### 抄録

In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: 1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the 'explosion of complexity' problem in backstepping method and the complexities introduced by SVC are overcome; 2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small L∞ ^{a} tracking performance; 3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly bounded^{b} and can be made arbitrarily small. Simulation results show the validity of the proposed method.^{a}Here, the L∞ norm is defined as x ∞ =Δ t≥q 0 x(t) and we say x L∞ when x ∞ exists.^{b}Here, we say x(t) is ultimately uniformly bounded if there exist positive constants b and c, independent of t_{0} ≥ 0, and for every a\in (0,c), there is T=T(a,b), independent of t_{0}, such that x(t_{0})x(t)b, t t_{0}+T.

元の言語 | English |
---|---|

記事番号 | 8482338 |

ページ（範囲） | 61918-61933 |

ページ数 | 16 |

ジャーナル | IEEE Access |

巻 | 6 |

DOI | |

出版物ステータス | Published - 2018 1 1 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Materials Science(all)
- Engineering(all)

### これを引用

*IEEE Access*,

*6*, 61918-61933. [8482338]. https://doi.org/10.1109/ACCESS.2018.2873660

**Decentralized Adaptive Quantized Excitation Control for Multi-Machine Power Systems by Considering the Line-Transmission Delays.** / Zhang, Xiuyu; Li, Bin; Zhu, Guoqiang; Chen, Xinkai; Zhou, Miaolei.

研究成果: Article

*IEEE Access*, 巻. 6, 8482338, pp. 61918-61933. https://doi.org/10.1109/ACCESS.2018.2873660

}

TY - JOUR

T1 - Decentralized Adaptive Quantized Excitation Control for Multi-Machine Power Systems by Considering the Line-Transmission Delays

AU - Zhang, Xiuyu

AU - Li, Bin

AU - Zhu, Guoqiang

AU - Chen, Xinkai

AU - Zhou, Miaolei

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: 1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the 'explosion of complexity' problem in backstepping method and the complexities introduced by SVC are overcome; 2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small L∞ a tracking performance; 3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly boundedb and can be made arbitrarily small. Simulation results show the validity of the proposed method.aHere, the L∞ norm is defined as x ∞ =Δ t≥q 0 x(t) and we say x L∞ when x ∞ exists.bHere, we say x(t) is ultimately uniformly bounded if there exist positive constants b and c, independent of t0 ≥ 0, and for every a\in (0,c), there is T=T(a,b), independent of t0, such that x(t0)x(t)b, t t0+T.

AB - In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: 1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the 'explosion of complexity' problem in backstepping method and the complexities introduced by SVC are overcome; 2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small L∞ a tracking performance; 3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly boundedb and can be made arbitrarily small. Simulation results show the validity of the proposed method.aHere, the L∞ norm is defined as x ∞ =Δ t≥q 0 x(t) and we say x L∞ when x ∞ exists.bHere, we say x(t) is ultimately uniformly bounded if there exist positive constants b and c, independent of t0 ≥ 0, and for every a\in (0,c), there is T=T(a,b), independent of t0, such that x(t0)x(t)b, t t0+T.

KW - Dynamic surface control (DSC)

KW - hysteresis quantizer

KW - L1 tracking performance

KW - large-scale multi-machine power system

KW - static var compensator (SVC)

UR - http://www.scopus.com/inward/record.url?scp=85054505351&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054505351&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2018.2873660

DO - 10.1109/ACCESS.2018.2873660

M3 - Article

AN - SCOPUS:85054505351

VL - 6

SP - 61918

EP - 61933

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8482338

ER -