A non-unit protection scheme for double circuit series capacitor compensated transmission lines

Swetapadma, Aleena; Mishra, Praveen Kumar; Yadav, Anamika; Abdelaziz, Almoataz Y.

doi:10.1016/j.epsr.2017.04.002

Cited by 42 publications

(24 citation statements)

References 27 publications

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“…Different ANN architectures for fault location estimation have been designed according to different fault type, ie, total 4 fault location modules for LG, LL, LLG, and LLLG types of faults. When the fault occurs, first, the fault is detected and classified by using any 1 method given in Moravej et al 18 or Swetapadma et al 19 Once the fault type is identified by using the methods given in Moravej et al 18 or Swetapadma et al, 19 then based on the fault type occurred, the particular type of fault location module is selected, which estimates the location of fault phase wise. It is worth mentioning here that in the proposed scheme, the 4 types of fault locators are trained with both common single-location faults and multilocation faults data set.…”

Section: Proposed Artificial Neural Network-based Fault Location Scmentioning

confidence: 99%

“…18 Recently, k-nearest neighbor-based fault detection, classification, and location estimation in SCCTL has been reported by authors. 19 Furthermore, dynamic state estimation-based fault location estimation has been proposed in Liu et al 20 Nonetheless, all the aforementioned fault location algorithms are relevant to normal shunt faults occurring at single location and are not applicable for multilocation faults. Multilocation faults may occur in different locations in different phases at same or different time during thunder storms.…”

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confidence: 99%

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An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

Swetapadma

Yadav

2018

Int Trans Electr Energ Syst

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During thunder storms, multilocation faults may occur at different locations in different phases of the 3 phase transmission lines at same or different time. This paper proposes an artificial neural network-based solution to locate the multilocation faults in double-circuit series capacitor compensated transmission lines (SCCTLs), unlike previous works that only locate the fault at single location. Although various fault location schemes have been proposed for normal shunt faults occurring at 1 location in SCCTL, nevertheless, finding the locations of multilocation faults in double circuit SCCTL has not been addressed so far. The proposed artificial neural network-based method determines the location of multilocation faults by using current and voltage signals of 1 end of line only, thus avoiding the need of communication link. The signals are preprocessed by using discrete wavelet transform. A comparative study of various neural networks such as feed-forward back-propagation network with Levenberg-Marquardt algorithm, Elman recurrent neural network with gradient descent algorithm, radial basis function neural network has been carried out. The proposed method is not affected by variation in different parameters viz. fault type, fault location, fault inception angle, fault resistance, degree of series compensation, and location of series capacitor. The key advantage of the method is that it correctly estimates the locations of multilocation faults as well as single-location fault, thus making it more reliable and accurate as compared to conventional fault location schemes.

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Section: Proposed Artificial Neural Network-based Fault Location Scmentioning

confidence: 99%

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confidence: 99%

An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

Swetapadma

Yadav

2018

Int Trans Electr Energ Syst

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“…In [23], an intelligent relaying scheme based on decision tree (DT) has been validated in the real-time digital simulator (RTDS). Recently, discrete wavelet transform and k-nearest neighbour have been used for fault detection, classification, and location estimation in a double circuit series compensated line in [24]. e main challenges in fault classification in an extrahigh-voltage (EHV) series compensated transmission line are as follows:…”

Section: Introductionmentioning

confidence: 99%

Combined DFT and Fuzzy Based Faulty Phase Selection and Classification in a Series Compensated Transmission Line

Mishra

Yadav

2019

Modelling and Simulation in Engineering

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The conventional distance protection scheme malfunctions sometimes in case of a fixed series capacitor compensated transmission line due to the change in relaying impedance of the protected line during faulty conditions. In order to mitigate this problem, a combined discrete Fourier transform and fuzzy (CDFTF) based algorithm has been proposed in this paper. This method has been tested on a 400 km, 735 kV series compensated transmission line network and WSCC 3-machine 9-bus system for all fault types using MATLAB/Simulink and PSCAD platforms, respectively. A fixed series capacitor is located at the middle of the protected line. The fundamental components of phase currents, phase voltages, and zero-sequence current are fed as inputs to the proposed scheme. The fault detection, faulty phase selection, and fault classification are achieved within 1/2–1 cycle of power frequency. The proposed CDFTF-based scheme is less complex and is better than other data mining techniques which require huge training and testing time. Test results corroborate the proposed scheme reliability with wide variations in fault location, fault resistance, fault inception angle, evolving faults, compensation level, and heavy load interconnection. The results discussed in this work indicate that the proposed technique is resilient to wide variations in fault and system conditions.

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“…One approach proposes to apply machine learning and intelligent technique for compensated transmission line protection, such as artificial neural network [9,10], the support vector machines [11,12], ensemble decision tree [13,14], pattern recognition [15], echo state networks [16], k-nearest neighbour algorithm [17], and hybrid Wavelet-ANN [18] etc. Although the results presented in the aforementioned papers are encouraging, the performance of these methods depends on the experience gained from the training input-output examples, which needs a large number of training sets and training time.…”

Section: Introductionmentioning

confidence: 99%

Boundary protection for series‐compensated transmission lines

Qi¹,

Wang³

et al. 2019

IET Generation, Transmission & Distribution

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A non-unit protection scheme for double circuit series capacitor compensated transmission lines

Cited by 42 publications

References 27 publications

An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

An artificial neural network-based solution to locate the multilocation faults in double circuit series capacitor compensated transmission lines

Combined DFT and Fuzzy Based Faulty Phase Selection and Classification in a Series Compensated Transmission Line

Boundary protection for series‐compensated transmission lines

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