ENG740 Engineering Research Methods and Postgraduate Studies Assignment Sample
Designing and building an integrated system for automatic solar tracking and following system for solar collectors using fuzzy algorithms and mechatronics
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Introduction
The world’s most abundant and cleanest energy source is solar power. The resource is virtually inexhaustible and will likely be our primary source of energy for many years [1,2]. In addition to industrial, commercial and residential uses, solar power has also been applied to military applications. Photovoltaic (PV) cells generate solar energy based on the conditions of the environment such as irradiation, sunlight incident angle, temperature, and load.
Renewable energy sources such as solar are becoming more common in recent years [1]. There are two major categories of solar technology. There are two types of thermal solar technologies: thermal solar technologies and concentrated solar power (CSP). Other solar energy conversion methods involve direct conversion of solar energy to electrical energy through the use of photovoltaic cells. Photovoltaic cells can produce more electricity if several factors are in place. A solar tracking system (STS) is used to monitor the Sun from sunrise to sunset in order to gain maximum power from it (see Figure 1).
A single-axis or dual-axis STS can be used. Panels can be moved in the azimuth direction (east-west) with single-axis systems. The STS can be moved in both north-south (Zenith) and east-west (Zero) directions. Three types of STS can be distinguished by their operating modes: chronological, passive, and active STS. In passive STS, elements such as matter or shape memory alloys are used to control thermal expansion. The Clifford and Eastwood work used bimetallic strips of aluminum and steel mounted symmetrically on either side of a horizontal axis. When bimetallic strips are placed far from the sun, the farther away strip absorbs solar radiation, while the other remains shaded [1].
Two identical cylindrical tubes are exposed to the sun’s rays from different angles, causing one of the tubes to evaporate and produce a weight difference, which allows the photovoltaic panel to move in one direction [3]. Based on a shape memory alloy, Poulek developed a passive single-axis STS. Due to the heat engine-like properties of the alloy used during the thermal cycle, even at low temperatures it can easily deform. At temperatures above the conversion temperature, the alloy will return to its original shape. In this way, the alloy is capable of following the sun [4]. Using a box-type solar cooker along the azimuth, Farooqui described a new mechanism for tracking it single-axis. As the tracking power is generated by the stored gravitational energy in the spring, no external power source is required for the system. In the development of passive STS using gravity, Natarajan and Srinivas tested it experimentally. By controlling the rate at which liquids drop or fill into the gravity system, they tried to minimize tracking load and error [6]. In passive tracking systems, there is no electronic motor or control and no electricity is used, although accuracy is limited.
Solar tracking is done with optical sensors in the active STS. The solar panel is rotated to face the sun once the position of the sun is determined by the error signals measured by the sensors [7]. There are two parts to an active STS system. There are two parts to an active STS system. An active STS system has two parts: electro-optical and microprocessor-controlled STS. It relies on two well-positioned photoresistors being illuminated differently due to their difference in light intensity. When both photo resistors have the same resistance value, the motor is moved in one direction by an electronic circuit. The installation of such systems must be highly precise to ensure proper operation [8]. By employing this method, Papageorgiou created a single-axial STS containing three Light Dependent Resistors (LDR) sensors and a DC motor. The three LDRs determined the position and status of the Sun. With the circuit designed, he was able to track the sun from east to west at a speed of 0.011 revolutions per minute. A mechanism was designed by Sefa et al. for moving the solar panels east-west. Two LDRs were used to control the movement of the single-axis system. By removing the connection point in the middle of the resistors, a control circuit was established by connecting the LDRs in series. Based on the results, it was found that the single-axis tracking system had a higher power collection efficiency than a fixed system [10].
Further, solar PV has a low efficiency when it comes to producing maximum output power from sunlight. Many research works have been conducted to solve this problem by using solar tracking systems to increase the efficiency of output power. To keep the panels of the solar PV system perpendicular to the direction of the solar radiation, the panels are tracked by the tracking system. As a mechatronic system, solar-tracking systems incorporate elements of mechanics, electronics, and information technology [11]. A solar tracking system has been able to increase energy efficiency by 35% per year [12].
This study will design and develop dual axis solar tracking systems using four LDR sensors to compare light intensity. It is also highlighted that fuzzy logic algorithms provide the best positioning accuracy. Furthermore, the development of the system should be robust based on materials of high quality but at an affordable cost [13]. Additionally, the developed solar tracking system’s power efficiency will be compared with a non-tracing system under similar conditions.
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Literature Review
Abdullah [14] presented a computerized method for tracking the sun’s movement with a solar still. A comparison of sun tracked solar stills and fixed stills revealed a 22% increase in productivity due to a 2% increase in overall efficiency due to sun tracking. It showed that sun tracking is able to boost productivity better than fixed systems. Sun trackers lower the thermal capacity of water while increasing its temperature. Increased temperatures lead to more evaporation, which in turn leads to higher distillation rates.
Tudorache [15] examined the “performance of a solar tracking PV panel developed by University Politehnica of Bucharest in cooperation with Technosoft International SRL”. Comparing the equipment to a fixed solar panel, experimentally tested its performance. Solar tracking panels of single axis type are studied in this paper in order to estimate their performance. Using a DC motor controlled by an intelligent drive unit and receiving input signals from light intensity sensors, the studied device automatically finds the optimal PV panel position in relation to the sun. Approximately 57.55% more energy was produced by the solar tracking panel on a given day than the fixed panel. A mobile PV panel with an oversized tracking mechanism becomes less attractive than a fixed one when considering its own energy consumption. Under the same experimental conditions, the higher-power PV panels may produce more energy when driven by the same tracking mechanism, for example about 38% more energy in case of a 100 Wp PV panel.
In his study [16], Barsoum explains that research has proved that using a solar tracking system with one axis of freedom increases energy output by approximately 20%, while using a system with two axes can increase energy output by more than 40%.
In this work, we developed and implemented a solar tracking system that uses sensors to detect solar radiation. Peripheral Interface Controllers were used to detect sunlight and control solar panel placement. They actuated motors to position the solar panels where maximum sunlight was being reflected. Using the photocells, this device detects sunlight, actuates the motor, and positions the solar panel where it receives maximum sunlight.
Solar assist plug-in hybrid electric tractors (SAPHTs) are electric vehicles with solar panels on board. Mousazadeh et al. [17] studied how to measure and maximize collected energy from the solar panels. Solar tracking systems were designed and implemented. As the SAPHT is a mobile structure, a sun-tracker that is independent of time and date was desirable. On a mobile structure a sun tracking system based on four light dependent resistive sensors was built and evaluated. The sun-tracking system proved to be 30% more efficient than the fixed position mode in experimental tests. In total, the PV array covered approximately 6 square meters and has a maximum output power of 540 watts. Direct sunlight was detected with four LDR sensors. The obstructions between each pair of LDRs were used as shading devices. As an interface between the hardware and the software, an electronic drive board based on a microcontroller was used. To control the actuators for each motor, power MOSFETs were used. The experimental results indicated the system to be highly effective and robust. The results of the April tests indicated that a sun-tracking system improved collected energy by about 30% over a horizontally fixed one.
The bi-directional tracking system described in Okpeki et al. [18] is designed and constructed. The constructed device integrated an inverter of 900V and a battery of 12volts, 100AH. There are three parameters that determine the amount of power generated by photovoltaic panels: the type and area of the materials, the intensity of the sunlight, and the wavelength of the solar radiation. A solar panel’s first parameter, the material and its size, had been fully improved and standardized through advancements in technology. These two parameters were fully considered in this research work as this device ensures maximum intensity of sunlight hitting the panel surface from sunrise to sunset. Tracking the sun accurately used fewer power resources than the tracker. Our research revealed that the tracker system has a very low total cost of construction.
A sliding mode control law is used by Rhif et al., [19], to track the sun with a dual axis sun tracker. As a result of the lack of sensors and two degrees of freedom, the sun tracker considered in this study is significant. During the course of five years, this tracker will record the sun’s position every second during the day. After sunset, the tracker returns to its original position corresponding to sunrise. These dual axis autonomic sun trackers increase solar energy production by over 40%. A sliding mode observer with a high estimation quality and robustness is successful in the tracking process.
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Discussion
In electronic control systems, Artificial Intelligence is a widely used technology. The science of Intelligent Control focuses on controlling various applications using different types of intelligent methods. Such machines that behave like humans are designed using both machine learning and expert systems technologies. As well as performing tasks in many real-world applications, they are capable of making decisions. As a result, people would be able to make decisions more quickly and effectively. In the past, several studies have investigated the use of different artificial intelligence models to control solar tracking systems – “Logistic Regression (LR), Support Vector Regression (SVR), Fuzzy Logic (FL), Multi-layer Perceptron (MLP), Adaptive Neural Fuzzy Inference System (ANFIS), and Genetic Algorithms (GA), etc.” [20-23].
Using fuzzy logic principle, solar tracking systems can be controlled intelligently. The fuzzy logic principle has been applied in several intelligent solar tracking controllers around the world. A proposed fuzzy logic model varies primarily in the type of fuzzy logic it uses, the architecture used, and the variables employed as inputs and outputs. Solar tracking systems can be controlled using both Mamdani and Sugeno inferences. To determine the optimal tilt and orientation angles for dual-axis solar tracking system, time, day, and month variables were used as input attributes [24]. Based on practical datasets, the controller proposes to predict tilt and orientation angles efficiently. Moreover, light sensors were used as input attributes to predict the direction and angle of movement of a servomotor using light intensity measurements [25]. A field-programmable gate array (FPGA) was used to implement the designed intelligent solar tracking controller. Experimental and simulation testing was used to test and evaluate the proposed control system, resulting in simpler, faster, and more precise control of the solar tracking system. In addition, different intelligent classifiers were used to predict the vertical and horizontal motions of solar tracking systems based on input variables obtained directly from sensors [26]. We used four light sensor readings to calculate luminous values ranging from 0 to 1000 Lux using resistance values. A DC motor driven dual axis solar tracking system based on fuzzy logic and artificial fuzzy inference models was developed. Several limitations need to be accounted for even though the proposed models were almost always in agreement with the actual data. A method was proposed for determining the direction of solar tracking systems without determining its angle value. It is inefficient to propose a dual axis tracking system by using the variables of a fixed tracking system and guaranteeing that the expected results will be accurate. Furthermore, the variables of the system should be measured and processed on an hourly basis, as they take a long time to process.
Another intelligent control model, neural networks, are used to control many types of solar panels. Between the two models, the main differences are the network architecture and the attributes used as inputs and outputs. To predict the acceptance angle of dual-axis solar tracking controller, we used day, time, and longitude as inputs to a neural network controller. Controlling dual-axis tracking systems using neural net-works principle increases their performance. When using time, longitude, and random signals, however, the system can become overly complex, take a long time to process, and consume too much energy. To improve solar tracking system performance, this controller improves the neural networks training and validation.
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Conclusions
By tracking the maximum intensity of solar radiation, a solar tracking system uses an intelligent method. The availability of solar energy and its cost-effectiveness have made it one of the most useful alternative forms of energy. As much direct solar irradiance as possible must be captured in order to maximize the output power of solar energy technology. Dual-axis tracking systems could be improved using neural networks based controllers. A combination of time, longitude, and random signal generation will, however, increase the complexity of the algorithm, increase the processing time, and cause the system to consume more energy. In this paper, a dual-axis tracking controller is proposed in order to improve the training and validation of neural networks for improving solar tracking.
References
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