Decentralized Stability Enhancement of DFIG-Based Wind Farms in Large Power Systems: Koopman Theoretic Approach

Ahmed Husham,Innocent Kamwa,M A Abido,Hussein Supreme

doi:10.1109/access.2022.3157747

Abstract

This paper proposes a data-centric model predictive control (MPC) for supplemental control of a DFIG-based wind farm (WF) to improve power system stability. The proposed method is designed to control active and reactive power injections via power converters to reduce the oscillations produced by the WF during disturbance conditions. Without prior knowledge of the system model, this approach utilizes the states measurements of the DFIG subsystem for control design. Therefore, a data-driven optimal controller with a decentralized feature is developed. The learning process is based on Koopman operator theory where the unknown nonlinear dynamics of the DFIG is reconstructed by lifting the nonlinear dynamics to a linear space with an approximate linear state evolution. Extended dynamic mode decomposition (EDMD) is then applied to determine the lifted-state space matrices for the proposed Koopman-based model predictive controller (KMPC) design. The effectiveness of the proposed scheme is tested on New England IEEE 68-bus 16-machine system under three-phase fault conditions. The results ascertain the effectiveness of the proposed scheme to enhance the system damping characteristics.

Highlights

Extensive studies have been conducted to damp the oscillatory modes using power system stabilizers (PSSs) [1], [2] and Flexible AC transmission systems (FACTS) that have good performance under a wide range of network conditions [3], [4] PSS and FACTS are difficult to tune which may jeopardize their performance in damping the inter-area modes under varying network conditions induced by high penetration of intermittent renewable energy resources
SIMULATION RESULTS In the previous section, we have shown the mathematical model of the DFIG and presented an overview of Koopman theory that is used to establish the basis of the data-driven model predictive control (MPC)
We show the simulations to demonstrate the ability of the proposed scheme to improve the dynamic stability of wind-integrated power systems

Summary

Introduction

Extensive studies have been conducted to damp the oscillatory modes using power system stabilizers (PSSs) [1], [2] and Flexible AC transmission systems (FACTS) that have good performance under a wide range of network conditions [3], [4] PSS and FACTS are difficult to tune which may jeopardize their performance in damping the inter-area modes under varying network conditions induced by high penetration of intermittent renewable energy resources. Transmission system operators (TSOs) have altered grid code criteria in response to the growing number of wind generation facilities around the world [9] These grid regulations require wind farms to provide ancillary services such as inertial support, frequency regulation, and damping control. All these services are conventionally provided by synchronous generators This large-scale integration of wind farms, delivers an extreme challenge to handle the time-varying characteristics of such resources and is usually accompanied by modeling uncertainties [10]. Ref [12] suggested a robust design of multimachine PSSs based on the simulated annealing optimization approach These solutions, are analytical model-based, resulting in sophisticated designs and complex controls. Whereas model-based design gives an appropriate solution for oscillation occurrences in principle, optimality and resilience are seldom attained in practice due to: 1) The real parameters of the devices (e.g., HVDC stations and SGs (synchronous generator)) are difficult to determine due to operating conditions dependence and parameter uncertainty

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Conclusion