Research Papers - Department of Electrical and Electronic Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/679

Browse

Filters

2022 - 20262

Advanced Search

Filter by

Settings

Author: search.filters.author.Jayasekara, SHas files: Yes

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationEmbargo
    Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study
    (Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, H
    In palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.
  • Thumbnail Image
    PublicationEmbargo
    Tamper Proof Smart Energy Metering System with Power Factor Correction and Surge Protection
    (IEEE, 2022-10-28) Sandarenu, E.A.P.; Jayasekara, S; Jathunga, T. G
    Currently, Sri Lankan electricity suppliers have adapted a postpaid tariff system, in which the consumers with a contract demand equal to or less than 42 kVA are only charged for the active power consumption. This tariff method has several disadvantages for both consumers and electrical suppliers in many ways. Lower power factors, Electricity theft, tampered meters, avoided bill payments, and manual meter reading procedures have caused considerable losses for the electricity suppliers. Similarly, consumers are also discomforted by the postpaid nature of this tariff method since it has caused long queues in the bill payment locations. To overcome above problems, this paper presents a smart energy meter with automated billing capabilities, power factor correction, and tamper protection. This meter is currently intended only for single-phase consumers, in which consumers are allowed to choose the payment method from prepaid or postpaid according to their preference.