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

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.

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

“…ANFIS is a hybrid system that uses ANN and fuzzy inference. It can handle complex data [37]. Time series data from the 25 kV class insulator is filtered using wavelet packet transform, which is used as input to the model for time series forecasting.…”

Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

“…Additionally, the outcome was enhanced ANN-based optimization algorithms, namely the artificial neural network-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) [24]. ANN-based optimization algorithms are applied to develop the scheduling controller used on the VPP system for many reasons, such as because of powerful optimization techniques, good search exploration process, fast convergence for solution compared with other conventional optimization techniques, and resistance to trapping in local minima [26][27][28][29]. All those previous algorithms have only focused on training and testing ANNs on similar loading conditions, as well as on the feasibility of implementing these optimization techniques [30].…”

Comparison of Screening Configurations for the Mitigation of Voltages and Currents Induced on Pipelines by HVAC Power Lines

“…Existing numerical methodologies for the analysis of such interference cases include analytical methodologies based on transmission-line theory and the computation of the mutual impedance between earth-return conductors [15,16]. Finite element analysis (FEA) and combinations between FEA and circuital methods and neural networks [17] are employed as well.…”