Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, Chin

Estimating the Returns to Investment in Louisiana\u27s Agricultural Research System.

This study demonstrates one way in which the combined nonparametric and parametric estimates of returns-to-research can be used to build a stronger argument for public investment in agricultural research. The data used in this study were constructed from time series information covering the period 1949--95. Tornqvist-Theil quantity indices were calculated to determine the implicit state price for each input category. The returns-to-research in Louisiana agriculture were estimated using both nonparametric and parametric estimators, with appropriate emphasis given to lag structures, data coherence, and functional forms. Model misspecification testing for the parametric model was examined. Internal rates of return were calculated from the empirical estimates. Results indicate that Louisiana agricultural research investments significantly contribute to productivity growth. Returns exhibited a pattern that has been observed in a previous national nonparametric study and numerous parametric studies of returns to agricultural research. The overall return pattern exhibited an investment return life cycle as long as 30 years, in which flows of research returns can be divided into four stages; incubation, growth, maturity, and senescence. Public and private researches appear to play alternative and complementary roles in Louisiana agricultural productivity. The public research plays a major role in the third and final periods of the cycle. Nearly 95 percent of the benefits from agricultural research flowed between 15 years and 30 years after the initial investment, with a peak at 24 years. Private investment largely affected the second and third stages of the overall return life cycle. Private return patterns had a stronger impact in the short term and the peak effect occurred 7--8 years earlier than those for public research investment. The extension service played an important role in the productivity growth period. The results suggest that internal rates of returns to agricultural research investment in Louisiana are at least 20 percent for public investment and 18 percent for private investment. Results also show that nonparametric and parametric approaches can generate consistent results and the nonparametric and parametric approaches play complementary roles. Comprehensive approaches to model misspecification. testing used in this study provided better insight into sources of possible misspecifications

A Free Industry-grade Education Tool for Bulk Power System Reliability Assessment

A free industry-grade education tool is developed for bulk-power-system reliability assessment. The software architecture is illustrated using a high-level flowchart. Three main algorithms of this tool, i.e., sequential Monte Carlo simulation, unit preventive maintenance schedule, and optimal-power-flow-based load shedding, are introduced. The input and output formats are described in detail, including the roles of different data cards and results categorization. Finally, an example case study is conducted on a five-area system to demonstrate the effectiveness and efficiency of this tool.Comment: This paper was submitted to a conferenc

Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL framework (rewards, states, etc.) are also discussed in detail. Experiments on the IEEE 118-bus system by both shallow and deep neural networks demonstrate promising results in terms of reduced system collapse rates.Comment: This paper has been accepted and presented in the IEEE ISGT-Asia conference in 201

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities.Comment: This paper has been accepted by IEEE Transactions on Sustainable Energy and now still in online-publication phase, IEEE Transactions on Sustainable Energy. 201

Optimal Battery Energy Storage Placement for Transient Voltage Stability Enhancement

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is set up based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of fewer iterations for convergence with better solution qualities.Comment: This paper has been accepted by the 2019 IEEE PES General Meeting at Atlanta, GA in August 201