基于字典学习算法的调度运行信息稀疏编码方法
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摘要
在电力系统中,随着电网规模的不断扩大,运行信息的数据规模与复杂程度不断增大,对其编码效率提出了更高的要求。字典学习算法是当前应用较为广泛的人工智能算法,其优势在于能够给出一个标准的基础信息库,使得依据该库所得到运行信息表示形式相对稀疏。首先介绍了字典学习算例原理与实施方法;结合电力系统调度运行控制实际,提出了基于字典学习的调度运行信息稀疏编码方法。最后基于某电网实际,利用近二年的运行记录信息构建了调度运行字典。算例表明利于该字典所编码表示的调度运行记录稀疏度处于0.35～0.80之间,符合大数据处理的稀疏性要求。
In power system, the data size and complexity of the operation information are increasing quickly with the continuous expansion of power grid, which puts forward higher requirements for sparse coding efficiency. Dictionary learning algorithm is a widely used artificial intelligence algorithm at present. Its advantage is that it can provide a standard basic information base to present a relatively sparse representation for operation information based on the standard basic information base. The principle and implementation of dictionary learning algorithm was firstly introduced. Then a specific sparse coding method for dispatching operation information based on dictionary learning was proposed. Finally, based on the actual situation of a power grid, the dispatching operation dictionary was constructed with the operation record information of the last two years. The case study shows that the sparsity of the scheduling operation record encoded by the dictionary is between 0.35 and 0.80, which meets the sparsity requirement of big data processing.
In power system, the data size and complexity of the operation information are increasing quickly with the continuous expansion of power grid, which puts forward higher requirements for sparse coding efficiency. Dictionary learning algorithm is a widely used artificial intelligence algorithm at present. Its advantage is that it can provide a standard basic information base to present a relatively sparse representation for operation information based on the standard basic information base. The principle and implementation of dictionary learning algorithm was firstly introduced. Then a specific sparse coding method for dispatching operation information based on dictionary learning was proposed. Finally, based on the actual situation of a power grid, the dispatching operation dictionary was constructed with the operation record information of the last two years. The case study shows that the sparsity of the scheduling operation record encoded by the dictionary is between 0.35 and 0.80, which meets the sparsity requirement of big data processing.
引文
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[3] 薛盖超,何宇,王丹,等.基于态势感知理论的大电网运行控制系统设计及开发应用[J].机械与电子,2018,36(3):3-6,10.
[4] 周学斌,李晓明,李雷,等.采用长短期记忆网络与压缩感知实现电物理量轻型化方法[J].电力系统自动化,2019,43(1):102-109,241.
[5] 孙国栋,周振,王俊豪,等.基于稀疏编码空间金字塔匹配和GA-SVM的列车故障自动识别[J].光学精密工程,2018,26(12):3087-3098.
[6] 万源,张景会,陈治平,等.基于弹性网和直方图相交的非负局部稀疏编码[J].计算机应用,2019,39(3):706-711.
[7] 徐佳庆,万文,吕启.基于二阶矩稀疏编码的高光谱遥感图像分类[J].计算机科学,2018,45(9):288-293.
[8] 李军,於阳.基于稀疏编码的短期风电功率时间序列预测[J].电力系统保护与控制,2018,46(12):16-23.
[9] 孙利雷,秦进.稀疏字典学习方法综述[J].贵州大学学报(自然科学版),2018,35(5):81-86.
[10] 潘可佳,王鑫,杨帆,等.面向电力大数据的多源异构数据融合技术研究[J].机械与电子,2017,35(9):7-11.
[11] 薛文俊,谢淑翠,王至琪.基于层次化字典学习的图像超分辨率方法[J].计算机工程与设计,2019,40(2):488-491.
[12] 刘连,王孝通.基于多尺度图像块分类的字典学习算法[J].科学技术与工程,2019,19(4):150-154.
[13] 林杨,高思煜,刘同舜,等.基于深度学习的高速铣削刀具磨损状态预测方法[J].机械与电子,2017,35(7):12-17.
[14] 牛彪,李海洋.低字典相干性K-SVD算法研究[J].计算机与数字工程,2019,47(1):92-98.