WO2022027717A1 - 一种提高光伏发电低电压穿越能力的方法 - Google Patents

一种提高光伏发电低电压穿越能力的方法 Download PDF

Info

Publication number
WO2022027717A1
WO2022027717A1 PCT/CN2020/108683 CN2020108683W WO2022027717A1 WO 2022027717 A1 WO2022027717 A1 WO 2022027717A1 CN 2020108683 W CN2020108683 W CN 2020108683W WO 2022027717 A1 WO2022027717 A1 WO 2022027717A1
Authority
WO
WIPO (PCT)
Prior art keywords
axis
low voltage
time
photovoltaic
control
Prior art date
Application number
PCT/CN2020/108683
Other languages
English (en)
French (fr)
Inventor
冯仰敏
赵勇
杨沛豪
杨洋
常洋涛
刘庆元
赵文超
薛菲
Original Assignee
西安热工研究院有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 西安热工研究院有限公司 filed Critical 西安热工研究院有限公司
Publication of WO2022027717A1 publication Critical patent/WO2022027717A1/zh

Links

Images

Classifications

    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381Dispersed generators
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24Arrangements for preventing or reducing oscillations of power in networks
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20The dispersed energy generation being of renewable origin
    • H02J2300/22The renewable source being solar energy
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/50Photovoltaic [PV] energy
    • Y02E10/56Power conversion systems, e.g. maximum power point trackers

Definitions

  • the invention belongs to the technical field of photovoltaic power generation, and particularly relates to a method for improving the low voltage ride-through capability of photovoltaic power generation.
  • Photovoltaic is connected to the grid through the inverter device.
  • the power electronic components used in the inverter device will be damaged. Therefore, it is necessary to carry out research on improving the low voltage ride-through capability of photovoltaic power generation.
  • Photovoltaic inverter control adopts PI control, traditional PI control cannot achieve no static difference adjustment, and does not have the ability to quickly track voltage.
  • model predictive control has received extensive attention in the field of photovoltaic grid-connected inverter control because of its strong fast dynamic response capability, simultaneous control of multiple targets, and good output characteristics.
  • the problem is that this requires a higher-performance processor, which undoubtedly increases the cost and is not conducive to the promotion of the control algorithm.
  • the purpose of the present invention is to provide a method for improving the low voltage ride-through capability of photovoltaic power generation, specifically applying a multi-step model prediction control strategy to improve the photovoltaic low voltage ride-through capability, the method is to establish a mathematical model for the current loop control in the photovoltaic L-type inverter , and simplify and discretize it. Aiming at the cycle delay problem existing in traditional model prediction, a prediction method for the next two sampling cycles at the current sampling time is proposed, that is, the sampling prediction step size at time k becomes twice the original step size, so that it can maintain long-term Optimum state, improve the low voltage ride-through capability of photovoltaic power station.
  • the present invention adopts the following technical solutions to realize:
  • a method for improving the low voltage ride-through capability of photovoltaic power generation comprising the following steps:
  • step 5) discretizing the feedforward compensation term in step 4) to obtain the prediction equations of the d-axis and the q-axis at time k+1;
  • step 7) Bring the output control amount at time k of step 6) into the prediction equations of d-axis and q-axis in step 5) to obtain new d-axis and q-axis prediction equations;
  • the predicted value at time k is determined according to the control quantity at time k-1, the control increment at time k is 0, and the prediction model at time k+2 is obtained according to the prediction equations of the new d-axis and q-axis in step 7);
  • a further improvement of the present invention is that the mathematical model of the middle photovoltaic L-type inverter in step 1) in the abc three-phase coordinate system is:
  • L is the filter inductance
  • R is the line equivalent impedance
  • i a , ib , ic are the inverter output three-phase AC current
  • u a , ub , uc are the inverter output three-phase AC voltage
  • ea , eb , and ec are the load side voltages.
  • step 2 is: according to the mathematical model of the photovoltaic L-type inverter in the abc three-phase coordinate system in step 1), perform Park transformation to obtain the photovoltaic L-type inverter in Mathematical model in the dq two-phase coordinate system:
  • Tabc ⁇ dq0 is the Park transformation matrix
  • is the electrical angular velocity
  • step 3 is: simplify the two-dimensional voltage equation obtained through Park transformation in step 2) into a mathematical model of single input and single output:
  • step 4 the specific implementation method of step 4) is: the cross-coupling term between the d-axis and the q-axis in the single-input and single-output mathematical model obtained in step 3) is regarded as a disturbance, and the subsequent current control is obtained.
  • step 5 A further improvement of the present invention is that the specific implementation method of step 5) is as follows: the feedforward compensation term of step 4) is discretized to obtain the prediction equations of the d-axis and the q-axis at time k+1 in:
  • step 6 the output control quantity at time k is expressed as a prediction equation form:
  • u d (k) and u q (k) are the output control quantities at time k
  • ⁇ u d (k) and ⁇ u q (k) are the control increments at time k.
  • step 7 the specific implementation method of step 7) is as follows: the output control amount at time k of step 6) is brought into the prediction equations of d-axis and q-axis in step 5), and new d-axis and q-axis are obtained.
  • step 8 the predicted value at time k is determined according to the control amount at time k-1, the control increment at time k is 0, and according to the new d axis of step 7),
  • the prediction equation of the q-axis, the prediction model at time k+2 is obtained:
  • step 9) is: according to the actual working condition of photovoltaic power generation low voltage ride through, setting the objective function in: are the current reference values of the d and q axes, respectively; id (k+2) and i q (k+2) are the predicted current values of the d and q axes at k+2, respectively; ⁇ 1 and ⁇ 2 are the d-axis current errors, respectively , the weight of the q-axis current error in the optimized performance function; ⁇ 1 , ⁇ 2 are the d-axis control voltage variation and the q-axis control voltage variation, respectively.
  • the present invention has the following beneficial effects:
  • the present invention adopts the model predictive control method in the current PI control of the photovoltaic power generation grid-connected inverter to realize the current fast tracking performance, thereby improving the photovoltaic power generation low voltage ride-through capability.
  • the present invention proposes a prediction scheme for the next two sampling periods at the current sampling time, so as to maintain the long-term optimal state and improve the photovoltaic low voltage and low ride through voltage regulation capability.
  • Figure 1 is a model of photovoltaic power generation L grid-connected inverter
  • Figure 2 is a schematic diagram of model predictive control
  • Figure 3 is the low voltage ride-through requirement curve of photovoltaic power station
  • Figure 4 is a block diagram of photovoltaic inverter grid-connected control predicted by a multi-step model
  • Figure 5 is a simulation diagram of ground fault verification low voltage ride-through capability
  • Figure 6 is a three-phase short-circuit grounding, grid-side voltage and current simulation diagram;
  • Figure 6(a) is the three-phase voltage of the grid, and
  • Figure 6(b) is the three-phase current of the grid;
  • Figure 7 is a single-phase short-circuit grounding, grid-side voltage and current simulation diagram; Figure 7(a) is the three-phase voltage of the grid, and Figure 7(b) is the three-phase current of the grid.
  • a layer/element when referred to as being "on" another layer/element, it can be directly on the other layer/element or intervening layers/elements may be present therebetween. element.
  • a layer/element when a layer/element is “on” another layer/element in one orientation, then when the orientation is reversed, the layer/element can be "under” the other layer/element.
  • L is the filter inductance
  • R is the line equivalent impedance
  • i a , ib , ic are the inverter output three-phase AC current
  • u a , ub , uc are the inverter output three-phase AC voltage
  • e a , e b , and e c are the load side voltages.
  • the mathematical model of the photovoltaic L-type inverter in the dq two-phase coordinate system can be obtained as:
  • Tabc ⁇ dq0 is the Park transformation matrix
  • is the electrical angular velocity
  • the state quantity predictive control at the next moment should be carried out.
  • the minimum objective function should be used as the constraint condition, and the voltage vector Carry out optimization to achieve the optimal voltage vector.
  • the voltage vector is compared with the switching state to realize the PWM control of the photovoltaic grid-connected inverter.
  • the present invention applies the model predictive control algorithm to the current control system.
  • the low-voltage ride-through control of the photovoltaic grid-connected inverter based on the prediction of the single-step model does not consider that there is a period delay when the system samples and calculates the PWM duty cycle, and it is impossible to know whether the optimal control effect can be maintained in the future.
  • the present invention proposes a prediction scheme for the current value of the next two sampling periods (k+1, k+2) at the current sampling moment, so as to keep the long-term optimal state and achieve the optimal control effect.
  • the control variable at time k-1 determines the predicted value at time k, that is, ⁇ u d (k) and ⁇ u q (k) are both 0, then:
  • the prediction model at time k+2 can be obtained as:
  • the low-voltage ride-through control of photovoltaic grid-connected inverters based on multi-step model prediction proposed in the present invention is similar to the first-step prediction of traditional multi-step predictive control.
  • the current prediction value at time k+1 is based on the sampling value at time k.
  • the proposed scheme of the present invention is based on the sampling value at time k, and the prediction step size becomes 2T s , and the prediction value of each step in the traditional multi-step prediction control is based on the previous step. predicted value.
  • the photovoltaic grid-connected inverter control system needs to quickly provide reactive power support in the face of voltage sag conditions, which requires the current to have fast tracking and response capabilities.
  • the control scheme proposed in the present invention has better control performance under the same constraint conditions.
  • the photovoltaic power station To make the photovoltaic power station have low voltage ride-through capability, it is necessary to control id and i q under the condition of voltage sag.
  • the active power corresponding to id In the grid-connected stable state, the active power corresponding to id is the same as the apparent power, and the power corresponding to i q is no. The active power is 0.
  • the reference value of active and reactive current that is, the relationship between the target current and the rated current is:
  • the i q corresponding to the reactive power delivered to the grid side by the photovoltaic inverter grid-connected should have the ability to track the voltage changes at the grid-connected point in real time, and must meet the following requirements:
  • the objective function proposed by the present invention is:
  • ⁇ 1 and ⁇ 2 are the d-axis current errors, respectively , the weight of the q-axis current error in the optimized performance function; ⁇ 1 , ⁇ 2 are the d-axis control voltage variation and the q-axis control voltage variation, respectively.
  • three-phase ground faults and single-phase ground faults are respectively set on the line side of the simulation model to verify that the multi-step model predictive current control scheme proposed in the present invention can improve the grid-connected low voltage ride-through capability of photovoltaic inverters.
  • Three-phase grounding and A-phase grounding faults occur in the 0.55s line, and the 0.75s relay protection action removes the faults.
  • the grid-connected inverter control system using multi-step model predictive control can quickly issue current commands to provide voltage support for the grid. Comparing the bus voltage waveform obtained without model prediction in Figure (a), the per-unit value of the grid-connected point voltage has increased from the original 0.65 to 0.72. In Figure (b), the per-unit value of the grid-connected point voltage is increased from 0.68 to 0.75 as shown in Figure 5.
  • the three-phase grid-connected voltage can still be kept in balance during the fault period, and the amplitude conversion range is not Large, it can smoothly pass through the three-phase short-circuit ground fault period.
  • the three-phase current of the power grid can also be kept in balance during the fault, and the control scheme of the present invention can limit the current within 1.1 times of the rated current to meet the requirements of the regulations.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Electrical Variables (AREA)
  • Inverter Devices (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

一种提高光伏低电压穿越能力的方法,包括步骤:1)建立光伏L型逆变器数学模型;2)对步骤1)中数学模型进行Park变换,得到二维电压方程;3)化简步骤2)电压方程;4)将步骤3)中的d、q轴交叉耦合项作为电流控制前馈补偿;5)将步骤4)前馈补偿项进行离散,得到k+1时刻d、q轴预测方程;6)将k时刻输出控制量表示为预测方程;7)将步骤6)k时刻输出控制量带入步骤5)预测方程中,得到新d、q轴预测方程;8)根据k时刻控制增量为0和步骤7)的预测方程,得到k+2时刻预测模型;9)根据光伏低穿工况,设置目标函数;10)设置正常/电压跌落两种模式电流控制方式。采用模型预测在光伏逆变器控制中,提高低穿能力。

Description

一种提高光伏发电低电压穿越能力的方法 【技术领域】
本发明属于光伏发电技术领域,具体涉及一种提高光伏发电低电压穿越能力的方法。
【背景技术】
随着电网容量不断增加,光伏等新能源发电大规模接入,势必会对电力系统稳定性造成影响。光伏通过逆变装置实现并网,当网侧发生电压跌落事故,会造成逆变装置用到的电力电子元器件损坏。因此需要开展提高光伏发电低电压穿越能力的研究。
为了使光伏电站具备低电压穿越能力,目前主流方法是改进逆变器控制算法。光伏逆变器控制采用PI控制,传统PI控制无法实现无静差调节,不具备电压快速跟踪能力。
模型预测控制作为一种新兴控制策略,因为快速动态响应能力强,可同时控制多个目标,输出特性好,在光伏并网逆变器控制领域得到广泛关注,但该控制策略存在计算量大的问题,这就需要更高性能的处理器,这无疑增加了成本,不利于该控制算法的推广。
【发明内容】
本发明的目的是提供一种提高光伏发电低电压穿越能力的方法,具体应用多步模型预测控制策略提高光伏低电压穿越能力,该方法是对光伏L型逆变器中电流环控制建立数学模型,并对其进行简化和离散。针对传统模型预测存在的周期延时问题,提出一种在当前采样时刻对未来两个采样周期预测方法,即在k时刻 采样预测步长变为原些步长的2倍,使其保持长期的最优状态,提高光伏发电场站低电压穿越能力。
为达到上述目的,本发明采用以下技术方案予以实现:
一种提高光伏发电低电压穿越能力的方法,包括以下步骤:
1)建立光伏L型逆变器在abc三相坐标系下的数学模型;
2)对步骤1)中光伏L型逆变器在abc三相坐标系下的数学模型进行Park变换;
3)对步骤2)中经过Park变换得到的二维电压方程化简成单输入、单输出的数学模型;
4)将步骤3)中得到的单输入、单输出的数学模型中的d轴和q轴之间交叉耦合项视为扰动,得到后续电流控制系统中的前馈补偿项;
5)将步骤4)前馈补偿项进行离散化处理,得到k+1时刻d轴、q轴的预测方程;
6)将k时刻输出控制量表示为预测方程形式;
7)将步骤6)的k时刻输出控制量带入步骤5)的d轴、q轴的预测方程中,得到新的d轴、q轴的预测方程;
8)根据k-1时刻的控制量决定了k时刻的预测值,k时刻控制增量为0,并根据步骤7)的新d轴、q轴的预测方程,得到k+2时刻预测模型;
9)根据光伏发电低电压穿越实际工况,设置目标函数;
10)在光伏并网逆变器控制系统中设置正常/网侧发生低电压跌落,两种电流控制方式,一种是在光伏逆变并网正常运行时,多步模型预测控制中的电流参考值由外环电压给定;一种是网侧发生低电压跌落,多步模型预测控制中的电流参 考值由人工设定。
本发明进一步的改进在于,步骤1)的中光伏L型逆变器在abc三相坐标系下的数学模型为:
Figure PCTCN2020108683-appb-000001
其中:L为滤波电感、R为线路等效阻抗;i a、i b、i c为逆变器输出三相交流电流;u a、u b、u c为逆变器输出三相交流电压;e a、e b、e c为负载侧电压。
本发明进一步的改进在于,步骤2)的具体实现方法为:根据步骤1)中光伏L型逆变器在abc三相坐标系下的数学模型,进行Park变换,得到光伏L型逆变器在dq两相坐标系下的数学模型:
Figure PCTCN2020108683-appb-000002
其中:
Figure PCTCN2020108683-appb-000003
T abc→dq0为Park变换矩阵,ω为电角速度。
本发明进一步的改进在于,步骤3)的具体实现方法为:对步骤2)中经过Park变换得到的二维电压方程化简成单输入、单输出的数学模型:
Figure PCTCN2020108683-appb-000004
本发明进一步的改进在于,步骤4)的具体实现方法为:根据步骤3)得到 的单输入、单输出的数学模型中的d轴和q轴之间交叉耦合项视为扰动,得到后续电流控制系统中的前馈补偿项:
Figure PCTCN2020108683-appb-000005
本发明进一步的改进在于,步骤5)的具体实现方法为:将步骤4)前馈补偿项进行离散化处理,得到k+1时刻d轴、q轴的预测方程
Figure PCTCN2020108683-appb-000006
其中:
Figure PCTCN2020108683-appb-000007
本发明进一步的改进在于,步骤6)的具体实现方法为:将k时刻输出控制量表示为预测方程形式:
Figure PCTCN2020108683-appb-000008
其中:u d(k)和u q(k)为k时刻输出控制量,Δu d(k)、Δu q(k)为k时刻控制增量。
本发明进一步的改进在于,步骤7)的具体实现方法为:将步骤6)的k时刻输出控制量带入步骤5)的d轴、q轴的预测方程中,得到新的d轴、q轴的预测方程:
Figure PCTCN2020108683-appb-000009
其中:
Figure PCTCN2020108683-appb-000010
本发明进一步的改进在于,步骤8)的具体实现方法为:根据k-1时刻的控制量决定了k时刻的预测值,k时刻控制增量为0,并根据步骤7)的新d轴、q轴的预测方程,得到k+2时刻预测模型:
Figure PCTCN2020108683-appb-000011
其中:
Figure PCTCN2020108683-appb-000012
本发明进一步的改进在于,步骤9)的具体实现方法为:根据光伏发电低电压穿越实际工况,设置目标函数
Figure PCTCN2020108683-appb-000013
其中:
Figure PCTCN2020108683-appb-000014
分别为d、q轴电流参考值;i d(k+2)、i q(k+2)分别为d、q轴k+2时刻电流预测值;ε 1、ε 2分别为d轴电流误差、q轴电流误差在优化性能函数中所占的权重;λ 1、λ 2分别为d轴控制电压变化量、q轴控制电压变化量。
与现有技术相比,本发明具有以下有益效果:
1.本发明采用模型预测控制方法在光伏发电并网逆变器电流PI控制中,实现电流快速跟踪性能,进而提高光伏发电低电压穿越能力。
2.本发明针对传统模型预测存在的周期延时问题,提出一种在当前采样时刻对未来两个采样周期预测方案,使其保持长期的最优状态,提升光伏低电压低穿越电压调节能力。
【附图说明】
图1为光伏发电L并网逆变器模型;
图2为模型预测控制原理图;
图3为光伏电站低电压穿越要求曲线;
图4为采用多步模型预测的光伏逆变并网控制框图;
图5为接地故障验证低电压穿越能力仿真图;
图6为三相短路接地,网侧电压、电流仿真图;其中图6(a)为电网三相电压,图6(b)为电网三相电流;
图7为单相短路接地,网侧电压、电流仿真图;其中图7(a)为电网三相电 压,图7(b)为电网三相电流。
【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方案,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分的实施例,不是全部的实施例,而并非要限制本发明公开的范围。此外,在以下说明中,省略了对公知结构和技术的描述,以避免不必要的混淆本发明公开的概念。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都应当属于本发明保护的范围。
在附图中示出了根据本发明公开实施例的各种结构示意图。这些图并非是按比例绘制的,其中为了清楚表达的目的,放大了某些细节,并且可能省略了某些细节。图中所示出的各种区域、层的形状及它们之间的相对大小、位置关系仅是示例性的,实际中可能由于制造公差或技术限制而有所偏差,并且本领域技术人员根据实际所需可以另外设计具有不同形状、大小、相对位置的区域/层。
本发明公开的上下文中,当将一层/元件称作位于另一层/元件“上”时,该层/元件可以直接位于该另一层/元件上,或者它们之间可以存在居中层/元件。另外,如果在一种朝向中一层/元件位于另一层/元件“上”,那么当调转朝向时,该层/元件可以位于该另一层/元件“下”。
需要说明的是,本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便这里描述的本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外,术语“包括”和“具有” 以及他们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
下面结合附图对本发明做进一步详细描述:
参见图1,光伏L型逆变器在abc三相坐标系下的数学模型为:
Figure PCTCN2020108683-appb-000015
式中:L为滤波电感、R为线路等效阻抗;i a、i b、i c为逆变器输出三相交流电流;u a、u b、u c为逆变器输出三相交流电压;e a、e b、e c为负载侧电压。
对三相坐标系下的数学模型进行Park变换,可以得到光伏L型逆变器在dq两相坐标系下的数学模型为:
Figure PCTCN2020108683-appb-000016
式中:
Figure PCTCN2020108683-appb-000017
其中,T abc→dq0为Park变换矩阵,ω为电角速度。
将二维电压方程化简为单输入、单输出的数学模型,表达式为:
Figure PCTCN2020108683-appb-000018
将d轴和q轴之间的交叉耦合项视为扰动,作为后续电流控制系统中的前馈补偿项:
Figure PCTCN2020108683-appb-000019
如图2所示,为了对动态模型实现预测控制,以该模型当前状态量为基础,进行下一时刻的状态量预测控制,在预测过程中应以目标函数最小为约束条件,不断对电压矢量进行优化,达到电压矢量最优。将该电压矢量与开关状态相对,实现光伏并网逆变器的PWM控制。,为了使光伏并网逆变器具有低电压穿越能力,满足电压跌落情况下电流大幅调节特性,本发明将模型预测控制算法应用于电流控制系统中。
由式(3)可知:在d、q轴上,电流方程具有相同形式。设采样时间为T s,对式(4)进行离散化处理,可以得到k+1时刻d轴、q轴的预测方程:
Figure PCTCN2020108683-appb-000020
式中:
Figure PCTCN2020108683-appb-000021
为了表示方便,将k时刻输出控制量u d(k)和u q(k)写成:
Figure PCTCN2020108683-appb-000022
式中:Δu d(k)、Δu q(k)为k时刻控制增量。将上式带入式(7)可以得到:
Figure PCTCN2020108683-appb-000023
其中:
Figure PCTCN2020108683-appb-000024
基于单步模型预测的光伏并网逆变器低电压穿越控制并未考虑系统在采样、计算PWM占空比时存在一个周期延时,无法得知后续能否保持最优控制效果。本发明提出一种在当前采样时刻,对未来两个采样周期(k+1、k+2)的电流值进行预测方案,使其保持长期的最优状态,达到最优控制效果。
k-1时刻的控制量决定了k时刻的预测值,即Δu d(k)、Δu q(k)均为0,则:
Figure PCTCN2020108683-appb-000025
根据式(8)可以得到k+2时刻预测模型为:
Figure PCTCN2020108683-appb-000026
其中:
Figure PCTCN2020108683-appb-000027
本发明所提基于多步模型预测的光伏并网逆变器低电压穿越控制,与传统多步预测控制的第一步预测类似,k+1时刻的电流预测值是建立在k时刻的采样值;在第二步预测即k+2时刻,本发明所提方案是建立在k时刻的采样值,预测步长变为2T s,而传统多步预测控制中每一步预测值都是基于前一步的预测值。光伏并网逆变器控制系统面对电压跌落工况,需要快速提供无功支持,这就需要电流具有快速跟踪和响应能力。本发明所提控制方案在相同的约束条件下,具有更好的控制性能。
如图3所示,根据国家标准《光伏电站接入电网技术规定》,大中型光伏电站在电网发生接地故障要具备低电压穿越能力,为电网稳定提供支撑,图中:U N为光伏并网额定电压,U L为发生电压跌落光伏电站不脱网最低电压,一般为0.2 U N。根据《光伏电站接入电网技术规定》,当网侧发生电压跌落事故,光伏并网电压需保持在U L至少1s时间,电站才具备低电压穿越能力。
使光伏电站具有低电压穿越能力,需要在电压跌落工况下,对i d和i q进行控制,在并网稳定状态下,i d对应的有功功率与视在功率相同,i q对应的无功功率为0,为了穿越过低电压状态,需要控制系统按照有功、无功电流参考值
Figure PCTCN2020108683-appb-000028
进行有功、无功功率调节,有功和无功电流参考值即目标电流与额定电流的关系为:
Figure PCTCN2020108683-appb-000029
光伏逆变并网输送至网侧的无功功率对应的i q应具备实时跟踪并网点电压变化的能力,需满足:
Figure PCTCN2020108683-appb-000030
为了使光伏逆变并网控制系统具备低电压穿越能力,在应用多步模型预测电流控制算法时,需要设置目标函数来提升系统电流追踪性能。在模型预测电流控制中,目标是下一周期被控量的预测值与该量差值尽可能小,同时控制量也不要过大,基于此本发明所提目标函数为:
Figure PCTCN2020108683-appb-000031
式中:
Figure PCTCN2020108683-appb-000032
分别为d、q轴电流参考值;i d(k+2)、i q(k+2)分别为d、q轴k+2时刻电流预测值;ε 1、ε 2分别为d轴电流误差、q轴电流误差在优化性能函数中所占的权重;λ 1、λ 2分别为d轴控制电压变化量、q轴控制电压变化量。
如图4所示,光伏逆变并网两种电流控制方式,一种是在光伏逆变并网正常运行时,多步模型预测控制中的电流参考值由外环电压给定;一种是网侧发生低电压跌落,多步模型预测控制中的电流参考值由人工设定。
如图5所示,在仿真模型线路侧分别设置三相接地故障、单相接地故障,来验证本发明所提的多步模型预测电流控制方案可以提高光伏逆变并网低电压穿越能力。0.55s线路发生三相接地、A相接地故障,0.75s继电保护动作切除故障,采用多步模型预测控制的并网逆变器控制系统可以快速下达电流指令来为电网提供电压支持。在图(a)中对比未采用模型预测得到的母线电压波形,并网点电压标幺值由原些的0.65上升至0.72。在图(b)并网点电压标幺值由如图5所示,原些的0.68上升至0.75。
如图6所示,发生三相短路接地故障,采用本发明所提出的基于两步模型预测控制的光伏低电压穿越方案,在故障期间三相并网电压仍能保持平衡,幅值变换幅度不大,可以平稳穿越过三相短路接地故障时段。电网三相电流也可在故障期间保持平衡,采用本发明控制方案可以将电流限制在额定电流的1.1倍之内,满足规程要求。
如图7所示,当线路发生A相接地短路时,A相电压幅值减小,B、C相电压基本保持不变,当故障消除后三相电压又达到平衡。故障期间A、B、C相电流基本对称,幅值增长满足额定电流限制,故障消除后,很快恢复至系统额定运行。
以上内容仅为说明本发明的技术思想,不能以此限定本发明的保护范围,凡是按照本发明提出的技术思想,在技术方案基础上所做的任何改动,均落入本发明权利要求书的保护范围之内。

Claims (10)

  1. 一种提高光伏发电低电压穿越能力的方法,其特征在于,包括以下步骤:
    1)建立光伏L型逆变器在abc三相坐标系下的数学模型;
    2)对步骤1)中光伏L型逆变器在abc三相坐标系下的数学模型进行Park变换;
    3)对步骤2)中经过Park变换得到的二维电压方程化简成单输入、单输出的数学模型;
    4)将步骤3)中得到的单输入、单输出的数学模型中的d轴和q轴之间交叉耦合项视为扰动,得到后续电流控制系统中的前馈补偿项;
    5)将步骤4)前馈补偿项进行离散化处理,得到k+1时刻d轴、q轴的预测方程;
    6)将k时刻输出控制量表示为预测方程形式;
    7)将步骤6)的k时刻输出控制量带入步骤5)的d轴、q轴的预测方程中,得到新的d轴、q轴的预测方程;
    8)根据k-1时刻的控制量决定了k时刻的预测值,k时刻控制增量为0,并根据步骤7)的新d轴、q轴的预测方程,得到k+2时刻预测模型;
    9)根据光伏发电低电压穿越实际工况,设置目标函数;
    10)在光伏并网逆变器控制系统中设置正常/网侧发生低电压跌落,两种电流控制方式,一种是在光伏逆变并网正常运行时,多步模型预测控制中的电流参考值由外环电压给定;一种是网侧发生低电压跌落,多步模型预测控制中的电流参考值由人工设定。
  2. 根据权利要求1所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤1)的中光伏L型逆变器在abc三相坐标系下的数学模型为:
    Figure PCTCN2020108683-appb-100001
    其中:L为滤波电感、R为线路等效阻抗;i a、i b、i c为逆变器输出三相交流电流;u a、u b、u c为逆变器输出三相交流电压;e a、e b、e c为负载侧电压。
  3. 根据权利要求2所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤2)的具体实现方法为:根据步骤1)中光伏L型逆变器在abc三相坐标系下的数学模型,进行Park变换,得到光伏L型逆变器在dq两相坐标系下的数学模型:
    Figure PCTCN2020108683-appb-100002
    其中:
    Figure PCTCN2020108683-appb-100003
    T abc→dq0为Park变换矩阵,ω为电角速度。
  4. 根据权利要求3所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤3)的具体实现方法为:对步骤2)中经过Park变换得到的二维电压方程化简成单输入、单输出的数学模型:
    Figure PCTCN2020108683-appb-100004
  5. 根据权利要求4所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤4)的具体实现方法为:根据步骤3)得到的单输入、单输出的数 学模型中的d轴和q轴之间交叉耦合项视为扰动,得到后续电流控制系统中的前馈补偿项:
    Figure PCTCN2020108683-appb-100005
  6. 根据权利要求5所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤5)的具体实现方法为:将步骤4)前馈补偿项进行离散化处理,得到k+1时刻d轴、q轴的预测方程
    Figure PCTCN2020108683-appb-100006
    其中:
    Figure PCTCN2020108683-appb-100007
    Figure PCTCN2020108683-appb-100008
  7. 根据权利要求6所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤6)的具体实现方法为:将k时刻输出控制量表示为预测方程形式:
    Figure PCTCN2020108683-appb-100009
    其中:u d(k)和u q(k)为k时刻输出控制量,Δu d(k)、Δu q(k)为k时刻控制增量。
  8. 根据权利要求7所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤7)的具体实现方法为:将步骤6)的k时刻输出控制量带入步骤5)的d轴、q轴的预测方程中,得到新的d轴、q轴的预测方程:
    Figure PCTCN2020108683-appb-100010
    其中:
    Figure PCTCN2020108683-appb-100011
  9. 根据权利要求8所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤8)的具体实现方法为:根据k-1时刻的控制量决定了k时刻的预测值,k时刻控制增量为0,并根据步骤7)的新d轴、q轴的预测方程,得到k+2时刻预测模型:
    Figure PCTCN2020108683-appb-100012
    其中:
    Figure PCTCN2020108683-appb-100013
  10. 根据权利要求9所述的一种提高光伏发电低电压穿越能力的方法,其特征在于,步骤9)的具体实现方法为:根据光伏发电低电压穿越实际工况,设置目标函数
    Figure PCTCN2020108683-appb-100014
    其中:
    Figure PCTCN2020108683-appb-100015
    分别为d、q轴电流参考值;i d(k+2)、i q(k+2)分别为d、q轴k+2时刻电流预测值;ε 1、ε 2分别为d轴电流误差、q轴电流误差在优化性能函数中所占的权重;λ 1、λ 2分别为d轴控制电压变化量、q轴控制电压变化量。
PCT/CN2020/108683 2020-08-04 2020-08-12 一种提高光伏发电低电压穿越能力的方法 WO2022027717A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010773385.0 2020-08-04
CN202010773385.0A CN111917130A (zh) 2020-08-04 2020-08-04 一种提高光伏发电低电压穿越能力的方法

Publications (1)

Publication Number Publication Date
WO2022027717A1 true WO2022027717A1 (zh) 2022-02-10

Family

ID=73287062

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/108683 WO2022027717A1 (zh) 2020-08-04 2020-08-12 一种提高光伏发电低电压穿越能力的方法

Country Status (2)

Country Link
CN (1) CN111917130A (zh)
WO (1) WO2022027717A1 (zh)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114719333A (zh) * 2022-04-14 2022-07-08 兰州理工大学 一种多逆变器电磁供暖系统及其输出功率预测控制方法
CN115395561A (zh) * 2022-08-23 2022-11-25 特变电工科技投资有限公司 一种弱网下光伏逆变器低电压穿越控制方法及系统
CN116131368A (zh) * 2023-03-07 2023-05-16 天津大学 适用于双馈风电场低电压穿越期间最大有功功率输出的控制方法
CN116545003A (zh) * 2022-12-30 2023-08-04 中国电力科学研究院有限公司 一种主动支撑型变流器的机电暂态稳定控制方法及系统
CN117294202A (zh) * 2023-09-20 2023-12-26 哈尔滨工业大学(威海) 一种基于lcl耦合网络的电机模拟器及其建模方法

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112467788B (zh) * 2020-11-18 2023-03-10 西安热工研究院有限公司 一种减少光伏模型预测控制系统低电压穿越时稳态误差的方法
CN112350352A (zh) * 2020-11-20 2021-02-09 西安热工研究院有限公司 一种增加储能无功调节速率的方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102832643A (zh) * 2012-09-19 2012-12-19 中船重工鹏力(南京)新能源科技有限公司 一种基于逆系统的三相光伏并网逆变器的控制方法
US20190140453A1 (en) * 2017-11-07 2019-05-09 Zhehan Yi Model Predictive Controller for Autonomous Hybrid Microgrids
CN110045610A (zh) * 2019-04-18 2019-07-23 中国地质大学(武汉) 逆变器改进型多步模型预测控制方法、设备及存储设备
CN111030105A (zh) * 2019-12-25 2020-04-17 国网节能服务有限公司 一种基于三相级联h桥的光伏发电系统低电压穿越方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102832643A (zh) * 2012-09-19 2012-12-19 中船重工鹏力(南京)新能源科技有限公司 一种基于逆系统的三相光伏并网逆变器的控制方法
US20190140453A1 (en) * 2017-11-07 2019-05-09 Zhehan Yi Model Predictive Controller for Autonomous Hybrid Microgrids
CN110045610A (zh) * 2019-04-18 2019-07-23 中国地质大学(武汉) 逆变器改进型多步模型预测控制方法、设备及存储设备
CN111030105A (zh) * 2019-12-25 2020-04-17 国网节能服务有限公司 一种基于三相级联h桥的光伏发电系统低电压穿越方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WANG XIAOHUI, LIU XIANG-CHEN;KOU SHUI-CHAO;YANG PEI-HAO: "Current Control Method for ACIM Based on Two-step Model Predictive Control)", POWER ELECTRONICS, ZHONGGUO DIANGONG JISHU XUEHUI DIANLI DIANZI XUEHUI, CN, vol. 53, no. 8, 31 August 2019 (2019-08-31), CN , pages 52 - 55, XP055895108, ISSN: 1000-100X *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114719333A (zh) * 2022-04-14 2022-07-08 兰州理工大学 一种多逆变器电磁供暖系统及其输出功率预测控制方法
CN115395561A (zh) * 2022-08-23 2022-11-25 特变电工科技投资有限公司 一种弱网下光伏逆变器低电压穿越控制方法及系统
CN116545003A (zh) * 2022-12-30 2023-08-04 中国电力科学研究院有限公司 一种主动支撑型变流器的机电暂态稳定控制方法及系统
CN116545003B (zh) * 2022-12-30 2024-06-07 中国电力科学研究院有限公司 一种主动支撑型变流器的机电暂态稳定控制方法及系统
CN116131368A (zh) * 2023-03-07 2023-05-16 天津大学 适用于双馈风电场低电压穿越期间最大有功功率输出的控制方法
CN117294202A (zh) * 2023-09-20 2023-12-26 哈尔滨工业大学(威海) 一种基于lcl耦合网络的电机模拟器及其建模方法
CN117294202B (zh) * 2023-09-20 2024-06-04 哈尔滨工业大学(威海) 一种基于lcl耦合网络的电机模拟器及其建模方法

Also Published As

Publication number Publication date
CN111917130A (zh) 2020-11-10

Similar Documents

Publication Publication Date Title
WO2022027717A1 (zh) 一种提高光伏发电低电压穿越能力的方法
WO2022036787A1 (zh) 一种利用自适应虚拟参数提高风电并网一次调频性能的方法
WO2018121732A1 (zh) 一种基于非线性状态观测器的微电网分散式电压控制方法
WO2018153222A1 (zh) 一种基于内模控制的微电网并离网平滑切换控制方法
CN107732956A (zh) 变功率跟踪轨迹的两级式光伏并网系统低电压穿越方法
Zhao et al. Harmonic characteristics and control strategies of grid-connected photovoltaic inverters under weak grid conditions
CN110289618B (zh) 一种多功能储能变流器并网电能质量补偿控制方法
CN105763094A (zh) 一种基于电压前馈和复合电流控制的逆变器控制方法
WO2022227401A1 (zh) 微电网群同期控制方法和系统
CN103886146A (zh) 一种提高直流系统抑制换相失败能力的控制参数优化方法
CN104569696A (zh) 基于dq变换的电压标幺值正反馈的孤岛检测方法
CN105743091A (zh) 一种有源电力滤波器的双环解耦控制方法
CN108462213B (zh) 基于守恒功率理论的多功能并网逆变器控制方法及系统
CN105629730A (zh) 一种基于神经网络滑模控制的upfc控制方法
CN105305498A (zh) 一种大功率光伏并网逆变器低电压穿越控制方法
Wang et al. Coordinated multiple HVDC modulation emergency control for enhancing power system frequency stability
CN113991715A (zh) 非理想电网下中压直挂不对称混合储能系统控制方法
CN104113079B (zh) Mppt控制方法和系统
CN102025163B (zh) 一种用于动态无功补偿控制器的调节方法
CN112467788B (zh) 一种减少光伏模型预测控制系统低电压穿越时稳态误差的方法
CN104600746B (zh) 区域光伏储能系统并网变流器无源非线性控制方法
Li et al. Crowbar Resistance Setting and its Influence on DFIG Low Voltage Based on Characteristics
CN113489018B (zh) 一种电压源型储能电站支撑慢过程电压跌落的控制方法
CN104007349A (zh) 基于小波变换的模糊控制孤岛检测方法
Luo et al. Development of fast simulation models of photovoltaic generation system based on MATLAB

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20948522

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20948522

Country of ref document: EP

Kind code of ref document: A1