| 基于机器学习算法的电网运行方式计算实践研究 |
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| 引用本文： | 曾思成,陈勇,郭中华,宋荣昌.基于机器学习算法的电网运行方式计算实践研究[J].北京理工大学学报,2017,37(S2):193-197. |
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| 作者姓名： | 曾思成 陈勇 郭中华 宋荣昌 |
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| 作者单位： | 中国电力科学研究院有限公司, 北京 100192,中国电力科学研究院有限公司, 北京 100192;华中科技大学 计算机科学与技术学院, 湖北, 武汉 430074,中国电力科学研究院有限公司, 北京 100192,北京理工大学 机电学院, 北京 100081 |
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| 基金项目： | 国家自然科学基金资助项目（60217289） |
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| 摘 要： | 电力系统中电网运行方式的变化会引起了电力设备参数的变化，运行方式的计算量会随着电网复杂程度的增加而逐渐增大，为解决这种工作量繁重而又重复性的工作，结合TensorFlow架构的优势和机器学习算法对电网运行方式计算进行实践研究，结合实际需求对线性回归算法、逻辑回归二分类算法以及深度神经网络算法进行对比分析，给出了性能评价指标.
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| 关 键 词： | 机器学习 电网运行方式 TensorFlow |
| 收稿时间： | 2017/9/20 0:00:00 |
The Practice Research of Power System Operation Mode Calculation Based On Machine Learning |
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| ZENG Si-cheng,CHEN Yong,GUO Zhong-hua and SONG Rong-chang.The Practice Research of Power System Operation Mode Calculation Based On Machine Learning[J].Journal of Beijing Institute of Technology(Natural Science Edition),2017,37(S2):193-197. |
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| Authors: | ZENG Si-cheng CHEN Yong GUO Zhong-hua and SONG Rong-chang |
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| Institution: | China Electric Power Research Institute, Beijing 100192, China,China Electric Power Research Institute, Beijing 100192, China;School of computer science and technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China,China Electric Power Research Institute, Beijing 100192, China and School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | The change of power system operation mode can cause the change of the power equipment parameters, operation mode of the calculation with the power grid is gradually increasing with the increase of complexity. In order to solve the heavy workload and repetitive tasks, this paper describes how to take the advantage of TensorFlow architecture and machine learning algorithm to study thegrid operation mode calculation. The linear regression algorithm, logical regression two-classification algorithm and depth neural network algorithm are analyzed comparatively. Index of performance evaluation can be provided combining with the actual requirements. |
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| Keywords: | machine learning power system operation mode tensorFlow |
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