**1. Introduction**

Ice accumulation on insulator strings has been recognized as a serious threat for power systems operating in many atmospheric icing regions [1–5]. These hazards can be mainly divided into two categories: one is serious ice accretion, which will cause structural problems, for instance, wire breakage, tower collapse, etc., and the other is insulation problems, for instance, the icicle will change the distribution of insulators electric field significantly, which reduces its insulation performance and can lead to an ice flashover accident easily. Therefore, it is necessary to improve the ability for monitoring the operation of iced outdoor insulator strings for preventing structural accidents and icing flashover.

Over the past decades, many investigations have researched monitoring methods to reduce icing accidents [6–16]. In these investigations, the characteristics of iced insulators surface phenomena were extracted by image processing method for monitoring.

Liu et al. studied the performance of insulators under icing conditions, recorded the test process with a high-speed camera and analyzed the flashover characteristics of iced insulators and the growth characteristics of ice pillars based on image processing technology [17,18]. According to the characteristic value of a flashover image, the flashover process is divided into di fferent stages, and the quantitative analysis method of flashover risk value of iced insulators is proposed. At the same time, the growth characteristics of ice pillars at di fferent edges and the variation characteristics of surface discharge are calculated and analyzed. The research results can be used to evaluate the main hazards of iced outdoor insulators and improve the safety of iced suspension insulators. Hao et al. used the image processing method to study the natural icing of glass insulator strings in service. Based on the grab segmentation method, by identifying the convex defects of Icelandic contour, the algorithm of graphical shed spacing and graphical shed overhanging is proposed [19,20]. This method can identify the most serious icing situation when the insulator cover is completely bridged. The bridge position can also be detected, including the left, right, or both sides of the insulator string in the image. Yang et al. proposed a method for identifying the ice coating type of an in-service glass insulator based on the texture feature description operator [21]. A uniform local binary model (ULBP) and an improved uniform local binary model (IULBP) are used to extract the texture features of ice cover types. The experimental results show that, due to the di fferent texture features of each kind of ice, IULBP has a good recognition e ffect on six kinds of ice. Zhu et al. proposed an image recognition algorithm for monitoring the icicle length and insulator ice bridging condition. The saliency analysis algorithm is applied to the extract region of the insulator ice layer and the length of the insulator icicle was calculated by the Fourier transform of the pixel distribution curve [22]. Pernebayeva et al. studied a Gabor filtering algorithm for extracting a set of Gabor phase congruency features from insulator images for the presence or absence of snow, ice, and water droplets by utilizing the minimum distance nearest neighbor classifier [23]. Vita, V. et al. constructed di fferent neural network models for insulator contamination identification using di fferent structures, learning algorithms, and transfer functions. All the models are compared and analyzed, the best model is found, and the calculation results match the experimental results [24]. Chen et al. applied digital image processing technologies such as gray level transformation, image sharpening, image segmentation, and edge detection to the research of structural images, and e ffectively extracted the e ffective information in the image [25]. Gilboa, G. et al. use the free Schrodinger equation and extended the linear and nonlinear scale space generated by the intrinsic real value di ffusion equation to the complex di ffusion process, thus obtaining two examples of nonlinear complex processes which play an important role in image processing: One is the regularized impact filter for image enhancement, the other is the denoising process keeping slope [26]. From comparative analysis of the research on other aspects, few investigations have been conducted on monitoring and diagnostic of insulator strings in extreme weather environments, which need further research to decrease icing accidents.

Therefore, in this paper, for improving the ability to judge the icing degree risk of outdoor insulators and reduce icing accidents caused by ice-covered insulators, the features of insulator surface performance was extracted by an image processing method in order to monitor icicle length and the ice bridging state of iced outdoor insulator strings. The tests were conducted at CIGELE Laboratories. The test specimen was the five units' suspension ceramic insulators, which were artificially accreted with wet-grown ice in the cold-climate room of CIGELE. The procedure of ice accumulation was recommended by IEEE Standard 1783/2009. The surface phenomena of the insulators during the icing accretion were recorded by a high-speed video camera with a rate of six thousand frames per second.

#### **2. Test Setup and Procedures**

The test specimen is the five units' suspension ceramic insulators. The picture and parameters are shown in Table 1.


**Table 1.** Configuration, dimensions, and parameters of each unit of the test specimen.

The tests were conducted in an artificial climate room with a length of 4.8 m, a width of 2.8 m, and a height of 3.5 m of CIGELE Laboratories, as shown in Figure 1 [27]. By using the proportional integral and differential system, the freezing devices can make the ambient temperature drop to −12 ◦C after the test setup was fixed. The spray device mainly consists of a water supplying system and wind blowing equipment. Ice was formed from super-cooled droplets produced by the former system through 4 oscillating nozzles. The latter system produced a relatively uniform airflow by using a series of fans with a diffusing honeycomb panel. The test power source was supplied by an AC test transformer with a rated capacity of 240 kV·A.

**Figure 1.** Artificial cold-climate room.

The surfaces of the insulator sheds were cleaned by deionized water before the ice accretion. The insulators needed under the setting ambient temperature last about sixteen hours to give all the experimental setup enough time to reach the same temperature as that of the test environment. The AC voltage of 75 kVrms (15 kVrms per unit) was energized on insulators during ice accretion for simulating the operating environment. Meanwhile, the water supply system started to spray freezing droplets on the insulators' surface. The water conductivity was set at 30 μS/cm by mixing deionized water and sodium chloride. The wind speed was fixed at 3.3 m/s to blow on the windward side of insulators in the ice accumulation period. When ice accumulation duration reached 90 min, the applied voltage and

spray device were turned off immediately and the icing process is stopped. The ice accretion process on insulators was photographed during the whole experiment [28,29].

#### **3. Image Processing of Ice-Covered Insulator**

The iced insulator image of recording was influenced by various factors, such as the glazed icing, which is a smooth and transparent structure with unobvious transverse volume change. It is difficult to identify the overall state of iced insulators; and therefore, low quality images were attained. Therefore, in order to improve the image quality, an image processing method is proposed for recognizing the insulators' bridged state and extracting the characteristic values for the warning of icing accidents.

#### *3.1. Enhancement of Image*
