Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
item: Thesis-Full-text
Correction of ships’ steering angle for safe maneuvering against strong winds using microcontroller
(2025) Thambugala, TASH; Ruwanthika, RMM
The maritime industry, responsible for approximately 80% of global trade volume, faces significant challenges posed by adverse weather conditions, particularly strong winds. Over-steering in such conditions increases the risk of capsizing, leading to substantial economic and human losses. This research presents the development of a microcontroller-based system designed to adjust ship steering angles in real-time, accounting for wind conditions and enhancing navigational safety. The system inte- grates various sensors, including wind speed and direction transducers, gyro modules, and GPS receivers, to continuously monitor both environmental conditions and navi- gational parameters. Data collected from these sensors are processed by a microcon- troller, which utilizes a developed mathematical model to assess the impact of wind forces on the ship’s motion. Based on this assessment, the model calculates the neces- sarysteeringangleadjustmentstocounteracttheeffectsofwindforces,ensuringstable and safe maneuvering. Key factors considered in the model include wind force, cen- trifugal force, and heel angle, providing a comprehensive evaluation of ship stability under varying wind conditions. The system is designed to be compatible with existing steeringmechanisms,ensuringcost-effectivenessandeaseofimplementation. Simula- tionsdemonstratethesystem’sabilitytopredictandcorrectsteeringanglesaccurately, significantly reducing the risk of over-steering and capsizing. Rigorous testing has validatedthesystem’sperformance,showingsubstantialimprovementsinnavigational safety understrongwindconditions. Theproposedsolution offers arobustmethodfor enhancing maritime safety, protecting both crew and cargo.
item: Thesis-Full-text
Oil condition prediction in diesel generators using acoustic signals
(2025) Wijesuriya, WAH; Jayasekara, B
This research proposes a novel method for predicting the oil condition in CAT diesel generators using acoustic signals, aiming to overcome the limitations of traditional Scheduled Oil Sampling (S·O·SSM) testing methods. Conventional S·O·SSM programs often face challenges such as delayed results, sampling errors, and high operational costs. To address these, the study develops a non-invasive acoustic monitoring technique that correlates generator sound patterns with oil health indicators. Audio samples were collected using a Blue Yeti microphone under controlled conditions, and corresponding physical oil samples were subjected to standard S·O·SSM laboratory analysis. Various time-domain and frequency-domain features, including MFCCs, Zero Crossing Rate, and Spectral Centroid, were extracted using the Librosa library. A neural network model was developed to classify oil condition states into "Action Required," "No Action Required," or "Monitor," based on these acoustic features. Testing the system on a dataset of 75 samples showed encouraging results, with the second iteration achieving improved classification accuracy. Findings confirm that generator acoustic emissions change with oil degradation, validating the hypothesis that sound can predict oil condition. Conclusions highlight that this method enables timely, cost-effective oil monitoring without operational disruption. Recommendations for future work include optimizing the neural network through learning rate adjustments, particularly expanding the sample dataset, exploring ensemble methods, and validating performance across different generator types to enhance system robustness and industrial applicability.
item: Thesis-Abstract
Enhancing the productivity of cement grinding system by observing the vibration response
(2023) Thennakoon, PS; Jayasekara, AGBP
At a cement plant, the grinding process is the last phase of production. the process of turning kiln-ground cement clinker into final cement by mixing it with 4-5% gypsum, limestone, and potential additives. The grinding of cement must be fine enough to meet the strength properties requirements. For a particular cement type, the productivity of grinding process and the loading of ball mill has a proportional relationship, but it's important to note that the relationship is not always straightforward and can be influenced by various factors such as the characteristics of the raw material (mainly clinker), the design and condition of the mill, the speed of the mill, and the size and shape of the grinding media. As of now, there are no reliable ways for identifying the mill blockage condition of a cement ball mill, which occurs when the mill is suddenly overloaded with material to the point of obstruction and rapid drop in grinding productivity. Mill operators intentionally reduce the grinding output by feeding the mill with less material in order to prevent overloading and subsequent mill failure. This results in a less efficient and more power-intensive grinding process. There are very few external controls that can be used to create better conditions. Only the data extracted from sensors fixed at mill motor bearings do not provide accurate readings for mill fill level. Additional vibration responses and torque responses need to be considered for better fill level predictions. Time domain vibration signals are those that are obtained through the use of an accelerometer. Sensor array design and development has been done according to capture features of vibration signals of mill at various feed rates. Proper filtering has been used to remove noises of vibration signals. Fast Fourier Transform (FFT), with the use of DALOG BusyBee software has been used to extract features from time constrained vibration information. The features extracted were utilized as an ANN’s input parameters. The material feed rate to the ball mill is estimated using the ANN's output. Regression-based Deep Learning neural network fit for the cement mill operation automation and cement mill feed rate can be predicted without forcing mill blockages by analysis of vibration responses of mill motor and mill gearbox and torque response of mill shaft.
item: Thesis-Full-text
Identification of the key factors and development of strategies for promoting residential customer participation in demand response programs
(2024) Chathruangi, MAIN; Hemapala, KTMU
Effective Demand Response (DR) design plays a crucial role in mitigating peak demand increases and price volatility. Understanding diverse customer behavior, particularly in the residential sector, is pivotal for successful DR implementation. Recently, there has been a growing focus on price-based demand response programs, offering increased flexibility and potential for enhanced demand responsiveness. The success of these programs hinges on encouraging greater residential customer participation, necessitating the formulation of more effective incentive schemes. DR programs, including direct load control, empower utilities to manage peak loads by temporarily reducing electricity demand. While the efficacy of direct load control on individual appliances is well- established, utility demand-side management strategies often integrate multiple DR programs to optimally reduce demand during peak events.
Power distribution transformers are one of the key assets to be managed by the utility. The aging of transformers causes operational and financial burdens on the utility. Insulation failure due to overloading is the primary cause of the aging of the transformer. In Sri Lanka high peak demand appears at nighttime due to the residential loads are increasing. Therefore, the transformers are overloading at nighttime. Instead, the concept of demand response can be used to reduce the transformer overloading at the nighttime peak considering the requirements of the load. In this, I have demonstrated how Demand Response can positively affect the lifespan of the transformer.
To characterize the psychological behaviors of the customer by analyzing the conduct of a customer survey among the residential customers of the overloaded feeder. From this, the available capacity of DR can be measured. The consumer willingness to participate in DR is also measured by identifying the key factors the customer expects to participate in such program.
After that, analyze the customer load profiles and transformer load profiles to find the necessary load reduction in the night peak.Finally, based on the available capacity, a DR scheduling model is developed.
Then, according to the scheduling results, an incentive scheme is proposed to evaluate the impact of DR on generation adequacy and analyze the benefits to the utility and the customers who participated in DR.
item: Thesis-Abstract
Analysis of electric vehicle adoption in Sri Lanka : techno-economic and environmental perspectives
(2025) Abeygunawardena, NJ; Wijayapala, WDAS
As a developing nation in Asia, Sri Lanka has become increasingly reliant on fossil fuel-based energy since the late 1900s. The transportation sector, in particular, heavily depends on fossil fuels, contributing significantly to environmental degradation and rising fuel demand. While battery electric vehicles (BEVs) have emerged globally as a sustainable alternative, Sri Lanka has yet to experience a major shift in BEV adoption.
This study aims to theoretically assess the techno-economic and environmental feasibility of switching from internal combustion engine (ICE) vehicles to BEVs in the Sri Lankan context. It also compares the environmental impact of BEVs with hybrid electric vehicles (HEVs) and ICE vehicles available in the local market.
A techno-economic model was developed using survey data. The Nissan Leaf was selected as the representative BEV, while the Toyota Aqua and Toyota Vitz represented the HEV and ICE categories, respectively. Using the net present value (NPV) method, the levelized cost of mileage (LCOM) was calculated. Results showed that the Nissan Leaf had the lowest LCOM at 14.72 LKR/km, followed by the Aqua at 24.26 LKR/km, and the Vitz at 31.27 LKR/km.
The Toyota Vitz has the highest greenhouse gas (GHG) emissions, at 0.261 kg CO₂/km, according to the life cycle assessment, followed by the Toyota Aqua with emissions of 0.153 kg CO₂/km. However, despite higher production-phase emissions from battery manufacturing, the Nissan Leaf has the lowest emissions, measuring 0.108 kg CO₂/km. Over their lifetime, BEVs are the most environmentally friendly choice due to the notable decrease in operational phase emissions.
In conclusion, BEVs are both economically and environmentally advantageous, particularly if Sri Lanka can further decarbonize its electricity grid. A greener energy mix would enhance the sustainability and appeal of BEVs as a viable transport solution for the country.