Optimization of fuzzy-based MPPT controller via metaheuristic techniques for stand-alone PV systems

Due to the alteration of power-voltage characteristics of solar module output under multiple environmental conditions such as solar irradiation and ambient temperature, these systems hardly function at maximum power point (MPP). However, maximum power point tracking (MPPT) plays a significant role in their efficiency. On the other hand, solar module characteristics are extremely nonlinear and their slope on either side of MPP is asymmetric. Thus using a nonlinear control method which has the potential of adapting the operating point of the system to MPP seems useful. This has motivated authors to present MPPT method which maximizes PV's output power by tracking MPP continuously. In the present study, a fuzzy logic controller (FLC) is presented for MPPT in photovoltaic systems. Four optimization algorithms are presented in this paper for optimizing fuzzy membership functions (MFs) and generating proper duty cycle for MPPT. The presented algorithms include: Teaching Learning Based Optimization (TLBO), Firefly Algorithm (FFA), Biogeography based optimization (BBO), and Particle Swarm Optimization (PSO), which are all described and simulated. Finally, to validate performance of the proposed optimized FLC, it is compared with other algorithms such as symmetrical fuzzy logic controller (SFLC) and conventional Perturbation and Observation (P&O). According to the simulation results, P&O algorithm shows significant oscillations, energy loss, and in some cases, it cannot obtain MPP. Simulation results also indicate that TLBO and FFA based asymmetric fuzzy MFs not only increase MPPT convergence speed but also enhance tracking accuracy in comparison with symmetric fuzzy MFs and asymmetric fuzzy MFs based on BBO and PSO.     

Comprehensive review and performance evaluation of maximum power point tracking algorithms for photovoltaic system

A photovoltaic array is environmentally friendly and a source of unlimited energy generation. However, it is presently a costlier energy generation system than other non-renewable energy sources. The main reasons are seasonal variations and continuously changing weather conditions, which affect the amount of solar energy received by the solar panels. In addition, the non-linear characteristics of the voltage and current outputs along with the operating environment temperature and variation in the solar radiation decrease the energy conversion capability of the photovoltaic arrays. To address this problem, the global maxima of the PV arrays can be tracked using a maximum power point tracking algorithm (MPPT) and the operating point of the photovoltaic system can be forced to its optimum value. This technique increases the efficiency of the photovoltaic array and minimizes the cost of the system by reducing the number of solar modules required to obtain the desired power. However, the tracking algorithms are not equally effective in all areas of application. Therefore, selecting the correct MPPT is very critical. This paper presents a detailed review and comparison of the MPPT techniques for photovoltaic systems, with consideration of the following key parameters: photovoltaic array dependence, type of system (analog or digital), need for periodic tuning, convergence speed, complexity of the system, global maxima, implemented capacity, and sensed parameter(s). In addition, based on real meteorological data (irradiance and temperature at a site located in Addis Ababa, Ethiopia), a simulation is performed to evaluate the performance of tracking algorithms suitable for the application being studied. Finally, the study clearly validates the considerable energy saving achieved by using these algorithms.     

A novel maximum power point tracking in partially shaded PV systems using a hybrid method

To exploit photovoltaic systems, a major point that merits attention is to find maximum output power for the efficiency increase. The output power of solar cells depends on the ambient temperature and intensity of solar radiation. The cloud phenomenon creates a partially shaded on solar arrays; in this condition, the power–voltage curve of a solar array has several local maximum points. When there is uniform radiation, conventional methods of maximum power point tracking (MPPT) can be used. However, these methods are not efficient in partially shaded, due to the existence of several Maximum Power Points (MPPs) in the power-voltage characteristic. In this paper, a novel method for MPP tracking under the partially shaded is proposed, which is a combination of observational tracking and constant–voltage methods. When there is uniform radiation, the tracking operation is observed and tracked by a fuzzy logic–based approach. The proposed method is based on the existence of a relationship between radiation intensity and MPP voltage. In the existence of this relationship, the MPP voltage can be calculated by measuring the intensity of radiation at any moment. In addition, partially shaded works using the constant-voltage method. To verify the simulation results, laboratory implementation was performed. The results show that with considering the MPPs, the output power increases about 10% while the partially shaded is applied.     

Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power?voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.     

Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms

This work presents an experimental comparison of two algorithms developed in order to maximize the output power from a photovoltaic (PV) system for the same given set of conditions. The maximum power point tracking (MPPT) methods proposed in this study are two extended algorithms: Perturb and Observe and Incremental Conductance. The numerical modelling of the PV system shows the MPPT interest and then the extended MPPT algorithms are highlighted. In this paper, a PV system based on a boost converter as MPPT device is considered. A programmable DC electronic load is fed by two identical PV systems in which the MPPT control converter algorithms are different. This experimental platform operates under the same conditions such as changing solar radiation and cell temperature. The experimental results obtained with a dSPACE controller board show the MPPT energy efficiency of the proposed algorithms.     

A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.     

Highly efficient maximum power point tracking using DC–DC coupled inductor single-ended primary inductance converter for photovoltaic power systems

Solar photovoltaics (PVs) have nonlinear voltage–current characteristics, with a distinct maximum power point (MPP) depending on factors such as solar irradiance and operating temperature. To extract maximum power from the PV array at any environmental condition, DC–DC converters are usually used as MPP trackers. This paper presents the performance analysis of a coupled inductor single-ended primary inductance converter for maximum power point tracking (MPPT) in a PV system. A detailed model of the system has been designed and developed in MATLAB/Simulink. The performance evaluation has been conducted on the basis of stability, current ripple reduction and efficiency at different operating conditions. Simulation results show considerable ripple reduction in the input and output currents of the converter. Both the MPPT and converter efficiencies are significantly improved. The obtained simulation results validate the effectiveness and suitability of the converter model in MPPT and show reasonable agreement with the theoretical analysis.     

Maximum power extraction from photo-voltaic power generator with adaptive MPP tracker

Power generation with the help of Photovoltaic (PV) arrays is emphasized increasingly and regarded as an important resource of power energy in the coming years. As the power supplied by PV arrays depends upon the insolation, temperature and array voltage, it is necessary to control the operating point to extract the maximum power from the PV arrays. A number of methods for Maximum Power Point Tracking (MPPT) has been reported in the literature. This paper discusses an adaptive method as well as compares with the conventional fixed step size method, effectively improves the MPPT speed and accuracy simultaneously. An adaptive algorithm and two phase dc-dc Converter is exercised as a MPP tracker. Ripple reduction is possible at input and output side of the converter. Mathematical models of converter are developed using state space averaging technique. The tracking responses of the system operating at the solar array MPP are evaluated. A theoretical analysis of the new algorithm in connection with dc-dc converter is provided and its feasibility is also verified by simulation results.     

基于并联光伏组件外特性的最大功率点跟踪方法

当阴影条件变化时,并联光伏组件的全局最大功率点(MPP)会随之改变.为了实现太阳能发电最大化,要求最大功率点跟踪(MPPT)方法始终能实时而准确地锁定住并联光伏组件的全局MPP.不同阴影条件下并联光伏组件会呈现不同的外特性特征,如多阶梯的电流电压特性以及多峰值的功率电压特性.基于此现象,该文提出一种基于并联光伏组件外特...     

A comprehensive simulator for assessing the reliability of a photovoltaic panel peak power tracking system

When designing a maximum power point tracking (MPPT) algorithm, it is often difficult to correctly predict, before field testing, the behavior of this MPPT under varying solar irradiation on photovoltaic (PV) panels. A solution to this problem is to design a maximum power point trackers simulator of a PV system used to test MPPT algorithms. This simulator must have the same role as the MPPT card of the PV panel and thus will fully emulate the response of a real MPPT card of the PV panel. Therefore, it is a good substitute to help to test the peak power trackers of the PV system in the laboratory. This paper describes a simple peak power trackers simulator of the PV system which has a short response time thus, can be used to test MPPT algorithms under very rapid variation condition. The obtained results and the theoretical operation confirm the reliability and the superior performance of the proposed model.     

High-accuracy maximum power point estimation for photovoltaic arrays

Quantitative information regarding the maximum power point (MPP) of photovoltaic (PV) arrays is crucial for determining and controlling their operation, yet it is difficult to obtain such information through direct measurements. PV arrays exhibit an extremely nonlinear current-voltage (I-V) characteristic that varies with many complex factors related to the individual cells, which makes it difficult to ensure an optimal use of the available solar energy and to achieve maximum power output in real time. Finding ways to obtain the maximum power output in real time under all possible system conditions are indispensable to the development of feasible PV generation systems. The conventional methods for tracking the MPP of PV arrays suffer from a serious problem that the MPP cannot be quickly acquired. Based on the p-n junction semiconductor theory, we develop a prediction method for directly estimating the MPP for power tracking in PV arrays. The proposed method is a new and simple approach with a low calculation burden that takes the resistance effect of the solar cells into consideration. The MPP of PV arrays can be directly determined from an irradiated I-V characteristic curve. The performance of the proposed method is evaluated by examining the characteristics of the MPP of PV arrays depending on both the temperature and irradiation intensity, and the results are discussed in detail. Such performance is also tested using the field data. The experimental results demonstrate that the proposed method helps in the optimization of the MPP control model in PV arrays.     

Evaluation of a photovoltaic energy mechatronics system with a built-in quadratic maximum power point tracking algorithm

The historically high cost of crude oil price is stimulating research into solar (green) energy as an alternative energy source. In general, applications with large solar energy output require a maximum power point tracking (MPPT) algorithm to optimize the power generated by the photovoltaic effect. This work aims to provide a stand-alone solution for solar energy applications by integrating a DC/DC buck converter to a newly developed quadratic MPPT algorithm along with its appropriate software and hardware. The quadratic MPPT method utilizes three previously used duty cycles with their corresponding power outputs. It approaches the maximum value by using a second order polynomial formula, which converges faster than the existing MPPT algorithm. The hardware implementation takes advantage of the real-time controller system from National Instruments, USA. Experimental results have shown that the proposed solar mechatronics system can correctly and effectively track the maximum power point without any difficulties.     

A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach

The maximum power point tracking (MPPT) in the PV system has become complex due to the stochastic nature of the load, intermittency in solar irradiance and ambient temperature. To address this problem, a novel Grasshopper optimized fuzzy logic control (FLC) approach based MPPT technique is proposed in this paper. In this proposed MPPT, grasshopper optimization is used to tune the membership functions (MFs) of FLC to handle all uncertainties caused by variable irradiances and temperatures. The performance of the proposed grasshopper optimized FLC based MPPT is studied under rapidly changing irradiance and temperature. The proposed MPPT overcomes the limitations such as slow convergence speed, steady-state oscillations, lower tracking efficiency as encountered in conventional methods viz. perturb & observed (P&O) and FLC techniques. The feasibility of the proposed MPPT is validated through experimentation. The effectiveness of the proposed scheme is compared with P&O and also with FLC MPPT.     

Modeling and maximum power point tracking (MPPT) method for PV array under partial shade conditions

Influenced by partial shade, PV module aging or fault, there are multiple peaks on PV array's output power–voltage (P–V) characteristic curve. Conventional maximum power point tracking (MPPT) methods are effective for single peak P–V characteristic under uniform solar irradiation, but they may fail in global MPP tracking under multi-peak P–V characteristics. Existing methods in literature for this problem are still unsatisfactory in terms of effectiveness, complexity and speed. In this paper, we first analyze the mathematical model of PV array that is suitable for simulation of complex partial shade situation. Then an adaptive MPPT (AMPPT) method is proposed, which can find real global maximum power point (MPP) for different partial shade conditions. When output characteristic of PV array varies, AMPPT will adjust tracking strategies to search for global peak area (GPA). Then it is easy for conventional MPPT to track the global MPP in GPA. Simulation and experimental results verify that the proposed AMPPT method is able to find real global MPP accurately, quickly and smoothly for complex multi-peak P–V characteristics. Comparison analysis results demonstrate that AMPPT is more effective for most shade types.     

An efficient single‐sensor global maximum power point tracking method for partially shaded photovoltaic battery chargers

Solar energy and other renewables like geothermal, biomass, and wind energy can minimize the release of the CO₂ and other harmful gases produced in case of fossil fuel. Low efficiency is the main drawback of the solar photovoltaic system specifically under partial shadowing condition (PSC). Commonly, with uniform solar radiation distribution, the power‐voltage graph has single maximum power point (MPP). The single MPP can be definitely extracted by any traditional tracker like perturb and observe as an example. However, during PSC, the situation is completely different since the power‐voltage curve has many MPPs (ie, multiple local points and single global point). The conventional MPP tracking methods cannot discriminate among local peaks and global peak; consequently, they can be easily trapped on the first local peak. Therefore, smart MPPTs based on modern optimization are required to track the global MPP. Most of MPPT tracking methods in the literature require both voltage and current sensors, and sometimes the control system needs an additional solar irradiance sensor and/or temperature sensor, which increase the system cost. In this paper, for the first time, a simple single‐sensor–based global MPP tracking method for partially shaded photovoltaic battery chargers is proposed. A deterministic particle swarm optimizer is utilized to extract the global MPP. Several patterns of PSC are considered to test and evaluate the proposed strategy. The obtained results confirm the efficacy of a single‐sensor–based global MPP tracking method to catch the global MPP accurately. Considering this research reduces the number of sensors, cost, and difficulty and consequently increases the power density of the MPP tracking methods under partial shadowing conditions.     

Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations

This paper analyses the operation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the SPV modules by changing the duty ratio of the boost converter. The duty ratio of the boost converter is calculated for a given solar irradiance and temperature condition by a closed-loop control scheme. The ANFIS is trained to generate maximum power corresponding to the given solar irradiance level and temperature. The response of the ANFIS-based control system is highly precise and offers an extremely fast response. The response time is seen as nearly 1 ms for fast varying cell temperature and 6 ms for fast varying solar irradiance. The simulation is done for fast-changing solar irradiance and temperature conditions. The response of the proposed controller is also presented.     

Fractional order extremum seeking approach for maximum power point tracking of photovoltaic panels

Due to the high interest in renewable energy and diversity of research regarding photovoltaic (PV) array, a great research effort is focusing nowadays on solar power generation and its performance improvement under various weather conditions. In this paper, an integrated framework was proposed, which achieved both maximum power point tracking (MPPT) and minimum ripple signals. The proposed control scheme was based on extremum-seeking (ES) combined with fractional order systems (FOS). This auto-tuning strategy was developed to maximize the PV panel output power through the regulation of the voltage input to the DC/DC converter in order to lead the PV system steady-state to a stable oscillation behavior around the maximum power point (MPP). It is shown that fractional order operators can improve the plant dynamics with respect to time response and disturbance rejection. The effectiveness of the proposed controller scheme is illustrated with simulations using measured solar radiation data.     

A fast and low cost analog maximum power point tracking method for low power photovoltaic systems

Low power photovoltaic (PV) systems are commonly used in stand-alone applications. For these systems, a simple and cost-effective maximum power point tracking (MPPT) solution is essential. In this paper, a fast and low cost analog MPPT method for low power PV systems is proposed. By using two voltage approximation lines (VALs) to approximate the maximum power point (MPP) locus, a low-complexity analog MPPT circuit can be developed. Theoretical derivation and detailed design procedure will be provided in this paper. The proposed method boasts the advantages such as simple structure, low cost, fast tracking speed and high tracking efficiency. To validate the correctness of the proposed method, simulation and experimental results of an 87 W PV system will also be provided to demonstrate the effectiveness of the proposed technique.     

Adaptive maximum power point tracking based on Kalman filter for hydrogen fuel cell in hybrid unmanned aerial vehicle applications

In this paper, an adaptive real-time estimation method based on Kalman filter is proposed for tracking the maximum power point (MPP) of a hydrogen fuel cell (FC) in hybrid unmanned aerial vehicle (UAV) applications. To achieve the adaptive MPP tracking (MPPT), a mathematical model for the hydrogen FC is established. Then, the recursive least square method is employed to identify the initial values of model parameters. On this basis, the MPP of the hydrogen FC under steady conditions can be derived. Furthermore, the state and observation equations based on Kalman filter are introduced to adaptively estimate the model parameters in real-time. Moreover, the real-time model parameters would be used to optimize the MPP in accordance with the operating conditions such that the adaptive MPPT can be achieved. Finally, various simulations and experiments are conducted to verify the effectiveness and accuracy of the adaptive MPPT for the hydrogen FC in hybrid UAV applications. Results show that the adaptive MPPT can not only track the MPP accurately in real-time, but also reduce the oscillation of the hydrogen FC. Compared with the MPPT methods based on perturb and observe (P&O) and particle swarm optimization (PSO), the maximum power tracking error of the adaptive MPPT can be improved by 2.83% and 1.10%, respectively.