Evaluation of Computational Complexity for Distribution Systems State Estimation

IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–12

Abstract

Distribution system state estimation (DSSE) is a key monitoring function for system operators to automate, control and operate the active distribution grids. The computational complexity of the DSSE algorithm is a design factor for implementing a real-time monitoring system. As a common criterion, the elapsed time has been used in many works to determine if a developed DSSE solver, regardless of data acquisition and bad data detection steps, can satisfy the requirement of real-time operation. This measure, however, is machine-dependent and thus may not be a good indicator to show the computational burden of a state estimation method in run-time. This article proposes instead to use floating point operation (FLOP) which is a machine-independent computational complexity measure. The number of FLOPs is a suitable relative performance indicator in determining how fast a method could be executed. In this paper, the computational cost of different DSSE algorithms is mathematically derived in terms of FLOPs. On this basis, this article shows that FLOP is a better measure than the more commonly used elapsed time for making design decisions about the choice of the DSSE method based on computational complexity.

Authors 3

1. Behzad Zargar Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, Germany

   Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany

2. Ferdinanda Ponci Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, Germany

   Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany

3. Antonello Monti Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems

   RWTH Aachen University

   Affiliation as printed

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, Aachen, Germany

   Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany