In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way A...In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way AF relaying mode with channel estimation error, the resultant instantaneous SNRs at end nodes is obtained. Then, by using a high SNR approximation, outage possibility is acquired and its simple closed-form expression is represented. Specially, for using the energy resource more efficiently, a low-complexity power allocation and transmission mode selection policy is proposed to enhance the energy efficiency of two-way AF relay system. Finally, relay priority region is identified in which cooperative diversity energy gain can be achieved. The computer simulations are presented to verify our analytical results, indicating that the proposed policy outperforms direct transmission by an energy gain of 3 dB at the relative channel estimation error less than 0.001. The results also show that the two-way AF relaying transmission loses the two-way AF relaying transmission loses its superiority to direct transmission in terms of energy efficiency when channel estimation error reaches 0.03.展开更多
保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive princip...保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。展开更多
为了提高故障检测准确率,提出了基于动态受控主元分析(dynamic controlled principal component analysis,DCPCA)模型的故障检测方法。首先,利用DCPCA提取动态受控主元(dynamic controlled principal component,DCPC),所得DCPC包含过程...为了提高故障检测准确率,提出了基于动态受控主元分析(dynamic controlled principal component analysis,DCPCA)模型的故障检测方法。首先,利用DCPCA提取动态受控主元(dynamic controlled principal component,DCPC),所得DCPC包含过程的自回归特性和与控制输入之间的动态因果关系,使得构建的DCPCA模型更精确。然后,针对传统方法只对过程变量进行静态空间结构的故障检测,忽略了动态特性的问题,基于DCPCA模型适时应用检测综合指标,对系统进行静态重构误差和动态模型误差的双重检测,使得检测结果更全面。最后,基于田纳西-伊斯曼(Tennessee-Eastman,TE)过程的仿真结果验证了所提方法的可行性和有效性。展开更多
基金Project(IRT0852) supported by the Program for Changjiang Scholars and Innovative Research Team in University,ChinaProject(2012CB316100) supported by the National Basic Research Program of China+2 种基金Projects(61101144,61101145) supported by the National Natural Science Foundation of ChinaProject(B08038) supported by the "111" Project,ChinaProject(K50510010017) supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way AF relaying mode with channel estimation error, the resultant instantaneous SNRs at end nodes is obtained. Then, by using a high SNR approximation, outage possibility is acquired and its simple closed-form expression is represented. Specially, for using the energy resource more efficiently, a low-complexity power allocation and transmission mode selection policy is proposed to enhance the energy efficiency of two-way AF relay system. Finally, relay priority region is identified in which cooperative diversity energy gain can be achieved. The computer simulations are presented to verify our analytical results, indicating that the proposed policy outperforms direct transmission by an energy gain of 3 dB at the relative channel estimation error less than 0.001. The results also show that the two-way AF relaying transmission loses the two-way AF relaying transmission loses its superiority to direct transmission in terms of energy efficiency when channel estimation error reaches 0.03.
文摘保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。
文摘为了提高故障检测准确率,提出了基于动态受控主元分析(dynamic controlled principal component analysis,DCPCA)模型的故障检测方法。首先,利用DCPCA提取动态受控主元(dynamic controlled principal component,DCPC),所得DCPC包含过程的自回归特性和与控制输入之间的动态因果关系,使得构建的DCPCA模型更精确。然后,针对传统方法只对过程变量进行静态空间结构的故障检测,忽略了动态特性的问题,基于DCPCA模型适时应用检测综合指标,对系统进行静态重构误差和动态模型误差的双重检测,使得检测结果更全面。最后,基于田纳西-伊斯曼(Tennessee-Eastman,TE)过程的仿真结果验证了所提方法的可行性和有效性。
