In this work finite state automata or finite state machine based directional protection scheme is... more In this work finite state automata or finite state machine based directional protection scheme is proposed for transmission lines. Phase angle of positive sequence current is used as input to finite state automata based fault direction estimation module. Finite state automata is used as fault pattern recognizer which estimates the direction of fault. The output of the proposed FSM based scheme will be 'À1' for reverse section faults and '1' for fault in primary section faults. The performance of the proposed technique is evaluated using data simulated for variation of fault type, fault inception angle, fault location, power flow angle, reverse power flow and fault resistance. Accuracy of the method is found to be 100% from all 11,500 fault cases. Proposed technique does not use voltage unlike conventional directional relaying schemes due to which there is no issue regarding close-in fault detection. The reach setting of the proposed method is up to 99.9% of line length which has advantage over conventional relaying schemes which have reach up to 80-85% of line. Although proposed method is a pattern recognition based technique, it does require an extra training module unlike artificial neural network to estimate the direction correctly. The proposed technique is effective because it do not require any training and the computation complexity is very less as compared to training based algorithms. Proposed method is also tested in an existing power system network of India, which shows accurate result in estimating fault direction.
Abstract Most of the high impedance faults (HIF) remain un-detected by the conventional relays du... more Abstract Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions.
This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all ... more This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all types of fault in parallel lines using one-terminal measurement. Discrete Fourier Transform (DFT) is used for pre-processing the signals and then the standard deviation of one cycle of pre-fault and one cycle of post-fault samples are used as inputs to k-NN algorithm. The results obtained under various fault conditions demonstrate the high accuracy of the proposed scheme to estimate the fault location. The accuracy of the k-NN-based fault location scheme is not affected by alteration in fault type including inter-circuit faults, fault location, fault inception angle, fault resistance, and pre-fault load angle.
This paper proposes a fault distance estimation scheme for fixed series capacitor compensated par... more This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately.
In this paper, a novel two-terminal fault location approach utilizing traveling-waves for series ... more In this paper, a novel two-terminal fault location approach utilizing traveling-waves for series compensated transmission line connected to wind farms is presented. The approach is based on accurate estimation of arrival time of waves (ATWs) at both ends of the line. The first step in estimation of ATWs is the application of estimation of signal parameters via rotational invariant techniques (ESPRIT) on three-phase instantaneous voltage signals measured at both ends of the line after inception of fault in order to obtain reconstructed signals. The next step of the approach involves calculation of errors between aerial-modes of original and reconstructed signals (obtained via ESPRIT). Finally, the peak of squared-errors are utilized to determine ATWs. The simulation is carried out in PSACAD/EMTDC environment on IEEE-14 bus test system with a line having 50 % series compensation connected to 300 MW wind farm. The efficacy of FLE approach is validated under different fault scenarios and found to be accurate regarding fault location estimation.
Protection and Control of Modern Power Systems, Nov 25, 2016
As it is crucial to protect the transmission line from inevitable faults consequences, intelligen... more As it is crucial to protect the transmission line from inevitable faults consequences, intelligent scheme must be employed for immediate fault detection and classification. The application of Artificial Neural Network (ANN) to detect the fault, identify it's section, and classify the fault on transmission lines with improved zone reach setting is presented in this article. The fundamental voltage and current magnitudes obtained through Discrete Fourier Transform (DFT) are specified as the inputs to the ANN. The relay is placed at section-2 which is the prime section to be protected. The ANN was trained and tested using diverse fault datasets; obtained from the simulation of different fault scenarios like different types of fault at varying fault inception angles, fault locations and fault resistances in a 400 kV, 216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB. The simulation outcomes illustrated that the entire shunt faults including forward and reverse fault, it's section and phase can be accurately identified within a half cycle time. The advantage of this scheme is to provide a major protection up to 99.5% of total line length using single end data and furthermore backup protection to the forward and reverse line sections. This routine protection system is properly discriminatory, rapid, robust, enormously reliable and incredibly responsive to isolate targeted fault.
In this paper, a decision tree regression (DTR) based fault distance estimation scheme for double... more In this paper, a decision tree regression (DTR) based fault distance estimation scheme for double circuit transmission lines is presented. Fault location is estimated using the information obtained from fault events data. The decision tree (DT) regression was chosen because it requires less training time, offers greater accuracy with large data set and robustness than all other techniques like artificial neural network, support vector machines, adaptive neuro-fuzzy inference system etc. Hitherto, DT has been used for fault detection/classification, but it has not been used for fault location. Three phase current and voltage signals measured at one end of the line are used as inputs to fault location network. The proposed method does not require communication link as it uses only one end measurements. Signals are processed with two signal processing techniques, discrete Fourier transforms (DFT) and discrete wavelet transform (DWT). A comparative study of both the techniques has been carried out to observe the effect of signal processing on the fault location estimation method. The proposed method is tested on three test systems: namely, the 2-bus, the WSCC-9-bus, and the IEEE 14-bus test systems. The test results confirms that the proposed DTR based algorithm is not affected by the variation in fault type, fault location, fault inception angle, fault resistance, pre-fault load angle, SCC, load variation and line parameters. The proposed scheme is relatively simple and easy in comparison with complex equation based fault location estimation methods.
Advances in Artificial Neural Systems, Dec 28, 2014
Contemporary power systems are associated with serious issues of faults on high voltage transmiss... more Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.
2020 First International Conference on Power, Control and Computing Technologies (ICPC2T)
In this study, the discrete Meyer wavelet transform (DMWT) is exploited for the recognition of fa... more In this study, the discrete Meyer wavelet transform (DMWT) is exploited for the recognition of faults and categorization of faulted phase in the wind park incorporated series compensated transmission line (WPISCTL). The DMWT is immensely inspected using the simulation model of the WPISCTL. The three-phase fault currents measured by the transducers linked at the generator-bus of the power system network are inserted to the proposed DMWT-based design for fault recognition and faulted phase categorization. The recognition of fault and categorization of faulted phase are studied with great appropriateness.
2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020
This paper proposes a hybrid relaying scheme for the recognition of cross-country faults (CCFs), ... more This paper proposes a hybrid relaying scheme for the recognition of cross-country faults (CCFs), inter-circuit faults (ICFs), evolving faults (EFs), and multi-location faults (MLFs) in a 400-kV dual-circuit TL (DCTL) coupled with wind turbo-generators (WTGs). The proposed hybrid scheme is designed using discrete Fourier transform (DFT) and discrete daubechies wavelet transform (DDWT). The proposed hybrid relaying scheme is used to recognize the fault, discriminate between the healthy and the faulty phases, and categorize the faulty phase type in a DCTL. The three-phase fault currents synchronized with respect to time and measured at Bus-M of the DCTL are used to evaluate the coefficients of DFT and thereafter the computed hidden fault current characteristics from the coefficients of DFT are given as input attributes to the DDWT algorithm for the relaying of DCTL. The proposed DFT-DDWT based hybrid scheme is widely tested using the MATLAB/Simulink model of a 200-km distributed parame...
This paper describes fault detection and classification scheme for protection of doubly feeded di... more This paper describes fault detection and classification scheme for protection of doubly feeded distribution line using fuzzy logic system with penetration of the wind farm driven by doubly fed induction generator. The simulation study of doubly feeded distribution line system consists of 120-kV, 60-Hz source and 9-MW wind farm connected to distribution line of 30 km length, which is modelled using pi block in Simulink toolbox of MATLAB 2013a. The proposed method has been exhaustively examined with large variety of fault situations with different fault parameters like all ten types of fault, fault inception angle and fault location. The proposed scheme also identifies the evolving fault and classifies the faulty phase(s) as well. Simulation study shows that the developed scheme works accurately for huge number of fault cases with half cycle detection time.
This paper presents a non-unit protection scheme for series capacitor compensated transmission li... more This paper presents a non-unit protection scheme for series capacitor compensated transmission lines (SCCTL) using discrete wavelet transform and k-nearest neighbor (k-NN) algorithm. All the protective relaying functions such as fault detection, fault classification, faulty phase identification and fault location estimation have been considered in this work. Such a comprehensive work providing all protective relaying functions for protection of double circuit SCCTL utilizing k-NN has not been reported so far. The signal processing and feature extraction are done using discrete wavelet transform due to its capability to differentiate between high and low frequency transient components. For fault detection and classification, only approximate wavelet coefficient of current signal up to level 1 has been used; while for k-NN location estimation, both voltage and current signals of the two circuits are decomposed up to level 3 have been used. Finally, the standard deviation of one cycle pre-fault and one cycle post-fault samples of the approximate wavelet coefficients are calculated to form the feature vector for the k-NN-based algorithm. The performance of the proposed technique is evaluated for large number of fault events with variation in fault type including inter-circuit faults, fault inception angle, fault location and fault resistance. The change in position of series capacitor and different degree of compensation has been discussed. The accuracy of the proposed k-NN-based fault detection and classification module is 100% for all the tested fault cases with a decision period of less than half cycle. The k-NN-based fault location scheme estimates the location of fault with ≤1% error for most of the tested fault cases, which is an exceptional attribute of the proposed scheme as compared with 10-15% error of conventional distance relaying scheme.
In this work finite state automata or finite state machine based directional protection scheme is... more In this work finite state automata or finite state machine based directional protection scheme is proposed for transmission lines. Phase angle of positive sequence current is used as input to finite state automata based fault direction estimation module. Finite state automata is used as fault pattern recognizer which estimates the direction of fault. The output of the proposed FSM based scheme will be 'À1' for reverse section faults and '1' for fault in primary section faults. The performance of the proposed technique is evaluated using data simulated for variation of fault type, fault inception angle, fault location, power flow angle, reverse power flow and fault resistance. Accuracy of the method is found to be 100% from all 11,500 fault cases. Proposed technique does not use voltage unlike conventional directional relaying schemes due to which there is no issue regarding close-in fault detection. The reach setting of the proposed method is up to 99.9% of line length which has advantage over conventional relaying schemes which have reach up to 80-85% of line. Although proposed method is a pattern recognition based technique, it does require an extra training module unlike artificial neural network to estimate the direction correctly. The proposed technique is effective because it do not require any training and the computation complexity is very less as compared to training based algorithms. Proposed method is also tested in an existing power system network of India, which shows accurate result in estimating fault direction.
Abstract Most of the high impedance faults (HIF) remain un-detected by the conventional relays du... more Abstract Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions.
This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all ... more This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all types of fault in parallel lines using one-terminal measurement. Discrete Fourier Transform (DFT) is used for pre-processing the signals and then the standard deviation of one cycle of pre-fault and one cycle of post-fault samples are used as inputs to k-NN algorithm. The results obtained under various fault conditions demonstrate the high accuracy of the proposed scheme to estimate the fault location. The accuracy of the k-NN-based fault location scheme is not affected by alteration in fault type including inter-circuit faults, fault location, fault inception angle, fault resistance, and pre-fault load angle.
This paper proposes a fault distance estimation scheme for fixed series capacitor compensated par... more This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately.
In this paper, a novel two-terminal fault location approach utilizing traveling-waves for series ... more In this paper, a novel two-terminal fault location approach utilizing traveling-waves for series compensated transmission line connected to wind farms is presented. The approach is based on accurate estimation of arrival time of waves (ATWs) at both ends of the line. The first step in estimation of ATWs is the application of estimation of signal parameters via rotational invariant techniques (ESPRIT) on three-phase instantaneous voltage signals measured at both ends of the line after inception of fault in order to obtain reconstructed signals. The next step of the approach involves calculation of errors between aerial-modes of original and reconstructed signals (obtained via ESPRIT). Finally, the peak of squared-errors are utilized to determine ATWs. The simulation is carried out in PSACAD/EMTDC environment on IEEE-14 bus test system with a line having 50 % series compensation connected to 300 MW wind farm. The efficacy of FLE approach is validated under different fault scenarios and found to be accurate regarding fault location estimation.
Protection and Control of Modern Power Systems, Nov 25, 2016
As it is crucial to protect the transmission line from inevitable faults consequences, intelligen... more As it is crucial to protect the transmission line from inevitable faults consequences, intelligent scheme must be employed for immediate fault detection and classification. The application of Artificial Neural Network (ANN) to detect the fault, identify it's section, and classify the fault on transmission lines with improved zone reach setting is presented in this article. The fundamental voltage and current magnitudes obtained through Discrete Fourier Transform (DFT) are specified as the inputs to the ANN. The relay is placed at section-2 which is the prime section to be protected. The ANN was trained and tested using diverse fault datasets; obtained from the simulation of different fault scenarios like different types of fault at varying fault inception angles, fault locations and fault resistances in a 400 kV, 216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB. The simulation outcomes illustrated that the entire shunt faults including forward and reverse fault, it's section and phase can be accurately identified within a half cycle time. The advantage of this scheme is to provide a major protection up to 99.5% of total line length using single end data and furthermore backup protection to the forward and reverse line sections. This routine protection system is properly discriminatory, rapid, robust, enormously reliable and incredibly responsive to isolate targeted fault.
In this paper, a decision tree regression (DTR) based fault distance estimation scheme for double... more In this paper, a decision tree regression (DTR) based fault distance estimation scheme for double circuit transmission lines is presented. Fault location is estimated using the information obtained from fault events data. The decision tree (DT) regression was chosen because it requires less training time, offers greater accuracy with large data set and robustness than all other techniques like artificial neural network, support vector machines, adaptive neuro-fuzzy inference system etc. Hitherto, DT has been used for fault detection/classification, but it has not been used for fault location. Three phase current and voltage signals measured at one end of the line are used as inputs to fault location network. The proposed method does not require communication link as it uses only one end measurements. Signals are processed with two signal processing techniques, discrete Fourier transforms (DFT) and discrete wavelet transform (DWT). A comparative study of both the techniques has been carried out to observe the effect of signal processing on the fault location estimation method. The proposed method is tested on three test systems: namely, the 2-bus, the WSCC-9-bus, and the IEEE 14-bus test systems. The test results confirms that the proposed DTR based algorithm is not affected by the variation in fault type, fault location, fault inception angle, fault resistance, pre-fault load angle, SCC, load variation and line parameters. The proposed scheme is relatively simple and easy in comparison with complex equation based fault location estimation methods.
Advances in Artificial Neural Systems, Dec 28, 2014
Contemporary power systems are associated with serious issues of faults on high voltage transmiss... more Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.
2020 First International Conference on Power, Control and Computing Technologies (ICPC2T)
In this study, the discrete Meyer wavelet transform (DMWT) is exploited for the recognition of fa... more In this study, the discrete Meyer wavelet transform (DMWT) is exploited for the recognition of faults and categorization of faulted phase in the wind park incorporated series compensated transmission line (WPISCTL). The DMWT is immensely inspected using the simulation model of the WPISCTL. The three-phase fault currents measured by the transducers linked at the generator-bus of the power system network are inserted to the proposed DMWT-based design for fault recognition and faulted phase categorization. The recognition of fault and categorization of faulted phase are studied with great appropriateness.
2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2020
This paper proposes a hybrid relaying scheme for the recognition of cross-country faults (CCFs), ... more This paper proposes a hybrid relaying scheme for the recognition of cross-country faults (CCFs), inter-circuit faults (ICFs), evolving faults (EFs), and multi-location faults (MLFs) in a 400-kV dual-circuit TL (DCTL) coupled with wind turbo-generators (WTGs). The proposed hybrid scheme is designed using discrete Fourier transform (DFT) and discrete daubechies wavelet transform (DDWT). The proposed hybrid relaying scheme is used to recognize the fault, discriminate between the healthy and the faulty phases, and categorize the faulty phase type in a DCTL. The three-phase fault currents synchronized with respect to time and measured at Bus-M of the DCTL are used to evaluate the coefficients of DFT and thereafter the computed hidden fault current characteristics from the coefficients of DFT are given as input attributes to the DDWT algorithm for the relaying of DCTL. The proposed DFT-DDWT based hybrid scheme is widely tested using the MATLAB/Simulink model of a 200-km distributed parame...
This paper describes fault detection and classification scheme for protection of doubly feeded di... more This paper describes fault detection and classification scheme for protection of doubly feeded distribution line using fuzzy logic system with penetration of the wind farm driven by doubly fed induction generator. The simulation study of doubly feeded distribution line system consists of 120-kV, 60-Hz source and 9-MW wind farm connected to distribution line of 30 km length, which is modelled using pi block in Simulink toolbox of MATLAB 2013a. The proposed method has been exhaustively examined with large variety of fault situations with different fault parameters like all ten types of fault, fault inception angle and fault location. The proposed scheme also identifies the evolving fault and classifies the faulty phase(s) as well. Simulation study shows that the developed scheme works accurately for huge number of fault cases with half cycle detection time.
This paper presents a non-unit protection scheme for series capacitor compensated transmission li... more This paper presents a non-unit protection scheme for series capacitor compensated transmission lines (SCCTL) using discrete wavelet transform and k-nearest neighbor (k-NN) algorithm. All the protective relaying functions such as fault detection, fault classification, faulty phase identification and fault location estimation have been considered in this work. Such a comprehensive work providing all protective relaying functions for protection of double circuit SCCTL utilizing k-NN has not been reported so far. The signal processing and feature extraction are done using discrete wavelet transform due to its capability to differentiate between high and low frequency transient components. For fault detection and classification, only approximate wavelet coefficient of current signal up to level 1 has been used; while for k-NN location estimation, both voltage and current signals of the two circuits are decomposed up to level 3 have been used. Finally, the standard deviation of one cycle pre-fault and one cycle post-fault samples of the approximate wavelet coefficients are calculated to form the feature vector for the k-NN-based algorithm. The performance of the proposed technique is evaluated for large number of fault events with variation in fault type including inter-circuit faults, fault inception angle, fault location and fault resistance. The change in position of series capacitor and different degree of compensation has been discussed. The accuracy of the proposed k-NN-based fault detection and classification module is 100% for all the tested fault cases with a decision period of less than half cycle. The k-NN-based fault location scheme estimates the location of fault with ≤1% error for most of the tested fault cases, which is an exceptional attribute of the proposed scheme as compared with 10-15% error of conventional distance relaying scheme.
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Papers by Anamika Yadav