The short-term power variations of renewable energy sources result in system state fluctuations and deviations from the scheduled operating point. Tracking short-term power variations and maintaining the system's ...
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The short-term power variations of renewable energy sources result in system state fluctuations and deviations from the scheduled operating point. Tracking short-term power variations and maintaining the system's optimality is challenging for traditional optimization methods due to their highly time-consuming nature. This paper proposes a short-timescale self -adaptive optimization strategy based on the nonlinear affine transformation to deal with this problem. Firstly, the multi -period optimization model is established and solved by considering static power -frequency characteristics to obtain an optimal operating point on a longer temporal scale, with the state security margins reserved using chance constraint programming. Next, analogously with the Taylor series, the nonlinear relationship between the system state variable and short-term power fluctuations is revealed through an analytic expression of the nonlinear affine transformation. Then, a self -adaptive optimization algorithm based on the nonlinear affine transformation is proposed to achieve frequency and voltage optimizations on a shorter temporal scale. With less communication, self -adaptive optimization is implemented at the local bus level to achieve more optimal states for short-term renewable power variations rapidly. Finally, simulations demonstrate that the proposed optimization strategy can effectively enhance frequency and voltage qualities, and decrease objective function, thereby improving the operation safety and economy.
In this paper, we construct a novel algorithm for the split common fixed point problem for two demicontractive operators in Hilbert spaces. By using inertial self-adaptive algorithms, we obtain strong convergence resu...
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In this paper, we construct a novel algorithm for the split common fixed point problem for two demicontractive operators in Hilbert spaces. By using inertial self-adaptive algorithms, we obtain strong convergence results for finding a solution of the split common fixed point problems. Applications to solving the split minimization problem and the split feasibility problem are included. Our results extend and generalizemany previously known results in this research area. Moreover, numerical experiments are supplied to demonstrate the convergence behavior and efficiency of the proposed algorithm.
In this paper, we introduce two inertial accelerated algorithms for solving the split common fixed-point problem of directed operators in real Hilbert space. The proposed iterative algorithms combine the primal-dual m...
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In this paper, we introduce two inertial accelerated algorithms for solving the split common fixed-point problem of directed operators in real Hilbert space. The proposed iterative algorithms combine the primal-dual method and the inertial method with the self-adaptive stepsizes such that the implementation of our algorithms does not need any prior information about bounded linear operator norms. Under suitable conditions, the weak and strong convergence results of the algorithms are obtained. Numerical results which involve image restoration problems are reported to show the effectiveness of the proposed algorithms.
The split feasibility problem is to find a pointx*with the property thatx*is an element of CandAx*is an element of Q, whereCandQare nonempty closed convex subsets of real Hilbert spacesXandY, respectively, andAis a bo...
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The split feasibility problem is to find a pointx*with the property thatx*is an element of CandAx*is an element of Q, whereCandQare nonempty closed convex subsets of real Hilbert spacesXandY, respectively, andAis a bounded linear operator fromXtoY. The split feasibility problem models inverse problems arising from phase retrieval problems and the intensity-modulated radiation therapy. In this paper, we introduce a new inertial relaxedCQalgorithm for solving the split feasibility problem in real Hilbert spaces and establish weak convergence of the proposedCQalgorithm under certain mild conditions. Our result is a significant improvement of the recent results related to the split feasibility problem.
In this paper, we introduce two self-adaptive algorithms for solving a class of non-Lipschitz equilibrium problems. These algorithms are very simple in the sense that at each step, they require only one projection ont...
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In this paper, we introduce two self-adaptive algorithms for solving a class of non-Lipschitz equilibrium problems. These algorithms are very simple in the sense that at each step, they require only one projection onto a feasible set. Their convergence can be established under quite mild assumptions. More precisely, the weak (strong) convergence of the first algorithm is proved under the pseudo-paramonotonicity (strong pseudomonotonicity) conditions, respectively. Especially, the convexity in the second argument of the involving bifunction is not required. In the second algorithm, the weak convergence is established under the pseudomonotonicity. Moreover, it is proved that under some additional conditions, the solvability of the equilibrium problem is equivalent to the boundedness of the sequences generated by the proposed algorithms. Some applications to the optimization problems and variational inequality problems as well as to transport equilibrium problems are also considered.
作者:
Ye, XiaosenZhao, JiaqingTsinghua Univ
Collaborat Innovat Ctr Adv Nucl Energy Technol Inst Nucl & New Energy Technol Key Lab Adv Reactor Engn & SafetyMinist Educ Beijing 100084 Peoples R China
Conventional digital image correlation (C-DIC) combined with a rotated Gaussian weight (RGW) function for subset has demonstrated attractive ability in resolving heterogeneous deformation parameters. To further improv...
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Conventional digital image correlation (C-DIC) combined with a rotated Gaussian weight (RGW) function for subset has demonstrated attractive ability in resolving heterogeneous deformation parameters. To further improve the performance, the selection of an optimum weight function becomes the key issue. In this paper, a novel rotated sigmoid weight (RSW) function is proposed. RSW function aims to get a more uniform weight distribution near the subset center, and to realize the continuous change of the equivalent subset size as well. The performance of the RSW function is compared with the Gaussian weight (GW) function, RGW function, the rotated inverse distance weight (RIDW) function and the inverse distance square weight (RIDSW) function through Star 5 image set from the DIC challenge 2.0 and the simulated image set. A total of six methods with different weight functions and rotated weights are systematically compared. The experiment results clearly show that DIC combined with RSW function (i.e. RSW-DIC) has the best spatial resolution without sacrificing much measurement resolution. The spatial resolution of RSW-DIC is only about half of the other methods for both first- and second-order shape functions when a big initial subset size is adopted.
self-adaptive algorithms are presented for solving the split common fixed point problem of demicontractive operators in Hilbert spaces. Weak and strong convergence theorems are given under some mild assumptions.
self-adaptive algorithms are presented for solving the split common fixed point problem of demicontractive operators in Hilbert spaces. Weak and strong convergence theorems are given under some mild assumptions.
As frost accumulates on the heat exchanger surface with time, system operating performance will be dramatically degraded, and limit its use in climates susceptible to frost formation. A novel self-adaptive control str...
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As frost accumulates on the heat exchanger surface with time, system operating performance will be dramatically degraded, and limit its use in climates susceptible to frost formation. A novel self-adaptive control strategy of frost prevention and retardation for air source heat pumps (ASHP) is introduced in this paper. The control strategy relies on a new thermodynamic model, which involves a Dimensionless Artificial Neural Network (DANN) correlation model describing frost accumulation for ASHP on the air-side of the fin-and-tube heat exchanger. The dimensionless parameters of this DANN model, including the ambient conditions, 6 commonly used refrigerants, and the geometric parameters of the heat exchanger, are considered in the model. To enhance the reliability of DANN, we develop a self-adaptive algorithm, including determining the optimal transfer algorithm and selecting the number of neurons in the hidden layer, for the DANN model. Results show a limited relative error (7.55%) between calculated values and experimental data, which help researchers and manufacturers analyze the complicated frosting process and design the new ASHPs more reasonably in different regions with different ambient conditions. (C) 2020 Elsevier Inc. All rights reserved.
This paper proposes an accelerated algorithm for the split common fixed point problem, based on viscosity approximation methods and inertial effects. The main result will be applied to image restoration problems. This...
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This paper proposes an accelerated algorithm for the split common fixed point problem, based on viscosity approximation methods and inertial effects. The main result will be applied to image restoration problems. This algorithm is constructed in such a way that its step sizes and the norm of a given linear operator are not related. Under some conditions, the strong convergence of the algorithm is obtained. Numerical investigations are carried out in order to illustrate high-performance of the present work, mainly using processing duration and the signal-to-noise ratio. It is also shown that this proposed algorithm is more efficient and effective than the published algorithm by Yao et al.
In this paper, we introduce a new step size strategy for projection-type algorithms for solving strongly pseudomonotone equilibrium problems in a Hilbert space. In contrast to the work by Anh et al. (Numer algorithms....
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In this paper, we introduce a new step size strategy for projection-type algorithms for solving strongly pseudomonotone equilibrium problems in a Hilbert space. In contrast to the work by Anh et al. (Numer algorithms. 10.1007/s11075-018-0578-z, 2017) and by Santos et al. (Comput Appl Math 30:91-107, 2011), our methods do not require the step sizes being square summable. Moreover, at each step of the proposed algorithms, instead of solving a constrained problem, we only have to solve an unconstrained problem and compute a projection onto the feasible set or its intersection with a closed sphere. The strong convergence of the proposed algorithms is proven without any Lipschitz-type condition. Also, we evaluate the convergence rate of these algorithms. Using cutting hyperplanes, we refine the feasible set at the beginning of our algorithms. Thanks to this, we can apply the new algorithms to the equilibrium problems with non-closed and non-convex feasible set. Some numerical experiments and comparisons confirm efficiency of the proposed modification.
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