Master of Science By Research

Permanent URI for this collectionhttp://192.248.9.226/handle/123/11526

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Browsing Master of Science By Research by Author "Gopura, RARC"

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    item: Thesis-Full-text
    Hybrid approach for financial forecasting with support vector machines
    (2015-08-28) Roshan, WDS; Gopura, RARC; Jayasekara, AGBP
    Financial markets are the biggest business platforms in the world. Therefore, financial forecasting is getting a lot of attention in today’s economic context. Accurate forecast is beneficial to broker firms, governments, individuals etc. Vast range of forecasting methods, models have introduced by the research community. However, the risk involved with trading on those markets are very high. Such complexity makes a difficulty of making consistent profit. Building an accurate forecasting model is still an active and interesting research area for the academic community. Recently, nonlinear statistical models such as neural network, support vector machine have shown greater capability to forecast financial markets over conventional methods. This dissertation pro-posed a hybrid support vector machine model which consists of wavelet transform and k-means clustering for foreign exchange market forecasting. The proposed model analyzes the trends and makes a forecast by entirely depending on the past exchange data. Wavelet transform is used to remove the noise of the time series. K-means clustering cluster the input space according to the similarities of the input vectors and finally support vector models make a forecast for the relevant cluster. The proposed hybrid forecasting system was tested on real market environment to check the fore-casting capability. Auto trading algorithm developed on ‘metatrader4’ platform used the forecast of the model to trade on the real conditions. Results confirmed that the proposed model can fore-cast price movements with greater accuracy that leads to profitable trades on foreign exchange market
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    item: Thesis-Abstract
    Multi agent based control and protection for an inverter based microgrid
    (2014-05-31) Kulasekera, AL; Gopura, RARC; Hemapala, KTMU
    Legacy electrical infrastructure is failing to provide the reliability and the resiliency towards faults expected by the modern energy intensive society. Implementation of smarter microgrids, are proving to be part of the solution for this. Development of such systems requires distributed intelligence capabilities absent in legacy control systems. This research focuses on proposing a dual layered, multi agent based control system for distributed control of a microgrid aimed at intentional islanding and dynamic load management. The architecture consists of two layers; primary strategic level layer and secondary execution level layer. The Control agent in the primary, or the strategic level, is capable of supervising the secondary layer agents. The proposed Multi Agent System (MAS) controller provides islanding capabilities to a microgrid during disturbances in the main utility grid. During islanded operation, the MAS is able to maintain the supply to the most critical local loads. If the priority levels of the loads change after loads are shed, the MAS is able to reassign power to the revised most critical load/s. The proposed multi agent based control architecture is developed using the JADE platform and it is used to control a microgrid simulated in MATLAB/SIMULINK. In order to validate the effectiveness of the proposed method, investigations are carried out for islanding and dynamic load management scenarios simulated on the test network. The results of these studies show the capability of developing a reliable control mechanism for islanding operation of microgrids based on the proposed concept.