Flexible load microgrid system control method based on model-free adaptive sliding mode control

Abstract

The application discloses a flexible load micro-grid system control method based on a model-free adaptive sliding mode control, which comprises the following steps: selecting an access port of a time division multiplexing switch according to output power of a power supply side and load side demand; performing pseudo partial derivative estimation and resetting according to input variation and output variation; taking a current time system output signal, an expected output signal and the pseudo partial derivative as inputs of a model-free adaptive control law to obtain a first control input quantity; constructing a discrete sliding mode surface according to a system output error, combining the current time system output signal, the expected output signal and the pseudo partial derivative as inputs of a sliding mode control law to obtain a second control input quantity; obtaining a system input quantity according to the first control input quantity and the second control input quantity, generating a modulation signal and acting on the time division multiplexing switch. The introduction of the sliding mode control accelerates the convergence speed of the model-free adaptive controller, and the dynamic performance of the system when accessing different loads is improved through the model-free adaptive sliding mode control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power flexible load micro-grid system control, in particular to a flexible load micro-grid system control method based on model-free adaptive sliding mode control. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Micro-grid, as a new technical form of integration of source, network and load resources on the distribution side, is an effective means to promote on-site consumption of large-scale distributed new energy and realize local aggregation and interaction of multiple new elements. However, the current micro-grid technology and application deployment mode do not match the inherent characteristics of new energy well, resulting in low utilization rate of new energy and serious "curtailment of wind and light" phenomenon.

[0004] In addition, the volatility and intermittency of new energy generation make it difficult to consume in the power system. In the actual dispatching and operation of the power system, especially during the peak load period, the supply of new energy power often cannot meet the system peak shaving demand. This mismatch between supply and demand not only limits the further development of new energy, but also affects the investment return of investors on new energy projects, and fails to achieve the expected investment effect.

[0005] In order to improve the operation efficiency of the power system and the utilization rate of non-stable renewable energy, the traditional micro-grid needs to be changed to a power flexible load micro-grid system, and it is of great significance to realize flexible connection of source and load by using time division multiplexing switch components. However, time division multiplexing switch components need to constantly switch power supply to different loads, different power supply characteristics are different, and the voltage levels required by the loads may be different, and the system is easily affected by changes in internal and external parameters such as load disturbance, making it difficult to achieve fast and stable switching. In view of the complex requirements of time division multiplexing switch components for different power sources and load connections in the power flexible load micro-grid system, it is particularly important to develop a control method that can meet these requirements to ensure that the micro-grid system can maintain stable and efficient operation under varying power sources and loads.

[0006] Model-free adaptive control (MFAC) is a data-driven controller that only needs input and output (I / O) data of the system to achieve the control target. It describes the relationship between input and output of the system through dynamic linearization method, without the need for accurate model of the system, so it has good robustness. However, the traditional model-free adaptive controller takes the actual output of the power system reaching the expected reference value as the main control target, and does not consider the relationship between the output change of the power system in the design of the control input criterion function, so the convergence speed is slow and the dynamic performance is poor. SUMMARY

[0007] To solve the above problems, the present application provides a flexible load micro-grid system control method based on model-free adaptive sliding mode control, first, by judging the load demand and the output of different power sources, the time division switch access port is selected; then the dynamic performance of the power flexible load micro-grid system when accessing different loads is improved through model-free adaptive sliding mode control, the introduction of sliding mode control further accelerates the convergence speed of the model-free adaptive controller, and the control problem of quickly and stably accessing different loads by different power sources through the time division multiplexing switch component is solved.

[0008] To achieve the above purpose, the present application adopts the following technical scheme:

[0009] In the first aspect, the present application provides a flexible load micro-grid system control method based on model-free adaptive sliding mode control, comprising:

[0010] The flexible load micro-grid system is composed of power supply side and load side based on time division multiplexing switch array, and the access port of time division multiplexing switch is selected according to the output power of power supply side and the demand of load side;

[0011] The system input signal and the system output signal at the current time are respectively processed by difference, and then the pseudo partial derivative estimation and reset are performed according to the obtained input variation and output variation, so as to obtain the pseudo partial derivative;

[0012] The system output signal at the current time, the expected output signal and the pseudo partial derivative are taken as the input of the model-free adaptive control law, and thus the first control input quantity is obtained;

[0013] The discrete sliding mode surface is constructed according to the system output error, and the system output signal at the current time, the expected output signal and the pseudo partial derivative are taken as the input of the sliding mode control law, and thus the second control input quantity is obtained;

[0014] The system input quantity is obtained according to the first control input quantity and the second control input quantity, the modulation signal is generated after the system input quantity is modulated and acts on the time division multiplexing switch, so as to realize the control of the flexible load micro-grid system.

[0015] As an optional implementation, the pseudo partial derivative estimation is:

[0016]

[0017] Wherein, The estimated value of the pseudo partial derivative φ(k) is η∈(0,1) is the step factor; μ is the weight factor; Δv o (k) is the output variation between the system output signal at k time and the system output signal at k-1 time; Δv i(k-1) is the input variation between the system input signal at k-1 time and the system input signal at k-2 time;

[0018] The pseudo partial derivative is reset as:

[0019]

[0020] where ε is a sufficiently small positive number, and φ(0) is the initial value of φ(k).

[0021] As an optional implementation, the model-free adaptive control law is:

[0022]

[0023] where ρ is a convergence factor; v iMFAC (k) is the first control input; v i (k-1) is the system input signal at k-1 time; m>0 is a weight factor; λ>0 is a weight factor; φ(k) is a pseudo partial derivative; v o * (k+1) is the desired output signal; v o (k) is the system output signal at k time.

[0024] As an optional implementation, the process of constructing a discrete sliding surface according to the system output error is:

[0025] The discrete sliding surface s(k) at the current time is constructed as: s(k) = e(k) = v o * (k) - v o (k) ;

[0026] where e(k) is the system error at the current time, v o * (k) is the desired output signal at k time, v o (k) is the actual system output signal at k time.

[0027] Thus, the discrete sliding surface s(k+1) at the next time is:

[0028] s(k+1) = e(k+1) = v o * (k+1) - φ(k)Δv i (k) - v o (k) ;

[0029] where e(k+1) is the system error at the next time; v o * (k+1) is the desired output signal; φ(k) is a pseudo partial derivative; Δv i(k) is the input variation between the system input quantity at k moment and the system input quantity at k-1 moment.

[0030] As an alternative embodiment, the sliding mode reaching law is designed based on discrete sliding surface:

[0031] s(k+1)-s(k)=-qTs(k)-rTsat((s(k));

[0032] Thus, the sliding mode control law is:

[0033]

[0034] wherein v iSM (k) is the second control input quantity; q and r are sliding mode coefficients, T is the discrete sampling time, and sat((s(k)) is a saturation function.

[0035] As an alternative embodiment, the system input quantity v i (k) is obtained according to the first control input quantity and the second control input quantity. i (k)=v iMFAC (k)+w*v iSM (k); wherein w is a weight factor, and v iSM (k) is the second control input quantity; and v iMFAC (k) is the first control input quantity.

[0036] In a second aspect, the present application provides a flexible load microgrid system control system based on model-free adaptive sliding mode control, comprising:

[0037] A selection module is configured to select the access port of the time-division multiplexing switch according to the output power of the power supply side and the load side demand, so that the flexible load microgrid system is composed of the power supply side and the load side based on the time-division multiplexing switch array.

[0038] A pseudo partial derivative estimation module is configured to perform differential processing on the system input signal and the system output signal at the current moment, and then perform pseudo partial derivative estimation and reset according to the obtained input variation and output variation, so as to obtain the pseudo partial derivative.

[0039] A model-free adaptive control module is configured to take the system output signal at the current moment, the expected output signal and the pseudo partial derivative as the input of the model-free adaptive control law, so as to obtain the first control input quantity.

[0040] A sliding mode control module is configured to construct a discrete sliding surface according to the system output error, and take the system output signal at the current moment, the expected output signal and the pseudo partial derivative as the input of the sliding mode control law, so as to obtain the second control input quantity.

[0041] The modulation module is configured to obtain a system input quantity according to the first control input quantity and the second control input quantity, generate a modulation signal by modulating the system input quantity, and act on the time division multiplexing switch to realize control of the flexible load microgrid system.

[0042] In a third aspect, the present application provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, when the computer instructions are run by the processor, the method of the first aspect is completed.

[0043] In a fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, when the computer instructions are executed by the processor, the method of the first aspect is completed.

[0044] In a fifth aspect, the present application provides a computer program product comprising a computer program, when the computer program is executed by the processor, the method of the first aspect is completed.

[0045] Compared with the prior art, the present application has the following beneficial effects:

[0046] The present application proposes a flexible load microgrid system control method based on model-free adaptive sliding mode control, first, by judging the load demand and the output of different power sources, the time division switch access port is selected, then the dynamic performance of the power flexible load microgrid system when accessing different loads is improved through model-free adaptive sliding mode control, the convergence speed of the model-free adaptive controller is further accelerated by introducing the sliding mode control, the control problem of quickly and stably accessing different loads by different power sources through the time division multiplexing switch component is solved, the dynamic performance of the power flexible load microgrid system is improved, and the dynamic response capability of the traditional model-free adaptive control is further improved.

[0047] The present application adopts the time division multiplexing switch component to realize the flexible connection of source and load, and improves the operation efficiency of the power system and the utilization rate of unstable renewable energy.

[0048] The present application designs a model-free adaptive sliding mode control strategy, which is applied to the Boost converter, and can effectively improve the dynamic performance of the power flexible load microgrid system, and the designed model-free adaptive sliding mode control strategy can be applied not only to the Boost converter, but also to a general data-driven controller, which can be flexibly applied to various industrial scenes.

[0049] The present application considers the relationship of the output change in the control input criterion function of the model-free adaptive controller, and designs a sliding mode control strategy to further accelerate the convergence speed of the traditional model-free adaptive control and enhance the dynamic response capability of the system.

[0050] Advantages of the additional aspects of the application will become apparent in the following description. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained according to the provided drawings without creative labor.

[0052] Figure 1 The structural block diagram of the power flexible load microgrid system is shown in Fig. 1.

[0053] Figure 2 The principle schematic diagram of the flexible load microgrid system control method based on model-free adaptive sliding mode control provided by Embodiment 1 of the present application is shown in Fig. 2.

[0054] Fig. 3(a) is a waveform diagram of the DC bus voltage and load current when the DC voltage changes under different load demands when a traditional model-free adaptive controller is used.

[0055] Fig. 3(b) is a waveform diagram of the DC bus voltage and load current when a model-free adaptive sliding mode controller is used.

[0056] Fig. 4(a) is a waveform diagram of the load-side inverter output power and load current when a traditional model-free adaptive controller is used under sudden load increase.

[0057] Fig. 4(b) is a waveform diagram of the load-side inverter output power and load current when a model-free adaptive sliding mode controller is used under sudden load increase. DETAILED DESCRIPTION

[0058] The present application will be further described below in combination with the drawings and embodiments.

[0059] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0060] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0061] The embodiments in the present application and the features in the embodiments can be combined with each other in the case of no conflict.

[0062] Embodiment 1

[0063] The embodiment provides a flexible load micro-grid system control method based on model-free adaptive sliding mode control (MFASMC), comprising:

[0064] The flexible load micro-grid system is composed of a power supply side and a load side based on a time division multiplexing switch array, and the access port of the time division multiplexing switch is selected according to the output power of the power supply side and the demand of the load side;

[0065] The system input signal and the system output signal at the current time are respectively subjected to differential processing, and then pseudo partial derivative estimation and resetting are performed according to the obtained input variation and output variation, so as to obtain a pseudo partial derivative;

[0066] The system output signal at the current time, the expected output signal and the pseudo partial derivative are taken as the input of the model-free adaptive control law, so as to obtain a first control input quantity;

[0067] A discrete sliding mode surface is constructed according to the system output error, and the system output signal at the current time, the expected output signal and the pseudo partial derivative are taken as the input of the sliding mode control law, so as to obtain a second control input quantity;

[0068] The system input quantity is obtained according to the first control input quantity and the second control input quantity, the system input quantity is modulated to generate a modulation signal, and the modulation signal is applied to the time division multiplexing switch, so as to realize the control of the flexible load micro-grid system.

[0069] As shown in Figure 1 The structure block diagram of the power flexible load micro-grid system is shown. The power flexible load micro-grid system realizes the optimal distribution and stable operation of the internal power of the micro-grid through multi-source multi-load time multiplexing. The power supply side and the load side are composed of an equivalent flexible load network based on a time division multiplexing switch array, the port of the time division multiplexing switch is determined according to the output power of the power supply side and the demand of the load side, and the dynamic adaptation between the fluctuating source and load is realized through the cooperative control of the time division multiplexing switch array.

[0070] In the embodiment, the cooperative control strategy of the time-sharing multiplexing switch includes: using a four-port time-sharing multiplexing switch. In order to improve the direct utilization rate of new energy, the time-sharing multiplexing switch preferentially selects to access photovoltaic, wind power and other non-stable power sources. Since the output of photovoltaic and wind power has randomness and volatility, when the output power of the non-stable power source cannot meet the load demand, a stable power source such as a distribution network is accessed.

[0071] If there is a non-stable power source that meets the load demand, the non-stable power source with the most matched output power and load demand is preferentially selected as the access port of the time-sharing multiplexing switch.

[0072] After selecting the appropriate power port through the cooperative control strategy of the time-sharing multiplexing switch, since different loads have different requirements for voltage levels, a DC / DC converter is usually needed in front of the load side to adjust the direct current voltage. Therefore, the control strategy designed in the embodiment is applied to the DC / DC converter to realize the stability of the direct current voltage when accessing different power sources and adapt to the requirements of different loads.

[0073] In the embodiment, a model-free adaptive sliding mode control method is designed for the DC / DC converter in the power flexible load microgrid system. The controller is designed by taking the Boost converter in the DC / DC converter as an example. Specifically, the control method includes a dynamic linearization module, a model-free adaptive control module and a sliding mode control module. The mathematical model of the system is described by the dynamic linearization method, the pseudo partial derivative (PPD) is introduced to estimate the input and output changes of the system in real time, and the accurate mathematical model of the system is not needed; the model-free adaptive control module is designed to realize efficient and stable control of the Boost converter, and the dynamic performance of the model-free adaptive controller is further enhanced through the sliding mode control module.

[0074] Figure 2 The following describes the dynamic linearization module, the model-free adaptive control module and the sliding mode control module in detail. Figure 2

[0075] 1. Dynamic linearization module.

[0076] The Boost converter is described as:

[0077] v o (k+1)=f(v o (k),L,v o (k-n o ),v i (k),L,v i (k-n i ))(1) ​

[0078] where v i (k) ∈ R is the system input at time k, denoted as the PWM modulated voltage in Boost converter; v o (k) ∈ R is the system output at time k, v o (k+1) ∈ R is the system output at time k+1, denoted as the load side DC bus output voltage; n o , n i is the system order; f(...) is an unknown nonlinear function.

[0079] Based on the system controllability and the existence of continuous partial derivative of f(...) and the satisfaction of Lipschitz constraint condition, a tight format dynamic linearization method is established, and the above equation is transformed into:

[0080] v o (k+1) = v o (k) + φ(k)Δv i (k) (2)

[0081] where Δv i (k) = v i (k) - v i (k-1), represents the input variation between the system input v i (k) at time k and the system input v i (k-1) at time k-1; φ(k) represents the pseudo partial derivative, which is a time-varying quantity, used to dynamically describe the relationship between the input variation Δv i (k) and the output variation Δv o (k); since it only contains the I / O data of the system, equation (2) can be applied to single-input single-output nonlinear systems, and it belongs to a general data-driven controller.

[0082] In order to estimate the pseudo partial derivative φ(k) in real time, a cost function J(φ(k)) as shown in equation (3) is designed by using projection algorithm:

[0083]

[0084] where φ (k) is the estimated value of the pseudo partial derivative, μ is the weight factor, v o (k-1) ∈ R is the system output at time k-1, Δv i (k-1) = v i (k-1) - v i (k-2), represents the input variation between the system input v i (k-1) at time k-1 and the system input v i (k-2) at time k-2.

[0085] Finding the extreme values ​​of equation (3) with respect to φ(k) yields a pseudo-partial derivative estimation algorithm:

[0086]

[0087] Where η∈(0,1) is the step size factor, the purpose of which is to make the control algorithm more flexible and general; Δv o (k) represents the change in system output at time k and at time k-1.

[0088] To ensure that the controller is bounded and convergent, a pseudo-partial derivative reset algorithm is introduced:

[0089]

[0090] Where ε is a sufficiently small positive constant, and φ(0) represents the initial value of φ(k).

[0091] 2. Model-free adaptive control module.

[0092] To control the Boost converter, a traditional model-free adaptive controller uses the following control input criterion function J(v i (k)):

[0093] J(v i (k))=|v o * (k+1)-v o (k+1)| 2 +λ|v i (k)-v i (k-1)| 2 (5)

[0094] Where λ>0 is a weighting factor used to limit changes in the control input; v o * (k+1) represents the desired output signal, which can be considered as the desired output voltage v of the Boost converter. ref .

[0095] The control input criterion function in equation (5) only considers the steady-state relationship between the actual output signal and the desired output signal. Therefore, the system output change is introduced to accelerate the convergence speed of the controller, resulting in the improved control input criterion function G(v). i (k)):

[0096] G(v i (k))=J(v i (k))|+m|v o (k+1)-v o (k)| 2 (6)

[0097] where m>0 is a weight factor.

[0098] Substitute equation (6) into equation (2) and take the derivative of v i (k) to get the model-free adaptive control law:

[0099]

[0100] where p is a convergence factor to make the control algorithm more general; v iMFAC (k) represents the first control input obtained by the model-free adaptive control law.

[0101] 3. A sliding mode control module.

[0102] To further enhance the convergence speed of the model-free adaptive controller, a discrete sliding surface s(k) is constructed:

[0103] s(k) = e(k) = v o * (k) - v o (k) (8)

[0104] where e(k) represents the system error, v o * (k) is the desired output signal at time k, and v o (k) is the actual system output signal at time k.

[0105] Substitute equation (8) into equation (2) to get:

[0106] s(k+1) = e(k+1) = v o * (k+1) - φ(k)Δv i (k) - v o (k) (9)

[0107] To reduce the chattering of the sliding mode control, a sliding mode reaching law is designed:

[0108] s(k+1) - s(k) = -qTs(k) - rTsat((s(k)) (10)

[0109] where q, r are the sliding mode coefficients, T is the discrete sampling time, and sat((s(k)) is the saturation function.

[0110] Substitute equation (10) into equation (9) to get the sliding mode control law:

[0111]

[0112] where v iSM (k) is the second control input.

[0113] Applying the sliding mode control law to the traditional model-free adaptive control law, i.e., combining equations (7) and (11), we obtain equation (12):

[0114] v i (k)=v iMFAC (k)+w*v iSM (k)(12)

[0115] Here, w represents the weighting factor, which makes the control algorithm more generalized.

[0116] Finally, the system input v obtained through model-free adaptive sliding mode control i (k) is modulated to generate a PWM modulation signal, which is then applied to the switching devices (time-division multiplexing switches) of the Boost converter to realize the control of the power flexible load microgrid system.

[0117] Figure 3(a) shows the DC bus voltage and load current waveforms under different load demands when using a traditional model-free adaptive controller; Figure 3(b) shows the DC bus voltage and load current waveforms when using a model-free adaptive sliding mode controller. It can be seen that both methods can achieve stable voltage rise. Compared with the traditional method, the model-free adaptive sliding mode controller has a faster adjustment time, the DC voltage can reach the desired value faster, and there is less voltage fluctuation.

[0118] Figure 4(a) shows the output power and load current waveforms of the inverter on the load side when using a traditional model-free adaptive controller under sudden load increases; Figure 4(b) shows the output power and load current waveforms of the inverter on the load side when using a model-free adaptive sliding mode controller under sudden load increases. It can be seen that, compared with the traditional method, the model-free adaptive sliding mode controller can adapt its output power to load changes more quickly, enabling the system to stabilize, and has a lower current THD, thus enhancing the dynamic performance of the power flexible load microgrid system.

[0119] Example 2

[0120] This embodiment provides a control system for a flexible load microgrid system based on model-free adaptive sliding mode control, including:

[0121] The selection module is configured such that the flexible load microgrid system is composed of a power supply side and a load side based on a time-division multiplexing switch array, and the access port of the time-division multiplexing switch is selected according to the output power of the power supply side and the demand of the load side.

[0122] The pseudo partial derivative estimation module is configured to perform differential processing on the system input signal and the system output signal at the current moment, and perform pseudo partial derivative estimation and reset according to the obtained input variation and output variation, so as to obtain a pseudo partial derivative;

[0123] The model-free adaptive control module is configured to take the system output signal at the current moment, the expected output signal and the pseudo partial derivative as inputs of a model-free adaptive control law, so as to obtain a first control input quantity;

[0124] The sliding mode control module is configured to construct a discrete sliding mode surface according to the system output error, and take the system output signal at the current moment, the expected output signal and the pseudo partial derivative as inputs of a sliding mode control law, so as to obtain a second control input quantity;

[0125] The modulation module is configured to obtain a system input quantity according to the first control input quantity and the second control input quantity, generate a modulation signal by modulating the system input quantity, and act on a time division multiplexing switch, so as to control the flexible load microgrid system.

[0126] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the above modules have the same examples and application scenarios as the corresponding steps, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0127] In more embodiments, there are also provided:

[0128] An electronic device includes a memory and a processor, and computer instructions stored in the memory and running on the processor, when the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For brevity, it will not be repeated here.

[0129] It should be understood that in the embodiments, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, ready-to-program gate arrays FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0130] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a part of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0131] A computer readable storage medium for storing computer instructions, when the computer instructions are executed by the processor, the method described in Embodiment 1 is completed.

[0132] The method in embodiment 1 can be directly embodied as being completed by a hardware processor or being completed by a combination of hardware and software modules in the processor. The software modules can be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, or the like. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the above method in combination with hardware. To avoid repetition, no longer detailed description is made herein.

[0133] A computer program product includes a computer program, which, when executed by a processor, implements the method described in embodiment 1.

[0134] The present application also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, for example, instructions embodied in program modules, executed by devices at a destination real or virtual processor to perform processes / methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functionality of program modules can be combined or split between program modules as desired. Machine executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0135] Computer program code for carrying out operations of the present application can be written in one or more programming languages. The computer program code can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, transform and control transmits the flow diagrams and / or block diagrams into a machine that implements the functions / acts specified in the flow diagrams and / or block diagrams. The computer program code can also be loaded onto a computer, a processor or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flow diagrams and / or block diagrams.

[0136] In the context of the present application, computer program code or related data can be carried by any suitable carrier, to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals can include electrical, optical, radio frequency, sound, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0137] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the present embodiment can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0138] Although the specific embodiments of the present application are described above in combination with the drawings, it is not a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A control method for a flexible load microgrid system based on model-free adaptive sliding mode control, characterized in that, include: The flexible load microgrid system consists of a power supply side and a load side based on a time-division multiplexing switch array. The access port of the time-division multiplexing switch is selected according to the output power of the power supply side and the demand of the load side. After performing differential processing on the system input signal and system output signal at the current moment, pseudo-partial derivatives are estimated and reset based on the obtained input and output changes to obtain pseudo-partial derivatives. The first control input is obtained by taking the current system output signal, the desired output signal, and the pseudo-partial derivative as the inputs of the model-free adaptive control law; A discrete sliding surface is constructed based on the system output error. The current system output signal, the desired output signal, and the pseudo-partial derivative are used as inputs to the sliding control law, thereby obtaining the second control input quantity. The system input is obtained based on the first control input and the second control input. The system input is modulated to generate a modulated signal, which is then applied to the time-division multiplexing switch to realize the control of the flexible load microgrid system. By introducing changes in system output to accelerate the convergence speed of the controller, an improved control input criterion function is obtained. : The model-free adaptive control law is: ； in, It is the convergence factor; It is the first control input; The system input signal at time k-1; It is a weighting factor; It is a weighting factor; It is a pseudo-partial derivative; The desired output signal; Let k be the system output signal at time k. It is a weighting factor; Design of sliding mode convergence law based on discrete sliding surface: ； Therefore, the sliding mode control law is: ； in, This is the second control input. The sliding mode coefficient is... For discrete sampling time, sat (( s ( k )) is a saturation function.

2. The control method for a flexible load microgrid system based on model-free adaptive sliding mode control as described in claim 1, characterized in that, The pseudo-partial derivative estimate is: ； in, It is a pseudo-partial derivative The estimated value; It is the step size factor; It is a weighting factor; Let be the change in system output signal between time k and time k-1; This represents the change in system input signal between time k-1 and time k-2. The pseudo-partial derivatives are reset to: ； in, It is a sufficiently small positive number. yes The initial value.

3. The control method for a flexible load microgrid system based on model-free adaptive sliding mode control as described in claim 1, characterized in that, The process of constructing a discrete sliding surface based on the system output error is as follows: Construct the discrete sliding surface at the current moment : ; in, The system error at the current moment. Let k be the desired output signal at time k. This represents the actual system output signal at time k; Therefore, the discrete sliding surface at the next moment for: ； in, This represents the systematic error at the next moment. The desired output signal; It is a pseudo-partial derivative; Let be the change in system input at time k and system input at time k-1.

4. The control method for a flexible load microgrid system based on model-free adaptive sliding mode control as described in claim 1, characterized in that, System input quantities obtained based on the first control input quantity and the second control input quantity for: ;in, As a weighting factor, This is the second control input. It is the first control input.

5. A flexible load microgrid system control system based on model-free adaptive sliding mode control using the method described in claim 1, characterized in that, include: The selection module is configured such that the flexible load microgrid system is composed of a power supply side and a load side based on a time-division multiplexing switch array, and the access port of the time-division multiplexing switch is selected according to the output power of the power supply side and the demand of the load side. The pseudo-partial derivative estimation module is configured to perform differential processing on the current system input signal and system output signal, and then estimate and reset the pseudo-partial derivatives based on the obtained input and output changes to obtain the pseudo-partial derivatives. The model-free adaptive control module is configured to take the current system output signal, the desired output signal, and the pseudo-partial derivatives as inputs to the model-free adaptive control law, thereby obtaining the first control input quantity; The sliding mode control module is configured to construct a discrete sliding surface based on the system output error, and combine the current system output signal, the desired output signal and the pseudo-partial derivative as inputs to the sliding mode control law, thereby obtaining the second control input quantity; The modulation module is configured to obtain the system input based on the first control input and the second control input, modulate the system input to generate a modulation signal, and apply it to the time-division multiplexing switch to realize the control of the flexible load microgrid system.

6. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-4.

8. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-4.

Images