Modified Perturb and Observe Approach in MPPT for a Standalone Photovoltaic System

This paper proposes a modified perturb and observe algorithm approach to increase the output power of an independent photovoltaic system. Today, photovoltaic application as renewable energy power plant has been prevalent. This popularity is because photovoltaic power plants are easy to apply on-grid and off-grid schemes. In standalone power generation applications, the increased photovoltaic output power is of great help to users as it contributes to increasing overall system efficiency. The perturb and observe algorithm has been known as a reliable and inexpensive method. However, the performance still needs to be improved. Therefore, this study proposes a modified perturb and observe algorithm approach. The research results show the superiority of the proposed method

New and improved solutions for the configuration, management and operation of large-scale photovoltaic power plants using hybrid energy storage system

This thesis is presented in the context of multiple efforts being made by research groups in order to disseminate and evolve the technological knowledge in the renewable energy (RE) field. The motivation of this study is based on the main disadvantages of photovoltaic (PV) solar power generation compared to conventional energy sources, such as intermittency, non-dispatchability, and unreliability. From those drawbacks, the aim is to find creative solutions to mitigate these issues with a power plant combining PV and hybrid energy storage systems (HESS). In doing so, hybrid power plants represent a very interesting option for power generation and grid integration of PV systems, since they allow increased efficiency in the energy generation, as well as storing and supporting RE generation when needed.The storage systems are particularly important to deal with the intermittent nature of solar radiation. A wide variety of storage systems exists nowadays, all of them based on different operating principles and target applications. A thorough review of the literature has exposed that, depending on the application, a certain type of storage could be preferred above others. Besides, two or more different technologies working together can present complementary features. In this sense, the HESS can accomplish higher efficiencies and improved systems for grid connection. The objective is to develop new solutions to improve the configuration, management, and operation of PV plants with energy storage, designing the necessary control techniques and validating them through real-time simulations. In this context, this thesis presents a large-scale PV power plant with HESS, through a DC/DC impedance source converter (DC/DC-ZSC), and a new simplified model (SM) of the quasi Z-source inverter with battery energy storage (qZSI-BES) attached directly to the Z-network without an extra DC/DC converter. The HESS consists of battery arrays (BES) and ultracapacitors (UC). The newly designed SM is implemented, assessed, and validated experimentally in laboratory through a TYPHOON HIL system and a dSPACE MicroLabBox control board. Three different energy management strategies are implemented, using two of them advanced control techniques based on fuzzy logic. The control loops of the active, reactive, BES, and UC power have been conveniently deployed. The results obtained are coherent with the expected responses, observing an appropriate power balance and grid energy dispatch. This thesis aims for a relevant contribution in the development of low polluting energy sources that can fulfill the growing electricity demand. Following the global trends, and recognizing the importance of this knowledge for the scientific community and for society, this thesis will provide new solutions in the field of solar PV generation. In view of the fact that research has mainly focused on small-scale hybrid power plants so far, further studies are required regarding the configuration, design, control, energy management, operation, and problems associated with large PV power plants with hybrid storage

Modelling, Simulation and Analysis of a Buck-Boost Converter for Photovoltaic Application. Modello, Simulazione ed Analisi di un Convertitore Buck-Boost per Applicazione Fotovoltaica

La tesi tratta della construzione ed analisi di un modello di un convertitore buck-boost, interagente in una microgrid con una batteria e il suo convertitore associato, un inverter con PLL e sistema di controllo. Si analizza se le non linearità  introdotte dai vari elementi della rete influiscono sui sistemi di controllo e sulla stabilità  del modello

Design and Implementation of Control Techniques of Power Electronic Interfaces for Photovoltaic Power Systems

The aim of this thesis is to scrutinize and develop four state-of-the-art power electronics converter control techniques utilized in various photovoltaic (PV) power conversion schemes accounting for maximum power extraction and efficiency. First, Cascade Proportional and Integral (PI) Controller-Based Robust Model Reference Adaptive Control (MRAC) of a DC-DC boost converter has been designed and investigated. Non-minimum phase behaviour of the boost converter due to right half plane zero constitutes a challenge and its non-linear dynamics complicate the control process while operating in continuous conduction mode (CCM). The proposed control scheme efficiently resolved complications and challenges by using features of cascade PI control loop in combination with properties of MRAC. The accuracy of the proposed control system’s ability to track the desired signals and regulate the plant process variables in the most beneficial and optimised way without delay and overshoot is verified. The experimental results and analysis reveal that the proposed control strategy enhanced the tracking speed two times with considerably improved disturbance rejection. Second, (P)roportional Gain (R)esonant and Gain Scheduled (P)roportional (PR-P) Controller has been designed and investigated. The aim of this controller is to create a variable perturbation size real-time adaptive perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. The proposed control scheme resolved the drawbacks of conventional P&O MPPT method associated with the use of constant perturbation size that leads to a poor transient response and high continuous steady-state oscillations. The prime objective of using the PR-P controller is to utilize inherited properties of the signal produced by the controller’s resonant path and integrate it to update best estimated perturbation that represents the working principle of extremum seeking control (ESC) to use in a P&O algorithm that characterizes the overall system learning-based real time adaptive (RTA). Additionally, utilization of internal dynamics of the PR-P controller overcome the challenges namely, complexity, computational burden, implantation cost and slow tracking performance in association with commonly used soft computing intelligent systems and adaptive control strategies. The experimental results and analysis reveal that the proposed control strategy enhanced the tracking speed five times with reduced steady-state oscillations around maximum power point (MPP) and more than 99% energy extracting efficiency.Third, the interleaved buck converter based photovoltaic (PV) emulator current control has been investigated. 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 cancellation in the presence of parameter uncertainties. 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 multi-phase converters has been 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. Fourth, a symmetrical pole placement Method-based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller has been designed and investigated. The proposed PR-P controller resolved the issues associated with the use of the PI controller which are tracking repeating control input signal with zero steady-state and mitigating the 3rd order harmonic component injected into the grid for single-phase PV systems. Additionally, the PR-P controller has overcome the drawbacks of frequency detuning in the grid and increase in the magnitude of odd number harmonics in the system that constitute the common concerns in the implementation of conventional PR controller. Moreover, the unprecedented design process based on changing notch filter dynamics with symmetrical pole placement around resonant frequency overcomes the limitations that are essentially complexity and dependency on the precisely modelled system. The verification and validation process of the proposed control schemes has been conducted using MATLAB/Simulink and implementing MATLAB/Simulink/State flow on dSPACE Real-time-interface (RTI) 1007 processor, DS2004 High-Speed A/D and CP4002 Timing and Digital I/O boards

Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform

Publisher Copyright: © 2023 by the authors.This article deals with fault detection and the classification of incipient and intermittent open-transistor faults in grid-connected three-level T-type inverters. Normally, open-transistor detection algorithms are developed for permanent faults. Nevertheless, the difficulty to detect incipient and intermittent faults is much greater, and appropriate methods are required. This requirement is due to the fact that over time, its repetition may lead to permanent failures that may lead to irreversible degradation. Therefore, the early detection of these failures is very important to ensure the reliability of the system and avoid unscheduled stops. For diagnosing these incipient and intermittent faults, a novel method based on a Walsh transform combined with a multilayer perceptron (MLP)-based classifier is proposed in this paper. This non-classical approach of using the Walsh transform not only allows accurate detections but is also very fast. This last characteristic is very important in these applications due to their practical implementation. The proposed method includes two main steps. First, the acquired AC currents are used by the control system and processed using the Walsh transform. This results in detailed information used to potentially identify open-transistor faults. Then, such information is processed using the MLP to finally determine whether a fault is present or not. Several experiments are conducted with different types of incipient transistor faults to create a relevant dataset.publishersversionpublishe

Power quality enhancement in a grid-integrated photovoltaic system using hybrid techniques

In recent years, the photovoltaic (PV) system was designed to supply solar power through photovoltaic arrays. The PV generator exhibits nonlinear voltage–current characteristics and its maximum power point tracking (MPPT), which varies with temperature and radiation. In the event of non-uniform solar insolation, several multiple maximum power points (MPPs) appear in the power–voltage characteristic of the PV module. Thus, a hybrid combination of binary particle swarm optimization (BPSO) and grey wolf optimization (GWO) is proposed herein to handle multiple MPPs. This combination is nowhere found in the literature, so the author chose this hybrid technique; and the main advantage of the proposed method is its ability to predict the global MPP (GMPP) in a very short time and to maintain accurate performance, even under different environmental conditions. Moreover, a 31-level multilevel inverter (MLI) was designed with a lower blocking voltage process to reduce the complexity of the circuit design. The entire system was executed in the MATLAB platform to examine the performance of the PV system, which was shown to extract a maximum power of 92.930 kW. The simulation design clearly showed that the proposed method with a 31-level MLI achieved better results in terms of total harmonic distortion (THD) at 1.60%, which is less when compared to the existing genetic algorithm (GA) and artificial neural networks (ANNs).The authors would like to thank the Spanish Ministerio de Ciencia, Innovación y Universidades (MICINN)‐Agencia Estatal de Investigación (AEI) and the European Regional De‐ velopment Funds (ERDF), by grant PGC2018‐098946‐B‐I00 funded by MCIN/ AEI /10.13039/501100011033/ and by ERDF.ERDF A way of making Europe.Peer ReviewedPostprint (published version

"Integrating AI and AEM Electrolyzer for Green Hydrogen Production: Optimization of Solar-Powered Electrolysis in Residential Energy Management"

openThis master's thesis presents a comprehensive study on the forecasting of short-term power generation in a grid-connected hybrid solar photovoltaic (PV) system through the utilization of an artificial intelligence (AI) model. The research integrates weather data and solar PV electricity production data to develop and optimize a Long Short-Term Memory (LSTM) based AI model. The year 2021's solar PV and weather data were utilized for training and validating the model. Additionally, AEM electrolyzer was optimized to efficiently produce hydrogen using surplus electricity generated by the solar PV system . The investigation identified notable correlations between solar radiation, solar energy, UV index, and various other weather parameters with solar PV power generation. These correlations played a significant role in enhancing the accuracy of the AI model in predicting power generation. Various LSTM model structures were evaluated, and a two-layer LSTM model demonstrated superior performance, achieving an accuracy of approximately 80%. Furthermore, surplus electricity generated by the system, averaging 10 kWh during the daytime was calculated and analyzed. The economic viability of the hybrid system was also established, as the cost of electricity generated through the hybrid system was less than half of the grid energy price, meeting the regulatory standards.Optimizing the AEM electrolyzer revealed that a configuration with a few standby parallel AEM electrolyzers was optimal for utilizing excess electricity effectively. Further than that scheduling the parallel system in hourly basis for the days ahead, would help to have more conveniently benefit from this system. In conclusion, this research presents promising avenues for future studies aimed at further enhancing the efficiency and sustainability of renewable energy systems. Prospective research includes real-time integration of weather updates for AI models, advanced energy storage systems, demand-side management strategies, comparison of machine learning algorithms, optimized hydrogen production, and the evaluation of the integrated model in a microgrid setting. These future directions aim to contribute to the wider adoption of renewable energy sources and facilitate the transition towards a more sustainable energy future.This master's thesis presents a comprehensive study on the forecasting of short-term power generation in a grid-connected hybrid solar photovoltaic (PV) system through the utilization of an artificial intelligence (AI) model. The research integrates weather data and solar PV electricity production data to develop and optimize a Long Short-Term Memory (LSTM) based AI model. The year 2021's solar PV and weather data were utilized for training and validating the model. Additionally, AEM electrolyzer was optimized to efficiently produce hydrogen using surplus electricity generated by the solar PV system . The investigation identified notable correlations between solar radiation, solar energy, UV index, and various other weather parameters with solar PV power generation. These correlations played a significant role in enhancing the accuracy of the AI model in predicting power generation. Various LSTM model structures were evaluated, and a two-layer LSTM model demonstrated superior performance, achieving an accuracy of approximately 80%. Furthermore, surplus electricity generated by the system, averaging 10 kWh during the daytime was calculated and analyzed. The economic viability of the hybrid system was also established, as the cost of electricity generated through the hybrid system was less than half of the grid energy price, meeting the regulatory standards.Optimizing the AEM electrolyzer revealed that a configuration with a few standby parallel AEM electrolyzers was optimal for utilizing excess electricity effectively. Further than that scheduling the parallel system in hourly basis for the days ahead, would help to have more conveniently benefit from this system. In conclusion, this research presents promising avenues for future studies aimed at further enhancing the efficiency and sustainability of renewable energy systems. Prospective research includes real-time integration of weather updates for AI models, advanced energy storage systems, demand-side management strategies, comparison of machine learning algorithms, optimized hydrogen production, and the evaluation of the integrated model in a microgrid setting. These future directions aim to contribute to the wider adoption of renewable energy sources and facilitate the transition towards a more sustainable energy future

The design, management and testing of a solar vehicle's energy strategy

In recent years the interest in implementing solar energy on vehicles (electrical and hybrid) has grown significantly [1]. There are currently limitations in this sector, such as the low energy density (efficiency of conversion) of this source, but it is still a renewable resource and as such, there is a growing interest [1]. A “smart” energy strategy implemented on a solar/electrical vehicle, in order to increase its energy harvesting volume, could enhance the growth of this sector. A tracking algorithm for a solar vehicle’s MPPT (Maximum Power Point Tracker) can be designed to source solar energy very effectively and to increase the speed of finding (tracking) this optimal sourcing point (solar panel voltage and current). Even though there are many different MPPT algorithms, it was decided that most of them were designed for stationary MPPT applications and the dynamics of implementing a MPPT on a vehicle create some unique scenarios. These include: Shadow flicker. This is rhythmic, rapid moving shadows across a solar panel, such as shadows from a line of trees: Rapid changes in solar panel orientation due to the road surface/relief; Rapid changes in panel temperature due to the location of the vehicle. The aim of the research can be divided into three outcomes: 1 Creating a “Smart” energy strategy/control, 2 Implement the new control system on a solar vehicle’s MPPT, and 3 Harvesting maximum energy from solar panels using the new energy strategy. The term “smart” is used to indicate the ability of the MPPT algorithm to be updated and improved based on previous results. A MPPT and scaled solar vehicle is designed and manufactured in order to test the MPPT algorithm. The purpose of using a self-developed experimental setup is to have more control over the system variables as well as having the maximum freedom in setting up the system parameters

Integrated with Boost-Buck-Boost Topology on Solar & Wind Renewable Energy Resources

The integration was started with wind farms. When the price for photovoltaic panels became affordable, the penetration of PV became to be used more often but not necessarily at the same level of power as wind. For medium to high power the PV’s are modularly used. Many studies propose small power integration (few kW) for both wind and solar PV as hybrid stand-alone systems. Other studies added fuel cells and batteries creating the concept of multi-port system. I have proposed a double-port boost-buck-boost (BBB) topology that enhances the power capability of the PV-Wind power system during partial solar irradiation and weak winds. In this paper a hybrid power electronics interface that combines the energy from solar photovoltaic panel and wind generator into a small scale stand-alone system is proposed. After the description of operation of this Dual-port interface, a simulation model for 1 kW PV array integrated together with a 1.5 kW wind generator was developed and simulation results are presented