836 research outputs found
Intelligent Fault Analysis in Electrical Power Grids
Power grids are one of the most important components of infrastructure in
today's world. Every nation is dependent on the security and stability of its
own power grid to provide electricity to the households and industries. A
malfunction of even a small part of a power grid can cause loss of
productivity, revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid and take
protective measures accordingly even before a serious anomaly takes place. To
achieve this objective, we have set out to create an artificially intelligent
system which can analyze the grid information at any given time and determine
the health of the grid through the usage of sophisticated formal models and
novel machine learning techniques like recurrent neural networks. Our system
simulates grid conditions including stimuli like faults, generator output
fluctuations, load fluctuations using Siemens PSS/E software and this data is
trained using various classifiers like SVM, LSTM and subsequently tested. The
results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid
architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure
BanditQ -- No-Regret Learning with Guaranteed Per-User Rewards in Adversarial Environments
Classic online prediction algorithms, such as Hedge, are inherently unfair by
design, as they try to play the most rewarding arm as many times as possible
while ignoring the sub-optimal arms to achieve sublinear regret. In this paper,
we consider a fair online prediction problem in the adversarial setting with
hard lower bounds on the rate of accrual of rewards for all arms. By combining
elementary queueing theory with online learning, we propose a new online
prediction policy, called BanditQ, that achieves the target rate constraints
while achieving a regret of in the full-information setting. The
design and analysis of BanditQ involve a novel use of the potential function
method and are of independent interest
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