Machine Learning-Based Predictive State Estimation
This talk presents the machine-based predictive state estimation method developed in Grid Optimization with Solar (GO-Solar) project.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Engineering topics
Yang, Rui
Publications and source records attributed to Yang, Rui.
Matrix Completion Using Alternating Minimization for Distribution System State Estimation
This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Analyzing the Effects of Cyberattacks on Distribution System State Estimation: Preprint
Key components of power systems—such as energy management systems, automatic generation control, and state estimation—are under serious vulnerability from cyber attacks. Cyber threats in electric grids have increased significantly because of the increased interconnectivity of supervisory control and data acquisition systems and public network infrastructure. As the penetration level of distributed energy resources increases, it is imperative to employ system-monitoring techniques such as state estimation for the reliable operation of distribution systems. Recently, multiple methods have been developed that exploit the low rank property of distribution system state matrix and are robust to bad data, such as matrix completion. This paper analyzes the impact of various realistic cyber attack scenarios on matrix completion. Realistic cyber attack scenarios are converted into data corruption models that are used in an extensive simulation of a custom IEEE 123-bus system.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Matrix Completion Using Alternating Minimization for Distribution System State Estimation: Preprint
This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Joint Estimation of Behind-the-Meter Solar Generation in a Community
Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.
behind-the-meter solar generation↗
Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint
Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.
Bayesian structural time series↗
Predictive Analytics for Behind-the-Meter Resources
This presentation discusses the development of the predictive analytics for behind-the-meter resources.
behind-the-meter resources↗
Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation
Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.
bayesian structural time series↗
3D printed Mg-NiTi interpenetrating-phase composites with high strength, damping capacity, and energy absorption efficiency
It is of significance, but still remains a key challenge, to simultaneously enhance the strength and damping capacities in metals, as these two properties are often mutually exclusive. Here, we provide a multidesign strategy for defeating such a conflict by developing a Mg-NiTi composite with a bicontinuous interpenetrating-phase architecture through infiltration of magnesium melt into three-dimensionally printed Nitinol scaffold. The composite exhibits a unique combination of mechanical properties with improved strengths at ambient to elevated temperatures, remarkable damage tolerance, good damping capacities at differing amplitudes, and exceptional energy absorption efficiency, which is unprecedented for magnesium materials. The shape and strength after deformation can even be largely recovered by heat treatment. This study offers a new perspective for the structural and biomedical applications of magnesium.
36 MATERIALS SCIENCE↗
Robust Matrix Completion State Estimation in Distribution Systems
Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Integration of Total-Sky Imager Data with a Physics-Based Smart Persistence Model for Intra-Hour Forecasting of Solar Radiation
Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Our previous study introduced a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposed the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle were accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique was used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction were predicted for future time steps using a two-stream approximation and a 5-minute exponential weighted moving average, respectively. The model evaluation indicated the estimation and forecast of cloud fraction mostly contributed to the uncertainty of the PSPI though it overcame the persistence and smart persistence models in all forecast time horizons between 5 and 60 minutes. This study aims to enhance the PSPI by ingesting surface-based observations of cloud fraction from a total sky imager (TSI). The estimation and forecast of cloud albedo is correspondingly improved by utilizing the cloud fraction observations and thus leads to more accurate GHI forecast. Various time-series analysis methods are also investigated on the forecasting of cloud fraction and cloud albedo for further improving the GHI forecast. These improvements are valuable for many applications, such as forecasting energy use for buildings, grid operations, and ultimately bringing down the cost of solar energy.
14 SOLAR ENERGY↗
Estimation of Behind-the-Meter Solar Generation by Integrating Physical with Statistical Models
Accurate estimation of solar photovoltaic (PV) generation is crucial for distribution grid control and optimization. Unfortunately, most of the residential solar PV installations are behind-the-meter. Thus, utilities only have access to the net load readings. This paper presents an unsupervised framework for estimating solar PV generation by disaggregating the net load readings. The proposed framework synergistically combines a physical PV system performance model with a statistical model for load estimation. Specifically, our algorithm iteratively estimates solar PV generation with a physical model and electric load with the Hidden Markov model regression. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is validated against net load and solar PV generation data gathered from residential customers located in Austin, Texas. The validation results show that our method reduces mean squared error by 44% compared to the state-of-the-art disaggregation algorithm.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Robust Matrix Completion State Estimation in Distribution Systems: Preprint
Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Crystal Structures of the Full-Length Murine and Human Gasdermin D Reveal Mechanisms of Autoinhibition, Lipid Binding, and Oligomerization
Not Available
59 BASIC BIOLOGICAL SCIENCES↗
Study on Mechanical and Thermal Properties of Poly(Lactic acid)/Poly(Butylene adipate-co-terephthalate)/Office Wastepaper Fiber Biodegradable Composites
Office wastepaper fibers(OWF)/poly(butylene adipate-co-terephthalate) (PBAT)/poly(lactic acid) (PLA) biodegradable composites with different mass ratio were made by melt blending and injection moulding process. At the same time, the use of MAPLA and KH560 was studied as a potential approach for improving interfacial adhesion between OWF, PBAT and PLA. The results revealed that with the increasing of PBAT, the notched impact strength of PLA/PBAT/OWF composites can be first increased and then decreased. When the content of PBAT was 20 wt%, the notch impact strength of PLA/PBAT/OWF composite was the highest, increased by 291% compared with pure PLA. TGA results revealed that the onset degradation temperature of the composites can be increased and its thermal decomposition step can be changed. According to the crystallization and melting performance table, OWF can act as nucleating agent to promote the crystallization properties of composites, while PBAT can prevent the crystallization and the higher the content is, the more obvious it is. It can be seen from the SEM figures that the addition of MAPLA and KH560 simultaneously makes the composites interface bond closer, thus improving the interface performance of PLA/20PBAT/OWF composite. Among the three kinds of modified composites, PLA/20PBAT/OWF/MK composite has the lowest water absorption and the best comprehensive performance.
36 MATERIALS SCIENCE↗
High‐Throughput Growth of Microscale Gold Bicrystals for Single‐Grain‐Boundary Studies
Abstract The study of grain boundaries is the foundation to understanding many of the intrinsic physical properties of bulk metals. Here, the preparation of microscale thin‐film gold bicrystals, using rapid melt growth, is presented as a model system for studies of single grain boundaries. This material platform utilizes standard fabrication tools and supports the high‐yield growth of thousands of bicrystals per wafer, each containing a grain boundary with a unique <111> tilt character. The crystal growth dynamics of the gold grains in each bicrystal are mediated by platinum gradients, which originate from the gold–platinum seeds responsible for gold crystal nucleation. This crystallization mechanism leads to a decoupling between crystal nucleation and crystal growth, and it ensures that the grain boundaries form at the middle of the gold microstructures and possess a uniform distribution of misorientation angles. It is envisioned that these bicrystals will enable the systematic study of the electrical, optical, chemical, thermal, and mechanical properties of individual grain boundary types.
Gan, Lucia T.↗
State Estimation in Low-Observable Distribution Systems Using Matrix Completion: Preprint
The need for distribution system state estimation is on the rise because of the increased penetration of distributed energy resources and flexible load. To manage the distribution systems in real time, operators need to first overcome the challenge of low observability in distribution systems. Also, because of the amount of data present from smart meters, distributed generation measurements, switches, etc., the ideal distribution state estimation methods need to be able to process heterogeneous data. In this paper, an algorithm is developed for voltage phasor estimation in low-observability distribution systems. The algorithm is based on the matrix completion approach from signal processing. The traditional matrix completion formulation is augmented with power-flow constraints to improve results while requiring less data. This method can also use all types of measurements (voltage magnitude, voltage angle, real power, reactive power) to complete the state matrix.
24 POWER TRANSMISSION AND DISTRIBUTION↗