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With the rapid expansion of urban power grids and the widespread adoption of renewable energy sources, conventional fault-diagnosis methods for power grids face significant challenges in handling the complexities of modern, highly interconnected systems. While artificial intelligence approaches offer promising alternatives for power grid fault diagnosis, the scarcity of labeled grid data severely hinders the application of AI techniques in this domain. To tackle these issues, this study introduces a semi-supervised learning framework featuring self-supervised learning and an adaptive threshold mechanism (SAT-SSL) designed for fault detection and classification in power grids. Compared with existing approaches, the proposed method substantially reduces reliance on labeled data while maintaining excellent recognition performance. First, a frequency-domain analysis is applied to power grid data to identify and filter anomalous events, which are then classified and labeled based on their visual characteristics, thereby constructing a dedicated power grid dataset. Next, the Yule–Walker algorithm is employed to extract features from the power grid data. A semi-supervised learning framework is subsequently developed, integrating a self-supervised loss and a dynamic threshold mechanism to enhance the model’s information extraction capacity and adaptability across diverse operational scenarios. Finally, the constructed power grid dataset, together with two standard benchmark datasets, is utilized to evaluate the model’s effectiveness. Experimental results demonstrate that the proposed model maintains a low error rate across multiple scenarios and varying label proportions. Specifically, on the power grid dataset, retaining only 5% of the labels yields an error rate of just 6.15%, confirming the method’s ability to achieve precise fault detection and classification even with very limited labeled data.