Proposed Maximum Power Point Tracking Technique Research Articles

Improved photovoltaic energy production under partial shading using an innovative MPPT controller based on Flying Squirrel Search Optimization algorithm

This paper explores the issue of partial shading (PS) in photovoltaic systems and proposes a solution using the Flying Squirrel Search Optimization (FSSO) algorithm. PS results in power losses, and our study aims to enhance photovoltaic energy production by introducing a new Maximum Power Point Tracking (MPPT) controller based on the FSSO algorithm. This paper presents a controller designed to efficiently track the maximum power in the presence of partial shading. The controller uses the Modified ODD-EVEN (MOE) configuration, the Modified Symmetrical Array (MSA), and the Total-Cross-Tied (TCT). The performance of the FSSO algorithm was compared with that of GWO and PSO. A bidirectional DC-DC converter was integrated to connect the PV system to a battery. The proposed methods were also applied to an electric vehicle powered by a PV battery. The FSSO proposed in this study demonstrates an efficiency of over 99%, outperforming other methods in tracking the maximum power point and converging faster to the overall maximum power point. The results indicate that convergence time can be improved from 16% to over 60%. Furthermore, the proposed MPPT technique effectively minimizes over 80% of random oscillations. The proposed method reduces losses and maximizes fill factor compared to alternative algorithms, based on two observed shading cases. When connecting the system to a battery, the FSSO algorithm outperforms the PSO by more than 34.78% and the GWO by 49.02%. Although slightly inferior to the PSO, the FSSO still performs significantly in the context of an electric vehicle powered by a photovoltaic battery.

Design and investigation of high power quality PV fed DC-DC boost converter

This study presents a new improved voltage gain dc-dc converter architecture to maximize solar photovoltaic (PV) power output. The maximum power point tracking (MPPT) method utilizes particle swarm optimization (PSO)–based artificial neural networks (ANN) to reduce the oscillations of output electrical performance at the maximum power point (MPP). For any solar cell temperature and irradiance, the ANN delivers the electrical current and voltage outputs at the MPP and thus could reach the MPP in minimum instances. A DC-DC converter needs specific characteristics to work with photovoltaic systems. These include higher voltage development to meet increased DC link voltage requirements, the continuous input current to extend the PV system life, common grounding to prevent electromagnetic interference, and reduced electrical stress with fewer components. This paper combines a quasi-switched switch inductor network and extendable diode capacitor modules to achieve the quality mentioned earlier. The converter offers constant input current, high efficiency (95 %) with considerable gain (12 times higher than input), lower voltage stress (almost one-fifth of output voltage), and a common grounding feature. The effectiveness of the proposed MPPT technique is analyzed in terms of higher efficiency, MPP tracking time, gain, and the normalized voltage stress on diodes. The experimental step is developed to validate the simulation (Simulink and Psim) results of the present research work.

Analysis of deep learning models for estimation of MPP and extraction of maximum power from hybrid PV-TEG: A step towards cleaner energy production

The Hybrid Photovoltaic-Thermoelectric generation system (PV-TEG) exhibits immense potential for effectively harnessing solar energy. However, the overall effectiveness of the hybrid PV-TEG system is hindered by the relatively low efficiency of both PV and thermoelectric generation (TEG). To overcome this limitation, this research investigates the application of an evolutionary Neural Network (NN)-based Maximum Power Point Tracking (MPPT) control technique. This proposed control technique utilizes a Growth Optimizer Algorithm (GO-algorithm) to optimize the weights and biases of a Deep Neural Network (DNN), enabling real-time tracking of the global maximum power point (GMM). Furthermore, to fortify the robustness of the MPPT control, a modified Fractional Order Proportional-Integral-Derivative (FOPID) controller is used whose gains are tuned using the GO-algorithm (GO-FOPID). Experimental evaluations conclusively demonstrate the efficacy of the evolutionary NN-based MPPT control technique in enhancing the generation efficiency of hybrid PV-TEG systems. The effectiveness of the proposed MPPT technique is validated through a comparative analysis with other MPPT techniques. The GO-DNN-based MPPT controller excels in its real-time global maxima tracking capability, exhibiting minimal power oscillations with average efficiency of 99.928% and an exceptionally low tracking time of less than 5ms. This superior performance is verified by comparative, simulation, quantitative, and statistical analyses, thereby highlighting the efficiency, tracking time, stability, and fault detection capabilities of the GO-DNN and GO-FOPID controllers across various practical scenarios.

An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

PurposeTo generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time.Design/methodology/approachIn this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence.FindingsThe effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques.Originality/valueThis paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems

In partial shading situations, the power–voltage (P–V) characteristics of photovoltaic (PV) systems become more complex due to many local maxima. Hence, traditional maximum power point tracking (MPPT) techniques fail to recognize the global maximum power point (MPP), resulting in a significant drop in the produced power. Global optimization strategies, such as metaheuristic approaches, efficiently address this issue. This work implements the recent “particle swarm optimization through targeted position-mutated elitism” (PSO-TPME) with a reinitialization mechanism on a PV system under partial shading conditions. The fast-converging and global exploration capabilities of PSO-TPME make it appealing for online optimization. PSO-TPME also offers the flexibility of tuning the particle classifier, elitism, mutation level, and mutation probability. This work analyzes several PSO-TPME parameter settings for the MPPT of partially shaded PV systems. Simulations of the PV system under varying shading patterns show that PSO-TPME, with balanced exploitation–exploration settings, outperforms PSO in terms of convergence speed and the amount of captured energy during convergence. Furthermore, simulations of partial shading conditions with fast-varying, smooth, and step-changing irradiance demonstrated that the proposed MPPT technique is capable of dealing with these severe conditions, capturing more than 97.7% and 98.35% of the available energy, respectively.

A novel ROA optimized Bi-LSTM based MPPT controller for grid connected hybrid solar-wind system

PurposeRenewable energy sources such as solar photovoltaic (PV) and wind are ubiquitous because of their lower environmental impact. Output from solar PV and wind turbines is unstable; hence, this article aims to propose an effective controller to extract maximum available power.Design/methodology/approachBy focusing on the varying nature of solar irradiance and wind speed, the paper presents the maximum power point tracking (MPPT) technique for renewable energy sources, and power regulation is made by the novel inverter design. Moreover, a DC–DC boost converter is adopted with solar PV, and a doubly fed induction generator is connected with the wind turbine. The proposed MPPT technique is used with the help of a rain optimization algorithm (ROA) based on bi-directional long short-term memory (Bi-LSTM) (ROA_Bi-LSTM). In addition, the sinusoidal pulse width modulation inverter is used for DC–AC power conversion.FindingsThe proposed MPPT technique has jointly tracked the maximum power from solar PV and wind under varying climatic conditions. The power flow to the transmission line is stabilized to protect the load devices from unregulated frequency and voltage deviations. The power to the smart grid is regulated by three-level sinusoidal pulse width modulation inverter.Originality/valueThe methodology and concept of the paper are taken by the author on their own. They have not taken a duplicate copy of any other research article.

Reduced oscillations based perturb and observe solar maximum power point tracking scheme to enhance efficacy and speed of a photovoltaic system

This paper puts forward a reduced oscillation based perturb and observe (ROP&O) maximum power point (MPP) tracking (MPPT) technique to mitigate the probability of loss of tracking direction and to reduce oscillations around MPP when the solar photovoltaic (PV) array is subjected under periodically changing irradiance. The proposed technique retains the structure of conventional perturb and observe (P&O) technique additionally incorporating a unique structure of dynamic step sizing, along with proportional-integral (PI) controller which potently alters the duty cycle (𝐷) of the DC-DC boost power converter (BPC). The ROP&O MPPT technique is compared with conventional P&O and incremental conductance (IC) schemes in terms of tracking efficacy (𝜂), ripples in PV voltage and PV current, convergence time, and the error rates. The efficacy of the proposed scheme lies between 99.06% to 99.80%. Moreover, the time to obtain MPP is 0.018 sec. which is about five times faster than the P&O technique and fifteen times faster than the IC technique. Also, the proposed MPPT technique is benchmarked using three-phase grid integration, and the power quality of the grid current is observed in terms of total harmonic distortion (THD).

Single-phase grid-connected PV system with golden section search-based MPPT algorithm

Maximum power point tracking (MPPT) is a technique employed for with variable-power sources, such as solar, wind, and ocean, to maximize energy extraction under all conditions. The commonly used perturb and observe (P&O) and incremental conductance (INC) methods have advantages such as ease of implementation, but they also have the challenge of selecting the most optimized perturbation step or increment size while considering the trade-off between convergence time and oscillation. To address these issues, an MPPT solution for grid-connected photovoltaic (PV) systems is proposed that combines the golden section search (GSS), P&O, and INC methods to simultaneously achieve faster convergence and smaller oscillation, converging to the MPP by repeatedly narrowing the width of the interval at the rate of the golden ratio. The proposed MPPT technique was applied to a PV system consisting of a PV array, boost chopper, and inverter. Simulation and experimental results verify the feasibility and effectiveness of the proposed MPPT technique, by which the system is able to locate the MPP in 36 ms and regain a drifting MPP in approximately 30 ms under transient performance. The overall MPPT efficiency is 98.99%.

Hybrid MPPT Technique PSO-P&O Applied to Photovoltaic Systems Under Uniform and Partial Shading Conditions

The PV array must always operate at the Global Maximum Power Point (GMPP) in order to make the most of the energy generated. In this context, some classical techniques achieve this goal satisfactorily under uniform shading conditions. However, environmental conditions vary during the day and these methods fail to extract the highest power available under partial shading conditions. The present paper aims to propose a hybrid Maximum Power Point Tracking (MPPT) technique that uses Particle Swarm Optimization (PSO) and Perturb & Observe (P&O) methods, applied to photovoltaic systems under uniform and partial shading conditions. The photovoltaic system was modeled in the PSIM software. The proposed MPPT technique is compared with the classical P&O, standard PSO and a hybrid P&O-PSO techniques. The simulation results showed that the proposed hybrid algorithm can track GMPP under uniform and partial shading conditions and tracking time is 50% shorter than standard PSO technique. Furthermore, the proposed method manages to extract 0.3% more electricity from the photovoltaic system compared to the P&O-PSO hybrid

Fuzzy based incremental conductance algorithm stabilized by an optimal integrator for a photovoltaic system under varying operating conditions

ABSTRACT 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.

High Performance MPPT Approach for Off-Line PV System Equipped With Storage Batteries and Electrolyzer

The current publication is directed to achieve a high-performance stand-alone PV system having the capability of tracking maximum output power, providing fixed output DC voltage, and attaining efficient system utilization, under different irradiation levels. A new maximum power point tracking (MPPT) approach integrating the incremental conductance algorithm and fuzzy logic control, and enhanced with PI-controller, was proposed to track maximum power. To provide fixed output DC voltage and approaching full system utilization, the PV system is equipped with a battery bank, electrolyzer; as a dump load, and buck-boost converter, with two controllers. The results of the proposed MPPT technique; modified incremental conductance (MINC), are compared with the corresponding results of three prevalently implemented MPPT algorithms: perturbed and observed (P&O), modified variable step-size P&O (VSZ-PO) and the ordinarily incremental conductance (INC). The highest output power, best tracking efficiency and best output power response are achieved by utilizing the proposed MPPT method. The results of the output voltage response and electrolizer on/off states confirm the ability of the PV scheme to provide fixed DC voltage and attain efficient system utilization, under varying irradiances.

Photovoltaic and Thermoelectric Generator Combined Hybrid Energy System with an Enhanced Maximum Power Point Tracking Technique for Higher Energy Conversion Efficiency

In this paper, the design and performance investigation of the hybrid photovoltaic–thermoelectric generator (PV–TEG) system with an enhanced fractional order fuzzy logic controller (FOFLC)-based maximum power point tracking (MPPT) technique is presented. A control strategy of the variable incremental conduction (INC) method is employed using FOFLC for the MPPT control technique to efficiently harvest the maximum power from the PV module. The fractional factor α used in the MPPT control algorithm is a supporting fuzzy logic controller (FLC) for the accurate tracking of the maximum power point (MPP) and to maintain the constant output after reaching the MPP. In the proposed system configuration, the TEG is mounted with the PV panel for generating the extra electrical power using the waste heat energy produced on the PV panel due to the incident solar irradiation. The PV and TEG are connected electrically in series to increase output voltage level and thereby improve the power output. The hybrid energy module has better energy conversion efficiency when compared to the standalone PV array. The performance of the proposed MPPT technique is studied for the PV–TEG hybrid energy module under various thermal and electrical operating conditions using a MATLAB software-based simulation. The results of the FOFLC-based MPPT technique are compared with the conventional perturb and observe (P&O) and FLC-based P&O methods. The proposed MPPT technique confirms its effectiveness in extracting the maximum power in terms of speed and accuracy. Moreover, the PV and TEG combined system provides higher energy efficiency than the individual PV module.

An Enhanced Maximum Power Point Tracking Method for Thermoelectric Generator Using Adaptive Neuro-Fuzzy Inference System

The design and performance study of a variable fractional factor combined adaptive neuro-fuzzy inference system (VFANFIS) based maximum power point tracking (MPPT) technique for the thermoelectric generator (TEG) is presented. In this proposed MPPT technique, the fractional factor is used in such a way to achieve the variable tracking step size based on the operating point of the TEG in the power versus voltage (P–V) curve. The larger tracking step size move the TEG system operating point quickly towards the maximum power point (MPP), suppose the TEG operating point reaches near to MPP then the tracking step size becomes smaller to maintain stable output without any oscillation. In the proposed algorithm, the fractional factor value which is determined based on the change in voltage of the TEG is used expand or contract the input domain range of the ANFIS by which the variable tracking step size is achieved. The energy efficiency of the TEG can be improved by attaining the MPP quickly and maintaining steady-state output around the MPP. The effectiveness of the VFANFIS-based MPPT technique is demonstrated with MATLAB simulation study under different thermal and electrical operating condition. The study results are confirmed that the proposed MPP tracker performs better to extract the maximum power from the TEG by achieving the MPP quickly and accurately when compared to the conventional incremental conduction (INC) based MPPT technique.

New Photovoltaic Module Model And A Comparative Study of MPPT Control Techniques Based On Neural Networks

Nowadays, renewable energy resources play an important role in replacing conventional fossil fuel energy resources. Solar photovoltaic (PV) energy is a very promising renewable energy resource, which rapidly grew in the past few years. The main problem of the solar photovoltaic is with the variation of the operating conditions of the array, the voltage at which maximum power can be obtained from it likewise changes. In this paper, is first performed the modelling of a solar PV panel using MATLAB/Simulink. After that, a maximum power point tracking (MPPT) technique based on artificial neural network (ANN) is applied in order to control the DC-DC boost converter. This MPPT controller technique is evaluated and compared to the “perturb and observe” technique (P&O). The simulation results show that the proposed MPPT technique based on ANN gives faster response than the conventional P&O technique, under rapid variations of operating conditions. This comparative study is made in terms of temporal variations of the duty cycle (D), the output power ( out P ), the output current ( out I ), the efficiency, and the reference current ( ref I ). The efficiency, D, out P , and out I are the output of the boost DC-DC, and ref I is itsinput. The different temporal variations of the efficiency, D, ref I , out P , and out I (for the two cases: the first case, when T = 25°C and G =1000 W/m2 and the second case, when T and G are variables), show negligible oscillations around the maximum power point. The used MPPT controller based on ANN has a convergence time better than conventional P&O technique.

A current sensor based adaptive step‐size MPPT with SEPIC converter for photovoltaic systems

Efficient maximum power point tracking (MPPT) is an important problem for renewable power generation from photovoltaic systems. In this work, a current sensor based MPPT algorithm using an adaptive step-size for a single ended primary inductance converter (SEPIC) based solar photovoltaic system is proposed. Due to lower sensitivity of power to current perturbation as compared to the voltage one, such a scheme is shown to yield better efficiency at steady-state. A new adaptation scheme is also proposed for faster convergence of the MPPT technique. Hence, the proposed scheme yields better transient as well as steady-state performance. A prototype converter is used along with digital implementation of the proposed MPPT technique to demonstrate the superiority of the proposed algorithm over the fixed step-size and voltage based ones. Simulation and experimental results corroborates the same.

Improved Proportional-Integral Coordinated MPPT Controller with Fast Tracking Speed for Grid-Tied PV Systems under Partially Shaded Conditions

When a photovoltaic (PV) system is exposed to physical objects and cloud coverage and connected to bypass diodes, a partial shading condition (PSC) occurs, which causes a global maximum power point (GMPP) and numerous local maximum power points (LMPPs) on the power-voltage (P-V) curve. Unlike conventional MPPT techniques that search for multiple LMPPs on the P-V curve, it is possible to track GMPP straightaway by designing a simple but robust MPPT technique that results in faster tracking speed and low power oscillations. Hence, in this study, an improved proportional-integral (PI) coordinated Maximum Power Point Tracking (MPPT) algorithm is designed to enhance the conversion efficiency of a PV system under PSC with fast-tracking speed and reduced power oscillations. Here, PI controllers are used to mitigating the steady-state errors of output voltage and current of PV system that later on passed through an incremental conductance (INC) algorithm to regulate the duty cycle of a dc–dc boost converter in order to ensure fast MPPT process. The PV system is integrated with the grid through an H-bridge inverter, which is controlled by a synchronous reference frame (SRF) controller. Tracking speed and steady-state oscillations of the proposed MPPT are evaluated in the MATLAB/Simulink environment and validated via a laboratory experimental setup using Agilent solar simulator and dSPACE (DS1104) controller. Results show that the proposed MPPT technique reduces the power fluctuations of PV array significantly and the tracking speed of the proposed method is 13% and 11% faster than the conventional INC and perturb and observe (P&O) methods respectively under PSCs.

Predictive Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell System

This project aims to design a predictive maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system (PEMFC). This predictive MPPT includes the predictive control algorithm of a DC-DC boost converter in the fully functional mathematical modeling of the PEMFC system. The DC-DC boost converter is controlled by the MPPT algorithm and regulates the voltage of the PEMFC to extract the maximum output power. All simulations were performed using MATLAB software to show the power characteristics extracted from the PEMFC system. As a result, the newly designed predictive MPPT algorithm has a fast-tracking of maximum power point (MPP) for different fuel cell (FC) parameters. It is confirmed that the proposed MPPT technique exhibits fast tracking of the MPP locus, outstanding accuracy, and robustness with respect to environmental changes. Furthermore, its MPP tracking time is at least five times faster than that of the particle swarm optimizer with the proportional-integral-derivative controller method.

Group Teaching Optimization Algorithm Based MPPT Control of PV Systems under Partial Shading and Complex Partial Shading

The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

A Variable Fractional Order Fuzzy Logic Control Based MPPT Technique for Improving Energy Conversion Efficiency of Thermoelectric Power Generator

Thermoelectric generation technology is considered to be one of the viable methods to convert waste heat energy directly into electricity. The utilization of this technology has been impeded due to low energy conversion efficiency. This paper aims to improve the energy conversion efficiency of the thermoelectric generator (TEG) model with a novel maximum power point tracking (MPPT) technique. A variable fractional order fuzzy logic controller (VFOFLC)-based MPPT technique is proposed in the present work in which the operating point of the TEG is moved quickly towards an optimal position to increase the energy harvesting. The fraction order term α, introduced in the MPPT algorithm, will expand or contract the input domain of the fuzzy logic controller (FLC to shorten the tracking time and maintain a steady-state output around the maximum power point (MPP). The performance of the proposed MPPT technique was verified with the TEG model by simulation using MATLAB /SIMULINK software. Then, the overall performance of the VFOFLC-based MPPT technique was analyzed and compared with Perturb and observe (P&O) and incremental resistance (INR)-based MPPT techniques. The obtained results confirm that the proposed MPPT technique can improve the energy conversion efficiency of the TEG by harvesting the maximum power within a shorter time and maintaining a steady-state output when compared to other techniques.