The use of artificial intelligence in power systems has gained significant attention in recent years due to its potential to enhance efficiency and improve decision-making. One of the key areas where artificial intelligence can make a significant impact is in the diagnosis and maintenance of power systems assets, such as transformers. Physics-informed machine learning and complex-impedance features have been proposed as potential solutions to improve the diagnosis and maintenance of these assets.
Research has shown that physics-informed machine learning can be used to automate and robustly discover systems in power systems assets. For instance, a study published in the journal Elsevier BV demonstrated the effectiveness of physics-informed machine learning in automated system discovery. The study highlighted the potential of this approach to improve the efficiency and accuracy of system discovery, which is critical for maintaining the reliability and efficiency of power systems.
Another area where artificial intelligence can make a significant impact is in the diagnosis of transformer winding faults. Complex-impedance features have been proposed as a potential solution to improve the diagnosis of these faults. A study published in the journal Elsevier BV demonstrated the effectiveness of complex-impedance features in finite-element classification with laboratory frequency-response corroboration. The study highlighted the potential of this approach to improve the accuracy and efficiency of transformer winding-fault diagnosis.
Despite the potential benefits of artificial intelligence in power systems, there are also important questions about the role of human judgment and intuition in these fields. As artificial intelligence becomes more prevalent in power systems, there is a risk that human judgment and intuition may be overlooked or undervalued. However, human judgment and intuition are critical components of decision-making in power systems, and it is essential to ensure that they are not lost in the transition to artificial intelligence.
In conclusion, the use of physics-informed machine learning and complex-impedance features has the potential to improve the diagnosis and maintenance of power systems assets, such as transformers. However, it is essential to ensure that human judgment and intuition are not overlooked or undervalued in the transition to artificial intelligence. Further research is needed to fully explore the potential benefits and challenges of artificial intelligence in power systems.