Time Domain Simulation of DFIG-Based Wind Power System using Differential Transform Method
Pradeep Singh, Upasana Buragohain, Nilanjan Senroy

TL;DR
This paper introduces a novel non-iterative time-domain simulation method using Differential Transform Method (DTM) for DFIG-based wind power systems, enabling efficient solutions of complex non-linear DAEs without higher-order derivatives.
Contribution
The paper presents a new application of DTM and MsDTM to simulate wind power systems, improving computational efficiency and convergence over traditional methods.
Findings
DTM provides accurate series solutions for non-linear DAEs.
The proposed method outperforms RK-4 in numerical simulations.
Series convergence is enhanced with multi-step DTM.
Abstract
This paper proposes a new non-iterative time-domain simulation approach using Differential Transform Method (DTM) to solve the set of non-linear Differential-Algebraic Equations (DAEs) involved in a DFIG-based wind power system. The DTM is an analytical as well as numerical approach applied to solve high dimensional non-linear dynamical systems and the solution can be expressed in the form of a series. In this approach, there is no need to compute higher-order derivatives as DAEs are converted into a set of linear equations after applying transformation rules so that the power series coefficients can be computed directly. The transformation rules are used to transform power system models of various devices, such as induction generator, wind turbine, rotor and grid side converter, which includes trigonometric, square root, exponential functions etc. Further, to increase the interval of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsElectromagnetic Simulation and Numerical Methods · Numerical methods for differential equations · Model Reduction and Neural Networks
