INGENIEUR
depend on the power monitoring instruments to continuously monitor power delivered to customer sites and record the disturbance waveforms . The consequence of this method is that large data files are produced for power quality engineers to inspect in the disturbance waveforms . Therefore , the development of CI methods in signal analysis for automatically characterising and identifying various power quality disturbances reduces the data workload of engineers [ 8 ]. The application of CI-based methods mainly performs pattern recognition or signal processing to detect , localise , estimate , and classify disturbances in the supply lines .
Power System Reliability
Power system reliability will improve when transformer incipient faults are detected and eliminated before they deteriorate to a severe state . Therefore , the search for a reliable method for incipient fault detection in power transformers is still a topic of interest in many utilities .
Reliable and stable flow of energy is essential for all electrical utility companies , making power transformer one of the most important assets and largest investments . For these reasons , the condition assessment and incipient fault analysis of transformer are high priority .
Energy Security
With the rapid rate of population growth , urbanisation , and economic growth , the demand of electricity continues to grow . Hence the first thing to take into consideration is the condition assessment of the electrical equipment . It is of utmost importance to make sure the equipment ( especially the transformer ) is operating at its planned working condition to deliver the optimum efficiency .
Energy security can be improved by ensuring the availability and continual uninterrupted flow of energy supply to meet the demand of the nation , which impacts national security . The security of energy supply becomes a risk management strategy with strong references to cost effectiveness associated with both energy supply and energy demand . The question is how to interpret a situation in which the imbalance between the supply and the demand for electricity is so big that it leads to a loss much larger than it would have been if the condition assessment and incipient fault analysis of the transformer had been taken care of in advance . Energy security concerns the ability to satisfy energy needs by the energy systems . This stems above all from the risk of interruptions of electricity delivery at determined times .
Does this mean a stick-to-the-bone Dissolved Gas in Oil ( DGA ) expert need to abandon his classical arsenal ?
No , of course not . Digitalisation and CI technologies do not replace conventional methods , but with the availability of digitalisation and CI analyses , better plans can be made with regard to fault diagnosis of power transformers . As such , the necessary decision-making processes can be accelerated , avoiding power disruption due to downtime of the transformers . In other words , these models complement the conventional methods by providing learning capabilities to maximise reliability , optimise operations , and minimise maintenance costs . CI-based models can help revolutionise the current diagnostic practices , allowing industry practitioners to reap the benefits of CI in the maintenance of power transformers in the long run .
Conclusion
As a key enabler in IR4.0 , considerable advancement in digitalisation and CI is expected to take place , resulting in new preventive maintenance regimes for power transformers that are more critical than ever in creating a sustainable future . Through better monitoring and improved maintenance , CI methods can reduce the frequency of unplanned outages , as well as limit the duration of downtime by quickly detecting the point of failure and recognising the cause of failure . This enhances the productivity , safety , accessibility , and sustainability of electrical power system , and improves the resilience and reliability of supply . Information and communication technologies are also changing markets , businesses and employment . New business models are emerging .
12 VOL 89 JANUARY-MARCH 2022