[1] YANG Z K, LIU C Y, SONG X L, et al. Application of RBF neural network PID in wet flue gas desulfurization of thermal power plant[C]//International Conference on Machine Learning and Cybernetics. Jeju: IEEE, 2016. [2] 朱竹军. 基于专家控制的炉外湿法脱硫自动控制研究及应用[J]. 自动化与仪表, 2019, 34(1): 24-27 ZHU Zhujun. Research and application of wet desulfurization automatic control based on expert control[J]. Automation & Instrumentation, 2019, 34(1): 24-27 [3] 罗睿, 徐衍安, 王毅, 等. 电厂厂级监控信息系统移动端开发及应用[J]. 热力发电, 2018, 47(6): 137-142 LUO Rui, XU Yan’an, WANG Yi, et al. Development and application of mobile terminal in plant-level supervisory information system for power plant[J]. Thermal Power Generation, 2018, 47(6): 137-142 [4] 侯子良. 火电厂厂级控制系统概念探讨[J]. 中国电力, 2011, 44(6): 20-21, 25 HOU Ziliang. Conception of plant supervisory control system in fossil fired power plant[J]. Electric Power, 2011, 44(6): 20-21, 25 [5] ZHANG J, CAO W L, WANG B S, et al. Realization for data communications of the supervisory information system of plant level in power plant[C]// International Conference on Industrial Technology. IEEE, 2015. [6] 顾慧, 乔宗良, 司风琪, 等. 一种基于EKFCM算法的电站脱硫系统目标工况库的建立方法[J]. 中国电机工程学报, 2015, 35(15): 3859-3864 GU Hui, QIAO Zongliang, SI Fengqi, et al. A target conditions library method in power plant desulphurization system based on EKFCM algorithm[J]. Proceedings of the CSEE, 2015, 35(15): 3859-3864 [7] 许彦斌, 赵明, 吕金花, 等. 基于工况划分的机组优化运行寻优方法[J]. 节能技术, 2018, 36(2): 139-144 XU Yanbin, ZHAO Ming, LV Jinhua, et al. Optimization of unit operation based on operating condition[J]. Energy Conservation Technology, 2018, 36(2): 139-144 [8] 刘延泉, 牛成林, 程海燕, 等. 330MW机组湿法烟气脱硫控制系统目标值优化[J]. 中国电力, 2012, 45(4): 68-72 LIU Yanquan, NIU Chenglin, CHENG Haiyan, et al. Target optimization of wet flue gas desulfurization control system in 330 MW unit[J]. Electric Power, 2012, 45(4): 68-72 [9] 王珊, 刘明, 严俊杰. 采用粒子群算法的热电厂热电负荷分配优化[J]. 西安交通大学学报, 2019, 53(9): 159-166 WANG Shan, LIU Ming, YAN Junjie. Optimizing heat-power load distribution of thermal power plants based on particle swarm algorithm[J]. Journal of Xi'an Jiaotong University, 2019, 53(9): 159-166 [10] BEZDEK J C. Pattern recognition with fuzzy objective function algorithms[J]. Advanced applications in pattern recognition, 1981, 22(1171): 203-239. [11] 刘辉舟, 周开乐, 胡小建. 基于模糊负荷聚类的不良负荷数据辨识与修正[J]. 中国电力, 2013, 46(10): 29-34 LIU Huizhou, ZHOU Kaile, HU Xiaojian. Bad data identification and correction based on load clustering by FCM algorithm[J]. Electric Power, 2013, 46(10): 29-34 [12] 王锡辉, 陈厚涛, 朱晓星, 等. 基于模糊C均值聚类的电站锅炉燃烧在线诊断[J]. 热力发电, 2019, 48(9): 77-82 WANG Xihui, CHEN Houtao, ZHU Xiaoxing, et al. Online diagnosis for combustion of power station boilers based on fuzzy C mean clustering[J]. Thermal Power Generation, 2019, 48(9): 77-82 [13] XIE X L, BENI G A. A validity measure for fuzzy clustering algorithm[J]. IEEE Trans on pattern Anal Machine Intel. IEEE, 1991(8): 841-846. [14] AJMAL M M, ALHASHMI H M, KHAN M M. Implementing sustainable procurement strategy in the oil and gas sector: analytic hierarchy process approach[J]. International Journal of Service Science, Management, Engineering, and Technology,2021,12(2):1-10. [15] 李远远, 刘光前. 基于AHP-熵权法的煤矿生产物流安全评价[J]. 安全与环境学报, 2015, 15(3): 29-33 LI Yuanyuan, LIU Guangqian. Research on the safety evaluation of coal mine production logistics based on AHP-entropy method[J]. Journal of Safety and Environment, 2015, 15(3): 29-33 [16] 蔡宁, 张则强, 邹宾森, 等. 考虑能耗的多目标拆卸线平衡优化与层次分析法决策[J]. 计算机集成制造系统, 2019, 25(1): 125-136 CAI Ning, ZHANG Zeqiang, ZOU Binsen, et al. Multi-objective disassembly line balancing optimization and analytic hierarchy process decision-making considering energy consumption[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 125-136 [17] 赵洪山, 张健平, 李浪. 基于最优权重和隶属云的风电机组状态模糊综合评估[J]. 中国电力, 2017, 50(5): 88-94 ZHAO Hongshan, ZHAO Jianping, LI Lang. Fuzzy comprehensive assessment of wind turbines status based on optimal weight and membership cloud[J]. Electric Power, 2017, 50(5): 88-94 [18] WEI L, YUAN Z X, YAN Y Y, et al. Evaluation of energy saving and emission reduction effect in thermal power plants based on entropy weight and PROMETHEE method[C]//Chinese Control and Decision Conference. IEEE, 2016. [19] SUN Y Y, WANG Y M, GUO L L, et al. The comparison of optimizing SVM by GA and grid search[C]// International Conference on Electronic Measurement & Instruments. IEEE, 2018. [20] 田芳, 周孝信, 于之虹. 基于支持向量机综合分类模型和关键样本集的电力系统暂态稳定评估[J]. 电力系统保护与控制, 2017, 45(22): 1-8 TIAN Fang, ZHOU Xiaoxin, YU Zhihong. Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J]. Power System Protection and Control, 2017, 45(22): 1-8 [21] JINDAL A, DUA A, KAUR K, et al. Decision tree and SVM-based data analytics for theft detection in smart grid[J]. IEEE Transactions on Industrial Informatics, 2016: 1-1.
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