Development of a novel predictive based Smart Distribution Management System (S-DMS) to maximize the rooftop PV absorption capacity of last mile networks.
The Sri Lankan Government promotes solar power through ‘Surya Bala Sangrahamaya’ and is hoping to develop 1000 MW of PV systems by 2024. Currently solar PV power systems in excess of 100 MW are in operation with roof-top solar modules making a small but significant contribution. However, in some areas, CEB is not allowed to connect roof-top PV modules due to the fears of possible over voltages in lines, overloading of lines and losing the supply security. Even though rooftop PV modules can be looked at as a burden to the last mile networks, if they are properly controlled and supervised they will bring many technical and economic advantages for the last mile networks.Therefore, in this research network management strategies executed through a Smart Distribution Management System (S-DMS) that integrates different controllable entities within the last mile network is considered to support these networks thus increasing the absorbability of rooftop PV. The management of the following controllable entities through a S-DSM in a coordinated manner will be investigated:Smart inverters of rooftop PV systems that can provide grid support through active and reactive power control,Smart meters connected to the consumer premises that can make the load flexible by controlling, shifting or switching off some smart and non-critical loads and charging and discharging plug in electric vehicles,Smart transformer at the origin of the last mile network that can change its secondary side voltage continuously to manage the network voltages.
Funding : NSF Sri Lanka, RG/2018/EA&ICT/01 (Rs. 2 million)
Investigators : Dr. M.P.B. Ekanayake, Dr. Roshan Godaliyadda, Prof. J.B. Ekanayake
Duration : March 2019 - March 2021
Research Assistant : W.G. Chaminda Bandara
W.G. Chaminda Bandara, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, J.B. Ekanayake, Coordinated photovoltaic re-phasing: A novel method to maximize renewable energy integration in low voltage networks by mitigating network unbalances, Applied Energy, Volume 280, 2020, 116022, ISSN 0306-2619. (more...)
Wele Gedara Chaminda Bandara, Dilini Almeida, Roshan Indika Godaliyadda, Mervyn Parakrama Ekanayake, Janaka Ekanayake, A complete state estimation algorithm for a three-phase four-wire low voltage distribution system with high penetration of solar PV, International Journal of Electrical Power & Energy Systems, Volume 124, 2021, 106332, ISSN 0142-0615. (more...)
A. Balachandran, G. W. K. Prabhath, W. G. C. Bandara, G. M. R. I. Godaliyadda, M. P. B. Ekanayake and J. B. Ekanayake, "Reactive Power Compensation for Voltage Violations in Distribution Network," 2019 14th Conference on Industrial and Information Systems (ICIIS), Kandy, Sri Lanka, 2019, pp. 245-250, doi: 10.1109/ICIIS47346.2019.9063332.
Fetal Movement Detection and Condition Monitoring.
: One of the main methods available to monitor fetal wellbeing is to monitor fetal movement patterns. It is one of the basic indicators of fetal health. Currently this is mainly done by the mother herself, which is very unstructured and time consuming. Therefore, the main target of this project is to come up with an end-to-end solution to monitor fetal movements reliably. An accelerometric sensor was developed and data were obtained. This data was highly contaminated with noise such as maternal movements, maternal laugh, maternal cough, etc. Therefore, several novel advanced signal processing algorithms were utilized to extract fetal movement signals. Initially algorithms such as Spectral clustering, Convoluted Neural Networks (CNN) and Non-Negative Matrix Factorization (NMF) based techniques were implemented. Currently more advanced machine learning algorithms such as Variable Auto encoders are being utilized.
Investigators : Dr. M.P.B. Ekanayake, Dr. G.M.R.I. Godaliyadda, Dr. J. Wijayakulasooriya, Dr. R.M.C.J.Rathnayake
Collaborators : Department of Obstetrics and Gynecology, Faculty of Medicine, University of Peradeniya.
Duration : May 2019- May 2021
W. A. W. S. Wasalaarachchi et al., "Fetal Movements Identification Based on Non-negative Matrix Factorization and Spectral Clustering,"2019 14th Conference on Industrial and Information Systems (ICIIS), Kandy, Sri Lanka, 2019, pp. 266-271, doi: 10.1109/ICIIS47346.2019.9063351.
Delay, Upekha; Dissanayake, Sajan; Nawarathne, Thoshara;Wasalaarachchi, Wishmi; Hetti Arachchi, Anupama; Abeywardhana, Sachitha ; Withanage, Thanushi; Gunarathne, Samitha; Ekanayake, Mervyn Parakrama; Godaliyadda, G. M. R. I. ; Wijayakulasooriya, J. V. ; Ratnayake, Chathura (2019), “Fetal Movement Detection Dataset Recorded Using MPU9250 Tri-Axial Accelerometer”,Mendeley Data, V2, doi: 10.17632/7svcy4cscy.2.
M. S. L. Gunarathne, W.T.Ruwanga , U. H. Delay, B. M. T. M. Nawarathne, M.P.B. Ekanayake, G.M.R.I Godaliyadda,J. V. Wijayakulasooriya, R. M. C. J. Rathnayake.“Standardized Fourth Central Moment Based Three Step Algorithm for Fetal Movements Identification” International Conference on Advances in Computing and Technology ICACT 2020. (more...)
U. H. Delay, B. M. T. M. Nawarathne, D. W. S. V. B. Dissanayake, M.P.B. Ekanayake, G.M.R.I Godaliyadda, J. V. Wijayakulasooriya, R. M. C. J. Rathnayake. “Non Invasive Wearable Device for FetalMovement Detection” 2020 15th International Conference onIndustrial and Information Systems (ICIIS).(in press).
Upekha Delay, Thoshara Nawarathne, Sajan Dissanayake, Roshan Godaliyadda, Chathura Rathnayake, Parakrama Ekanayake, Janaka Wijayakulasooriya. “Novel Non-Invasive In-house Fabricated Wearable System with a Hybrid Algorithm for Fetal Movement Recognition” PloS one.(under review).
W.T. Ruwanga, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, J.V. Wijayakulasooriya, and R.M.C.J.Rathnayake, ”Combining Local Non-negative Matrix Factorization and Spectral Clustering to Detect Fetal Movements”, Women in Engineering Symposium 2020 (IEEE WIESymp2020),PaperID22, Online Conference held on:11.15.2020.
Modification of Fractal Microstrip Antennas using Genetic Algorithms to Improve their Performance.
Fractal antennas operate in multiple frequencies that depend on the number of iterations and the scale factor. However, the higher resonant frequencies are multiples of the fundamental frequency and therefore, the fractal antenna may not resonate at the frequencies the designer needs. Also the fractal antennas have no control over antenna characteristics such as the bandwidth and gain. This research project focuses on improving the performance of fractal microstrip antennas by modifying the fractals using genetic algorithms. Promising results have been obtained on improving the gain and bandwidth of Sierpinski carpet antennas with a less intensive genetic algorithm optimization process when compared with such a process required for an ordinary microstrip antenna.
Funding : Peradeniya University Research grant- URG/2016/31/E
Principal Investigator : Prof. D. N. Uduwawala
Other Investigators : Dr. Jeevani Jayasinghe, Wayamba University of Sri Lanka
Duration : 2018-2020
Research Assistant : N. Narampanawe
D. N. Uduwawala and J. W. Jayasinghe, “Performance Improvement of Patch Antennas using Modified Fractals,” Proc. Peradeniya UniversityInternational Research Sessions (iPURSE), Peradeniya, Sri Lanka, Vol. 22, pp 13, Sept. 2019.
D. Uduwawala, J. Jayasinghe and N. Narampanawe, “Genetically Designed High Gain Sierpinski Carpet Fractal Antenna,” Proc. 2019 IEEE 14th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, pp. 11-14, Dec. 2019.
Status : Completed
Development of a multispectral imaging system for quality inspection.
Research Area : Signal Processing
A multispectral imaging system was developed which consists of nine spectral bands ranging from 400 nm - 1000 nm. The device was used to study the potential of multispectral imaging for food quality assessment. The device was also used to investigate the effect of contaminants in transformer insulators.
Following two studies were conducted with the collaboration of Faculty of Agriculture, University of Peradeniya.
Validation of multispectral imaging for the detection of selected adulterants in turmeric samples
Quantitative assessment of adulteration and reuse of coconut oil using transmittance multispectral imaging
Following study is an ongoing study with the collaboration of The National Aquatic Resources Research and Development Agency.
Use of multispectral imaging for fish quality assessment
Following study is an ongoing study on utilizing multispectral techniques in insulator quality assessment.
A Novel Multispectral Image-Based Technique to Assess the Contamination level on Insulators.
One of the most important problems that insulators are subjected to is degradation, deterioration and contamination. The contaminants affect the external insulation performance of insulators and they reduce the effective creepage distance of an insulator. In order to avoid sudden electrical failures, insulators need to be properly maintained. Here the inventors proposed a method which utilizes the multispectral imaging techniques.
Other Investigators : Dr. V.R. Herath,Dr. Roshan Godaliyadda,Dr. Parakrama Ekanayake, Prof. M.A.R.M Fernando
Collaborators : Sri Lanka Technological Campus, Faculty of Agriculture,University of Peradeniya, Faculty of Science, University of Peradeniya.The National Aquatic Resources Research and Development Agency.
W.G.C. Bandara, G.W.K. Prabhath, D.W.S.C.B. Dissanayake, H.M.V.R. Herath, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, D. Demini, T. Madhujith, “Validation of multispectral imaging for the detection of selected adulterants in turmeric samples”, (2019), Journal of Food Engineering, Volume 266, 2020,109700, ISSN 0260-8774.(more...)
G. W. K. Prabhath, W. G. C. Bandara, D. W. S. C. B. Dissanayake, H. M. V. R. Herath, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, S. M. D. Demini, and T. Madhujith, “Multispectral Imaging for Detection of Adulterants in Turmeric Powder,” in Optical Sensors and Sensing Congress (ES, FT, HISE, Sensors), OSA Technical Digest (Optical Society of America, 2019), San Jose McEnery Convention Center, San Jose, California, United States, paper HTu3B.3.(more...)
H. M. H. K. Weerasooriya, H. M. S. Lakmal, D. Y. L. Ranasinghe, W. G. C. Bandara, H. M. V. R. Herath, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, and T. Madujith, Transmittance Multispectral Imaging for Edible Oil Quality Assessment in Imaging and Applied Optics Congress, OSA Technical Digest (Optical Society of America, 2020), paper JW5C.8 (more...)
S. Herath, H.K. Weerasooriya, D.Y.L. Ranasinghe, W.G.C. Bandara, H.M.V.R. Herath, G.M.R.I Godaliyadda, M.P.B. Ekanayake, Terrence Madhujith Quantitative Assessment of Adulteration and Reuse of Coconut Oil Using Transmittance Multispectral Imaging.(more...)
Hyperspectral Imaging for remote sensing applications.
Remote sensing is the process of monitoring and detecting the physical characteristics of an area using reflected and emitted solar radiation at a distance. Typically satellites or aircrafts are used to acquire images using multispectral and hyperspectral cameras specially developed for remote sensing purposes. Owing to the ability to capture more spectral information, hyperspectral imagery is widely popular amongst the remote sensing community and in recent years an amalgam of applications and research has been proposed. The project was focused on two distinct aspects related to hyperspectral remote sensing: unmixing of hyperspectral images and mineral indication using hyperspectral images.
Hyperspectral unmixing (HU) is a crucial step in the hyperspectral image (HSI) analysis. It aims at decomposing the observed spectrum at each pixel into a collection of constituent endmembers, weighted by their abundances. For this several algorithms were proposed based on various premises such as signal and image processing, graph signal processing, and autoencoder architectures that are described below.
A novel blind HU algorithm referred to as Kurtosis-based Smooth Nonnegative Matrix Factorization (KbSNMF) which incorporates a novel constraint based on the statistical independence of the probability density functions of endmember spectra. Imposing this constraint on the conventional NMF framework promotes the extraction of independent endmembers while further enhancing the parts-based representation of data.
In a close spatial neighborhood, due to spatial autocorrelation, the abundances of an endmember tend to be similar to each other. Therefore, defining each abundance map as a signal on a suitable graph enables the HU problem to be analyzed for the first time from a graph signal processing perspective. This project introduces a novel Laplacian regularizer based on the l1-norm, where graph spectral analysis is utilized to show that the regularizer has natural piecewise smooth (PWS) signal promotion and noise rejection capabilities. A graph-based blind HU algorithm is developed by incorporating this regularizer and an l1/2-sparsity constraint into the NMF problem. Since the featured regularizer exploits the PWS property of abundance maps, the proposed method is effective in HU.
A novel architecture is proposed to perform blind unmixing in hyperspectral images. The proposed architecture consists of convolutional layers followed by an autoencoder. The encoder transforms the feature space produced through convolutional layers to a latent space representation. Then, from these latent characteristics, the decoder reconstructs the roll-out image of the monochrome image which is at the input of the architecture; and each single-band image is fed sequentially.
Mineral indication is the process of identification and classification of minerals using spectral images. The scope of the project was to develop an algorithm to identify and quantify the availability of limestone and establish the ground truths with the collaboration of the Department of Geology, University of Peradeniya, in the Northern region of Sri Lanka around the Jaffna peninsula
Principal Investigator : Dr. G.M.R.I. Godaliyadda
Other Investigators : Dr. H.M.V.R. Herath, Dr. M.P.B. Ekanayake
Collaborators : Sri Lanka Technological Campus, Faculty of Science, University of Peradeniya.
B. Rathnayake, E. M. M. B. Ekanayake, K. Weerakoon, G. M. R. I. Godaliyadda, M. P. B. Ekanayake and H. M. V. R. Herath, "Graph-Based Blind Hyperspectral Unmixing via Nonnegative Matrix Factorization," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 9, pp. 6391-6409, Sept. 2020, doi: 10.1109/TGRS.2020.2976799. (more...)
D. Y. L. Ranasinghe et al., "Hyperspectral Imaging Based Method to Identify Potential Limestone Deposits," 2019 14th Conference on Industrial and Information Systems (ICIIS), Kandy, Sri Lanka, 2019, pp. 135-140, doi: 10.1109/ICIIS47346.2019.9063280. (more...)
Yasiru Ranasinghe, Sanjaya Herath, Kavinga Weerasooriya, Mevan Ekanayake, Roshan Godaliyadda, Parakrama Ekanayake, and Vijitha Herath, "Convolutional Autoencoder for Blind Hyperspectral Image Unmixing", 2020 15th Conference on Industrial and Information Systems (ICIIS), Ropar, India, 2020. (more...)
E.M.M.B. Ekanayake, B. Rathnayake, D.Y.L. Ranasinghe, S. Herath, G.M.R.I. Godaliyadda, H.M.V.R. Herath, and M.P.B. Ekanayake, "Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence", Apr 2020 [online]. (more...)