Several types of superconducting devices require quench detection systems to safely operate. Detection has to happen in real time to avoid damage. Superconducting magnets for particle accelerators are one example of such device. The quench, that is the resistive runaway resulting from the transition from superconductive to the normal-conducting state must be detect as soon as possible to avoid thermal and voltage related damage to the magnet. In this work, we discuss advanced implementations of low latency systems deployed to deal with this critical problem for both Low and High Temperature Superconductive (LTS and HTS) devices. The diverse challenges posed by LTS and HTS based apparatus demand concurrent systems utilizing sensor arrays, and data fusion when early quench detection with reduced false detection rate is required. System implementation utilizing the latest developments in hardware, and signal processing algorithms engineered for fast quench detection developed at Lawrence Berkeley National Laboratory (LBNL) are shown. Experimental results from measurements carried out with both HTS and LTS superconducting magnets and undulators are analyzed. These results illustrate existing system capabilities and limitations to properly operate and detect a quench. Enhanced implementations under development utilizing current detection hardware with the aid of machine learning based algorithms are explored. The capability of these algorithms to minimize erroneous quench detection while potentially improving detection time are discussed as the next logical step for this kind of system.
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