Adaptive Damping-Inertia Control Method for Energy Storage for Wind Power Maximum Power Point Tracking

Abstract

The present invention discloses an energy storage adaptive damping-inertia control method for wind power maximum power tracking. By designing adaptive compensation terms of inertia control parameters and damping control parameters, substituting into typical virtual synchronous control motion equations to obtain adaptive virtual synchronous control expressions, designing parameter adaptive adjustment rules and setting control parameter selection ranges, the method can achieve the virtual synchronous external characteristics of the grid connection point interface while maximizing the output of the wind farm, thereby improving the frequency stability of the grid connection point.

Description

Technical Field

[0001] The present invention relates to the technical field of stable control of new energy power systems, and in particular to an energy storage adaptive damping-inertia control method for wind power maximum power tracking. Background Art

[0002] Due to the low inertia characteristics of wind farms, as the penetration rate of wind power continues to increase, the frequency stability of the power grid decreases, which seriously affects the safe and stable operation of the power grid. Electrochemical energy storage has a fast response and flexible regulation, which is the key to solving the power balance problem and system stability risks caused by the connection of a large number of new energy sources to the grid. Equipping wind farms with energy storage can improve the frequency response characteristics of wind farms and alleviate the impact of intermittent, random and volatile new energy sources. At present, newly built wind farms in most provinces in my country are required to be equipped with energy storage of 5% to 20% of their rated output.

[0003] The virtual synchronization control strategy can achieve virtual synchronization of the grid-connected interface of power electronic devices by adjusting the control strategy of power electronic devices. Compared with traditional power sources, the virtual synchronization control strategy has active / reactive decoupling control and four-quadrant control capabilities, and the parameters of the rotor motion equation are all virtual quantities, so the dynamic adjustment capability is more flexible than that of thermal power units. If the wind farm operates in the virtual synchronization control mode, although it can improve the frequency stability of the grid connection point, this control mode cannot achieve the maximum power point tracking (MPPT) of the wind farm, and the power generation of the wind farm will drop significantly. In addition, in the current virtual synchronization control strategy of wind farm supporting energy storage, the virtual inertia and damping are generally fixed, and cannot be adaptively adjusted according to the operating status, making it difficult to achieve the best oscillation damping and inertia support effect of energy storage, and unable to maximize the frequency stability of the wind storage system. Summary of the invention

[0004] In order to solve the technical problem that the current virtual synchronization control strategy of the supporting energy storage of the wind farm cannot be adaptively adjusted according to the operating status, the present invention provides an energy storage adaptive damping-inertia control method for wind power maximum power tracking, which can achieve frequency stability of the wind farm grid connection point through supporting energy storage while achieving the maximum output of the wind farm.

[0005] In order to achieve the above technical purpose, the technical solution of the present invention is:

[0006] An energy storage adaptive damping-inertia control method for wind power maximum power tracking, characterized by comprising the following steps:

[0007] For wind farms operating in the maximum power point tracking (MPPT) control mode and the energy storage system supporting the wind farm, an adaptive damping-inertia controller is used to control the energy storage system. The adaptive damping-inertia controller is implemented through the following process:

[0008] Step 1, setting adaptive compensation items for the inertia control parameters and damping control parameters in the virtual synchronous control respectively, and then substituting the adaptive compensation items into the typical virtual synchronous control motion equations to obtain the adaptive damping-inertia control expression;

[0009] Step 2, according to the relationship between the frequency deviation and the frequency deviation change rate in the transient process and the inertia control parameter and the damping control parameter in the virtual synchronous control, the adjustment rule of the adaptive compensation item is set;

[0010] Step three, establish a small signal model to analyze the operating boundary, and then derive the value ranges of the selected damping control parameters and inertia control parameters in turn through small signal analysis, so as to obtain the energy storage adaptive damping-inertia controller.

[0011] The method, in step 1, the adaptive virtual synchronization control expression is:

[0012]

[0013] Where dω / dt is the differential term of the difference between the virtual rotor angular velocity of the wind-storage system connected to the grid, including the wind farm and the energy storage system supporting the wind farm, and the grid angular velocity. J0 and D0 are the initial values ​​of the inertia control parameter and damping control parameter of the energy storage system, respectively. K J and K D are the compensation coefficients of the inertia control parameter and damping control parameter of the energy storage system, and ΔP is the active power generated by the energy storage system.

[0014] In the method, in step 2, the adjustment rule of the adaptive compensation term is:

[0015] When Δω·(dω / dt)>0, J comp Rising, D comp Decrease; when Δω·(dω / dt)<0, J comp Down, D comp rise;

[0016] Where Δω is the difference between the virtual rotor angular velocity at the grid connection point and the grid angular velocity, J comp and D comp are the compensation items of the inertia control parameter and damping control parameter of the energy storage system, K J and K D are the compensation coefficients of inertia control parameter and damping control parameter respectively, and J=J0+Jcomp , D=D0+D comp , J and D are the inertia control parameter and damping control parameter D in virtual synchronous control respectively.

[0017] The method described, the step three comprises:

[0018] A small signal model of the system with grid-connected wind farms is established, and the characteristic roots of the state space equations in the small signal model are obtained. By adjusting the inertia control parameter J and the damping control parameter D, the motion trajectory of the characteristic roots changing with the control parameters is obtained, thereby obtaining the operating boundary. Under the condition that the oscillation mode damping DR>0.1 corresponding to the characteristic roots is satisfied, the value ranges of the damping control parameters and the inertia control parameters are selected in combination with the operating boundary.

[0019] An electronic device, comprising:

[0020] one or more processors;

[0021] a storage device for storing one or more programs,

[0022] When the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0023] A computer readable medium stores a computer program, which implements the above method when executed by a processor.

[0024] The technical effect of the present invention is that the present invention can avoid real-time acquisition and calculation of the frequency deviation and frequency deviation change rate of the system, and can realize smooth adaptive energy storage damping-inertia control of the energy storage converter by transforming the typical virtual synchronous control active loop. The present invention has strong compatibility and scalability. J and K D When both are not equal to 0, the control strategy is energy storage adaptive damping-inertia control; when K J ≠0, K D = 0, the control strategy is adaptive virtual inertia control. J =0, K D ≠0, the control strategy is adaptive virtual damping control; when K J and K D When both are equal to 0, the control strategy is a typical fixed parameter virtual synchronous control. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A topological structure diagram of a wind farm and its supporting energy storage system in a specific embodiment of the present invention;

[0026] Figure 2 A flow chart is designed for the control method of the present invention.

[0027] Figure 3 It is a power angle response curve analysis diagram of the VSG corresponding to step S1 of the method of the present invention;

[0028] Figure 4 A control block diagram of an energy storage system designed according to the steps of the inventive method in a specific embodiment of the present invention;

[0029] Figure 5 This is a graph showing how the characteristic root loci obtained in step 3 of the method vary with control parameters in a specific implementation manner of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further described clearly and completely in conjunction with the accompanying drawings. It should be noted that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The present invention can be applied to a wind farm with supporting energy storage to achieve maximum power point tracking (MPPT) of the wind farm and improve the frequency stability of the wind storage system grid connection point interface. The method includes the following steps:

[0032] S1: Derivation of adaptive virtual synchronous control expression. Derived adaptive virtual synchronous control expression, designed adaptive compensation terms of inertia control parameters and damping control parameters, and substituted them into the typical virtual synchronous control motion equation.

[0033] S2: Design of adaptive adjustment rules for control parameters. Analyze the relationship between frequency deviation and frequency deviation change rate in transient process and inertia control parameters and damping control parameters in virtual synchronous control, and set adaptive compensation mode.

[0034] S3: Setting the range of control parameters. Establish the small signal model analysis operation boundary, and derive the value range of the selected damping control parameters and inertia control parameters in turn through small signal analysis, so as to complete the design of the energy storage adaptive damping-inertia controller.

[0035] S4: Joint operation of wind and energy storage systems. The wind farm controller is operated in MPPT control mode, and the wind farm supporting energy storage is operated in virtual synchronization control mode. This can maximize the output of the wind farm while achieving the virtual synchronization external characteristics of the grid connection point interface, thereby improving the frequency stability of the grid connection point.

[0036] In the above steps:

[0037] In S1, adaptive compensation items are added to the inertia control parameter J and damping control parameter D in the virtual synchronous control as shown in equations (1) and (2), where J0 and D0 are the fixed inertia control parameter and damping control parameter when no compensation item is added in the virtual synchronous control, J comp and D comp are the adaptive compensation items of inertia control parameters and damping control parameters, K J and K D are the compensation coefficients of inertia control parameters and damping control parameters respectively, Δω is the difference between the virtual rotor angular velocity at the grid connection point and the angular velocity of the grid, and dω / dt is the differential term of the difference between the virtual rotor angular velocity at the grid connection point of the wind-storage system including the wind farm and the energy storage system supporting the wind farm and the angular velocity of the grid.

[0038]

[0039]

[0040] Substitute the inertia control parameter J and damping control parameter D after adding the adaptive compensation term into the typical second-order synchronous generator motion equation The virtual synchronous control equation including inertia and damping compensation is shown in equation (3).

[0041]

[0042] Rearranging equation (3) into a quadratic equation of dω / dt yields:

[0043]

[0044] According to Vieta's theorem, the two roots of a quadratic equation can be solved.

[0045]

[0046] The cases of Δω·(dω / dt)>0 and Δω·(dω / dt)<0 both exist in transient processes, so the unreasonable negative roots are discarded.

[0047] By rationalizing the numerator and denominator, we can obtain the expression of energy storage adaptive damping-inertia control as shown in formula (6).

[0048]

[0049] That is, according to the above formula, the differential term dω / dt is obtained through two control parameters, thereby obtaining the final speed control amount.

[0050] In S2, when Δω·(dω / dt)>0, it is necessary to increase the system inertia to enhance the anti-interference ability of the system, but it will sacrifice a certain response speed, so the damping should be reduced at the same time. When Δω·(dω / dt)<0, it is necessary to reduce the inertia to make the system response speed faster, but the anti-interference ability of the system is reduced, so the damping should be increased while reducing the inertia. Therefore, when Δω·(dω / dt)>0, J comp Rising, D comp Decrease; therefore, when Δω·(dω / dt)<0, J comp Down, D comp rise.

[0051] In S3, further, reasonable initial control parameters are selected for the energy storage adaptive damping-inertia control, and J0 and D0 are selected as appropriate parameter values. A small signal model of the system including the wind farm connected to the grid is established, and the characteristic roots are obtained by the state space equation in the small signal model. By adjusting the inertia control parameter J and the damping control parameter D, the motion trajectory of the characteristic root as the control parameter changes can be obtained. The control parameter selection should satisfy the oscillation mode damping DR>0.1 corresponding to the characteristic root.

[0052] The following is an example of a specific wind farm case:

[0053] The wind farm and its supporting energy storage topology in this embodiment are as follows: Figure 1 The wind farm has 23 double-fed wind turbines with a unit capacity of 2.2MW and a rated capacity of 50.6MW. The wind farm supporting energy storage is configured at 10% of the rated capacity of the wind farm, with an energy storage capacity of 5MW / 10MWh. The parameters of the wind turbines are shown in Table 1, and the parameters of the lithium battery energy storage are shown in Table 2.

[0054] Energy storage adaptive damping-inertia control method for maximizing wind farm tracking planned power generation, specific control parameter design Figure 2 As shown. Through steps S1-S2, the expression of energy storage adaptive damping-inertia control is obtained as shown in equation (6). The control block diagram of the energy storage system using virtual synchronous control is shown in Figure 3 shown.

[0055] Table 1 Related parameters of doubly-fed wind turbine

[0056]

[0057] Table 2 Lithium battery energy storage related parameters

[0058]

[0059] After selecting reasonable simulation parameters for adaptive virtual synchronous control in step S3, the wind farm is connected to the IEEE 3-machine 9-bus system and its characteristic root changes with the control parameters are analyzed. Figure 4 As shown. By changing the root locus corresponding to the parameter change, reasonable parameters are selected for the energy storage adaptive virtual synchronization control, and finally the J0 value is determined to be 27, the D0 value is 90, and the K J The value is 0.45, K D The value is -0.65. The inertia control parameter J and the damping control parameter D are adjusted by the adaptive compensation parameter J comp and D comp Make dynamic adjustments.

[0060] The wind farm is operated in MPPT control mode, and the energy storage is operated in the adaptive virtual synchronous control proposed in the present invention. A three-phase short-circuit fault is set on the wind farm grid-connected busbar, and the fault is cleared after 0.15s. The dynamic response of the system under different control strategies of the wind farm supporting energy storage is compared. The method of the present invention is energy storage adaptive damping-inertia control (AVSG control), and the comparison methods are (a) wind farm without supporting energy storage, (b) wind farm supporting energy storage is PQ control, and (c) wind farm supporting energy storage is traditional virtual synchronous control (VSG control). The time domain simulation results of the wind farm's transient response to large disturbances at the wind farm grid connection point under different control strategies are shown in the figure. Figure 5 As shown, the comparison of relevant frequency indicators is shown in Table 3.

[0061] from Figure 5 From the comparison of the frequency indicators in the simulation and Table 3, it can be seen that the control strategy proposed in the present invention has a better suppression effect on frequency oscillation; compared with the typical fixed parameter VSG control, the adaptive damping-inertia control proposed in the present invention reduces the frequency fluctuation range by 20.59%, shortens the adjustment time by 57%, and greatly improves the transient performance of the system; and the wind turbine has a better output effect when operating in the MPPT mode.

[0062] Table 3 Dynamic response of wind-storage system under transient short-circuit fault

[0063]

[0064] According to an embodiment of the present invention, the present invention also provides an electronic device and a computer-readable medium.

[0065] Electronic equipment includes:

[0066] one or more processors;

[0067] a storage device for storing one or more programs,

[0068] When the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0069] In specific use, users can interact with servers, which are also electronic devices, through electronic devices that are terminal devices and based on the network to achieve functions such as receiving or sending messages. Terminal devices are generally various electronic devices equipped with display devices and used based on human-computer interfaces, including but not limited to smart phones, tablet computers, laptops and desktop computers. Various specific application software can be installed on the terminal devices as needed, including but not limited to web browser software, instant messaging software, social platform software, shopping software, etc. The server is a network service end used to provide various services.

[0070] Similarly, the computer-readable medium of the present invention stores a computer program thereon, and when the program is executed by a processor, the method of the embodiment of the present invention is implemented.

[0071] The above-mentioned embodiments only express one or several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An energy storage adaptive damping-inertia control method for wind power maximum power tracking, characterized in that: The following steps are involved: For wind farms operating in the maximum power point tracking (MPPT) control mode and the energy storage system supporting the wind farm, an adaptive damping-inertia controller is used to control the energy storage system. The adaptive damping-inertia controller is implemented through the following process: Step 1, setting adaptive compensation items for the inertia control parameters and damping control parameters in the virtual synchronous control respectively, and then substituting the adaptive compensation items into the typical virtual synchronous control motion equations to obtain the adaptive damping-inertia control expression; Step 2, according to the relationship between the frequency deviation and the frequency deviation change rate in the transient process and the inertia control parameter and the damping control parameter in the virtual synchronous control, the adjustment rule of the adaptive compensation item is set; Step 3: Establish a small signal model to analyze the operating boundary, and then derive the value ranges of the selected damping control parameters and inertia control parameters in turn through small signal analysis, so as to obtain an energy storage adaptive damping-inertia controller; In the step 1, the adaptive damping-inertia control expression is: Where dω / dt is the differential term of the difference between the virtual rotor angular velocity of the wind-storage system connected to the grid, including the wind farm and the energy storage system supporting the wind farm, and the grid angular velocity. J0 and D0 are the initial values ​​of the inertia control parameter and damping control parameter of the energy storage system, respectively. K J and K D are the compensation coefficients of the inertia control parameter and damping control parameter of the energy storage system, ΔP is the active power generated by the energy storage system; In the step 2, the adjustment rule of the adaptive compensation term is: When Δω·(dω / dt)>0, J comp Rising, D comp Decrease; when Δω·(dω / dt)<0, J comp Down, D comp rise; Where Δω is the difference between the virtual rotor angular velocity at the grid connection point and the grid angular velocity, J comp and D comp are the compensation items of the inertia control parameter and damping control parameter of the energy storage system, K J and K D are the compensation coefficients of inertia control parameter and damping control parameter respectively, and J=J0+J comp , D=D0+D comp , J and D are the inertia control parameter and damping control parameter D in virtual synchronous control respectively.

2. The method according to claim 1, characterized in that The step three comprises: A small signal model of the system with grid-connected wind farms is established, and the characteristic roots of the state space equations in the small signal model are obtained. By adjusting the inertia control parameter J and the damping control parameter D, the motion trajectory of the characteristic roots changing with the control parameters is obtained, thereby obtaining the operating boundary. Under the condition that the oscillation mode damping DR>0.1 corresponding to the characteristic roots is satisfied, the value ranges of the damping control parameters and the inertia control parameters are selected in combination with the operating boundary.

3. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-2.

4. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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