Improving the Performance of DC Microgrids by Utilizing Adaptive Takagi-Sugeno Model Predictive Control

In naval direct current(DC)microgrids,pulsed power loads(PPLs)are becoming more prominent.A solar sys-tem,an energy storage system,and a pulse load coupled directly to the DC bus compose a DC microgrid in this study.For DC mi-crogrids equipped with sonar,radar,and other sensors,pulse load research is crucial.Due to high pulse loads,there is a possibility of severe power pulsation and voltage loss.The original contribution of this paper is that we are able to address the nonlinear problem by applying the Takagi-Sugeno(TS)model formulation for naval DC microgrids.Additionally,we provide a nonlinear power observer for estimating major disturbances affecting DC microgrids.To demonstrate the TS-potential,we examine three approaches for mitigating their negative effects:instantaneous power control(IPC)control,model predictive control(MPC)formulation,and TS-MPC approach with compensated PPLs.The results reveal that the TS-MPC approach with adjusted PPLs effectively shares power and regulates bus voltage under a variety of load conditions,while greatly decreasing detrimental impacts of the pulse load.Additionally,the comparison confirmed the efficiency of this technique.Index Terms-DC microgrids(MG),model predictive control(MPC),pulsed power loads(PPLs),nonlinear power observer,Takagi-Sugeno(TS)fuzzy model.

Hybrid SDS and WPT-IBBO-DNM Based Model for Ultra-short Term Photovoltaic Prediction

Accurate photovoltaic(PV)power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency.This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model(DNM)in this paper.This model is trained using improved biogeography-based optimization(IBBO),a technique that incorporates a domestication operation to increase the performance of classical biogeography-based optimization(BBO).To be more precise,a similar day selection(SDS)technique is presented for selecting the training set,and wavelet packet transform(WPT)is used to divide the input data into many components.IBBO is then used to train DNM weights and thresholds for each component prediction.Finally,each component’s prediction results are stacked and reassembled.The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre(DKASC)in Alice Springs.Simulation results indicate the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting(PVPF).