Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons.