Multiple regression analysis of performance parameters of a binary cycle geothermal power plant

Abstract

Regression analysis of a 7.35 MWe existing binary geothermal power plant is conducted using actual plant data to assess the plant performance. The thermo physical properties of geothermal fluid and ambient conditions, which are brine (geothermal water) temperature and flow rate, steam and NCGs (non-condensable gases) flow rates and ambient air temperature, directly affect power generation from a geothermal power plant. Generally, amount of power generated is calculated by deterministic formulations of thermodynamics. However, the data would be probabilistic because inputs may be measured by uncalibrated devices or some parameters may be neglected during the calculation. In these cases, the performance of power plant may be estimated by using regression analysis and then changing of plant performance may be monitored overtime. All measured parameters on DORA-1 Geothermal Power Plant from 2006 to 2012 and 49,411 hourly time series data are used in this study. A review of the available literature indicates this paper is the first study to focus on the prediction of power generation of a geothermal power plant by using multiple linear regression analysis. In this study, annual multiple linear regression models are developed to estimate the performance of a geothermal power plant. These models are tested by using classical assumptions of linear regressions and positive serial autocorrelation is found in all models. Autocorrelations are eliminated by using Orcutt-Cochran method. Although the performance model trends, from 2006 to 2008, are found to be close, the performance status of the plant is generally variable from year to year. According to annual regression models, since 2009, the plant performance started to decline with 270 kW(e) electricity generation capacity. The total degradation of the plant performance reached 760 kW(e) capacity by 2012. (C) 2014 Elsevier Ltd. All rights reserved.