The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To ...The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.展开更多
大规模电动汽车(plug-in electric vehicle,PEV)和风光等可再生能源发电并网使配电网分布式电源(distributed generation,DG)定容选址需考虑更多的不确定因素,为此,利用机会约束规划方法建立了以环境效益、供电可靠性、DG总费用和有功...大规模电动汽车(plug-in electric vehicle,PEV)和风光等可再生能源发电并网使配电网分布式电源(distributed generation,DG)定容选址需考虑更多的不确定因素,为此,利用机会约束规划方法建立了以环境效益、供电可靠性、DG总费用和有功损耗最优为目标的DG优化配置模型,并提出蒙特卡洛模拟嵌入改进量子粒子群(improved quantum particle swarm optimization algorithm-Monte Carlo simulation,IQPSO-MCS)的方法进行求解。在优化配置中考虑了风电、光伏、微型燃气轮机3种DG的选址和定容;并针对输出功率不确定的风力发电、光伏发电和电动汽车建立了概率模型,利用蒙特卡洛模拟法将随机性问题转化为确定性问题,实现含不确定因素的配电网随机潮流计算;最后由带自适应变异机制的IQPSO算法全局寻优得到最优配置方案。以IEEE 33节点测试配电系统为例,验证了所提模型和方法的有效性和实用性。展开更多
基金This work is supported by the National Science Foundation of China(61806219,61703426,and 61876189)by National Science Foundation of Shaanxi Provence(2021JM-226)by the Young Talent fund of the University,and the Association for Science and Technology in Shaanxi,China(20190108,20220106)by and the Innovation Capability Support Plan of Shaanxi,China(2020KJXX-065).
文摘The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.
