Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Using Neuro Fuzzy Modeling

Al‐Badi, Abdullah; Ghania, Samy M.; El-Saadany, Ehab F.

doi:10.1109/tpwrd.2008.2002657

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…The level of the induced pipelines voltage, depends on diverse factors, such as the level of OHL voltage, parallelism length, the separation distance, resistivity of the ground, the load current magnitude, OHL configuration, and the material of pipeline coating and the mitigation system. The level of fault current, the separation distance, mitigation system, and the resistivity of the ground have the higher impacts on the induced pipeline voltage, as reported in [26]. Generally, the single phase to ground fault condition is considered to evaluate the induced voltage on neighboring pipelines as it is the most frequent fault in the distribution and transmission power system and it produces very high level of induced voltage on the metallic pipelines.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…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. 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.…”

Section: Literate Reviewmentioning

confidence: 99%

“…Still, the FLSs are based on predetermined "if/then" rules that disrupt the capability of fuzzy systems to adapt and learn from different conditions. To overcome this shortcoming and offer a universal estimator approach with a high capability for the computation of induced voltages, hybridization of ANN with FLS as a neuro-fuzzy system has been proposed by the authors in [26]. The adaptive neuro-fuzzy inference system (ANFIS) has a higher degree of generalization and consistency.…”

Section: Literate Reviewmentioning

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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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Literate Reviewmentioning

confidence: 99%

Section: Literate Reviewmentioning

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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“…[22] The obtained optimal NN configuration is a feed-forward structure with 30 neurons on the first hidden layer and 25 neurons on the second hidden layer, using the tansig transfer function on the hidden layers and the purelin transfer function on the output layer. Fig.…”

Section: Obtained Operating Mode Predictionsmentioning

confidence: 99%

Operating mode prediction of a microwave heating system using artificial intelligence techniques

Czumbil

Micu

Hathazi

2013

2013 8th International Symposium on Advanced Topics in Electrical Engineering (Atee)

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For drying and decontaminating agricultural seeds a laboratory stand using microwave heating technology has been developed. In order to optimize the heating process the current paper presents an artificial intelligence based system, applied to predict the operating mode of the developed stand for different wave guide and loading bed configurations. Obtained results are used to identify the optimal microwave system configuration in case of corn seeds treatment and for future development of an industrial scale microwave treatment apparatus.

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“…Magnetic fields induce currents in the body, which are greatest near the body's periphery and smallest at its center. There are several factors that affect the level of magnetic field produced by overhead transmission lines: the arrangement of transmission lines, the amount of current flow though the lines, the height of conductors from the ground, the balance between phases, and the presence of earth conductor [14][15][16][17][18][19]. It should be noted that higher voltage lines usually carry higher currents and have larger spacing between conductors.…”

Section: Magnetic Fieldsmentioning

confidence: 99%

Measurement and Analysis of Extremely Low Frequency Electromagnetic Field Exposure in Oman

Al‐Badi¹

2012

JEMAA

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Concern about the possible health effects from exposure to extremely low frequency (ELF) electromagnetic fields is increasing worldwide. This paper presents analyses of measurements of low frequency magnetic and electric fields caused by conductors carrying current in the Oman electric power system. Measurements were taken during the summer period at around 3 PM to capture peak loads. Field measurements were from several high voltage environments which including different types of electricity infrastructure including overhead lines, underground cables and substations. The measured values were compared to the International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards for both general public and occupational exposure. All the Oman values of electric and magnetic fields are lower than the recommended values provided by ICNIRP

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Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Using Neuro Fuzzy Modeling

Cited by 14 publications

References 18 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

Operating mode prediction of a microwave heating system using artificial intelligence techniques

Measurement and Analysis of Extremely Low Frequency Electromagnetic Field Exposure in Oman

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