A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.