Over the past decades, electric power systems (EPSs) have undergone an evolution from an ordinary bulk structure to intelligent flexible systems by way of advanced electronics and control technologies. Moreover, EPS has become a more complex, unstable and nonlinear structure with the integration of distributed energy resources in comparison with traditional power grids. Unlike classical approaches, physical methods, statistical approaches and computer calculation techniques are commonly used to solve EPS problems. Artificial intelligent (AI) techniques have especially been used recently in many fields. Deep neural networks have become increasingly attractive as an AI approach due to their robustness and flexibility in handling nonlinear complex relationships on large scale data sets. Major deep learning concepts addressing some problems in EPS have been reviewed in the present study by a comprehensive literature survey. The practices of deep learning and its combinations are well organized with up‐to‐date references in various fields such as load forecasting, wind and solar power forecasting, power quality disturbances detection and classifications, fault detection power system equipment, energy security, energy management and energy optimization. Furthermore, the difficulties encountered in implementation and the future trends of this method in EPS are discussed subject to the findings of current studies. It concludes that deep learning has a huge application potential on EPS, due to smart technologies integration that will increase considerably in the future.

中文翻译：

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电力系统的深度学习方法和应用：全面回顾

在过去的几十年中，电力系统（EPS）经历了从普通的散装结构到借助先进的电子技术和控制技术的智能柔性系统的演变。而且，与传统的电网相比，随着分布式能源的整合，EPS已成为一种更加复杂，不稳定和非线性的结构。与经典方法不同，物理方法，统计方法和计算机计算技术通常用于解决EPS问题。人工智能（AI）技术最近已在许多领域中使用。由于深度神经网络在处理大规模数据集上的非线性复杂关系方面具有鲁棒性和灵活性，因此作为一种AI方法，深层神经网络已变得越来越有吸引力。一项全面的文献调查在本研究中对解决EPS中某些问题的主要深度学习概念进行了回顾。深度学习的实践及其组合组织得很好，并在各个领域提供了最新的参考资料，例如负荷预测，风能和太阳能预测，电能质量扰动检测和分类，电力系统设备故障检测，能源安全，能源管理和能源优化。此外，在当前研究发现的基础上，讨论了EPS中该方法的实施中遇到的困难和未来的趋势。结论是，由于智能技术的集成，深度学习在EPS上具有巨大的应用潜力，而智能技术的集成将在未来大大增加。