5. Multi-information fusion

The electric network method, surface electromagnetic field method, time di fference positioning method, and electromagnetic imaging method consider the physical characteristics of the grounding grid corrosion. However, these methods do not reveal the nature of corrosion and can only solve the problem of corrosion degree detection. The electrochemical method can detect the corrosion rate but not the degree of corrosion. Multi-information fusion [52], combined with multiple methods, is expected to simultaneously achieve corrosion degree and corrosion rate detection. Therefore, tapping the potential of existing methods, implementing two or more corrosion detection methods in one device, solving the problem of simultaneous detection of corrosion degree and corrosion rate, and improving the practicality of the detection method are future development directions.

#### 6. Life prediction of grounding grid

With the development of power grid technology and the improvement of voltage levels, the safe operating life of grounding grids has begun to receive attention. It is expected to realize the life prediction of the grounding grid by detecting the corrosion degree of the grounding grid conductor, the corrosion rate, and the parameters of soil aeration, water content, salt content, pH value, soil resistivity, oxidation-reduction potential and soil temperature. Multidisciplinary intersection, multi-information fusion, new sensing technology, big data platform [53] and intelligent computing will certainly promote the maturity and application of grounding grid detection technology and life prediction. These bring new opportunities for research in the field of grounding grid testing.

**Author Contributions:** Formal analysis, Z.F. and X.W; Methodology, X.W. and X.X.; Project administration, Z.F., Q.W. and S.Q.; Software, S.Q.; Supervision, Q.W.; Validation, N.F.; Writing—original draft, Z.F. and X.W.; Writing—review & editing, X.X. and N.F.

**Funding:** This research was funded by National Key R&D Program of China, gran<sup>t</sup> number 2017YFC0601804, and National Natural Science Foundation of China, gran<sup>t</sup> number 1777017.

**Acknowledgments:** This work was supported in part by the National Key R&D Program of China (No. 2017YFC0601804) and the National Natural Science Foundation of China (No. 51777017).

**Conflicts of Interest:** The authors declare no conflict of interest.
