Hydrogen energy

with its abundant reserves

green and low-carbon characteristic

high energy density

diverse sources

and wide applications

is gradually becoming an important carrier in the global energy transformation and development. In this paper

the off-grid wind power hydrogen production system is considered as the research object

and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell

including underload

overload

variable load

and start- stop are analyzed. On this basis

the characteristic extraction of wind power output data after noise reduction is carried out

and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability. The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system. The historical output data of an actual wind farm are used for the case study

and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production. The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems

and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.