基于深度学习的光伏储能电站负荷模糊逻辑优化控制算法

Fuzzy Logic Optimization Control Algorithm for Load ofPhotovoltaic Energy Storage Power Station Based on Deep Learning

传统的负荷优化控制算法应用在光伏储能电站中,不能根据实时非线性负荷变化问题进行相应优化控制调整,造成负荷功率的浪费,因此,提出一种基于深度学习的光伏储能电站负荷模糊逻辑优化控制算法,以更好地适应光伏储能电站运行工况的变化。分析机组故障趋势计算参数劣化度,得到光伏出力情况,获取储能电站机组带负荷能力,模糊逻辑下建立负荷优化问题模型,根据模糊逻辑控制器示意图,建立优化目标函数和功率平衡的约束条件,基于深度学习对模型进行求解,实现负荷分配控制的优化。为验证所设计算法的有效性,将传统负荷控制算法与所设计的光伏储能电站负荷控制算法进行对比。结果表明,计及功率概率性平衡时,所设计的控制算法调节响应方面较好,不存在弃光现象,降低了电站总输出功率。

The traditional load optimization control algorithm applied in photovoltaic energy storage stations cannot optimize and adjust the control based on real-time nonlinear load changes,resulting in waste of load power.A deep learning-based fuzzy logic optimization control algorithm for photovoltaic energy storage station load is proposed to better adapt to changes in operating conditions of photovoltaic energy storage stations.The unit failure trend is analyzed,the parameter degradation degree is calculated,the photovoltaic output situation is obtained,the load capacity of the energy storage power station unit is obtained,the load optimization model is established under fuzzy logic,the optimization objective function and power balance constraint conditions are established according to the fuzzy logic controller diagram,and the model is solved based on deep learning to realize the optimization of load distribution control.To verify the effectiveness of the design algorithm,a comparison is made between the traditional load control algorithm and the designed load control algorithm for photovoltaic energy storage stations.The results show that when considering the probability balance of power,the designed control algorithm has good response in terms of regulation,and there is no phenomenon of light abandonment,reducing the total output power of the power station.