The integration of renewable energy with alkaline water electrolyzers (AWEs) for hydrogen production is critical for decarbonization. However, inherent power fluctuations significantly increase the incidence of electrolyzer faults compared to grid-powered systems. Traditional threshold-based fault responses—limited to alarm triggering and shutdown—lack online diagnostic capabilities, necessitating costly post-failure inspections and posing operational risks. To address this challenge, this paper proposes a principal component analysis (PCA)-based fault diagnosis methodology for AWEs. The approach utilizes only normal operation data to establish a multivariate statistical model. Fault detection is achieved by monitoring deviations in T2 and SPE statistics beyond their confidence limits (α = 0.01), while fault isolation is performed through contribution analysis of feature variables to these statistical indices. Experimental validation using two independent datasets (479-h and 15-h operation records) demonstrates that the method: (1) accurately detects incipient faults (e.g., temperature anomalies and gas impurity events) within seconds, and (2) identifies root-cause variables with the maximal contribution dominance. This model-free strategy eliminates the dependency on fault training data, offering a practical solution for enhancing operational reliability in renewable-energy-driven hydrogen production systems.