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Wyszukujesz frazę "power margin" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Ultra-low Power FinFET SRAM Cell with Improved Stability Suitable for Low Power Applications
Autorzy:
Birla, Shilpi
Powiązania:
https://bibliotekanauki.pl/articles/226772.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
FinFET
RSNM
WSNM
hold margin
subthreshold
leakage power
Opis:
In this paper, a new 11T SRAM cell using FinFET technology has been proposed, the basic component of the cell is the 6T SRAM cell with 4 NMOS access transistors to improve the stability and also makes it a dual port memory cell. The proposed cell uses a header scheme in which one extra PMOS transistor is used which is biased at different voltages to improve the read and write stability thus, helps in reducing the leakage power and active power. The cell shows improvement in RSNM (Read Static Noise Margin) with LP8T by 2.39x at sub-threshold voltage 2.68x with D6T SRAM cell, 5.5x with TG8T. The WSNM (Write Static Noise Margin) and HM (Hold Margin) of the SRAM cell at 0.9V is 306mV and 384mV. At sub-threshold operation also it shows improvement. The Leakage power reduced by 0.125x with LP8T, 0.022x with D6T SRAM cell, TG8T and SE8T. Also, impact of process variation on cell stability is discussed.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 603-609
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A self-scheduling strategy of virtual power plant with electric vehicles considering margin indexes
Autorzy:
Jiao, Fengshun
Deng, Yongsheng
Li, Duo
Wei, Bo
Yue, Chengyan
Cheng, Meng
Zhang, Yapeng
Zhang, Jiarui
Powiązania:
https://bibliotekanauki.pl/articles/949880.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electric vehicle (EV)
response time margin (RTM)
scheduling strategy
state of charge margin (SOCM)
virtual power plant (VPP)
Opis:
From the perspective of a virtual power plant (VPP) with electric vehicles (EVs), a self-scheduling strategy considering the response time margin (RTM) and state of charge margin (SOCM) is proposed. Firstly, considering the response state of the state of charge (SOC) and charge-discharge state of EVs, a VPP based response capacity determination model of EVs is established. Then, RTM and SOCM indexes are introduced on the basis of the power system scheduling target and the EV users’ traveling demands. The RTM and SOCM indices are calculated and then are used to generate a priority sequence of responsive EVs for the VPP. In the process of the scheduling period and rolling iteration, the scheduling schemes of the EVs in the VPP for multiple time periods are determined. Finally, the VPP self-scheduling strategy is validated by taking an VPP containing three kinds of EV users as an example. Simulation results show that with the proposed strategy, the VPP is able to respond to the scheduling power from the power system, while ensuring the traveling demands of the EV users at the same time.
Źródło:
Archives of Electrical Engineering; 2020, 69, 4; 907-920
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Short-term wind power combined prediction based on EWT-SMMKL methods
Autorzy:
Li, Jun
Ma, Liancai
Powiązania:
https://bibliotekanauki.pl/articles/1955182.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
combined model
empirical wavelet transform
prediction
soft margin multiple kernel learning
wind power
model łączony
empiryczna transformacja falkowa
przewidywanie
miękki margines uczenia wielokrotnego jądra
energia wiatrowa
Opis:
Since wind power generation has strong randomness and is difficult to predict, a class of combined prediction methods based on empirical wavelet transform (EWT) and soft margin multiple kernel learning (SMMKL) is proposed in this paper. As a new approach to build adaptive wavelets, the main idea is to extract the different modes of signals by designing an appropriate wavelet filter bank. The SMMKL method effectively avoids the disadvantage of the hard margin MKL method of selecting only a few base kernels and discarding other useful basis kernels when solving for the objective function. Firstly, the EWT method is used to decompose the time series data. Secondly, different SMMKL forecasting models are constructed for the sub-sequences formed by each mode component signal. The training processes of the forecasting model are respectively implemented by two different methods, i.e., the hinge loss soft margin MKL and the square hinge loss soft margin MKL. Simultaneously, the ultimate forecasting results can be obtained by the superposition of the corresponding forecasting model. In order to verify the effectiveness of the proposed method, it was applied to an actual wind speed data set from National Renewable Energy Laboratory (NREL) for short-term wind power single-step or multi-step time series indirectly forecasting. Compared with a radial basic function (RBF) kernel- based support vector machine (SVM), using SimpleMKL under the same condition, the experimental results show that the proposed EWT-SMMKL methods based on two different algorithms have higher forecasting accuracy, and the combined models show effectiveness.
Źródło:
Archives of Electrical Engineering; 2021, 70, 4; 801-817
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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