CN111555357A - 一种光伏发电优化控制的方法 - Google Patents
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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Abstract
本发明公开了一种光伏发电优化控制的方法;利用新型电子器件忆阻器的忆阻特性,提出了一种用于并网控制的忆阻器神经网络优化算法,改善了光伏发电系统的控制性能。本发明公开的一种光伏发电优化控制方法,不仅实现了对电流的跟踪控制,而且有效减少了谐波电流含量,提高了光伏发电效率。
Description
技术领域
本发明涉及光伏发电领域,特别涉及一种光伏发电优化控制的方法。
背景技术
作为一种清洁高效的新能源,近年来光伏发电得到了迅猛的发展。并网发电不仅是光伏利用的发展趋势,也是太阳能发电规模化发展的必然方向。在实际的光伏发电系统中,谐波电流含量较高,这个问题不仅会降低电能的利用率,使电气设备过热、绝缘老化、产生噪声、缩短使用寿命,严重时将会导致电气设备发生故障、烧毁、引起继电保护等自动装置的错误动作,干扰周围的电子设备。为了更高效、环保地利用太阳能,实现对谐波电流的有效抑制是光伏发电中的一个重要研究方向。
发明内容
为了解决背景技术中的问题,本发明公开了一种光伏发电优化控制的方法,包括以下步骤:
步骤1:建立一种基于忆阻器神经网络优化控制的光伏发电系统模型;
步骤2:建立一种忆阻器神经网络,忆阻神经网络选用J-I-L 结构,输入层为J个节点,隐含层为I个节点,输出层为 L个节点,Gij为连接输入层和隐含层之间的权值,Gjl为隐含层与输出层间的权值,△Gij为权值的更新值;
隐含层的输入和输出为:
其中,Roff和Ron为忆阻器的两个极限电阻值,uv为离子移动速率,D为两层二氧化钛总厚度,v为激励脉冲,△t为激励脉冲时间;
输出层的输入和输出为:
具体运行如下:
(1)初始化忆阻器神经网络,给出输入层、输出层中神经元的个数以及初始参数;
(2)采样得到指定电流i* c和输出电流ic,得到误差电流e(k)= ic-i* c;
(3)更新忆阻器神经网络权值;
(4)计算忆阻器神经网络输出,确定所述PID模块的最优控制参数;
(5)令 k=k+1,返回(2);
步骤3:误差电流e(k)被输入到经过忆阻器神经网络优化后的PID模块产生作用于所述SPWM模块的控制信号;
步骤4:所述SPWM模块的输出信号作用于所述光伏逆变器,使得光伏发电系统的输出电流ic跟踪指定电流i* c。
有益效果:
本专利公开的一种光伏发电优化控制的方法,结合新型电子器件忆阻器搭建出一种基于忆阻器神经网络优化控制的光伏发电系统,不仅实现了对电流的跟踪控制,而且降低了谐波电流对输出电流的影响。
附图说明
图1 是本发明实施例的一种基于忆阻器神经网络优化控制的光伏发电系统框图。
图2 是本发明实施例的一种基于忆阻器神经网络优化控制的光伏发电系统的输出电流波形。
具体实施方式
为了使本技术领域的人员更好地理解本发明实施例的方案,下面结合附图和实施方式对本发明实施例作进一步的详细说明。
如图1所示,一种光伏发电优化控制的方法的具体实施包括以下步骤:
步骤1:建立一种基于忆阻器神经网络优化控制的光伏发电系统模型;
步骤2:建立一种忆阻器神经网络,忆阻神经网络选用J-I-L 结构,输入层为J个节点,隐含层为I个节点,输出层为 L个节点,Gij为连接输入层和隐含层之间的权值,Gjl为隐含层与输出层间的权值,△Gij为权值的更新值;
隐含层的输入和输出为:
其中,Roff和Ron为忆阻器的两个极限电阻值,uv为离子移动速率,D为两层二氧化钛总厚度,v为激励脉冲,△t为激励脉冲时间;
输出层的输入和输出为:
具体运行如下:
(1)初始化忆阻器神经网络,给出输入层、输出层中神经元的个数以及初始参数;
(2)采样得到指定电流i* c和输出电流ic,得到误差电流e(k)= ic-i* c;
(3)更新忆阻器神经网络权值;
(4)计算忆阻器神经网络输出,确定所述PID模块的最优控制参数;
(5)令 k=k+1,返回(2);
步骤3:误差电流e(k)被输入到经过忆阻器神经网络优化后的PID模块产生作用于所述SPWM模块的控制信号;
步骤4:所述SPWM模块的输出信号作用于所述光伏逆变器,使得光伏发电系统的输出电流ic跟踪指定电流i* c。
为验证上述方法的可实现性,本实施例基于上述步骤,在Matlab13.0环境中进行了仿真, 图2显示了一种基于忆阻器神经网络优化控制的光伏发电系统的输出电流波形。
当然,本发明还可有其他多种实施例,在不背离本发明精神及其实质的情况下,熟悉本领域的技术人员当可根据本发明作出各种相应的改变和变形,但这些相应的改变和变形都应属于本发明所附的权利要求的保护范围。
Claims (1)
1.一种光伏发电优化控制的方法,其特征在于,包括以下步骤:
步骤1:建立一种基于忆阻器神经网络优化控制的光伏发电系统模型;
步骤2:建立一种忆阻器神经网络,忆阻神经网络选用J-I-L 结构,输入层为J个节点,隐含层为I个节点,输出层为 L个节点,Gij为连接输入层和隐含层之间的权值,Gjl为隐含层与输出层间的权值,△Gij为权值的更新值;
隐含层的输入和输出为:
其中,Roff和Ron为忆阻器的两个极限电阻值,uv为离子移动速率,D为两层二氧化钛总厚度,v为激励脉冲,△t为激励脉冲时间;
输出层的输入和输出为:
具体运行如下:
(1)初始化忆阻器神经网络,给出输入层、输出层中神经元的个数以及初始参数;
(2)采样得到指定电流i* c和输出电流ic,得到误差电流e(k)= ic-i* c;
(3)更新忆阻器神经网络权值;
(4)计算忆阻器神经网络输出,确定所述PID模块的最优控制参数;
(5)令 k=k+1,返回(2);
步骤3:误差电流e(k)被输入到经过忆阻器神经网络优化后的PID模块产生作用于所述SPWM模块的控制信号;
步骤4:所述SPWM模块的输出信号作用于所述光伏逆变器,使得光伏发电系统的输出电流ic跟踪指定电流i* c。
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CN115047753A (zh) * | 2022-06-16 | 2022-09-13 | 中国科学院光电技术研究所 | 一种基于模糊算法的自适应忆阻pid控制器设计方法 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102495953A (zh) * | 2011-11-29 | 2012-06-13 | 河北省电力建设调整试验所 | 基于采集到的电能质量数据和环境参数对光伏数据进行分析评估和发电负荷预测的方法 |
CN105305446A (zh) * | 2015-10-22 | 2016-02-03 | 南京亚派科技股份有限公司 | 基于智能控制的谐波电流跟踪方法 |
CN106532749A (zh) * | 2016-12-27 | 2017-03-22 | 合肥工业大学 | 一种微电网不平衡功率和谐波电压补偿系统及其应用 |
CN107533668A (zh) * | 2016-03-11 | 2018-01-02 | 慧与发展有限责任合伙企业 | 用于计算神经网络的节点值的硬件加速器 |
CN109659940A (zh) * | 2019-02-25 | 2019-04-19 | 南京工程学院 | 一种用于微电网特定次谐波补偿的储能变流器控制方法 |
CN109960307A (zh) * | 2019-03-01 | 2019-07-02 | 湖南诺诚光伏能源有限公司 | 一种光伏离网逆变器mppt自抗扰控制方法 |
CN110651330A (zh) * | 2017-05-22 | 2020-01-03 | 佛罗里达大学研究基金会 | 二分忆阻网络中的深度学习 |
-
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- 2020-06-04 CN CN202010497754.8A patent/CN111555357A/zh active Pending
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102495953A (zh) * | 2011-11-29 | 2012-06-13 | 河北省电力建设调整试验所 | 基于采集到的电能质量数据和环境参数对光伏数据进行分析评估和发电负荷预测的方法 |
CN105305446A (zh) * | 2015-10-22 | 2016-02-03 | 南京亚派科技股份有限公司 | 基于智能控制的谐波电流跟踪方法 |
CN107533668A (zh) * | 2016-03-11 | 2018-01-02 | 慧与发展有限责任合伙企业 | 用于计算神经网络的节点值的硬件加速器 |
CN106532749A (zh) * | 2016-12-27 | 2017-03-22 | 合肥工业大学 | 一种微电网不平衡功率和谐波电压补偿系统及其应用 |
CN110651330A (zh) * | 2017-05-22 | 2020-01-03 | 佛罗里达大学研究基金会 | 二分忆阻网络中的深度学习 |
CN109659940A (zh) * | 2019-02-25 | 2019-04-19 | 南京工程学院 | 一种用于微电网特定次谐波补偿的储能变流器控制方法 |
CN109960307A (zh) * | 2019-03-01 | 2019-07-02 | 湖南诺诚光伏能源有限公司 | 一种光伏离网逆变器mppt自抗扰控制方法 |
Non-Patent Citations (2)
Title |
---|
夏思为: "《基于忆阻神经网络PID控制器设计》", 《计算机学报》 * |
魏江涛: "《忆阻神经网络在有源电力滤波器中的应用》", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115047753A (zh) * | 2022-06-16 | 2022-09-13 | 中国科学院光电技术研究所 | 一种基于模糊算法的自适应忆阻pid控制器设计方法 |
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