基于数据—物理融合的直流系统后续换相失败预测方法

doi:10.12204/j.issn.1000-7229.2021.05.008

电力建设 ›› 2021, Vol. 42 ›› Issue (5): 69-80.doi: 10.12204/j.issn.1000-7229.2021.05.008

基于数据—物理融合的直流系统后续换相失败预测方法

汤奕¹, 顾锐¹, 戴剑丰¹, 郑晨一¹, 张超明¹, 党杰²

1.东南大学电气工程学院, 南京市 210096
2.国家电网有限公司华中分部,武汉市430077

收稿日期:2020-07-31 出版日期:2021-05-01 发布日期:2021-05-06
通讯作者: 戴剑丰
作者简介:汤奕(1977),男,博士,教授,主要从事电力系统稳定分析、电力信息物理系统等方面的研究工作;|顾锐(1997),男,硕士研究生,主要研究方向为高压直流输电;|郑晨一(1994),男,博士研究生,主要研究方向为电力系统稳定分析与控制、高压直流输电;|张超明(1995),男,硕士研究生,主要研究方向为电力系统稳定分析与控制;|党杰(1981),女,教授级高级工程师,研究方向为电力系统稳定分析与控制、网源协调、新能源并网等。
基金资助:
国家电网有限公司华中分部科技项目“适应大规模强稀疏性新能源接入的受端电网特性分析与运行控制技术研究”(SGHZ0000DKJS1900226)

Subsequent Commutation Failure Prediction of HVDC by Integrating Physical-Driven and Model-Driven Methods

TANG Yi¹, GU Rui¹, DAI Jianfeng¹, ZHENG Chenyi¹, ZHANG Chaoming¹, DANG Jie²

1. School of Electrical Engineering, Southeast University, Nanjing 210096, China
2. Central China Branch of State Grid Corporation of China, Wuhan 430077, China

Received:2020-07-31 Online:2021-05-01 Published:2021-05-06
Contact: DAI Jianfeng
Supported by:
Central China Branch of State Grid Corporation of China Research Program “Characteristics Analysis and Operation Control Technology Research on Power Grid Adapting to Large-scale and Strong Sparse New Energy”(SGHZ0000DKJS1900226)

摘要/Abstract

摘要：

换相失败是高压直流输电系统最常见的故障之一,对换相失败的有效预测有利于交直流系统的安全稳定。但是,与首次换相失败相比,后续换相失败机理较为不明,影响因素更加复杂,当前研究尚难以实现对后续换相失败的有效预测。因此,文章提出了一种基于数据-物理融合模型的后续换相失败预测方法。基于对换相过程的机理分析,首先将电力系统固有响应形式进行时域-频域转换,得到考虑电压谐波的换相电压预测值。然后,基于叠加定理计算直流电流,从而实现对熄弧角的预测。为进一步提高预测精度,将与熄弧角相关的电气量作为输入特征,建立基于数据驱动的预测误差修正模型,对机理分析得到的熄弧角预测值进行校正。最后,在电磁暂态仿真软件中搭建测试系统,结果验证了文章所提方法的有效性。

关键词: 高压直流输电, 换相失败预测, 数据-物理融合

Abstract:

Commutation failure (CF) is one of the most common faults in traditional HVDC system. Effective prediction of CF is beneficial to the safety and stability of the power system. The physical-driven prediction method can effectively reflect the causal law but it is difficult to establish a precise model. Data-driven prediction method has the advantage of efficient training, but the prediction accuracy depends on a large number of high-quality training samples. Combining the advantage of physical-driven and data-driven methods, a CF prediction method is proposed. In the part of physical-driven, the inherent response of the power system is transformed from time-domain to frequency-domain to obtain the predicted commutation voltage. Then the predicted DC current can be obtained according to the superposition theorem. Finally, the predicted extinction angle can be calculated according to the commutation mechanism. In the part of data-driven, the amplitude and phase of each harmonic of the commutation voltage are taken as the input characteristics, and the extinction angle predicted by the physical-driven method can be modified. According to the results of the test system built in electromagnetic transient simulation software, the validation of the proposed method is verified.

Key words: HVDC transmission, commutation failure prediction, integration of physical-driven and model-driven method

中图分类号:

TM73

汤奕, 顾锐, 戴剑丰, 郑晨一, 张超明, 党杰. 基于数据—物理融合的直流系统后续换相失败预测方法[J]. 电力建设, 2021, 42(5): 69-80.

TANG Yi, GU Rui, DAI Jianfeng, ZHENG Chenyi, ZHANG Chaoming, DANG Jie. Subsequent Commutation Failure Prediction of HVDC by Integrating Physical-Driven and Model-Driven Methods[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 69-80.

图/表 19

图1 三相全波桥式逆变器拓扑结构

Fig.1 The topology of three-phase full-wave bridge inverter

图2 换相过程换相电压及对应阀电流

Fig.2 Commutation voltage and corresponding valve current during the commutation

图3 数据-物理融合的换相失败预测方法示意图

Fig.3 Commutation failure prediction by integrating physical-driven and model-driven

图4 熄弧角预测示意图

Fig.4 The diagram of extinction angle prediction

图5 换相电压预测流程

Fig.5 The flow chart of commutation voltage prediction

图6 换相过程等值电路及简化等值电路

Fig.6 The equivalent and simplified equivalent circuit during the commutation process

图7 直流电流稳态响应及暂态响应等值电路

Fig.7 The equivalent circuit of steady-state and transient response of DC current

图8 故障期间换相电压和熄弧角特征对应关系

Fig.8 The commutation voltage and the corresponding extinction angle during the failure

图9 测试系统一拓扑结构

Fig.9 The topology of test system one

图10 测试系统三拓扑结构

Fig.10 The topology of test system 3

表1 测试系统二、三受端系统及故障设置相关参数

Table 1 Relevant fault setting parameters of test system 2 and 3

图11 换相电压三序分量预测值和实际值对比

Fig.11 Comparison between the predicted and actual values of three-sequence components of commutation voltage

表2 换相电压预测误差对比表

Table 2 Comparison of prediction errors of commutation voltage

表3 直流电流预测相关参数

Table 3 Parameters for DC current prediction

图12 直流电流时域响应的预测值和实际值对比

Fig.12 Comparison of predicted and actual values of the DC current time domain response

图13 min_γ的实际值及预测值对比图

Fig.13 Predicted and actual values of min_γ under different prediction methods

图14 不同场景下3种方法预测精度对比

Fig.14 Comparison of prediction accuracy of three kinds of methods under different scenes

图15 训练样本数对数据-物理融合方法的影响

Fig.15 The influence of training samples on the physical and data-driven prediction method

图16 采样样本数对数据-物理融合方法的影响

Fig.16 The influence of samples on the physical and data-driven prediction method

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基于数据—物理融合的直流系统后续换相失败预测方法

Subsequent Commutation Failure Prediction of HVDC by Integrating Physical-Driven and Model-Driven Methods

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