Arrester is used to protect high voltage equipment or electric power lines from permanent or temporary overvoltage. It is imperative to perform a frequent monitoring on the condition of the arrester as this device will prevent damage to the power system. When there is an AC operating voltage applied across the arrester body, there is a small leakage current flowing to the ground terminal of the arrester. Currently, the third harmonic component of the leakage current has been used to identify the condition of the arrester whether it is still safe to be used. However, measurements of the leakage current and its harmonic components pose some difficulties. Moreover, the usage of a new technique based on thermal condition in monitoring the performance of arrester has been studied widely. The thermal condition of an arrester can be used to support the efficiency of the monitoring process. This research proposes to investigate the correlation between two variables, namely the third harmonic leakage current, and the arrester housing surface temperature (representing the thermal condition of the arrester) using a Radial Basis Function (RBF) Neural Network analysis. In addition, this research also studies the effect of ambient temperature on the correlation between the two variables. The leakage current values were measured using a current shunt and a digital storage oscilloscope, and then analyzed using Fast Fourier Transform to obtain its harmonic component. The surface thermal profile of the arrester body was captured using a thermal camera and then further analyzed to obtain several key representative parameters including the maximum, minimum, average, and standard deviation temperatures. These temperature parameters, together with the ambient temperature, were used as input variables while the third harmonic leakage current magnitude as a target to the proposed radial basis function neural network. The ambient temperature was then omitted in a repeated computation. From the radial basis function analyses, the two mentioned variables are positively correlated. Also, the ambient temperature has an effect on this correlation, whereby it is advisable also include the ambient temperature in the ANN computation to minimize the error. The results from all experimental data (500 training, 61 testing) show that a 97% accuracy in categorizing the arrester condition (either good or bad) is successfully achieved. Thus, it can be concluded that there is a good correlation between the third harmonic leakage current and the thermal image of an arrester which means the thermal image can be used as an alternative technique for zinc oxide surge arrester monitoring without the need to measure the leakage current.
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