DISHONEST GAUSS–NEWTON METHOD‐BASED POWER SYSTEM STATE ESTIMATION ON A GPU

The power system can change very fast in reality. To control the system in real time, it is important to accelerate the computation processes related to it. As the states estimator is one of the most time‐consuming processes, it needs to be made fast and scalable. Different methods can be found in the literature to serve the purpose. It is important to explore the current parallel computation technology to develop the fastest estimator. To find the fastest estimator, the dishonest Gauss–Newton method is implemented in a graphics processing unit (GPU) in this study. As the method is not explored extensively in the literature, its accuracy is investigated first. Then different aspects of the parallel implementation are explained. It takes a few hundreds of microseconds for IEEE 118‐bus systems that is found to be the fastest in the existing reported times. For very large systems, the required configuration of a GPU and the corresponding time are also estimated. At the end, the distributed method‐based parallelization is also implemented.