Teaching Sparse Matrices Storing and Processing Techniques. Case Study for the Power Systems Field

AbstractThe power flow computing and optimal power flow computing are the most used tools within the power systems field. Matrices are very often used for these kinds of analyses. Usually, within the power systems field, sparse matrices are involved. In the case of large-scale power systems, these matrices have to be stored in the computer memory. It is very inefficient to store (if it is possible) the entire matrix, as it is, with all the 0 elements involved. Two drawbacks are highlighted: unproductive memory use and increased computing time. Thus, methods have to be developed to store only the non-zero elements, but also knowing all the real correspondences between the elements. Within the paper the authors are presenting, in a tutorial-manner, followed by examples, several self-developed methods have evolved to solve this problem. These methods are presented to the students, and they are asked to apply them to several cases studies. Their level of attention and concentration is then analyzed by the teacher. For the examples stage, within the teaching process, students are involved. In the following they are requested to solve different case studies in small teams. The overall results are analyzed