This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary erro...This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.展开更多
基金Supported by the National Natural Science Foundation of China(61374044)Shanghai Science Technology Commission(12510709400)+1 种基金Shanghai Municipal Education Commission(14ZZ088)Shanghai Talent Development Plan
文摘This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.
文摘随着不确定性可再生能源大规模并网,电网频率特性日益复杂。传统火电机组具有响应时间长、无法准确跟踪指令等问题,亟须利用储能提高火电机组参与自动发电控制(automatic generation control,AGC)调频时的调节性能。首先,针对调频考核规则,建立调频性能指标数学模型,并考虑火储系统出力特性,结合改进层次分析法校正调频子指标权重系数,以此构建以调频性能最优为目标的第一阶段优化模型;在此基础上,为了减少储能荷电状态(state of charge,SOC)越限和深度充放情况,以储能SOC偏差最小为目标构建第二阶段优化模型。仿真验证表明:所提的两阶段调频方法能够提高火储联合系统的调频性能和调频收益,同时有效减少储能深度充放情况和工作寿命损耗,提高储能辅助调频服务的可持续性。
