Numerical interpolation methods applied in electromagnetic interference problems

Micu, Dan D.; Ceclan, Andrei; Lıngvay, Iosif; Simion, Emil

doi:10.24084/repqj06.283

Cited by 3 publications

(2 citation statements)

References 7 publications

(8 reference statements)

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“…Among different AI-based techniques artificial neural networks (ANNs) are the most broadly used approaches for modeling the induced voltages [22]. ANNs are of interest for predicting the induced voltages on metallic pipelines placed in high voltage OHLs RoW due to their capabilities such as precise pattern learning, a memory to recall information from past experience, determine relations between dependent (output) and independent (input) variables and discover diverse discriminators in the complex system [1,20,[23][24][25].…”

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

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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.

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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

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“…Layers which contain the neurons interposed between the global inputs of the neural network and the inputs of the neurons from the output layer are called hidden layers. Usually, there are used feed-forward NN which contains a hidden layer and an output layer [20].…”

Section: Design Of Ann Torque Ripple Optimizationmentioning

confidence: 99%