Biblio

GIS equipment is an important component of power system, and mechanical failure often occurs in the process of equipment operation. In order to realize GIS equipment mechanical fault intelligent detection, this paper presents a mechanical fault diagnosis model for GIS equipment based on cross-validation parameter optimization support vector machine (CV-SVM). Firstly, vibration experiment of isolating switch was carried out based on true 110 kV GIS vibration simulation experiment platform. Vibration signals were sampled under three conditions: normal, plum finger angle change fault, plum finger abrasion fault. Then, the c and G parameters of SVM are optimized by cross validation method and grid search method. A CV-SVM model for mechanical fault diagnosis was established. Finally, training and verification are carried out by using the training set and test set models in different states. The results show that the optimization of cross-validation parameters can effectively improve the accuracy of SVM classification model. It can realize the accurate identification of GIS equipment mechanical fault. This method has higher diagnostic efficiency and performance stability than traditional machine learning. This study can provide reference for on-line monitoring and intelligent fault diagnosis analysis of GIS equipment mechanical vibration.

At present, the AE method has the advantages of live measurement, online monitoring and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. In this paper, development of a data processing software for PD acoustic detection based on a general fast analysis algorithm model is introduced. With considering the signal flow chart of current acoustic detection system widely used in operation and maintenance of power system equipments, the main function of the developed PD AE signals analysis software was designed, including the detailed analysis of individual data file, identification with phase compensation based on 2D PRPD histograms, batch processing analysis of data files, management of discharge fingerprint library and display of typical defect discharge data. And all of the corresponding developed software pages are displayed.

Nowadays, the detection of acoustical emission is widely used for fault diagnosis of gas insulated substations (GIS) in normal operation and factory tests, which is called 'non-conventional' method recommended in the standard IEC TS 62478-2016 and GIGRE D1.33 444. In this paper, to develop a data analyzer for acoustic detection (AD) system to make an assistant diagnosis for technical personnel or equipment operation and maintenance personnel, based on the previous research on the experimental research, pattern identification with phase compensation and the software development, the algorithm model design and its application is given in detail. For the acoustical emission signals (n, ti, qi), the BP artificial neural network optimized by genetic algorithm (GA-BP) is used as a classifier based on the fingerprint consisting of several statistic operators, which are derivate form typical 2D histograms of PRPD with identification with phase compensation (IPC). Experimental results show that the comprehensive algorithm model designed for identification is practical and effective.

At present, the acoustic emission (AE) method has the advantages of live measurement and easy fault location, so it is very suitable for insulation defect detection of power equipments such as GIS, etc. While the conventional AE detection system or instruments always can't give a right discrimination result, because them always work based on the reference voltage or phase information from an auxiliary 220V voltage signal source rather than the operation high voltage (HV) with the real phase information corresponding to the detected AE pulsed signals. So there is a random phase difference between the reference phase and operation phase. The discharge fingerprint formed by the detected AE pulsed signals with reference phase using the same processing process is compared to the discharge fingerprint database formed in the HV laboratory with the real phase information, therefore, the system may not be able to discriminate the discharge mode of the field measured data from GIS in substation operation. In this paper, in order to design and develop a general fast analysis algorithm model for PD acoustic detection system to make an assistant diagnosis, the pattern identification with phase compensation was designed and applied. The results show that the method is effective and useful to deatl with AE signals meased in operation situation.

This study presents spatial analysis of Dengue Fever (DF) outbreak using Geographic Information System (GIS) in the state of Selangor, Malaysia. DF is an Aedes mosquito-borne disease. The aim of the study is to map the spread of DF outbreak in Selangor by producing a risk map while the objective is to identify high risk areas of DF by producing a risk map using GIS tools. The data used was DF dengue cases in 2012 obtained from Ministry of Health, Malaysia. The analysis was carried out using Moran's I, Average Nearest Neighbor (ANN), Kernel Density Estimation (KDE) and buffer analysis using GIS. From the Moran's I analysis, the distribution pattern of DF in Selangor clustered. From the ANN analysis, the result shows a dispersed pattern where the ratio is more than 1. The third analysis was based on KDE to locate the hot spot location. The result shows that some districts are classified as high risk areas which are Ampang, Damansara, Kapar, Kajang, Klang, Semenyih, Sungai Buloh and Petaling. The buffer analysis, area ranges between 200m. to 500m. above sea level shows a clustered pattern where the highest frequent cases in the year are at the same location. It was proven that the analysis based on the spatial statistic, spatial interpolation, and buffer analysis can be used as a method in controlling and locating the DF affection with the aid of GIS.

Artificial monitoring is no longer able to match the rapid growth of cybercrime, it is in great need to develop a new spatial analysis technology which allows emergency events to get rapidly and accurately locked in real environment, furthermore, to establish correlative analysis model for cybercrime prevention strategy. On the other hand, Geography information system has been changed virtually in data structure, coordinate system and analysis model due to the “uncertainty and hyper-dimension” characteristics of network object and behavior. In this paper, the spatial rules of typical cybercrime are explored on base of GIS with Internet searching and IP tracking technology: (1) Setup spatial database through IP searching based on criminal evidence. (2)Extend GIS data-structure and spatial models, add network dimension and virtual attribution to realize dynamic connection between cyber and real space. (3)Design cybercrime monitoring and prevention system to discover the cyberspace logics based on spatial analysis.