Operation anomalies are common phenomena in large-scale solar farms. Effective anomaly detection and classification is essential for improving operation reliability and electricity generation. However, this is a challenging task due to the high complexity and wide variety of frequently occurring anomalies. Furthermore, existing pre-installed supervisory control and data acquisition systems (SCADA) can only provide a limited amount of information regarding the healthy condition of solar farms, making accurate anomaly detection and classification difficult. This paper presents a data-driven anomaly detection and classification solution, which can accurately detect and classify diverse photovoltaic system anomalies. The proposed solution does not require additional equipment or non-SCADA data collection. More specifically, the proposed work consists of two methods: (1) a hierarchical context-aware anomaly detection method using unsupervised learning, and (2) a multimodal anomaly classification method. The proposed solution has been deployed in two large-scale solar farms (39.36 MWp and 21.62 MWp). Multi-month operation demonstrates the effectiveness, robustness, as well as cost- and computation-efficiency of the proposed solution.