Department of Electrical Engineering
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Item Design and Implementation of a Hybrid Supervisory Controller for the Speed Control of PMSM under Load Variations(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Tayyab; SP21-REE-005; Dr. Mirza Tariq Hamayun; LHR TP 8474The control of speed in Permanent Magnet Synchronous Motors (PMSM) is a crucial aspect in various industrial domains, including electric vehicles, automation, and sustainable energy infrastructures. The maintenance of consistent speed control in PMSM encounters difficulties when faced with fluctuations in load. This research aims to demonstrate the implementation of a Proportional-Integral (PI) controller, Artificial Neural Networks (ANNs), and a hybrid controller which is a combination of PI and ANN in the speed regulation of PMSM while considering the variations in load. The hybrid controller that merges the benefits of a PI controller and an ANN controller is utilized to address the challenge of load variation. The PI controller is a reliable and consistent method of speed control, while the ANN contributes to adaptive control and improved performance in the presence of dynamic loads. The hybrid controller operates through the iterative adjustment of control inputs in response to motor feedback. The proportional and integral gains of the PI controller are tuned using ANN. The ANN learns the complex link between input parameters (e.g., motor speed, load torque) and the needed control action through a learning process. The controller's PI component delivers control signals proportionate to the difference between the reference and real motor speeds, resulting in fast responsiveness and stability. This allows the controller to adjust to changing load conditions and optimize the motor's reaction to keep the target speed. The controllers have been implemented in MATLAB/SIMULINK and it has been observed that the PI controller produced the mean square error (MSE) and root mean square error (RMSE) of 0.389 and 0.6236 respectively. The ANN model produced the MSE and RMSE of 0.021155 and 0.1454 respectively. Then the MSE and RMSE produced by PI-ANN are observed as 0.000458 and 0.02144 respectively. The results demonstrate that the PI-ANN controller produced the least MSE and RMSE followed by ANN and PI. There are various advantages of using a PI-ANN hybrid controller for PMSM speed control on load variation. It improves system adaptability by allowing the controller to modify control parameters in response to real time load variances.Item Handoff Management in Light Fidelity Networks(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Waseem Ahmad,; SP21-RCS-005; Dr. Muhammad Farooq-i-Azam; LHR TP 8057Handoff Management in Light Fidelity Networks Many technologies have been considered to handle the speedy developing range of cell phones and their extended requests for service providers. Among those technologies, Li-Fi is the one that can provide better communication and cover the spectrum shortage. However, Li-Fi has a short range and different service outages. Therefore, Li-Fi is integrated with Wi-Fi to provide better communication in the form of a hybrid Wi-Fi Li-Fi system. This thesis investigates handoff in hybrid light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) networks, i.e., HLWNets. The hybrid network has more challenges to overcome the issue of the small spectrum regions of Li-Fi access points and the interference issue of two different networks. Li-Fi has a short coverage range, while Wi-Fi covers a great area. Due to this, to provide reliable coverage in HLWNets, we integrated Li-Fi networks with Wi-Fi networks. In HLWNets, we face the issue of frequent handoffs. Different methods have been introduced to resolve this issue. In this research, our proposed method is presented based on a signal-to-interference ratio (SINR) scheme instead of reference signal received power (RSRP) because it is a standard scheme but requires additional signals during transmission. The simulation results show that the proposed technique reduces frequent handoffs between Wi-Fi and Li-Fi. During the proposed method, we introduced the AWGN channel while transmitting data from the transmitter to provide reliable data on the receiver. This method also offers better throughput than a standalone Li-Fi system. We have developed a simulation network with handoff between fully working access points of hybrid Li-Fi and Wi-Fi. The simulation results work on the throughput and diminish the packet delay and drop ratio. The algorithm used for load balancing is based on evolutionary game theory for hybrid Li-Fi/Wi-Fi networks. The proposed greedy algorithm also overcomes the frequent handoffs in a hybrid Li-Fi/Wi-Fi network and provides rapid information transmission that improves the quality of service (QoS). We used vertical handoff and horizontal handoff based on the threshold. The greedy algorithm is used to decrease the data loss and improve throughput. The user is connected to a Li-Fi access point (AP) mostly. When light intensity decreases, we communicate to other AP based on the threshold value. If no high-intensity Li-Fi AP is available, the user is associated with Wi-Fi AP. The RSSI method moves the user from VIII Wi-Fi AP to Li-Fi AP. This process decreases the data loss rate and improves throughput and fast data transmission.Item Non-Invasive Fault Detection and Localization of Induction Motors(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) TEHSEEN JAVAID; , SP21-REE-016; Dr. Saleem Akhtar; Dr. Saleem AkhtarWith the rapid rise of Industry 4.0, the growing use of sensors, the rapid development of the Internet of Things (IoT), and the use of artificial intelligence techniques, smart factories may automate their operations to greatly enhance their efficiency and quality of output. However, ultimately, even the most well-maintained equipment have defects. Given that Prognostics and Health Management (PHM) is essential for optimal machine performance, Predictive Maintenance (PdM) is an emerging topic within maintenance methodologies with the objective of predicting failure prior to its occurrence in order to schedule maintenance only when it is necessary. Deep learning is a useful technique for using big data for data-driven fault diagnostic approaches, since data can be generated at an unprecedented rate. Diagnostic of faults of induction motors is playing an important role in industries. Fault detection of induction motors is one of the most noteworthy need of the industries. Several motor components (rotor, bearings, insulation, stator and rotor circuits) deteriorate with time and stress. Degraded electric rotating motor parts can lead to machine accidents and downtimes. Localizing faults, repairing, or replacing a damaged motors takes time and money. In this thesis, a non invasive acoustic signal-based fault monitoring and localization using machine learning (ML) will be designed and analyzed for the induction motors. A mic will be installed near induction motor to extract the dataset of faulty motors and healthy motors based on acoustic signals. Audial based fault detection of induction motors monitors the system and detects the faults earlier to improve the technical issues, cost reduction and high reliability. We will develop this system by using machine learning taking the computational values of the acoustic signals, splitting the audio, amplitude scaling. This thesis investigates two distinct fault diagnosis approaches related with predictive maintenance: anomaly identification via a fault classifier recurrent neural network and failure mode and effects analysis (RNN). The technology under consideration is an industry-standard AC induction motor. The results demonstrate excellent performance and suggest the method's potential for industrial applications.