基于触发角偏差补偿的连续换相失败抑制措施

逆变侧交流系统发生不对称故障后容易导致连续换相失败,威胁电网的安全稳定运行。针对这一问题,基于不对称故障后直流控制系统的响应规律,根据直流系统稳态运行曲线将故障及恢复过程分为2个阶段,发现在阶段2即故障稳定恢复阶段,存在换相面积需求量逐渐增加而换相面积最大提供量逐渐减小的趋势,并且在此过程中触发角指令值与触发角实际值还存在偏差,触发角偏差将进一步削减换相面积最大提供量,是引发连续换相失败的重要原因。基于此,分析了触发角偏差的来源以及触发角偏差对控制系统的影响,提出一种基于触发角偏差补偿的连续换相失败抑制措施,并在PSCAD/EMTDC中基于CIGRE HVDC标准模型进行仿真验证。仿真结果表明,所提措施能够有效抑制连续换相失败。     

一种基于熵源分离模型的可重构安全原语

在信息爆炸时代,信息的安全问题受到了广泛关注。在物联网设备的加密协议中,物理不可克隆函数(PUF)与真随机数发生器成为加密协议中基本的安全原语,提供了轻量级的解决方案。文章提出了一种熵源分离模型,能够分离环形振荡器中抖动(真随机数发生器的熵)和工艺偏差(PUF熵)引起的延时。基于该模型,在FPGA上设计了一种可重构的双工作模式电路,通过改变模式可分别生成PUF和真随机数。相较于FPGA上独立设计的PUF和真随机数发生器,该结构具有资源开销小、面积利用率高、功耗低等优势。实验结果表明,生成的PUF稳定性高、唯一性强、均匀性好;真随机数序列均通过了NIST测试,具有高随机性和不可预测性。     

基于改进算法的10kV配电网合环稳态电流计算

为了保证合环操作的顺利进行,提出了一种10 kV配电网的合环电流计算方法,重点对合环冲击电流计算过程进行改进,将最大电流出现时刻和所在相纳入考虑。首先,通过理论分析推导,介绍了所提的10 kV配电网合环前稳态电流和合环冲击电流的计算方法;然后,给出了合环操作判定条件的流程;最后,基于MATLAB/Simulink搭建了10 kV配电网的合环仿真模型,对理论分析进行了仿真验证。仿真结果表明,提出的计算方法具有较高的准确性,对实际工程中合环电流计算和合环操作判定具有一定的指导意义。     

虚拟惯量控制响应延时对控制效果的影响分析

电力电子接口电源通过虚拟惯量控制模拟同步机惯量响应,但控制过程存在响应延时。全面分析了延时对虚拟惯量控制效果的影响,发现延时并非越小越好。虚拟惯量控制可以降低频率变化率和最大频差两个指标。响应延时增大,扰动后短时间内平均频率变化率变大,但是,最大频差先下降后上升,并在某个延时取值处获得最优值。而且延时增大时调频功率峰值和能量峰值均降低,控制成本更小。虚拟惯量控制的目标不同时,对延时的要求不同。分析结论有助于更加全面地认识虚拟惯量控制的效果并指导实际应用。     

基于自适应积分滑模与扰动观测的多PMSM同步控制

为了克服多电机差速失步振荡及同步稳定性变差等问题,提出了基于新型自适应积分滑模和扰动观测的多 PMSM 同步控制方法。首先,构建了一种改进型偏差耦合同步控制结构。其次,设计了基于新型自适应积分滑模(new adaptive integral sliding mode control, NAISMC)的 PMSM 控制器。同时,利用滑模扰动观测器(sliding mode disturbance observer, SMDO)对电机负载扰动进行观测,并与改进的偏差耦合同步控制结构相结合,提出了基于NAISMC与SMDO的多 PMSM 的改进型偏差耦合同步控制方法。最后,进行了实验分析。其结果表明所提出的同步控制方法能有效地保证4个电机具有良好的同步性能,提高了其抗扰动的能力,确保了多电机同步的稳定性。     

Effect of process parameters and optimization for photochemical machining of brass and german silver

This article focuses on parametric optimization for photochemical machining (PCM) of brass and german silver. The aim of the study is to analyze the effect of control parameters on response measures, that is, surface roughness, material removal rate, and edge deviation and optimization of parameters considering different weight percentage for each performance measure. The control parameters have been selected as etchant concentration, etching temperature, and etching time. Using full factorial method of design of experiments, PCM has been carried out using ferric chloride as etchant. Surface roughness and edge deviation should be less, while material removal rate is desired high. For satisfying this multi-objective condition, overall evaluation criteria (OEC) have been formulated by assigning different and equal weight percentage to response measures. The optimized condition for particular OEC is obtained, and analysis of variance (ANOVA) has been performed for observing effect of control parameters on response measures. Surface topography study has been performed using scanning electron microscopy, and material composition analysis has been carried out using energy dispersive spectroscopy. The surface roughness is observed lower for brass, while the edge deviation is found lesser for german silver. The material removal rate is observed higher for brass compared to german silver.     

CUSUM Control Charts for the Monitoring of Zero‐inflated Binomial Processes

Zero‐inflated probability models are used to model count data that have an excessive number of zeros. These models are mostly useful in modeling high‐yield or health‐related processes. The zero‐inflated binomial distribution is an extension of the ordinary binomial distribution that takes into account the excess of zeros. In this paper, one‐sided cumulative sum (CUSUM)‐type control charts are proposed for monitoring increases or decreases in the parameter p of a zero‐inflated binomial process. The results of an extensive numerical study concerning the statistical design of the proposed schemes as well as their practical implementation are provided. Copyright © 2015 John Wiley & Sons, Ltd.     

Monitoring the Coefficient of Variation Using the Side Sensitive Group Runs Chart

Many quality characteristics have means and standard deviations that are not independent. Instead, the standard deviations of these quality characteristics are proportional to their corresponding means. Thus, monitoring the coefficient of variation (CV), for these quality characteristics, using a control chart has gained remarkable attention in recent years. This paper presents a side sensitive group runs chart for the CV (called the SSGR CV chart). The implementation and optimization procedures of the proposed chart are presented. Two optimization procedures are developed, i.e. (i) by minimizing the average run length (ARL) when the shift size is deterministic and (ii) by minimizing the expected average run length (EARL) when the shift size is unknown. An application of the SSGR CV chart using a real dataset is also demonstrated. Additionally, the SSGR CV chart is compared with the Shewhart CV, runs rules CV, synthetic CV and exponentially weighted moving average CV charts by means of ARLs and standard deviation of the run lengths. The performance comparison is also conducted using EARLs when the shift size is unknown. In general, the SSGR CV chart surpasses the other charts under comparison, for most upward and downward CV shifts. Copyright © 2015 John Wiley & Sons, Ltd.     

On the Modelling and Monitoring of General Inflated Poisson Processes

In this work, we propose and study general inflated probability distributions that can be used for modelling and monitoring unusual count data. The considered models extend the well‐known zero‐inflated Poisson distribution because they allow the excess of values, other than zero. Four simple upper‐sided control schemes are considered for the monitoring of count data based on the proposed general inflated Poisson distributions, and their performance is evaluated under various out‐of‐control situations. The usefulness of the considered models and techniques is illustrated via two real‐data examples, while practical guidelines are provided as well. Copyright © 2015 John Wiley & Sons, Ltd.     

A short note on the effect of sample size on the estimation error in Cp

Process capability indices such as C_p are used extensively in manufacturing industries to assess processes in order to decide about purchasing. In practice, the parameter for calculating C_p is rarely known and is frequently replaced with estimates from an in-control reference sample. This article explores the optimal sample size required to achieve a desired error of estimation using absolute percentage error of different C_p estimates. Moreover, some practical tools are created to allow practitioners to find sample size in different situations.