Hydropower contributes around 20% to the world electricity supply and is considered as the most important, clean, emissions free and economical renewable energy source. Total installed capacity of Hydropower generation is approximately 777GW in the world (2998TWh/year). Furthermore, estimated technically feasible hydropower potential in the world is 14000TWh/year.
The hydropower is the major renewable energy source in many countries and running at a higher plant-factor. Bearing overheating is one of the major problems for continues operations of hydropower plants. Objective of this work is to model and simulate dynamic variation of temperatures of bearings (generator guide bearing, turbine guide bearing, thrust bearing) of a hydropower generating unit.
The temperature of a bearing is depends on multiple variables such as temperatures of ambient air, cooling water and cooling water flow-rate, initial bearing temperatures, duration of operation and electrical load. Aim of this study is to minimize the failures of hydropower plants due bearing temperature variations and to improve the plant-factor. The bearing heat exchange system of a hydropower plant is multi-input (MI) and multi-output (MO) system with complex nonlinear characteristics. The heat transfer pattern is compel in nature and involves with large number of variables.
Therefore, it is difficult to use conventional modelling methods to model a system of this nature. So that Neural Network (NN) method has been selected as the best where past input and output data is available, and the input characteristics can be mapped in order to develop a model. In this report a neural network model is developed to model the hydropower plant, using Matlab neural network tool box and matlab as the implementation language.
Source: KTH
Author: Gunasekara, Cotte Gamage Sarathchandra
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