Design of an Adaptive Constrained Based Neuro-fuzzy Controller for Fault Detection of a Power Plant System

This paper proposes an adaptive constraint based framework for fault detection of a complex thermal power plant system. In many complex systems, representation of precise and crisp constraints uses formal specification languages such as Object Constraint Language (OCL). Here, a constraint based neuro-fuzzy controller to tackle imprecise constrained objects is proposed. The proposed inference system is used to identify the intensity of faults by mapping instance values against the constraints. The imprecise constraints defined as fuzzy constraints are prioritized using fuzzy weights assigned to rules. Back-propagation algorithm is used to train and calibrate the controller to capture the dynamic behavior of the system. The system is adopted into a coal-fired Thermal power plant system, which is controlled by different parameters in a constrained environment. The output of the neuro-fuzzy inference system is compared against actual plant site alarm that functions based on rule based system and has been found that the intensity of faults can be accurately determined.