Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm

S. Hr. Aghay Kaboli, Amer Al Hinai, A.H. Al-Badi, Yassine Charabi and Abdulrahim Al Saifi
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S. Hr. Aghay Kaboli: Sustainable Energy Research Center, Sultan Qaboos University, Muscat 123, Oman
Amer Al Hinai: Sustainable Energy Research Center, Sultan Qaboos University, Muscat 123, Oman
A.H. Al-Badi: Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Yassine Charabi: Center for Environmental Studies and Research, Sultan Qaboos University, Muscat 123, Oman
Abdulrahim Al Saifi: Petroleum Development Oman (PDO), Muscat 100, Oman

Energies, 2019, vol. 12, issue 19, 1-18

Abstract: The electromagnetic interference (EMI) generated by high voltage power systems can cause a serious problem for nearby electrically conductive structures, such as railroads, communication lines, or pipelines, that would place a system’s integrity and the operational safety of the structure at high level of risk. According to the IEEE standard-80, by implementing a well-designed mitigation system, the induced voltage on neighboring electrically conductive structure can reach a harmless level. The mitigation system can enhance the overall integrity of pipelines and provide higher operation safety for personal during working on the exposed parts of metallic pipelines or conductive appurtenances. An accurate prediction about the level of induced voltage is absolutely necessary to design a suitable mitigation system for metallic pipelines. Thus, in this work a hybrid prediction methodology composed of an adaptive neuro-fuzzy inference system (ANFIS) and a backtracking search algorithm (BSA) is developed to accurately predict the electromagnetic inference’s effects on metallic pipelines with shared right-of-way (RoW) and high voltage overhead lines (OHLs). Through the combination of BSA as a robust and efficient optimization algorithm in the learning process of an ANFIS approach, a hybrid data mining algorithm has been developed to predict the induced voltage on mitigated and unmitigated pipelines more accurately and reliably. The simulation results are validated by data sets observed from the Current Distribution, Electromagnetic Interference, Grounding and Soil Structure Analysis (CDEGS) software. From the simulation results it was confirmed that the proposed hybrid method is effective in accurately predicting the induced voltage on pipelines with changing system parameters. Furthermore, to evaluate the precision and applicability of the developed approach in this paper, its estimates are compared with the results obtained from an artificial neural network (ANN), a support vector regression (SVR) and an ANFIS optimized by other well-known optimization algorithms. The obtained results indicate higher accuracy of the developed hybrid method over other artificial intelligence based approaches.

Keywords: adaptive neuro-fuzzy inference system; backtracking search algorithm; electromagnetic interference; overhead line; pipeline (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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