A Novel Algorithm for Fault Type Fast Diagnosis in Overhead Transmission Lines Using Hidden Markov Models

Jannati, Mohsen; Jazebi, Saeed; Vahidi, B.; Hosseinian, Seyed Hossein

doi:10.5370/jeet.2011.6.6.742

Cited by 10 publications

(7 citation statements)

References 29 publications

(31 reference statements)

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“…Hence, auto‐closure with fixed time setting was not required for transmission line. Jannati et al proposed a powerful pattern recognizer termed as hidden Markov model for classification of the faults based on waveform and characteristics in probabilistic manner . The probabilistic measurement provided an extension of power system in to a new era called HVDC transmission system to improve the reliability of the system.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the fault diagnosis evolved in research works to ensure the reliability of the system. 3 Based on the study of fault waveform and associated characteristics, the probabilistic classifier or powerful pattern recognizer such as hidden Markov model 4 introduced in the fault analysis. Hidden Markov model has the ability to solve the nonlinear high dimensional problems and classify the disturbances in power system.…”

Section: Introductionmentioning

confidence: 99%

“…The recovery of positive voltage sequence and the reduction of negative voltage sequence is a difficult process in presence of fault in grid. Fault detection algorithms focus the 2 parameters such as short time interval analysis and transient signal levels. The use of wavelet transform techniques aims to analyze the faults in short time intervals.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices

Vijayakumar¹,

Veeraraghavan

2017

Int. Trans. Electr. Energ. Syst.

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Summary Major attributes of an electrical power network are the service continuity and high reliability. The occurrence of fault in the transmission line causes the disruption of attributes. If any fault occurs in the transmission line, the reactive power is generated to compensate the failure. The reactive power compensation is based on the utilization of flexible AC transmission system devices like static synchronous compensator (STATCOM) for power regulation. Many research works investigated the use of flexible AC transmission system in power systems for effective fault identification, classification, and localization. In traditional methods, the influence of fault on network parameters such as consumer gateway communication overhead, memory usage, consumer computational overhead, and critical time was more. This paper introduces a novel framework to identify, classify, and rectify the faults in the power transmission line and reduces the influence level of network parameters. Initially, the proposed algorithm detects the fault and ant colony optimization (ACO) and particle swarm optimization (PSO) are used to optimize the time and rectify the fault by the reactive power injection from static synchronous compensator. Finally, the identified faults are classified using relevance vector machine. The best fitness value by ACO‐PSO assures an effective reactive power generation and fault rectification. The proposed method outperforms the existing models regarding the detection, rectification of fault, and critical time required. The comparative analysis of proposed (ACO‐PSO) method with the existing methods confirms the effectiveness in assurance of high reliability and service continuity.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices

Vijayakumar¹,

Veeraraghavan

2017

Int. Trans. Electr. Energ. Syst.

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“…The faults followed by the arc phenomena are permanent faults making significant changes in the TL characteristics. The faults which appear as a consequence of the arc and are cleared by temporary removing the faulted phase are transient faults . According to the experience, 70% to 90% of faults in the TLs are SLG faults, with a very high percentage being transient in nature .…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of a real‐time adaptive single‐phase auto‐reclosure for power transmission lines

Jannati

Moshtagh

2020

Int Trans Electr Energ Syst

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Summary In order to distinguish permanent single line‐to‐ground faults from transient faults and prevent reclosing in the case of a permanent fault occurrence, an adaptive algorithm is proposed. The proposed algorithm prevents unsuccessful reclosing if the event is transient by identifying the extinguishing time of the secondary arc, thus preventing long interruptions in the electricity production. Furthermore, an adaptive auto‐reclosure is constructed based on the proposed algorithm. Extensive simulations in the EMTP environment and performing tests by the constructed reclosure and a test relay device (Vebko) using practical data verify the speed and accuracy of the proposed algorithm.

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“…Yusuff et al [11] proposed a transmission line fault location scheme, combining wavelet packet decomposition and support vector regression. Jannati et al [12] presented the application of a hidden Markov model (HMM) to classify faults in overhead power transmission lines. Kang and Liao [13] proposed a method with an optimal estimator to locate short-circuit faults on a double-circuit transmission line.…”

Section: Introductionmentioning

confidence: 99%

Icing load accretion prognosis for power transmission line with modified hidden semi‐Markov model

Rui

2014

IET Generation, Transmission & Distribution

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Ice accretion on power transmission lines is one of the major causes for cable failure in Zhaotong area, Yunnan Province, South China. This study proposes a method to predict the remaining-dangerous time (RDT) of the icing load accretion on an interval of the power transmission lines with modified hidden semi-Markov model (HSMM). Based on the predicted RDT of the cables during ice accretion, the appropriate preventative measures can be scheduled in advance by electric power companies. The estimation model with the learning algorithm of support vector machine for icing load accretion is built through historical icing load accretion data and meteorological conditions first. Then, the estimated icing load accretion sequence can be obtained through the estimation model by forecasting the meteorological conditions. The modified HSMM method can eliminate the possible underflow issue during computation, and be used to build the RDT prognosis model. With the estimated icing load accretion sequence and RDT prognosis model, the authors can predict RDT of the icing load accretion on an interval of the power transmission lines. The developed prognosis algorithm is verified through collected meteorological conditions and icing load accretion data on the Dazheng 73# power transmission line in Zhaotong area, Yunnan Province, South China.

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A Novel Algorithm for Fault Type Fast Diagnosis in Overhead Transmission Lines Using Hidden Markov Models

Cited by 10 publications

References 29 publications

An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices

An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices

Hardware implementation of a real‐time adaptive single‐phase auto‐reclosure for power transmission lines

Icing load accretion prognosis for power transmission line with modified hidden semi‐Markov model

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