Collaborative Research: FMitF: Track I: Towards Verified Robustness and Safety in Power System-Informed Neural Networks

合作研究：FMitF：第一轨：实现电力系统通知神经网络的鲁棒性和安全性验证

• 批准号: 2319242
• 负责人: Kaidi Xu
• 金额: $ 37.37万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319242&HistoricalAwards=false
• 关键词: Collaborative; Research; FMitF; Track; Towards; Collaborative; Research; FMitF; Track; Towards

Neural Networks (NNs) have revolutionized the way we operate and manage modern power systems, providing remarkable solutions to modeling complex non-linear relationships and performing pattern recognition tasks using abundant data collected by state-of-the-art monitoring sensors. Despite the promising advantages, the efficiency and reliability of these models can be negatively impacted by noisy or biased power measurements and the unpredictability of renewable energy sources. The NN-based models are further complicated by their inherent non-linear, high-dimensional nature and vulnerability to adversarial attacks. Recognizing the risks associated with empirical methods that lack formal robustness guarantees, especially in a field where model failures can lead to disastrous real-world consequences, this project seeks to enhance the security and reliability of power systems by optimizing the cutting-edge NN verifier (alpha, beta-CROWN) tailored to the characteristics of modern power systems. The resulting improvements aim to provide power grid operators with safe, dependable tools to operate the power systems. Moreover, this project also intends to support education and research initiatives, encompassing the fields of machine learning and power system, for both bachelor's and master's degree students.With a vision to bridge the existing gap between the power systems and the robust neural network verification techniques, this project is divided into three thrusts. In Thrust I, the project will extend the applications of NN verifiers to topology-aware power systems, examining different scenarios that include complete and incomplete verification on various model structures and adjusting branch and bound heuristics accordingly. Thrust II will enhance the effectiveness of current NN verifiers by incorporating power system static and dynamic constraints and further improve verification efficiency through certifiable training. Lastly, in Thrust III, the project will develop specially designed verifiers for power systems to serve as a novel tool for sensitivity analysis-based power system planning. This last component incorporates verification approaches for the first time, utilizing explainable Artificial Intelligence within power systems. Collectively, these research efforts will revolutionize people’s understanding and application of formal robustness verification techniques to power systems, ensuring the security and dependability of modern power networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经网络（NNS）彻底改变了我们运营和管理现代电力系统的方式，为使用最先进的监视传感器收集的丰富数据提供了建模复杂的非线性关系和执行模式识别任务的显着解决方案。尽管有希望的优势，但这些模型的效率和可靠性可能会受到嘈杂或有偏见的功率测量以及可再生能源的不可预测性的负面影响。基于NN的模型将其继承的非线性，高维质和对对抗性攻击的脆弱性更加复杂。认识到与缺乏正式鲁棒性保证的经验方法相关的风险，尤其是在模型失败会导致现实世界后果的领域，该项目旨在通过优化针对现代电力系统特征量身定制的尖端NN验证器（Alpha，Beta-Crown）来增强电力系统的安全性和可靠性。最终的改进旨在为电网操作员提供安全，可靠的工具以操作电源系统。此外，该项目还打算支持教育和研究计划，涵盖机器学习和电力系统的领域，包括学士学位和硕士学位的学生。有了弥合电源系统与强大的神经网络验证技术之间现有差距的愿景，该项目分为三个推力。在推力I中，该项目将将NN验证者的应用扩展到拓扑感知的功率系统，研究不同的方案，包括对各种模型结构的完整验证和不完整的验证，并相应地调整分支和绑定的启发式方法。推力II将通过合并电源系统的静态和动态约束来提高当前NN验证者的有效性，并通过对验证训练进一步提高验证效率。最后，在推力III中，该项目将开发专门为电力系统设计的验证器，以作为基于灵敏度分析的电力系统计划的新工具。最后一个组件首次采用了验证方法，利用电力系统中的可解释的人工智能。总的来说，这些研究工作将彻底改变人们对电力系统正式鲁棒性验证技术的理解和应用，从而确保现代电力网络的安全性和可靠性。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。