Detection and Location of High Impedance Faults in Multiconductor Overhead Distribution Lines Using Power Line Communication Devices

Milioudis, A. N.; Ανδρέου, Γεώργιος; Labridis, Dimitris P.

doi:10.1109/tsg.2014.2365855

Cited by 135 publications

(60 citation statements)

References 42 publications

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“…The duration of 14 cycles are applied. It shows that the proposed HS-TBFO technique is comparatively lower than the state-of-the-art works though the maximum fault detection time is at C = 14, comparatively, optimization of fault detection time achieves at C = 8, with 11.88% and 20.60% improvement compared to DL-HIF [1] and PAD-IR [2].…”

Section: Fault Identified 100mentioning

confidence: 92%

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An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Gopila¹,

Gnanambal²

2016

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Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequencybased Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.

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Section: Fault Identified 100mentioning

confidence: 92%

“…Detection and Location of High Impedance Faults (DL-HIF) [1] was performed using power line communication devices resulted in the ability to effectively identify and detect HIFs. The DL-HIF method applied on multiconductor overhead distribution networks to detect the fault rate in an efficient manner.…”

Section: Introductionmentioning

confidence: 99%

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Gopila¹,

Gnanambal²

2016

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“…In addition, even if a main distribution line fault occurs and is identified, the localization of the exact fault position can significantly facilitate the maintenance personnel. Various efforts concerning the main distribution line fault localization have already been presented in [31], [39]- [42]. In this paper, the identification of a main distribution line fault is secured via the study of the behavior of reflection coefficients that come from the hybrid method.…”

Section: Faults and Instabilities In Ov MV Bpl Topologiesmentioning

confidence: 99%

“…The OV MV MTL configuration, which is examined in this paper, is presented in Fig. 1(a) [16], [18], [20], [28]- [30] while the analysis concerning the impact of imperfect ground on broadband signal propagation and transmission via OV MV MTL configurations are analyzed in [6], [7], [16], [18], [20], [31]- [33].…”

Section: Introductionmentioning

confidence: 99%

Main Line Fault Localization Methodology in Smart Grid – Part 1: Extended TM2 Method for the Overhead Medium-Voltage Broadband over Power Lines Networks Case

Lazaropoulos

2017

Tr Ren Energy

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These three papers cover the overall methodology for the identification and localization of faults that occur in main transmission and distribution lines when broadband over power lines (BPL) networks are deployed across the transmission and distribution power grids, respectively. In fact, this fault case is the only one that cannot be handled by the combined operation of Topology Identification Methodology (TIM) and Instability Identification Methodology (FIIM). After the phase of identification of main distribution line faults, which is presented in this paper, the main line fault localization methodology (MLFLM) is applied in order to localize the faults in overhead medium-voltage BPL (OV MV BPL) networks. The main contribution of this paper, which is focused on the identification of the main distribution line faults, is the presentation of TM2 method extension through the adoption of coupling reflection coefficients. Extended TM2 method is analyzed in order to identify a main distribution line fault regardless of its nature (i.e., short-or open-circuit termination). The behavior of the extended TM2 method is assessed in terms of the main line fault nature and, then, its results are compared against the respective ones during the normal operation, which are given by the original TM2 method, when different main distribution line fault scenarios occur. Extended TM2 method acts as the introductory phase (fault identification) of MLFLM.

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“…However, recent studies outline that today's OV HV BPL networks are not providing sufficient bandwidth for supporting the latest ultra-broadband applications (such as high-definition television, voice over internet protocol, and console gaming) or some state-of-art sophisticated smart grid applications (such as energy trade, electric vehicle power management, etc) in a reliable way [7], [9], [11], [14], [15]. Towards that direction, the integration of today's OV HV BPL networks -i.e., single-input single-output (SISO) ones-with multiple-input multiple-output (MIMO) technology features, which are firstly presented in [12], [13], [16]- [21], is decisive for the further deployment of OV HV BPL networks.…”

Section: Introductionmentioning

confidence: 99%

Capacity Performance of Overhead Transmission Multiple-Input Multiple-Output Broadband over Power Lines Networks: The Insidious Effect of Noise and the Role of Noise Models

Lazaropoulos

2016

Tr Ren Energy

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Extending the analysis already presented in [1], this paper considers broadband potential of overhead (OV) transmission multiple-input multiple-output (MIMO) broadband over power lines (BPL) networks when different noise conditions occur and different well-proven noise models are adopted. The contribution of this paper is two-fold. First, the broadband potential of a great number of indicative OV high-voltage (HV) BPL topologies and of MIMO transmission schemes is studied in terms of appropriate capacity metrics. The relevant numerical results reveal the significant dependence of ΜΙΜΟ capacity metrics on noise conditions. Second, various well-known BPL noise models from the literature are compared on the basis of their achieved OV HV MIMO BPL capacity. Through the careful study of the capacity results of noise models, it is demonstrated that spectrally flat additive white Gaussian noise (AWGN) may be comfortably assumed as an efficient noise model in transmission MIMO BPL networks. Also in MIMO BPL networks, the comparative capacity analysis of noise models shows small differences among them in the 3-88MHz frequency range.

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Detection and Location of High Impedance Faults in Multiconductor Overhead Distribution Lines Using Power Line Communication Devices

Cited by 135 publications

References 42 publications

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Main Line Fault Localization Methodology in Smart Grid – Part 1: Extended TM2 Method for the Overhead Medium-Voltage Broadband over Power Lines Networks Case

Capacity Performance of Overhead Transmission Multiple-Input Multiple-Output Broadband over Power Lines Networks: The Insidious Effect of Noise and the Role of Noise Models

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