Small Signal Stability with the Householder Method in Power Systems

Voltage collapse in power systems is still considered the greatest threat, especially for the transmission system. This is directly related to the quality of the power, which is characterized by the loss of a stable operating point and the deterioration of voltage levels in the electrical center of the region exposed to voltage collapse. Numerous solution methods have been investigated for this undesirable degradation. This paper focuses on the steady state/dynamic stability subcategory and techniques that can be used to analyze and control the dynamic stability of a power system, especially following a minor disturbance. In particular, the failure of one generator among the network with a large number of synchronous generators will affect other synchronous generators. This will become a major problem and it will be difficult to find or resolve the fault in the network due to there being too many variables, consequently affecting the stability of the entire system. Since the solution of large matrices can be completed more easily in this complex system using the Householder method, which is a small signal stability analysis method that is suggested in the thesis, the detection of error and troubleshooting can be performed in a shorter period of time. In this paper, examples of different rotor angle deviations of synchronous generators were made by simulating rotor angle stability deviations up to five degrees, allowing the system to operate stably, and concluding that the system remains constant.

Automatic Defect Detection and Classification of Terminals in a Bussed Electrical Center Using Computer Vision

In this chapter, an intelligent Computer Vision (CV) system, for the automatic defect detection and classification of the terminals in a Bussed Electrical Center (BEC) is presented. The system is able to detect and classify three types of defects in a set of the seven lower pairs of terminals of a BEC namely: a) twisted; b) damaged and c) missed. First, an environment to acquire a total of 56 training and test images was created. After that, the image preprocessing is performed by defining a Region Of Interest (ROI) followed by a binarization and a morphological operation to remove small objects. Then, the segmentation stage is computed resulting in a set of 12-14 labeled zones. A vector of 56 features is extracted for each image containing information of area, centroid and diameter of all terminals segmented. Finally, the classification is performed using a K-Nearest Neighbor (KNN) algorithm. Experimental results on 28 BEC images have shown an accuracy of 92.8% of the proposed system, allowing changes in brightness, contrast and salt and pepper noise.