多故障工况微网并离网切换教学实验设计

微网MG(Microgrid)在发生故障时参数呈现多变特性,为让学生能够直观地了解不同故障工况下MG特征参数演变规律,设计了MG多故障工况下的模拟实验。首先,选择MG控制策略,分析MG故障下的并离网条件;其次,设计MG多工况下的仿真模拟、实验验证流程与方案,分析不同工况下实验模拟数据的演变特征;最后,通过仿真与实际相结合,加深学生对MG在故障过程中的变化特性的理解,提高学生动手实践能力。

Hybrid Support Vector Machines-Based Multi-fault Classification

Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.

Online Supervision of Penicillin Cultivations Based on Rolling MPCA

To reduce the variations of the production process in penicillin cultivations, a rolling multivariate statis-tical approach based on multiway principle component analysis (MPCA) is developed and used for fault diagnosis of penicillin cultivations. Using the moving data windows technique, the static MPCA is extended for use in dy-namic process performance monitoring. The control chart is set up using the historical data collected from the past successful batches, thereby resulting in simplification of monitoring charts, easy tracking of the progress in each batch run, and monitoring the occurrence of the observable upsets. Data from the commercial-scale penicillin fer-mentation process are used to develop the rolling model. Using this method, faults are detected in real time and the corresponding measurements of these faults are directly made through inspection of a few simple plots (t-chart, SPE-chart, and T2-chart). Thus, the present methodology allows the process operator to actively monitor the data from several cultivations simultaneously.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring

In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.

Transient Analysis and Control Strategies for a Quad-lnverter Multiphase Multilevel Motor Drive in Faulty Conditions

In this paper, post-fault-tolerant control strategies for quad-inverter multiphase-multilevel induction motor drives are investigated. More specifically, four standard two-level three-phase VSIs （voltage source inverters） supplying the open-end windings of a dual three-phase induction motor is considered, quadrupling the power capability of a single VSI with given voltage and current ratings. In healthy conditions, the control algorithm is able to generate multi-level voltage waveforms, equivalent to the ones of a three-level inverter and to share the total motor power among the four dc sources in each switching period. This sharing capability is investigated under post-fault operating conditions, when one VSI must be completely insulated due to a severe failure on it. In this case, the conversion power unit can operate with a reduced power rating by a proper modulation of the remaining three VSIs. The whole ac motor drive has been numerically implemented, and the effectiveness of the proposed control strategies under healthy and post-fault operating conditions have been proved.

DC Pole-to-Pole Short-Circuit Behavior Analysis of Modular Multilevel Converter

This paper presents the behavior analysis of modular multilevel converter under DC pole-to-pole short-circuit fault, which is an important issue in fault management, electrical system design and MMC based power system protection and control. Firstly, the transient behavior is analyzed and the conduction overlap- ping angle γ, is defined. Secondly, seven possible short-circuit current paths induced by different γ values are identified, and the corresponding engineering short-circuit current calculation methods for both AC and DC sides are proposed. And then, the influences of impedance distribution factor κ and equivalent short-circuit resistance Rsc on short-circuit currents are elaborated the proposed analysis methods. Finally, case study is used to verify the effectiveness of