基于瞬时负荷统计特性检测电能质量扰动源

提出一种基于检测点产生的瞬时负荷统计特性快速确认扰动源的方法,展示了该原理在谐波、电压凹陷、电压膨胀、瞬间电压降落四种扰动,在MATLAB下的仿真和计算结果。结果表明在每种情况下此方法都可以对扰动源进行迅速准确的检测。同时还对每种情况计算电压与实际电压的偏离程度进行了分析,并求出了各种情况下的电压偏离方差。最后,根据扰动源产生原因对扰动源类型的识别进行了研究。     

计及能量互保行为的综合能源园区多时间尺度调度

综合能源园区的运行控制应兼顾多方市场主体利益诉求,针对不同类型园区的供能差异化需求,提出计及用户诉求的日前调度与实时调度相结合的综合能源园区多时间尺度优化调度策略。首先,构建包含智能用电管理单元、电—气耦合联供单元、电动汽车(EV)充电单元和用户电—冷用能调度单元的园区基本架构,并分析市场环境下的园区运行方法;其次,在日前调度中建立以运营商利润和用户评价为目标的园区非合作博弈模型,确定园区能价和用能安排;再次,针对日前购电偏差问题,建立考虑不同类型用户用能诉求的含能量互保行为的实时能量偏差应对模型;最后,以14节点配电网和7节点天然气管网组成的微能源网为场景,对所提模型进行仿真验证,证明所提模型的有效性。结果表明,所提多时间尺度优化调度策略能够提升园区多方市场主体经济效益,以及兼顾用户多样化用能需要的可行性。     

大功率缺失下频率最低点估计的低阶仿真模型

作为频率监视的关键参量和频率控制的决策依据,大功率缺失下频率响应过程中的频率最低点需要被准确估计,包括最大频率偏移和频率最低点时间。提出了一种大功率缺失下频率最低点估计的低阶仿真建模方法。通过对调门扰动响应微分进行多项式拟合,描述了调速器的冲激响应特性。基于各机组调速器响应特性的频域变换,建立了系统等效调速器模型。依据大功率缺失下频率下降过程的形态特征,评估了低阶仿真模型的有效性。算例结果表明,所提建模方法能够显著降低频率响应模型阶数,有效提升仿真求解效率,充分保证了频率最低点的计算精度。     

RPS与偏差电量考核下考虑微电网群的售电公司盈利模型

可再生能源配额制（renewable portfolio standard, RPS）和偏差电量考核机制是近几年推出的2项重要电改政策,使售电公司面临着双重考核压力。为量化分析RPS和偏差电量考核对售电公司的影响,综合考虑可再生能源配额制与偏差电量考核机制,以售电公司利润最优为目标,构建了2种售电公司盈利模型,即售电公司独自经营盈利模型和售电公司与微电网群代理商合作的盈利模型。售电公司与微电网群代理商合作的盈利模型的提出是为降低RPS考核成本,减少偏差电量以及兼顾微电网群绿色环保、灵活可控等优点。文章利用广东省实际交易数据对比分析了售电公司在2种盈利模型下的最优利润。分析结果表明,售电公司与微电网群代理商合作,降低了RPS与偏差电量考核成本,提高了整体利润,基于夏普利（Shapley）值分配方法,售电公司与微电网群代理商的盈利均能达到最大,为售电公司的最优经营策略。     

激光对中仪的原理及应用

介绍了电动机水泵机组等设备对中的必要性;阐述了激光对中仪的基本原理及使用过程中温度对精度的影响。将激光对中仪与国内普遍使用的千分表对中仪进行技术比较,通过实例检验,证明激光对中仪对我国企业的设备管理和节省生产成本有重要作用。     

A quick deviation zone fitting in coordinate metrology of NURBS surfaces using principle component analysis

Evaluation of the tolerance zone using discrete measured points plays a critical role in today’s manufacturing, metrology, and many industrial applications. The deviation zone is typically evaluated using a fitting method that locates an ideal desired geometry corresponding to a set of measured points while a function of the Euclidean distances of the measured points to the ideal surface becomes minimum. This paper presents a quick and reliable algorithm called Dynamic Principle Component Alignment (DPCA) for fitting complex surfaces to the coordinate metrology measured points using the information that is dynamically generated by Principal Component Analysis (PCA) of the measurement data and the corresponding fitted geometry. The developed algorithm efficiently eliminates the necessity for applying commonly used optimization methods for the fitting (localization) process, which decreases the computational cost and uncertainty of the evaluation process. Moreover, DPCA is very reliable and practical in coordinate metrology with large data sets in processes such as laser scanning and other optical methods. The results show that the proposed methodology more accurately finds fitting parameters in comparison with the other commonly used methods while the computational cost is considerably reduced.     

A practical sampling method for profile measurement of complex blades

Blade is one of the most important parts in turbine machinery. The complex geometry of blades not only makes them difficult to fabricate, but also leads them difficult to inspect. Typically, the surface of blades is measured by using coordinate measuring machine (CMM). Since the measurement time and cost increase proportionally as the increase of measurement points, it is essential to sample measurement points which can represent entire blade with sufficient confidence and accuracy. In order to achieve a certain allowable deviation with a suitable set of points, a practical sampling method for surface measurement of blades was studied. Firstly, the leading edge curve and trailing edge curve were supposed to represent the twisted and bend information of blades. A sampling method based on maximum chordal deviation for leading edge curve and trailing edge curve was researched. Further, a fusion approach for sampling points on both edge curves, which determine the cross-sections, was proposed. Secondly, the inspection points sampling method for sectional curves were investigated. Finally, two simulation and one experimental examples were used to demonstrate the sampling methodology. The results indicated that the approach of this study can ensure the measurement precision at high curvature potion by measuring a small number of points.     

Efficient driver drowsiness detection at moderate levels of drowsiness

Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.