An Adaptive Optimization Method for LFOD Enhancement in DFIG Integrated Smart Grids

Tat Kei Chau; Samson Shenglong Yu; Tyrone Fernando; Herbert Ho Ching Iu

doi:10.1109/ISCAS.2018.8351473

An Adaptive Optimization Method for LFOD Enhancement in DFIG Integrated Smart Grids

Tat Kei Chau, Samson Shenglong Yu, Tyrone Fernando, Herbert Ho Ching Iu

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

2 Citations (Scopus)

Abstract

This paper proposes a load-oriented control parameters optimization strategy for Doubly Fed Induction Generator (DFIG) to enhance Low-Frequency Oscillation Damping (LFOD) and improve stability of a power system. Enabled by the smart grid measuring technologies, frequency deviations of generators of interest are obtained and employed as the input signals of the designed Supplementary Damping Controller (SDC) of DFIG. In order to acquire the optimal load-oriented control parameters, an hour-ahead load-forecasting scheme is devised, using Artificial Neural Network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique for the purpose of minimizing the Critical Damping Index (CDI) of the power system. Numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller in smart grids integrated with wind power generators.

Original language	English
Title of host publication	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Volume	2018-May
ISBN (Electronic)	9781538648810
DOIs	https://doi.org/10.1109/ISCAS.2018.8351473
Publication status	Published - 26 Apr 2018
Event	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Firenze Fiera Congress and Exhibition Center, Florence, Italy Duration: 27 May 2018 → 30 May 2018

Conference

Conference	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/Territory	Italy
City	Florence
Period	27/05/18 → 30/05/18

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISCAS.2018.8351473

Cite this

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title = "An Adaptive Optimization Method for LFOD Enhancement in DFIG Integrated Smart Grids",

abstract = "This paper proposes a load-oriented control parameters optimization strategy for Doubly Fed Induction Generator (DFIG) to enhance Low-Frequency Oscillation Damping (LFOD) and improve stability of a power system. Enabled by the smart grid measuring technologies, frequency deviations of generators of interest are obtained and employed as the input signals of the designed Supplementary Damping Controller (SDC) of DFIG. In order to acquire the optimal load-oriented control parameters, an hour-ahead load-forecasting scheme is devised, using Artificial Neural Network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique for the purpose of minimizing the Critical Damping Index (CDI) of the power system. Numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller in smart grids integrated with wind power generators.",

keywords = "ANN, Damping controller, DFIG, Load forecasting, Low-frequency oscillation, Smart grids",

author = "Chau, {Tat Kei} and Yu, {Samson Shenglong} and Tyrone Fernando and Iu, {Herbert Ho Ching}",

year = "2018",

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doi = "10.1109/ISCAS.2018.8351473",

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volume = "2018-May",

booktitle = "2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 ; Conference date: 27-05-2018 Through 30-05-2018",

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Chau, TK, Yu, SS, Fernando, T & Iu, HHC 2018, An Adaptive Optimization Method for LFOD Enhancement in DFIG Integrated Smart Grids. in 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. vol. 2018-May, 8351473, IEEE, Institute of Electrical and Electronics Engineers, USA, 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018, Florence, Italy, 27/05/18. https://doi.org/10.1109/ISCAS.2018.8351473

An Adaptive Optimization Method for LFOD Enhancement in DFIG Integrated Smart Grids. / Chau, Tat Kei; Yu, Samson Shenglong; Fernando, Tyrone et al.
2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. Vol. 2018-May USA: IEEE, Institute of Electrical and Electronics Engineers, 2018. 8351473.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

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AU - Chau, Tat Kei

AU - Yu, Samson Shenglong

AU - Fernando, Tyrone

AU - Iu, Herbert Ho Ching

PY - 2018/4/26

Y1 - 2018/4/26

N2 - This paper proposes a load-oriented control parameters optimization strategy for Doubly Fed Induction Generator (DFIG) to enhance Low-Frequency Oscillation Damping (LFOD) and improve stability of a power system. Enabled by the smart grid measuring technologies, frequency deviations of generators of interest are obtained and employed as the input signals of the designed Supplementary Damping Controller (SDC) of DFIG. In order to acquire the optimal load-oriented control parameters, an hour-ahead load-forecasting scheme is devised, using Artificial Neural Network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique for the purpose of minimizing the Critical Damping Index (CDI) of the power system. Numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller in smart grids integrated with wind power generators.

AB - This paper proposes a load-oriented control parameters optimization strategy for Doubly Fed Induction Generator (DFIG) to enhance Low-Frequency Oscillation Damping (LFOD) and improve stability of a power system. Enabled by the smart grid measuring technologies, frequency deviations of generators of interest are obtained and employed as the input signals of the designed Supplementary Damping Controller (SDC) of DFIG. In order to acquire the optimal load-oriented control parameters, an hour-ahead load-forecasting scheme is devised, using Artificial Neural Network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique for the purpose of minimizing the Critical Damping Index (CDI) of the power system. Numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller in smart grids integrated with wind power generators.

KW - ANN

KW - Damping controller

KW - DFIG

KW - Load forecasting

KW - Low-frequency oscillation

KW - Smart grids

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M3 - Conference paper

AN - SCOPUS:85057100324

VL - 2018-May

BT - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018

Y2 - 27 May 2018 through 30 May 2018

ER -

An Adaptive Optimization Method for LFOD Enhancement in DFIG Integrated Smart Grids

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