206 research outputs found
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
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