Abstract.The present global energy scenario, in which fossil fuels play a preponderant role, faces significant energy and environmental challenges. To help address these challenges, countries across the world are increasing the contribution made by renewable energy (RE) resources to their energy supplies. Feed-in Tariff (FiT) is one of the most effective incentive policies used to promote the RE sector especially at the micro-generation level. The key objective of the FiT schemes is to provide financial support to residential, industrial and commercial consumers to encourage them to become RE producers. Solar photovoltaic (PV) is one of the most promising RE technologies. This paper provides an overview of the solar PV developments in the Association of South East Asian Nation (ASEAN) countries. It reflects upon the RE trends in the world as well as providing an introduction to the ASEAN countries. It reviews the progress of solar PV in each of the ASEAN countries especially in terms of RE policies, growth in terms of PV installations and research and development activities. Finally, the paper presents conclusions and a set of recommendations. Out of the 10 ASEAN countries, 5 have implemented FiT as a key policy incentive to stimulate the progress of RE. It is found that the ASEAN countries have great potential for solar PV in term of their annual solar insolation levels, which ranging from 1,460 to 1,892 kWh/m 2 per year.
Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.
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