper proposes an artificial neural network maximum power point tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis- tor (IGBT) power switch. The reference voltage for MPPT is obtained by artificial neural network (ANN) with gradient descent momentum algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera- ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information sup- plied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lith- ium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.

Assessment of high-gain quadratic boost converter with hybrid-based maximum power point tracking technique for solar photovoltaic systems

Solar photovoltaic(SPV)modules have a low output voltage and are load-dependent.Therefore,it is critical that the SPV system has an adequate DC-DC converter to regulate and improve the output voltage to get maximum output voltage.To meet load requirements,the voltage must be increased,necessitating the use of energy-efficient power electronic converters.The performance of an SPV system coupled to a high-gain quadratic boost converter(HG-QBC)with a load is investigated in this paper.The suggested HG-QBC for the SPV system at a lower value of duty ratio provides high voltage gain with a boost factor of four times.An analytical comparison is carried out with the various existing boost converters in terms of the components and the boost factor.The issue of locating the maximum power generation point from the SPV system is crucial.As a result,choosing an appropriate maximum power point tracker(MPPT)-based technique to obtain the peak power output of the SPV system under the rapidly varying atmospheric conditions is vital.To determine the highest output power of an SPV system,a hybrid-based MPPT with a neural network assisted by a perturb and observe(P&O)technique is proposed.For the HG-QBC,a comparison of the proposed MPPT with a traditional P&O-based MPPT is illustrated.The comparative analysis takes into account rise time,settling time and voltage ripples.The output voltage and power characteristics of the proposed model are analysed under constant and varying irradiation conditions using MATLAB®/Simulink®.The results of a hybrid-based MPPT show that the oscillations are minimum at the maximum power point with fewer ripples of 0.20%and a settling time of 1.2 s in comparison with the other two techniques.

Continuous Mixed p-norm Control Scheme with Reweighted L_(0) norm Variable Step Size for Mitigating Power Quality Problems of Grid Coupled Solar PV Systems

In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)of a voltage source inverter(VSI)control scheme.The efficacy of the system is determined by considering unbalanced grid voltage,DC offset,voltage sag and swell,unbalanced load and variations in solar insolation.RLo-VSSCMPN is used for inverter control and it ex-tracts fundamental components of load current for generating the reference grid current with a faster convergence rate and lesser steady state oscillations.With the proposed control,harmonics in the grid current follows the IEEE-519 norm and the performance is also satisfactory under varying environmental/load conditions.The power generated from SPvGS is transferred optimally using a DC-DC boost converter utilizing the incremental conductance(INC)maximum power point technique.The proposed system is simulated using MATLAB/Simulink 2018a and test results are verified experimentally using dSPACE1202 in the laboratory to ensure the validity of a novel proposed robust RLo-VSSCMPN.Index Terms-INC maximum power point tracker,power quality,reweighted LoVSSCMPN algorithm,solar PV generating system,total harmonic distortion,voltage source inverter.

Modeling and control of photovoltaic energy conversion connected to the grid

This paper presents modeling and control of a photovoltaie generator （PVG） connected to the grid. The parameters of the PVG have been identified in previous work （series and parallel resistance, reverse saturation current and thermal voltage） using Newton-Raphston and the gradient algorithm. The electrical energy from a PVG is transferred to the grid via two static converters （DC/DC and DC/AC）. The objective of the proposed control strategy is to maximize energy captured from the PVG. The adapted control law for extracting maximum power from the PVG is based on the incremental conductance algorithm. The developed algorithm has the capability of searching the maximum photovoltaic power under variable irradiation and temperature. To control the DC/AC inverter, an intelligent system based on two structures is constructed： a current source control structure and a voltage source control structure. The system has been validated by numerical simulation using data obtained from the PVG installed in the laboratory research （INSAT, Tunisia）.

Design of a P-&-O algorithm based MPPT charge controller for a stand-alone 200W PV system

Solar cells convert sun light into electricity,but have the major drawbacks of high initial cost,low photo-conversion efficiency and intermittency.The current-voltage characteristics of the solar cells depend on solar insolation level and temperature,which lead to the variation of the maximum power point(MPP).Herein,to improve photovoltaic(PV)system efficiency,and increase the lifetime of the battery,a microcontroller-based battery charge controller with maximum power point tracker(MPPT)is designed for harvesting the maximum power available from the PV system under given insolation and temperature conditions.Among different MPPT techniques,perturb and observe(P&O)technique gives excellent results and thus is used.This work involves the design of MPPT charge controller using DC/DC buck converter and microcontroller.A prototype MPPT charge controller is tested with a 200 W PV panel and lead acid battery.The results show that the designed MPPT controller improves the efficiency of the PV panel when compared to conventional charge controllers.