摘要
波浪能的波动性和随机性较大,为满足并网要求,需提高波浪发电系统输出功率的平滑性。采用蓄电池/超级电容混合储能对波浪发电系统输出功率进行平滑,利用极限学习机算法滚动预测波浪发电系统输出功率,获取周期内平均功率。结合蓄电池能量密度高和超级电容功率密度高的特点,根据预测功率来控制蓄电池和超级电容的充放电,平滑波电系统在不同波浪条件下的输出功率。通过在规则波浪和突变波浪条件下的仿真,验证了其有效性。
Due to the fluctuation and randomness of wave energy,the output power of the wave power system must be smoothed in order to meet the requirements of grid-connected.The hybrid energy storage system (HESS) composed of batteries and super-capacitors was adopted to smooth the output power of the wave energy conversion system.The extreme learning machine was used to predict the output power of wave generation and obtain average output power constantly.Considering the high energy density of batteries and the high power density of super-capacitors,the charging and discharging of batteries and super-capacitors were controlled by the predicted power to smooth the output power under different wave conditions.The simulations were carried out under the condition of regular and abrupt waves to test the effectiveness of the proposed strategies.
作者
潘一夫
杨俊华
PAN Yi-fu;YANG Jun-hua(Faculty of Automation,Guangdong University of Technology,Guangzhou Guangdong 510006,China)
出处
《计算机仿真》
北大核心
2018年第12期68-72,共5页
Computer Simulation
基金
国家自然科学基金资助项目(513770265)
广东省科技计划项目(2016B090912006)
广东省自然科学基金项目(2015A030313487)
广东省教育部产学研合作专项资金(2013B090500089)
关键词
波浪发电系统
功率预测
混合储能系统
极限学习机算法
功率平滑控制
Wave power system
Power prediction
Hybrid energy storage system
Extreme learning machine
Power smoothing control