Modeling of a Combined Cycle Gas Turbine (CCGT) Using an Adaptive Neuro-Fuzzy System

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

“…For training, the input and output data are obtained from the parameters of the problem being analyzed, and the ANFIS model presents a fuzzy inference system (FIS) for which the parameters of the membership function are tuned or refined by means of a classic optimization method or a metaheuristic optimization method. The optimization is done in the training step in order to minimize the error function or the difference between the targets or desired values and the output values [8]. A number of training algorithms have been presented in the literature, e.g., backpropagation as part of heuristic methods, evolutionary algorithms like particle swarm optimization (PSO), and genetic algorithm (GA), etc.…”

“…Some studies have successfully applied ANFIS in the prediction of power generation in solar power plants, control and modeling of interconnected combined cycle gas turbine plants, and diagnosis of these systems. However, until recently, no work applying the ANFIS model in the design or analysis of the efficiency or the performance of a combined cycle gas turbine was found in the literature [8]. To date, this gap is further widened by the integration of renewable energy systems, namely solar systems, in conventional combined cycle gas turbine plants in an effort to reduce their carbon footprint and increase the overall efficiency of the plants.…”

“…In an effort to identify the operating variables that can improve the efficiency of a combined cycle gas turbine (CCGT), Rodriguez et al [8] modeled the cycle using an adaptive neuro-fuzzy inference system (ANFIS). Three input variables, namely, the compression ratio in the gas cycle, the pressure of the bled steam for water heating, and the heat lost to the steam turbine exhaust, were considered.…”