Performance Analysis of an Improved Variable Step-Size IC MPPT Technique for SPV System

To mitigate problems like energy shortage and environmental degradation caused by conventional energy sources, it needs to shift from conventional energy to non-conventional energy sources. The solar photovoltaic (PV) technology emerges as one of the leading non-conventional energy sources. To operate a PV system at its maximum efficiency, it requires the operating module at maximum power output point (MPP). There are many conventional as well as advanced maximum power point tracking (MPPT) techniques developed and implemented for a solar PV system. This work provides a comparative analysis of the conventional and fuzzy logic controller (FLC)-based MPPT techniques for a solar PV system. In this study, performance of two conventional techniques is considered, i.e., perturb and observe (P & O) and incremental conductance (IC) MPPT technique. The analysis of the PV system is performed for different solar irradiance levels. The comparative study is evaluated for complexity, tracking time, efficiency, range of power, voltage, and current oscillations. The DC-DC boost converter is used for operation of the proposed PV system at MPP. The implementation of the entire system and control algorithms has been modeled in MATLAB Simulink.

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

ABSTRACTThis paper presents the modelling, simulation and hardware implementation of the incremental conductance (INC) maximum power point tracking (MPPT) algorithm. The extraction of maximum power from the solar photovoltaic system was made by integrating the PV module with a DC–DC converter and utilising the MPPT algorithm. In this work, the INC MPPT algorithm is practically implemented on an Arduino Uno board to track the maximum power point (MPP) and also simulated in Matlab/Simulink under the same environmental conditions. The performance of the INC algorithm was also compared with the conventional Perturb and Observe (P&O) MPPT algorithm. The experimental and simulation results are provided for both MPPT techniques. Matlab/Simulink was used for simulation studies.

A high conversion ratio DC/DC converter with an active zero-voltage switching (ZVS) snubber circuit incorporated with maximum power point tracking (MPPT) techniques is presented in the paper. The MPPT controllers employ photovoltaic (PV) systems to make the converter output track the maximum PV output power. The active snubber circuit restricts the voltage stresses on the power switches. The simulation and experimental results verify high conversion ratio converter performance. The tracking performance simulations of the perturbation and observation (P&O) and incremental conductance (INC) MPTT are compared in the PV charging system. The tracking performances by INC MPPT controller and waveform of the PV charging system are presented.

In this work, a simulation study and comparative analysis of conventional Maximum Power Point Tracking (MPPT) technique, i.e., Incremental Conductance (IC) with nonlinear technique, i.e., Ripple Correlation Control (RCC), has been done. Photovoltaic (PV) array and the DC/DC boost converter along with MPPT control strategy have been implemented and simulations analysis has been presented for both the techniques for various solar irradiance levels at 25 °C temperature. The system has been designed to achieve 450 V and 10 kW output at 1000 W/m2 irradiation, which is attained through a DC/DC boost converter. Simulation results show that the IC technique’s output is very much affected by oscillations under steady-state and varying environmental conditions, which affects the system’s stability; whereas, RCC removes all ripples from voltage, current, and power outputs in steady-state and upto some extent in transient state, due to its inherent ripple removal feature. However, during transitions RCC also exhibits underdamped response, but it is minimum as compared to other MPPT techniques.KeywordsPhotovoltaic (PV)DC/DC Boost converterMaximum Power Point Tracking (MPPT)IrradianceRipplesIncremental Conductance (IC)Ripple Correlation Control (RCC)

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

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.

In the work a study is performed between different maximum power point tracking (MPPT) techniques for DC grid connected solar PV applications. To harness the maximum power from available input under changing weather conditions MPPT controller is used. Different MPPT techniques namely Perturb and Observe (P&O), Incremental Conductance (In &C), Fuzzy logic and Random Search Method (RSM) based MPPT techniques are implemented here. Due to simple computation, absence of derivatives RSM guarantees global convergence, unlike other methods. Simulation is performed in MATLAB/Simulink environment to identify efficient MPP tracker.

This paper presents a single-phase standalone photovoltaic (PV) system with two stages of converters. The aim of this work is to track the maximum power point (MPP) so as to transfer the maximum available power to the load and to control the output current in order to feed the AC load by a sinusoidal current. These goals are attained by using the sliding mode to design control laws in order to command the boost DC-DC and the inverter switches. Thus, a maximum power point tracking (MPPT) and an output current controller based on the sliding mode are proposed. The innovative aspect of this work is to propose a standalone PV system with the controllers based only on the sliding mode control approach. The proposed system is modeled and simulated under MATLAB Simulink under fast variations of irradiance and temperature. Then, the obtained results using the suggested MPPT are compared to those using the incremental conductance (IC) method. These results demonstrate the superiority of the sliding mode MPPT in terms of the tracking speed, the efficiency, and the time of response. Moreover, the current controller provides an output current of high quality with a THD of 3.47%. Furthermore, for accurate results, these controllers are evaluated under the fluctuations of two daily climatic profiles (sunny and cloudy) and compared those of the IC method. The results illustrate that the sliding mode MPPT has the potential of generating more electrical energy than the IC MPPT with benefits of up to 13.02% for the sunny daily profile and 27.57% for the cloudy one.

Photovoltaic (PV) systems are gradually becoming the key renewable source of electrical energy. But the nonlinear characteristics of the PV module require special techniques to harvest maximum power. The techniques used to track the Maximum Power Point (MPP) from the nonlinear I-V characteristics through power electronic circuits are known as Maximum Power Point Tracking (MPPT) techniques. Although enormous MPPT techniques have been anticipated in literature, each endures its own advantages and disadvantages. This paper proposes a novel hybrid technique to enhance the performance of Incremental conductance (InC) technique. Our proposed technique removes the limitations of InC under rapid changing environmental condition. It improves the efficiency and reduces the oscillation power loss under steady state. A hybrid combination of variable-step sized Incremental Conductance with Fractional Short Circuit Current (FSCC) technique is proposed in this research work. Initially, the FSCC forces the PV system to work near MPP and then InC technique track the exact peak. Matlab/Simulink software is used to implement the proposed hybrid technique. Finally, the results verify its superior performance under rapidly changing environmental conditions.

Purpose Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques. Design/methodology/approach A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance. Findings MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower. Originality/value In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.

Integrating Photovoltaic plants into electric power system always involve challenges related to the dynamic performance of the power system. These challenges stem primarily from the natural characteristics of the Photovoltaic plants which differ in many aspects from conventional plants. Several natural conditions, such as solar irradiance and ambient temperature, do affect the output power of the PV systems. The Maximum Power Point Tracking (MPPT) techniques for controlling DC-DC boost Converter opt to reaching the maximum output power. Various MPPT-based techniques are discussed in paper in order to highlight the uncertainties almost always associated with conventional algorithm and propose a modified incremental conductance algorithm that responds accurately to solar irradiance variations. The recommended proposed algorithm shows zero oscillation in the power of the solar module after the maximum power point (MPP) is tracked. The results of the various studied algorithms (“Perturb & Observe”, Modified, Conventional, Variable step-size Incremental Conductance) are presented herein and duly compared.

Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE

This paper presents the design and analysis of a Grey Wolf Optimization (GWO)-based PI controller for a solar-fed Brushless DC (BLDC) motor with Incremental Conductance (INC) MPPT technique. Conventional MPPT methods such as Perturb and Observe (P&O) and PSO suffer from steady-state oscillations, slower tracking, and limited optimization performance. The proposed system integrates the INC MPPT technique with GWO-tuned PI controller for enhanced performance. The solar PV array is connected to a DC-DC boost converter and three-phase inverter feeding the BLDC motor. The INC MPPT method ensures accurate and fast maximum power point tracking with reduced oscillations under varying environmental conditions, while the GWO algorithm optimally tunes the PI controller parameters by minimizing the Integral of Absolute Error (IAE). MATLAB/Simulink simulation demonstrates 99.4% MPPT tracking efficiency, 1.0% steady-state speed error, 35ms settling time, and 38% reduction in IAE compared to conventional PSO-tuned controllers, validating the approach for solar-powered electric drives in water pumping and EV applications. Keywords: BLDC Motor, Solar PV, Grey Wolf Optimization, INC MPPT, PI Controller, Speed Control, Renewable Energy Drive