342 research outputs found
PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning
Deep learning has emerged as an effective solution for addressing the
challenges of short-term voltage stability assessment (STVSA) in power systems.
However, existing deep learning-based STVSA approaches face limitations in
adapting to topological changes, sample labeling, and handling small datasets.
To overcome these challenges, this paper proposes a novel phasor measurement
unit (PMU) measurements-based STVSA method by using deep transfer learning. The
method leverages the real-time dynamic information captured by PMUs to create
an initial dataset. It employs temporal ensembling for sample labeling and
utilizes least squares generative adversarial networks (LSGAN) for data
augmentation, enabling effective deep learning on small-scale datasets.
Additionally, the method enhances adaptability to topological changes by
exploring connections between different faults. Experimental results on the
IEEE 39-bus test system demonstrate that the proposed method improves model
evaluation accuracy by approximately 20% through transfer learning, exhibiting
strong adaptability to topological changes. Leveraging the self-attention
mechanism of the Transformer model, this approach offers significant advantages
over shallow learning methods and other deep learning-based approaches.Comment: Accepted by IEEE Transactions on Instrumentation & Measuremen
A Survey of DeFi Security: Challenges and Opportunities
DeFi, or Decentralized Finance, is based on a distributed ledger called
blockchain technology. Using blockchain, DeFi may customize the execution of
predetermined operations between parties. The DeFi system use blockchain
technology to execute user transactions, such as lending and exchanging. The
total value locked in DeFi decreased from \$200 billion in April 2022 to \$80
billion in July 2022, indicating that security in this area remained
problematic. In this paper, we address the deficiency in DeFi security studies.
To our best knowledge, our paper is the first to make a systematic analysis of
DeFi security. First, we summarize the DeFi-related vulnerabilities in each
blockchain layer. Additionally, application-level vulnerabilities are also
analyzed. Then we classify and analyze real-world DeFi attacks based on the
principles that correlate to the vulnerabilities. In addition, we collect
optimization strategies from the data, network, consensus, smart contract, and
application layers. And then, we describe the weaknesses and technical
approaches they address. On the basis of this comprehensive analysis, we
summarize several challenges and possible future directions in DeFi to offer
ideas for further research
Horizontal heat flux between urban buildings and soil and its influencing factors
The soil temperature near four external walls with different orientations was investigated in spring and summer. In both seasons, the soil temperature was higher in the positions closest to the buildings, suggesting that the buildings were a heat source for the soil surrounding them. Therefore, it could be confirmed that there was lateral heat transfer between the soil and the buildings. Based on this, a soil heat flux plate was set between the soil and the buildings to investigate the horizontal heat flux. The data showed diurnal variations of the horizontal heat flux in both spring and summer. In order to determine the factors that influenced the horizontal heat flux and to provide a basis to understand its mechanism, the correlations between the data of several meteorological factors and the horizontal heat flux were analysed. The results showed that solar radiation was significantly correlated with the horizontal heat flux (p0.05), such as that for soil moisture. The stepwise regression results indicated that the relative importance of these meteorological factors was 48.63, 21.94, 14.44, 8.12 and 6.87% for solar radiation, soil temperature, air temperature, relative humidity and soil moisture, respectively, on a diurnal scale
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach
The electric vehicle (EV) and electric vehicle charging station (EVCS) have
been widely deployed with the development of large-scale transportation
electrifications. However, since charging behaviors of EVs show large
uncertainties, the forecasting of EVCS charging power is non-trivial. This
paper tackles this issue by proposing a reinforcement learning assisted deep
learning framework for the probabilistic EVCS charging power forecasting to
capture its uncertainties. Since the EVCS charging power data are not standard
time-series data like electricity load, they are first converted to the
time-series format. On this basis, one of the most popular deep learning
models, the long short-term memory (LSTM) is used and trained to obtain the
point forecast of EVCS charging power. To further capture the forecast
uncertainty, a Markov decision process (MDP) is employed to model the change of
LSTM cell states, which is solved by our proposed adaptive exploration proximal
policy optimization (AePPO) algorithm based on reinforcement learning. Finally,
experiments are carried out on the real EVCSs charging data from Caltech, and
Jet Propulsion Laboratory, USA, respectively. The results and comparative
analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle
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