# A Comprehensive Survey for Deep-Learning-Based
## 2 Related Works on Abnormality Detection Based on Multimodal Image Data
Furthermore, [PERSON] et al. [39] proposed an object recognition system that could be used for fast electric power line damage detection after a storm. Due to the limited resolution of satellite optical images, they are not suitable for some more detailed abnormality detection tasks. In recent research, optical satellite imagery has often been used to monitor vegetation cover and the hidden dangers of tree barriers in power grid abnormality detection. The transmission line tree barrier means that the distance between the tree and the wire is constantly being reduced during the growth of the tree, which causes the electric field on the wire surface to be distorted. The strength of the electric field formed between the wire and the tree increases, and this eventually causes the line to flash over, triggering a power outage accident. This survey paper will be restricted to image-data-based abnormality detection, which is a core technique in WAMPAC for smart grids. For the abnormality detection problem, with the introduction given in [5], the related methods can be broadly categorized into three classes: model-driven [4; 5], data-driven [6; 7; 40; 41; 42; 43; 44], and machine-learning-based [45; 46; 47]. The model-driven solutions provide a rigorous analysis that is based on solving power flows equations [48]. In contrast, the data-driven ones provide sound solutions based on the statistics extracted from a large amount of data [49; 50; 51]. Consider the wide placement of PMUs, this work will focus on the data-driven abnormal detection methods. Interested readers are referred to the works of [1; 52; 53; 54; 55] for comprehensive reviews and comparisons. It is worth pointing out that excellent survey works exist in the literature. For example, ref. [56] comprehensively introduced abnormality detection using data-mining techniques. In addition, abnormality detection based on time-series data has been presented by [57]. Moreover, deep-learning-based abnormality detection was described in [58; 59]. However, none of them present a comprehensive analysis of deep-learning-based abnormality detection using multimodal image data. The main contributions that distinguish our survey from those already in the literature [56; 57; 58; 59; 60] are the following:  
1. To the best of our knowledge, this is the first survey paper on abnormality detection in smart grids using multimodal image data, which can provide solution examples both in the air and over the air. 2. This survey paper is formatted in a comprehensive manner so that interested readers can obtain a wide range of knowledge, which may help them in developing their own ideas in the research area of abnormality detection in smart grids. 3. This survey compares both the methods and application scenarios so that interested readers will understand the cons and pros of the employed/compared methods or data quickly, further serving as a road map for academic researchers to start their own works and avoid duplication. The remainder of this paper is organized as follows. First, Section 2 and Section 3 will introduce image-data-based abnormality detection over short distances. We then look at abnormality detection over large distances in Section 4. Furthermore, we provide the related methodologies in Section 5. Lastly, Section 6 concludes this work.