Robust phase detection in distribution systems

Modarresi, Mohammad Sadegh; Huang, Tong; Ming, Hao; Xie, Le

doi:10.1109/tpec.2017.7868279

Cited by 10 publications

(14 citation statements)

References 16 publications

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“…Any phase identification algorithm that relies on exploiting the correlations between the voltage magnitude measurements of loads would essentially be neutralized starting at ν = 0.5% for a 7-day window. This fading structure behavior has also been observed by Modarresi et al [23].…”

Section: B Singular Value Decomposition (Svd)supporting

confidence: 84%

“…Surprisingly, there have not been many attempts in the phase identification literature to directly remove noise from sensor measurements as a preprocessing step. Modarresi et al [23] model the IEEE 13-bus test feeder using a neural network and use the voltage magnitude measurements that are generated by the network as denoised measurements. The network is composed of a 9-node input dense layer, 10-node dense hidden layer, and an 18-node dense output layer (they don't indicate the activation functions used).…”

Section: Related Workmentioning

confidence: 99%

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Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2022

Preprint

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Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution<br>system. However, real world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system aren’t overloaded which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach results in a reduction in frobenius error and an increase in average phase identification accuracy over a year of time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br>

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Section: B Singular Value Decomposition (Svd)supporting

confidence: 84%

Section: Related Workmentioning

confidence: 99%

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Any phase-identification algorithm that relies on exploiting the correlations between the voltage magnitude measurements of loads would essentially be neutralized, starting at ν = 0.5%, for a 7-day window. This fading structure behavior has also been observed by Modarresi et al [23]. In the second row of figure 4, we can see that the SVD and SVHT can recover a good approximation of the original correlation structure for ν = 0.1%, ν = 0.2%, and ν = 0.5%.…”

Section: B Singular Value Decomposition (Svd)supporting

confidence: 84%

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Zaragoza¹,

Rao²

2021

Preprint

View full text Add to dashboard Cite

Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution system. However, real-world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data-driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system are not overloaded, which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We use Singular Value Decomposition (SVD) with the optimal Singular Value Hard Threshold (SVHT) as part of a feature engineering pipeline to denoise data matrices of voltage magnitude measurements. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of time series data. K- medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.<br><br>

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“…BP neural network proposed by scientists led by Rumelhart and McClelland is a feedforward neural network implemented by a back-propagation algorithm [33], which is characterized by distributed storage and parallel cooperative processing of information, and its hidden layer can solve non-linear problems. Neural networks were usually used to find problems in electricity system configurations to prevent losses [34]. In addition, it can be used as an input for data generation for the scenario approach theory mentioned [35] for the case of energy system scheduling.…”

Section: Development Of Four Methodsmentioning

confidence: 99%

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Wang

Zhan

2019

Sustainability

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Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

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Robust phase detection in distribution systems

Cited by 10 publications

References 16 publications

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Denoising with Singular Value Decomposition for Phase Identification in Power Distribution Systems

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

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