An approach for establishing key parameter system and evaluating the state of transmission line

Yan, Yingjie; Sheng, Gehao; Chen, Yufeng; Jiang, Xiuchen; Guo, Zhihong; Du, Xiuming

doi:10.1080/1448837x.2015.1092926

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Very common among these connection on the field includes, star-star, delta-delta, star-delta, delta-star, open-delta and Scott connection [2] , [3] , [4] , [5] , [6] . In distribution substations, the delta-star connection transformers are commonly used by service providers in making electricity supply available to their prospective customers [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] . If applied as step-up transformer, the secondary compartment will be star connected; But when applied as step-down transformer, secondary compartment will be star connected because the supply of electricity to customers will require a grounded neutral (Primary distribution) [2] , [3] , [4] , [5] , [6] .…”

Section: Methods Detailsmentioning

confidence: 99%

“…In power generation analysis, the magnitude and displacement of the 3 phase output are observed to be approximately balanced compared to what is obtainable in both distribution and transmission networks [7] , [8] , [9] , [10] . The aftermath of this is that, unbalanced Voltage for example deteriorates the operation and performance of electrical equipment such as distribution transformers [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] . Consequently, this paper aims at analysing some three phase distribution transformers in a network of a power utility company having an unbalance voltage conditions.…”

Section: Methods Detailsmentioning

confidence: 99%

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On-load measurement method for the reliability of distribution transformers

et al. 2020

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Transformer is the most important equipment used in power system. It's ensure network stability and reliability. Transformer fails over time due to several factors such as overload, poor insulation, cellulose deterioration, poor dielectric strength of the oil etc. However, transformer owners are finding it difficult to monitor the performance of distribution transformers (DT) in their place of application which results to system failure. Analyses of this distribution transformer performance required that distribution transformer be put on-load and the secondary terminals link to Power Quality and Energy Analyzer equipment (Fluke 435 Series II). This paper presents a methodology of carrying out experimental set up of on-load reliability measurements of the two distribution transformers, 500 kVA and 300 kVA, 11/0.415 kV as case study. Recommendations for the operations and maintenance engineers in charge to comply are discussed. This approach using the prescribed power quality equipment gives a reliable experimental method of evaluating the performance of distribution transformers. • Maintaining the selected distribution transformer on – load prior to and during the experiment. • The setting of experimental circuit to determine the operational parameters involving DT connection to Power Quality and Energy Analyzer (PQEA) equipment. • It utilize personal computer to download the measured parameters for graphical and statistics analysis.

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Section: Methods Detailsmentioning

confidence: 99%

Section: Methods Detailsmentioning

confidence: 99%

On-load measurement method for the reliability of distribution transformers

et al. 2020

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“…The literature [12] summarizes the characteristics of transmission and substation equipment condition monitoring data, and completes data cleaning of the original condition information by constructing time series models, estimating model parameters, identifying abnormal data types, and repairing and correcting data. The literature [13] uses big data analysis methods based on time series analysis and unsupervised learning to monitor abnormal data and test and analyze the transmission and substation status equipment. The above data cleaning methods only consider the anomalies of a single sequence and ignore the correlation between the sequences of power data state quantities.…”

Section: Introductionmentioning

confidence: 99%

Identification and Calibration Method of Deviation of Main Transformer Online Monitoring Date Groups

Yuan

Zhu

Tong³

et al. 2023

IEEJ Transactions Elec Engng

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Dissolved gas in transformer oil (DGA) online monitoring data can reflect equipment insulation performance, which provides an important basis for transformer condition assessment. The accuracy of online monitoring data directly affects the correctness of transformer condition assessment, and the health condition of the online monitoring data device also affects the effectiveness and reasonableness of online monitoring data. With the accumulation of operation time, the DGA online monitoring device faces many problems, resulting in the overall deviation of this indicator data from the actual value, and the transformer online monitoring device cannot play its monitoring effect. To solve these problems, this paper firstly linearizes the data of the DGA online monitoring device by segmentation, extracts the characteristics of the line segment curve, constructs a segmented correlation mining model, and mines the correlation of different indicators of DGA. Then, according to the change of correlation strength and weakness between online monitoring data in time sequence, the data deviation is tracked. Finally, a multi‐indicator back propagation (BP) neural network algorithm optimized by the genetic simulated annealing algorithm is constructed to calibrate the deviated data. The analysis shows that the method can identify the population deviation of online monitoring data and calibrate the deviated data effectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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