Browsing by Author "Abeygunawardane, SK"

Now showing 1 - 11 of 11

item: Article-Full-text
Adequacy evaluation of composite power systems using an evolutionary swarm algorithm
(IEEE, 2022) Amarasinghe, PAGM; Abeygunawardane, SK; Singh, C
The generation and transmission capacities of many power systems in the world are significantly increasing due to the escalating global electricity demand. Therefore, the adequacy evaluation of power systems has become a computationally challenging and time-consuming task. Recently, population-based intelligent search methods such as Genetic Algorithms (GAs) and Binary Particle Swarm Optimization (BPSO) have been successfully employed for evaluating the adequacy of power generation systems. In this work, the authors propose a novel Evolutionary Swarm Algorithm (ESA) for the adequacy evaluation of composite generation and transmission systems. The random search guiding mechanism of the ESA is based on the underlying philosophies of GAs and BPSO. The main objective of the ESA is to find out the most probable system failure states that significantly affect the adequacy of composite systems. The identified system failure states can be directly used to estimate the system adequacy indices. The proposed ESA-based framework is used to evaluate the adequacy of the IEEE Reliability Test System (RTS). The estimated annualized and annual adequacy indices such as Probability of Load Curtailments (PLC), Expected Duration of Load Curtailments (EDLC), Expected Energy Not Supplied (EENS) and Expected Frequency of Load Curtailments (EFLC) are compared with those obtained using Sequential Monte Carlo Simulation (SMCS), GA and BPSO. The results show that the accuracy, computational efficiency, convergence characteristics, and precision of the ESA outperform those of GA and BPSO. Moreover, compared to SMCS, the ESA can estimate the adequacy indices in a more time-efficient manner.
item: Conference-Full-text
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.
item: Conference-Full-text
Application of machine learning algorithms for solar power forecasting in Sri Lanka
(Institute of Electrical and Electronics Engineers, Inc., 2018-09) Amarasinghe, PAGM; Abeygunawardane, SK; Samarasinghe, R; Abeygunawardana, S
Reliability and stability of a power system get decrease with the integration of large proportion of renewable energy. Renewable sources such as solar and wind are highly intermittent, and it is difficult to maintain system stability with intolerable proportion of renewable energy injection. Solar power forecasting can be used to improve system stability by providing approximated future power generation to system control engineers and it will facilitate dispatch of hydro power plants in an optimum way. Machine Learning (ML) algorithms have shown great performance in time series forecasting and hence can be used to forecast power using weather parameters as model inputs. This paper presents the application of several ML algorithms for solar power forecasting in Buruthakanda solar park situated in Hambantota, Sri Lanka. The forecasting performance of implemented ML algorithms is compared with Smart Persistence (SP) method and the research shows that the ML models outperforms SP model.
item: Conference-Full-text
Application of metaheuristic algorithms for generation system adequacy evaluation
(IEEE, 2023-12-09) Amarasinghe, PAGM; Abeygunawardane, SK; Abeysooriya, R; Adikariwattage, V; Hemachandra, K
The evaluation of generation system adequacy has become a complex procedure due to the variability of renewable power generation. Renewable power models associated with Monte Carlo Simulation (MCS) require a considerable amount of processing power, especially when periodical variations of renewables are modeled. This paper analyses the application of different metaheuristic algorithms for evaluating the adequacy of renewable-rich power generation systems. The IEEE reliability test system is modified and used for conducting several case studies. The utilized metaheuristic algorithms are validated using sequential simulations and it is found that problem-specific Evolutionary Swarm Algorithm (ESA) provides more accurate estimations for generation system adequacy indices. In this study, the improvement of generation system adequacy is analyzed when integrating different renewable power proportions into the system. The intra-day reliability variation of the system is analyzed for different solar and wind penetration levels. The reliability improvement provided by renewable generators to the generation system adequacy is quantified by estimating the respective Effective Load Carrying Capabilities (ELCCs) of solar and wind generation. The ELCCs of 100 MW solar and 100 MW wind generation are found to be 26 MW and 43 MW, respectively.
item: Conference-Abstract
Capacity credit evaluation of wind and solar power generation using non sequential Monte Carlo simulation
Amarasinghe, PAGM; Abeygunawardane, SK
Renewable power, especially wind and solar integration to the power grid is gaining more attention nowadays. However, the contribution of wind and solar generators to the power system reliability is significantly low due to the diurnal and seasonal variations and intermittency of solar irradiance and wind speed. Capacity credit provides an idea of actual solar PV or wind capacity contribution to the power system reliability. In this paper, the non-sequential Monte Carlo simulation is used to obtain reliability curves to evaluate the capacity credit of solar PV and wind power facilities situated in Sri Lanka (SL). Moreover, SL capacity values are compared with capacity values of wind and solar generation in Brussels, Belgium which has a temperate maritime climate. The impact of power system reliability level and seasonal renewable power variations on capacity credit values are explored using several case studies. Results show that for SL, the wind capacity value significantly varies with seasons whereas the solar PV capacity value remains the same throughout the year.
item: Conference-Abstract
Design and implementation of an automated load scheduling and monitoring system
Liyanage, BLCB; Tharushika, GHAS; Liyanage, KLSV; Porambage, MD; Abeygunawardane, SK; Wijesiriwardana, R
An automated load scheduling and monitoring system is implemented by using smart sockets, a central control unit, and a user interface. This system can perform real-time power monitoring and appliance scheduling tasks and can be used with either demand response (DR) or demand-side management (DSM) applications with an ultimate target of reducing energy usage by means of effective controlling of electrical appliances. In the proposed system, the inter integrated circuit (I2C) protocol is used as the communication method between smart sockets and the central control unit. Message queuing telemetry transport (MQTT) protocol is used for the communication between the central unit and the web interface. Utilization of the above protocols plays a major role in enhancing the reliability of the entire system. Further realtime power monitoring of the connected appliances can be performed through an android application. Experimental results reveal that the designed smart sockets are capable of accurately and precisely monitoring the power consumption of connected devices. Maximum communication distance of 17m is achieved with I2C communication without using any bus extender. Modular approach in the software and hardware architecture of the system provides provisions for effective implementation of DR and DSM schedules.
item: Conference-Full-text
Impact of solar pv systems on the reliability of power distribution systems
(IEEE, 2020-07) Meegoda, MSS; Balasooriya, BADT; Dharmapriya, EGMC; Kumara, JIAN; Amarasinghe, PAGM; Abeygunawardane, SK; Weeraddana, C; Edussooriya, CUS; Abeysooriya, RP
The integration of solar PV into the power distribution systems has been significantly increasing. Hence, distributed solar PV will become a ubiquitous component of modern power distribution systems. This paper investigates the impact of solar PV systems on the adequacy of power distribution systems. Firstly, the system Energy Not Supplied (ENS) is calculated for the RBTS bus 2 system when there is a line outage. The AC optimal power flow analysis is used to evaluate the distribution system states. Different levels of solar PV integration and various solar PV placements are considered to analyse the impact of solar integration on the reliability of power distribution networks. Then, Monte Carlo simulation is used to obtain the ENS of the system by simulating the distribution system operation. The forced outage rates of the system components are considered in the study to model the stochastic operation of the system. The results show that solar PV can significantly reduce the ENS of the system when there is an outage in the system. When 0.75, 1.5, 2.25 MW and 4.5 MW solar capacities are integrated, the daytime ENS is reduced by 5.9 %, 11.6 %, 17.1 % and 32.6 % respectively.
item: SRC-Report
An Improved Markov maintenance model for power system equipment [abstract]
(2019) Abeygunawardane, SK
Distribution utilities compute several reliability indices to assess the reliability performance of power systems. In reliability-based planning, these indices are computed using reliability planning models. Such models require failure rates and mean downtimes of distribution feeders as inputs. At present, there is a lack of models for calculating operational mean downtimes of distribution feeders. This paper proposes a Markov model which represents failures in a feeder and operations circumstances in fault recovery. This proposed model can efficiently calculate the operational mean downtime of a feeder, using analytical equations. An algorithm and a graphical user interface are also developed based on the proposed model, in order to integrate the proposed model with software tools. Two case studies are conducted on two selected feeders, using actual data obtained from failure and repair histories and experts’ opinion. Results of case studies show the applicability of our proposed Markov model-based algorithm to calculate mean downtimes of distribution feeders. The Markov model is validated by comparing results provided by the proposed algorithm with the results obtained using Monte Carlo simulation. The proposed Markov model-based algorithm would be very useful for utilities to calculate operational mean downtimes required for reliability-based planning models.
item: Article-Full-text
Kernel density estimation based time-dependent approach for analyzing the impact of increasing renewable on generation system adequacy
(Elsevier, 2020) Abeygunawardane, SK; Singh, C; Amarasinghe, PAGM
Integration of non-conventional renewables such as wind and solar to the power system may affect the system reliability, especially when the proportion of renewable power in the system is large. Therefore, with a significant level of renewable penetration, the intermittency and both diurnal and seasonal variations of renewable power generation should be deliberately modeled in order to accurately quantify the power system reliability. This paper presents a novel method based on Kernel Density Estimation (KDE) for modeling intermittency and both diurnal and seasonal variations of wind and solar power generation using historical renewable power generation data. The proposed KDE based renewable power models are used with non-sequential Monte Carlo simulation to evaluate the generation system adequacy. Several case studies are conducted on IEEE reliability test system to analyze the impact of increasing renewables on the generation system adequacy. The results show that the generation system adequacy tends to decay exponentially when the renewable integration is increased. It is shown that the reliability values obtained using the proposed approach are very close to those provided by the time-consuming sequential simulations. Importance of modeling seasonal variations of wind and solar is also investigated.
item: Conference-Full-text
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.
item: Conference-Full-text
Smart energy tracking system for domestic consumers
(IEEE, 2016-05) Sameera, NHK; Ovinda, JAPA; Palayangoda, NA; Sanjeewa, AD; Abeygunawardane, SK; Jayasekara, AGBP; Bandara, HMND; Amarasinghe, YWR
Demand side management provides benefits to both the utility and the consumers. The effective implementation of demand side management can be achieved by introducing a proper energy management system. To popularize such an energy management system among domestic consumers in developing countries, initial cost of the system should be affordable and it should be able to assist the consumers to significantly reduce their electricity bill. This paper proposes a smart energy tracking system that can monitor the energy usage at different time slots, compare the energy usage with past data and provide details about the daily energy usage and helpful instructions to manage the energy consumption. Hence, the proposed system assists the consumers to reduce their electricity bill and the utilities to implement demand side management in the domestic sector.