Masume Khodsuz

Bio: Masume Khodsuz is an academic researcher from Babol Noshirvani University of Technology. The author has contributed to research in topics: Surge arrester & Harmonic. The author has an hindex of 7, co-authored 16 publications receiving 134 citations.

Monitoring and identification of metal–oxide surge arrester conditions using multi-layer support vector machine

Abstract: Metal–oxide surge arresters (MOSAs) are essential equipments for power system protection and devices from lightning and switching transient overvoltages. Therefore, their operating condition and diagnosis are very important. In this study, a multi-layer support vector machine (SVM) classifier has been used for MOSA conditions monitoring based on experimental tests. Three features are extracted based on the test results for determining surge arresters operating conditions including clean virgin, ultraviolet (UV) aged clean surface, surface contaminations after and before UV housing ageing, and degraded varistors along active column. Then, the multi-layer SVM classifier is trained with the training samples, which are extracted by the above data processing. Finally, the five fault types of surge arresters are identified by this classifier. The test results show that the classifier has an excellent performance on training speed and reliability which confirm the high applicability of introduced features for correct diagnostic of surge arresters conditions.

Evaluation of ultraviolet ageing, pollution and varistor degradation effects on harmonic contents of surge arrester leakage current

Abstract: Metal-oxide surge arresters are one of the most important equipments for power system protection against switching and lightning over-voltages. Surge arresters leakage currents increase by the operating time. In this study, the important factors on leakage current variations such as surface contamination, ultraviolet ageing and varistor degradation have been studied. To accomplish this purpose, experimental tests have been performed on various polymer housed surge arresters. Fast Fourier transform analysis has been performed on measured leakage currents. Results show that ultraviolet radiation and varistor degradation affect resistive harmonic components, especially the third and fifth harmonics. Moreover, it is observed that surface contamination has influence on fundamental harmonic variation more than ultraviolet ageing. In addition, combination of ultraviolet radiation and pollution had more effect on leakage currents. The investigation and the discussion of the results can be used to easily analyse arresters condition leading to effective schedule maintenance.

Optimal design of corona ring on HV composite insulator using PSO approach with dynamic population size

Abstract: This paper deals with the use of corona ring at the HV end fitting for improving the electric field and potential distributions and then for minimizing the corona discharges on 230 kV AC transmission line composite insulator. A single corona ring is installed at the energized end side. Three-dimensional finite element method (FEM) software is employed to compute the electric field. As the performance of high voltage insulator strings closely depends on designs and locations of corona ring, the effects of the corona ring radius, the ring tube radius and the ring vertical position are examined. The minimization of the electric field necessitates the optimization of corona ring. For this purpose, new nonlinear mathematical objective function linking the electric field strength to the corona ring structure parameters is established. The optimization problem is achieved by minimizing the objective function using a modified particles swarm optimization (PSO) algorithm with a dynamic population size. The algorithm adjusts the size of population for each iteration. Based on the average value and the best solution of the objective function, we propose a new mathematical model to update the population size. This algorithm enables the population size reduction leading to computing time decrease. According to the results, FEM-PSO hybridization technique could be very helpful in optimization of corona ring design.

A novel differential particle swarm optimization for parameter selection of support vector machines for monitoring metal-oxide surge arrester conditions

Abstract: Since metal-oxide surge arresters are the important over-voltage protection equipments used in power systems, their operating conditions must be monitored on a timely basis to give an alarm as soon as possible in order to increase the reliability of a power system. The paper proposes a novel differential particle swarm optimization-based (DPSO-based) support vector machine (SVM) classifier for the purpose of monitoring the surge arrester conditions. A DPSO-based technique is investigated to give better results, which optimizes the parameters of SVM classifiers. Three features are extracted as input vectors for evaluating five arrester conditions, including normal (N), pre-fault (A), tracking (T), abnormal (U) and degradation (D). Meanwhile, a comparative study of fault diagnosis is carried out by using a DPSO-based ANN classifier. The results obtained using the proposed method are compared to those obtained using genetic algorithm (GA) and particle swarm optimization (PSO). The experiments using an actual dataset will expectably show the superiority of the proposed approach in improving the performance of the classifiers.

Online condition monitoring of surge arresters based on third-harmonic analysis of leakage current

Abstract: Surge arresters play an important role in maintaining the reliability and protection of the high voltage power systems. Various diagnostic online monitoring methods of surge arrester are based on the decomposition of the measured leakage current into their capacitive and resistive components. The arrester's resistive component of the leakage current, in particular, the third harmonic component, is known as to be directly related to the degree of degradation of the ZnO blocks. This paper proposes an online monitoring technique for the surge arrester under dry and pollution conditions when measuring either the total leakage current or both the internal and external leakage currents of three different types of surge arresters. This technique based on feature extraction of the magnitudes, frequencies, and phase angles of all frequency components of the real measured current signals using Prony analysis-Hilbert transform approach and MATLAB program. The observed results illustrate the viability of this technique for online monitoring of surge arrester. Furthermore, the effect of voltage harmonics on the measured leakage currents is investigated through surge arrester model simulation performed with PSCAD program.

Measurement and Modelling of Leakage Current Behaviour in ZnO Surge Arresters under Various Applied Voltage Amplitudes and Pollution Conditions

Abstract: In zinc oxide (ZnO) surge arresters, leakage current usually flows across the arrester under normal operating condition. Leakage current is one of the factors which contribute towards degradation of surge arresters and therefore, it is very important to monitor the condition of surge arrester. In this work, the behaviour of leakage current in a ZnO surge arrester during normal operation, under different voltage amplitudes, wetness and pollution conditions was analysed. An 11 kV surge arrester model in three-dimensional space was subjected to finite element analysis (FEA) to determine the leakage current under different conditions. The results from the FEA model were compared with the measurement results to validate the model that has been developed. From comparison between the measurement and simulation results, physical parameters of a surge arrester that influence the leakage current under different conditions of the surge arrester were identified from the model. Through this work, a better understanding of leakage current behaviour can be attained, which may help in condition monitoring analysis on surge arrester in electrical utilities.