## 7 Conclusions  
This study assessed envelope-aided ML-based electrical fault type detection algorithms for electrical distribution grids with an IB-PV array. The electrical substation grid emulation was established by considering three utilities (utilities \(A\), \(B\), and \(C\)). Utility \(A\) included a substation (34.5 kV) and feeders (12.47 kV) fed by utility \(B\). Subsequently, utility \(B\) consisted of the generation, transmission, and sub-transmission of a grid. Furthermore, utility \(C\) was designed with a substation (12.47 kV) and IB-PV array farm. The power grid signals were recorded in the emulated substation using relays and meter equipment. The examination of the proposed algorithms was executed using these recorded power grid signals. The main achievement of this examination was that the ML-based models were trained by considering another substation configuration but were tested with modern electrical grids' measured power signals. The results provide valuable insights for smart grid systems that leverage the protection and control of power grid systems. For instance, under the test of data from various power relays and meters, the envelope-based fault region detection algorithm obtained an area of interest in the signal (start/end points of fault) precisely. Further, the first ML-based fault presence detection model achieved 100% prediction accuracy and F1-Score by using the SVM algorithm with raw data, the AP, FFT, and PSD features, and the CNN algorithm with AP, FFT, PSD, whereas the second ML-based fault type detection model on specific fault type also reached 100% prediction accuracy and F1-Score with the same ML algorithms and feature extraction method combination. However, the processing time of the algorithms demonstrated the optimal combination for ML models, which was achieved with the SVM algorithm with the PSD feature (0.057 s average processing time) and the CNN algorithm with the FFT feature (0.04 s average processing time). Further, as outlined in the IEEE 242 standard [54], the fundamental features of power grid protection system applications specify that the maximum response times for primary and secondary (backup) protection equipment are defined based on the specifications of the relevant power system and fault conditions. In future research, we plan to investigate the applicability of the proposed method in a real-world scenario, taking into account end-to-end time measurements, including fault identification, signal propagation, and protection activation. Conceptualization, O.A., E.C.P., A.R.E., N.S. ; methodology, O.A., E.C.P., A.R.E., N.S. ; validation, A.R.E. ; formal analysis, O.A., E.C.P., A.R.E. ; investigation, O.A., E.C.P., A.R.E. ; resources, O.A., E.C.P., A.R.E., N.S., Y.G. ; data curation, O.A., E.C.P. ; writing--original draft preparation, O.A., E.C.P., A.R.E. ; writing--review and editing, O.A., E.C.P., A.R.E., N.S., Y.G., M.M.O., N.B., A.Y. All authors have read and agreed to the submitted version of the manuscript. This research is supported by the US Department of Energy (DOE) Office of Electricity under Contract DE-AC05-00 OR22725 with UT-Battelle LLC for the US DOE. Data Availability Statement: Data are unavailable owing to privacy. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00 OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ([[http://energy.gov/downloads/doe-public-access-plan](http://energy.gov/downloads/doe-public-access-plan)]([http://energy.gov/downloads/doe-public-access-plan](http://energy.gov/downloads/doe-public-access-plan))). The authors declare no conflicts of interest.