A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization f...A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.展开更多
<span style="font-family:Verdana;">This paper represents</span> <span style="font-family:Verdana;">a continuation of</span><span style="color:#C45911;"> <...<span style="font-family:Verdana;">This paper represents</span> <span style="font-family:Verdana;">a continuation of</span><span style="color:#C45911;"> </span><span><span style="white-space:nowrap;"><a href="#ref1" target="_blank">[1]</a></span><span style="font-family:Verdana;"> and</span> <span style="white-space:nowrap;"><a href="#ref2" target="_blank">[2]</a></span></span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">Here, we consider the numerical analysis of a non-trivial frictional contact problem in a form of a system of evolution nonlinear partial differential equations. The model describes the equilibrium of a viscoelastic body in sliding contact with a moving foundation. The contact is modeled with a multivalued normal compliance condition with memory term restricted by a unilateral constraint and is associated with a sliding version of Coulomb’s law of dry friction. After a description of the model and some assumptions, we derive a variational formulation of the problem, which consists of a system coupling a variational inequality for the displacement field and a nonlinear equation for the stress field. Then, we introduce a fully discrete scheme for the numerical approximation of the sliding contact problem. Under certain solution regularity assumptions, we derive an optimal order error estimate and we provide numerical validation of this result by considering some numerical simulations in the study of a two-dimensional problem.</span>展开更多
构网型储能设备(grid-forming energy storage system,GFM-ESS)因具备主动构网和提供惯量支持等能力,已成为解决大规模新能源接入引发宽频振荡问题的重要手段。然而,GFM-ESS的容量配置直接影响系统投资成本,在保障系统稳定性的前提下如...构网型储能设备(grid-forming energy storage system,GFM-ESS)因具备主动构网和提供惯量支持等能力,已成为解决大规模新能源接入引发宽频振荡问题的重要手段。然而,GFM-ESS的容量配置直接影响系统投资成本,在保障系统稳定性的前提下如何实现其经济性配置,成为当前研究热点。现有研究多侧重于提升系统稳定裕度,较少考虑GFM-ESS容量与选址的协同优化,且缺乏统一的量化指标指导配置方案的制定。为此,本工作基于s域模态分析理论,构建了一条集模态识别、关键节点筛选与容量协同优化于一体的完整技术链条,并提出一种考虑经济性约束的GFM-ESS容量与选址协同优化方法。首先,通过归一化模态参与因子对各母线的振荡模态贡献进行定量评估,识别影响不稳定模态的关键节点;针对系统多模态振荡特性,引入粒子群优化(particle swarm optimization,PSO)算法,在预选节点上对储能容量进行协同优化,以满足预设的稳定裕度指标。仿真结果表明,所提技术链能够有效识别关键控制节点,并以较小的GFM-ESS投入容量将原本不稳定的系统恢复至稳定状态,且具备明确的稳定裕度,为新型电力系统中GFM-ESS的合理配置提供了理论支撑。展开更多
基金supported by the National Natural Science Foundation of China(61571131 11604055)
文摘A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.
文摘<span style="font-family:Verdana;">This paper represents</span> <span style="font-family:Verdana;">a continuation of</span><span style="color:#C45911;"> </span><span><span style="white-space:nowrap;"><a href="#ref1" target="_blank">[1]</a></span><span style="font-family:Verdana;"> and</span> <span style="white-space:nowrap;"><a href="#ref2" target="_blank">[2]</a></span></span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">Here, we consider the numerical analysis of a non-trivial frictional contact problem in a form of a system of evolution nonlinear partial differential equations. The model describes the equilibrium of a viscoelastic body in sliding contact with a moving foundation. The contact is modeled with a multivalued normal compliance condition with memory term restricted by a unilateral constraint and is associated with a sliding version of Coulomb’s law of dry friction. After a description of the model and some assumptions, we derive a variational formulation of the problem, which consists of a system coupling a variational inequality for the displacement field and a nonlinear equation for the stress field. Then, we introduce a fully discrete scheme for the numerical approximation of the sliding contact problem. Under certain solution regularity assumptions, we derive an optimal order error estimate and we provide numerical validation of this result by considering some numerical simulations in the study of a two-dimensional problem.</span>
