摘要
深度学习是感知智能电网暂态安全状态的有效方法,针对多层重构学习过程低维特征及结构参数难以全局寻优的问题,提出了一种改进深度置信网络(Deep Belief Network,DBN)方法。首先,该方法利用SMOTE过采样算法,增加样本多样性,促使DBN深层架构的挖掘。其次,直接面向噪声样本,DBN通过网络中各神经元吉布斯抽样的二值状态,增强重构特征的抗噪能力。最后,建立了基于遗传算法(Genetic Algorithm,GA)的GA-DBN模型,有效解决DBN结构参数调试繁琐的问题,确保DBN高精度地从底层量测数据提取低维特征,提高安全分类精度。新英格兰10机39节点系统的仿真实验表明,在样本不平衡、含噪声情况下,所提算法比其他算法的失稳漏判率降低,辩识准确率和F;分数提升。
Deep learning shows superiority in transient security situational awareness of smart grids. However, it is hard to find optimal parameters for low-dimensional features and network structures in multi-layer network reconstruction.Thus an improved deep belief network(DBN) is proposed. In this method, the SMOTE algorithm is first adopted for oversampling to balance the proportion of data samples. This ensures mining of the deep structure of DBN. Then the binary neuron from Gibbs sampling is used in DBN to improve the noise immunity in the reconstruction process. Given the repeated manual debugging of network structure, a genetic algorithm(GA)-based GA-DBN model is finally employed to achieve global optimization. The low-dimensional features are abstracted accurately from the high-dimensional measurement data at the bottom layer and the classification precision is guaranteed. A test on the New England 39-bus system with imbalance and noise samples shows that the proposed method outperforms the existing methods in accuracy,F;score and missing alarm.
作者
李海英
沈益涛
罗雨航
LI Haiying;SHEN Yitao;LUO Yuhang(Department of Electrical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Sichuan Water Conservancy Vocational College,Chengdu 611231,China)
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2022年第5期171-177,共7页
Power System Protection and Control
基金
国家自然科学基金项目资助(51777126)。
关键词
暂态安全感知
深度置信网络
SMOTE
遗传算法
样本不平衡
transient security situational awareness
deep belief network
SMOTE
genetic algorithm
sample imbalance
