How to identify new clusters that the training data has never seen
A possible way to do this is to learn some compressed latent representation of your training data, and check how well new data matches this representation. 

In practice, you could try training an autoencoder on your training data, by minimizing the reconstruction error between its input (your raw features) and its output (the reconstruction).

After training, you can check whether a new operational state comes up by checking if the reconstruction error is larger than a threshold. The intuition behind this is that if the new data belongs to a known or normal operational state that the model learnt from the training data, the model will be able to reconstruct it properly and the error will be low.