The electricity production transition leads to the development of low-carbon content renewable energy resources, such as photovoltaic (PV) panels. Due to the later, utilities operators see a significant drop in mid-day net load, raising concerns that the conventional power system will be unable to accommodate the ramp rate and change needed to fully utilize solar energy. Also, this could result in overgeneration and curtailed renewable energy, increasing its cost and reducing its environmental benefit. This paper presents and quantifies a solution that has the potential to help solve this issue and help the development of more PV panels. It consists of using electric water heater tanks to store the excess energy produced by the PV panels by heating the water above the typical setpoint during mid-day hours. Simulations have been run through EnergyPlus (EP) using the average profile of hot water consumption in the US and modified scheduled of water heater to show the feasibility and impact of the methodology. Across California, it has been evaluated that up to 59 GWh of energy could be potentially stored per day during mi-day hours through this technique, reducing by 97% the current need to add expensive energy storage devices, such as batteries, to flatten the duck curve.

This work presents a new online method for monitoring the health of multiple motors using only aggregate current measurements. The approach is attractive because it could significantly lower the cost of machinery monitoring. This work demonstrates that motor current signature analysis (MCSA)can be applied for fault detection using bus-level measurements. The primary contribution is a signal-detection algorithm that can accurately detect fault-related signals having nearly the same exact frequency. The approach has been tested in the laboratory using two identical motors connected in parallel, and it has successfully detected the existence of faults in both motors.

This paper builds a case for the importance of building energy models. It goes on to study the current techniques in calibration and their strengths and flaws. Ultimately a new method to identify the most influential parameters in building energy simulation is proposed. Through an RMSE based method of calibration, these parameters are calibrated and interpreted for reinforcing the reliability of the method and pointing out the weaknesses of the model. This process is also a starting step in the development of future aid packages for building operators and smart fault detector systems in buildings.

This paper provides a closed-form model for a new approach that has been shown to significantly mitigate the likelihood of instability when connecting a single-phase PV inverter to a weak grid. The authors use this model to predict stability when connecting to a purely inductive grid with impedance as high as 10% per unit.

This paper describes how the non-intrusive load monitoring technique can be applied to develop a powerful framework for grid-responsive buildings, particularly in the small commercial and residential sector. Specifically, recent advances have made it cost-effective to monitor a number of electrical circuits in a building using low-cost embedded platforms that can send their data to cloud-computing and data-storage services. This approach significantly reduces the effort required to deploy and commission monitoring solutions, specifically because the cloud allows one to create a large library of load patterns and it provides a powerful platform for computation. This approach thus solves one of the major challenges in power monitoring and energy management, which has been the development of robust unsupervised learning algorithms that eliminate the need for costly human involvement. The cloud also provides an opportunity to learn fault patterns and to predict the performance of individual building systems. This paper describes the unsupervised training that can be performed in the cloud using intuition about the underlying physics of major load devices. It also describes how load on-off patterns can be used for fault detection as well as load prediction and control and the deployment of powerful algorithms for a large data set obtained across a large portfolio is discussed.

This paper describes a time-domain approach for estimating the state of health (SOH) and remaining useful life (RUL) of a battery, with particular emphasis on LiFePO4cells. In this methodology, a small test signal is superimposed on top of the battery load to trigger its transient dynamics. The resulting terminal voltage and current are measured, and a nonlinear least-squares routine is used to estimate the impedance parameters of the battery model. These parameter values are then used to track the SOH and RUL over time. Experimental results are presented. The approach requires minimal hardware and could be used to form the basis of a robust on-line monitoring system.

The early detection of incipient faults is desirable in mission-critical applications such as shipboard propulsion drives. This paper presents an online condition-monitoring approach for detecting early stage faults in IGBTs. The proposed algorithm extracts important device features (i.e. on-state resistance, gate charge, etc.) and compares them to healthy values recorded over a range of operating conditions. The algorithm is based on principal-components analysis (PCA). An experimental implementation in an IGBT-based drive is described, and results recorded with two different faults over a range of operating conditions are presented. The scheme integrates well with new FPGA-based gate drives and provides a powerful alternative to rules-based fault detection.

This paper introduces a new interference-cancellation technique for use in biomedical instruments and other devices impacted by line-frequency interference. This approach uses a Fourier decomposition to construct sinusoids that are subtracted from the corrupted input signal. The method has been implemented both in hardware and in software. Both systems have been designed using tools and techniques that have been successfully employed in power-system monitoring applications. Experimental results demonstrate the capabilities of both prototypes. The software-based approach uses a Kalman filter to drive a synchronization and resampling process that provides a simple and effective digital solution. Both systems are shown to be particularly useful in cases where the signal-to-noise ratio falls below unity.

This paper describes a transient-based approach for estimating the state-of-charge (SOC) and state-of-health (SOH) of a lithium-ion battery. In this methodology, a small test signal is superimposed on top of the battery load to trigger its transient dynamics. The resulting terminal voltage and current are measured, and a non-linear least-squares routine is used to estimate the impedance parameters of the battery model. These parameter values are then passed to an H∞filter that estimates the open-circuit voltage while the battery is under load. Experimental results are presented. The approach requires minimal hardware and could be used to form the basis of a robust on-line monitoring system.

Techniques for rotating two- and three-dimensional (2-D and 3-D) images using fast Fourier transforms (FFT's) are presented. The methods are applications of the multidimensional chirp algorithm. In the 2-D case, one chirp transformation is sufficient, requiring four 2-D FFT's, In the 3-D case, two successive chirp transformations are required, needing six 3-D FFT's.