Fault Diagnosis of Power System Considering Communication System Information Transmission Error

Zhang, Haibo; Ge, Dongyu; Su, Weizhi; Zhang, Li

doi:10.1109/pesgm.2018.8585790

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This puts forward higher requirements for fault diagnosis of power grid [1]. At present, there are many fault diagnosis technologies and methods, including Expert System (ES) [2], Bayesian Network (BN) [3], Fuzzy Theory (FT) [4], Artificial Neural Network (ANN) [5,6], Genetic Algorithms (GA) [7], Petri Net (PN) [8], Analytic Model [9]. These related research studies not only ensure the safety and stable operation of power grid but also provides the basis for further improving the power grid fault diagnosis technology.…”

Section: Introductionmentioning

confidence: 99%

Partition Fault Diagnosis of Power Grids Based on Improved PNN and GRA

Zhang

et al. 2020

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

With the increase of energy demand, the scale of power grid is expanding, and the difficulty of power grid fault diagnosis is increasing. Aiming at the problem of large power grid fault diagnosis, a method of partition fault diagnosis based on improved Probabilistic neural network (PNN) and gray relational analysis (GRA) integral is proposed. Firstly, the large power grid divided into small areas for fault diagnosis through power grid partition, which reduces the difficulty of fault diagnosis. Then the PNN diagnosis module is established by the PNN optimized by GA-CPSO for diagnosing the power grid fault. Finally, the faults in the overlapping area are reanalyzed by the GRA method, in order to realize the accurate fault diagnosis of the whole power grid. The feasibility and effectiveness of the method are analyzed by two cases. The diagnosis results show that the method can effectively identify the faults in the nonoverlapping area and the overlapping area, and has strong fault tolerance and high diagnosis accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Partition Fault Diagnosis of Power Grids Based on Improved PNN and GRA

Zhang

et al. 2020

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

Fault Diagnosis of Low-Noise Amplifier Circuit Based on Fusion Domain Adaptation Method

Zhang,

Du,

Zhou

et al. 2024

Actuators

View full text Add to dashboard Cite

The Low-Noise Amplifier (LNA) is a critical component of Radio Frequency (RF) receivers. Therefore, the accuracy of LNA fault diagnosis significantly impacts the overall performance of the entire RF receiver. Traditional LNA fault diagnosis is typically conducted under fixed conditions, but varying factors in practical applications often alter the circuit’s parameters and reduce diagnostic accuracy. To address the issue of decreased fault diagnosis accuracy under varying external or internal conditions, a fusion domain adaptation method based on Convolutional Neural Networks (CNNs), referred to as FDA, is proposed. Firstly, a domain-adaptive diagnostic model was established based on the feature extraction capabilities of CNNs. The powerful deep feature extraction capabilities of CNNs and the adaptability of domain adaptation methods to changing conditions are leveraged to enhance both the generalization ability of diagnostic models and the environmental adaptability of diagnostic techniques. Secondly, the fusion of feature-mapping domain adaptation and adversarial domain adaptation further enhances the convergence speed and diagnostic accuracy of the LNA cross-domain fault diagnosis model in the target domain. Finally, various cross-domain experiments were conducted. The FDA method achieved an average fault diagnosis rate of 90.19%, which represents an improvement of over 30% in accuracy compared to a CNN and also shows enhancements over individual domain-adaptation methods.

show abstract

Fault Diagnosis of Power System Considering Communication System Information Transmission Error

Cited by 2 publications

References 7 publications

Partition Fault Diagnosis of Power Grids Based on Improved PNN and GRA

Partition Fault Diagnosis of Power Grids Based on Improved PNN and GRA

Fault Diagnosis of Low-Noise Amplifier Circuit Based on Fusion Domain Adaptation Method

Contact Info

Product

Resources

About