<p>Fault diagnostic methods with fuzzy
logic methods, SVM, KNN and artificial intelligence systems have been used in
complex systems such as wind turbines, gas turbines, power distribution
systems, power transformers and rotary machines, but in the specific field of
distributed control systems, the vacancy of this topic is strongly felt. Due to
the need of the industry to detect faults quickly and in a timely manner in all
modes of sensors, actuators, outputs and control logics to maintain expensive, valuable
resources, important and complex equipment, it is very necessary to enter this
topic. In this paper, a suitable theoretical and practical basis for diagnosing
various types of faults in the DCS of a gas refinery is done. The fact that the
operator quickly identifies the area and the cause of the fault can avoid huge
losses in terms of downtime. Automation of fault diagnosis in DCS has not been
explicitly mentioned in any article or book, and here the plan is presented for
the first time. In this design, we connect MATLAB
classification apps to the industrial system like DCS, then data are analyzed
by SVM and KNN methods to detect faults. The results show that faults can be
detected with a probability of more than 85% accuracy without the need for
on-site expert force and with much less time.</p>
Control strategy and site selection of a shunt active filter for damping of harmonic propagation in power distribution systems
