CN116581770A - Micro-grid system VSG double-droop control method based on self-adaptive neural network - Google Patents
Micro-grid system VSG double-droop control method based on self-adaptive neural network Download PDFInfo
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Abstract
本发明公开了基于自适应神经网络的微网系统VSG双下垂控制方法,所述的微网系统架构主要由光伏发电单元、蓄电池储能单元、逆变器模块、LC滤波模块、VSG控制模块组成,其中,光伏发电单元前级采用DC‑DC升压变换器实现最大功率点追踪,后级为DC‑AC并网逆变器,与储能单元并联于直流侧,引入VSG为系统提供惯量和阻尼,建立自适应神经网络控制器,通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,结合设定的虚拟参量选取规则得到虚拟参数J和D,通过自适应神经网络控制器输出的虚拟参量调节微网系统的频率和电压,完成双下垂控制。通过本发明,可以实现双下垂控制策略,提高了微网并网时的稳定性。The invention discloses a micro-grid system VSG double droop control method based on an adaptive neural network. The micro-grid system architecture is mainly composed of a photovoltaic power generation unit, a storage battery energy storage unit, an inverter module, an LC filter module, and a VSG control module. , where the front stage of the photovoltaic power generation unit uses a DC‑DC boost converter to achieve maximum power point tracking, and the rear stage is a DC‑AC grid-connected inverter, which is connected in parallel with the energy storage unit on the DC side, and VSG is introduced to provide inertia and Damping, establish an adaptive neural network controller, obtain the angular frequency offset and the change slope of the angular frequency in real time through the neural network controller, combine the set virtual parameter selection rules to obtain the virtual parameters J and D, and control it through the adaptive neural network The virtual parameters output by the controller adjust the frequency and voltage of the microgrid system to complete the double droop control. Through the invention, the double droop control strategy can be realized, and the stability of the microgrid when connected to the grid is improved.
Description
技术领域technical field
本发明涉及清洁能源领域,具体是基于自适应神经网络的微网系统VSG双下垂控制方法。The invention relates to the field of clean energy, in particular to a microgrid system VSG double droop control method based on an adaptive neural network.
背景技术Background technique
近年来,光伏以及风电等新能源发电凭借着清洁、高渗透率、可持续利用等特点被广泛应用于大电网中。与此同时也带来了一些严峻的挑战,由于新能源发电具有随机性和波动性,对系统的稳定性造成极大的影响,微电网的提出旨在解决这些弊端,成为当今的研究热点,但微电网在并网时由于系统缺少阻尼和惯量,在受到扰动时容易造成频率和电压崩溃,因此为了使频率和电压能够适应系统的变化,保证系统安全运行,在微电网中引入虚拟同步发电机控制技术(Virtual Synchronous Generator,VSG),它具有同步发电机的惯性和阻尼特性,应用于微电网中为其提供了惯性和阻尼,进而保证了系统输出频率和电压的稳定。VSG的惯量和阻尼参数灵活可变,因此为了提高虚拟同步机的稳定控制,可以利用自适应的方法来对VSG进行改进。现有技术提出了一种基于模糊控制的自适应参数控制策略,根据频率偏差和变化率来自适应惯量和阻尼参数,但未考虑下垂控制特性的影响。现有技术充分考虑了功角与有功功率之间的关系和角频率振荡过程,给出转动惯量和阻尼系数的选取原则,有效的提高了光伏并网的稳定性。但是并未考虑电压的影响。现有技术针对不同工况下电网对频率和转动惯量的需求,提出了一种频率以及虚拟惯量等多参数的自适应控制策略,实现了电网调频以及并/离网切换,但并未考虑阻尼变化对其的影响。现有技术为了解决并网时产生的电压电流冲击问题,采用一种模型预测控制的方法,利用频率的变化来制定模型预测控制的加权系数自适应规则,极大的改善了在预同步和运行模式之间切换时的动态响应,但是文中没有提到对频率的调节。现有技术采用输出速度反馈调节阻尼,利用功角特性提出参数自适应的控制策略,降低了频率偏差,同时抑制了功率超调,但未考虑虚拟惯量对系统的影响。现有技术[9]提出一种基于自适应前馈控制的双机并联VSG控制策略。通过角频率的变化自适应增加前馈量补偿系统的功率缺额,提高了系统的响应速度和功率分配精度,抑制系统频率波动和有功功率振荡,保证了系统的快速稳定和安全性,但是Q-U下垂控制未考虑。现有技术基于利用模糊算法来实现VSG虚拟惯量和阻尼系数自适应调节,该策略能够合理地抑制瞬态过程中VSG频率和功率的波动,维持电网的稳定运行,但采用模糊控制方法导致参数调节并不准确。In recent years, new energy power generation such as photovoltaics and wind power has been widely used in large power grids due to its cleanliness, high penetration rate, and sustainable utilization. At the same time, it also brings some serious challenges. Due to the randomness and volatility of new energy power generation, it has a great impact on the stability of the system. The proposal of the microgrid aims to solve these drawbacks and has become a research hotspot today. However, due to the lack of damping and inertia of the system when the microgrid is connected to the grid, it is easy to cause frequency and voltage collapse when it is disturbed. Therefore, in order to make the frequency and voltage adapt to system changes and ensure the safe operation of the system, virtual synchronous power generation is introduced into the microgrid. Machine control technology (Virtual Synchronous Generator, VSG), which has the inertia and damping characteristics of synchronous generators, is applied to the microgrid to provide inertia and damping, thereby ensuring the stability of the system output frequency and voltage. The inertia and damping parameters of the VSG are flexible and variable, so in order to improve the stability control of the virtual synchronous machine, an adaptive method can be used to improve the VSG. The prior art proposes an adaptive parameter control strategy based on fuzzy control, which adapts the inertia and damping parameters according to the frequency deviation and rate of change, but does not consider the influence of the droop control characteristics. The existing technology fully considers the relationship between power angle and active power and the angular frequency oscillation process, and gives the selection principles of moment of inertia and damping coefficient, which effectively improves the stability of photovoltaic grid connection. However, the effect of voltage is not considered. In the existing technology, a multi-parameter adaptive control strategy such as frequency and virtual inertia is proposed to meet the requirements of power grids for frequency and moment of inertia under different working conditions, which realizes frequency modulation and on/off grid switching, but does not consider damping impact of changes on it. In the existing technology, in order to solve the voltage and current impact problem generated when connecting to the grid, a model predictive control method is adopted, and the weight coefficient adaptive rule of the model predictive control is formulated by using the change of frequency, which greatly improves the pre-synchronization and operation. The dynamic response when switching between modes, but the adjustment of the frequency is not mentioned in the text. The existing technology adopts the output speed feedback to adjust the damping, and uses the power angle characteristic to propose a parameter adaptive control strategy, which reduces the frequency deviation and suppresses the power overshoot, but does not consider the influence of virtual inertia on the system. The prior art [9] proposes a dual-machine parallel VSG control strategy based on adaptive feedforward control. Adaptively increase the power deficit of the feedforward compensation system through the change of angular frequency, improve the response speed and power distribution accuracy of the system, suppress the system frequency fluctuation and active power oscillation, and ensure the rapid stability and safety of the system, but Q-U droop Controls are not considered. The existing technology is based on the use of fuzzy algorithm to realize the adaptive adjustment of VSG virtual inertia and damping coefficient. This strategy can reasonably suppress the fluctuation of VSG frequency and power in the transient process and maintain the stable operation of the power grid. However, the fuzzy control method leads to parameter adjustment. Not exactly.
发明内容Contents of the invention
本发明的目的在于克服现有技术的不足,提供基于自适应神经网络的微网系统VSG双下垂控制方法,所述的微网系统架构主要由光伏发电单元、蓄电池储能单元、逆变器模块、LC滤波模块、VSG控制模块组成,其中,光伏发电单元前级采用DC-DC升压变换器实现最大功率点追踪,后级为DC-AC并网逆变器,与储能单元并联于直流侧,引入VSG为系统提供惯量和阻尼,建立自适应神经网络控制器,通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,结合设定的虚拟参量选取规则得到虚拟参数J和D,通过自适应神经网络控制器输出的虚拟参量调节微网系统的频率和电压,完成双下垂控制。The purpose of the present invention is to overcome the deficiencies of the prior art and provide a micro-grid system VSG double droop control method based on an adaptive neural network. The micro-grid system architecture is mainly composed of a photovoltaic power generation unit, a battery energy storage unit, and an inverter module , LC filter module, and VSG control module. Among them, the front stage of the photovoltaic power generation unit adopts a DC-DC boost converter to achieve maximum power point tracking, and the rear stage is a DC-AC grid-connected inverter, which is connected in parallel with the energy storage unit in DC On the other hand, VSG is introduced to provide inertia and damping for the system, an adaptive neural network controller is established, and the angular frequency offset and angular frequency change slope are obtained in real time through the neural network controller, and the virtual parameter J is obtained by combining the set virtual parameter selection rules. and D, adjust the frequency and voltage of the microgrid system through the virtual parameters output by the adaptive neural network controller, and complete the double droop control.
进一步的,所述的建立自适应神经网络控制器,通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,结合设定的虚拟参量选取规则得到虚拟参数J和D,包括:Further, in the establishment of an adaptive neural network controller, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, and the virtual parameters J and D are obtained in combination with the set virtual parameter selection rules, including:
首先通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,即:Firstly, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, namely:
其次神经网络控制器根据给定的虚拟参量选取规则得到虚拟参数J和D即:Secondly, the neural network controller obtains the virtual parameters J and D according to the given virtual parameter selection rules, namely:
式中:J0和D0代表经神经网络调节输出的惯量和阻尼参数;Kj和Kd分别为惯量和阻尼的调节系数;Tj和Td是参数变化的上下限。In the formula: J 0 and D 0 represent the inertia and damping parameters adjusted and output by the neural network; K j and K d are the adjustment coefficients of inertia and damping respectively; T j and T d are the upper and lower limits of parameter changes.
进一步的,所述的通过自适应神经网络控制器输出的虚拟参量调节微网系统的电压包括:Further, said adjusting the voltage of the microgrid system through the virtual parameter output by the adaptive neural network controller includes:
通过神经网络输出的虚拟参量调节电压:The voltage is adjusted by the virtual parameters output by the neural network:
V=(LfCf)-1[ωiiqLf-rfid-vodi-iodiLf+vid]V=(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ]
式中:vodi是VSG输出的d轴电压分量,Lf、Cf、rf分别代表LC滤波器的电感、电容和电阻,iodi是VSG输出的d轴电流分量,id、iq为VSG输入的d、q轴电流分量,vid是VSG输入的d轴电流分量,ωi为VSG输出的角频率;In the formula: v odi is the d-axis voltage component of the VSG output, L f , C f , r f represent the inductance, capacitance and resistance of the LC filter respectively, i odi is the d-axis current component of the VSG output, i d , i q d and q-axis current components input by the VSG, v id is the d-axis current component input by the VSG, and ω i is the angular frequency output by the VSG;
跟踪误差为:The tracking error is:
式中: In the formula:
另一个误差变量e2i表示为:Another error variable e 2i is expressed as:
e2i=divodi-αij e 2i =d i v odi -α ij
式中:αij是一个虚拟控制量。In the formula: α ij is a virtual control quantity.
进一步的,所述的通过自适应神经网络控制器输出的虚拟参量调节微网系统的角频率包括:Further, said adjusting the angular frequency of the microgrid system through the virtual parameter output by the adaptive neural network controller includes:
由下垂控制可得:From droop control:
ωi=ωni-kppi ω i =ω ni -k p p i
式中:ωi使VSG输出的角频率,ωni是角频率的标幺值,Pi为滤波之后输出的有功功率。In the formula: ω i is the angular frequency output by the VSG, ω ni is the per unit value of the angular frequency, and P i is the output active power after filtering.
调节的误差为:The adjusted error is:
角频率输出为:The angular frequency output is:
本发明的有益效果是:本发明所提出的技术方案能够自适应的对频率、电压、有功功率和无功功率的控制,实现了双下垂控制策略,提高了微网并网时的稳定性。The beneficial effects of the present invention are: the technical solution proposed by the present invention can control the frequency, voltage, active power and reactive power adaptively, realize the double droop control strategy, and improve the stability of the microgrid when connected to the grid.
具体实施方式Detailed ways
下面结合进一步详细描述本发明的技术方案,但本发明的保护范围不局限于以下所述。The technical solution of the present invention will be further described in detail below, but the protection scope of the present invention is not limited to the following description.
为了使本发明的目的,技术方案及优点更加清楚明白,对本发明进行进一步详细说明。应当理解,此处所描述的具体实施例仅用以解释本发明,并不用于限定本发明,即所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。In order to make the object, technical solution and advantages of the present invention clearer, the present invention is further described in detail. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention, that is, the described embodiments are only some of the embodiments of the present invention, but not all of the embodiments.
因此,以下对本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围,而是仅仅表示本发明的选定实施例。基于本发明的实施例,本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例,都属于本发明保护的范围。需要说明的是,术语“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。Accordingly, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention. It should be noted that relative terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. There is no such actual relationship or order between them.
而且,术语“包括”,“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程,方法,物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程,方法,物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程,方法,物品或者设备中还存在另外的相同要素。Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed other elements of, or also include elements inherent in, such a process, method, article or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.
以下结合实施例对本发明的特征和性能作进一步的详细描述。The characteristics and performance of the present invention will be described in further detail below in conjunction with the examples.
基于自适应神经网络的微网系统VSG双下垂控制方法,所述的微网系统架构主要由光伏发电单元、蓄电池储能单元、逆变器模块、LC滤波模块、VSG控制模块组成,其中,光伏发电单元前级采用DC-DC升压变换器实现最大功率点追踪,后级为DC-AC并网逆变器,与储能单元并联于直流侧,引入VSG为系统提供惯量和阻尼,建立自适应神经网络控制器,通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,结合设定的虚拟参量选取规则得到虚拟参数J和D,通过自适应神经网络控制器输出的虚拟参量调节微网系统的频率和电压,完成双下垂控制。A microgrid system VSG double droop control method based on an adaptive neural network. The microgrid system architecture is mainly composed of a photovoltaic power generation unit, a battery energy storage unit, an inverter module, an LC filter module, and a VSG control module. The front stage of the power generation unit adopts a DC-DC boost converter to achieve maximum power point tracking, and the rear stage is a DC-AC grid-connected inverter, which is connected in parallel with the energy storage unit on the DC side. VSG is introduced to provide inertia and damping for the system, and an automatic Adapt to the neural network controller, obtain the angular frequency offset and the change slope of the angular frequency in real time through the neural network controller, combine the set virtual parameter selection rules to obtain the virtual parameters J and D, and output the virtual parameters through the adaptive neural network controller The parameters adjust the frequency and voltage of the microgrid system to complete the double droop control.
进一步的,所述的建立自适应神经网络控制器,通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,结合设定的虚拟参量选取规则得到虚拟参数J和D,包括:Further, in the establishment of an adaptive neural network controller, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, and the virtual parameters J and D are obtained in combination with the set virtual parameter selection rules, including:
首先通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,即:Firstly, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, namely:
其次神经网络控制器根据给定的虚拟参量选取规则得到虚拟参数J和D即:Secondly, the neural network controller obtains the virtual parameters J and D according to the given virtual parameter selection rules, namely:
式中:J0和D0代表经神经网络调节输出的惯量和阻尼参数;Kj和Kd分别为惯量和阻尼的调节系数;Tj和Td是参数变化的上下限。In the formula: J 0 and D 0 represent the inertia and damping parameters adjusted and output by the neural network; K j and K d are the adjustment coefficients of inertia and damping respectively; T j and T d are the upper and lower limits of parameter changes.
进一步的,所述的通过自适应神经网络控制器输出的虚拟参量调节微网系统的电压包括:Further, said adjusting the voltage of the microgrid system through the virtual parameter output by the adaptive neural network controller includes:
通过神经网络输出的虚拟参量调节电压:The voltage is adjusted by the virtual parameters output by the neural network:
V=(LfCf)-1[ωiiqLf-rfid-vodi-iodiLf+vid]V=(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ]
式中:vodi是VSG输出的d轴电压分量,Lf、Cf、rf分别代表LC滤波器的电感、电容和电阻,iodi是VSG输出的d轴电流分量,id、iq为VSG输入的d、q轴电流分量,vid是VSG输入的d轴电流分量,ωi为VSG输出的角频率;In the formula: v odi is the d-axis voltage component of the VSG output, L f , C f , r f represent the inductance, capacitance and resistance of the LC filter respectively, i odi is the d-axis current component of the VSG output, i d , i q d and q-axis current components input by the VSG, v id is the d-axis current component input by the VSG, and ω i is the angular frequency output by the VSG;
跟踪误差为:The tracking error is:
式中: In the formula:
另一个误差变量e2i表示为:Another error variable e 2i is expressed as:
e2i=divodi-αij e 2i =d i v odi -α ij
式中:αij是一个虚拟控制量。In the formula: α ij is a virtual control quantity.
进一步的,所述的通过自适应神经网络控制器输出的虚拟参量调节微网系统的角频率包括:Further, said adjusting the angular frequency of the microgrid system through the virtual parameter output by the adaptive neural network controller includes:
由下垂控制可得:From droop control:
ωi=ωni-kppi ω i =ω ni -k p p i
式中:ωi使VSG输出的角频率,ωni是角频率的标幺值,Pi为滤波之后输出的有功功率。In the formula: ω i is the angular frequency output by the VSG, ω ni is the per unit value of the angular frequency, and P i is the output active power after filtering.
调节的误差为:The adjusted error is:
角频率输出为:The angular frequency output is:
具体的,微网系统架构主要由光伏发电单元、蓄电池储能单元、逆变器模块、LC滤波模块、VSG控制模块组成,其中,光伏发电单元前级采用DC-DC升压变换器实现最大功率点追踪,后级为DC-AC并网逆变器,与储能单元并联于直流侧,引入VSG为系统提供惯量和阻尼,实现双下垂稳定控制,提高系统的稳定性。Specifically, the microgrid system architecture is mainly composed of photovoltaic power generation units, battery energy storage units, inverter modules, LC filter modules, and VSG control modules. Among them, the front stage of photovoltaic power generation units uses DC-DC boost converters to achieve maximum power. Point tracking, the rear stage is a DC-AC grid-connected inverter, which is connected in parallel with the energy storage unit on the DC side, and the VSG is introduced to provide inertia and damping for the system to achieve double droop stability control and improve system stability.
VSG的外环控制为功率控制环,内环为双下垂控制,通过调节虚拟惯量和阻尼来影响VSG对频率、电压的控制,让其偏差降低到允许范围内,从而影响双下垂控制,实现并网的目标。The outer loop control of the VSG is a power control loop, and the inner loop is a double droop control. By adjusting the virtual inertia and damping, the control of the frequency and voltage of the VSG is affected, and the deviation is reduced to the allowable range, thereby affecting the double droop control. net target.
VSG通过转子运动方程来实现频率控制,为了方便对变流逆变器进行控制,采用降阶的方式来对模型进行处理,对虚拟同步机进行建模可得转子运动方程:VSG realizes frequency control through the rotor motion equation. In order to facilitate the control of the converter inverter, the model is processed by reducing the order, and the rotor motion equation can be obtained by modeling the virtual synchronous machine:
式中:J代表虚拟同步机的转动惯量,ω为转子的瞬时角频率,ω0是转子的额定角频率,D为VSG的阻尼参数,δ为VSG的功角,Pm代表机械功率。虚拟转动惯量J可以降低频率的偏差,阻尼系数D可以抑制电压波动,降低其偏差。根据VSG的Q-U下垂控制特性,可得到VSG的电压控制方程为:In the formula: J represents the moment of inertia of the virtual synchronous machine, ω is the instantaneous angular frequency of the rotor, ω 0 is the rated angular frequency of the rotor, D is the damping parameter of the VSG, δ is the power angle of the VSG, and P m represents the mechanical power. The virtual moment of inertia J can reduce the frequency deviation, and the damping coefficient D can suppress the voltage fluctuation and reduce its deviation. According to the QU droop control characteristics of VSG, the voltage control equation of VSG can be obtained as:
Uq=Kq(Qref-Q)+Un (2)U q =K q (Q ref -Q)+U n (2)
式中:Uq代系统优化后想要的到的目标电压,Kq代表无功下垂系数,Qref代表系统无功功率参考值,Q为VSG的实时无功功率,Un是额定电压。In the formula: U q represents the desired target voltage after system optimization, K q represents the reactive power droop coefficient, Q ref represents the system reactive power reference value, Q is the real-time reactive power of VSG, U n is the rated voltage.
由VSG的f-p下垂控制特性可得频率控制方程:The frequency control equation can be obtained from the f-p droop control characteristic of VSG:
式中:f为想要调节的目标频率,fn是额定频率,p和Pref分别为有功功率的参考值和实际值,Kp是有功功率的下垂控制系数。In the formula: f is the target frequency to be adjusted, f n is the rated frequency, p and P ref are the reference value and actual value of active power respectively, and K p is the droop control coefficient of active power.
为了保障输出电压的稳定性,采用励磁调节,励磁调节控制方程为[17-18]:In order to ensure the stability of the output voltage, the excitation regulation is adopted, and the control equation of the excitation regulation is [17-18]:
E=kv∫[Kq(Qref-Q)+(U0-Uq)] (4)E=k v ∫[K q (Q ref -Q)+(U 0 -U q )] (4)
结合式(1)、(2)、(3)、(4)可得双下垂闭环函数Combining formulas (1), (2), (3), and (4), the double-sag closed-loop function can be obtained
传统的虚拟同步机利用式(1)的转子运动方程来调节输出参数,通过事先选取好的虚拟参量对频率和电压进行调控,这种方法有很大的局限性,不能实时的对应系统的变化做出最佳的调控,会使系统的频率和电压造成较大的误差影响并网的稳定性,因此本文研究了一种自适应虚拟参数的方法来解决这一难题。The traditional virtual synchronous machine uses the rotor motion equation of formula (1) to adjust the output parameters, and adjusts the frequency and voltage through the virtual parameters selected in advance. This method has great limitations and cannot respond to system changes in real time. Making the best regulation will cause large errors in the frequency and voltage of the system and affect the stability of grid connection. Therefore, this paper studies a method of adaptive virtual parameters to solve this problem.
由经典控制理论可知VSG的角频率以及阻尼比分别为:According to the classical control theory, the angular frequency and damping ratio of the VSG are:
式中: In the formula:
通过式(5)、(6)可知虚拟转动惯量J和阻尼系数D对系统的影响,当J取定值时,极点会随阻尼系数D改变而改变,当D逐渐增大时极点会由负虚轴逐渐向负实轴靠近,这时系统的稳定性也在逐步提高,而当阻尼系数D(假定D=30)取定值时,虚拟惯量J的变化也给极点带来了很大的影响,如果J值较小,就会出生欠阻尼的情况,降低系统的稳定性。然而如果取用较大的J,虽然会对频率起到一定的支撑作用,但这中选择会产生极大的频率偏差,容易导致系统崩溃。Through equations (5) and (6), we can know the influence of virtual moment of inertia J and damping coefficient D on the system. When J takes a fixed value, the pole will change with the change of damping coefficient D. When D gradually increases, the pole will change from negative The imaginary axis is gradually approaching the negative real axis, and the stability of the system is gradually improving at this time, and when the damping coefficient D (assuming D=30) takes a fixed value, the change of the virtual inertia J also brings a great change to the pole Influence, if the J value is small, there will be an underdamped situation, which will reduce the stability of the system. However, if a larger J is used, although it will play a certain role in supporting the frequency, this choice will produce a huge frequency deviation, which will easily lead to a system crash.
通过分析可以得出虚拟惯量J和阻尼系数D都会对系统的稳定性带来影响。因此为了系统能够应对各种突发情况而导致的频率波动等问题,应该同时调节J和D提高系统的稳定性。Through the analysis, it can be concluded that both the virtual inertia J and the damping coefficient D will affect the stability of the system. Therefore, in order for the system to cope with frequency fluctuations caused by various emergencies, J and D should be adjusted at the same time to improve the stability of the system.
有功功率呈现振荡衰减的变化规律,其临界稳定点也在不断地发生变化,角频率的变化可以分成四个阶段,第1阶段:[t1-t2]时刻角频率的变化斜率从正值逐渐减小到零,从而导致角频率逐渐增大,因此与额定角频率的误差也在不断增加,与之对应的功角变化为从a点向b点运动,有功也在不断增加,影响系统并网的稳定性,因此需要增大惯量J和阻尼D的取值,抑制角频率偏差和变化率带来的不利因素,第2阶段:[t2-t3]时刻角速度变化斜率由零开始逐渐减小到负值,角速度的值也逐渐接近额定值,但仍存在误差,此过程功角变化为从b点向c点运动,有功功率变化率由正值减小到零,功率达到最大值,因此为了使角频率快速下降到额定角速度,应该降低惯量的取值,如果角频率偏移量过大时,可以增大阻尼D来进一步抑制其偏差,剩余两个阶段的变化同前两阶段的变化规律相同。由此分析可得惯量J和阻尼D的选取规则,如表1所示。The active power presents a change law of oscillation attenuation, and its critical stable point is also constantly changing. The change of angular frequency can be divided into four stages. The first stage: the change slope of angular frequency at time [t 1 -t 2 ] changes from a positive value to gradually decreases to zero, resulting in a gradual increase in the angular frequency, so the error with the rated angular frequency is also increasing, and the corresponding power angle changes from point a to point b, and the active power is also increasing, which affects the system Grid-connected stability, so it is necessary to increase the values of inertia J and damping D to suppress the unfavorable factors brought by angular frequency deviation and rate of change, the second stage: the angular velocity change slope starts from zero at time [t 2 -t 3 ] Gradually decreases to a negative value, and the value of the angular velocity gradually approaches the rated value, but there is still an error. In this process, the power angle changes from point b to point c, the rate of change of active power decreases from positive to zero, and the power reaches the maximum value, so in order to quickly drop the angular frequency to the rated angular velocity, the value of the inertia should be reduced. If the angular frequency offset is too large, the damping D can be increased to further suppress the deviation. The changes in the remaining two stages are the same as the first two The changes in stages are the same. From this analysis, the selection rules of inertia J and damping D can be obtained, as shown in Table 1.
表1惯量J和阻尼D的选取规则Table 1 Selection rules for inertia J and damping D
人工神经网络可以理解为由大量的神经元通过连接权值组成,能够进行信息储存、并行处理、自发学习等操作。人工神经网络的算法主要为BP(Back Propagation)算法,它又被称之为反向传播算法。BP算法可以看成是一种函数,由非线性可以变化的单元构成,具有一些非线性能力,比如说映射能力,而且可以灵活处理网络的学习系数等问题,同时在其它领域比如:信号处理、故障诊断、智能控制等具有广阔的应用前景。Artificial neural network can be understood as composed of a large number of neurons connected by weights, capable of information storage, parallel processing, spontaneous learning and other operations. The algorithm of the artificial neural network is mainly the BP (Back Propagation) algorithm, which is also called the back propagation algorithm. The BP algorithm can be regarded as a function, which is composed of non-linear and changeable units. It has some nonlinear capabilities, such as mapping capabilities, and can flexibly handle problems such as network learning coefficients. At the same time, it can be used in other fields such as: signal processing, Fault diagnosis, intelligent control, etc. have broad application prospects.
通过虚拟参数的选取规则,为了抑制频率和电压的偏移量,实现稳定的双下垂控制,从而实现稳定并网,因此采用神经网络控制的思想实时的调节虚拟参量,其控制方法下所示:Through the selection rules of virtual parameters, in order to suppress the offset of frequency and voltage, realize stable double droop control, and realize stable grid connection, so the idea of neural network control is adopted to adjust virtual parameters in real time, and the control method is as follows:
首先通过神经网络控制器实时获取角频率偏移量和角频率的变化斜率,即:Firstly, the angular frequency offset and the change slope of the angular frequency are obtained in real time through the neural network controller, namely:
其次神经网络控制器根据给定的虚拟参量选取规则得到虚拟参数J和D,即:Secondly, the neural network controller obtains the virtual parameters J and D according to the given virtual parameter selection rules, namely:
式中:J0和D0代表经神经网络调节输出的惯量和阻尼参数;Kj和Kd分别为惯量和阻尼的调节系数;Tj和Td是参数变化的上下限。In the formula: J 0 and D 0 represent the inertia and damping parameters adjusted and output by the neural network; K j and K d are the adjustment coefficients of inertia and damping respectively; T j and T d are the upper and lower limits of parameter changes.
通过神经网络输出的虚拟参量可以去实时的调节电压:The virtual parameters output by the neural network can adjust the voltage in real time:
V=(LfCf)-1[ωiiqLf-rfid-vodi-iodiLf+vid] (11)V=(L f C f ) -1 [ω i i q L f -r f i d -v odi -i odi L f +v id ] (11)
式中:vodi是虚拟同步机输出的d轴电压分量,Lf、Cf、rf分别代表LC滤波器的电感、电容和电阻,iodi是VSG输出的d轴电流分量,id、iq为VSG输入的d、q轴电流分量,vid是VSG输入的d轴电流分量。ωi为VSG输出的角频率。此外跟踪误差为:In the formula: v odi is the d-axis voltage component output by the virtual synchronous machine, L f , C f , r f represent the inductance, capacitance and resistance of the LC filter respectively, i odi is the d-axis current component output by the VSG, i d , i q is the d and q axis current components of VSG input, and v id is the d axis current component of VSG input. ω i is the angular frequency of VSG output. In addition, the tracking error is:
由上式可知虚拟同步机利用下垂系数分配无功功率时,无法做到精准调节电压,因此提前设定好误差函数eli,可以实现电压和无功功率折中调配,但是定义的误差函数需要对电压进行二次控制,即e1i减少vodi-vref之间的偏差,通过神经网络控制器使这个误差减小,因此式(12)可以变换为:It can be seen from the above formula that when the virtual synchronous machine uses the droop coefficient to allocate reactive power, it cannot accurately adjust the voltage. Therefore, the error function e li is set in advance to achieve a compromise between voltage and reactive power. However, the defined error function requires The voltage is controlled twice, that is, e 1i reduces the deviation between v odi -v ref , and the error is reduced through the neural network controller, so the formula (12) can be transformed into:
式中:另一个误差变量e2i可表示为:In the formula: Another error variable e 2i can be expressed as:
e2i=divodi-αij (14)e 2i =d i v odi -α ij (14)
式中:αij是一个虚拟控制量。In the formula: α ij is a virtual control quantity.
利用李雅普诺夫理论来校验其稳定性Using Lyapunov's theory to check its stability
设定李雅普函数为:Set the Lyape function as:
对其微分并且带入式(13)可得:Differentiate it and put it into formula (13) to get:
将式(14)代入式(16)可以得到:Substituting formula (14) into formula (16) can get:
式中αi用以下公式来表示:In the formula, α i is expressed by the following formula:
将式(18)代入式(17)可得:Substituting formula (18) into formula (17) can get:
由以上公式得到另一个李雅普函数为:Another Lyape function obtained from the above formula is:
结合式(11)、(19)、(20)可得该模型的控制器:Combining equations (11), (19) and (20), the controller of this model can be obtained:
vid=-e1i-k2ie2i+Fi(xi) (21)v id =-e 1i -k 2i e 2i +F i (x i ) (21)
上述可表示为:the above Can be expressed as:
利用神经网络将可以转变为:Using a neural network to can be transformed into:
神经网络控制器来实时调节,首先由下垂控制可得:The neural network controller is used for real-time adjustment, first of all, it can be obtained from the droop control:
ωi=ωni-kppi (24)ω i =ω ni -k p p i (24)
式中:ωi使虚拟同步机输出的角频率,ωni是角频率的标幺值,Pi为滤波之后输出的有功功率。In the formula: ω i is the angular frequency output by the virtual synchronous machine, ω ni is the per unit value of the angular frequency, and P i is the output active power after filtering.
调节的误差可以表示为:The adjusted error can be expressed as:
选择的李雅普诺夫函数为:The chosen Lyapunov function is:
对其微分得到:Differentiate it to get:
vfi=eωidi (27)v fi =e ωi d i (27)
将式(25)代入式(27)可得:Substituting formula (25) into formula (27) can get:
将式(24)代入(28)可得:Substitute (24) into (28) to get:
然后采用神经网络控制,即:Then use neural network control, namely:
因此角速度输出为:So the angular velocity output is:
式中:是神经网络的权值,λi(xi)是神经网络控制的近似误差。In the formula: is the weight of the neural network, and λ i (xi ) is the approximate error controlled by the neural network.
通过神经网络控制器将误差控制在合理范围内,利用李雅普诺夫理论可以判定其经过神经网络控制后系统稳定性有极大的提升。The error is controlled within a reasonable range through the neural network controller, and the Lyapunov theory can be used to determine that the system stability has been greatly improved after the neural network control.
通过神经网络来控制器来调节频率,从而保证有功功率可以按需分配,实现p-f的稳定控制。为了使ωi与ωref的误差在合理的范围内,利用神经网络控制器来实时调节,首先由下垂控制可得:The frequency is adjusted by the controller through the neural network, so as to ensure that the active power can be distributed on demand and realize the stable control of pf. In order to keep the error between ω i and ω ref within a reasonable range, the neural network controller is used to adjust in real time, firstly, it can be obtained by droop control:
ωi=ωni-kppi (24)ω i =ω ni -k p p i (24)
式中:ωi使虚拟同步机输出的角频率,ωni是角频率的标幺值,Pi为滤波之后输出的有功功率。In the formula: ω i is the angular frequency output by the virtual synchronous machine, ω ni is the per unit value of the angular frequency, and P i is the output active power after filtering.
调节的误差可以表示为:The adjusted error can be expressed as:
选择的李雅普诺夫函数为:The chosen Lyapunov function is:
对其微分得到:Differentiate it to get:
vfi=eωidi (27)v fi =e ωi d i (27)
将式(25)代入式(27)可得:Substituting formula (25) into formula (27) can get:
将式(24)代入(28)可得:Substitute (24) into (28) to get:
然后采用神经网络控制,即:Then use neural network control, namely:
因此角速度输出为:So the angular velocity output is:
以上所述仅是本发明的优选实施方式,应当理解本发明并非局限于本文所披露的形式,不应看作是对其他实施例的排除,而可用于各种其他组合、修改和环境,并能够在本文所述构想范围内,通过上述教导或相关领域的技术或知识进行改动。而本领域人员所进行的改动和变化不脱离本发明的精神和范围,则都应在本发明所附权利要求的保护范围内。The above descriptions are only preferred embodiments of the present invention, and it should be understood that the present invention is not limited to the forms disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and Modifications can be made within the scope of the ideas described herein, by virtue of the above teachings or skill or knowledge in the relevant art. However, changes and changes made by those skilled in the art do not depart from the spirit and scope of the present invention, and should all be within the protection scope of the appended claims of the present invention.
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