基于暂态关键特征逻辑推理的复杂电网 响应驱动暂态稳定性判别

doi:10.19595/j.cnki.1000-6753.tces.230857

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摘要

暂态稳定实时性判别是响应式稳定控制的核心,新能源和直流输电并网下电网的结构、运行和响应特征复杂程度骤增,现有实时判稳方法的准确性和泛化性面临挑战。本文针对含新能源和直流输电的复杂电网暂态稳定性,提出了一种基于可量测暂态能量特征的自适应逻辑推理判稳方法,实现无电网模型依赖且具有可解释性的实时稳定性判别。首先,基于发电机转子运动的能量关系,结合特勒根定理构建了面向复杂电网响应信息的能量函数并论证了其守恒性;而后,根据系统动能-势能能量转换特征定义了稳定预判因数,并结合最大功角差构成了判稳关键特征量,提出了基于自适应模糊推理神经网络 (adaptive network based fuzzy inference system,ANFIS)的关键特征量与稳定状态之间的映射模型,实现了暂态稳定性实时推理评估。最后,在简单系统中量化分析了关键特征量与系统稳定性间的关系,并在修改后含新能源与直流输电的IEEE10机39节点系统中验证了本文所提方法的有效性与泛化性。

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	杨浩
	伍柏臻
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	蔡国伟
	刘萌

关键词 ：暂态稳定性, 响应驱动, 特勒根定理, 能量函数, 复杂电网, 自适应逻辑推理

Abstract：

Real-time transient stability assessment is the core of response-driven stability control. With renewable energy and DC transmissions connecting to power systems, the complexity of grid’s structure, operation and characteristics increases significantly, which have challenged the effectiveness and accuracy of existing stability assessment methods. For real-time stability assessment in the complex power system including renewable energy and DC transmissions, an adaptive network based fuzzy inference system (ANFIS) based model-free and interpretable stability assessment method was proposed by using measurable transient energy features. First, based on the energy relation of generator motion equation and the Tellegen’s theorem, the transient energy function using the response information of the complex grid was constructed and its energy conservation was also validated. Then, according to the conversion characteristic between kinetic energy and potential energy, the stability prediction factor was defined, which was combined with maximum angle deviation to obtain the key features of the stability judgment. A mapping model between the key features and stability status based on ANFIS adaptive logic reasoning was proposed to realize real-time prediction of transient stability. Finally, the quantitative analysis of the relationship between the key features and system stability status was illustrated in a simple system, and the effectiveness and generalization of the proposed method were verified in the modified IEEE 10-machine 39-bus system with new energies and DC transmissions.
Firstly, based on the energy relation of the generator rotor movement and the Tellegen’s theorem, a refined energy function model was developed by using the response information of a complex grid, and the energy conservation property of the model was proven. The energy function model can depict the energy response characteristics of various components in the system. By introducing the virtual impedance branch, the potential energy of each component can be conveniently calculated using the pre-fault equilibrium point of power system, which does not depend on the component model and its parameters, showing good generality. Secondly, the conversion characteristics between kinetic energy and potential energy were analyzed under stable and unstable conditions, and then a stability prediction factor (SPF) were defined at the maximum potential energy point. Together with the maximum angle deviation (MAD) at the same point, the critical features SPF and MAD for stability assessment were obtained, which have strong representational ability to indicate the stable/unstable status of the system. Finally, a response-driven stability discrimination model was constructed with the key features SPF and MAD. The stability discrimination model was established based on logical reasoning ANFIS, which can obtain an interpretable mapping model between the key features and the stable/unstable status of the system. This model enables real-time transient stability assessment with interpretability.
This paper quantitatively analyzed the relationship between the key features (SPF and MAD) and system stability status in a modified simple IEEE 9-bus system , and evaluated the effectiveness and generalization of the proposed stability discrimination model in a modified IEEE 10-machine 39-bus system with new energy sources and DC transmissions. The simulation results demonstrated that the proposed key features SPF and MAD, showing strong physical attributes associated with the system stability, can effectively reflect the system stability status. Compared to other AI stability discrimination models, the proposed data-driven ANFIS stability discrimination model had a higher accuracy of stability discrimination in a complex power grid. Thus, it can be effectively applied in subsequent emergency response-driven stability control.
The conclusions of this paper are given as follows: 1) The key features SPF and MAD, extracted from the constructed energy function model, have a strong correlation with the system stable/unstable status. They can directly characterize the system stability condition compared to other response features. 2) The data-driven ANFIS based stability discrimination model establishes the if-then logical inference process between the input features (SPF and MAD) and the system stable/unstable status. It can detect the unstable condition in real-time and show higher accuracy of stability discrimination in a complex power grid compared to other AI stability discrimination methods. 3) The proposed stability analysis method maintains high accuracy even in the presence of changes in the power grid topology, showing good generalization performance.

Key words： Transient stability response-driven Tellegen's theorem transient energy function complex power system adaptive logical reasoning

收稿日期: 2023-06-06

PACS:

TM712

基金资助:

国家重点研发计划资助项目：响应驱动的大电网稳定性智能增强分析与控制技术（2021YFB2400800）

通讯作者: 刘铖男,1985年生,硕士研究生导师,工学博士,研究方向为电力系统稳定分析与控制。E-mail：05dylc@163.com

作者简介: 杨浩男,1988年生,硕士研究生导师,工学博士,研究方向为电力系统安全稳定分析与控制,人工智能技术在电力系统中的应用。E-mail：hao_yang@neepu.edu.cn

引用本文:

杨浩, 伍柏臻, 刘铖, 孙正龙, 蔡国伟, 刘萌. 基于暂态关键特征逻辑推理的复杂电网响应驱动暂态稳定性判别[J]. 电工技术学报, 0, (): 230857-. Yang Hao, Wu Baizhen, Liu Cheng, Sun Zhenglong, Cai Guowei, Liu Meng. Response-Driven Transient Stability Assessment for Complex Power Grids Based on Logical Reasoning with Transient Key Feature. Transactions of China Electrotechnical Society, 0, (): 230857-.

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[1] 陈国平, 李明节, 许涛, 等. 关于新能源发展的技术瓶颈研究[J]. 中国电机工程学报, 2017, 37(1): 20-27.
Chen Guoping, Li Mingjie, Xu Tao, et al.Study on technical bottleneck of new energy development[J]. Proceedings of the CSEE, 2017, 37(1): 20-27.
[2] 肖湘宁. 新一代电网中多源多变换复杂交直流系统的基础问题[J]. 电工技术学报, 2015, 30(15): 1-14.
Xiao Xiangning.Basic problems of the new complex AC-DC power grid with multiple energy resources and multiple conversions[J]. Transactions of China Electrotechnical Society, 2015, 30(15): 1-14.
[3] 孟沛彧, 向往, 潘尔生, 等. 分址建设直流输电系统拓扑方案与运行特性研究[J]. 电工技术学报, 2022, 37(19): 4808-4822.
Meng Peiyu, Xiang Wang, Pan Ersheng, et al.Research on topology and operation characteristics of HVDC transmission system based on site-division construction[J]. Transactions of China Electrotechnical Society, 2022, 37(19): 4808-4822.
[4] 叶瑞丽, 刘瑞叶, 刘建楠, 等. 直驱风电机组风电场接入后的电力系统暂态稳定计算[J]. 电工技术学报, 2014, 29(6): 211-218.
Ye Ruili, Liu Ruiye, Liu Jiannan, et al.Transient stability calculation of power system integrated with direct-drive wind farm with permanent magnet synchronous generators[J]. Transactions of China Electrotechnical Society, 2014, 29(6): 211-218.
[5] 郑超, 孙华东, 牛振波. 暂态功角多群失稳机制及基于广域支路响应的阻断控制[J]. 电网技术, 2023, 47(2): 722-731.
Zheng Chao, Sun Huadong, Niu Zhenbo.Transient rotor angle multi-group instability mechanism and fast blocking control based on wide area branches’ response[J]. Power System Technology, 2023, 47(2): 722-731.
[6] 吴政球, 荆勇. 基于时域仿真的暂态稳定裕度灵敏度分析[J]. 中国电机工程学报, 2001, 21(6): 19-24.
Wu Zhengqiu, Jing Yong.Transient stability sensitivity based on time domain simulation[J]. Proceedings of the CSEE, 2001, 21(6): 19-24.
[7] Lee J, Chiang H D.A singular fixed-point homotopy method to locate the closest unstable equilibrium point for transient stability region estimate[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2004, 51(4): 185-189.
[8] 林玉章, 蔡泽祥. 基于PEBS法的交直流输电系统暂态稳定分析[J]. 电力自动化设备, 2009, 29(1): 24-28.
Lin Yuzhang, Cai Zexiang.Transient stability analysis of AC/DC power system based on PEBS method[J]. Electric Power Automation Equipment, 2009, 29(1): 24-28.
[9] Chiang H D, Wu F F, Varaiya P P.Foundations of the potential energy boundary surface method for power system transient stability analysis[J]. IEEE Transactions on Circuits and Systems, 1988, 35(6): 712-728.
[10] 李锡林,查晓明,田震,等.考虑频率突变影响的孤岛微电网系统建模和基于Lyapunov第二法的暂态稳定性分析[J/OL].电工技术学报: 1-14[2023-05-29].
Li Xilin, Zha Xiaoming, Tian Zhen, et al.Modeling of Island Microgrid Considering Frequency Mutation and Transient Stability Analysis Based on Lyapunov Second Method[J/OL]. Transactions of China Electrotechnical Society: 1-14[2023-05-29].
[11] Chiang H D, Wu F, Varaiya P.Foundations of direct methods for power system transient stability analysis[J]. IEEE Transactions on Circuits and Systems, 1987, 34(2): 160-173.
[12] 黄通, 王杰. 基于改进EEAC法的随机复杂多机系统的暂态稳定性分析[J]. 电网技术, 2017, 41(4): 1174-1182.
Huang Tong, Wang Jie.Transient stability analysis of stochastic complex multi-machine system based on improved extended equal-area criteria[J]. Power System Technology, 2017, 41(4): 1174-1182.
[13] Paudyal S, Ramakrishna G, Sachdev M S.Application of equal area criterion conditions in the time domain for out-of-step protection[J]. IEEE Transactions on Power Delivery, 2010, 25(2): 600-609.
[14] 魏少攀, 杨明, 韩学山, 等. 基于相轨迹MLE指标的暂态功角稳定在线辨识[J]. 电力系统自动化, 2017, 41(16): 71-79.
Wei Shaopan, Yang Ming, Han Xueshan, et al.Online identification for transient angle stability based on MLE index of phase trajectory[J]. Automation of Electric Power Systems, 2017, 41(16): 71-79.
[15] Wei Shaopan, Yang Ming, Qi Junjian, et al.Model-free MLE estimation for online rotor angle stability assessment with PMU data[J]. IEEE Transactions on Power Systems, 2018, 33(3): 2463-2476.
[16] 谢欢, 张保会, 于广亮, 等. 基于相轨迹凹凸性的电力系统暂态稳定性识别[J]. 中国电机工程学报, 2006, 26(5): 38-42.
Xie Huan, Zhang Baohui, Yu Guangliang, et al.Power system transient stability detection theory based on characteristic concave or convex of trajectory[J]. Proceedings of the CSEE, 2006, 26(5): 38-42.
[17] 范新凯, 杨松浩, 赵一铭, 等. 基于相轨迹凹凸性的暂态失稳判据准确性验证[J]. 电力自动化设备, 2021, 41(1): 159-165, 171, 166.
Fan Xinkai, Yang Songhao, Zhao Yiming, et al. Accuracy verification of transient instability criterion based on concavity and convexity of phase trajectory[J]. Electric Power Automation Equipment, 2021, 41(1): 159-165, 171, 166.
[18] 郑超, 孙华东, 赵兵. 基于关键支路sBTTC指数幅相相关性的主导稳定形态判别与多级主动控制[J]. 中国电机工程学报, 2022, 42(7): 2474-2486.
Zheng Chao, Sun Huadong, Zhao Bing.Dominant stability mode discrimination and multilevel stability control based on amplitude-phase correlation of critical branch’s sBTTC index[J]. Proceedings of the CSEE, 2022, 42(7): 2474-2486.
[19] 郑超, 孙华东, 张爱军, 等. 基于广域支路响应的功角与电压主导稳定模式判别及紧急控制[J]. 中国电机工程学报, 2021, 41(18): 6148-6160.
Zheng Chao, Sun Huadong, Zhang Aijun, et al.Discrimination of rotor angle stability and voltage stability based on wide-area branch response and emergency control[J]. Proceedings of the CSEE, 2021, 41(18): 6148-6160.
[20] 蔡国伟, 穆钢, K W Chan, 等. 基于网络信息的暂态稳定性定量分析: 支路势能法[J]. 中国电机工程学报, 2004, 24(5): 1-6.
Cai Guowei, Mu Gang, Chan K W, et al.Branch potential energy method for power system transient stability assessment based on network dynamic variables[J]. Proceedings of the CSEE, 2004, 24(5): 1-6.
[21] Padiyar K R, Krishna S.Online detection of loss of synchronism using energy function criterion[J]. IEEE Transactions on Power Delivery, 2006, 21(1): 46-55.
[22] Amjady N, Majedi S F.Transient stability prediction by a hybrid intelligent system[J]. IEEE Transactions on Power Systems, 2007, 22(3): 1275-1283.
[23] 李扬, 李国庆, 顾雪平, 等. 基于集成OS-ELM的暂态稳定评估方法[J]. 电工技术学报, 2015, 30(14): 412-418.
Li Yang, Li Guoqing, Gu Xueping, et al.Transient stability assessment of power systems based on ensemble OS-ELM[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 412-418.
[24] 周艳真, 吴俊勇, 冀鲁豫, 等. 基于两阶段支持向量机的电力系统暂态稳定预测及预防控制[J]. 中国电机工程学报, 2018, 38(1): 137-147, 350.
Zhou Yanzhen, Wu Junyong, Ji Luyu, et al.Two-stage support vector machines for transient stability prediction and preventive control of power systems[J]. Proceedings of the CSEE, 2018, 38(1): 137-147, 350.
[25] You Dahai, Wang Ke, Ye Lei, et al.Transient stability assessment of power system using support vector machine with generator combinatorial trajectories inputs[J]. International Journal of Electrical Power & Energy Systems, 2013, 44(1): 318-325.
[26] 王同文, 管霖, 章小强, 等. 基于扩展k阶近邻法的电力系统稳定评估新算法[J]. 电力系统自动化, 2008, 32(3): 18-21, 75.
Wang Tongwen, Guan Lin, Zhang Xiaoqiang, et al.A new method for power system stability assessment based on extended k-nearest neighbor classifier[J]. Automation of Electric Power Systems, 2008, 32(3): 18-21, 75.
[27] 王步华, 朱劭璇, 熊浩清, 等. 基于长短期记忆神经网络的检修态电网暂态稳定评估方法[J]. 电气技术, 2023, 24(1): 29-35, 43.
Wang Buhua, Zhu Shaoxuan, Xiong Haoqing, et al.Assessment method of transient stability for maintenance power system based on long short term memory neural network[J]. Electrical Engineering, 2023, 24(1): 29-35, 43.
[28] 房佳姝, 刘崇茹, 苏晨博, 等. 基于自注意力Transformer编码器的多阶段电力系统暂态稳定评估方法[J]. 中国电机工程学报, 2023, 43(15): 5745-5759.
Fang Jiashu, Liu Chongru, Su Chenbo, et al.Multi-stage transient stability assessment of power system based on self-attention transformer encoder[J]. Proceedings of the CSEE, 2023, 43(15): 5745-5759.
[29] 陈灏颖,管霖.基于主动迁移学习的电力系统拓扑自适应暂态稳定评估[J/OL].中国电机工程学报:1-14[2023-04-06].
Chen Haoying, Guan Lin.Anactive transfer learning scheme for power system transient stability assessment adaptive to the topological variability[J/OL]. Proceedings of the CSEE: 1-14[2023-04-06].
[30] 汪小明, 刘涤尘, 吴军, 等. 基于能量函数法的电网暂态稳定性分析[J]. 电网技术, 2011, 35(8): 114-118.
Wang Xiaoming, Liu Dichen, Wu Jun, et al.Energy function-based power system transient stability analysis[J]. Power System Technology, 2011, 35(8): 114-118.
[31] 王科, 秦文萍, 张宇, 等. 双馈风机等效惯量控制比例系数对系统功角首摆稳定的影响机理分析[J]. 电工技术学报, 2023, 38(3): 741-753.
Wang Ke, Qin Wenping, Zhang Yu, et al.Mechanism analysis of effect of equivalent proportional coefficient of inertia control of DFIG on stability of first swing of power angle[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 741-753.
[32] 贾天下, 孙华东, 赵兵, 等. 基于结构保持能量函数的电力系统暂态稳定分析方法研究[J]. 中国电机工程学报, 2020, 40(9): 2819-2826.
Jia Tianxia, Sun Huadong, Zhao Bing, et al.Research on transient stability analysis method of power system based on network structure preserving energy function[J]. Proceedings of the CSEE, 2020, 40(9): 2819-2826.
[33] 梁得亮, 丁文, 鱼振民. 基于自适应网络模糊推理系统的开关磁阻电机建模方法[J]. 中国电机工程学报, 2008, 28(9): 86-92.
Liang Deliang, Ding Wen, Yu Zhenmin.Modeling for switched reluctance motor based on adaptive network-based fuzzy inference system[J]. Proceedings of the CSEE, 2008, 28(9): 86-92.
[34] 于希宁, 程锋章, 朱丽玲, 等. 基于T-S模型的自适应神经模糊推理系统及其在热工过程建模中的应用[J]. 中国电机工程学报, 2006, 26(15): 78-82.
Yu Xining, Cheng Fengzhang, Zhu Liling, et al.ANFIS modeling based on T-S model and its application for thermal process[J]. Proceedings of the CSEE, 2006, 26(15): 78-82.
[35] Sauer PW., Pai M., Power system dynamics and stability. Prentice Hall Upper Saddle River, NJ,1998, vol. 4.
[36] 卢锦玲, 郭鲁豫. 基于改进深度残差收缩网络的电力系统暂态稳定评估[J]. 电工技术学报, 2021, 36(11): 2233-2244.
Lu Jinling, Guo Luyu.Power system transient stability assessment based on improved deep residual shrinkage network[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2233-2244.
[37] 李扬, 顾雪平. 基于改进最大相关最小冗余判据的暂态稳定评估特征选择[J]. 中国电机工程学报, 2013, 33(34): 179-186, 27.
Li Yang, Gu Xueping.Feature selection for transient stability assessment based on improved maximal relevance and minimal redundancy criterion[J]. Proceedings of the CSEE, 2013, 33(34): 179-186, 27.
[38] 王怀远, 陈启凡. 基于代价敏感堆叠变分自动编码器的暂态稳定评估方法[J]. 中国电机工程学报, 2020, 40(7): 2213-2220, 2400.
Wang Huaiyuan, Chen Qifan.A transient stability assessment method based on cost-sensitive stacked variational auto-encoder[J]. Proceedings of the CSEE, 2020, 40(7): 2213-2220, 2400.