Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.This research was funded by the Basque Government through the project EKOHEGAZ (ELKARTEK KK-2021/00092), by the Diputación Foral de Álava (DFA), through the project CONAVANTER, and by the UPV/EHU, through the project GIU20/063

Global maximum power tracking of PV system under partial shading

The increased usage of electrical energy in the recent times leads to a greater demand. It invites large development in the production of electrical energy from renewable energy sources. It involves more evolving technologies. Out of all energy extraction from solar would be abundant. Photovoltaic (PV) are one such components helps in deriving large amounts of energy, this has become more easiest method due to its economic liabilities and the world has aimed its interest in developing the PV technology, which gives clean energy. This paper objective is to implement various Maximum Power Point Tracking (MPPT) algorithms, mainly Cuckoo Search Algorithm, fuzzy logic control (FLC) and conventional perturb and observe (P&O), incremental conductance (INC) on solar PV systems. These controlled MPPT algorithms helps in driving DC-DC boost converter, which helps to obtain maximum output from the PV Panels/cells/modules/Arrays. The obtained results are compared with each other under several operating conditions. The operating conditions include change in irradiance, change in temperature dynamically, and partial shading on PV panels. The implemented MPPT algorithms require only the PV array voltage and current to control DC-DC converter, which makes them economically feasible and attractive. From the results, it can be observed that Cuckoo search algorithm gives better results under partial shading situations

Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Solar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER). Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak power point tracking in solar photovoltaic systems by bringing agility, data-driven decision-making, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA). The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability

Fast‐converging robust PR‐P controller designed by using symmetrical pole placement method for current control of interleaved buck converter‐based PV emulator

In this study, the interleaved buck converter-based photovoltaic (PV) emulator current control is presented. A proportional-resonant-proportional (PR-P) controller is designed to resolve the drawbacks of conventional PI controllers in terms of phase management, which means balancing currents evenly between active phases to avoid thermally stressing and provide optimal ripple cancelation in the presence of parameter uncertainties. The resonant path of the controller (PR) with a constant proportional unity gain is designed considering the changing dynamics of a notch filter by pole placement method (adding mutually complementary poles to the notch transfer function) at PWM switching frequency. The proportional gain path (P) of the controller is used to determine the compatibility of the controller with parameter uncertainty of the phases and designed by utilizing loop-shaping method. The proposed controller shows superior performance in terms of 10 times faster-converging transient response, zero steady-state error with significant reduction in current ripple. Equal load sharing that constitutes the primary concern in multiphase converters is achieved with the proposed controller. Implementing of robust control theory involving comprehensive time and frequency domain analysis reveals 13% improvement in the robust stability margin and 12-degree bigger phase toleration with the PR-P controller. In addition to these, the proposed unconventional design process of the controller reduces the computational complexity and provides cost-effectiveness and simple implementation. Moreover, implementing of auxiliary resistor-capacitor (RC) circuits parallel with the inductors to sense the current in each phase removes the need for current measurement sensors that contribute to overall cost of the system

Development of new parameter extraction schemes and maximum power point controllers for photovoltaic power systems

In the recent years, in every parts of the world, focus is on supplementing the conventional fossil fuel based power generation with power generated from renewable sources such as photovoltaic (PV) and wind systems. PV technology is one of the fastest growing energy technologies in the world owing to its abundant availability. But unfortunately, the cost of PV energy is higher than that of other electrical energy from other conventional sources.Therefore, a great deal of research opportunities lie in applying power electronics and control technologies for harvesting PV power at higher efficiencies and efficient utilization. Simulation and control studies of a PV system require an accurate PV panel model. Further, for efficient utilization of the available PV energy, a PV system should operate at its maximum power point (MPP). A maximum power point tracker (MPPT) is needed in the PV system to enable it to operate at the MPP.The output characteristic of a PV system is non-linear and its output power fluctuates to a large extent in accordance with the variation of solar irradiance and temperature. A lot of research is being pursued on this area and several MPPT techniques have been proposed and implemented. But, still there is a lot of scope on designing new parameter extraction algorithms to achieve fast and accurate extraction of PV panel parameters. Further, there is need of development of efficient MPPT algorithms that can be adapted to different weather conditions with minimal fluctuations in input PV current and voltage.The work described in the thesis involves development of some new parameter extraction and robust adaptive MPPT algorithms. Two parameter extraction algorithms have been proposed namely a hybrid Newton Raphson method (hybrid NRM) and an evolutionary computational technique called Bacterial Foraging Optimization (BFO). These two parameter extraction techniques are found to be extracting parameters of a PV panel accurately in all weather conditions with less computational overhead. Further, these two parameter extraction techniques do not suffer from singularity problem during convergence. BFO technique being a global optimization technique provides accurate PV panel parameters

Design and Control of Power Converters 2019

In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc

Bidirectional Electric Vehicles Service Integration in Smart Power Grid with Renewable Energy Resources

As electric vehicles (EVs) become more popular, the utility companies are forced to increase power generations in the grid. However, these EVs are capable of providing power to the grid to deliver different grid ancillary services in a concept known as vehicle-to-grid (V2G) and grid-to-vehicle (G2V), in which the EV can serve as a load or source at the same time. These services can provide more benefits when they are integrated with Photovoltaic (PV) generation. The proper modeling, design and control for the power conversion systems that provide the optimum integration among the EVs, PV generations and grid are investigated in this thesis. The coupling between the PV generation and integration bus is accomplished through a unidirectional converter. Precise dynamic and small-signal models for the grid-connected PV power system are developed and utilized to predict the system’s performance during the different operating conditions. An advanced intelligent maximum power point tracker based on fuzzy logic control is developed and designed using a mix between the analytical model and genetic algorithm optimization. The EV is connected to the integration bus through a bidirectional inductive wireless power transfer system (BIWPTS), which allows the EV to be charged and discharged wirelessly during the long-term parking, transient stops and movement. Accurate analytical and physics-based models for the BIWPTS are developed and utilized to forecast its performance, and novel practical limitations for the active and reactive power-flow during G2V and V2G operations are stated. A comparative and assessment analysis for the different compensation topologies in the symmetrical BIWPTS was performed based on analytical, simulation and experimental data. Also, a magnetic design optimization for the double-D power pad based on finite-element analysis is achieved. The nonlinearities in the BIWPTS due to the magnetic material and the high-frequency components are investigated rely on a physics-based co-simulation platform. Also, a novel two-layer predictive power-flow controller that manages the bidirectional power-flow between the EV and grid is developed, implemented and tested. In addition, the feasibility of deploying the quasi-dynamic wireless power transfer technology on the road to charge the EV during the transient stops at the traffic signals is proven

Optimization approaches for parameter estimation and maximum power point tracking (MPPT) of photovoltaic systems

Optimization techniques are widely applied in various engineering areas, such as modeling, identi�cation, optimization, prediction, forecasting and control of complex systems. This thesis presents the novel optimization methods that are used to control Photovoltaic (PV) generation systems. PV power systems are electrical power systems energized by PV modules or cells. This thesis starts with the introduction of PV modeling methods, on which our research is based. Parameter estimation is used to extract the parameters of the PV models characterizing the utilized PV devices. To improve e�ciency and accuracy, we proposed sequential Cuckoo Search (CS) and Parallel Particle Swarm Optimization (PPSO) methods to extract the parameters for di�erent PV electrical models. Simulation results show the CS has a faster convergence rate than the traditional Genetic Algorithm (GA), Pattern Search (PS) and Particle Swarm Optimization (PSO) in sequential processing. The PPSO, with an accurate estimation capability, can reduce at least 50% of the elapsed time for an Intel i7 quad-core processor. A major challenge in the utilization of PV generation is posed by its non linear Current-Voltage (I-V ) relations, which result in the unique Maximum Power Point (MPP) varying with di�erent atmospheric conditions. Maximum Power Point Tracking (MPPT) is a technique employed to gain maximum power available from PV devices. It tracks operating voltage corresponding to the MPP and constrains the operating point at the MPP. A novel model-based two-stage MPPT strategy is proposed in this thesis to combine the o�ine maximum power point estimation using the Weightless Swarm Algorithm (WSA) with an online Adaptive Perturb & Observe (APO) method. In addition, an Approximate Single Diode Model (ASDM) is developed for the fast evaluations of the output power. The feasibility of the proposed method is veri�ed in an MPPT system implemented with a Single-Ended Primary-Inductor Converter (SEPIC). Simulation results show the proposed MPPT method is capable of locating the operating point to the MPP under various environmental conditions

Predictive-TOPSIS-based MPPT for PEMFC Featuring Switching Frequency Reduction

A maximum power point tracking (MPPT) for a proton exchange membrane fuel cell (PEMFC) using a combination of conventional finite control set model predictive control (FCS-MPC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed in this paper. The key idea is to maximize the power generation from a PEMFC while minimizing the switching frequency of the power converter. The FCS-MPC technique is formulated to track the maximum power of PEMFC highly affected by ever-changing internal parameters. Meanwhile, the TOPSIS algorithm is applied to overcome the potential weaknesses of insulated-gate bipolar transistor (IGBT), which can only withstand a lower switching frequency. In this project, all simulations were run using MATLAB software to display the output power of the PEMFC system. As a result, the proposed predictive-TOPSIS-based MPPT algorithm can track the MPP for various PEMFC parameters within 0.019 s with an excellent accuracy up to 99.11%. The proposed MPPT technique has fast-tracking of the MPP locus, excellent accuracy, and robustness to environmental changes

Issues in Design of Maximum-Power-Point-Tracking Control – Power Electronics Perspective

This thesis provides a comprehensive study of the dynamic characteristics and operation of maximum-power-point-tracking (MPPT) dc-dc converters, especially for those parts that concern the MPPT-control design. The study concentrates on the widely-utilized heuristic perturb-based MPPT algorithms and their design constraints when equipped with photovoltaic-interfacing converter. The main objective is to provide an explicit formulation of the input-power dynamics of the photovoltaic-generator-interfacing dcdc converter for addressing the MPP-tracking control. The dynamics introduce design constraints for the aforementioned MPPT-control algorithms and provide tools for deterministic MPPT design.A photovoltaic (PV) generator has nonlinear current-voltage characteristics with a particular maximum power point (MPP), which depends on the environmental factors such as temperature and irradiation. Thus, to ensure the maximization of the power extracted from the PV source, the interfacing power converter must be capable of controlling its parameters, i.e., changing its input voltage and current levels based on the MPP of the PV generator. That is done by implementing an MPPT controller, which generates the reference control signal for the interfacing converter. Despite the way of implementation, the fundamental operation is to nd the electrical operating point, i.e., the voltage and the current, at which the PV generator either generates the maximum power or follows a given power reference at every time instant. However, the dynamic characteristics of a photovoltaic generator are determined by the environmental conditions as well as the dynamics of the interfacing converter, which creates limitations for the MPPT-control design. It has been noticed recently that the characteristic curve of a PV generator can be separated into three dierent operation regions each having their distinct characteristics. Thus, to ensure reliability and eciency of a maximum-power tracking, all of these regions should be analyzed separately and choose the condition corresponding to the slowest settling dynamics of the PV system. Up to now, that is not completely recognized, and deterministic analytical models are missing to provide design guidelines for the MPPT-control design.This thesis presents a detailed dynamic model for PV-generator power dynamics in case of open-loop and closed-loop-operated switched-mode dc-dc converter. Two common design examples of closed-loop-operated converters were provided, where the closed-loop dynamics of the converter was slow and fast by adjusting the control bandwidth and phase margin of the feedback loop. With the developed models, a proper evaluation of the MPPT control imposed by the converter dynamics was presented. Thus, previously developed design guidelines were revised, or new guidelines were established