基于宽度学习的风光容量配置研究
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摘要
针对风光发电的大量接入,将引起配电网规划与运行特征的根本性改变问题,提出一种基于宽度学习的风光容量配置方法。利用网络节点电压、风光电源出力等数据对宽度学习容量配置模型进行训练,模型精度和结果的合理性采用均方根误差和电压稳定性评价指标进行评估。以IEEE 33节点系统作为算例进行仿真,给出了满足总投资成本和网络有功损耗最小的容量配置结果,并与支持向量机、核极限学习机进行对比分析,验证了所提模型和方法的可行性和有效性。
A large number access of wind and photovoltaic power generations will cause fundamental changes in the planning and operation characteristics of distribution networks. This paper proposes a capacity selection method based on board learning system. The board learning capacity configuration model is trained by using data such as network node voltage and power output. The accuracy of the model and the rationality of the results are evaluated by using root mean square error and voltage stability evaluation indicators. The IEEE 33 node system is used as an example to simulate. The capacity selection results,which satisfy the total investment cost and the network active loss,are obtained. And compare with the support vector machine and the kernel extreme learning machine to verify the feasibility and effectiveness of the proposed model and method.
A large number access of wind and photovoltaic power generations will cause fundamental changes in the planning and operation characteristics of distribution networks. This paper proposes a capacity selection method based on board learning system. The board learning capacity configuration model is trained by using data such as network node voltage and power output. The accuracy of the model and the rationality of the results are evaluated by using root mean square error and voltage stability evaluation indicators. The IEEE 33 node system is used as an example to simulate. The capacity selection results,which satisfy the total investment cost and the network active loss,are obtained. And compare with the support vector machine and the kernel extreme learning machine to verify the feasibility and effectiveness of the proposed model and method.
引文
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[14]蒋斌,吴健,冯亮,等.含分布式发电的有源配电网电压稳态指标计算研究[J].电子测量与仪器学报,2017,31(6):885-891.Jiang Bin,Wu Jian,Feng Liang,et al.Research on static voltage stability calculation indicator of active distribution network with distributed generation[J].Journal of Electronic Measurement and Instrumentation,2017,31(6):885-891.
[2]曾奕铭,张慧媛,龚仁敏.一种对配网继电保护影响最小的分布式电源的最优接入方式[J].电测与仪表,2017,54(10):55-62.Zeng Yiming,Zhang Huiyuan,Gong Renmin.An optimal access method of distributed generation with minimal impact on distribution network relay protection[J].Electrical Measurement&Instrumentation,2017,54(10):55-62.
[3]Canny,J.A computational approach to edge detection[J].IEEE transactions on pattern analysis and machine intelligence,1986,8(6):679-98
[4]张昊宇,姚钢,殷志柱,等.基于小波神经网络与KNN机器学习算法的六相永磁同步电机故障态势感知方法[J].电测与仪表,2019,56(2):1-9.Zhang Haoyu,Yao Gang,Yin Zhizhu,et al.Faul tstate perception method for six-phase PMSM based on wavelet neural network and KNNmachine learning algorithm[J].Electrical Measurement&Instrumentation,2019,56(2):1-9.
[5]Zhulin Liu,C L Philip Chen.Broad Learning System:Structural Extensions Onsingle-layer and Multi-layer Neural Networks[C]//2017 International Conference on Security,Pattern Analysis,and Cyber-netics(SPAC),2017.
[6]C L P Chen,C-Y Zhang.Data-intensive Applications,Challenges,Techniques and Technologies:A Survey on Big Data[J].Information Sciences,2014,275:314-347.
[7]曾宪华,侯苏丽.基于宽度学习的集成超分辨率重建方法[J].计算机工程与设计,2016,37(9):2526-2532.Zeng Xianhua,Hou Suli.Integrated super-resolution reconstruction method based on broad-learning[J].Computer Engineering and Design,2016,37(9):2526-2532.
[8]袁利平,陈川南.人工智能视域下的宽度学习及在教育中的应用[J].远程教育杂志,2018,36(4):49-56.Yuan Liping,Chen Chuannan.Broad Learning System and Its Educational Applications in the View of Artificial Intellegence[J].Journal of Distance Education,2018,36(4):49-56.
[9]郑云飞,陈霸东.基于最小p-范数的宽度学习系统[J].模式识别与人工智能,2019,32(1):51-57.Zheng Yunfei,Chen Badong.Least p-Norm Based Broad Learning System[J].Pattern Recognition and Artificial Intelligence,2019,32(1):51-57.
[10]贾晨,刘华平,续欣莹,等.基于宽度学习方法的多模态信息融合[J].智能系统学报,2019,14(1):150-157.Jia Chen,Liu Huaping,Xu Xinying,et al.Multi-modal information fusion based on broad learning method[J].CAAI Transactions on Intelligent Systems,2019,14(1):150-157.
[11]Zhang Zhe,Li Gengyin,Wei Junqiang.Probabilistic evaluation of voltage quality in distribution networks considering the stochastic characteristic of distributed generators[J].Proceedings of the CSEE,2013,33(13):150-156.
[12]Evangelopoulos V A,Georgilakis P S.Optimal distributed generation placement under uncertainties based on point estimate method embedded genetic algorithm[J].IET Generation,Transmission and Distribution,2014,8(3):389-400.
[13]汤泽琦,吕智林.孤岛微网多场景机会约束动态调度[J].电测与仪表,2018,55(12):66-73.Tang Zeqi,Lv Zhilin.Multi-scenario chance constrained dynamic scheduling in islanded micro-grid[J].Electrical Measurement&Instrumentation,2018,55(12):66-73.
[14]蒋斌,吴健,冯亮,等.含分布式发电的有源配电网电压稳态指标计算研究[J].电子测量与仪器学报,2017,31(6):885-891.Jiang Bin,Wu Jian,Feng Liang,et al.Research on static voltage stability calculation indicator of active distribution network with distributed generation[J].Journal of Electronic Measurement and Instrumentation,2017,31(6):885-891.