A project report submitted in partial fulfillment of the requirements for the award of Bachelor of Science. This shows that the agent is successful in achieving its goal which is the coordinate of the Sun.
General Introduction
In a CPV system, external factors such as the mechanical structure of the concentrator and an excessively high solar concentration ratio affect the sensitivity of the concentrator. In addition, reinforcement learning is used to determine the motor's range of motion and orient the solar concentrator in the correct position.
Importance of the Study
In recent years, artificial intelligence (AI) has been applied to solar concentrator systems to increase the accuracy of predicting the position of the sun and thereby reduce tracking errors in the sun tracking algorithm. Therefore, an artificial intelligence integrated sun tracking system is proposed to reduce tracking errors by detecting the Sun using an object detection algorithm.
Problem Statement
Aim and Objectives
Scope and Limitation of the Study
Report Overview
Introduction
Concentrator Photovoltaics (CPV)
Type of Concentrators
This concentrator uses the optical properties of a curved parabolic surface to concentrate the incident solar radiation towards a central point. A refractive optics concentrator using Fresnel lenses refracts light to focus solar radiation incident on the surface of the lens into a solar cell.
Non-Imaging Dish Concentrator (NIDC)
The origin of the coordinate system is located at the center of the NIDC with the coordinates assigned as (0,0,0). The coordinate of the central point of each facet mirror is represented by the matrix 𝑀𝑖,𝑗 and is written as.
Sun Tracking System
Non-Imaging Dish Concentrator (NIDC)
The sun tracking algorithm requires several input parameters to accurately determine the position of the sun. The angle of declination, 𝛿 refers to the angle between the equatorial plane and a line drawn from the center of the earth to the center of the sun. The angle of declination varies with the seasons as the Earth orbits the sun on the ecliptic plane, and can be approximated as.
In an azimuth-elevation solar tracking system, the motion of the solar concentrator depends on the azimuth and elevation angles. The solar azimuth angle, 𝐴 is the angle between the projection of the center of the sun on the horizontal plane that is due south. The solar elevation angle is the angle of the sun's elevation, which is between the direction of the sun and the horizon.
Artificial Intelligence
- Recent Developments in AI Integrated Sun-Tracking System In recent years, many studies on the AI integrated sun tracking systems have
- Artificial Neural Networks (ANN)
- Background of Computer Vision
- Object Detection
- YOLOv7
- Reinforcement Learning
- Q-Learning
The system uses object detection algorithms with region proposal techniques and convolutional neural network (CNN) to track the position of the sun. The accuracy of the neural network is improved as more training data is fed into the neural network. The output of the algorithm is the bounding box coordinates, width, height and object class.
Object segmentation divides the image into different areas based on the characteristics of the pixels. The reinforcement learning model will continue to learn and the best solution will be determined based on the maximum reward. The transition probability is the probability that the agent will move from its current state, 𝑆𝑡, to the next state, 𝑆𝑡 + 1, and perform an action, 𝐴𝑡.
The transition probability indicates that the agent's behavior is probable and the agent may not behave as instructed. In reinforcement learning, Q-Learning is a type of value-based learning algorithms where the objective of the agent is to optimize the value function.
Summary
In the update rule, the learning rate and the discount rate are two hyperparameters that require adjustment to improve Q-Learning. The discount rate is a parameter to encourage the agent to reach its goal faster as it discounts its future value at the next state, 𝑆𝑡 + 1. The learning rate determines the weights of the current value compared to its future value.
Introduction
System Design of the AI Sun-Tracking System
Design Architecture
Motor Control Unit
Arduino
Arduino's Stepper library is imported into the code, which provides the 'Stepper' function. The minimum number of steps per movement is defined as 200 steps, which allows accurate movement of the sun tracking system. In manual tracking, the CPV is controlled using the joystick connected to the Arduino UNO.
Manual tracking is used to preset the starting position of the CPV to orient to the sun or manually adjust the position of the CPV. In Auto Tracking, the CPV is controlled by the AI system, which consists of the YOLOv7 object detection algorithm and the Q-Learning reinforcement learning algorithm. YOLOv7 will get the coordinates for the Sun and pass the coordinates to Q-Learning.
This will allow Q-Learning to get the minimum steps required for the CPV to align with the Sun's coordinates. The Arduino UNO will receive the required minimum steps and orient the CPV to the correct position.
AI Sun-Tracking System
- Custom Dataset of Sun and Clouds
- Model Training for Object Detection
- Model Training for Reinforcement Learning
- AI Sun-Tracking Algorithm
A custom dataset consisting of the class 'sun' and 'clouds' is created using the labelImg software. The class 'CENTERTARGET' is defined to provide the center of the camera for the agent to move from the center to the sun coordinate. In the 'motor movement' function, the agent is trained to move from the center point to the target point through a series of rewards.
The Q-learning training process starts by creating a new Q-table to store the Q-values of the training process for the motor control process. To obtain a better model, the unsaturated epsilon approach is chosen where the agent's behavior is determined by a randomly generated number. However, if the generated number is less than epsilon, the agent's next move is determined randomly.
Therefore, a python script is required to integrate both the machine learning algorithm and form a complete AI sun tracking algorithm. The function 'plot_box_point' is created to plot the center point of the bounding box.
Summary
This signal is defined as 0, 1, 2, and 3, which are the motion options for the CPV system, which translates into the clockwise and counterclockwise motion of the tilt and roll motors. The official YOLOv7 detect.py file has been modified to include both object detection and Q-learning. This midpoint is useful because it extracts the coordinate of the target's center, which in our case is the sun.
Once the classes and dependencies are created, a pipeline is created for detecting sun and cloud objects. The Arduino function will then send the signal that is the movement of the CPV from the Jupyter Notebook to the Arduino. YOLOv7 is chosen as the object detection algorithm while Q-learning is chosen for motor control in this proposed AI tracking system.
The process for training the deep learning model is introduced for both YOLOv7 and Q-learning. A new python script for the AI sun tracking algorithm is created, designed to integrate both YOLOv7 and Q-learning algorithms.
Introduction
Performance of the Model Training
YOLOv7
One parameter that can affect the performance of a convolutional neural network is the batch size of a deep learning model. In general, model accuracy has been shown to increase with the number of batch sizes (Radiuk, 2017). However, when training the model on the custom dataset for sun and clouds, the mAP is not in an increasing trend, where the lowest batch size of 4 shows the highest mAP.
This situation may be due to the low number of samples as the largest batch size the laptop in use can handle is a batch size of 16 and the training process takes a very long time compared to other models. The higher mAP for batch size 4 may also be due to the mAP for batch size 4 being at a local maximum, thus requiring a larger batch size to obtain an increasing trend. Thus, the upward trend can be visible if a large amount of batch sizes are present.
It is also shown that the effect of batch size on model accuracy can vary depending on the data set used (Masters & Luschi, 2018). Therefore, a batch size of 4 was chosen for the model, as it has the highest mAP and lowest loss.
Q-Learning
Model Performance
Confidence Value for AI Sun-Tracking Algorithm
The left image has a confidence level of 0.10, while the right image has a confidence level of 0.30. However, there are cases where if the confidence value is set too low, wrong objects are detected, as shown in Figure 4-9. When the confidence value is set to 0.10, objects are detected that are not the actual target object.
This may be due to the insufficient amount of training images, which may cause the algorithm to detect the wrong object because it has similar shape, color or brightness. However, this issue is resolved when the confidence value is set to 0.30, where only the actual 'Sun' is detected. The left image is at confidence level of 0.10 while the right image is at confidence level of 0.30.
Evaluating the Model at Different Conditions
From left to right: (Left) Detection of sun during sunny day, (Middle) Detection of sun during sunset, (Right) Detection of sun when partially blocked. The value next to the class 'Sun' is the confidence level of the detection algorithm, which is above 90% for each case. The agent in Q-learning has 4 movement options which are left, right, up and down.
When the semi-solar point is reached, the agent will determine the path and the minimum steps the center must take to reach the semi-solar point, as shown in Figure 4.11. The movement of the agent will determine the signal sent to the Arduino, which will control the clockwise and counterclockwise movement of the tilt and rotate motors. With Q-learning, the same AI sun tracking algorithm can be applied to any type of sun tracking system, whether it is a heliostat, flat PV or CPV system, as Q-learning can automatically adjust the sun tracking position. system that the sun coordinate is always in the center of the webcam.
Also, encoders are no longer needed, because the Q-learning can automatically and continuously feed back the errors and adjust the solar tracker accordingly. The distance traveled and the minimum steps are determined for each sun detection, as shown in Figure 4 from left to right.
Summary
Conclusions
Recommendations for Future Work
YOLOv7: Trainable bag-free items set new state-of-the-art for real-time object detectors.
