针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)...针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)算法与扰动观察P&O(perturbation and observation)法混合控制的光伏系统最大功率跟踪策略。SFO算法同时使用旗鱼(捕食者)和沙丁鱼(猎物)2个种群,可保证粒子在全局空间探索。所提混合算法先利用SFO算法快速跟踪到最大功率点附近,再利用小步长P&O法对最大功率点进行精细搜索,最后利用分段步长的方法同时兼顾MPPT搜索速度和搜索精度的要求。仿真结果表明,所提混合控制策略有效提升了控制系统的响应速度及跟踪精度,提升了系统的稳定性。展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolut...为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。展开更多
In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm...In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm which was recently reported in literature.Stability analysis has been done to verify the suitability of the proposed structure for industrial processes.The proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC system.Furthermore,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.展开更多
文摘针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)算法与扰动观察P&O(perturbation and observation)法混合控制的光伏系统最大功率跟踪策略。SFO算法同时使用旗鱼(捕食者)和沙丁鱼(猎物)2个种群,可保证粒子在全局空间探索。所提混合算法先利用SFO算法快速跟踪到最大功率点附近,再利用小步长P&O法对最大功率点进行精细搜索,最后利用分段步长的方法同时兼顾MPPT搜索速度和搜索精度的要求。仿真结果表明,所提混合控制策略有效提升了控制系统的响应速度及跟踪精度,提升了系统的稳定性。
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。
文摘In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm which was recently reported in literature.Stability analysis has been done to verify the suitability of the proposed structure for industrial processes.The proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC system.Furthermore,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.
