scholarly journals Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6563
Author(s):  
Yinfeng Zhao ◽  
Shutang You ◽  
Mirka Mandich ◽  
Lin Zhu ◽  
Chengwen Zhang ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Transient Stability ◽  
Operating Conditions ◽  
Power Grids ◽  
System Model ◽  
Remedial Action ◽  
Interconnected Power Grids ◽  
Simulation Results ◽  
Corrective Actions

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

scholarly journals A New Method to Determine the Impact of Individual Field Quantities on Cycle-to-Cycle Variations in a Spark-Ignited Gas Engine

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4136
Author(s):  
Clemens Gößnitzer ◽  
Shawn Givler
Keyword(s):  
Kinetic Energy ◽  
Flow Field ◽  
Turbulent Kinetic Energy ◽  
Negative Impact ◽  
Operating Conditions ◽  
Engine Operation ◽  
Gas Engine ◽  
Cycle Simulation ◽  
Simulation Results ◽  
The Impact

Cycle-to-cycle variations (CCV) in spark-ignited (SI) engines impose performance limitations and in the extreme limit can lead to very strong, potentially damaging cycles. Thus, CCV force sub-optimal engine operating conditions. A deeper understanding of CCV is key to enabling control strategies, improving engine design and reducing the negative impact of CCV on engine operation. This paper presents a new simulation strategy which allows investigation of the impact of individual physical quantities (e.g., flow field or turbulence quantities) on CCV separately. As a first step, multi-cycle unsteady Reynolds-averaged Navier–Stokes (uRANS) computational fluid dynamics (CFD) simulations of a spark-ignited natural gas engine are performed. For each cycle, simulation results just prior to each spark timing are taken. Next, simulation results from different cycles are combined: one quantity, e.g., the flow field, is extracted from a snapshot of one given cycle, and all other quantities are taken from a snapshot from a different cycle. Such a combination yields a new snapshot. With the combined snapshot, the simulation is continued until the end of combustion. The results obtained with combined snapshots show that the velocity field seems to have the highest impact on CCV. Turbulence intensity, quantified by the turbulent kinetic energy and turbulent kinetic energy dissipation rate, has a similar value for all snapshots. Thus, their impact on CCV is small compared to the flow field. This novel methodology is very flexible and allows investigation of the sources of CCV which have been difficult to investigate in the past.

A Simple Design Approach for Excitation Controller and Power System Stabilizer

2010 ◽  
Author(s):  
G. Fusco ◽  
M. Russo
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Short Circuit ◽  
Design Procedure ◽  
Voltage Regulation ◽  
Operating Conditions ◽  
Power System Stabilizer ◽  
Simple Design ◽  
Wide Range ◽  
System Stabilizer

This paper proposes a simple design procedure to solve the problem of controlling generator transient stability following large disturbances in power systems. A state-feedback excitation controller and power system stabilizer are designed to guarantee robustness against uncertainty in the system parameters. These controllers ensure satisfactory swing damping and quick decay of the voltage regulation error over a wide range of operating conditions. The controller performance is evaluated in a case study in which a three-phase short-circuit fault near the generator terminals in a four-bus power system is simulated.

scholarly journals Meat Demand in South Korea: An Application of the Restricted Source-Differentiated Almost Ideal Demand System Model

2007 ◽  
Vol 39 (1) ◽  
pp. 47-60 ◽  
Author(s):  
Shida Rastegari Henneberry ◽  
Seong-huyk Hwang
Keyword(s):  
United States ◽  
South Korea ◽  
The United States ◽  
System Model ◽  
Demand System ◽  
Almost Ideal Demand System ◽  
The South ◽  
Meat Demand ◽  
South Korean ◽  
Meat Market

The first difference version of the restricted source-differentiated almost ideal demand system is used to estimate South Korean meat demand. The results of this study indicate that the United States has the most to gain from an increase in the size of the South Korean imported meat market in terms of its beef exports, while South Korea has the most to gain from this expansion in the pork market. Moreover, the results indicate that the United States has a competitive advantage to Australia in the South Korean beef market. Results of this study have implications for U.S. meat exports in this ever-changing policy environment.

A Robust Trajectory Tracking Control Scheme of Two-Link Flexible Manipulator

2011 ◽  
Vol 328-330 ◽  
pp. 2108-2112
Author(s):  
Jing Shuang Lu ◽  
Chun Mei Du ◽  
Rui Zhou ◽  
Na Li
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Vertical Plane ◽  
Flexible Manipulator ◽  
System Model ◽  
Error Signal ◽  
Dynamics Model ◽  
Trajectory Tracking Control ◽  
Control Scheme ◽  
Simulation Results

A simple dynamics model is established based on the two-link flexible manipulator moving within the vertical plane, and a robust simple control scheme is put forward. The advantages of this scheme are simple and good robustness. Only the error signal is needed when designing the control scheme and the acquirement of control signal does not depend on the system model. The simulation results show that this method has a good robustness and stability.

Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Short Term ◽  
Term Memory ◽  
Rectangular Pattern ◽  
Useful Life ◽  
Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

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