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The paper presents an on-line sequential extreme learning machine (OS-ELM) based fast and accurate adaptive distance relaying scheme (ADRS) for transmission line protection. The proposed method develops an adaptive relay characteristics suitable to the changes in the physical conditions of the power systems. This can efficiently update the trained model on-line by partial training on the new data to reduce the model updating time whenever a new special case occurs. The effectiveness of the proposed method is validated on simulation platform for test system with two terminal parallel transmission lines with complex mutual coupling. The test results, considering wide variations in operating conditions of the faulted power network, indicate that the proposed adaptive relay setting provides significant improvement in the relay performance. [ABSTRACT FROM AUTHOR]

This paper proposes a synchrophasors-assisted intelligent relaying scheme for transmission lines compensated by Unified Power Flow Controller (UPFC). The algorithm uses a new relaying signal termed as imaginary part of integrated impedance (IPII). The synchronized phasor measurements at both ends of the transmission line are used to extract voltage and current phasors from instantaneous voltage and current signals. The voltage and current phasors are utilized to derive IPII of each phase. Further, IPII of each phase is used as input to a data-mining model termed as decision tree (DT) which provides the final relaying decision. The proposed algorithm is validated on real-time digital simulator (RTDS) platform, and the results obtained indicate that the proposed scheme is both dependable and secure in protecting transmission system compensated by UPFC. [ABSTRACT FROM AUTHOR]

This paper proposes a new cross-differential protection scheme for a parallel transmission system in the presence of a unified power-flow controller (UPFC) in one line. A cumulative summation technique using three-phase current signals at the sending end is used for fault detection. The faulty phases are then identified by computing the spectral energies of the phase currents. The spectral energy of the phase currents is computed by applying a computationally fast efficient powerful tool known as first Discrete S-Transform (FDST), suitable for analyzing power system signals. The proposed scheme has been extensively tested on different fault conditions with a wide variation in operating parameters. The test results indicate that it can accurately detect and classify the faults with clear discrimination between internal and external faults on a transmission line with a response time of a maximum one cycle from fault inception. [ABSTRACT FROM AUTHOR]

This paper presents a data-mining model for fault-zone identification of a flexible ac transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides effective decision on fault-zone identification. Half-cycle postfault current and voltage samples from the fault inception are used as an input vector against target output “1” for the fault after TCSC/UPFC and “-1” for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate reliable identification of the fault zone in FACTS-based transmission lines. [ABSTRACT FROM PUBLISHER]

Transmission line distance relaying for flexible AC transmission lines (FACTS) including thyristor controlled series compensator (TCSC), STATCOM, SVC and unified power flow controller (UPFC) has been a very challenging task. A new approach for fault zone identification and fault classification for TCSC and UPFC line using decision tree (DT) is presented. One cycle post fault current and voltage samples from the fault inception are used as input vectors against target output ‘1’ for fault after TCSC/UPFC and ‘0’ for fault before TCSC/UPFC for fault zone identification. Similarly, the DT-based classification algorithm takes one cycle data from fault inception of three phase currents along with zero-sequence current and voltage, and constructs the optimal DT for classifying all ten types of shunt faults in the transmission line fault process. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network including noisy environment. The results indicate that the proposed method can reliably identify the fault zone and classify faults in the FACTs-based transmission line in large power network. [ABSTRACT FROM AUTHOR]

The fault location algorithm based on a differential equation-based approach for a transmission line employing a unified power flow controller (UPFC) using synchronised phasor measurements is presented. First, a detailed model of the UPFC and its control is proposed and then, it is integrated into the transmission system for accurately simulating fault transients. The method includes the identification of fault section for a transmission line with a UPFC using a wavelet-fuzzy discriminator. Features are extracted using a wavelet transform and the normalised features are fed to the fuzzy logic systems for the identification of fault section. After the identification of the fault section, the control shifts to the differential equation-based fault locator that estimates the fault location in terms of the line inductance up to the fault point from the relaying end. Shunt faults are simulated with wide variations in operating conditions and a pre-fault parameter setting. The instantaneous fault current and voltage samples at the sending and receiving ends are fed to the designed algorithm sample by sample, which results in the fault location in terms of the line inductance. The proposed method is tested for different fault situations with wide variations in operating conditions in the presence of a UPFC. [ABSTRACT FROM AUTHOR]

A new approach for distance relaying of transmission line using machine intelligence technique such as support vector machine (SVM) is presented. The proposed SVM technique is used for faulty phase selection and ground detection in different fault situations that occur on large power transmission line. Post-fault current and voltage samples for one-fourth cycle (five samples) are used as inputs to SVM 1, which provide output for faulty phase selection. SVM 2 is trained and tested with zero-sequence components of fundamental, third and fifth harmonic components of the post-fault current signal to provides the involvement of ground in the fault process. The polynomial and Gaussian kernel SVMs are designed to provide the most optimised boundary for classification. The total time taken for faulty phase selection and ground detection is 10 ms (half cycle) from the inception of fault. Also the proposed technique is tested on experimental set-up with different fault situations. The test results are compared with those of the radial basis function neural network and were found to be superior with respect to efficiency and speed. The classification test results from SVMs are accurate for simulation model and experimental set-up, and thus provide fast and robust protection scheme for distance relaying in transmission line. [ABSTRACT FROM AUTHOR]

A new approach for the protection of thyristor-controlled series-compensated (TCSC) line using wavelet packets transform (WPT) is presented. The proposed method uses one cycle post-fault-current samples just after fault inception, which is processed through WPT and decomposed into various decomposition levels. The decomposed components are grouped together to provide different frequency sub-bands. Then the phase selection signal (PSS) and section identification signal (SIS) are computed to identify the faulty phase and faulty section, respectively, involved in the fault process in transmission line including TCSC. A threshold value (THD) is selected for PSS, and PSS above THD describes the faulty phase involved, otherwise not. Similarly, another THD is selected for SIS, and SIS below THD describes fault that includes TCSC, otherwise fault that does not include TCSC. As PSS takes half cycle after fault inception to identify the faulty phase and then triggers SIS, the faulty phases and faulty sections are identified within one cycle of fault inception. The proposed WPT algorithm is also tested on physical transmission line model with TCSC, under wide variations in operating conditions and provides accurate results. Thus, the proposed method provides accurate and fast protection measures for TCSC-based line. [ABSTRACT FROM AUTHOR]

A new approach for protection of parallel transmission lines is presented using a time–frequency transform known as the S-transform that generates the S-matrix during fault conditions. The S-transform is an extension of the wavelet transform and provides excellent time localisation of voltage and current signals during fault conditions. The change in energy is calculated from the S-matrix of the current signal using signal samples for a period of one cycle. The change in energy in any of the phases of the two lines can be used to identify the faulty phase based on some threshold value. Once the faulty phase is identified the differences in magnitude and phase are utilised to identify the faulty line. For similar types of simultaneous faults on both the lines and external faults beyond the protected zone, where phasor comparison does not work, the impedance to the fault point is calculated from the estimated phasors. The computed phasors are then used to trip the circuit breakers in both lines. The proposed method for transmission-line protection includes all 11 types of shunt faults on one line and also simultaneous faults on both lines. The robustness of the proposed algorithm is tested by adding significant noise to the simulated voltage and current waveforms of a parallel transmission line. A laboratory power network simulator is used for testing the efficacy of the algorithm in a more realistic manner. [ABSTRACT FROM AUTHOR]

Distance protection of flexible ac transmission lines, including the thyristor-controlled series compensator (TCSC), static synchronous compensator, and static var compensator has been a very challenging task. This paper presents a new approach for the protection of TCSC line using a support vector machine (SVM). The proposed method uses postfault current samples for half cycle (ten samples) from the inception of the fault and firing angle as inputs to the SVM. Three SVMs are trained to provide fault classification, ground detection, and section identification, respectively, for the line using TCSC. The SVMs are trained with polynomial kernel and Gaussian kernel with different parameter values to get the most optimized classifier. The proposed method converges very fast with fewer numbers of training samples compared to neural-network and neuro-fuzzy systems which indicates fastness and accuracy of the proposed method for protection of the transmission line with TCSC. [ABSTRACT FROM AUTHOR]