Power Quality Enhancement in Hybrid Photovoltaic-Battery System based on three–Level Inverter associated with DC bus Voltage Control

This modest paper presents a study on the energy quality produced by a hybrid system consisting of a Photovoltaic (PV) power source connected to a battery. A three-level inverter was used in the system studied for the purpose of improving the quality of energy injected into the grid and decreasing the Total Harmonic Distortion (THD). A Maximum Power Point Tracking (MPPT) algorithm based on a Fuzzy Logic Controller (FLC) is used for the purpose of ensuring optimal production of photovoltaic energy. In addition, another FLC controller is used to ensure DC bus stabilization. The considered system was implemented in the Matlab /SimPowerSystems environment. The results show the effectiveness of the proposed inverter at three levels in improving the quality of energy injected from the system into the grid.Peer reviewedFinal Published versio

Discrete Model-Predictive-Control-Based Maximum Power Point Tracking for PV Systems:Overview and Evaluation

The main objective of this work is to provide an overview and evaluation of discrete model-predictive control (MPC)-based maximum power point tracking (MPPT) for photovoltaic systems. A large number of MPC-based MPPT methods have been recently introduced in the literature with very promising performance; however, an in-depth investigation and comparison of these methods has not been carried out yet. Therefore, this paper has set out to provide an in-depth analysis and evaluation of MPC-based MPPT methods applied to various common power converter topologies. The performance of MPC-based MPPT is directly linked with the converter topology, and it is also affected by the accurate determination of the converter parameters; sensitivity to converter parameter variations is also investigated. The static and dynamic performance of the trackers is assessed according to the EN 50530 standard, using detailed simulation models, and validated by experimental tests. The analysis in this work aims to present useful insight for practicing engineers and academic researchers when selecting the maximum power point tracker for their application.</p

Model Predictive Control Technique of Multilevel Inverter for PV Applications

Renewable energy sources, such as solar, wind, hydro, and biofuels, continue to gain popularity as alternatives to the conventional generation system. The main unit in the renewable energy system is the power conditioning system (PCS). It is highly desirable to obtain higher efficiency, lower component cost, and high reliability for the PCS to decrease the levelized cost of energy. This suggests a need for new inverter configurations and controls optimization, which can achieve the aforementioned needs. To achieve these goals, this dissertation presents a modified multilevel inverter topology for grid-tied photovoltaic (PV) system to achieve a lower cost and higher efficiency comparing with the existing system. In addition, this dissertation will also focus on model predictive control (MPC) which controls the modified multilevel topology to regulate the injected power to the grid. A major requirement for the PCS is harvesting the maximum power from the PV. By incorporating MPC, the performance of the maximum power point tracking (MPPT) algorithm to accurately extract the maximum power is improved for multilevel DC-DC converter. Finally, this control technique is developed for the quasi-z-source inverter (qZSI) to accurately control the DC link voltage, input current, and produce a high quality grid injected current waveform compared with the conventional techniques. This dissertation presents a modified symmetrical and asymmetrical multilevel DC-link inverter (MLDCLI) topology with less power switches and gate drivers. In addition, the MPC technique is used to drive the modified and grid connected MLDCLI. The performance of the proposed topology with finite control set model predictive control (FCS-MPC) is verified by simulation and experimentally. Moreover, this dissertation introduces predictive control to achieve maximum power point for grid-tied PV system to quicken the response by predicting the error before the switching signal is applied to the converter. Using the modified technique ensures the iii system operates at maximum power point which is more economical. Thus, the proposed MPPT technique can extract more energy compared to the conventional MPPT techniques from the same amount of installed solar panel. In further detail, this dissertation proposes the FCS-MPC technique for the qZSI in PV system. In order to further improve the performance of the system, FCS-MPC with one step horizon prediction has been implemented and compared with the classical PI controller. The presented work shows the proposed control techniques outperform the ones of the conventional linear controllers for the same application. Finally, a new method of the parallel processing is presented to reduce the time processing for the MPC

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

A cost effective computational design of maximum power point tracking for photo-voltaic cell

Maximum Power Point Tracking (MPPT) is one of the essential controller operations of any Photo-Voltaic (PV) cell design. Developing an efficient MPPT system includes a significant challenge as there are various forms of uncertainty factors that results in higher degree of fluctuation in current and voltage in PV cell. After reviewing existing system, it has been found that there is no presence of any benchmarked model to ensure a better form of computational model. Hence, this paper presents a novel and very simple design of MPPT without using any form of complex design mechanism nor including any form of frequently used iterative approach. The proposed model is completely focused on developing an algorithm that takes the input of voltage (open circuit), current (short circuit), and max power in order to obtain the peak power to be extracted from the PV cells. The study outcome shows faster response time and better form of analysis of current-voltage-power for given state of PV cells

Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators

[EN] In this work, new results are presented on the implementation of predictive diagnosis techniques on isolated photovoltaic (PV) systems and installations. The novelties introduced in this research focus on the additional advantages obtained from the point of view of predictive diagnosis of faults caused by partial shading in isolated PV installations using maximum power point tracking (MPPT) regulators. MPPT regulators are comparatively more appropriate than pulse width modulation (PWM) solar regulators in order to implement fault diagnosis systems. MPPT regulators have a physical separation between the electrical parameters belonging to the part of the solar panel with respect to the batteries part. Therefore, these electrical parameters can be used to obtain early predictive symptoms of the effects of partial shading with a greater level of observation and sensitivity. Additionally, modifications are proposed in the PV system assembly to obtain greater homogeneity of all the panels regarding the solar irradiance reception angle.García Moreno, E.; Quiles Cucarella, E.; Correcher Salvador, A.; Morant Anglada, FJ. (2022). Predictive Diagnosis Based on Predictor Symptoms for Isolated Photovoltaic Systems Using MPPT Charge Regulators. Sensors. 22(20):1-33. https://doi.org/10.3390/s22207819133222

A cost-effective and optimized maximum powerpoint tracking system for the photovoltaic model

Solar energy is naturally available from sun, and it can be extracted by using a photovoltaic (PV) cell. However, solar energy extraction entirely depends on the climatic conditions and angle of rays falling on PV cells. Hence, maximum powerpoint tracking (MPPT) is considered in most areas under variable climatic conditions, which acts as a controller unit for PV cells. MPPT can enhance the efficiency of PV cells. However, designing an MPPT model is challenging as different uncertainties in the climatic condition may lead to more fluctuations in voltage and current in PV cells. Under the shaded condition, the PV cell may have other MPPT points that lead to the PV cell’s low efficiency in analyzing maximum power. Hence, this paper introduces a cost-effective and optimized system for the PV model that can find optimal power and improve PV cells’ efficiency. The proposed system achieves better computational performance with ~35% and ~42% than existing MPPT techniques. The improved particle swarm optimization (PSO) is smoother due to the enhanced form of MPP tracking. Hence, improved PSO takes 0.038 sec while the existing PSO technique takes 0.045 sec to obtain the MPP tracking