This paper is an experimental study on the optimal operating conditions of direct charging type electrospray for particulate matter collection. To perform the research, a direct charging type electrospray visualization system was configured to photograph the spray shape of microdroplets, and experiments were performed with varying electrode distance, flow rate, and applied voltage, which are the main factors affecting the particulate matter collection efficacy. Through image processing, the total number of microdroplets according to each condition was analyzed, and the number of microdroplets with a diameter of 1.5 mm or less was confirmed. In addition, by calculating the number of microdroplets per power consumption according to the applied voltage, the optimal operating conditions were derived in terms of energy consumption efficacy, and the microdroplet size distribution was analyzed under the optimal operating conditions. As a result of the experiment, it was confirmed that the optimal operating condition was at a flow rate of 10 mL/min and a voltage of -20 kV in case of 5 mm electrode distance, and at a flow rate of 15 mL/min and a voltage of -30 kV in case of 100 mm electrode distance.

This study proposes a crack identification algorithm to analyze the surface condition of porcelain insulators and to efficiently visualize cracks. The proposed image processing algorithm for crack identification consists of two primary steps. In the first step, the brightness is eliminated by converting the image to the lab color space. Then, the background is removed by the K-means clustering method. After that, the optimum image treatment is applied using morphological image processing and median filtering to remove unnecessary noise, such as blobs. In the second step, the preprocessed image is converted to grayscale, and any cracks present in the image are identified. Next, the region properties, such as the number of pixels and the ratio of the major to the minor axis, are used to separate the cracks from the noise. Using this image processing algorithm, the precision of crack identification for all the sample images was approximately 80%, and the F1 score was approximately 70. Thus, this method can be helpful for efficient crack monitoring.