Forecasting the remaining resource of hybrid systems with renewable energy sources
DOI:
https://doi.org/10.31548/energiya6(76).2024.065Abstract
Hybrid energy systems combine several energy sources: solar panels, wind turbines, mini-hydropower plants, fossil fuel sources (diesel generators). These energy sources complement each other in order to ensure uninterrupted power supply to consumers regardless of weather conditions and time of year.
With the continuously increasing complexity of hybrid systems, the interaction between logical components and continuous physical processes described by a particular kind of mathematical models becomes relevant.
It is therefore an important task to predict the remaining lifetime of hybrid renewable energy systems during their operational phase. This problem has to be solved, for example, when replacing equipment that has reached its end of life and when extending its service life.
The aim of the research is to develop a mathematical model and tools for residual life assessment of hybrid systems with renewable energy sources.
Controlling the technical condition of systems involves assessing changes in diagnostic parameters.
The choice and justification of the function describing the process of parameter change is an important point in the development of forecasting methods.The choice of the approximating function ultimately determines the error and labour intensity of forecasting.
The relations for the wear model of the hybrid system with renewable energy sources based on the assumption of the automodel of this process are obtained. In the general case, the process of changing the system parameters can have areas of both deceleration and acceleration of wear.
A mathematical model for predicting the residual life of a hybrid system with renewable energy sources based on the results of observations of its condition during operation is proposed.
This method allows to organise preventive maintenance and repair of the system taking into account its current technical condition, which will reduce operating costs.
Key words: forecasting, hybrid power system, diagnostic parameter, wear, residual life
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