Biblio

Edge detection is the key and difficult point of machine vision and image processing technology. The traditional edge detection algorithm is sensitive to noise and it is difficult to accurately extract the edge of the image, so the effect of image processing is not ideal. To solve this problem, people in the industry use the structural element features of morphological edge detection operator to extract the edge features of the image by carefully designing and combining the structural elements of different sizes and directions, so as to effectively ensure the integrity of edge information in all directions and eliminate large noise at the same time. This paper first introduces the traditional edge detection algorithms, then summarizes the edge detection algorithms based on mathematical morphology in recent years, finds that the selection of multi-scale and multi-directional structural elements is an important research direction, and finally discusses the development trend of mathematical morphology edge detection technology.

The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However, the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.

The application of network technology and high-tech equipment in power systems has increased the degree of grid intelligence, and malicious attacks on smart grids have also increased year by year. The wrong data injection attack launched by the attacker will destroy the integrity of the data by changing the data of the sensor and controller, which will lead to the wrong decision of the control system and even paralyze the power transmission network. This paper uses the measured values of smart grid sensors as samples, analyzes the attack vectors maliciously injected by attackers and the statistical characteristics of system data, and proposes a false data injection attack detection strategy. It is considered that the measured values of sensors have spatial distribution characteristics, the Gaussian mixture model of grid node feature vectors is obtained by training sample values, the test measurement values are input into the Gaussian mixture model, and the knowledge of clustering is used to detect whether the power grid is malicious data attacks. The power supplies of IEEE-18 and IEEE-30 simulation systems was tested, and the influence of the system statistical measurement characteristics on the detection accuracy was analyzed. The results show that the proposed strategy has better detection performance than the support vector machine method.