Artificial Intelligence Techniques Applied to Electromagnetic Interference Problems

Czumbil, Levente; Micu, Dan D.; Ceclan, Andrei

doi:10.1007/978-3-642-04292-8_75

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…Initially, feed-forward neural networks with one output layer and one hidden layer were tested (figure 6). Some of the obtained results were already presented in (Micu et al 2009) and (Czumbil et al 2009). In the following a more detailed study is presented.…”

Section: Results Obtained With Feed-forward Neural Networkmentioning

confidence: 68%

Recurrent Neural Networks and Soft Computing

ElHefnawi¹,

Mysara²

2012

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ITI) bioinformatics track. Many publications on topics including rational drug design, docking, and optimizing gene therapy for Influenza, Hepatitis C Virus (HCV), and different cancers, structural bioinformatics and motif predictions applications in virology, and applications of phylogenetic analysis. These were in reputed journals like Bioinformatics (Oxford University Press), PLOS1 (Public library of science), journal of molecular recognition, European journal of medicinal chemistry, and Virology Journal.Mr Mohamed Mysara, MSc graduate from the University of Nottingham, Dec/2010, acquiring the ITI prize for the best dissertation. Mr Mysara is working in the X Contents

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Section: Results Obtained With Feed-forward Neural Networkmentioning

confidence: 68%

Recurrent Neural Networks and Soft Computing

ElHefnawi¹,

Mysara²

2012

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“…Generally, the generated coupling mechanism and electromagnetic fields are modeled through the differential equations. The dedicated assessment approaches, such as finite difference method (FDM) [1] or the finite element method (FEM) [16] are commonly used to solve the differential equations. The EMI problems are transferred to a pure numerical simulation by FEM [17].…”

Section: Literate Reviewmentioning

confidence: 99%

“…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]. The ANN can be applied for predicting induced voltage on metallic pipelines on account of its ability for handling noisy information, having memory and parallel calculation architecture.…”

Section: Literate Reviewmentioning

confidence: 99%

“…Rule 2: If x is A2 and y is B2 then z is f2(x, y; p2, q2, r2) = x p2+y q2 +r2 (1) where fi (x, y; pi, qi, ri) is a polynomial function providing the output of fist order Sugeno FIS. In this function, the two inputs of the ANFIS model are represented by x and y, and the output of the ANFIS model is indicated by z.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…Thus, to effectively supply such continuously increasing demands, it is essential to expand the existing high voltage overhead lines (OHLs), water, gas, and oil supply pipelines or construct new ones. To reduce both construction costs and environmental damage, a set of government regulations has been generated that limit the access to new distribution and transmission corridors [1][2][3]. Therefore, high voltage OHLs are constructed with the shared transmission and distribution corridors for water, gas, and oil pipelines.…”

Section: Introductionmentioning

confidence: 99%

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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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