# A Comprehensive Survey for Deep-Learning-Based
## 3 Abnormal Detection with Images over Short Distances
### Infrared Image
#### 3.2.2 Infrared-Image-Based Anomaly Detection for Intelligent Sensor Technology in Smart Grids  
Due to the wide distribution of power grid equipment, we need to carry out abnormality monitoring for each centralized power grid environment. Therefore, [PERSON] et al. [26] proposed using a set of integrated miniaturized intelligent detection equipment to collect data from a centralized power grid environment, using an autonomous and wireless multivariable intelligent sensor, which can collect and verify the temperature data in the high-voltage disconnector, and transmit the data obtained by the sensor to the instrument system of the remote terminal for analysis, identifying the cause of the temperature in the high-voltage disconnector. This method can successfully detect the abnormal temperature of the high-voltage disconnector in the distribution substation in a smart grid and accurately locate the abnormal temperature position. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method Category**} & \multicolumn{1}{c}{\begin{tabular}{c} **Scenario of Anomaly** \\ **Detection** \\ \end{tabular} } & \multirow{2}{*}{**Advantage**} & \multirow{2}{*}{**Shortcoming**} \\ \hline Low-Voltage Pulse & \begin{tabular}{c} Abnormal components \\ of circuit \\ \end{tabular} & \begin{tabular}{c} High precision, low \\ energy consumption \\ \end{tabular} & \begin{tabular}{c} Danger, sensitive \\ to voltage fluctuations \\ \end{tabular} \\ \hline Intelligent Sensor & Conductor defect & \begin{tabular}{c} High reliability \\ high stability \\ \end{tabular} & \begin{tabular}{c} High power \\ consumption \\ \end{tabular} \\ \hline Internet of Things & \
\begin{tabular}{c} Abnormal leakage of \\ power grid system \\ \end{tabular} &
\begin{tabular}{c} Security, fast \\ data processing \\ \end{tabular} & Low resolution \\ \hline \hline \end{tabular}
\end{table} 
Table 2: In view of the abnormal phenomena in the power grid environment, we use the following three kinds of abnormal detection technologies and methods. Figure 1: The abnormality detection of power grid insulators using low-voltage pulse and infrared sensing detection technology. The concepts and tools of power management and energy efficiency of the smart grid have been widely studied and promoted [22; 81]. Therefore, in the process of distributing information to the whole power grid infrastructure, multiple variables describing the equipment operation status and conditions are monitored in real time. These variables are very important for the operation of each process, and perfect operation and maintenance standards should be followed, so as to better follow the standards within the power grid operation environment. Figure 2 shows the position of a remotely operated high-voltage disconnector where an abnormality has been detected using an autonomous, wireless multivariable intelligent sensor. Significant advances in different technical fields such as sensors [82], microsystems, wireless networks, and network service programming make it possible to improve this method to a higher level by combining maintenance, operation, and engineering, through the whole life cycle of assets in the power grid [83]. Specifically, some applications have solved the problem of real-time monitoring of assets, including transformers, circuit breakers, and overhead power lines [84; 85; 86]. In addition, the use of energy collection to power wireless sensor nodes in utilities is also reported [87; 88]. However, the common status of remotely operated high-voltage disconnectors in primary distribution substations is indirectly evaluated by monitoring their respective motor operating mechanisms [8]. In fact, this method only reports motor drive, so it cannot provide confirmation of the forward movement of the actual disconnect switch, let alone ensure that the closed position is correct (i.e., the two components are correctly aligned).