| Summary: | Gas flaring for years has been a major environmental problem in many parts of the world. One way of solving the problem of gas flaring is to effectively utilize the abundant supply of gas for power generation. To effectively utilize gas for power generation requires highly efficient gas turbines and power facilities. Traditional methods of assessing the efficiency of power generation turbines do not take into consideration the stochastic nature of gas input and power output. This is because in a power generation system, as in any typical production system, there is generally marked variability in both input (gas) and output (power) of the process. This makes the determination of the relationship between input and output quite complex. This work utilized Box-Jenkins transfer function modelling technique, an integral part of statistical principle of time series analysis to model the efficiency of a gas power plant. This improved way of determining the efficiency of gas power generation facilities involves taking input–output data from a gas power generation process over a 10-year period and developing transfer function models of the process for the ten years, which are used as performance indicators. Based on the performance indicators obtained from the models, the results show that the efficiency of the gas power generation facility was best in the years 2007–2011 with a coefficient of performance of 0.002343345. Similarly, with a coefficient of performance of 0.002073617, plant performance/efficiency was worst in the years 2002–2006. Using the traditional method of calculating efficiency the values of 0.2613 and 0.2516 were obtained for years 2002–2006 and 2007–2011 respectively. The result is remarkable because given the state of the facilities, it correctly predicted the period of expected high system performance i.e. 2002–2006 period, but the traditional efficiency measurement method failed to do so. Ordinarily, using efficiency values obtained through the traditional method as the metric, the system managers would assume that the period 2002–2006 was better than in the period 2007–2011 whereas the reverse is the case. The result of this study is expected to open new ways to improving maintenance effectiveness and efficiency of gas power generation facilities.
|
|---|
