Prognostics for energy and smart grid applications
Chair: Dr. Ki Yong OH (Chung-Ang University)
Evolution of the Dynamic Response and Its Effects on Serviceability of Offshore Wind Turbines with Stochastic Loads and Soil Degradation Dr. Bogdan I. EPUREANU, Woochul NAM and Ki Yong OH (University of Michigan)
Novel methods combined with an integrated simulation platform are proposed and used to ensure a twenty-year lifespan of an offshore wind turbine and substructure (OWT&S). These methods and the platform enable the estimation of the long-term evolution of dynamic responses of the OWT&S due to the degradation of soil modulus under stochastic loading conditions. Specifically, stress power spectrums and two probability distribution functions of soils (a Rayleigh distribution function and a Gaussian distribution function) are employed to calculate the probability distribution of degradation for all locations in soils. Moreover, a new method of using the derivative of degradation functions and inverse functions of the degradation functions is proposed to calculate the mean degradation index. These methods significantly decrease computational effort, which is a critical drawback of previous methods. Case studies demonstrate that the dimensions of the substructures significantly affect the evolution of the dynamic responses, suggesting that the evolution of dynamic responses should be considered in the design step to secure the serviceability of OWTs.
Battery prognostics based on discharge voltage drop for energy storage applications Prof. Jaewook LEE, Dongjin KIM, Seok Goo KIM, Jooho CHOI, Hwa Seob SONG and Sang Hui PARK (Korea Aerospace University, Hyosung Corporation)
This study proposes an approach that can predict the end of Li-ion battery life using the discharge voltage drop curve during its use in the energy storage system (ESS). The approach is developed based on the findings that the voltage drop in Li-ion batteries increases as the battery undergoes cycles, and it can be related with the residual capacity. The key idea is to insert the additional cycle of full charging and discharge with constant c-rate during the usage of the ESS. In this cycle, the relation between the voltage drop and capacity is established off-line via regression technique. Then this is applied to estimate the SOH and RUL on-line during the battery cycles. Particle filter (PF) algorithm is applied to this end, in which the degradation and regression models are taken as the state and measurement models respectively, and the capacity is estimated in the form of samples. The obtained samples are then used to predict its behavior in the future, from which the RUL distribution is determined. Conclusion of the study is that the voltage drop in Li-ion batteries can be a good indicator of the battery health and PF is a useful tool that can predict the RUL accurately even when the charge-discharge conditions change in the middle of the usage cycles.
evelopment of Field-applicable Health Monitoring Method for Photovoltaic Module Array Mr. Hyeonseok LEE, Wonwook OH and Changwoon HAN (Korea Electronics Technology Institute)
Solar cell modules are connected in serial and parallel in a photovoltaic system. Power conversion efficiency is typically real-time monitored for each serially connected string in modern photovoltaic systems. All the solar cell modules degrade over time and the most degraded module in a string decide the output level of the string. In this study, we suggest a heath monitoring methods which enable to detect the most degraded module in a string without separating the module from the string.
We suggest placing a non-transparent film on a module in a string to make an artificial shading effect on the module and monitoring the current-voltage curve of the string as placing the film to the next one. We show analytically that the most degradation module can be detected by comparing all the string current-voltage curves. We demonstrate the method for a field photovoltaic string. We also analyze the economic benefits by replacing the most degraded module in optimal times.