Estimation for constant-stress accelerated life test from generalized half-normal distribution

In the constant-stress accelerated life test, estimation issues are discussed for a generalized half-normal distribution under a log-linear life-stress model. The maximum likelihood estimates with the corresponding fixed point type iterative algorithm for unknown parameters are presented, and the least square estimates of the parameters are also proposed. Meanwhile, confidence intervals of model parameters are constructed by using the asymptotic theory and bootstrap technique. Numerical illustration is given to investigate the performance of our methods.

Robust Reactive Power Scheduling of Distribution Networks Based on Modified Bootstrap Technique

The uncertainties of the power load,wind power,and photovoltaic power lead to errors between point prediction values and real values,which challenges the safe operation of distribution networks.In this paper,a robust reactive power scheduling(RRPS)model based on a modified bootstrap technique is proposed to consider the uncertainties of power loads and renewable energy sources.Firstly,a deterministic reactive power scheduling(DRPS)model and an RRPS model are formulated.Secondly,a modified bootstrap technique is proposed to estimate prediction errors of power loads and renewable energy sources without artificially assuming the probability density function of prediction errors.To represent all possible scenarios,point prediction values and prediction errors are combined to construct two worst-case scenarios in the RRPS model.Finally,the RRPS model is solved to find a scheduling scheme,which ensures the security of distribution networks for all possible scenarios in theory.Simulation results show that the worst-case scenarios constructed by the modified bootstrap technique outperform popular baselines.Besides,the RRPS model based on the modified bootstrap technique balances economics and security well.

A common-gate bootstrapped CMOS rectifier for VHF isolated DC-DC converter

A common-gate bootstrapped CMOS rectifier dedicated for VHF（very high frequency） isolated DCDC converter is proposed.It uses common-gate bootstrapped technique to compensate the power loss due to the threshold voltage,and to solve the reflux problem in the conventional rectifier circuit.As a result,it improves the power conversion efficiency（PCE） and voltage conversion ratio（VCR）.The design saves almost 90%of the area compared to a previously reported double capacitor structure.In addition,we compare the previous rectifier with the proposed common-gate bootstrapped rectifier in the case of the same area;simulation results show that the PCE and VCR of the proposed structure are superior to other structures.The proposed common-gate bootstrapped rectifier was fabricated by using CSMC 0.5 μm BCD process.The measured maximum PCE is 86%and VCR achieves 77%at the operating frequency of 20 MHz.The average PCE is about 79%and average VCR achieves71%in the frequency range of 30-70 MHz.Measured PCE and VCR have been improved compared to previous results.

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems.However,the volatility and intermittence of wind power pose uncertainties to traditional point prediction,resulting in an increased risk of power system operation.To represent the uncertainty of wind power,this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network(GNN)and an improved Bootstrap technique.Specifically,adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective.Then,the graph convolutional network(GCN)and bi-directional long short-term memory(Bi-LSTM)are proposed to capture spatiotemporal features between nodes in the graph.To obtain highquality prediction intervals(PIs),an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively.Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph,and the prediction results outperform popular baselines on two real-world datasets,which implies a high potential for practical applications in power systems.