Model-based Maximum Power Point Tracking Research Articles

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Performance Evaluation of PV Model-Based Maximum Power Point Tracking Techniques

Maximum power point tracking (MPPT) techniques extract the ultimate power from the photovoltaic (PV) source. Therefore, it is a fundamental control algorithm in any PV configuration. The research in this area is rich and many MPPT methods have been presented in the literature. However, in the current study, we focus on the PV model-based MPPT algorithms. In this regard, the classification of this category can be mainly divided into curve fitting methods and techniques based on the mathematical model or characteristics of the PV source. The objective of the PV model-based MPPT algorithm is to allocate the position of the maximum power point (MPP). Thus, no searching efforts are required to capture that point, which makes it simple and easy to implement. Consequently, the aim of this study is to give an overview of the most commonly utilized model-based MPPT methods. Furthermore, discussion and suggestions are also addressed to highlight the gap in this area. The main methods from the literature are compared together. The comparison and evaluation are validated using an experimental hardware-in-the-loop (HIL) system, where high efficiency (more than 99%) can be obtained with a simple calculation procedure and fast convergence speed.

Analysis of Single-Diode PV Model and Optimized MPPT Model for Different Environmental Conditions

The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application to avoid errors. However, predicting the cohesive relationship between current and voltage and estimating the parameters of a single diode model become a perplexing task due to insufficient data in the datasheet of PV panels. This research work presents single-diode solar PV system simulation analysis under different conditions, and the performance is improved by introducing an optimization-based maximum power point tracking (MPPT) strategy. Before simulation, a mathematical model for a single diode and optimization approaches are presented in this research work. Particle swarm optimization (PSO), genetic algorithm (GA), BAT optimization, and grey wolf optimization (GWO) model-based MPPT circuits are designed, and the performances are comparatively analyzed. The simulation results identify the nonlinear relationship between current and voltage and between power and voltage as characteristic curves for different temperature and irradiance values. For maximum power (Pmax), the maximum peak point tracking power and efficiency are analyzed to verify the optimization-based MPPT system. The simulation results demonstrate that the GWO model obtains a maximum tracking efficiency (TE) of 98%, which is much better than that of other optimization techniques.

Model-Based Maximum Power Point Tracking Algorithm With Constant Power Generation Capability and Fast DC-Link Dynamics for Two-Stage PV Systems

In this paper, a model-based maximum power point tracking (MPPT) technique is presented for a two-stage grid-connected photovoltaic (PV) system, where the loci of the maximum power points (MPPs) is specified accurately based on a new formulation. In this formulation, the effect of both irradiance and temperature is taken into consideration, whereas the irradiance is estimated to reduce the cost of the system and enhance its reliability. Furthermore, constant power generation (CPG) is integrated with the developed MPPT method to facilitate other power regulation schemes in the PV system. The proposed methodology is compared with the well-known perturb and observe (P&O) method for evaluation. Additionally, a modified version of the P&O is included in the comparison for better assessment. The effect of different partial shading conditions on the system’s performance is also investigated. The DC-link PI controller is replaced with a new adaptive DC-link controller to enhance the transient behavior of the PV system. Moreover, the suggested DC-link controller removes the DC offsets, which appear in case of gradient increase or decrease in the input PV power. In contrast to the conventional PI controller, which has poor performance at such circumstances. The active and reactive power exchange with the grid is managed using a computationally efficient finite-set model predictive control (FS-MPC) algorithm. Furthermore, switching frequency minimization is added as a secondary objective using a weighting factorless procedure. The grid-voltage sensors are eliminated and estimated using an extended Kalman filter (EKF). The overall control strategy is evaluated using experimental implementation, hardware-in-the-loop (HIL), and matlab/simulink.

High Efficiency Operation of Photovoltaic System with Differential Power Processing Based on Voltage Reference Model

The purpose of this paper is to quickly and accurately perform the maximum power point tracking (MPPT) of a photovoltaic panel in accordance with the surrounding environment of variously changing photovoltaic systems. In this photovoltaic system, a power conversion system is configured with a differential power processing (DPP) structure to perform MPPT, and a method for improving this MPPT performance is proposed. MPPT is performed using the charging characteristics of the attached capacitor for voltage smoothing of the MPPT PV panel based on the proposed voltage reference model. The voltage reference model-based MPPT was applied to the proposed DPP structured photovoltaic system. Therefore, by enabling the expansion of the application of a high-efficiency photovoltaic system that has better power generation performance and can be adapted to various climate and temperature changes, a safer power generation system having less power loss was implemented to verify performance. It is also expected that by reducing the number of sensors in the system, annual maintenance costs will be reduced.

An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems

In this article, a modified control structure for a single-stage three phase grid-connected photovoltaic (PV) system is presented. In the proposed system, the maximum power point tracking (MPPT) function is developed using a new adaptive model-based technique, in which the maximum power point (MPP) voltage can be precisely located based on the characteristics of the PV source. By doing so, the drift problem associated with the traditional perturb and observe (P&O) technique can be easily solved. Moreover, the inverter control is accomplished using a predictive dead-beat function, which directly estimates the required reference voltages from the commanded reference currents. Then, the reference voltages are applied to a space vector pulse width modulator (SVPWM) for switching state generation. Furthermore, the proposed inverter control avoids the conventional and known cascaded loop structure of the voltage oriented control (VOC) method by elimination of the outer PI controller, and hence the overall control strategy is simplified. The proposed system is compared with different MPPT techniques, including the conventional P&O method and other techniques intended for drift avoidance. The evaluation of the suggested control methodology depends on various radiation profiles created in MATLAB. The proposed technique succeeds at capturing the maximum available power from the PV source with no drift in comparison with other methods.

Model‐based maximum power point tracking for photovoltaic panels: parameters identification and training database collection

Module-level distributed maximum power point tracking (MPPT) represents an attractive solution for photovoltaic systems installed in dense urban areas, where panels are often subject to different solar irradiance levels. Model-based MPPT algorithms are particularly suitable for the purpose: they enable good steady-state accuracy and fast dynamics thanks to an underlying parametric model of the panel. The target of the present study is deeply investigating the estimation of the model parameters, and the collection of the training database, since they heavily affect overall performance. In this work, parameter values result by maximising energy production considering the training database; under some simplifications, it leads to a weighted least squares problem that can be easily solved. One of the main advantages is the robustness in the presence of some identification data that have been collected under partially shadowed conditions. Moreover, the possibility to gather the training database by running a perturb and observe MPPT is investigated and tested for the first time. Energy production is allowed also during this stage, thus opening the way to a periodic update of the parameters to follow degradation and time drift of the module. Experimental results show that performance is virtually the same as that obtained by computing parameters from a large set of volt-ampere characteristics.

Hybrid Maximum Power Point Tracking Technique for PV Modules Based on a Double-Diode Model

Photovoltaic (PV) systems are more efficient when operated in its maximum power point (MPP). Even with the existence of high number of MPP tracking (MPPT) techniques, it is possible to improve the MPPT efficiency. One way to achieve this improvement is to use model-based MPPT techniques, since they can estimate the MPP with reasonable accuracy, accelerating the MPP search process. Based on this motivation, the main objective of this article is to propose an efficient double-diode model-based + perturb and observe (P&O) heuristic-based hybrid MPPT technique. The proposition has the following three unique features: the double-diode model used in the MPPT technique has the capability of extrapolating the I-V curves of the PV modules for any environmental condition, even for temperature and irradiance values not used during the model building process; a parameter estimation technique that uses a combination of analytical equations and pattern search optimization algorithm is also presented for this unique model; and an irradiance estimation algorithm is also proposed, avoiding the irradiance sensor usually needed in model-based and hybrid MPPT techniques. Experimental results demonstrate that the integration of the model, the irradiance estimator, and reduced step P&O algorithm is an effective hybrid MPPT solution for PV systems.

Development of a High-Performance, FPGA-Based Virtual Anemometer for Model-Based MPPT of Wind Generators

Model-based maximum power point tracking (MPPT) of wind generators (WGs) eliminates dead times and increases energy yield with respect to iterative MPPT techniques. However, it requires the measurement of wind speed. Under this premise, this paper describes the implementation of a high-performance virtual anemometer on a field programmable gate array (FPGA) platform. Said anemometer is based on a growing neural gas artificial neural network that learns and inverts the mechanical characteristics of the wind turbine, estimating wind speed. The use of this device in place of a conventional anemometer to perform model-based MPPT of WGs leads to higher reliability, reduced volume/weight, and lower cost. The device was conceived as a coprocessor with a slave serial peripheral interface (SPI) to communicate with the main microprocessor/digital signal processor (DSP), on which the control system of the WG was implemented. The best compromise between resource occupation and speed was achieved through suitable hardware optimizations. The resulting design is able to exchange data up to a 100 kHz rate; thus, it is suitable for high-performance control of WGs. The device was implemented on a low-cost FPGA, and its validation was performed using input profiles that were experimentally acquired during the operation of two different WGs.

Model-Based MPPT for Shaded and Mismatched Modules of Photovoltaic Farm

Photovoltaic modules (PVM) of same rating and manufacturer have unique characteristics in real time due to manufacturing dispersion. This unique maximum power point (MPP) of each module is tracked by the proven distributed module level maximum power point tracking (DMMPPT). Since the conventional slow iterative MPPT algorithms in DMMPPT fail under the rapidly varying environmental conditions, the model-based algorithm (MBA) is used in this paper due to its swift tracking and character leaning nature. By identifying the actual behavior of each module, the new MBA is implemented here in compensation power dc–dc converter for the distributed model-based maximum power point tracking (CPDC-DMBMPPT). The flyback converter-based CPDC converter, which provides the current or voltage compensation is used to operate the module in its own MPP without any compromise even at the mismatched or partial shaded condition. As the individual module parameters used in MBA are estimated during the installation, the costly high precision measuring instruments are avoided in the consumer site. Also, due to the LabVIEW-based centralized control, updates in the MBA becomes easy without changing the individual controllers in Photovoltaic farms. The proposed methodology and its proven outcomes are discussed through the simulation and hardware outputs.

Model-based maximum power point tracking for wind generators

A model-based maximum power point tracking solution for wind systems is presented in this paper. The strategy uses the Loss-free resistor concept and a generator model, which relates the load impedance required to produce the maximum power depending on the generator rotor speed. The strategy performance is validated through simulation and the emulation of a multi-machine wind generation system. With the proposed strategy, it is possible to extract the maximum power of each generator and, therefore, the maximum power of the generation system.

Fast Power-Peaks Estimator for Partially Shaded PV Systems

Model-based maximum power point tracking (MPPT) techniques have been developed recently to improve the dynamic and steady-state performance of MPPT. Although they are successfully implemented in homogeneous photovoltaic (PV) systems, there is still no model-based MPPT for partially shaded PV systems, mainly because the available models are complex and time consuming. This paper develops a fast modeling approach for partially shaded PV systems. By utilizing three developed rules that govern the formation of power peaks in partially shaded PV systems, the proposed approach can quickly find the power peaks of these systems without simulating the entire power curve. The effectiveness of the proposed approach in finding the power peaks of PV systems quickly and accurately is verified using MATLAB-Simulink and real-time simulator in hardware in the loop application. Moreover, a model-based MPPT is developed utilizing the proposed modeling method. The developed MPPT successfully improves the dynamic performance of power extraction, guarantees the operation on the global maximum power peak, and eliminates oscillating steady-state power losses.

Model-Based Method for Partially Shaded PV Module Hot-Spot Suppression

Hot Spot Suppression is a new method to operate a PV module that prevents the formation of hot spots without the need for bypass diodes. By comparing the operating voltage, current and temperature to an intrinsic I(V) model based on calibrated flash test data, Hot Spot Suppression determines the solar modules' shading condition and, if required, seeks a new operating power point that reduces the formation of hot spots on shaded cells. Hot Spot Suppression works in concert with a model-based Maximum Power Point Tracking algorithm to seek the best operating point for maximum output power while limiting stress on shaded cells on the solar module. Theoretical and experimental results show that the power density dissipated in the shaded cell(s) is reduced by a factor of four when the operating conditions are determined by Hot Spot Suppression.