An Analysis of Harmonic and Interharmonic Contribution of Electric Arc Furnace by Using Periodogram

Abstract

Fast and accurate detection of the harmonic and interharmonic contribution of electric arc furnace (EAF) is crucial in identifying and to mitigate the undesired effects to the system. In this paper, periodogram, a fast and accurate technique is introduced for the analysis of the contribution. Based on a rule-based classifier and the threshold settings that referred to the IEEE Standard 1159 2009, the analysis of the harmonic and interharmonic contribution of EAF are carried out successfully. Moreover, the impact of contribution is measured using total harmonic distortion (THD) and total non-harmonic distortion (TnHD). In addition, periodogram also gives 100 percent correct detection and able to analyze the contribution impact. It is proven that the proposed method is accurate, fast and cost efficient for analyzing the impact of harmonic and interharmonic of EAF.

Using the constant current ‒ constant voltage converters to effectively reduce voltage fluctuations in the power supply systems for electric arc furnaces

“…The spectrum of interharmonics for the furnace with a capacity of 100 tons is in the frequency range of 0.01-2.0 Hz; their energy is about 20 % of all mixed spectrum energy [5]. Study [6] showed the presence of a spectrum of interharmonics at frequencies above 200 Hz, as well as a constant component in the current of the arc whose value is correlated with the rated current of the furnace transformer magnetization. A wide range of higher harmonics was reported in [7]; the study results show the existence of individual harmonics of the furnace current with a value of 12 %, and a constant component of 3 %, of the rated current of the furnace.…”

Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

“…This paper proposes a high accuracy, fast estimation and costs efficient technique to identify and diagnose the type of multiple harmonic sources in the distribution system with single point measurement at the point of common coupling (PCC) by utilizing the machine learning techniques [28,29]. The machine learning such as support vector machine (SVM) and Naïve Bayes are used to classify and diagnose the location and type of harmonic sources.…”

Harmonic enhancement in microgrid with applications on sensitive loads

“…Harmonic distortion can decrease the accuracy of the electrical meter. Harmonic distortion may also cause excessive dissipation of electrical equipment so as to increase the cost of the bill [9], [10]. Furthermore, harmonic distortion causes electrical equipment not to work on the specified power quality standards causing a decrease in the lifetime of the electrical equipment [11], [12].…”