Artificially synthesizing network data for power system analysis

To evaluate the performance and robustness of new power system analysis algorithms, analytical justifications must be accompanied with enough test results comparing the performance of new algorithms with previously adopted ones. Thus, it is highly desirable to have realistic data of power networks of various types and sizes. It is evident that there are many difficulties associated with the collection of network data, especially for very large scale systems. Numerical testing is therefore mainly restricted to a few IEEE test networks or to special power networks whose data are not available to the general research community. This paper presents a new technique that synthetically generates realistic data for power networks of arbitrary size and complexity. While these networks are randomly generated, the software allows the user to specify the system dimension, type of the network, connectivity configurations and other network characteristics