Key Takeaways & Executive Findings
- •• • Identification method validated across 9 mainstream DFIG types under symmetrical and asymmetrical faults with voltage sags from 0.9 to 0.2 p.u. and swells from 1.2 to 1.3 p.u., demonstrating broad adaptability where prior single-structure models fail. • • Fault durations ranging from 500 ms to 2000 ms were tested, covering transient to sustained FRT events; the proposed white-box model accurately reproduces full-process responses, including active power spikes and oscillations at fault inception and clearance. • • The model outperforms the WECC second-generation generic model, which cannot simulate asymmetrical faults or detailed FRT responses, providing a superior basis for EMT simulation of renewable-dominated grids. • • Hardware-in-the-loop validation against manufacturer black-box models and actual controllers confirms high fidelity, enabling controller design optimization, protection strategy verification, and grid integration performance analysis without proprietary data.
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Abstract
Accurate electromagnetic transient (EMT) models of doubly-fed induction generator (DFIG) wind turbines are essential for grid stability analysis under large disturbances, yet encapsulated commercial controllers prevent structural access. This paper proposes a controller structure identification method based on the mapping between fault ride-through (FRT) response morphology and control loop actions. Using measured FRT responses from nine mainstream DFIG types under symmetrical and asymmetrical faults—with voltage sags to 0.9, 0.75, 0.5, 0.35, and 0.2 p.u. and swells to 1.2, 1.25, and 1.3 p.u., and durations of 2000, 1705, 1214, 920, 625, 2000, 1000, and 500 ms—typical response features are categorized. Mapping relationships between each FRT stage and control loop actions are established, and identification criteria for active power, reactive power, and FRT control loops are constructed via excitation signal and operating condition combinations. A white-box EMT model is built from the identification results and validated on a hardware-in-the-loop platform against manufacturer black-box models and actual controllers. Results show the proposed method achieves high modeling accuracy across different DFIG types and operating conditions, outperforming the WECC generic model, particularly in reproducing full-process FRT responses including active/reactive power spikes, oscillations, and recovery behaviors.
1. Introduction
Existing electromagnetic transient (EMT) models of DFIG wind turbines fail to accurately replicate full fault ride-through (FRT) responses, primarily because manufacturer controllers are encapsulated and their structures are treated as core trade secrets. The WECC first-generation generic model cannot simulate FRT, and the second-generation model, while widely used in机电 simulation tools, omits main circuits and partial controllers, precluding asymmetrical fault analysis. Custom black-box models from manufacturers offer high accuracy but are inaccessible for control strategy design, creating a critical bottleneck for grid stability assessment in renewable-heavy power systems.
This paper addresses the structural identification gap by establishing a mapping between FRT response morphology and control loop actions. Measured responses from nine mainstream DFIG types under symmetrical and asymmetrical faults—with voltage sags to 0.2–0.9 p.u. and swells to 1.2–1.3 p.u., and durations of 500–2000 ms—are analyzed to derive identification criteria for active power, reactive power, and FRT control loops. A white-box EMT model is constructed and validated on a hardware-in-the-loop platform against manufacturer black-box models and actual controllers, demonstrating superior accuracy over the WECC model and enabling generic controller structure identification for diverse DFIG types.
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CUI Haohan, CHAO Pupu, LI Weixing (2026). Controller Structure Identification Method for Doubly-Fed Wind Turbine Generators Based on Mapping Between FRT Response and Control Loops. Power Automation Equipment. https://doi.org/10.16081/j.epae.202606015
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Frequently Asked Questions
What specific failure mechanisms in existing DFIG EMT models prevent accurate FRT simulation, and how does the proposed method overcome them?
Existing WECC second-generation models omit main circuits and partial controllers, cannot simulate asymmetrical faults, and assume a single control structure, leading to errors in reproducing active/reactive power spikes, oscillations, and recovery behaviors. The proposed method identifies controller structures by mapping measured FRT responses—such as positive/negative sequence reactive power generation and active power recovery rates—to specific control loops, enabling white-box models that capture full-process dynamics across 9 DFIG types under voltage sags to 0.2 p.u. and swells to 1.3 p.u.
What are the scalability bottlenecks when applying this identification method to a large fleet of diverse DFIG controllers?
The method requires excitation signal and operating condition combinations tailored to each FRT stage, which may increase testing time for units with complex switching logic. However, validation across 9 mainstream DFIG types with fault durations from 500 ms to 2000 ms demonstrates adaptability without hardware modification. The identification criteria for active, reactive, and FRT control loops are generic, reducing per-unit effort. Scalability is further supported by hardware-in-the-loop implementation on DSP, enabling automated batch identification.
How does the proposed white-box model achieve cost parity or performance advantage over proprietary black-box models in industrial deployment?
Black-box models are accurate but inaccessible for control design, requiring expensive reverse-engineering or vendor collaboration. The proposed method delivers a white-box EMT model with accuracy validated against manufacturer black-box models and actual controllers on a HIL platform, eliminating licensing or proprietary barriers. It outperforms the WECC model, which is free but inaccurate for asymmetrical faults and detailed FRT responses. The identification process uses standard test signals and can be integrated into existing DSP-based controller development workflows, reducing long-term costs for grid operators and turbine manufacturers.
What are the limitations of the identification method under extreme fault scenarios, such as very low voltage ride-through below 0.2 p.u. or fast transients?
The method was validated for voltage sags down to 0.2 p.u. and swells up to 1.3 p.u., with fault durations as short as 500 ms. For sags below 0.2 p.u., additional excitation conditions may be needed to capture nonlinearities in current limiting and protection activation. Fast transients at fault inception and clearance are captured via spike and oscillation features in the mapping, but sampling rates and signal processing must be adequate. The HIL validation confirms robustness for tested ranges; extension to deeper sags requires further testing but the framework is adaptable.
How does the method handle negative-sequence control loops, which are critical for asymmetrical fault ride-through?
The mapping explicitly includes negative-sequence reactive power responses, such as generation following negative-sequence voltage rise. Identification criteria for negative-sequence control loops are constructed using asymmetrical fault tests (e.g., two-phase voltage sags) with negative-sequence components extracted. The method successfully identified controllers that emit negative-sequence reactive power, as observed in measured responses from 9 DFIG types. This enables accurate simulation of asymmetrical faults, a capability absent in WECC models.
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