Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network

Zhang, Tong; Sun, Limin; Liu, Jianchang; Huang, Yu; Zhou, Xiaoming; Gao, Lin; Zhang, Yingwei

doi:10.1080/15325008.2018.1460884

Cited by 22 publications

(3 citation statements)

References 37 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 clearly shows that the proposed method decreases 1.779% location error comparing with the location method in [32]. Furthermore, the model-based method has advantage of the operating speed and reduces respond time 63.583 s comparing with the iterative algorithm and the neural network with training process [32][33][34]. The main reason is that in the location stage, the Fibonacci search algorithm optimizes the iteration and reduces uncertainty range by x m and x m ′.…”

Section: Comparative Assessment For Location Accuracy and Respond Timementioning

confidence: 93%

See 1 more Smart Citation

Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

Zhang

Huang

Zeng

et al. 2020

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Section: Comparative Assessment For Location Accuracy and Respond Timementioning

confidence: 93%

“…Jiang [32] RBF [33] WNN [34] Location error (%) R f = 0.01 Ω 0.023 Table 3 Fault location result of different PMU installation scheme. phasors and synchronized current phasors at each DG.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

Zhang

Huang

Zeng

et al. 2020

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…THD comparative analysis of inrush current for all techniques are shown in Figure 12-Figure 15 for frequency of 30Hz & 80Hz. The comparative THD analysis of inrush current for all three techniques and existing methods (Tong et al, 2018;Wang et al, 2019;Zhang et al, 2013) are shown in Table 6, while graphical THD comparison for the three proposed methods is shown in Figure 16. From Table 6, it is observed that THD is found to be less with the proposed three methods in comparison with existing methods.…”

Section: Fault Diagnostic Under Flcmentioning

confidence: 99%

Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller

Singh

Saxena

Roy

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, performance analysis of power system network is carried out by injecting the inter-turn fault at the power transformer. The injection of inter-turn fault generates the inrush current in the network. The power system network consists of transformer, current transformer, potential transformer, circuit breaker, isolator, resistance, inductance, loads, and generating source. The fault detection and termination related to inrush current has some drawbacks and limitations such as slow convergence rate, less stability and more distortion with the existing methods. These drawbacks motivate the researchers to overcome the drawbacks with new proposed methods using wavelet transformation with sample data control and fuzzy logic controller. The wavelet transformation is used to diagnose the fault type but contribute lesser for fault termination; due to that, sample data of different signals are collected at different frequencies. Further, the analysis of collected sample data is assessed by using Z-transformation and fuzzy logic controller for fault termination. The stability, total harmonic distortion and convergence rate of collected sample data among all three methods (wavelet transformation, Z-transformation and fuzzy logic controller) are compared for fault termination by using linear regression analysis. The complete performance of fault diagnosis along with fault termination has been analyzed on Simulink. It is observed that after fault injection at power transformer, fault recovers faster under fuzzy logic controller in comparison with Z-transformation followed by wavelet transformation due to higher stability, less total harmonic distortion and faster convergence.

show abstract

Automatic Generation of Distribution Network Operation Data Report and Application of Data Mining

Song¹,

Gu²,

Lu³

et al. 2021

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

View full text Add to dashboard Cite

Fault Diagnosis and Location Method for Active Distribution Network Based on Artificial Neural Network

Cited by 22 publications

References 37 publications

Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller

Automatic Generation of Distribution Network Operation Data Report and Application of Data Mining

Contact Info

Product

Resources

About