Abstract
Decentralisation of generation and increasing utilisation of information communication systems bring challenges to present power system modelling approaches. This work applies functional modelling for monitoring and modelling of distributed energy resources, with wind turbine generator (WTG) application as a case study. First, the authors established a functional model of a generic WTG through the multilevel flow modelling approach. The model acts as basis of a state estimator (SE) for monitoring the WTG. Afterwards, the application of the SE is extended for wind power plant monitoring and control. The case study results show that the SE can efficiently limit the impact of information errors from different data integrity attacks during active power curtailment.
Highlights
The decentralisation of generation, through distributed energy resources (DERs), and higher utilisation of information and communication technology (ICT) infrastructure, drive the power system towards a cyber-physical system (CPS) [1]
In multilevel flow modelling (MFM), controls are represented in control flow structures (CFS), which evaluate the status on a function and can perform an actuation on a function to change the process operation
This paper proposes functional modelling for power system analysis as it transits towards a cyber-physical system, monitoring of DERs through state estimation
Summary
The decentralisation of generation, through distributed energy resources (DERs), and higher utilisation of information and communication technology (ICT) infrastructure, drive the power system towards a cyber-physical system (CPS) [1]. This paper proposes the application of functional modelling by high-level abstraction as a solution to model the operation of DERs. The approach offers a qualitative interpretation of the physical processes, while simultaneously maintaining an overview of the system. The work in [24] considers a doubly fed induction generator (DFIG) and Kalman filters to establish a SE connecting the collector system of the wind farm and the WTGs within All these methods require detailed and accurate information of model specific parameters to give accurate results, which is difficult to obtain and differentiate for each manufacturer and model. A similar approach through artificial neural networks is proposed in [26] intended for WPP and WTG applications These data-driven methods are highly dependent on the quality of the training set of data and do not require prior knowledge of the actual system.
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