Anomaly detection in thermal power plant using probabilistic neural network

Anomalies are integral part of every system's behavior and sometimes cannot be avoided. Therefore it is very important to timely detect such anomalies in real-world running power plant system. Artificial neural networks are one of anomaly detection techniques. This paper gives a type of neural network (probabilistic) to solve the problem of anomaly detection in selected sections of thermal power plant. Selected sections are steam superheaters and steam drum. Inputs for neural networks are some of the most important process variables of these sections. It is noteworthy that all of the inputs are observable in the real system installed in thermal power plant, some of which represent normal behavior and some anomalies. In addition to the implementation of this network for anomaly detection, the effect of key parameter change on anomaly detection results is also shown. Results confirm that probabilistic neural network is excellent solution for anomaly detection problem, especially in real-time industrial applications.