To address the scarcity of online fault diagnosis algorithms caused by strong nonlinear characteristics and multi-parameter coupling in pressurized water reactor (PWR) nuclear power plant steam generator systems, this paper proposes an online parameter identification method based on a self-inspired genetic algorithm (GA). First, a model-driven self-supervised GA framework is constructed based on parameter identification theory, transforming the fault diagnosis problem into the identification of key system performance parameters. By integrating a high-fidelity system mechanism model, the parameter identification task is reformulated as a function optimization problem, effectively overcoming the limitations imposed by nonlinearities and high-order differential terms in the system equations. Subsequently, a parameter identification method is developed by designing a fitness function based on dynamic time warping and optimizing the GA population iteration strategy using a quasi-Newton gradient descent approach. This replaces the global random search strategy with a gradient-directed search strategy, resolving the slow convergence issue of traditional GAs and meeting the requirements for online parameter identification. Finally, the proposed method is validated using both model data and real system simulator data. Compared to conventional GAs, it reduces parameter identification error by approximately 5% and decreases the average number of convergence steps by 47%, demonstrating the effectiveness of the self-inspired GA-based parameter identification method.