“…To do this, a double-layer artificial neural network with 20 neurons in the first hidden layer and 4 neurons in the second hidden layer is adopted. Finally, according to Equation (10), the output of ANN will determine the exact location of the faulty point with an average percentage error of about 0.0045%, which is a distance of 22.5 meter from defective spot. The flowchart of the main steps of the evaluated fault location method is shown in Figure 8.…”
Section: The Proposed Methodsmentioning
confidence: 99%
“…Wang and Hou [9] presented a TW fault location strategy for hybrid LCC-MMC-HVDC transmission systems based on the Hilbert-Huang transform. Wang et al [10] suggested a TW fault location method for LCC-MMC-HVDC hybrid DC transmission lines established on a capacitance-divided electronic voltage transformer (C-EVT), in which by analyzing the TW transmission characteristic of C-EVT, an algorithm is proposed based on the secondary differential voltage traveling wave (D-VTW). Wang et al [11] presented a fault localization approach suited for hybrid HVDC systems in which the fault location is obtained based on the functional association between the fault distance and the inverter side DC bus equivalent characteristic impedance.…”
In this article, a fault location technique based on artificial neural networks (ANN) for Terminal-Hybrid LCC-VSC-HVDC has been assessed and scrutinized. As is known, in conventional HVDC systems (LCC-based and VSC-based HVDCs), the same type of filter is used on both sides due to the use of similar converters in both sender and receiver terminals. In this article, it is concluded that due to the use of two different types of converters at the both ends of the utilized Terminal-hybrid LCC-VSC-HVDC system, and the use of different DC filters on both sides, fault location using positive and negative pole currents of the rectifier side has much better results than the rest of input signals. Therefore, it will be finalized that by increasing and designing suitable DC filters on the transmission line of HVDC systems, fault localization matter will be remarkably and surprisingly facilitated. Nowadays, the fault location of HVDC transmission lines with a value of more than 1% is generally discussed in most articles. In this research, the fault location with a value of 0.0045%, i.e., a distance of 22.5 meters from the fault point in the most satisfactory case is obtained, which shows the absolute feasibility of the ANN along with the wavelet transform. To validate the proposed method, a ±100 KV, Terminal-hybrid LCC-VSC-HVDC system is simulated via MATLAB. The outcomes verify that the proposed technique works perfectly under various fault locations, resistances, and fault types.
“…To do this, a double-layer artificial neural network with 20 neurons in the first hidden layer and 4 neurons in the second hidden layer is adopted. Finally, according to Equation (10), the output of ANN will determine the exact location of the faulty point with an average percentage error of about 0.0045%, which is a distance of 22.5 meter from defective spot. The flowchart of the main steps of the evaluated fault location method is shown in Figure 8.…”
Section: The Proposed Methodsmentioning
confidence: 99%
“…Wang and Hou [9] presented a TW fault location strategy for hybrid LCC-MMC-HVDC transmission systems based on the Hilbert-Huang transform. Wang et al [10] suggested a TW fault location method for LCC-MMC-HVDC hybrid DC transmission lines established on a capacitance-divided electronic voltage transformer (C-EVT), in which by analyzing the TW transmission characteristic of C-EVT, an algorithm is proposed based on the secondary differential voltage traveling wave (D-VTW). Wang et al [11] presented a fault localization approach suited for hybrid HVDC systems in which the fault location is obtained based on the functional association between the fault distance and the inverter side DC bus equivalent characteristic impedance.…”
In this article, a fault location technique based on artificial neural networks (ANN) for Terminal-Hybrid LCC-VSC-HVDC has been assessed and scrutinized. As is known, in conventional HVDC systems (LCC-based and VSC-based HVDCs), the same type of filter is used on both sides due to the use of similar converters in both sender and receiver terminals. In this article, it is concluded that due to the use of two different types of converters at the both ends of the utilized Terminal-hybrid LCC-VSC-HVDC system, and the use of different DC filters on both sides, fault location using positive and negative pole currents of the rectifier side has much better results than the rest of input signals. Therefore, it will be finalized that by increasing and designing suitable DC filters on the transmission line of HVDC systems, fault localization matter will be remarkably and surprisingly facilitated. Nowadays, the fault location of HVDC transmission lines with a value of more than 1% is generally discussed in most articles. In this research, the fault location with a value of 0.0045%, i.e., a distance of 22.5 meters from the fault point in the most satisfactory case is obtained, which shows the absolute feasibility of the ANN along with the wavelet transform. To validate the proposed method, a ±100 KV, Terminal-hybrid LCC-VSC-HVDC system is simulated via MATLAB. The outcomes verify that the proposed technique works perfectly under various fault locations, resistances, and fault types.
“…VMD is a frequency domain based adaptive completely non recursive signal processing method [6] . It directly specifies the number of decompositions and adaptively matches them based on the center frequency and bandwidth in the solution.…”
In this paper, a new Cable fault location scheme is proposed for the existing flexible DC Cable fault location methods, in which the fault signal is mixed with noise and the traveling wave speed is affected by line parameters. This scheme is based on the propagation characteristics of fault line mode voltage traveling waves in flexible DC transmission. Firstly, the fault voltage is effectively decomposed using Variational Mode Decomposition (VMD). Then, the decomposed intrinsic mode functions (IMF) are reconstructed to obtain the high-frequency components of the recorded fault information. Finally, the traveling wave head is calibrated using the Teager Energy Operator (TEO). A dual terminal ranging algorithm that is not affected by wave velocity has been proposed, which can effectively avoid the impact of fault traveling wave velocity uncertainty on the ranging results. A dual ended flexible DC power grid model was built in PSCAD/EMTDC, and a large number of simulation results show that the algorithm proposed in this paper can accurately calibrate the fault traveling wave head, has little interference from transition resistance, strong noise resistance, and can obtain high-precision fault location results in different fault locations.
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