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

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

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

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate Power-Voltage (P-V) curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximants method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P-V curve with moderate computational burden. The proposed method is illustrated in detail on a 4-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council (NPCC) 48-geneartor, 140-bus power system.Comment: Revised and Submitted to IEEE Transaction on Power System

Investor Target Prices

We argue that investors have target prices as anchors for the stocks that they own; once a stock exceeds target prices, investors are satisfied and more likely to sell the stock. This increased selling can generate a price drift after good news. Consistent with our argument, using analyst-target-price forecasts as a proxy, we provide evidence that the fraction of satisfied investors generates the post-earnings-announcement drift, and stocks with a high fraction of satisfied investors experience stronger selling around announcements. This pattern is stronger for stocks with low institutional ownership and high uncertainty.</p

Multi-Stage Holomorphic Embedding Method for Calculating the Power-Voltage Curve

The recently proposed non-iterative load flow method, called the holomorphic embedding method, may encounter the precision issue, i.e. nontrivial round-off errors caused by the limit of digits used in computation when calculating the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New Eng-land 39-bus power system and benchmarked with the result from the traditional continuation power flow method.Comment: This manuscript was submitted to IEEE Power Engineering Letters, which contains 2 pages and 4 figures. Minor modifications suggested from the first round review have been addressed and the manuscript has been submitted for the second round revie

Novel Low-Permittivity (Mg 1− x

The effects of B2O3–LiF addition on the phase composition, microstructures, and microwave dielectric properties of (Mg0.95Cu0.05)2SiO4 ceramics fabricated by a wet chemical method were studied in detail. The B2O3–LiF was selected as liquid-phase sintering aids to reduce the densification sintering temperature of (Mg0.95Cu0.05)2SiO4 ceramics. The B2O3 6%–Li2O 6%-modified (Mg0.95Cu0.05)2SiO4 ceramics sintered at 1200°C possess good performance of εr ∼ 4.37, Q×f ∼ 36,700 GHz and τf ∼ −42 ppm/°C