With only a single voltage time series recorded in fact,for accurately identifying the ferroresonance types,the delay coordinates method was applied to obtain the reconstructed phase space that is topologically equivalent to the phase space of the original system.Nonlinear dynamics methods based on the reconstructed phase space,e.g.phase plane,Poincaré section and correlation dimension,were used to express the dynamic characteristics of the time series and identify the type of ferroresonance based on them.The nonlinear dynamic characteristics analysis was conducted to 3 typical cases of measured time series of ferroresonance occurred in inductive voltage transformer in isolated neutral system.The phase plane trajectories and Poincaré sections of two time series cases of them show periodic motion characteristics,their correlation dimension estimation values respectively are 1.015 0±0.003 9,1.006 1±0.000 6;therefore their motion modes were identified as fundamental mode and subharmonic mode.The phase plane trajectory and Poincaré section of another time series case do not show periodic or quasiperiodic motion characteristics,its correlation dimension estimation value is 2.300 2±0.061 2;therefore its motion mode was identified as chaotic mode.The measured voltage time series is affected by many factors,the identification result of ferroresonance mode occurred is more accurate and convictive through synthesizing the features characterized by the above 3 analysis methods.

《单片机原理及接口技术》课程具有概念抽象、实践强的特点，本文针对机械类本科《单片机原理及接口技术》的教学现状，提出从课堂教学、实践教学、考核方式三方面进行改革。课堂教学与实践教学的一体化教学模式有效促进了学生的学习兴趣，使学生的单片机应用能力有较大提高，取得了良好的教学效果。

To increase the identification rate of measured overvoltage signals,the extracted features must be distinctive,stable,and explicitly and physically meaningful.Consequently,we put forward a new research method to extract features from measured overvoltage signals.Firstly,we analyzed overvoltage signals in their time-domains and frequency-domains by taking the physical process and influential factors of the signals generation into consideration;secondly,we determined the characteristics to meet the requirements mentioned above;finally,we selected appropriate signal processing techniques to extract corresponding features.Taking three types of common overvoltage signals(caused by capacitor bank energization,inductive lightning and single phase arc to ground,respectively) as examples,we extracted their features according to the proposed method.Then 98 samples of the mentioned three types of signals were identified by binary classifiers constructed based on support vector machines.It is found that the identification rates of the three types of signals are 100%,100%,and 97.3%,respectively,which is 99% in total for all the samples.The identification results validate the effectiveness of the extracted features for the three types of measured overvoltage signals;meanwhile,the proposed method can be referred by feature extractions of more types of measured overvoltage signals.

An effective classification and identification tree for automatically classifying overvoltages based on measured data is built.Firstly,the time domain features are extracted from three-phase overvoltage signals.The set of overvoltage category is classified into two subsets.Secondly,overvoltage signals are decomposed using discrete wavelet transform while other features are extracted from the wavelet transform domain.To make these features more distinctive,overvoltage signals belonging to different subsets each are resampled at different frequencies and decomposed to different resolution levels.Finally,binary classifiers based on the support vector machine are each built at a point on the classification tree and cross-validated using measured overvoltage data.The total identification rate is 95%,indicating that the classification tree can effectively classify overvoltage signals.

Ferroresonance overvoltage and over-current can be restrained using nonlinear dynamics. However, the ferroresonance simplified model, which cannot reflect the huge ferroresonance circuit in the grid precisely and cannot satisfy real-time analysis, is adopted in the traditional nonlinear control method. Owing to these shortcomings, the strategy of controlling ferroresonance overvoltage based on voltage time series is proposed. Based on the voltage time series analysis methods, the ferroresonance system is identified using the best embedding dimension and the best time delay obtained from the phase space reconstruction. Afterward, the feedback controller is designed based on the platform of the identified model. Results show that fundamental, subharmonic and chaotic ferroresonance overvoltage can all be controlled using the method presented in this paper even without the system parameters and the accurate system model. Therefore, the control strategy is generally more applicable.

It is desirable to detect and identify different overvoltage waveforms based on underlying causes to guarantee the safe operation of power system and improve the reliability of power supply. This paper builds a module-based scalable identification system for power system overvoltage events. Each module is able to extract predefined features and identify one specific overvoltage event by integrating one or two signal processing techniques with Support Vector Machines (SVM). Firstly, based on the priori knowledge about signals caused by various overvoltage events, one or two signal processing techniques are selected to analyze recorded overvoltage signals. The signal processing techniques include RMS method, Fourier and Wavelet transforms. Then, a feature vector different from others is defined for each category of overvoltage events. Finally each SVM is trained by using predefined feature vectors as inputs. The system is scalable and robust. If a new overvoltage event needs to be identified, a new module can be added without retraining the existed modules. The prototype of the system is cross-validated using 247 field-measured overvoltage waveforms which cover six types of overvoltage events and 46 unknown overvoltage waveforms. The total identification rate is 97%. It shows the system can classify overvoltage events effectively and smartly.

In this paper the working principle and performance character of both the manual and automatic Vicat apparatuses in existence are compared and analyzed,and their existing weakness and the urgent problem to be solved are pointed out.On this basis,adopting the technology of mechatronics and modern sensors,a new sort of full automatic Vicat apparatus that can fully satisfy the national standard in testing the setting time of the cement has been developed.This apparatus can be operated expediently with high precision,the test results are to be analyzed and computed automatically.This paper presents the working principle,performance character,key technology and prospect of application for the full automatic Vicat apparatus.

该文提出在住宅照明控制系统中采用程序控制，详细介绍了系统的软硬件设计方法，实现住宅照明的智能控制。