2019 IEEE 10th International Workshop on Applied Measurements for Power Systems (AMPS) 2019

DOI: 10.1109/amps.2019.8897757

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The Impact of Loads Aggregation and Correlation in Distribution State Estimation

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Amin Mokaribolhassan

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A Se In Dns With Limited Measurement Devices1

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Cited by 3 publications

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“…Learning-based methods require access to the ground-truth data, not applicable in DNs with limited measurements. Shafiei et al [16], [17] consider the spatial-temporal dependencies among customer loads in DNs to improve the accuracy of the pseudo measurements and DSSE. The aggregation of adjacent loads has been used to decrease the variability of load consumption and improve the spatial correlation in the subarea to increase the accuracy of the pseudo measurements.…”

Section: A Se In Dns With Limited Measurement Devicesmentioning

confidence: 99%

Distribution System State Estimation Using PV Separation Strategy in LV Feeders With High Levels of Unmonitored PV Generation

Mokaribolhassan

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et al. 2023

IEEE Systems Journal

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Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system's dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squaresbased estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator's accuracy is improved by using a new PV-scalingaided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

“…Learning-based methods require access to the ground-truth data, not applicable in DNs with limited measurements. Shafiei et al [16], [17] consider the spatial-temporal dependencies among customer loads in DNs to improve the accuracy of the pseudo measurements and DSSE. The aggregation of adjacent loads has been used to decrease the variability of load consumption and improve the spatial correlation in the subarea to increase the accuracy of the pseudo measurements.…”

Section: A Se In Dns With Limited Measurement Devicesmentioning

confidence: 99%

Distribution System State Estimation Using PV Separation Strategy in LV Feeders With High Levels of Unmonitored PV Generation

Mokaribolhassan

¹

,

²

,

³

et al. 2023

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system's dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squaresbased estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator's accuracy is improved by using a new PV-scalingaided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

Solar Generation Estimation in Distribution Network Using Limited Measurements

Mokaribolhassan

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,

²

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³

et al. 2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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One-Step Multilayer Unbalanced Three-Phase DSE for LV Distribution Networks

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et al. 2021

IEEE Systems Journal

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Efficiently solving unbalanced three-phase power flow in distribution grids is pivotal for grid analysis and simulation. There is a pressing need for scalable algorithms capable of handling large-scale unbalanced power grids that can provide accurate and fast solutions. To address this, deep learning techniques, especially Graph Neural Networks (GNNs), have emerged. However, existing literature primarily focuses on balanced networks, leaving a critical gap in supporting unbalanced three-phase power grids.This letter introduces PowerFlowMultiNet, a novel multigraph GNN framework explicitly designed for unbalanced three-phase power grids. The proposed approach models each phase separately in a multigraph representation, effectively capturing the inherent asymmetry in unbalanced grids. A graph embedding mechanism utilizing message passing is introduced to capture spatial dependencies within the power system network.PowerFlowMultiNet outperforms traditional methods and other deep learning approaches in terms of accuracy and computational speed. Rigorous testing reveals significantly lower error rates and a notable hundredfold increase in computational speed for large power networks compared to model-based methods.

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