Streamflow Predictive Model Using Reliable Distribution Method To Enhance Small Hydropower System Performance

• Main Author: Razi, Mashitah
• Format: Thesis
• Language: English; English
• Published: 2019
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/24676/1/Streamflow%20Predictive%20Model%20Using%20Reliable%20Distribution%20Method%20To%20Enhance%20Small%20Hydropower%20System%20Performance.pdf; http://eprints.utem.edu.my/id/eprint/24676/2/Streamflow%20Predictive%20Model%20Using%20Reliable%20Distribution%20Method%20To%20Enhance%20Small%20Hydropower%20System%20Performance.pdf; http://eprints.utem.edu.my/id/eprint/24676/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117635

Nowadays, increasing electricity demand and rural electrification upgrading make renewable energy becomes more popular. Some examples of renewable energy applied in Malaysia are solar, biomass and hydropower. For hydropower, it can be classified into several types and schemes. For types, it depends on the power generated by the system, while schemes, it related to its operating mechanism. In Malaysia, generally, the smallhydropower system tied with 21 years Feed-In-Tariff (FiT) policy offered by Sustainable Energy Development Authority (SEDA) and for rural electrification, small-hydropower system based on run-of-river schemes is much better in terms of total electricity generated, sustainability, reliability and environmental friendly. Therefore, to make sure the sustainability and reliability of the system, understanding the character of the streamflow is a must. In this research, average daily streamflow data from January to December 2016 from Sungai Perting, Bentong, Pahang small hydropower system were used as data analysis. Statistical analysis in boxplot using R-Software provides first statistical order characteristic of the streamflow in terms of maximum, minimum, mean, median, first quartile and third quartile of the data. While, for distribution analysis, Probability Distribution Function (PDF) model of Gumbel, Weibull 2-parameter, Lognormal and GEV are used to represent the data. Most researchers used Lognormal, Gumbel and Weibull and less study on GEV distribution. Therefore, the GEV is introduced to increase the reliable method applied. Best fit distribution is finding by the help of MATLAB Software based on MLE value. To obtain the performance of the system, the study will focus on the turbine power generated related to the streamflow. From the results, 8 out of 12 months of the streamflow distribution exhibit GEV function and have maximum MLE values compared to the others. As for the performance of the system, streamflow is directly proportional to the power generated by the turbine.