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Article Abstract

This paper presents a Fuzzy Inference System (FIS)-based method to detect a fault, identify the faulty section, and recognize the faulty pole in the Voltage Source Converter (VSC)-based Multi-Terminal High Voltage Direct Current (MT-HVDC) system. The method uses only rectifier end measurements of voltage and current signals. To achieve complete protection of the MT-HVDC system, three separate frameworks of FIS modules have been developed. The FIS-1 detects the presence of fault in either AC or DC segments. The FIS-2 identifies the fault section and eventually, the FIS-3 recognizes the faulty pole. The proposed method provides a rapid fault detection and it does not require any communication media as it is based on the rectifier-end measurements only. The efficacy of the implemented FIS-based method is evaluated in an MT-HVDC system simulated in MATLAB environment. Important highlights of this method are:-•FIS-based method is deployed using simple if-then rules, therefore very easy to implement.•Effortless means to exploit one end measurements of MTDC system in an environment accustomed to power engineer.•Proposed method provides a rapid fault detection and it does not require any communication link.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871324PMC
http://dx.doi.org/10.1016/j.mex.2023.102018DOI Listing

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This paper presents a Fuzzy Inference System (FIS)-based method to detect a fault, identify the faulty section, and recognize the faulty pole in the Voltage Source Converter (VSC)-based Multi-Terminal High Voltage Direct Current (MT-HVDC) system. The method uses only rectifier end measurements of voltage and current signals. To achieve complete protection of the MT-HVDC system, three separate frameworks of FIS modules have been developed.

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We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg-Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands.

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