# Real-Time Detection of Overloads on the Plasma-Facing Components of Wendelstein 7-X
## 4 Results from OP1.2 Campaign  
During the operation phase OP1.2, inertially-cooled test divertor units were used to test the island divertor concept of W7-X. This allowed us to test the imaging software for PFCs protection without danger of damaging the water-cooled high-heat-flux divertor. Note that a water-cooled divertor has similar dynamics to an inertially-cooled divertor in non-steady-state conditions when transitioning from one steady-state to another because of a change of the heat flux. Hence, the validation of the overload detection algorithm with an inertially-cooled divertor can be translated to a water-cooled one in non-steady-state conditions. In steady-state the temperature remains constant, no overload can occur, and no safety system is needed. During OP1.2, the system was not yet automated and it required manual inspection after the plasma discharges to assist the experiment leaders. We observed significant thermal events, and we reported them in [16]. These experiment programs make up a dataset of 25 discharges in different magnetic configurations. It includes high power discharges, overload experiments and unexpected events (hot spots, divertor overloads, strong leading edges, baffle overloads, and shine-through hot spots). This dataset has been used to test the Thermal Overload Detection algorithm. Note that this dataset is not valid for inferring the true alarm rate versus false alarm rate, since it consists of many extreme cases. It is not representative of a normal operation. Also note that the false alarm rate will depend on how the machine is operated. If the machine is operated close to the limits the false alarm rate will be higher than operating at low power with larger safety margins. The dataset is valid, however, for qualitative analysis. Since the system was not connected to the interlock during OP1.2 and the experiments were not interrupted, it is possible to measure the time difference between the time when the algorithm would have triggered the alarm (when the estimated risk reaches 1) and the time when the temperature reaches the limit. We define this as the anticipation time \(t_{anticipation}=t_{T=T_{limit}}-t_{risk=1}\), and we can measure it for each pulse. The algorithm tries to anticipate the overload and compensate for the system delay \(t_{max\_delay}\) (0.11 s). The goal is to have an anticipation time greater than the system delay. In this context, a late alarm occurs when the alarm is triggered after the temperature reaches the limit, when \(t_{anticipation}<t_{max\_delay}\). We consider a false alarm when the alarm is triggered and no overload occurs within (arbitrarily) 1 s window, when \(t_{anticipation}>1\) s. Since the alarm is triggered before the event occurs (it is predictive) it is not possible to deterministically relate an alarm to an overload. The longer the time exists between the alarm and the overload the less likely they are related. Hence the 1 s threshold used here is arbitrary. The reported results are obtained with the following parameter configuration: \(n=3\), \(\alpha=0.15,l=1.0\), \(A_{min}=0\). Note that, for this test, we do not consider a surface layer factor and that we assume that the measured surface temperature is the true bulk temperature of the tiles (\(S_{xy}=0\) in Equation (7)). Figure 10 shows the anticipation times distribution on the validation dataset comprising 238 infrared sequences (25 discharges \(\times\) 10 cameras, with some unavailable cameras for some shots). The results show no late alarms. The algorithm was also tested with 198 video sequence from randomly selected discharges. In these sequences no event occurred and no alarm was triggered. Figures 11 and 12 show two examples of events: an unexpected baffle overload and a divertor overload that, in this case, was part of the experiment design. Figure 11: A baffle overload example. (**a**) Maximum of the temperature over time.