Power grid fault diagnosis using polar PMU data plots

Zhang, Xu; Guo, Zixing; Zheng, Yuchuan; Liu, Jiang; Yan, Panfei; Zheng, Ling

doi:10.1016/j.ijepes.2022.108148

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…In China, researchers are actively contributing to the enhancement of power system stability and supply reliability through advancements in current phase detection and data mining technologies. Zhang, Guo & Zheng (2022) introduced a method for identifying the topology of LVDN substations based on voltage correlation analysis. This approach specifically targets topology identification challenges within LVDNs by employing EPD of time series voltage data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This approach specifically targets topology identification challenges within LVDNs by employing EPD of time series voltage data. Subsequently, voltage correlation analysis is applied to different time series data to discern the substation’s topology structure ( Zhang, Guo & Zheng, 2022 ). Li, Wang & Wang (2021) devised a PLTI method grounded in SVMs.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Xu,

Lv,

Wang

et al. 2024

PeerJ Computer Science

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At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Xu,

Lv,

Wang

et al. 2024

PeerJ Computer Science

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“…The data generated in the intelligent operation of power equipment can be used to assess the status of the equipment [8], and these data have the characteristics of wide distribution, great varieties, and massive amount, resulting in a slow and inefficient fault data analysis, so a fast and accurate fault analysis method is particularly important [9]. Data mining techniques can effectively improve the effect of fault data analysis.…”

Section: Clustering Analysis Algorithmmentioning

confidence: 99%

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

2023

IJOMAM

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Many fault-related data are generated in the intelligent operation of power equipment, and analyzing these data can help understand the operating condition of the equipment. This paper studied the clustering analysis methods and introduced K-means, kernel C-means (KCM), and fuzzy kernel C-means (FKCM) methods. The gray wolf optimization (GWO) algorithm was used to optimize the initial clustering center and kernel parameters of the FKCM algorithm to obtain the GWO-FKCM algorithm. Experiments were conducted taking transformer data as an example. It was found that the GWO-FKCM algorithm obtained the optimal clustering results at the 763rd iteration, and its accuracy rates for different fault types were all above 90%, with an average value of 92.92%, which was higher than the other clustering analysis methods. The results prove the reliability of the GWO-FKCM algorithm for equipment fault data analysis. This algorithm can be promoted and applied in actual power equipment.

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“…Ref. [9] converted collected data into polar coordinates, and the processed data were subsequently used as the input of a convolutional neural network. This greatly improved the diagnostic accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Lin,

Wang,

et al. 2024

IEEE Access

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Power grid fault diagnosis methods based on analytical models possess advantages such as logical rigor, strong interpretability, and high applicability. However, the existing design of their objective functions is presented as a 0-1 integer nonlinear programming model, making it difficult to approximate the optimal solution. To address this issue, this paper proposes an improvement to the expression of the second backup protection expectation involved in the diagnostic model. Thus, the order of the objective function is successfully reduced, transforming the original 0-1 integer nonlinear programming model into a constrained 0-1 integer linear programming model, which can be efficiently solved by typical commercial solvers based on a linear integer programming framework. Numerical results demonstrate that the solution time of the proposed linearized model is lower than that of existing nonlinear programming models, and the diagnostic accuracy of the proposed model is higher than that of other state-of-the-art methods.

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Power grid fault diagnosis using polar PMU data plots

Cited by 13 publications

References 14 publications

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Study on the Analysis of Fault Data During Intelligent Operation of Power Equipment With Cluster Analysis

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

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