EECon - 2021

Permanent URI for this collectionhttp://192.248.9.226/handle/123/17340

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3rd International Conference on Electrical Engineering 2021 (Pre Text)
(Institute of Electrical and Electronics Engineers, Inc., 2021) Abeykoon, AMHS; Velmanickam, L
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Medium-term load forecasting and error distribution for power system flexibility analysis
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Hamsa, S; Navaratne, US; Abeykoon, AMHS; Velmanickam, L
Load forecasting is used in many arenas of power system planning and analysis including expansion planning, load switching planning, and power system flexibility analysis etc. Medium-term load forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, load switching operation as well as power system flexibility studies. In this analysis four forecasting models were investigated in to study the feasibility of using them in the power system flexibility analysis. The forecasting methods studied in this research are namely, the multiple linear regression, the nonlinear regression (gaussian process), the auto regressive integrated moving average (ARIMA) model and the neural network. In order to analyse the flexibility of the power system, the feasibility of using those models were checked using the mean absolute percentage error (MAPE). The artificial neural network model was the most feasible method which had the low MAPE value to medium-term load forecasting among the models which were tested.
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Validation of Meteonorm 8 for energy estimation of solar power plants in Sri Lanka, using PVsyst software
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Viduruwan, G; Induranga, DKA; Abeykoon, AMHS; Velmanickam, L
The study presents a validation of using MT 8 (Meteonorm 8) international weather data provider's data for energy forecasting of photovoltaic energy. The study has used PVsyst software for a 1 MW DC photovoltaic solar power station in the Anuradhapura region, Sri Lanka, as a case study. A custom-built weather station situated at the solar power station premises has gathered weather data such as global horizontal irradiance (GHI), ambient temperature, module temperature, wind speed, wind direction, and rain data for six months. These weather station data are used as the inputs in PVsyst software for the PV energy forecasting and validating the model. Also, the study has used weather data of six months of MT8. According to the validated model, two different energy yield forecasting was done for both types of weather data, and a comparison is made with the actual PV energy generation of the solar power station with the simulation done using MT8 weather data. The simulation-based on MT8 shows a monthly generation error from -6.0% to 14.2% compared to the actual energy generation of the power station.
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Optimum wind turbine design and analysis to harvest wind energy from fast-moving vehicles on highways
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Widyalankara, N; Jayawickrama, NP; Ambegoda, D; Logeeshan, V; Abeykoon, AMHS; Velmanickam, L
Today, Renewable energy technology has become one of the most concerning technologies in the modern world. Among them, the wind is considered one of the fastest-growing green energy resources and is only hindered due to the fluctuations of wind regimes. When the vehicles are moving at high speed on highways, it induces a large wind force near the roads, and this unused and wasted energy can be directed to harvest electrical power using wind turbines. The wind, generated as a result of the rapid movement of vehicles, can be considered to have a continuing speed. This helps in addressing the issues brought up by fluctuations in the wind, which is the main setback on the use of wind power generation. This paper presents a design of an optimized wind turbine standalone system that can extract wind power from vehicles moving on highways, and the proposed system was implemented and simulated in MATLAB Simulink. Results are analyzed to verify the proposed concept
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Development of a software tool for power flow analysis in a distributed generation integrated radial distribution system
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Guruge, P; Kodikara, A; Karunasena, P; Arachchige, LNW; Abeykoon, AMHS; Velmanickam, L
Distribution system power flow analysis has significant differences when compared with that of transmission systems owing to their special topological properties. Hence, application of conventional power flow methods on illconditioned distribution systems brings out unsatisfactory results. Integration of distributed generation has caused the distribution system to become no longer passive in nature, highlighting the importance of more precise algorithms for power flow analysis. Solving power flow problems, which are mostly based on solving systems of non-linear algebraic equations can be efficiently done using software tools. However, most of the available software tools are focused on transmission systems and are very expensive. This study focuses on coming up with a software tool to solve power flow problems in distributed generation integrated radial distribution systems, which can be used as a teaching tool at universities. The proposed algorithm is based on the Forward Backward Sweep Method, and attempts have been made to model each distribution system component in a way that reflects the system parameters reasonably. Algorithm was tested on standard IEEE 4 Bus system, IEEE 13 Bus system and IEEE 15 Bus system, and the results were analyzed to assess the validity of the proposed algorithm.
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Short-term wind power forecasting using a Markov model
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Jeyakumar, P; Kolambage, N; Geeganage, N; Amarasinghe, G; Abeygunawardane, SK; Abeykoon, AMHS; Velmanickam, L
Large-scale wind power integration to power systems has been significantly increasing since the last decade. However, the reliability of power systems tends to degrade due to the intermittency and uncontrollability of wind power. Future wind power generation forecasts can be used to reduce the impacts of intermittency and uncontrollability of wind power on the reliability of power systems. This paper proposes a Markov chain-based model for the short-term forecasting of wind power. The first-order and second-order Markov chain principles are used as they require lesser memory and have lower complexities. Seasonal variation is also incorporated into the proposed model to further improve the accuracy. Results obtained from both Markov models are validated with real wind power output data and evaluated using evaluation metrics such as Mean Square Error and Root Mean Square Error. The results show that the accuracy of the first-order and second-order Markov models for a high wind regime is 81.33% and 82.61%, respectively and for a low wind regime is 83.50% and 89.27% respectively.
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Application of machine learning algorithms for predicting vegetation related outages in power distribution systems
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Melagoda, AU; Karunarathna, TDLP; Nisaharan, G; Amarasinghe, PAGM; Abeygunawardane, SK; Abeykoon, AMHS; Velmanickam, L
A large number of faults in power distribution systems is caused due to vegetation growing near power lines. Therefore, to maintain high system reliability, outages should be prevented as much as possible before they occur. This paper proposes a data-driven approach to predict vegetation-related outages in power distribution systems. Three Machine Learning (ML) methods i.e., the Neural Network (NN), Decision Tree Classifier (DTC) and Random Forest Classifier (RFC) are used to predict the vegetation-related outages. Historical outage data and weather data are used as the inputs to the ML methods. Then, the ML models are trained and used to predict the probability of occurrence of an outage in the next fourteen days. A risk map is generated by incorporating the geographical location of distribution feeders based on the predicted outage probabilities. Moreover, a real-time outage prediction platform is developed to provide the utilities a better insight into vegetation-related outages. The accuracy of predicting failures is found to be 72.57%, 84.06% and 93.79% for NN, DTC and RFC, respectively.
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Influence of water and powder contaminants on dielectric strength of transformer oil
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Abeyrathna, MSM; Karunaratne, MMLK; Kahingala, KPV; Samarasinghe, R; Lucas, JR; Abeykoon, AMHS; Velmanickam, L
Transformer failure statistics reveal that most of the transformer failures are due to insulation failures, where a considerable fraction of that percentage is caused by oil contamination. In this paper, the influence of water and powder contaminants on the electrical performance of transformer oil is investigated. The powder contaminant used is the fibrous dust obtained from the pressboard insulation of high voltage transformers. In a transformer, pressboard powder can easily contaminate the oil during the operation due to electrical and thermal stresses. Levels of powder contaminants selected for the research are 20mg, 40mg, and 80mg and added water levels were 1 ppm, 2 ppm, 3 ppm, and 4 ppm for an initial water level of 26ppm. Effect of contamination on AC breakdown voltage and visual streamer inception voltage of transformer oil was measured, and the results were statistically analyzed. This consists of Pearson correlation analysis, one-way multivariate analysis of variance and one-way analysis of variance. The correlation between the measured parameters of powder and water contamination levels was calculated with the Pearson correlation coefficient. The effect size which is a parameter that indicates how much the variance of the response (dependent) variable is described by the predictor(independent) variable was calculated using one-way multivariate analysis of variance and one-way analysis of variance methods. The results obtained from one-way multivariate analysis of variance showed an 86.1% effect size for the case of powder contamination. For the same case, with univariate one-way analysis of variance results, 88.8% and 96.2% effect sizes were calculated for breakdown voltage strength and visual streamer inception voltage respectively. For the water contamination case, one-way analysis of variance statistical method was utilized, and the effect size was calculated as 99.34% for the response variable (average of breakdown voltage and visual streamer inception voltage). Results of both cases made it evident that there is a significant effect of contamination on the dielectric strength of transformer oil. With a higher number of experimental results, the accuracy of the statistical analysis could be improved and with an increased number of response variables the analysis can be extended.
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Electric regenerative hybrid system for a traditional auto-rickshaw
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Bisht, H; Udupa, G; Abeykoon, AMHS; Velmanickam, L
In today’s world, traditional auto-rickshaws are one of the most commonly used mode so public transportation, and they also contribute significantly to air pollution.The fact that they use an internal combustion (i/c) engine is one of the key reasons for this.For generating power to run the vehicle, all internal combustion engines use petrol / diesel fuel.Fuel efficiency can been hanced via engine right-sizing,load leveling, and regenerative braking if conventional vehicles are convert ed to hybrid electric vehicles (HEVs) by adding an electric powert rain to the conventional power train.This study aims is to design a cost-effective electric regenerative hybrid system for traditional auto rickshaw that does not require major structural changes. The electric regenerative hybrid system would be capable of operating the auto rickshaw in two modes: electric mode and i/c engine mode.The brushless DC electric motor (BLDCmotor) in the auto rickshaw will be powered by a Lithium -ion battery in electric mode. In engine mode, the Auto Rickshaw will operate on an internal combustion engine while also generating electricity to charge the battery. The simulation was run on the Matlab/Simulink platform to look at battery and vehicle out put while using a battery pack.
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SOC level estimation of lithium-ion battery based on time series forecasting algorithms for battery management system
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Jeewandara, JMDS; Karunadasa, JP; Hemapala, KTMU; Abeykoon, AMHS; Velmanickam, L
To fulfill a reliable battery management system, a precise state of charge (SOC) estimation method for a battery energy storage system should be developed. This study makes two contributions to the battery management system. First, a combined electro-thermal battery model is proposed. To identify the electrical and thermal battery parameters, constant current -constant voltage (CC-CV) charge, constant current (CC) discharge, and pulse discharge tests should be performed on the lithium-ion battery cells and each of the above experiments, battery SOC level should be estimated precisely. The second study of this research is the development of the SOC level estimation method by using time series forecasting algorithms. In this study, six kinds of models are used in real-time, and each of the models is evaluated with the performance indices and the computational time, and finally, forecast diagrams are graphically represented for each of the experiments.
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Performance evaluation of a hybrid dual-axis solar tracking system
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Vidanapathirana, K; Kumarapeli, KAHS; Marasinghe, MADD; Amarasinghe, DP; Lucas, JR; Abeykoon, AMHS; Velmanickam, L
In a photo-voltaic solar energy system, solar tracking can harness more energy compared to a fixed system. This paper presents a design of a Manual/Automatic Hybrid Dual Axis Solar Tracking System that tracks the sun’s daily and seasonal motions. The hybrid tracker tries to ensure that the solar panel is always positioned perpendicular to the solar incident radiation by operating the tracker along two axes, of east to west (daily motion) and north to south (seasonal motion). The automatic tracking includes Time based Chronological Tracking and Light Dependent Resistor (LDR) based Active Tracking. Daily tracking is operated by a stepper motor while seasonal tracking is done manually by a gear system once a month. Results of the study show that the proposed hybrid dual- axis solar tracking system is capable of improving the overall energy efficiency by around 35%, over a fixed installation, in an economical manner.
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Techno-economic assessment of using utility scale battery storage to facilitate variable renewable energy (VRE) integration in Sri Lanka
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Kaushalya, KHA; Hemapala, KTMU; Abeykoon, AMHS; Velmanickam, L
Sri Lanka has envisaged an ambitious target for renewable capacity integration to the power system as indicated through favorable government policy targets and long-term generation expansion planning studies carried out by Ceylon Electricity Board. These renewable capacity additions are mainly comprised of wind and solar, both of which are variable renewable sources (VRE) which affect the operation and stability of the power system due to the inherent intermittency and variability of the sources. This rapid increase of VRE integration to the power system, calls for a more flexible power system and battery storage systems are emerging as one of the potential solutions to increase system flexibility. A technical and economic evaluation was carried out through the study on the use of battery storage compared to thermal generators in providing operational reserves to the system and reducing VRE curtailments which in turn support VRE integration. For technical analysis, dispatch simulation using Stochastic Dual Dynamic Programming (SDDP) was used and a cost benefit analysis based on the dispatch simulation results for 2022-2031 horizon were used for economic evaluation. It was observed that compared to thermal generators, battery storage contributed favorably in reducing VRE curtailments and providing operational reserves.
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Development of a cost-effective half-duplex power line communication system for low bandwidth home automation applications
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Wanninayake, WMSG; Kumarasiri, BU; Dharmaweera, MN; Pilanawithana, B; Abeykoon, AMHS; Velmanickam, L
This paper proposes a novel, cost-effective power line communication (PLC) system for low bandwidth home automation applications such as switching, dimming control, and periodic status reporting. The proposed system consists of a main controller node and several appliance nodes. Electrical appliances to be automated are connected through the appliance nodes to the power outlets (i.e., sockets). The main controller node, which is plugged into one of the power sockets, has an active internet connection. The home network with a star topology, where appliances are centred around the main controller node, communicates over the powerline. The proposed solution, which is a half-duplex communication system, controls appliances by sending commands from the main controller and retrieve sensor data from the appliances. The developed system provides a low-cost alternative to available solutions in the market. This paper highlights novel electronic engineering techniques used to maximize cost-effectiveness without compromising the overall quality of the proposed system.
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IoT based building energy management system
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Hettiarachchi, DG; Jaward, GMA; Tharaka, VPV; Jeewandara, JMDS; Hemapala, KTMU; Abeykoon, AMHS; Velmanickam, L
The ever-growing demand for energy and uncertainty of supply lead towards a major crisis in the energy sector, especially in building energy management. In case of power outages it is crucial to utilize the scarce power sources for the most vulnerable cause of demand. Furthermore, it is evident that due to the lack of monitoring and automation present in building energy management systems, a considerable percentage of energy wastage gets reported. Thus the need for a proper load forecasting methodology has arisen in the recent past. Researchers have formulated statistical methods and machine learning based models to facilitate energy forecasting for future periods. This paper addresses the load forecasting challenge by proposing an IoT (Internet of Things) based energy management system that incorporates an XGBoost (Extreme Gradient Boost) machine learning model to forecast energy consumption. The energy management system consists of a user-friendly central dashboard that acts as a mediator between a NodeMCU device and a cloud-hosted database with the aforementioned machine learning model. The paper concludes with a summarized discussion on the research.
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Techno-economic feasibility of implementing carbon capture and storage technology in Sri Lankan power sector
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Guruvita, KM; Damayanthi, RMT; Abeykoon, AMHS; Velmanickam, L
Earth is consistently getting hotter with the highest recorded global temperature was in 2020, surpassing the previous record in 2016. Global warming is the principle explanation behind the temperature increase on the planet. As one of the major greenhouse gases, carbon dioxide has a strong influence on the global warming. Fossil fuel-based power generation is one of the primary source that release carbon dioxide to the environment. Carbon Capture and Storage (CCS) is an emerging global technology to reduce the carbon dioxide emissions from fossil fuel power generation plants. However, this technology is highly capital and resource intensive and those vary from country to country as well. Therefore, it is essential to estimate the economic feasibility and the impacts on the environmental resources beforehand. This study is an effort to estimate the technical and economic feasibility of implementing CCS technology in the Sri Lankan fossil fuel power plants.
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An experimental study on smart electric bicycle with optimized power pack
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Silva, NCD; Withanage, WDSK; Jayasundara, VK; Randombage, SP; Randombage, PMTSA; Perera, WMKG; De Silva, MM; Wijesiriwardhana, R; Abeykoon, AMHS; Velmanickam, L
Energy-efficient transport systems orientation including the up take of electric vehicles provides socio-economic opportunities that generate multiplie reflects fortrade, employment, good quality of life,and improved health.Among many, electric bicycle (e-bicycle)is attracting significant attention worldwide due to its lightweight,compact design,andcheaperpricethan other electric vehicles. Many smart e-bicycle solutions have been advocated as important supportive tools to embrace-bicycles but failed to continue the paradigm due to the weight of the bicycle has increased. This paper proposes a novel approach to design an electrically assisted bicycle prototype including hardware, powerpack, and bicycle computer to control the motor using physiological information and the bicycle sensor data.The study presents the experimental results regarding fatigued etection based on heartrate, heart rate vary ability, bike speed data,cadence , and terrain data information for assisting the rider. Accordingly, the novelty of this study is to optimize the weight of thee-bicycle and implement a bicycle computer system to control the motor.
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A rectangular pulse current generator for calibration of magnetic field sensor
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Ouyang, H; Liu, H; Jin, X; Abeykoon, AMHS; Velmanickam, L
In order to study the calibration of magnetic field sensor, a compact rectangular pulse current generator(RPCG) is developed. Based on the LC network, the influence of loop parameters on rectangular pulse current waveform is studied using the software Multisim. The relationship between peak current, peak duration time and circuit parameters such as charging voltage, the number of links, inductor and its internal resistance, capacitor and load resistor are summarized. At the same time, a rectangular pulse current generator is designed to meet the project’s requirements. The maximum output current is 10 kA, and the peak duration time is 2 ms. The actual test results show that the waveform is in good agreement with the simulation analysis. The method can provide a reference and theoretical basis for a rectangular wave generator (including a voltage generator).
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Development of an effective 3D mapping technique for heritage structures
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Mahinda, MCP; Udhyani, HPAJ; Alahakoon, PMK; Kumara, WGCW; Hinas, MNA; Thamboo, JA; Abeykoon, AMHS; Velmanickam, L
Historical buildings and structures are import ant fora country and the world since they are the only living evidence of the engineering technology of man kind. Therefore, itis of utmost importance to protect and preserve them to safe guard the identity of the particular civilizations,retain the ircultural significance, and ensure the inaccessibility to present and future generations. Since many historic structures are in a partially dilapidated state, to prevent further deterioration,itis essential to record their present status in structural engineering and building material aspects. This is typically a costly,time-demanding, unsafe, and challenging task. As a solution,3 Dgeomatics technologies, and UAVsystemsarenowbeingusedintheworldtodocument existing structures,especially in difficult-to-accessare as.This is also usually a high-cost task.In this research, an effective technique for generating structural information of anexisting building or as imilarstructure to preserve the detail so for duplicating as a truereplicais developed.The developed solution is are mote-controllable custom-made drone system with a GoPro Hero7camera.Different type so fstructures were selected, and after running several test cases,the most suitable image- taking technique and the processing plat form were identified. The effecting factors to the accuracy such as the number of images taken,and overl appercentage of images were identified and their optimum contri bution detected.As a result,the proposed solution abled to yield a dimensional accuracy of 98.9%.This study concludes that the developed effective solution is avalid alternative to high-cost3Dmappingtechniques.
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Efficiency enhanced three-port DC-DC converter for MPPT controlled solar-battery systems
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Bopearachchi, L; Liyanage, HS; Dasanayake, VH; Chandima, DP; Bolonne, S; Abeykoon, AMHS; Velmanickam, L
In this paper, efficiency enhanced a three-port DC-DC converter for MPPT controlled Solar-Battery systems is proposed which consists of two separate power flow paths from solar panel to load and to the battery bank. In this converter, an integration of inductors is applied to maximize the gain of voltage and to reduce the effect of leakage inductance. Two active clamp circuits are applied for providing soft switching. Also, battery management system is designed so as to obtain minimum ripple current in charging/discharging cycles, the proposed system enhances battery efficiency and lifetime. Various converter operation modes as well as controller operation, necessary improvements are discussed in this paper. These control algorithms and control techniques have been implemented and tested in the simulated environment for a load of 100W/100V.
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Sample entropy analysis of cardiac and respiratory responses during four limbs exercise
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Bandara, KMELN; Wijesiriwardana, R; Abeykoon, AMHS; Velmanickam, L
This study was aimed to elucidate the effects of exercise on sample entropy of beat-to-beat (RR) intervals (SampEn-RR) and breath-to-breath (BB) intervals (SampEn- BB). A four limbs-based exercise equipment was used for the experiment and a wireless wearable respiration and heart rate monitoring system was used for recording the RR and BB intervals during lower limbs (LL), upper limbs (UL), and all four limbs (AL) exercises. A significant increment in both SampEn-RR and SampEn-BB was observed during exercises when compared with pre and post-exercise states. Further comparisons of statistical parameters during AL and UL exercises showed the highest and the lowest mean of SampEn values. Moreover, the comparison between exercise duration and the linear trend line gradients obtained from both SampEn- RR and SampEn-BB during exercise revealed an inversely proportional relationship between them. Thereby, this work supports analysis of trend line gradients of SampEn would be helpful to determine the fatigue level during exercise.

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