STUDY ON A NOVEL ADAPTIVE NOISE CANCELLATION ALGORITHM APPLIED TO CHARACTERISTIC EXTRACTING FOR THERMAL PROCESS

Along with the increasing requests of the control level for power plant operation, accurate state parameters are needed for the advanced control, diagnosis and optimization algorithm. But the signal of the state parameter is obscured by all kinds of noises in thermal system and difficult to analyze. To solve this problem, a novel least-mean-square(LMS) algorithm is used for characteristic extracting in the adaptive noise cancellation (ANC) problem. An improved LMS algorithm based on Sigmoid function was presented. The simulation result shows that a superior performance of the new algorithm in stationary environment and an equivalent performance in nonstationary environment. The experiment proves the method is effective and feasible for thermal processes signal analyzing.