PMU based adaptive zone settings of distance relays for protection of multi-terminal transmission lines

Mallikarjuna, Balimidi; Shanmukesh, Pudi; Dwivedi, Anmol; Reddy, M. Jaya Bharata; Mohanta, Dusmanta Kumar

doi:10.1186/s41601-018-0087-z

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…[26] analyzes the time differences between the wave travelling from the connection point to the three terminals and the wave from the fault point to the three terminals. Based on this relationship, the fault branch is determined, and then the fault distance between the fault point and the three terminals is calculated [27]. The above methods are all based on the multiterminal data, which require high accuracy of communication, and GPS time sampling [28][29][30].…”

Section: Of 22mentioning

confidence: 99%

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Shu

Han

Huang³

et al. 2020

Energies

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Due to the complex structure of the T-connection transmission lines, it is extremely difficult to identify the reflected travelling wave from the fault point and that from the connection point by the measurement from only one terminal. According to the characteristics of the structure of the T-connection transmission line, the reflection of the travelling wave within the line after the failure of different sections in T-connection transmission line are analyzed. Based on the lattice diagram of the travelling wave, the sequence of travelling waves detected at the measuring terminal varies with the fault distance and the faulty section. Moreover, the sequence of travelling waves detected in one terminal is unique at each faulty section. This article calculates the arrival time of travelling waves of fault points at different locations in different sections to form the collection of the travelling wave arrival time sequence. Then the sequence of travelling waves of the new added fault waveforms is extracted to compare with the sequences in the collection for the faulty section identification and fault location. This proposed method can accurately locate the fault with different fault types, fault resistances and system impedances by only single-terminal fault data. Both Power Systems Computer Aided Design/ Electromagnetic Transients including DC (PSCAD/EMTDC) and actual measurement data are implemented to verify the effectiveness of this method.

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Section: Of 22mentioning

confidence: 99%

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Shu

Han

Huang³

et al. 2020

Energies

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“…The PMU usage increased in 2004 when the U.S.-Canada investigation report of the 2003 blackout was released. The blackout report recognized that many of North America's major blackouts have been caused by inadequate situational awareness for grid operators and recommended the use of synchrophasor technology to provide real-time wide-area grid visibility [15], [16]. It was demonstrated that new technology and investment was needed to provide adequate situational awareness and the ability to react to such a major disturbance.…”

Section: Synchrophasor Data Description a Related Work Of Synchmentioning

confidence: 99%

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Zhang

2020

IEEE Access

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Deep learning techniques have been widely used for power system operations as a very hot topic in recent years. This paper proposed a spatiotemporal deep learning-based low frequency oscillation mode estimation method using synchrophasor data. The proposed deep learning method consists of the graph convolutional network (GCN) and the long short-term memory (LSTM). A graph network is used to model the power network in which the edges represent the connection relationships between system buses. Specifically, the GCN method is used to capture the topological structure of the power networks to obtain the spatial dependence. The LSTM model is used to capture the dynamic change of electrical variables that can be monitored by PMU devices through synchrophasor data to obtain the temporal dependence. Eventually, the proposed GCN-LSTM model is used to capture the spatiotemporal patterns and features of the system-wide synchrophasor data. To validate the effectiveness of the proposed method, we evaluate it on two classic IEEE simulation platform, i.e., IEEE 39-bus system and IEEE 118-bus system, by comparing with the Nonlinear Autoregressive Neural Network with External Input (NARX), the Gated Recurrent Unit (GRU) network, and single LSTM methods. It is demonstrated that the proposed GCN-LSTM method can achieve the best estimation results for different simulation platforms.

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“…Considering the investment savings and technical benefits over double-ended systems, multi-terminal transmission systems such as interconnected systems and teed connection transmission lines are increasingly performing in high voltage (HV) and ultra-high voltage power networks [1,2]. However, the protection of such multi-terminal transmission systems is more complex compared to double-ended systems [3]. The problem is that many subsystems and fault locations are relatively dispersed.…”

Section: Introductionmentioning

confidence: 99%

Identification of broken conductor faults in interconnected transmission systems based on discrete wavelet transform

Rashad,

Ibrahim,

Gilany

et al. 2024

PLoS ONE

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Interconnected transmission systems are increasingly spreading out in HV networks to enhance system efficiency, decrease reserve capacity, and improve service reliability. However, the protection of multi-terminal lines against Broken Conductor Fault (BCF) imposes significant difficulties in such networks as the conventional distance relays cannot detect BCF, as the BCF is not associated with a significant increase in current or reduction in voltage Traditionally, the earth fault relays in transmission lines may detect such fault; Nonetheless, it suffers from a long delay time. Moreover, many of the nearby earth fault relays detect the BCF causing unnecessary trips and badly affecting the system stability. In this article, a novel single-end scheme based on extracting transient features from current signals by discrete wavelet transform (DWT) is proposed for detecting BCFs in interconnected HV transmission systems. The suggested scheme unit (SSU) is capable of accurately detecting all types of BCFs and shunt high impedance faults (SHIFs). It also adaptively calculates the applied threshold values. The accurate selectivity in multi-terminal lines is achieved based on a fault directional element by analyzing transient power polarity. The SSU discriminates between internal/external faults effectively utilizing the time difference observed between the first spikes of aerial and ground modes in the current signals. Different fault scenarios have been simulated on the IEEE 9-Bus, 230 kV interconnected system. The achieved results confirm the effectiveness, robustness, and reliability of SSU in detecting correctly BCFs as well as the SHIFs within only 24.5 ms. The SSU has confirmed its capability to be implemented in interconnected systems without any requirement for communication or synchronization between the SSU installed in multi-terminal lines.

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PMU based adaptive zone settings of distance relays for protection of multi-terminal transmission lines

Cited by 11 publications

References 16 publications

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Low Frequency Oscillation Mode Estimation Using Synchrophasor Data

Identification of broken conductor faults in interconnected transmission systems based on discrete wavelet transform

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