Sponsored by SGCC Science and Technology Program
2017.07 - 2019.12
PMU & System Analytics Group, GEIRI North America
PMU and Machine Learning Based Load Modeling System Development and Applications
Yishen Wang
Principal Research Scientist
Publications
Tensor-Based Parameter Reduction of Dynamic Load Models with Variable Frequency Drive
Accurate load modeling is critical for credible power system stability analysis. Because of the increasing complexity in modern system …
Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network
This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart …
Real-time Energy Disaggregation at Substations with Behind-the-Meter Solar Generation
Energy Disaggregation at substations (EDS) is challenging because measurements are mostly aggregated over multiple types of loads, and …
Imitation and Transfer Q-learning-Based Parameter Identification for Composite Load Modeling
Fast and accurate load parameter identification has a large impact on power systems operation and stability analysis. This paper …
Mathematical representation of the WECC composite load model
The Western Electricity Coordinating Council (WECC) composite load model is a newly developed load model that has drawn great interest …
Evaluating Load Models and Their Impacts on Power Transfer Limits
Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. …
Self-organizing Probability Neural Network Based Intelligent Non-Intrusive Load Monitoring with Applications to Low-cost Residential Measuring Devices
Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring …
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach
With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is …
Global Sensitivity Analysis in Load Modeling via Low-rank Tensor
Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially …
Residential Customer Baseline Load Estimation Using Stacked Autoencoder with Pseudo-load Selection
Accurate estimation of customer baseline load (CBL) is a key factor in the successful implementation of demand response (DR). CBL …
Robust Time-Varying Synthesis Load Modeling in Distribution Networks Considering Voltage Disturbances
Uncertain power sources are increasingly integrated into distribution networks and causing more challenges for the traditional load …
Probabilistic Load Forecasting via Point Forecast Feature Integration
Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load …
Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were …
A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving
For the last ten years, utilities have observed on-going transitions on consumers’ load curves. The previously flat load curves …
Submodular Load Clustering with Robust Principal Component Analysis
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing …