基于HHT与滤波算法的风电波动平抑策略研究
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摘要
由于一阶低通滤波算法在实际工程中具有较好的实用性,因此用其进行风电功率波动的平抑。首先,应用希尔伯特黄变换(HHT)获取风电的主频率,以此求取初始滤波时间常数。然后,滤波时间常数最小作为目标,以风电并网波动率等为约束,动态调整滤波时间步长,从而实现对风电波动的平抑。再者,以混合储能经济性最优为目标,同时兼顾充放电功率和SOC等约束,实现储能总载荷在蓄电池和超级电容器之间的分配。最后,通过算例验证了控制策略的有效性。
The first-order low-pass filtering algorithm is used to stabilize wind power fluctuation due to its good practicability in practical engineering. Firstly,Hilbert Huang transform(HHT)was used to obtain main frequency of wind power,and corresponding filtering time constant could also be acquired. Furthermore,wind power fluctuation was mitigated by the adjustment of filtering time step,where filtering time constant minimization was chosen as objective function and many factors were selected as the constraint,such as grid-connected wind power fluctuation rate. Energy storage load distribution between the battery and super capacitor was achieved by calculating hybrid energy storage optimal economy,and charge or discharge power and SOC constraints were considered. Finally,an example was given to prove the effectiveness of the control strategy.
The first-order low-pass filtering algorithm is used to stabilize wind power fluctuation due to its good practicability in practical engineering. Firstly,Hilbert Huang transform(HHT)was used to obtain main frequency of wind power,and corresponding filtering time constant could also be acquired. Furthermore,wind power fluctuation was mitigated by the adjustment of filtering time step,where filtering time constant minimization was chosen as objective function and many factors were selected as the constraint,such as grid-connected wind power fluctuation rate. Energy storage load distribution between the battery and super capacitor was achieved by calculating hybrid energy storage optimal economy,and charge or discharge power and SOC constraints were considered. Finally,an example was given to prove the effectiveness of the control strategy.
引文
[1]闫鹤鸣,李相俊,麻秀范,等.基于超短期风电预测功率的储能系统跟踪风电计划出力控制方法[J].电网技术,2015,39(2):432-439.
[2]丁华杰,宋永华,胡泽春,等.基于风电场功率特性的日前风电预测误差概率分布研究[J].中国电机工程学报,2013,33(34):136-144.
[3] Sornsen P,Cutululisn A,Vigueras-Rodriguez A,et al. Power Fluctuations from Large Wind Farms[J]. IEEE Trans. on Power System,2007,22(3):958-965.
[4] Zhang Y X,Dong Z Y,Luo F J,et al. Optimal Allocation of Battery Energy Storage Systems in Distribution Networks with High Wind Power Penetration[J]. IET Renewable Power Generation,2016,10(8):1105-1113.
[5] AKHMATOV V. Analysis of Dynamic Behavior of Electric Power Systems with Large Amount of Wind Power[D]. Copenhagen,Denmark:Electric Power Engineering Technical University of Denmark,2003.
[6]谢应昭,卢继平,翁宗林,等.改善风电输出功率特性的复合储能系统优化配置[J].电网技术,2016,40(7):2052-2058.
[7]李圣清,徐天俊,张彬,等.光伏直流微网母线电压稳定控制[J].电气传动,2015,45(3):48-52.
[8]杨国华,朱向芬,周鑫,等.基于遗传算法的风电混合储能容量优化配置[J].电气传动,2015,45(2):50-53.
[9]雷珽,欧阳曾恺,李征,等.平抑风能波动的储能电池SOC与滤波协调控制策略[J].电力自动化设备,2015,35(7):126-131.
[10]程志江,李永东,谢永流,等.带超级电容的光伏发电微网系统混合储能控制策略[J].电网技术,2015,9(10):2739-2745.
[11]张野,郭力,贾宏杰,等.基于电池荷电状态和可变滤波时间常数的储能控制方法[J].电力系统自动化,2012,36(6):34-38.
[12]张国驹,唐西胜,齐智平.平抑间歇式电源功率波动的混合储能系统设计[J].电力系统自动化,2011,35(20):24-28.
[13]邹见效,戴碧蓉,彭超,等.基于荷电状态分级优化的混合储能风电功率平抑方法[J].电力系统自动化,2013,37(24):1-5.
[14]邓汇娟,张铁山,周小光,等.超导储能蓄电池混合储能在风力发电中的应用[J].电源学报,2013,11(1):25-29.
[15]韩晓娟,田春光,程成,等.基于经验模态分解的混合储能系统功率分配方法[J].太阳能学报,2014,35(10):1889-1896.
[16]杨裕生,程杰,曹高萍.规模储能装置经济效益的判据[J].电池,2011,41(1):19-21.
[2]丁华杰,宋永华,胡泽春,等.基于风电场功率特性的日前风电预测误差概率分布研究[J].中国电机工程学报,2013,33(34):136-144.
[3] Sornsen P,Cutululisn A,Vigueras-Rodriguez A,et al. Power Fluctuations from Large Wind Farms[J]. IEEE Trans. on Power System,2007,22(3):958-965.
[4] Zhang Y X,Dong Z Y,Luo F J,et al. Optimal Allocation of Battery Energy Storage Systems in Distribution Networks with High Wind Power Penetration[J]. IET Renewable Power Generation,2016,10(8):1105-1113.
[5] AKHMATOV V. Analysis of Dynamic Behavior of Electric Power Systems with Large Amount of Wind Power[D]. Copenhagen,Denmark:Electric Power Engineering Technical University of Denmark,2003.
[6]谢应昭,卢继平,翁宗林,等.改善风电输出功率特性的复合储能系统优化配置[J].电网技术,2016,40(7):2052-2058.
[7]李圣清,徐天俊,张彬,等.光伏直流微网母线电压稳定控制[J].电气传动,2015,45(3):48-52.
[8]杨国华,朱向芬,周鑫,等.基于遗传算法的风电混合储能容量优化配置[J].电气传动,2015,45(2):50-53.
[9]雷珽,欧阳曾恺,李征,等.平抑风能波动的储能电池SOC与滤波协调控制策略[J].电力自动化设备,2015,35(7):126-131.
[10]程志江,李永东,谢永流,等.带超级电容的光伏发电微网系统混合储能控制策略[J].电网技术,2015,9(10):2739-2745.
[11]张野,郭力,贾宏杰,等.基于电池荷电状态和可变滤波时间常数的储能控制方法[J].电力系统自动化,2012,36(6):34-38.
[12]张国驹,唐西胜,齐智平.平抑间歇式电源功率波动的混合储能系统设计[J].电力系统自动化,2011,35(20):24-28.
[13]邹见效,戴碧蓉,彭超,等.基于荷电状态分级优化的混合储能风电功率平抑方法[J].电力系统自动化,2013,37(24):1-5.
[14]邓汇娟,张铁山,周小光,等.超导储能蓄电池混合储能在风力发电中的应用[J].电源学报,2013,11(1):25-29.
[15]韩晓娟,田春光,程成,等.基于经验模态分解的混合储能系统功率分配方法[J].太阳能学报,2014,35(10):1889-1896.
[16]杨裕生,程杰,曹高萍.规模储能装置经济效益的判据[J].电池,2011,41(1):19-21.