Naslov
Recurent Neural Network as a Tool for Parameter Anomaly Detection in Thermal Power Plant

Autori
Hajdarević, Amel ; Banjanović-Mehmedović, Lejla ; Dzananović, Izet ; Mehmedović, Fahrudin ; Ayaz Ahmad, Mohamed

Izvornik
International journal of scientific and engineering research (2229-5518) 6 (2015), 8; 448-455

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, ostalo

Ključne riječi
Anomaly detection; Industrial application; Recurrent neural network;

Sažetak
Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. It is very important to timely detect parameter anomalies in real-world running thermal power plant system, which is one of the most complex dynamical systems. Artificial neural networks are one of anomaly detection techniques. This paper presents the Elman recurrent neural network as method to solve the problem of parameter anomaly detection in selected sections of thermal power plant (steam superheaters and steam drum). Inputs for neural networks are some of the most important process variables of these sections. 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 recurrent neural network is good approach for anomaly detection problem, especially in real-time industrial applications.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

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