Solar Maximum Power Point Tracking Research Articles

Comparative Analysis of Gravitational Search Algorithm and Particle Swarm Optimization for Solar MPPT

This work delves into the optimization of Maximum Power Point Tracking (MPPT) for photovoltaic (PV) systems through a comparative analysis of two advanced algorithms: the Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). With the escalating demand for renewable energy sources, enhancing the efficiency of solar panels has become crucial. MPPT techniques are pivotal in maximizing the power output from solar panels by adjusting to varying environmental conditions. This work implements GSA and PSO within a MATLAB environment to track the MPP of a solar cell array efficiently. The study systematically evaluates the performance, convergence speed, and stability of both algorithms under diverse operational scenarios. Preliminary findings indicate that GSA exhibits faster convergence and reduced oscillatory behaviour compared to PSO, suggesting a superior efficiency in tracking the MPP. This paper aims to provide a comprehensive comparison between GSA and PSO, offering insights into their applicability and effectiveness in optimizing solar energy systems, thereby contributing to the development of more efficient renewable energy solutions.

Overview of Solar Photovoltaic MPPT Methods: A State of the Art on Conventional and Artificial Intelligence Control Techniques

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel’s efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems’ classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study.

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

This study proposes a novel honey bee dancing (HBD) maximum power point tracking (MPPT) algorithm inspired by the foraging behavior of honey bees. When a bee finds nectar, it returns to the honeycomb and dances to inform others about the location of the nectar. Other bees then fly towards the location and gather the nectar. The proposed HBD algorithm uses five bees searching for the nectar who communicate with each other about the location and the quantity of nectar by dancing. Finally, the five bees found the location of the most nectar which is represented by the maximum power point. The proposed HBD algorithm applies to uniform irradiance condition (UIC) and partial shaded condition (PSC). It is then compared with the PV panel output power and load relationship (OPLR) algorithm and perturb and observe (P&O) algorithm. Experimental verification has been performed under both the UIC and PSC. At the UIC's irradiance level of 200 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 120 W, 118 W, and 94.5 W, respectively, with efficiencies of 99 %, 97 %, and 78 %. Additionally, under the PSC with an irradiance level of 600 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 218 W, 189.2 W, and 74.8 W, respectively, with efficiencies of 99 %, 86 %, and 34 %, and convergence times of 4.7 ms, 6.5 ms, and 6 ms, respectively. It is evident from the results that the solar MPPT performance of the proposed HBD algorithm is better than the OPLR algorithm and P&O algorithm. This method ingeniously combines the foraging behavior of bees with solar power generation to produce the maximum natural power. This approach does not require the development of photovoltaic (PV) panel specification data, complex calculations, and additional temperature meters and heliographs. It is highly efficient and has significant economic benefits.

IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Solar Maximum Power Point Tracking Algorithm Based on Wavelet Neural Network

With the development of human civilization, the shortage of resources and environmental pollution are increasing. Renewable energy is the only way to solve the problem of harmonious coexistence between human civilization and environment. More and more of them have come into our lives to reduce the burden of the earth and benefit mankind. Nowadays, due to the increasing gap of traditional power supply, solar energy has become a mainstream renewable energy, more and more applications, to provide us with green energy and improve our lives. The research on the application of solar energy system is of great academic value. This paper mainly studies the solar maximum power point tracking (MPPT) algorithm based on wavelet neural network (WNN). This paper analyzes several common maximum power tracking control methods, describes the structure design of WNN maximum power tracking control algorithm, and describes the structure and working principle of photovoltaic cells. In this paper, the output power of MPPT mode is analyzed and compared by simulating the use of solar photovoltaic system. In the meantime, the output voltage of solar charging panel in the charging process is studied. The experimental results show that the output power of the power supply in MPPT mode is kept at 0.2, and the output power of the solar panel is improved at every moment with the change of the external environment. At 9 o'clock, the output voltage of the solar panel is 3.6, and at 11 o'clock, the output voltage is 4.11.

Design and implementation of a novel multi-faceted-efficient pneumatic dual-axis solar tracker

PurposePhotovoltaic (PV) systems are experiencing exponential growth due to environmental concerns, unlimited and ubiquitous solar energy, and starting-to-make-sense panel costs. Alongside designing more efficient solar panels, installing solar trackers and special circuitry for optimizing power delivery to the load according to a maximum power point tracking (MPPT) algorithm are other ways of increasing efficiency. However, it is critical for any efficiency increase to account for the power consumption of any amendments. Therefore, this paper aims to propose a novel tracker while using MPPT to boost the PV system's actual efficiency accounting for the involved costs.Design/methodology/approachThe proposition is an experimental pneumatic dual-axis solar tracker using light-dependent resistor (LDR) sensors. Due to its embedded energy storage, the pneumatic tracker offers a low duty-cycle operation leading to tracking energy conservation, fewer maintenance needs and scalability potential. While MPPT assures maximum load power delivery, the solar PV's actual delivered power is calculated for the first time, accounting for the solar tracking and MPPT power costs.FindingsThe experiments' results show an increase of 37.6% in total and 35.3% in actual power production for the proposed solar tracking system compared to the fixed panel system, with an MPPT efficiency of 90%. Thus, the pneumatic tracking system offers low tracking-energy consumption and good actual power efficiency. Also, the newly proposed pneumatic stimulant can significantly simplify the tracking mechanism and benefit from several advantages that come along with it.Originality/valueTo the best of the authors’ knowledge, this work proposes, for the first time, a single-motor pneumatic dual-axis tracker with less implementation cost, less frequent operation switching and scalability potential, to be developed in future works. Also, the pneumatic proposal delivers high actual power efficiency for the first time to be addressed.

A comparative study between deterministic and two meta-heuristic algorithms for solar PV MPPT control under partial shading conditions

• Under partial shading conditions, adequate tuning of firefly and particle swarm optimization maximum power point tracking algorithms converge to the global maximum power point irrespectively of the starting voltage value. • Under partial shading conditions, there is no guarantee that perturb and observe algorithm will converge to the global maximum power point even for different starting voltage values. • The sensitivities of the parameters of firefly algorithm in improving the performance of the algorithm are investigated to find the best possible control parameters. • Firefly algorithm based maximum power point tracking performed slightly well than particle swarm optimization based maximum power point tracking for most irradiance patterns investigated in this paper. During partial shading conditions (PSC), the PV modules in a solar PV array become reverse biased and act as a load, resulting in hotspot issues. This can significantly decrease the efficiency of the solar PV. The general way to deal with PSC is to connect bypass diodes across the non-shaded PV module. However, this will alter the uniform characteristics of the PV array, resulting in multiple power peaks i.e., a multi modal landscape. The conventional gradient-based Perturb and Observe (PnO) algorithm generally used for maximum power point tracking (MPPT) in solar PV is ineffective in finding the maximum power during PSC because it is prone to converge to local optimum due to its nature of searching in a multimodal landscape. However, in this article an attempt is made to show how the PnO behaves in a multimodal landscape considering different starting points. Robust stochastic algorithms that are based on a population of search agents are used to guarantee convergence to the global MPP. In this paper, the maximum power point under PSC was tracked using two meta-heuristic algorithms namely, particle swarm optimization (PSO) and the firefly algorithm (FA). The performances of these algorithms in tracking the maximum power point are evaluated. The efficiency, standard deviation (STD), and root mean square error (RMSE) of the PSO and FA algorithms are compared to those of the PnO algorithm. The PSO was found to have the lowest RMSE, and the FA had the lowest STD. The efficiency of the PSO and FA were relatively the same. Simulation results show that PSO and FA-based MPPT algorithms can efficiently track the global maximum power point (GMPP) irrespectively of the starting points. On the other hand, PnO is shown to be unreliable under PSC even with different starting points.

Design and Analysis of P&O algorithm based MPPT solar system in PSIM

Conventional solar panels suffer with non-identical losses (resistive losses, diode leakage, loss of material properties etc.). To overcome these unwanted phenomena, a solar maximum power point tracking (MPPT) algorithm has been introduced. In this paper, the algorithms used for tracking the sun (or the solar maximum power point tracking algorithm) has been introduced & a brief description of the basic & easiest algorithm i.e., the P&O Algorithm using PSIM software has been provided to give a clear view about the concept & implementation of MPPT. Keywords: Photovoltaic, MPPT, P&O, PSIM

New Solar MPPT Control Technique Based on Incremental Conductance and Multi-Objective Genetic Algorithm Optimization

Solar energy is highly dependent on climatic conditions and swings sporadically, making photovoltaic integration into power systems difficult. In order to improve the design of power infrastructure and the successful deployment of decentralized and renewable energy sources, a new paradigm for the energy supply chain is established, leading to the creation of microgrids. Intelligence should be introduced at all levels of the grid, and it should act over a long period. This article introduces new research tools and methodologies to help utility engineers better understand the grid's potential impact from these new power sources. Therefore, this research proposes a novel PV solar system control method. The strategy is an innovative Maximum Power Point Tracking (MPPT) technology for solar systems. Designing an effective controller capable of lowering Power Stress inside the PV System is critical in this respect. In this research, the Proportional Integral (PI) controller has been utilized in conjunction with a heuristic technique termed Genetic Algorithm (GA) in order to regulate and stabilize the MPP of the power supplied by the PV system. Then the GA technique has been utilized to identify the ideal settings, based on the four performance indices of Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time Absolute Error (ITAE), and Integrated Time Squared Error (ITSE) (Kp, Ki). The simulation results show that the PV system can successfully monitor the required MPP.

New Solar MPPT Control Technique Based on Incremental Conductance and Multi-Objective Ant Colony Optimization

Solar energy is extremely reliant on climatic conditions, and fluctuates irregularly, forcing the integration of Photovoltaics PV into power grids problematic. A new paradigm for the energy supply chain is developed, leading to the development of microgrids, in order to enhance the design of power infrastructure and the effective deployment of decentralized and renewable energy sources. Intelligence should be added at all levels of the grid, acting across a variety of periods. This article presents new research tools and methodology to assist utility engineers in studying the grid's potential effect from these new power sources. A revolutionary PV solar system control method is proposed in this research, and the approach is an innovative Maximum Power Point Tracking (MPPT) technique for solar systems, which is based on adding a complicated function. This function tracks the Maximum Power Point (MPP) in PV systems by determining the duty cycle of the DC-DC converter in any environment and the load scenario. In this regard, designing an appropriate controller capable of reducing Power stress inside the PV System is essential. In this paper, the Proportional Integral (PI) controller has been used in conjunction with a metaheuristic approach called Ant Colony Optimization (ACO) in order to manage and stabilize the MPP of the power produced by the PV system. Then, utilizing the five multi-objective performances of Integrated Squared Error (ISE), Integrated Absolute Error (IAE), Integrated Time Absolute Error (ITAE), Integrated Time Squared Error (ITSE), and the Mean Squared Error (MSE), the ACO method has been used to determine the optimal parameters (Kp, Ki) (ITSE). The simulation results demonstrate a strong performance in monitoring the required MPP with the disrupted PV system.

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).

Photovoltaic-Based Electric Vehicle Charging Station using GA Algorithm

Detailed modeling of rapid charging Electric Vehicle (EV) stations connected to a hybrid grid-Renewable Energy Systems (RES), such as solar, mini-hydro, and wind, has been proposed. This assists in maximizing profit and lowering grid energy consumption. Since Photovoltaic (PV) systems exhibit several peaks under partial shading conditions; it is very difficult to track the global maximum point. Therefore, a broad search is needed to solve this nonlinear problem. Due to the variety of solutions, GA is preferred for solar MPPT. In comparison to the Genetic Algorithm (GA) and Maximum Power Point Tracking (MPPT) technique, the economic considerations obtained optimizes profit. It is also obvious that the proposed strategy lowers the grid's influence on the system network by capping the amount of electricity that may be exchanged between the system network and the grid. The voltage and current of PV array with GA method and GA MPPT technique results on the utility grid respectively.

Evaluation of Power Performance of a PV Module with MPPT Solution Using MATLAB Simulation

In the context of increasing emission of greenhouse gasses in the environment due to fossil fuel burning, this paper attempts to describe the significance of Maximum Power Point Tracking (MPPT) by investigating the power performance of photovoltaic modules with MATLAB simulation. MPPT algorithm was employed to secure maximum power from PV module. The boost converter whose pulse is linked to MPPT algorithm restricts the flow of load power and controls the current and voltage of PV panels. The whole design of the solar model, boost converter, and MPPT controlled algorithms was done in the SIMULINK to prioritize the system in simulation. The main concept employed in this paper was to develop a power generation process with MPPT algorithm and to provide information for future use. In this paper, all simulations along with the PV power generation process were done in MATLAB. This research could potentially play a vital role in mitigating the world fuel crisis.

Modelling and Simulation of Photo voltaic System Using Matlab/Simulink

In regulate to optimize the effectiveness of the solar cell well established and more efficient models are required to develop. In this paper mathematical modelling of solar cell and an algorithm called Maximum Power Point Tracking (MPPT) has been studied. Matlab has been used for the simulation of mathematical modelling. I – V characteristics of the solar cell and MPPT algorithm is studied. The single diode model is used for mathematical modelling of the photovoltaic system. The required parameter for the system is taken from the datasheet of industrialized company. The solar cell highly dependent on the weather condition therefore I – V characteristics studied with the change in temperature and irradiation of the modelled system.

Advanced Solar MPPT Techniques Under Uniform and Non-Uniform Irradiance: A Comprehensive Review

AbstractAdvancement of human civilization has led to rapid industrialization going in hand with urbanization and globalization, which have elevated energy demand worldwide. Conventional energy sources with the fear of being exhausted at a rapid pace seem to fail to quench inflated energy demand alone. Solar being a ubiquitous and eco-friendly source of renewable energy has become one of the most dominant forms of power generation implemented in diverse applications. To maximize the efficacy of the solar photovoltaic (SPV) system, it must be operated at maximum power point (MPP) that incorporates the use of maximum power point tracking (MPPT) algorithm. MPPT algorithm is a self-automated control technique that compels the SPV system to operate at MPP thereby harnessing maximum obtainable power under time-varying environmental conditions such as solar intensity, temperature, SPV module characteristics, and module shading. This paper puts forward an elaborated study on 27 MPPT techniques that are pervasive in the SPV system. The entire assessment deals with MPPT techniques employed under uniform solar insolation varying from time to time as well as global maximum power point tracking (GMMPT) techniques employed under partial shading (PS) conditions. Vivid comparisons among all the MPPT techniques along with their brief discussion, merits, and demerits have been done. Moreover, a detailed structure of error-based incremental conductance MPPT has been proposed.

Non-Linear Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking

In this paper, nonlinear sliding mode control (SMC) techniques formulated for extracting maximum power from a solar photovoltaic (PV) system under variable environmental conditions employing the perturb and observe (P and O) maximum power point tracking (MPPT) technique are discussed. The PV system is connected with load through the boost converter. A mathematical model of the boost converter is derived first, and based on the derived model, a SMC is formulated to control the gating pulses of the boost converter switch. The closed loop system stability is verified through the Lyapunov stability theorem. The presented control scheme along with the solar PV system is simulated in MATLAB (matric laboratory) (SMC controller and PWM (Pulse Width Modulation) part) and PSIM (Power electronics simulations) (solar PV and MPPT algorithm) environments using the Simcoupler tool. The simulation results of the proposed controller (SMC) are compared with the classical proportional integral derivative (PID) control scheme, keeping system parameters and environmental parameters the same.

Hybrid Loop Controlled Dual Axis Mechanical Solar Tracking Systems and MPPT for Nano/Micro Grid Applications

This paper emphasizes the important role of solar tracker systems in Micro/Nano grid level applications. Basically mechanical and electrical type trackers are different in operation but they are similar in way of tracking of solar energy. Mechanical trackers (Dual axis solar tracking system) are widely used where space is less and quality is more and with fixed panels solar MPPT is adoptable. Hybrid loop control which controls the relative motion of solar tracking system (STS) during the day as per the position of sun. Hybrid loop control (HLC) is effective rather than open and closed loop systems due its mode of control using sun position algorithm and sensors based as per the requirement and environmental conditions. Most commonly used sensor based trackers takes feedback to adjust the panel position in such a way to track the sun, but the situation when the sky is cloudy creates problem in performance of the system. The estimated accuracy is very close to ±0.0003º but it depends on the irradiation coming from sun. So, Sun tracking system by using solar position algorithm with accuracy is required in mechanical solar trackers and maximum power point tracking in electrical trackers. In order improve the efficiency of whole solar tracking system, we incorporated both open and closed loops i.e., hybrid loop control which plays important role to improve the overall performance. In view of that we incorporated both open and closed loops i.e., hybrid loop control. Here we presented Maximum power point tracking(MPPT) which is electrical tracking system simulated in MATLAB environment and also presented mechanical type of tracking system It is facilitated by using Programmable logic controller (PLC) to access more input/output controls and simulation has been done in CODESYS software using ladder logic programming

System Identification of a Solar Maximum Power Point Tracking System for Dual Axis

The project is on Maximum efficiency of a solar panel. As the sun is not constant at one place and by fixing the solar array at one place, maximum power generation is not possible. This project develops a solar power generation system using maximum power point tracking system. More direct sun light on Photovoltaic (PV) modules leads to enhanced energy yield. Therefore, tracking systems are implemented to improve the performance of PV system by tracking sun trajectory. The current design of Maximum Power Point Tracking (MPPT) trackers are more efficient than conventional single state solar power generation. Then by comparing two designs initially produced, Maximum Power Point Tracking (MPPT) tracking devices are found to be at better efficiency than static models. The system locates maximum- power generating point using an MPPT controller. The base line results of this study were obtained via physical implementation of simulated system to analyze the various parameters. The system then uses a quadratic-equation based algorithm that calculates the quadratic function corresponding to the maximum power generation point. With the advent of different applications of PV solar power, system planners have been implementing different strategies and techniques to maximize the output of solar system with commonly available technology in market. Additionally, various mathematical techniques were utilized to analyze the results gathered from the physical implementation. The simulation results also implemented to compare between the hardware and software of the project. The comparison of static Photovoltaic output graphs with real time measured values for the systems and a Dynamic Photovoltaic Maximum power point Solar Tracker (MPPT) system was observed. Both PV systems were implemented practically to get appropriate results and mainly all the equipment and services utilized in installation are widely available in local market.

Coordinated control of distributed generation system with improved generalised enhanced phase locked loop control algorithm

Here, a distributed generation system (DGS) is proposed that allows the different distributed energy resources to operate in coordination with others to provide the electricity in rural areas. Two individual controls are used in the DGS: (i) solar maximum power point tracking (MPPT) and the battery protection control, and (ii) voltage source converter (VSC) control. The proposed VSC control algorithm is based on an improved generalised filter algorithm for power quality improvement of the DGS. This control strategy effectively regulates the active power flow and provides the harmonics mitigation, reactive power compensation, and load balancing in the DGS with low steady-state error and fast dynamic response under high impulsive noise with DC-offset component. The solar MPPT control is designed on the basis of battery state of charge (SOC). In this control, the solar PV array operates in limited power point tracking (LPPT) after the storage battery surpasses the maximum SOC limit. In LPPT control, the solar PV MPPT operates in the voltage source operating region of PV array characteristic, thereby, reducing the power generation and avoids the overcharging of the storage battery. The proposed DGS performance is validated through test results under dynamics and steady-state conditions.

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

The Wireless Sensor Networks (WSN) are the basic building blocks of today’s modern internet of Things (IoT) infrastructure in smart buildings, smart parking, and smart cities. The WSN nodes suffer from a major design constraint in that their battery energy is limited and can only work for a few days depending upon the duty cycle of operation. The main contribution of this research article is to propose an efficient solar energy harvesting solution to the limited battery energy problem of WSN nodes by utilizing ambient solar photovoltaic energy. Ideally, the Optimized Solar Energy Harvesting Wireless Sensor Network (SEH-WSN) nodes should operate for an infinite network lifetime (in years). In this paper, we propose a novel and efficient solar energy harvesting system with pulse width modulation (PWM) and maximum power point tracking (MPPT) for WSN nodes. The research focus is to increase the overall harvesting system efficiency, which further depends upon solar panel efficiency, PWM efficiency, and MPPT efficiency. Several models for solar energy harvester system have been designed and iterative simulations were performed in MATLAB/SIMULINK for solar powered DC-DC converters with PWM and MPPT to achieve optimum results. From the simulation results, it is shown that our designed solar energy harvesting system has 87% efficiency using PWM control and 96% efficiency ( η s y s ) by using the MPPT control technique. Finally, an experiment for PWM controlled SEH-WSN is performed using Scientech 2311 WSN trainer kit and a Generic LM2575 DC-DC buck converter based solar energy harvesting module for validation of simulation results.