I- Introduction

II- Wind Turbine Generator Model

III- Dynamic State Estimation with a Nonlinear Unknown Input

IV- Case Studies

V- Conclusion

References

Abstract

This paper proposes a novel Kalman filtering based dynamic state estimation method, which addresses cases of models with a nonlinear unknown input, and it is suitable for wind turbine model state estimation. Given the complexity characterising modern power networks, dynamic state estimation techniques applied on renewable energy based generators, such as wind turbines, enhance operators’ awareness of the components comprising modern power networks. In this context, the method developed here is implemented on a doubly-fed induction generator based wind turbine, under unknown wind velocity conditions, as opposed to similar studies so far, where all model inputs are considered to be known, and this does not always reflect the reality. The proposed technique is derivative-free and it relies on the formulation of the nonlinear output measurement equations as power series. The effectiveness of the suggested algorithm is tested on a modified version of the IEEE benchmark 68-bus, 16-machine system.

INTRODUCTION

ELECTRIC power systems all over the world are undergoing significant changes, mainly driven by energy market liberalisation taking place in various countries, as well as the advent of renewable energy based power generators [1], [2]. The adoption of new technologies introduces complexity in terms of network control and operation, therefore, good knowledge of the behavioural model characterising the newly introduced devices is challenging but very important. On the other hand, the longstanding operation of power networks is associated with the existence of aging components which are likely to increase system stress and put system operation at risk, with a notable example being the 1994 North American blackouts in WECC [2], [3]. Given the aforementioned modern network challenges, dynamic security assessment (DSA) and wide area monitoring systems (WAMS) are useful approaches, providing insight regarding the system behaviour with respect to the advent of contingencies [4]. In this context, dynamic state estimation (DSE) is a useful tool to monitor the operational status of the system.