A framework for dynamic prediction of reliability weaknesses in power transmission systems based on imbalanced data

Sun, Chenhao; Wang, Xin; Zheng, Yongjun; Zheng, Feng

doi:10.1016/j.ijepes.2019.105718

Cited by 12 publications

(4 citation statements)

References 46 publications

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“…The power equipment operating status data will be sent to the regulation center in the future. The existing Supervisory Control and Data Acquisition with power Management Systems (SCADA/EMS) are challenging for dealing with the massive amount of monitoring data [ 20 ]. In order to enable monitoring devices of different manufacturers to be connected to a unified centralized monitoring system, a state monitoring center was established for power transmission and transformation equipment in many grid companies [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Most traditional grid SCADA systems collect data through circular inquiry, which cannot collect continuous monitoring data of power equipment. It is challenging to meet real-time processing requirements and dynamic access of streaming big data for power equipment monitoring [ 20 ]. The recent emergence of cloud computing technology may bring hope to difficulties in collecting, storing, and processing big data for power equipment monitoring [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

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Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The study of statistical data on the length, composition of electrical networks, the number and nature of failures in them allows further analysis of power supply system reliability and propose solutions to improve it [1]- [4]. Reliability analysis can be carried out in accordance with known methods [5]- [7]. Moreover, the documents regulating these indicators [8] are outdated and do not reflect the change in reliability indicators characteristic of existing networks.…”

Section: Introductionmentioning

confidence: 99%

Study of statistical data on the composition of electric distribution networks on the example of a Kaluga region district

et al. 2020

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Sustainable operation of the power grid complex is impossible without reliable and high-quality operation of 10/0.4 kV electrical distribution networks, which are the final link in the system for providing consumers with electric energy and are in direct interaction with a specific consumer. The study of statistical information on the composition of distribution networks makes it possible to draw conclusions about electrical network state, to implement recommendations for their development and to compare distribution electrical networks in different regions. This information is especially relevant for analyzing the reliability of power supply to consumers connected to the considered electrical distribution networks. Such indicators of network structure as the length of electric transmission lines of different voltages and power transmission schemes are studied. It also analyzes the number of damages in networks, the causes of these damages, data on the time of planned and emergency outages. This paper considers the structure of 10/0.4 kV electrical distribution networks located on the territory of one of the districts of the Kaluga region. The sample for distribution networks was: 1190 overhead transmission lines and 536 transformer substations. Consumers in the district are 21671 subscribers of individuals and 1986 subscribers of legal entities, that is, a total of 23657 metering points. On the lines of 0.4 kV and 10 kV, the percentage of their isolated design was revealed. The share of single-transformer and two-transformer versions of transformer substations is analyzed.

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“…the detection and classification of voltage events [10][11][12][13][14][15] • the calculation and prediction of power losses [16][17][18] • the diagnosis of faults in power transformers [19][20][21][22][23] • load forecasting [24][25][26][27][28][29] • load pattern segmentation [30][31][32][33] • fault detection [34][35][36][37][38] • fault prediction [39][40][41][42] • the defining of energy consumption [43][44][45][46][47][48] • the forecasting of energy gaining from renewable energy sources [49][50][51][52] • the reliability assessment of renewable sources of energy [53][54][55][56] • energy management in a household …”

mentioning

confidence: 99%

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

et al. 2020

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This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.

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A framework for dynamic prediction of reliability weaknesses in power transmission systems based on imbalanced data

Cited by 12 publications

References 46 publications

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Study of statistical data on the composition of electric distribution networks on the example of a Kaluga region district

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

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