Abstract
This paper presents a study of dynamic equivalence for doubly fed wind farms (DFWFs) in large power systems. The core idea is to find an attribute representing the critical characteristics of a doubly fed induction generator (DFIG) and use it for clustering in the equivalent modeling process. Using the voltage and flux linkage equations of DFIGs, we derived an electromagnetic power expression. Similar to the power angle concept for synchronous generators, an equivalent power angle (EPA) is proposed and shown to represent the dynamic characteristics of a DFIG in the test system. We introduce the similarity degree of the EPA based on similarity theory and construct a similarity index of the EPA for clustering a DFWF. A practical algorithm was developed for parameter aggregation of the equivalent model. In addition, a simulation of DFWFs in Yunnan, China, was conducted to show the feasibility and high precision of the proposed method.
Highlights
With the rapid developments in wind power in recent years, the doubly fed induction generator (DFIG) has become a popular type of wind turbine (WT) in most wind farms (WFs) and plays a prominent role in power systems
WFs usually consist of dozens or even hundreds of WTs, and this leads to tremendous challenges in the dynamic analysis of bulk power systems because detailed models of WFs create an enormous computational burden and are time-consuming
This paper proposes a novel method of dynamic equivalence for doubly fed wind farms (DFWFs)
Summary
With the rapid developments in wind power in recent years, the doubly fed induction generator (DFIG) has become a popular type of wind turbine (WT) in most wind farms (WFs) and plays a prominent role in power systems. A reasonable equivalent method with acceptable accuracies and flexibilities for various WFs is significant and can dramatically reduce the scale and time of the simulations To overcome these limitations, this paper proposes a novel method of dynamic equivalence for DFWFs. The core idea of this paper is inspired by the slow coherency and aggregation of synchronous generators that successfully use natural or inherent properties to derive reduced models and obtain the parameters [12]–[15]. This means that two DFIGs should be clustered into a coherent group when the active power and EPA curves of the two DFIGs are consistent. We apply the similarity degree to DFIGs and successfully cluster the DFIGs
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