Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2017.1343328
Neural network-based data-driven modelling of anomaly detection in thermal power plant
Lejla Banjanovic-Mehmedovic
; Department of Automation and Robotics, Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina
Amel Hajdarevic
; Ricardo Prague, Prague, Czech Republic
Mehmed Kantardzic
; Speed School of Engineering, University of Louisville, Louisville, KY, U.S.A.
Fahrudin Mehmedovic
; ABB Representation for Bosnia and Herzegovina, Tuzla, Bosnia and Herzegovina
Izet Dzananovic
; JP Elektroprivreda BiH–Power Plant Tuzla, Tuzla, Bosnia and Herzegovina
Sažetak
The thermal power plant systems are one of the most complex dynamical systems which must function properly all the time with least amount of costs. More sophisticated monitoring systems with early detection of failures and abnormal behaviour of the power plants are required. The detection of anomalies in historical data using machine learning techniques can lead to system health monitoring. The goal of the research is to build a neural network-based
data-driven model that will be used for 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. All of the inputs are observable from installed monitoring system of thermal power plant, and their anomaly/normal behaviour is recognized by operator’s experiences. The results of applying three different types of neural networks (MLP, recurrent and probabilistic) to solve the problem of anomaly detection confirm that neural network-based data-driven modelling has potential to be integrated in real-time health monitoring system of thermal power plant.
Ključne riječi
Anomaly detection; artificial neural networks; data-driven modelling approaches; thermal power plant
Hrčak ID:
203337
URI
Datum izdavanja:
19.9.2017.
Posjeta: 1.582 *