Accurate fault detection and root cause location are crucial to ensuring the efficiency and reliability of power distribution networks. The widespread deployment of sensors, such as Phasor Measurement Units (PMUs), has substantially increased multivariate time-series data, necessitating advanced methods to capture complex spatiotemporal dependencies of power distribution systems. However, existing approaches often overlook the simultaneous modeling of spatial and temporal dependencies, leading to increased false alarms and misdiagnoses. We propose Multivariate Spatial-Temporal Graph Convolutional Informer (MST-GCI), designed for long-term fault detection in complex power distribution networks, incorporating a novel Graph Convolutional Informer (GCformer) to better capture spatial and long-term temporal dependencies. Additionally, the correlation relationship learned by Multivariate Time-series Graph Learning (MTGL) and a Variational Autoencoder (VAE)-based fault scoring component are used to identify fault detection and precise root cause localization. Experimental evaluation in public datasets demonstrates that MST-GCI significantly outperforms eight state-of-the-art baseline models with respect to different types of fault detection accuracy. Further experiments show that MST-GCI effectively localizes root causes buses in complex power distribution network.