<![CDATA[The increasing complexity of modern power distribution systems has accelerated the need for advanced automation solutions to maintain grid reliability and efficiency. smart distribution systems (SDS), integrating distributed energy resources (DERs), internet of things (IoT) technologies, and advanced data analytics, are reshaping the conventional grid into a flexible and intelligent network. This review focuses on the application of deep learning (DL) techniques in enhancing automation within SDS, highlighting their role in key tasks such as anomaly detection, fault location, load forecasting, outage estimation, and customer clustering. Five DL models, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, deep neural networks (DNNs), autoencoders, and hybrid models, are evaluated using synthetic datasets that approximate real world grid behavior. Acknowledging the limitations of synthetic data, this review emphasizes the need for future validation using empirical datasets and adaptive learning techniques. Performance trends are qualitatively compared across models and tasks, with observations such as suitability of LSTMs for time series forecasting and CNNs for localized event detection. Challenges including data quality, computational costs, and implementation constraints are discussed, along with potential mitigation strategies such as lightweight model architectures and explainable artificial intelligence. A comparative perspective with traditional machine learning and physiscs-based models is also provided to highlight the unique advantages and tradeoffs of DL methods. The findings undescore the potential of DL in SDS automation while outlining key areas further research and real-world deployment.]]>
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