Maximum Power Point Algorithm Research Articles

Self fuzzy-genetic approach for boosting photovoltaic efficiency

The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34] and [35], in order to evaluate its effectiveness.

Design of a Seawater Desalination System with Two-Stage Humidification and Dehumidification Desalination Driven by Wind and Solar Energy

The paper presents a wind–photovoltaic-thermal hybrid-driven two-stage humidification and dehumidification desalination system for remote island regions lacking access to electricity and freshwater resources. By conducting an analysis of the wind and solar energy resources at the experimental site, a suitable wind power station and photovoltaic power station are constructed. The performance of the wind–solar complementary power generation system is then evaluated based on factors such as output power, seawater desalination load power, battery compensation output, system energy consumption, and water production costs. A variable step gradient disturbance method based on the power–duty ratio is proposed for tracking the maximum power point (MPPT) of wind power generation. The output power of the photovoltaic power generation system is optimized, employing a fuzzy logic control (FLC) method to track the MPPT of photovoltaic power generation. This approach effectively addresses the issues of slow speed and low accuracy encountered by traditional MPPT algorithms in tracking the maximum power point (MPP) of both photovoltaic and wind power generations. In order to ensure that the desalination system can operate stably under different weather conditions, eight working modes are designed, and a programmable logic controller (PLC) is used to control the system, which provides a guarantee for stable water production. Experimental results demonstrate that the system exhibits stable performance, achieving a maximum water output of 80.63 Kg/h and daily water yield is 751.32 Kg, the cost of desalination equipment is 1.4892 USD/t.

Particle Swarm Optimization (PSO) based Photovoltaic MPPT Algorithm under the Partial Shading Condition

Performance of solar photovoltaic (PV) system degrades considerably when the surface of the PV array is covered with shadows indicated by the reduction in the generated power. Partial shading condition causes the PV array to produce multiple peaks in PV’s characteristic curve causing the non-optimal power generation of the conventional maximum power point (MPPT) algorithm. Therefore, we proposed an optimization of PV system through the design of particle swarm optimization (PSO) algorithm in the MPPT in order to enhance the power extraction during shaded conditions. The evaluation of the performance of MPPT were carried out by comparing tracking time, tracking error and efficiency obtained by the PSO along with the two most used algorithms namely perturb and observe (P&O) and incremental conductance (IC). A partial shading generator function was designed to generate shading patterns randomly. The PV system consists of ten PV modules integrated with DC dummy load through DC-DC boost converter was simulated using MATLAB/SIMULINK. Simulation results show that the proposed PSO could obtain the global maximum power point (GMPP) while tracking performance of the P&O and IC MPPT both were stuck on the local maxima. Moreover, the optimization of PSO MPPT gave a promising result that the efficiency of the PV system was improved by around 4.66% on the dynamic shading conditions, while the MPPT’s tracking time is slowed down by 0.0025 s and 0.0105 s when its compared to the P&O and IC MPPT respectively.

A Unified Active Frequency Regulating and Maximum Power Point Tracking Strategy for Photovoltaic Sources

In order to optimize the extraction of solar energy, photovoltaic sources are commonly operated under the control of the so-called maximum power point (MPPT) strategy. However, as the rate of PV installations increases explosively, traditional MPPT algorithms may cause problems such as frequency deviation and power fluctuations, making system frequency stability a challenge due to the inherent intermittent and stochastic nature of PVs. Consequently, in order to reduce the investment and maintenance costs of storage systems, innovative control is expected for PV sources to provide ancillary services for the system, especially for weak systems such as microgrids. In this paper, a novel active power control (APC) strategy, based on characteristic curve fitting, is proposed to flexibly regulate the PV output power. The transient process performance and robustness of the system are improved with the proposed APC strategy. In conjunction, an f–P droop mechanism is designed to provide a frequency regulating (FR) service for the AC microgrid. The comprehensive control strategy unifies the FR function with the traditional MPPT function in a single control structure, allowing the PV source to operate either in the MPPT mode when the system frequency is nominal or in FR mode when the frequency exceeds it. The transition between MPPT and FR is autonomous and fully decentralized, which improves the PV generation efficiency as well as ensuring generation fairness among different parallel PV sources. Importantly, the proposed control strategy does not require any internal bundled energy within the PV generation system to achieve FR capability, but it effectively collaborates with the system-level energy storage system, thus reducing the necessary battery capacity. A detailed dynamic model of a PV generation system is constructed to validate the feasibility and effectiveness of the proposed control strategy.

Analysis of interleaved fuzzy Logic MPPT approach for PV system

In this article, a detailed novel interleaved fuzzy logic control based Maximum Power Point Algorithm was designed. It was derived from the conventional hill climbing method, which is used to track solar Photo Voltaic (PV) systems over changing temperature and irradiance. The proposed system composed of the PV panel source, Maximum Power Point Tracking (MPPT) with designed interleaved Fuzzy Logic controller, DC to DC charge controller, and load. The designed fuzzy logic controller contains the two fuzzy subsets, one fuzzy concentrate the change in power with respect to change in voltage and another fuzzy concentrate the change in power with respect to change in current, compare with two fuzzy model uses to track the path more perfect compare to existing method as a novelty of the article. The proposed interleaved fuzzy MPPT optimally tune the parameters of the controller. The proposed system analyzed the few sub criteria, such as time response, stability and power efficiency. The performance of the presented controller has been validated with conventional controllers such as fractional open circuit approach, fractional short circuit approach and hill climbing approach. The proposed leads the highest tracking performance compared to existing. In addition, the proposed performed effectively in different environmental conditions.

Energy yield estimation of on-vehicle photovoltaic systems in urban environments

Greenhouse gases from the propulsion systems of road transportations constitute a significant obstacle to achieve the Paris Agreement objectives. Nowadays, the substitution of conventional internal combustion engines with electric motors, along with electrochemical storage systems are the leading efforts to reduce the use of fossil fuels in road transportations. However, their limited driving range and the long charging times are the main technical factors that hinder the development of electromobility. Thus, energy harvesters and regeneration systems are increasingly incorporated in road vehicles, in order to increase their driving range. In this context, Vehicle Integrated and Applied Photovoltaics (VIAPVs) constitute an attractive prospect. The electricity yield for VIAPVs depends strongly on the route, the shadings due to the urban environment, the applied Maximum Power Point (MPPT) algorithm and the traffic conditions. In this paper, four commonly used commercial MPPT algorithms are experimentally evaluated, regarding their ability to extract the maximum available power simulating realistic city routes. The results show notable discrepancies in the performance of the studied algorithms, between terrestrial and VIAPV applications, highlighting the impact of poor MPPT performance in terms of power generation in moving vehicles.

Water Pumping System Supplied by a PV Generator and with a Switched Reluctance Motor Using a Drive Based on a Multilevel Converter with Reduced Switches

Pumping systems play a fundamental role in many applications. One of the applications in which these systems are very important is to pump water. However, in the real world context, the use of renewable energies to supply this kind of system becomes essential. Thus, this paper proposes a water pumping system powered by a photovoltaic (PV) generator. In addition, due to its interesting characteristics, such low manufacturing cost, free of rare-earth elements, simple design and robustness for pumping systems, a switched reluctance motor (SRM) is used. The power electronic system to be used in the PV generator and to control the SRM consists of a DC/DC converter with a bipolar output and a multilevel converter. The adopted DC/DC converter uses only one switch, so its topology can be considered as a derivation of the combination of a Zeta converter with a buck–boost converter. Another important aspect is that this converter allows continuous input current, which is desirable for PV panels. The topology selected to control the SRM is a multilevel converter. This proposed topology was adopted with the purpose of reducing the number of power semiconductors. A maximum power point algorithm (MPPT) associated with the DC/DC converter to obtain the maximum power of the PV panels is also proposed. This MPPT will be developed based on the concept of the time derivative of the power and voltage. It will be verified that with the increase in solar irradiance, the generated power will also increase. From this particular case study, it will be verified that changes in the irradiance from 1000 W/m2 to 400 W/m2 will correspond to a change in the motor speed from 1220 rpm to 170 rpm. The characteristics and operation of the proposed system will be verified through several simulation and experimental studies.

A Comprehensive Review on Latest State of the Art Practices in MPPT Algorithm

The performance of the solar charge controller depends largely on the maximum power point (MPPT) tracking algorithms. The review paper emphasis on the factors that influence the selection of MPPT, and performance of the charge controller employed in PV system. An efficient MPPT technique can achieve the purpose of energy saving, better extraction of power from solar. This paper presents a literature review on the basic MPPT techniques in detail along with their variants published by various researchers. The detailed study of MPPT techniques for improving the PV system performance, has been presented in this paper with emphasis on design and future MPPT techniques based on Moving Average Filter.

Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System

In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT control: neural network control (NNC) and the perturbation and observe method (PO), thus the PO method can offer better performance. Furthermore, this study aims to provide a comparison of the hybrid method with each algorithm 𝑃𝑂 and NNC. At the resulting duty cycle of the 2 methods, we applied the combination operation. A DC-DC boost converter is subjected to the hybrid MPPT control. This converter is part of a wind energy conversion system employing a permanent magnet synchronous generator (PMSG). The chain is modeled using MATLAB/Simulink software. The effectiveness of the controller is tested at varying wind speeds. In terms of the Integral time absolute error (ITAE), using the PO technique, the ITAE is 9.72. But, if we apply the suggested technique, it is smaller at 4.55. The corresponding simulation results show that the proposed hybrid method performs best compared to the PO method. Simulation results ensure the performance of the proposed hybrid MPPT control.

Reconfigured Photovoltaic Model to Facilitate Maximum Power Point Tracking for Micro and Nano-Grid Systems

PV systems are a popular energy resource, prevalent worldwide; however, shade faults manifested in PV systems limit its power conversion efficiency. The occurrence of multiple power peaks and their location are highly uncertain in PV systems; this necessitates the use of complex maximum power point tracking algorithms to introduce high voltage oscillations. To address this issue, a new reconfigurable PV array to produce a global maximum power point (GMPP) algorithm close to the Voc regions was introduced. This enables the use of a simple Perturb and Observe (P&O) algorithm to easily track GMPP. For reconfiguration, a simple 5 × 5 PV array is considered, and a new physical relocation procedure based on the position square method is proposed. Performance of the proposed reconfiguration model is tested for four various shade events and its row current evaluations are comprehensively analyzed. Furthermore, evaluations of fill factor, mismatch loss, and power loss are quantitatively compared against Dominance Square and TCT schemes. Since the power enhancement is ensured in a reconfigurable PV array, the fixed reconfiguration is tailored to suit residential PV and microgrid systems. For MPP evaluations, hardware demonstrations are performed with a lab scale prototype model developed with a PV simulator and a DC–DC power electronic interface. The I–V characteristics of conventional and reconfigured models are programmed into the simulator and the use of the hill climbing algorithm is validated. To analyze the voltage and power oscillations with MPP tracking, the PSO algorithm is also tested for two test patterns and its results are comprehensively studied.

Adaptive Control of Photovoltaic Systems Based on Dual Active Bridge Converters

Dual active bridge converters (DAB) are used to interconnect photovoltaic (PV) generators with AC and DC buses or isolated loads. However, a controller is needed to provide a stable and efficient operation of the DAB converter when the PV generator must be interconnected with a DC bus, which is particularly important in microinverter applications. Therefore, this paper proposes the design of a cascade controller for a PV system based on a DAB converter. The converter is controlled using a peak current control and an adaptive PI voltage control; thus the methodology to design the cascade controller is developed in two steps; first, the PV system formed by a PV generator, a DAB converter, and an inverter or load is introduced, including the description of the leakage current; as a second step, the model of the PV system to design the cascade controller is presented. Then, a relationship between the phase shift factor and the peak current of the leakage inductor is derived, which is used to design the peak current controller to ensure the DAB converter operation at the most efficient operating condition. On the other hand, an adaptive PI controller for the PV voltage is designed to ensure the reference tracking provided by a maximum power point (MPP) algorithm. The effectiveness of the proposed cascade controller is demonstrated through realistic examples simulated in PSIM. The power and control circuits implemented in PSIM are presented to encourage the use of the proposed solution. The simulation results confirm the correct operation of the control system, which mitigates the oscillatory perturbation produced by an inverter connected to the PV system, and also ensures the maximum power extraction from the PV panel by following the MPP reference.

Nonlinear backstepping control of a partially shaded photovoltaic storage system

Many power converter architectures and control approaches, both traditional and unconventional, have been developed, investigated, and adjusted to handle the challenge of tracking the maximum power points of a partially shaded photovoltaic (PV) system, which fluctuates with meteorological conditions (radiation and temperature). A DC-DC converter was used as the power conditioning unit to determine the system’s maximum efficiency. In this research, we focus on developing a nonlinear controller for a DC-DC converter to track the overall maximum power point in a PV storage system under partial shading situations. This study presents a combination of two MPP search algorithms with a backstepping controller. The particle swarm optimization (PSO) and variable step Perturb and Observe (P&O)with global scan (VSP&O/GS) algorithms supply the PV output reference voltage to the backstepping controller in order to recover the maximum power from photovoltaic (PV) systems. The simulation results of the methods compared to the proposed maximum power point (MPPT) algorithm are simulated and examined in the MATLAB/Simulink environment under non-uniform irradiation conditions. To demonstrate the performance and limits of each approach in tracking the maximum power point.

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

DC-Link Capacitor Diagnosis in a Single-Phase Grid-Connected PV System

The DC-link capacitor is one of the components that are more prone to faults in energy-distributed systems based on voltage source inverters. A predictive maintenance approach should allow to foresee the risk of an unexpected system shutdown. In this study, a two-stage diagnostic approach that is aimed at determining the health status of the DC-link capacitor in a single-phase grid-connected PV system was proposed. The equivalent series resistance (ESR) and the capacitance (C) values were used as indicators in the estimation of the degradation stage. Electrochemical impedance spectroscopy (EIS) was used to estimate the impedance curve of the DC-link capacitor, and a multi-fitting algorithm allowed us to determine the ESR and C parameters. A comparison between the estimated values C and ESR and the nominal values was used to quantify the fault severity. It was demonstrated that the EIS allowed the determination of the capacitor impedance regardless of the actual operating conditions of the photovoltaic generator, such as during irradiance changes and with the maximum power point algorithm turned off. By using the capacitor simplified model and a multi-fitting algorithm, the C and ESR values were estimated with an error that was lower than 1%. An analysis of the hardware required to implement the proposed approach in real applications by achieving the desired accuracy was also proposed.

IMPROVED PSO ALGORITHM FOR A PV SYSTEM UNDER PARTIAL SHADING CONDITION

Maximum power point tracking is essential for utilization of more amount of solar irradiation. Partial Shading condition is one of the reason behind not getting maximum power point. Partial shading condition may cause due to trees, chimney, clouds and so on. Due to partial shading, multiple peaks occurs in P-V characteristics of PV array. Conventional maximum power point algorithms are not able to track the maximum power point. To overcome this drawback, Particle swarm optimization algorithm is used for maximum power point tracking. Improved version of PSO also used in given paper for better results. In this paper comparative study of particle swarm optimization is done.In this paper particle swarm optimization with time varying inertia weight(PSO-TVIW) is applied for maximum power point tracking(MPPT). Results of classical PSO(particle swarm optimization) and POS-TVIW have analyzed. Effectiveness of both the algorithm have studied. Simulation of the PV system is done using MATLAB/Simulink.

Comparison of Maximum Power Point (PO & GMPP) Algorithm Under Varying Environmental Factors to Extract Maximum Peak Power from Photovoltaic System

Aim: This paper deals with efficiency enhancement on extraction of maximum peak power under varying environmental conditions from the photovoltaic green energy systems (PV). Materials & Methods: Perturb and Observe (P&O) and Global maximum power point (GMPP) algorithms have been implemented to analyse tracking efficiency. Results: Based on results obtained, GMPP has higher efficiency of about 93.37 % than PO has the efficiency of about 90.71%. Conclusion: GMPP appears to produce more consistent efficiency under varying environmental conditions than P&O MPPT algorithm for the selected data set.

Design of an efficient bipolar converter with fast MPPT algorithm for DC nanogrid application

SummaryA single‐input bipolar converter has been proposed along with a fast MPPT tracking algorithm. The proposed converter is used as an interface for PV panel to perform twofold task of effectively extracting the maximum power from the PV side and efficiently delivering the extracted power to the load. For effectively tracking the MPP (maximum power point) of PV panel, a modified perturb and observe (MP&O) algorithm has been proposed in the paper. Proposed algorithm works effectively even under fast variations of irradiation and ambient temperature. The proposed converter has been developed with a target application in DC nanogrid by providing two loading points with amplitude balanced bipolar outputs. Moreover, least input current ripple (compared with other conventional DC‐DC converters) aids the MPP algorithm in improving the tracking efficacy. As the proposed converter has synchronous switching operation at both output terminals referred to input side, the power conversion efficiency of the converter is very high. Two out of three switches used are ground referenced; thus, design complexity of the converter is greatly reduced. Proposed converter with the MP&O algorithm has been analyzed, and the simulation results are obtained using MATLAB/Simulink. A laboratory developed prototype is used to experimentally validate the proposed converter.

Implementation of Fuzzy logic control based MPPT for Photovoltaic system with Silicon Carbide (SiC) boost DC-DC converter

In recent days, photovoltaic (PV) system is the most promising renewable energy technologies and the PV cell has to operate at the optimum operating point to deliver maximum power. In order to obtain maximum power from PV, a maximum power point controller is required. This paper presents the simulation and hardware implementation of fuzzy logic (FL) maximum power point (MPPT) controller with FPGA technology for photovoltaic system. The MPPT algorithm is implemented for a Silicon carbide (SiC) MOSFET based boost DC-DC converter which provides fast switching, low losses and high voltage gain. The proposed MPPT algorithm is implemented on a SPARTAN/FPGA board platform based on the model developed and executed in MATLAB/SIMULINK. The entire system designed and implemented to hardware was successfully tested on a laboratory prototype PV array. The experimental results show the effectiveness and feasibility of the proposed controller and the results were satisfactory.

A novel exponent I–V relation based maximum power point algorithm for photovoltaic panel

• This paper introduces an explicit I–V relation formulated by three coefficients (known as an exponent model) to calculate accurate VMP using analytical expression with a single step. • A simple procedure for the determination of the exponent model coefficients in the standard test conditions (STC) and in the different environmental test conditions is described. Successively, the adjusted coefficients are used to calculate VMP . • The exponent model compares its accuracy with the universally adopted practical model (single-diode-model) in terms of the relative error in VMP and PMP tested in different environmental conditions. • The performance of the proposed model in different test conditions is simulated in detail using a PV system, and is also compared with, the P&O, the series-diode model based MPP (SDMPP) [24] and the Lambert W function based MPP (LWMPP). The efficiency comparison is conducted in the steady state and the dynamic conditions with proving the superiority of the proposed model. • The model has an additional feature of the estimation of the irradiance with an additional cost of PV temperature measurement. • The model successfully tracks the accurate MPP if 10% tolerance is considered in the output power of the PV modules manufactured by the same manufacturer. Moreover, the model performance is found marginally deviated in case of aging of a PV module. • In the laboratory, a prototype PV system with a boost converter, a resistive load and a controller (to regulate the boost converter voltage at the MPP) is designed and tested when the PV modules are placed in an open atmosphere under the clear sky. The experimental results are also compared with the P&O algorithm to establish the superiority of the proposed model. • The only limitation of the proposed model is a frequent adjustment of the coefficients with an additional cost of a PV temperature measurement in order to estimate accurate MPP. In this paper, a new model based MPP tracking algorithm in different environmental conditions is presented. A PV power converter driven by a maximum power point (MPP) controller adjusts the PV source voltage at the MPP ( V MP ) to harvest maximum power from the PV source. The proposed EXMPP algorithm calculates V MP using an analytical expression with a single step; derived from an explicit I–V relation. This algorithm overcomes voltage fluctuations in steady state condition which occurs in iterative based algorithms such as perturb-and-observe, incremental conductance and their improved versions. The algorithm also enhances the dynamic performance when it was compared to iterative and explicit model based algorithms. The algorithm is tested and validated in different climatic conditions using simulation study and experimental results. The proposed algorithm is also verified for a larger manufacturer tolerance and aging effect in the PV module. The proposed explicit I–V relation based EXMPP algorithm with module integrated converters provides an attractive performance in rapid environmental conditions changes.

An efficient datasheet-based parameters extraction method for two-diode photovoltaic cell and cells model

This paper proposes an efficient parameters extraction method for the two-diode model of photovoltaic cell and cells. Based on the data at standard test conditions provided by the photovoltaic datasheets, the parameters are all extracted by using derivative method at maximum power point and mountain-climbing algorithm, then the proposed two-diode model of the current voltage characteristic I–V of photovoltaic cell and cells are obtained. Compared with the popular Rp model and Ishaque’s two-diode model, The proposed two-diode model show that the accuracy is significantly higher than Rp model all the time, especially at the maximum power point. In addition, the parameters at different irradiance level and temperature are extracted, which also can simulate the current voltage characteristic I–V successfully at any condition, compared with experimental data.