An Assistive System for Thermal Power Plant Management

The estimation of available active power in coal-fired thermal power plant units involves considerable complexity and remains a critical task for plant operators. To avoid compromising system stability, operators often operate the thermal unit below its full capacity. To address this issue, the aim of this paper is to facilitate the process of estimating the maximum active electrical power by applying an assistive system based on ANFIS (Adaptive Neuro-Fuzzy Inference System), a method that combines the strengths of neural networks and fuzzy logic. Since the generated electric energy is directly linked to the amount of thermal energy produced, the analysis is focused on the boiler combustion process. It has been shown that the key factors in this process are the coal mills and their achievable capacity, as well as the calorific value of coal. Therefore, the proposed assistive system is based on the estimation of the available capacity of each active mill, which is then combined with the estimated calorific value of the coal to determine the achievable active electrical power of the unit. The conducted analysis and experiments confirm the validity of this approach.