Statistics shows that transients produced by lightning or momentary links with external objects, have produced more than 80% of faults in overhead lines. Reclosing of circuit breaker (CB) after a pre-defined dead time...Statistics shows that transients produced by lightning or momentary links with external objects, have produced more than 80% of faults in overhead lines. Reclosing of circuit breaker (CB) after a pre-defined dead time is very common however reclosing onto permanent faults may damage the power system stability and aggravate severe damage to the system. Thus, adaptive single-phase auto-reclosing (ASPAR) based on investigating existing electrical signals has fascinated engineers and researchers. An ASPAR blocks CB reclosing onto permanent faults and allows reclosing permission once secondary arc is quenched. To address the subject, there have been many ASPARs techniques proposed based on the features trapped in a faulty phase. This paper presents a critical survey of adaptive auto-reclosing schemes that have hitherto been applied to EHV transmission lines.展开更多
Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and...Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.展开更多
文摘Statistics shows that transients produced by lightning or momentary links with external objects, have produced more than 80% of faults in overhead lines. Reclosing of circuit breaker (CB) after a pre-defined dead time is very common however reclosing onto permanent faults may damage the power system stability and aggravate severe damage to the system. Thus, adaptive single-phase auto-reclosing (ASPAR) based on investigating existing electrical signals has fascinated engineers and researchers. An ASPAR blocks CB reclosing onto permanent faults and allows reclosing permission once secondary arc is quenched. To address the subject, there have been many ASPARs techniques proposed based on the features trapped in a faulty phase. This paper presents a critical survey of adaptive auto-reclosing schemes that have hitherto been applied to EHV transmission lines.
文摘Smart grid is envisaged as a power grid that is extremely reliable and flexible.The electrical grid has wide-area measuring devices like Phasor measurement units(PMUs)deployed to provide real-time grid information and resolve issues effectively and speedily without compromising system availability.The development and application of machine learning approaches for power system protection and state estimation have been facilitated by the availability of measurement data.This research proposes a transmission line fault detection and classification(FD&C)system based on an auto-encoder neural network.A comparison between a Multi-Layer Extreme Learning Machine(ML-ELM)network model and a Stacked Auto-Encoder neural network(SAE)is made.Additionally,the performance of the models developed is compared to that of state-of-the-art classifier models employing feature datasets acquired by wavelet transform based feature extraction as well as other deep learning models.With substantially shorter testing time,the suggested auto-encoder models detect faults with 100% accuracy and classify faults with 99.92% and 99.79%accuracy.The computational efficiency of the ML-ELM model is demonstrated with high accuracy of classification with training time and testing time less than 50 ms.To emulate real system scenarios the models are developed with datasets with noise with signal-to-noise-ratio(SNR)ranging from 10 dB to 40 dB.The efficacy of the models is demonstrated with data from the IEEE 39 bus test system.
