An artificial intelligence system for a complex electromagnetic field problem. II. Method implementation and performance analysis

Satsios, K.J.; Labridis, Dimitris P.; Dokopoulos, P.S.

doi:10.1109/20.737476

Cited by 10 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy and effectiveness of ANNs approaches are strongly associated with convergence speed, neural network architecture, and the weight updating algorithm [26]. The multilayer perceptron (MLP) with the error back propagation training method as an effective class of feedforward ANN was implemented in [27], to predict the level of induced voltages on a buried metallic pipeline, located in the electromagnetic field caused by a high voltage OHL during single phase to ground fault conditions. The resistivity of the ground, the distance between the OHL and buried metallic pipeline, the magnitude of fault current, and the connection of the pipeline to the mitigation system are considered as input for ANN in this study.…”

Section: Literate Reviewmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Literate Reviewmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Fuzzy systems have been successfully applied in control systems [19,20], system identification [21], and power systems [22]. Fuzzy logic has also been applied in the determination of magnetic fields based on finite element results [23,24]. The purposes of this paper are to show the investigation results obtained with the application of fuzzy logic in finding a relaxation factor and to compare its performance with other approaches.…”

Section: Introductionmentioning

confidence: 95%

Convergence improvement in two-dimensional finite element nonlinear magnetic problems—a fuzzy logic approach

Arjona

Escarela‐Perez

Melgoza-Vázquez

et al. 2005

Finite Elements in Analysis and Design

View full text Add to dashboard Cite

Artificial Intelligence Techniques Applied to Electromagnetic Interference Problems

2009

View full text Add to dashboard Cite

An artificial intelligence system for a complex electromagnetic field problem. II. Method implementation and performance analysis

Cited by 10 publications

References 6 publications

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

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

Convergence improvement in two-dimensional finite element nonlinear magnetic problems—a fuzzy logic approach

Artificial Intelligence Techniques Applied to Electromagnetic Interference Problems

Contact Info

Product

Resources

About