Abstract
With the development of modern society, the scale of the power system is rapidly increased accordingly, and the framework and mode of running of power systems are trending towards more complexity. It is nowadays much more important for the dispatchers to know exactly the state parameters of the power network through state estimation. This paper proposes a robust power system WLS state estimation method integrating a wide-area measurement system (WAMS) and SCADA technology, incorporating phasor measurements and the results of the traditional state estimator in a post-processing estimator, which greatly reduces the scale of the non-linear estimation problem as well as the number of iterations and the processing time per iteration. This paper firstly analyzes the wide-area state estimation model in detail, then according to the issue that least squares does not account for bad data and outliers, the paper proposes a robust weighted least squares (WLS) method that combines a robust estimation principle with least squares by equivalent weight. The performance assessment is discussed through setting up mathematical models of the distribution network. The effectiveness of the proposed method was proved to be accurate and reliable by simulations and experiments.
Highlights
Electric power is essential to modern society
Among all the newly-developed applications that aim at satisfying those new technological demands, the so-called wide area measurement system (WAMS) opens a new avenue for power system stability analysis and control, and it has been attracting increasing attention in recent years, since it is a powerful tool for power system monitoring, protection and control, and has been widely used in the energy management systems of power systems [8,9,10,11,12,13]
The computing processes of power system state estimation generally use the maximum likelihood estimation method, but bad data and outliers exist in the estimation process, whose standardized errors are larger than a pre-established tolerance, and they will affect the accuracy and state estimation effectiveness greatly [14,15,16], so it is important to research a robust estimation method to prevent the final estimate from being biased
Summary
Electric power is essential to modern society. Economic prosperity, national security, and standard of living depend on reliable electric power systems, and it’s very important for the power systems to obtain operating condition information about the state of the electric grid [1]. The computing processes of power system state estimation generally use the maximum likelihood estimation method, but bad data and outliers exist in the estimation process, whose standardized errors are larger than a pre-established tolerance, and they will affect the accuracy and state estimation effectiveness greatly [14,15,16], so it is important to research a robust estimation method to prevent the final estimate from being biased To address this problem, a variety of approaches have been proposed, including linear regression, artificial neural networks, fuzzy pattern matching, Kalman filter techniques, etc.
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