Why the energy is a good feature extraction for detect disturbances in signal processing?
In short, energy is not a good feature per se. Disturbance enhancement sometimes requires preprocessing, and might not be resistant to outliers. 
The notion of disturbance in your  question could be made more  precise. 

However, since the energy squares amplitudes of signal values, and is generally integrated over several samples, it has the ability to emphasize disturbances whose amplitude dynamics vary from the "normal" ones.

Aside, within the scope of automated detection, energies are quantities that are relatively easy to minimize: least-squares are common techniques in signal processing.

Yet I would not consider energy as a good feature in general. Depending on the need, other nonlinear operators can be used, as can be seen in the family of Teager Kaiser operators, see for instance: Signal processing using the Teager Energy Operator and other nonlinear operators.