Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degrad...Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.展开更多
At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in p...At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.展开更多
基金Projects(51475462,61374138,61370031)supported by the National Natural Science Foundation of China
文摘Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.
文摘At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.
