INGENIEUR
Figure 3 : The Bathtub curve of faults .
over the period 2016-40 . In other words , investment in power stations would be reduced by USD 34 billion per year and that in networks by USD 20 billion per year [ 1 ].
Revolutionise Power Transformer Maintenance
One of the single highest value components in the electrical power system , are power transformers , or simply transformers , comprising up to 60 % of the total investment [ 2 ]. Power transformers are at the heart of the power station . The flow of power and electricity into our homes and other locations would not be possible without the availability of power transformers . Power transformers , which enable the transmission of electricity to any location are indispensable in power transmission and distribution systems . Any failure or hidden defects in transformers can cause operational problems in the system , resulting in economic and resource losses . Early detection of abnormal conditions and fault diagnosis reduces productivity loss and avoids abnormal event management while the power station is still in a controllable operating condition . A lot of attention has been given to this topic because it is the central component of abnormal event management . Power transformers , being the core of the power system , play both technical and economic functions in a power system .
As transformers begin to age due to a high workload , several internal faults occur . The failure occurrence rate of a transformer can be described by the well-known “ bathtub curve ” in three stages : Infant , Normal Operating and Wear-Out , as depicted in Figure 3 . In the Infant stage , the failures are mainly caused by design and assembly errors . In the Normal Operating Stage , the transformer operates smoothly with a low failure risk . In the Wear-Out stage , the failures are mainly caused by ageing insulation and degeneration [ 3 ]. The functional lifespan of a transformer is approximately 40 to 50 years .
Digitalisation in IR4.0 presents abnormal event management strategies in three ways ;
( 1 ) online monitoring of the transformer ’ s condition which is now possible , thanks to the digital technology such as IoT , big data , and cloud services ,
( 2 ) use of Computational Intelligence ( CI ) to detect and diagnose transformer faults , and
( 3 ) the integration of online monitoring and CI in transformer asset management .
8 VOL 89 JANUARY-MARCH 2022