A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification

This paper presents a novel power-band-based data segmentation (PBDS) method to enhance the identification of meter phase and meter-transformer pairing. Meters that share the same transformer or are on the same phase typically exhibit strongly correlated voltage profiles. However, under high power consumption, there can be significant voltage drops along the line connecting a customer to the distribution transformer. These voltage drops significantly decrease the correlations among meters on the same phase or supplied by the same transformer, resulting in high misidentification rates. To address this issue, we propose using power bands to select highly correlated voltage segments for computing correlations, rather than relying solely on correlations computed from the entire voltage waveforms. The algorithm's performance is assessed by conducting tests using data gathered from 13 utility feeders. To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders. The verification results unequivocally demonstrate that the proposed algorithm surpasses existing methods in both accuracy and robustness.Comment: Submitted to the IEEE Transactions on Power Delivery. arXiv admin note: text overlap with arXiv:2111.1050

An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation

This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selection of similar days and iterative bidirectional-GB training. In the first step, pre- and post-event temperature profiles of the targeted CVR day are used to select similar days from historical non-CVR days. In the second step, the pre-event and post-event similar days are used to train two GBMs iteratively: a forward-GBM and a backward-GBM. After each iteration, the two generated CVR baselines are reconciled and only the first and the last points on the reconciled baseline are kept. The iteration repeats until all CVR baseline points are generated. We tested two gradient boosting methods (i.e., GBM and LighGBM) with two data resolutions (i.e., 15- and 30-minute). The results demonstrate that both the accuracy and performance of the algorithm are satisfactory.Comment: 5 pages, 8 figures, 2 table

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri