A neural network approach for the inductive coupling between overhead power lines and nearby metallic pipelines

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

doi:10.1109/isfee.2016.7803231

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…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]. However, the computation burden will exponentially increase by expanding dimensions of simulation geometry and simulation complexity as a huge number of extended meshes and parameters have to be assessed [18].…”

Section: Literate Reviewmentioning

confidence: 99%

“…The artificial intelligence (AI) based techniques have been considered a superior solution for the computation of AC induced voltages [17,20]. AI based approaches often warranty a sufficient degree of prediction accuracy for complex system modeling [21].…”

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%

“…Step 5: Boundary control. At the end of step 4, if an individual in generated offspring (T) violates the boundary condition, the control mechanism developed in step 5 is updated according to Equation (17)…”

Section: Backtracking Search Algorithm (Bsa)mentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Literate Reviewmentioning

confidence: 99%

Section: Literate Reviewmentioning

confidence: 99%

Section: Backtracking Search Algorithm (Bsa)mentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

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“…As the rails and the earth are only insulated by ballast beds or insulating gaskets, the gradual deterioration of insulation performance and the running of high-power electric locomotives (i.e., EMUs) will lead to the increased leakage current of the rails. Therefore, as opposed to steady-state AC interference of power systems caused by inductive coupling [11], the interference of the electrified railway traction power supply system is the instantaneous AC interference under the interaction of inductive coupling and resistive coupling [12,13].…”

Section: Introductionmentioning

confidence: 99%

Effects and Characteristics of AC Interference on Parallel Underground Pipelines Caused by an AC Electrified Railway

et al. 2018

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An AC electrified railway can interfere significantly with parallel underground pipelines in a public transportation corridor. Such interference may threaten the long-term transportation and the operational safety of the pipelines. Therefore, this paper presents a mathematical model to illustrate the electromagnetic coupling between an AC electrified railway and nearby parallel underground pipelines. As the basis of the engineering calculation, an integration simulation model based on CDEGS software, which included an electrified railway and pipeline system, was built and its correctness was validated by comparing it with a mathematical model. Also, the effects of electric traction load parameters, pipeline parameters, and soil structure parameters were analyzed, the results of which demonstrate the distribution characteristics of AC interference on parallel underground pipelines. Using a real case in China as an example, protection measures were introduced to decrease coupling interference and their effectiveness was verified by simulation calculations and field measurements.

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Method for characterization of soil electrical resistivity based on Wenner measurements by means of Nelder–Mead algorithm and FEM calculations

Stanišić

Radaković

2023

Electr Eng

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A neural network approach for the inductive coupling between overhead power lines and nearby metallic pipelines

Cited by 8 publications

References 11 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

Effects and Characteristics of AC Interference on Parallel Underground Pipelines Caused by an AC Electrified Railway

Method for characterization of soil electrical resistivity based on Wenner measurements by means of Nelder–Mead algorithm and FEM calculations

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