GASPRO International Journal of Eminent Scholars

The key to optimizing the system is to know the operating point of the system at the time of loading. This operating point can also be expressed as the power curve. The conventional method of identifying the operating points is through mathematical modelling. To model a good identification curve, some component’s characteristics need to be known in addition to proper observations, especially if the component variable is unknown. Typically, this type of scenario usually lead to a long identification process. This exploratory research presents the way to find out the power curve of a system without going through the mathematical modeling process, but by using the polynomial regression technique. This regression technique involves the use of empirical data of the power curve to form parameter on ss-caes prototype. The method is based on five approach model. In this model, the variation of loading sampling data used is aimed at finding the best sampling of prediction. The data is analyzed in the form of statistical parameters and graphs to show the evaluation process of the technique. From the results of the regression, it can be concluded that the power curve of ss-caes can be identified with a high correlation value of 0.997 (99,745% accuracy) and the best way to take samples of data to be used in this technique is presented in the paper.