@article{wang_zhu_liang_meng_kling_lubkeman_lu_2021, title={A Data-driven Pivot-point-based Time-series Feeder Load Disaggregation Method}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638034}, abstractNote={The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Wang, Jiyu and Zhu, Xiangqi and Liang, Ming and Meng, Yao and Kling, Andrew and Lubkeman, David and Lu, Ning}, year={2021} }
 @article{liang_meng_wang_lubkeman_lu_2021, title={FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets}, volume={12}, ISSN={["1949-3061"]}, url={https://doi.org/10.1109/TSG.2020.3025259}, DOI={10.1109/TSG.2020.3025259}, abstractNote={This article presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.}, number={2}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Liang, Ming and Meng, Yao and Wang, Jiyu and Lubkeman, David L. and Lu, Ning}, year={2021}, month={Mar}, pages={1163–1173} }
 @article{liang_meng_wang_lubkeman_lu_2021, title={FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638247}, abstractNote={This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Liang, Ming and Meng, Yao and Wang, Jiyu and Lubkeman, David and Lu, Ning}, year={2021} }
 @inproceedings{zhu_wang_mulcahy_lubkeman_lu_samaan_huang_2017, title={Voltage-load sensitivity matrix based demand response for voltage control in high solar penetration distribution feeders}, ISBN={9781538622124}, url={http://dx.doi.org/10.1109/pesgm.2017.8273887}, DOI={10.1109/pesgm.2017.8273887}, abstractNote={This paper presents a voltage-load sensitivity matrix (VLSM) based voltage control method to deploy demand response resources for controlling voltage in high solar penetration distribution feeders. The IEEE 123-bus system in OpenDSS is used for testing the performance of the preliminary VLSM-based voltage control approach. A load disaggregation process is applied to disaggregate the total load profile at the feeder head to each load nodes along the feeder so that loads are modeled at residential house level. Measured solar generation profiles are used in the simulation to model the impact of solar power on distribution feeder voltage profiles. Different case studies involving various PV penetration levels and installation locations have been performed. Simulation results show that the VLSM algorithm performance meets the voltage control requirements and is an effective voltage control strategy.}, booktitle={2017 IEEE Power & Energy Society General Meeting}, publisher={IEEE}, author={Zhu, Xiangqi and Wang, Jiyu and Mulcahy, David and Lubkeman, David L. and Lu, Ning and Samaan, Nader and Huang, Renke}, year={2017}, month={Jul} }