# PV Fault Detection Using Positive Unlabeled Learning
## 5 Conclusions  
To the best of our knowledge, this is the first time PU learning has been used for solar fault detection and classification. This, along with the introduction of the MLRf algorithm in Section 2.3, comprise the novel contributions in this paper. PU learning has the advantage over standard supervised and semi-supervised learning in that it does not require any labeled data from the \"good\" or STC class. This allows seemingly faultless data to avoid additional expensive scrutiny to confirm faultless status. Mistaking a low-level fault for STC or treating unlabeled data as negative can confuse a learning algorithm and create a poor learning model, as shown by the poor results of the naive PU algorithm in Figure 2 at lower values of \(c\). At the same time, PU learning algorithms such as MLR and MLRf are extremely effective, essentially matching the quality of a fully supervised model at all but the very lowest possible percentage of labels. With a small amount of labeled fault data, PU learning can accurately label the large amount of unknown data as well as creating an effective model for future data. Comparisons with a \"tiny\" supervised learning algorithm in Figure 2a-c,e with the same number of labeled samples split between the positive and negative classes illustrate the benefit of PU learning and the advantage found in using the unlabeled data as stated in [17].  
In addition to demonstrating the MLR algorithm on solar fault data, we proposed and evaluated a new MLRf algorithm developed for PU learning with application to solar fault datasets and other large datasets with few features. The MLRf algorithm has several components including feedback, feature enhancement, and feature pruning. Theseelements significantly increase the flexibility of the algorithm, though they do require additional hyperparameter tuning. They also raise the potential of overfitting concerns, though we did not see much evidence of this in our work.  
Simulations were performed for PU labeled fault detection and classification for a variety of different \(c\) values representing the percentage of known labels for the class of interest. Both the original MLR and the new MLRf algorithm provide extremely robust results, equaling or surpassing a fully supervised oracle algorithm when less than 10% of labels from the class of interest were available. These results are remarkable and confirm the results found in [13].  
Conceptualization, K.J., J.M., and A.S.; methodology, K.J.; software, K.J.; validation, K.J.; formal analysis, K.J., and A.S., investigation, K.J.; resources, K.J., A.S.; data curation, A.S.; writing--original draft preparation, K.J.; writing--review and editing, K.J., J.M., and A.S.; visualization, K.J.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by NSF IRES award 1854273, the NSF CPS grant 1646542, and the SenSIP I/UCRC 1540040.  
Data available through the PVWatts calculator on the NREL website [[https://www.nrel.gov](https://www.nrel.gov)]([https://www.nrel.gov](https://www.nrel.gov)) (accessed 16 June 2021) and described in reference [14]. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to potential copyright concerns as described at [[https://www.nrel.gov/disclaimer.html](https://www.nrel.gov/disclaimer.html)]([https://www.nrel.gov/disclaimer.html](https://www.nrel.gov/disclaimer.html)) (accessed on 6 June 2021).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.