面向运行场景变化的方差引导式域适应暂态稳定评估

doi:10.19595/j.cnki.1000-6753.tces.241774

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摘要

面对新能源电力系统的的不确定性和时变性,基于深度学习的暂态稳定评估模型在实际应用中效果会变差。为此,提出了一种面向运行场景变化的方差引导式域适应暂态稳定评估框架。首先,设计了一种增强式核函数,以提升模型对极端值的容忍度,从而增强其在处理具有长尾效应的暂态稳定数据时的鲁棒性。其次,提出了一种方差引导式域分布对齐机制。该机制基于增强式核函数构建了一组能表达分布方差特性的基,并构造了一个新的希尔伯特空间。在该空间中,通过方差引导式域分布差异度量精确量化源域和目标域间的分布差异,并通过不断缩小这些差异,实现域间分布的精细化对齐,从而提高模型的自适应能力。最后,推导了域适应泛化的误差界限,以保证所提方案的可靠性。在新英格兰10机39节点系统和中国西南某省电网上的测试结果验证了所提方案的优越性。

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关键词 ：暂态稳定评估, 域适应, 增强式核函数, 方差

Abstract：

The reliability of transient stability assessment methods based on deep learning typically depends on the assumption that data from the original (source) domain and the new (target) domain follow an independent and identical distribution. However, in renewable energy power systems, this assumption often fails due to the system’s complex and dynamic operating conditions. To address this challenge, some studies have attempted to map source and target domain data into a shared latent feature space using maximum mean discrepancy (MMD), which evaluates the mean difference between the two domains. By adjusting model parameters to reduce the MMD value between the source domain and the target domain in the feature space, these methods aim to achieve consistent feature representation and mitigate distributional differences across domains. However, the Gaussian kernel function used in MMD lacks robustness against the long-tail distribution characteristics of transient stability data. Moreover, MMD’s reliance on mean difference alone is insufficient to fully capture the volatility and dispersion of transient stability data, limiting its ability to achieve optimal domain alignment. To overcome this limitation, this paper proposes a variance-guided domain-adaptive transient stability assessment (TSA-VGA) framework designed to handle operational scenario changes.
Firstly, to address the issue of the Gaussian kernel function's insufficient robustness in handling transient stability data, an enhanced kernel function was proposed. This function accounted for both the long-tail distribution characteristics and abrupt variations in transient stability data, optimizing the model’s tolerance to extreme values through mathematical refinement, thereby improving its resilience in processing such data. Secondly, to address the insufficient inter-domain distribution alignment in MMD, a variance-guided domain distribution alignment mechanism was introduced. Built upon the improved kernel function, this mechanism constructed a basis set that captures the variance characteristics of distributions and established a new Hilbert space. In this space, the variance-guided domain distribution difference metric was employed to precisely quantify inter-domain distributional discrepancies. By iteratively minimizing these differences, the mechanism achieved refined inter-domain alignment, enhancing the model’s adaptability. Furthermore, both biased and unbiased estimators of the variance-guided distribution difference metric were formulated. The mathematical formulations were also derived to establish the error bounds between these two statistical estimators and the true distribution difference, providing theoretical support for cross-domain knowledge transfer in evaluation tasks.
In the case analysis, the effectiveness and accuracy of the TSA-VGA framework were thoroughly validated using the New England 10-machine 39-bus system. To further assess its practical applicability, the framework was also tested on a larger-scale provincial power grid in Southwest China with greater complexity. The results demonstrated that when using the New England 10-machine 39-bus system as the source domain and the provincial power grid as the target domain, the model achieved a prediction accuracy of 97.18% with only 500 target domain samples. This performance effectively meets the requirements for real-time applications in large-scale power systems.
This study presents the following conclusions: (1) An enhanced kernel function is designed to improve the model’s tolerance to extreme values, thereby effectively enhancing its robustness to transient stability data exhibiting long-tail characteristics. (2) A variance-guided domain distribution alignment mechanism is proposed. This mechanism constructs a set of basis functions that capture the variance characteristics of the distribution and defines a new Hilbert space. Within this space, the model can precisely quantify inter-domain distribution differences. By iteratively minimizing these differences, refined inter-domain alignment is achieved, thus enhancing the model’s adaptability.

Key words： Transient stability assessment domain-adaptive enhanced kernel function variance

收稿日期: 2024-10-11

PACS:

TM712

基金资助:

国家自然科学基金资助项目（52477096）

通讯作者: 王涛男,1976年生,博士,教授,研究方向为电力系统安全防御与恢复控制、智能技术在电力系统中的应用。E-mail：wtwxx@126.com

作者简介: 杨远男,1997年生,硕士研究生,研究方向为智能技术在电力系统中的应用。E-mail：yangyuan123654@163.com

引用本文:

王涛, 杨远, 申冰洁, 张丹. 面向运行场景变化的方差引导式域适应暂态稳定评估[J]. 电工技术学报, 0, (): 250427-. Wang Tao, Yang Yuan, Shen Bingjie, Zhang Dan. Variance-Guided Domain-Adaptive Transient Stability Assessment Framework. Transactions of China Electrotechnical Society, 0, (): 250427-.

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