Identifying Faulty Feeder for Single-Phase High Impedance Fault in Resonant Grounding Distribution System

The identification of faulty feeder for single-phase high impedance faults (HIFs), especially in resonant grounding distribution system (RGDS), has always been a challenge, and existing faulty feeder identification techniques for HIFs suffer from some drawbacks. For this problem, the fault transient characteristic of single-phase HIF is analyzed and a faulty feeder identification method for HIF is proposed. The analysis shows that the transient zero-sequence current of each feeder is seen as a linear relationship between bus transient zero-sequence voltage and bus transient zero-sequence voltage derivative, and the coefficients are the reciprocal of transition resistance and feeder own capacitance, respectively, in both the over-damping state and the under-damping state. In order to estimate transition resistance and capacitance of each feeder, a least squares algorithm is utilized. The estimated transition resistance of a healthy feeder is infinite theoretically, and is a huge value practically. However, the estimated transition resistance of faulty feeder is approximately equal to actual fault resistance value, and it is far less than the set threshold. According to the above significant difference, the faulty feeder can be identified. The efficiency of the proposed method for the single-phase HIF in RGDS is verified by simulation results and experimental results that are based on RTDS

Real-Time Queue Length Detection with Roadside LiDAR Data

Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed

Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem

In the job-shop scheduling field, timely and proper updating of the original scheduling strategy is an effective way to avoid the negative impact of disturbances on manufacturing. In this paper, a pure reactive scheduling method for updating the scheduling strategy is proposed to deal with the disturbance of the uncertainty of the arrival of new jobs in the job shop. The implementation process is as follows: combine data mining, discrete event simulation, and dispatching rules (DRs), take makespan and machine utilization as scheduling criteria, divide the manufacturing system production period into multiple scheduling subperiods, and build a dynamic scheduling model that assigns DRs to subscheduling periods in real-time; the scheduling strategies are generated at the beginning of each scheduling subperiod. The experiments showed that the method proposed enables a reduction in the makespan of 2–17% and an improvement in the machine utilization of 2–21%. The constructed scheduling model can assign the optimal DR to each scheduling subperiod in real-time, which realizes the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system