Real‐time condition monitoring of substation equipment using thermal cameras

The recent trend of decreasing reliability of the existing power grid due to various kinds of equipment failures at the transmission and distribution level, has led the researchers to develop advanced protection techniques. In distribution systems, the conventional substations are getting transformed to digital substations in the light of upcoming advanced technologies. The key component of such a digital substation is substation monitoring control center (SMCC). This study proposes a novel algorithm for the real‐time condition monitoring of substation equipment using thermal images obtained from thermal cameras. The processing of these is done at the remote terminal unit by extracting the speeded‐up robust features and passing the same through trained adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) classifier. The information based on the output at the SVM/ANFIS classifier is sent to the SMCC where appropriate actions are taken by the substation engineer. This novel real‐time monitoring of substation equipment overcomes the drawbacks of the conventional methodology of manual inspection done at a periodic interval thereby preventing the electrical assets from failure before any catastrophe would happen. Also, a comparative study establishes the superiority of SVM over ANFIS in identifying the critical fault conditions using thermal imaging.