Monitoring Of Surge Arresters Research Articles

New criteria for metal oxide surge arrester condition monitoring based on leakage current analysis: Considering non‐uniform pollution effect

AbstractThe role of surge arresters is vital in power systems' protection against transient overvoltages, which can arise due to occurrences like lightning strikes or switching operations. Based on the performance of the surge arrester, assessing its condition is an essential requirement for early detection of any damage or deterioration. This article proposes innovative criteria based on the surge arresters’ leakage current analysis for the classification of uniform and non‐uniform pollution levels. The leakage current signal of clean, uniform, and non‐uniform polluted surge arresters has been examined under different contamination levels, humidity, and non‐uniformity degrees. Based on the experimental results analysis, a new criterion has been proposed for assessing the surge arrester condition, which has resulted from the fifth and first‐order harmonics ratio of resistive leakage current. Analysis of the proposed criterion demonstrates that this index has a significant correlation to the pollution level. The proposed criterion ability has been established based on random forest, support vector machine, decision tree algorithm, and multi‐layer perceptron in Python language. The obtained criterion based on experimental data in different conditions has been employed for classifier training, which can be applied in the power network for better condition monitoring of surge arresters ensuring fast and accurate outcomes.

Machine Learning MOSA Monitoring System

This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

Development and Experimental Evaluation of Leakage Current Measurement Sensor for Electrical Power Apparatus

Abstract Leakage current measurement plays an important role in determining the healthiness of high voltage insulation of a power apparatus. High voltage (HV) insulation degrades due to electrical, thermal and mechanical ageing and their cumulative effects, and thus the leakage current of the insulation increase over a period of time. The accurate and real time measurement of leakage current enables the utilities to on-line access the healthiness of the insulation. This paper describes about the design, development, calibration and testing of a novel non-contact type passive leakage current sensor which can be used for monitoring of various electrical devices based on the analysis of the leakage current captured by the sensor. The developed sensor has been successfully used for measurement of leakage current in oil-filled HV bushings as well as in gas insulated surge arresters. The sensitivity of the developed sensor as per its design can be in the order of few milli amperes in case of bushing and in the order few micro amperes for surge arrester monitoring. The paper also reported a novel design of bushing adapter-sensor assembly for on-line measuring the leakage current in HV bushings without using the conventional capacitive or resistive tapping based measurements. Further, results covering experimental leakage current measurement on a 420 kV bushing using the developed adapter sensor assembly have also been presented. To establish capabilities of the developed sensor with optimized design parameters, laboratory measurements of leakage current on 420 kV and 145 kV GIS surge arresters have also been carried out and reported in the paper. Third harmonic content of surge arrester leakage current has also been estimated in-line with IEC requirement to assess the healthiness of the surge arrester. Finally, suitability of the developed sensor for leakage current measurement of above power apparatus equipment has also been analyzed and discussed.

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

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.

Leakage current measurement and health monitoring of surge arresters

Leakage current measurement and health monitoring of surge arresters

A Modified Time-Delay Addition Method to Extract Resistive Leakage Current of MOSA

Metal oxide surge arresters are one of the most important equipment for power system protection against switching and lightning over-voltages. High-energy stresses and environmental features are the main factors which degrade surge arresters. In order to verify surge arresters good condition, their monitoring is necessary. The majority of surge arrester monitoring techniques is based on total leakage current decomposition of their capacitive and resistive components. This paper introduces a new approach based on time-delay addition method to extract the resistive current from the total leakage current without measuring voltage signal. Surge arrester model for calculating leakage current has been performed in ATP–EMTP. In addition, the signal processing has been done using MATLAB software. To show the accuracy of the proposed method, experimental tests have been performed to extract resistive leakage current by the proposed method.

Harmonics ratios of resistive leakage current as metal oxide surge arresters diagnostic tools

Metal oxide surge arresters are important equipments that apply to power system for protection against switching and lightning over-voltages. Surge arrester monitoring technique based on leakage current analysis is a conventional monitoring method. Lack of proper features and thresholds is most important limitation of this method. In this paper, new monitoring indexes for surge arrester diagnostic are proposed to determine surge arrester condition under different situations including clean condition, surface contamination and ultraviolet aging. The experimental tests have been performed on polymer surge arresters to evaluate the ability of the proposed indexes for distinguishing the operating condition of surge arresters. Results have been shown the viability of the new indexes on surge arrester monitoring procedures. The investigation and the discussion of the produced results can give good ideas the electric engineers in order to easily analyze arresters’ condition leading to effective schedule maintenance.

Metal-oxide surge arrester monitoring and diagnosis by self-organizing maps

The main metal-oxide surge arrester are usually monitored by measuring the total leakage current and decomposing it into a capacitive component and a resistive component. However, these techniques present some limitations which make their implementation in the field quite difficult. This paper describes a monitoring technique based on classifying the harmonic characteristics of the leakage current. Self-organizing maps have been used in the classification process. Several station classes arresters were tested in the laboratory and their leakage current signals were recorded. Six different types of artificial imperfections were created in the arresters in order to assess the technical capability to discriminate arresters operating under different field conditions. The self-organizing maps are able to identify defective arresters with certainty of almost 98%. The results show that the technique is feasible for monitoring the condition of metal-oxide surge arresters.

Study on a New Method of Metal Oxide Surge Arrester Monitoring

Metal oxide surge arresters (MOA) is a power system overvoltage protection device, and the online monitoring of the MOA is an important means to ensure the safe operation of the power system. This paper presents a new MOA insulation defects monitoring method. This method is based on the relative change in capacitance to MOA insulation defects as characteristic parameter, the aging classified as a kind of local defects, the system disturbance contains higher harmonic of transient voltage and current as the signal source, dynamically generated MOA partial insulation discriminant function of the defects in the diagnosis. The maximum value of the discriminant function corresponds to the local insulation defects. The proposed method contributes to the MOA monitoring methods and the development of technology.

Artificial Neural Networks Used for ZnO Arresters Diagnosis

Lightning arresters provide protection for transmission lines and power equipments. During the occurrence of lightning or switching surges, a voltage level above the equipment insulation level can be reached. An arrester, properly working, is able to limit this voltage level avoiding damages to the protected equipment. A defective arrester may not be able to provide the proper protection level in substations, exposing the equipment and personnel to damage. Monitoring of surge arresters is usually conducted by means of current measurement or thermal images acquisition. But, among power companies, there is no model procedure for the monitoring conduction and analysis of the obtained results. Besides that, when some abnormality is detected, the arrester is replaced by a new one and no further study is conducted to evaluate what kind of problem happened to it. This work proposes a method for analysis of ZnO arresters by the study of failures and usage of artificial neural networks-ANN. The ANN is able to analyze the thermal profile, detect and classify patterns that could be undetected by a visual analysis.

Device for diagnostic monitoring of surge arresters

Basic reasons for the failure of nonlinear surge arresters are considered; most widely spread methods and devices for diagnostic monitoring of nonlinear surge arresters in the course of their operation are analyzed, and a new monitoring device registering discharge current pulses caused by lightning and switching voltage surges is proposed.

00/01921 Feasibility of fuel cell using dimethyl ether fuel

Metal oxide surge arresters are important equipments that apply to power system for protection against switching and lightning over-voltages. Surge arrester monitoring technique based on leakage current analysis is a conventional monitoring method. Lack of proper features and thresholds is most important limitation of this method. In this paper, new monitoring indexes for surge arrester diagnostic are proposed to determine surge arrester condition under different situations including clean condition, surface contamination and ultraviolet aging. The experimental tests have been performed on polymer surge arresters to evaluate the ability of the proposed indexes for distinguishing the operating condition of surge arresters. Results have been shown the viability of the new indexes on surge arrester monitoring procedures. The investigation and the discussion of the produced results can give good ideas the electric engineers in order to easily analyze arresters’ condition leading to effective schedule maintenance.