A Sliding Mode Multimodel Control for a Sensorless Photovoltaic System

In this work we will talk about a new control test using the sliding mode control with a nonlinear sliding mode observer, which are very solicited in tracking problems, for a sensorless photovoltaic panel. In this case, the panel system will has as a set point the sun position at every second during the day for a period of five years; then the tracker, using sliding mode multimodel controller and a sliding mode observer, will track these positions to make the sunrays orthogonal to the photovoltaic cell that produces more energy. After sunset, the tracker goes back to the initial position (which of sunrise). Experimental measurements show that this autonomic dual axis Sun Tracker increases the power production by over 40%

Fuzzy based sensorless tracking controller on the dual-axis PV panel for optimizing the power production

In general active sun trackers move because they respond to light sensors that measure the intensity of sunlight. However, sensor-based trackers are usually more expensive than sensor-less trackers. In addition, based on several studies, a comparison between the sensor and sensorless based tracker only reports lower tracking error and higher power generation for sensor-based than sensorless tracker. However, it does not include an analysis of energy use on the sensor. Therefore, this study aims to design a sensorless closed-loop tracking system for solar panels with two degrees of freedom. The tracking controller in this study is based on the Fuzzy Logic Controller (FLC) method. In this study, a dual-axis PV can increase power output by 20.2% compared to a fixed PV (0 ° axis position). In comparison to a fixed PV, dual-axis PV adjusts the solar panel perpendicular to the sun's position to optimize electrical conversion.In general active sun trackers move because they respond to light sensors that measure the intensity of sunlight. However, sensor-based trackers are usually more expensive than sensor-less trackers. In addition, based on several studies, a comparison between the sensor and sensorless based tracker only reports lower tracking error and higher power generation for sensor-based than sensorless tracker. However, it does not include an analysis of energy use on the sensor. Therefore, this study aims to design a sensorless closed-loop tracking system for solar panels with two degrees of freedom. The tracking controller in this study is based on the Fuzzy Logic Controller (FLC) method. In this study, a dual-axis PV can increase power output by 20.2% compared to a fixed PV (0 ° axis position). In comparison to a fixed PV, dual-axis PV adjusts the solar panel perpendicular to the sun's position to optimize electrical conversion

Maximum power point tracking and control of grid interfacing PV systems

Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power.Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power

A Sliding Mode Control for a Sensorless Tracker: Application on a Photovoltaic System

The photovoltaic sun tracker allows us to increase the energy production. The sun tracker considered in this study has two degrees of freedom (2-DOF) and especially specified by the lack of sensors. In this way, the tracker will have as a set point the sun position at every second during the day for a period of five years. After sunset, the tracker goes back to the initial position (which of sunrise). The sliding mode control (SMC) will be applied to ensure at best the tracking mechanism and, in another hand, the sliding mode observer will replace the velocity sensor which suffers from a lot of measurement disturbances. Experimental measurements show that this autonomic dual axis Sun Tracker increases the power production by over 40%

Wind energy harvester interface for sensor nodes

The research topic is developping a power converting interface for the novel FLEHAP wind energy harvester allowing the produced energy to be used for powering small wireless nodes. The harvester\u2019s electrical characteristics were studied and a strategy was developped to control and mainting a maximum power transfer. The electronic power converter interface was designed, containing an AC/DC Buck-Boost converter and controlled with a low power microcontroller. Different prototypes were developped that evolved by reducing the sources of power loss and rendering the system more efficient. The validation of the system was done through simulations in the COSMIC/DITEN lab using generated signals, and then follow-up experiments were conducted with a controllable wind tunnel in the DIFI department University of Genoa. The experiment results proved the functionality of the control algorithm as well as the efficiency that was ramped up by the hardware solutions that were implemented, and generally met the requirement to provide a power source for low-power sensor nodes

Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System

settings Open AccessArticle Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System by Mohamed Derbeli 1,2,* [OrcID] , Oscar Barambones 1 [OrcID] , Mohammed Yousri Silaa 1 [OrcID] and Cristian Napole 1 [OrcID] 1 Engineering School of Vitoria, University of the Basque Country UPV/EHU, Nieves Cano 12, 1006 Vitoria, Spain 2 National Engineering School of Gabes, University of Gabes, Omar Ibn-Elkhattab, 6029 Gabes, Tunisia * Author to whom correspondence should be addressed. Actuators 2020, 9(4), 105; https://doi.org/10.3390/act9040105 Received: 30 August 2020 / Revised: 25 September 2020 / Accepted: 10 October 2020 / Published: 16 October 2020 (This article belongs to the Section High Torque/Power Density Actuators) Download PDF Browse Figures Abstract Polymer electrolyte membrane (PEM) fuel cells demonstrate potential as a comprehensive and general alternative to fossil fuel. They are also considered to be the energy source of the twenty-first century. However, fuel cell systems have non-linear output characteristics because of their input variations, which causes a significant loss in the overall system output. Thus, aiming to optimize their outputs, fuel cells are usually coupled with a controlled electronic actuator (DC-DC boost converter) that offers highly regulated output voltage. High-order sliding mode (HOSM) control has been effectively used for power electronic converters due to its high tracking accuracy, design simplicity, and robustness. Therefore, this paper proposes a novel maximum power point tracking (MPPT) method based on a combination of reference current estimator (RCE) and high-order prescribed convergence law (HO-PCL) for a PEM fuel cell power system. The proposed MPPT method is implemented practically on a hardware 360W FC-42/HLC evaluation kit. The obtained experimental results demonstrate the success of the proposed method in extracting the maximum power from the fuel cell with high tracking performance.This work was partially supported by Eusko Jaurlaritza/Gobierno Vasco [grant number SMAR3NAK ELKARTEK KK-2019/00051]; the Provincial Council of Alava (DFA) [grant number CONAVAUTIN 2] (Collaboration Agreement)

A simple method for harvesting thermoelectric energy in home and industrial appliances heat cycle using peltier cells

Energy harvesting models are the focus of most countries, given that governments are becoming aware of the limitations of natural resources and the need to optimize them. On the other hand, different systems used in everyday life and in industrial environments involve the use of heat cycles, but in most cases, their thermoelectric energy is not recovered from these processes. Accordingly, this paper proposes to implement a model based on a low-cost Peltier array that can be attached to commonly used devices with heat cycles involving small temperature differences (∆T=25 °C). A maximum power point tracking (MPPT) method was used to extract the maximum power from this array. This device is thought to take advantage of home and industrial elements’ heat to power low-power system applications. The results show that this technology allows acceptable use and represents an effective recovery mechanism. This work represents a new approximation of the energy harvesting solutions from thermoelectric energy with future benefits, especially on the Internet of Things (IoT) applications, which has been one of the technology areas of most significant expansion and growth in recent decades. The IoT has opened significant challenges in the scientific community, especially regarding the energy supply methods of the IoT elements or nodes, considering that these elements can be located in places where it is impossible to wire to supply power and that use of batteries is unsustainable in the long term, also generating a negative environmental impact. The proposed system harvests energy from the temperature difference generated at a window, considering that the device is controlled environment within a roo

Wind Power

This book is the result of inspirations and contributions from many researchers of different fields. A wide verity of research results are merged together to make this book useful for students and researchers who will take contribution for further development of the existing technology. I hope you will enjoy the book, so that my effort to bringing it together for you will be successful. In my capacity, as the Editor of this book, I would like to thanks and appreciate the chapter authors, who ensured the quality of the material as well as submitting their best works. Most of the results presented in to the book have already been published on international journals and appreciated in many international conferences

Analytic Predictive of Hepatitis using The Regression Logic Algorithm

Hepatitis is an inflammation of the liver which is one of the diseases that affects the health of millions of people in the world of all ages. Predicting the outcome of this disease can be said to be quite challenging, where the main challenge for public health care services itself is due to a limited clinical diagnosis at an early stage. So by utilizing machine learning techniques on existing data, namely by concluding diagnostic rules to see trends in hepatitis patient data and see what factors are affecting patients with hepatitis, can make the diagnosis process more reliable to improve their health care. The approach that can be used to carry out this prediction process is a regression technique. The regression itself provides a relationship between the independent variable and the dependent variable. By using the hepatitis disease dataset from UCI Machine Learning, this study applies a logistic regression model that provides analysis results with an accuracy rate of 83.33

Influence Distribution Training Data on Performance Supervised Machine Learning Algorithms

Almost all fields of life need Banknote. Even particular fields of life require banknotes in large quantities such as banks, transportation companies, and casinos. Therefore Banknotes are an essential component in carrying out all activities every day, especially those related to finance. Through technological advancements such as scanners and copy machine, it can provide the opportunity for anyone to commit a crime. The crime is like a counterfeit banknote. Many people still find it difficult to distinguish between a genuine banknote ad counterfeit Banknote, that is because counterfeit Banknote produced have a high degree of resemblance to the genuine Banknote. Based on that background, authors want to do a classification process to distinguish between genuine Banknote and counterfeit Banknote. The classification process use methods Supervised Learning and compares the level of accuracy based on the distribution of training data. The methods of supervised Learning used are Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Naïve Bayes. K-NN method is a method that has the highest specificity, sensitivity, and accuracy of the three methods used by the authors both in the training data of 30%, 50%, and 80%. Where in the training data 30% and 50% value specificity: 0.99, sensitivity: 1.00, accuracy: 0.99. While the 80% training data value specificity: 1.00, sensitivity: 1.00, accuracy: 1.00. This means that the distribution of training data influences the performance of the Supervised Machine Learning algorithm. In the KNN method, the greater the training data, the better the accuracy