As smart grids (SG) increasingly rely on advanced technologies like sensors and communication systems for efficient energy generation, distribution, and consumption, they become enticing targets for sophisticated cyberattacks. These evolving threats demand robust security measures to maintain the stability and resilience of modern energy systems. While extensive research has been conducted, a comprehensive exploration of proactive cyber defense strategies utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This survey bridges this gap, studying the latest DL techniques for proactive cyber defense. The survey begins with an overview of related works and our distinct contributions, followed by an examination of SG infrastructure. Next, we classify various cyber defense techniques into reactive and proactive categories. A significant focus is placed on DL-enabled proactive defenses, where we provide a comprehensive taxonomy of DL approaches, highlighting their roles and relevance in the proactive security of SG. Subsequently, we analyze the most significant DL-based methods currently in use. Further, we explore Moving Target Defense, a proactive defense strategy, and its interactions with DL methodologies. We then provide an overview of benchmark datasets used in this domain to substantiate the discourse.{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.} The survey finally lists the challenges associated with deploying DL-based security systems within SG, followed by an outlook on future developments in this key field.

翻译：随着智能电网（SG）日益依赖传感器和通信系统等先进技术来实现高效发电、输电和用电，它们成为复杂网络攻击的诱人目标。这些不断演变的威胁要求采取稳健的安全措施，以维护现代能源系统的稳定性和弹性。尽管已有大量研究，但文献中对利用深度学习（DL）在智能电网中实施主动网络防御策略的综合探索仍显不足。本综述填补了这一空白，研究了用于主动网络防御的最新DL技术。综述首先概述了相关研究及我们的独特贡献，随后审视了SG基础设施。接着，我们将各种网络防御技术分为反应式和主动式两类。重点聚焦于基于DL的主动防御，我们提供了DL方法的全面分类，突出了它们在SG主动安全中的作用和相关性。随后，我们分析了当前使用的最重要的基于DL的方法。此外，我们探讨了移动目标防御（一种主动防御策略）及其与DL方法的交互作用。然后，我们概述了该领域使用的基准数据集以佐证论述。接着，我们对其实际意义及对智能电网网络安全的广泛影响进行了批判性讨论。最后，综述列出了在SG中部署基于DL的安全系统所面临的挑战，并对这一关键领域的未来发展进行了展望。