Machine learning applications in power system condition monitoring

While machine learning has made inroads into many industries, power systems have some unique application constraints and barriers that have motivated the creation of this Special Issue on their applications in condition monitoring. In recent years, power systems have undergone a once-in-a-generation transformation to accommodate lowcarbon technologies while supporting ever-higher expectations of their service level. New technology and legacy plants are expected to coexist seamlessly on networks that are being used outside of their original design specification through schemes such as dynamic rating. Condition monitoring is a possible way to facilitate this, but only if data can be reduced to an interpretable form, which is where machine learning offers leverage. Supporting existing domain expertise with higher-resolution operational insight unlocks the possibility for investment in condition monitoring, and here the design of appropriate analytics and automation is key. No matter the application—generation, transmission, distribution, or end use—power assets are diverse, and their performance is reflective of their health and operating environment.