Design and implementation of an I-V curvetracer dedicated to characterize PV panels

In recent years, solar photovoltaic energy is becoming very important in the generation of green electricity. Solar photovoltaic effect directly converts solar radiation into electricity. The output of the photovoltaic module MPV depends on several factors as solar irradiation and cell temperature. A curve tracer is a system used to acquire the PV current-voltage characteristics, in real time, in an efficient manner. The shape of the I-V curve gives useful information about the possible anomalies of a PV device. This paper describes an experimental system developed to measure the current–voltage curve of a MPV under real conditions. The measurement is performed in an automated way. This present paper presents the design, and the construction of I-V simple curve tracer for photovoltaic modules. This device is important for photovoltaic (PV) performance assessment for the measurement, extraction, elaboration and diagnose of entire current-voltage I-V curves for several photovoltaic modules. This system permits to sweep the entire I-V curve, in short time, with different climatic and loads conditions. An experimental test bench is described. This tracer is simple and the experimental results present good performance. Simulation and experimental tests have been carried out. Experimental results presented good performance

Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions

To satisfy global electrical energy requirements, photovoltaic (PV) energy is a promising source that can be obtained from the available alternative sources, but partial shading conditions (PSCs), which trap the local maxima power point instead of the global maxima peak power point (GMPP), are a major problem that needs to be addressed in PV systems to achieve the uninterruptable continuous power supply desired by consumers. To avoid these difficulties, a marine predator algorithm (MPA), which is a bio-inspired meta-heuristic algorithm, is applied in this work. The work is validated and executed using MATLAB/Simulink software along with hardware experimentation. The superiority of the proposed MPA method is validated using four different PSCs on the PV system, and their characteristics are compared to those of existing algorithms. The four different PSC outcomes in terms of GMPP are case 1 at 0.07 s 995.0 Watts; case 2 at 0.06 s 674.5 Watts; case 3 at 0.04 s 654.1 Watts; and case 4 at 0.04 s 364.2 Watts. The software- and hardware-validated results of the proposed MPA method show its supremacy in terms of convergence time, efficiency, accuracy, and extracted power.publishedVersio

Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

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Manta Ray Robot

The goal of this project was to improve UAV efficiency through use of biomimetic design. This was achieved through the application of a hydraulically actuated soft robotic fin. Drawing inspiration from the manta ray, a custom actuator was developed to achieve a feasible, lifelike locomotion method. The actuator was incorporated into a prototype robot to assess the performance and ease of integration

Modeling and Control Techniques in Smart Systems

Energy and food crisis are two major problems that our human society has to face in the 21st century. With the world’s population reaching 7.62 billion as of May 2018, both electric power and agricultural industries turn to technological innovations for solutions to keep up the increasing demand. In the past and currently, utility companies rely on rule of thumb to estimate power consumption. However, inaccurate predictions often result in over production, and much energy is wasted. On the other hand, traditional periodic and threshold based irrigation practices have also been proven outdated. This problem is further compounded by recent years’ frequent droughts across the globe. New technologies are needed to manage irrigations more efficiently. Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication, and ubiquitous computing technologies, high degree of information integration and automation are steadily becoming reality. More smart metering devices are installed today than ever before, enabling fast and massive data collection. Patterns and trends can be more accurately predicted using machine learning techniques. Based on the results, utility companies can schedule production more efficiently, not only enhancing their profitabilities, but also making our world’s energy supply more sustainable. In addition, predictions can serve as references to detect anomalous activities like power theft and cyber attacks. On the other hand, with wireless communication, real-time soil moisture sensor readings and weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers provide perfect platforms for complicated control algorithms. We designed and built a fully automated irrigation system at Bushland, Texas. It is designed to operate without any human intervention. Workers can program, move, and monitor multiple irrigation systems remotely. The algorithm that runs on the controls deserves more attention. AI and other state of art controlling techniques are implemented, making it much more powerful than any existing systems. By integrating professional crop yield simulation models like DSSAT, computers can run tens of thousand simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can find an optimum solution in minutes. The experience is then summarized as a policy and stored inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and update current policy with real harvest data. Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research. They represent our best knowledge in plant biology and can be very accurate when well calibrated. However, the calibration process itself is often time consuming, thus limiting the scale and speed of using these models. We made efforts to combine different models to produce a single accurate prediction using machine learning techniques. The process does not require manual calibration, but only soil, historical weather, and harvest data. 20 models were built, and their results were evaluated and compared. With high accuracy, machine learning techniques have shown a promising direction to best utilize professional models, and demonstrated great potential for use in future agricultural research

Bioelectrical measurement for sugar recovery of sugarcane prediction using artificial neural network

One of the problems in the sugar industry is lack of low cost, simple and accurate measurement techniques for sugar recovery of sugarcane in the field or laboratory. This study investigated the potential using of bioelectrical properties as a non-destructive technique for this purpose. A parallel plate capacitor was developed to measure the bioelectric properties of sugarcane in a lateral and longitudinal position of the samples. Eighteen internode samples from 3 sugarcane varieties were measured within 0.1-10 kHz frequency range of LCR meter and then was analyzed sugar recovery in the laboratory. The result showed that in the lateral position are more capacitive and resistive than the longitudinal position. Artificial neural network (ANN) was developed for prediction of sugar recovery as a function of bioelectrical properties. The best ANN model produces a high accuracy in the lateral bioelectrical measurement position with a correlation coefficient (R) > 0.90 and mean square error (MSE) <; 0.05. It showed that the ANN model based on bioelectrical properties had the potential to be developed as a simple technique to predict the sugar recovery of sugarcane