MPPT (Maximum Power Point Tracking) control method, device and equipment based on photovoltaic array and medium

Abstract

The invention relates to the technical field of solar power generation, and discloses an MPPT (Maximum Power Point Tracking) control method, device and equipment based on a photovoltaic array and a medium. The method comprises the following steps: acquiring environmental data and photovoltaic output data of a photovoltaic array; obtaining a target stability coefficient and target tracking data according to a preset deep learning training model; determining a voltage reference value based on the photovoltaic output data, the target tracking data and a preset reference voltage; searching a first target control parameter associated with the target stability coefficient and a second target control parameter associated with the target stability coefficient; determining a current reference value of a current control loop based on the voltage reference value; and determining a target output duty ratio of the pulse signal based on the current reference value, the photovoltaic output data and a second target control parameter, so that the photovoltaic array reaches the target maximum output power. The maximum power point tracking efficiency and precision can be improved.

Description

Background Art

[0002] At present, when the conversion efficiency of solar photovoltaic power generation is not very high, how to maximize the efficiency of photovoltaic power generation and the utilization rate of solar photovoltaic conversion is one of the difficulties faced by the promotion and application of photovoltaic power generation technology. Existing methods usually use the Maximum Power Point Tracking (MPPT) method to maximize the photovoltaic conversion, but the application effect of the existing MPPT control method in photovoltaic arrays is not mature and perfect, and there are shortcomings such as slow tracking speed, large steady-state error, and long control parameter debugging time. Summary of the invention

[0003] The present application provides a MPPT control method, device, equipment and medium based on a photovoltaic array to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a MPPT control method based on a photovoltaic array is provided, the method comprising: Obtain environmental data and photovoltaic output data of photovoltaic arrays; Acquire a target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and a preset deep learning training model; determining a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage; Searching for a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping relationship table, and searching for a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping relationship table; determining a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value; A target output duty cycle of the pulse signal is determined based on the current reference value, the photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal to enable the photovoltaic array to reach a target maximum output power.

[0006] In one embodiment of the present application, based on the above scheme, the photovoltaic output data includes the output voltage, output current and output power of the photovoltaic array, the environmental data includes the illuminance and air temperature, and the target tracking data includes the target tracking voltage range and the target tracking step size; the target stability coefficient and the target tracking data are obtained based on the photovoltaic output data, the environmental data and the preset deep learning training model, including: The output voltage, the output current, and the output power are normalized and subjected to hyperbolic tangent transformation one by one to obtain a target voltage parameter, a target current parameter, and a target power parameter; Inputting the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature into the deep learning training model, and obtaining the target stability coefficient, the target tracking voltage range and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature; The output power can be calculated based on the output voltage and the output current.

[0007] In one embodiment of the present application, based on the above solution, determining the voltage reference value of the voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage includes: Acquire the last output power of the photovoltaic array at the last moment based on the photovoltaic output data, and determine the disturbance attribute of the target tracking step length based on the last output power and the output power at the current moment; The voltage reference value is determined based on the disturbance property, the target tracking voltage range, and the reference voltage.

[0008] In one embodiment of the present application, based on the above scheme, the disturbance attribute includes a disturbance step and a disturbance step attribute, and determining the voltage reference value based on the disturbance attribute, the target tracking voltage range and the reference voltage includes: Determining whether the sum of the disturbance step length and the reference voltage is within the target tracking voltage range, and if so, taking the sum of the disturbance step length and the reference voltage as the voltage reference value; If the sum of the disturbance step length and the reference voltage is not within the target tracking voltage range, selecting a maximum value / minimum value in the target tracking voltage range as the voltage reference value based on the disturbance step length attribute; The unit of the disturbance step is voltage, and the disturbance step attribute is used to characterize the relationship between the target tracking step and the output voltage.

[0009] In one embodiment of the present application, based on the above scheme, the output voltage, the output current, and the output power are normalized and hyperbolic tangent transformed one by one to obtain a target voltage parameter, a target current parameter, and a target power parameter, including: Normalizing the output voltage with a preset rated voltage value to obtain a voltage normalized value; Normalizing the output current and a preset rated current value to obtain a current normalized value; Normalizing the output power and a preset rated power value to obtain a power normalized value; The voltage per unit value, the current per unit value, and the power per unit value are subjected to hyperbolic tangent transformation one by one to obtain the target voltage parameter, the target current parameter, and the target power parameter after conversion by the hyperbolic tangent function.

[0010] In one embodiment of the present application, based on the above solution, determining the current reference value of the current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value includes: A pid operation is performed based on a voltage error value between the output voltage and the voltage reference value and the first target control parameter to obtain a current reference value of the current control loop.

[0011] In one embodiment of the present application, based on the above scheme, determining the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter includes: A pid operation is performed based on the current error value between the output current and the current reference value and the second target control parameter to obtain the target output duty cycle of the pulse signal, so that the inverter can control the output current and output voltage of the photovoltaic array according to the pulse signal so that the photovoltaic array reaches the target maximum output power.

[0012] According to one aspect of an embodiment of the present application, there is provided an MPPT control device based on a photovoltaic array, the device comprising: A first acquisition unit, used to acquire environmental data and photovoltaic output data of the photovoltaic array; A second acquisition unit, configured to acquire a target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and a preset deep learning training model; a first determining unit, configured to determine a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage; A search unit, configured to search a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping relationship table, and to search a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping relationship table; a second determining unit, configured to determine a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value; A control unit is used to determine a target output duty cycle of a pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter, so that an inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array reaches a target maximum output power.

[0013] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program includes executable instructions. When the executable instructions are executed by a processor, the control method described in the above embodiment is implemented.

[0014] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; and a memory for storing executable instructions of the processors, wherein when the executable instructions are executed by the one or more processors, the one or more processors implement the control method as described in the above embodiment.

[0015] The beneficial effects of the present application are: by inputting photovoltaic output data and environmental data into a preset deep learning training model, the voltage reference value of the voltage control loop is determined according to the target tracking data output by the deep learning training model combined with the photovoltaic output data and the preset reference voltage. At the same time, the first target control parameter and the second target control parameter associated therewith are determined by the target stability coefficient, and the current reference value of the current control loop is determined based on the first target control parameter, the photovoltaic output data and the voltage reference value.

[0016] Finally, the target output duty cycle of the pulse signal is determined according to the obtained current reference value, the obtained photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array reaches the target maximum output power. The MPPT control method provided by the present application can quickly predict the accurate voltage reference value and current reference value based on the target stability coefficient and target tracking data obtained by the deep learning training model, and finally control the current of the photovoltaic array based on the current reference value, so that the photovoltaic array can quickly reach the corresponding target voltage according to the current adjustment value, and then the photovoltaic array can quickly reach the maximum power point at the target voltage, thereby improving the tracking efficiency and accuracy of the maximum power point. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following is a brief description of the drawings required for use in the description of the embodiments. Obviously, the drawings described are only part of the embodiments of the present application, not all of the embodiments, and those skilled in the art can also obtain other design solutions and drawings based on these drawings without creative work.

[0018] Figure 1 It is a flow chart of a MPPT control method based on a photovoltaic array according to an embodiment of the present application; Figure 2 The overall logic diagram of the MPPT control method based on the photovoltaic array according to the embodiment of the present application is shown; Figure 3 is a block diagram of an MPPT control device based on a photovoltaic array according to an embodiment of the present application; Figure 4 It is a schematic diagram of the system structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0020] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0021] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or micro-control node devices.

[0022] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0023] It should be noted that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0024] The implementation details of the technical solution of the embodiment of the present application are described in detail below: First of all, it should be noted that the preset deep learning training model is generated by collecting a large amount of sample data for training. There are three types of input samples, namely photovoltaic output data (photovoltaic parameters), illuminance and air temperature. Photovoltaic output data includes the output current, output voltage and output power of the photovoltaic array. By using multiple parameters as sample data to train the deep learning training model, the deep learning training model can quickly and accurately output the required results based on the input parameters.

[0025] The deep learning training model in the embodiment of the present application is a model-free adaptive control algorithm model of a multi-input multi-output system based on a compact dynamic linearization, which has multiple characteristics, such as: no need to master the precise understanding of the control process, the system does not need to include an identification mechanism, no manual parameter adjustment is required, and the stability of the closed-loop system can be guaranteed. Therefore, the input multiple parameters can be quickly adaptively adjusted to output the corresponding results.

[0026] However, in the embodiment of the present application, the result output by the deep learning training model is not the maximum power tracking voltage, but the target tracking voltage range and target tracking step size of the photovoltaic array and the target stability coefficient, and the target tracking step size is the MPPT algorithm step size. The voltage reference range and the MPPT algorithm step size are to obtain the voltage reference value more accurately, so that the photovoltaic array can quickly reach the voltage reference value, and then complete the tracking of the maximum power point.

[0027] After obtaining the voltage reference value, the photovoltaic array cannot be directly controlled to reach the voltage reference value. Instead, a pulse signal is needed to control the on-off time of the IGBT (insulated gate bipolar transistor) in the inverter to adjust the output current of the photovoltaic array. After the output current is adjusted, the output voltage of the photovoltaic array can naturally be adjusted. Then, according to the numerical calculation of the parameters, the output voltage of the photovoltaic array can reach the voltage reference value, thereby completing the tracking of the maximum power point.

[0028] According to one aspect of an embodiment of the present application, a MPPT control method based on a photovoltaic array is provided. Figure 1 Flow chart of a MPPT control method based on a photovoltaic array according to an embodiment of the present application, the method at least includes steps 110 to 160, which are described in detail as follows: In step 110 , environmental data and photovoltaic output data of the photovoltaic array are obtained.

[0029] Specifically, environmental data includes illuminance and temperature, while photovoltaic output data includes the output voltage, output current and output power of the photovoltaic array. By inputting the five parameters of the photovoltaic array output voltage, output current and output power, illuminance and temperature into the pre-trained deep learning training model, the corresponding target stability coefficient and target tracking data can be quickly output, and the target tracking data includes the target tracking voltage range and target tracking step size.

[0030] In step 120, a target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data are obtained based on the photovoltaic output data, the environmental data and a preset deep learning training model.

[0031] In one embodiment of the present application, the photovoltaic output data includes the output voltage, output current and output power of the photovoltaic array, the environmental data includes the illuminance and the air temperature, and the target tracking data includes the target tracking voltage range and the target tracking step size; the target stability coefficient and the target tracking data are obtained based on the photovoltaic output data, the environmental data and the preset deep learning training model, including: The output voltage, the output current, and the output power are normalized and subjected to hyperbolic tangent transformation one by one to obtain a target voltage parameter, a target current parameter, and a target power parameter; Inputting the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature into the deep learning training model, and obtaining the target stability coefficient, the target tracking voltage range and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature; The output power can be calculated based on the output voltage and the output current.

[0032] Specifically, please refer to Figure 2 As shown, Figure 2 The overall logic diagram of the MPPT control method proposed in the embodiment of the present application. The output voltage, the output current, and the output power are normalized and hyperbolic tangent transformed one by one to obtain target voltage parameters, target current parameters, and target power parameters. Normalization is to divide the actual output voltage, output current, and output power by the rated voltage, rated current, and rated power, respectively. The rated voltage, rated current, and rated power are obtained based on experimental experience and can also be set according to actual needs.

[0033] By performing hyperbolic tangent transformation on the normalized parameters, since the tangent function has the characteristics of monotonically increasing and fast changing speed, the target voltage parameters, target current parameters and target power parameters obtained after the transformation can change rapidly according to environmental data such as light intensity and temperature, so as to achieve the effect of adaptive and rapid adjustment.

[0034] In one embodiment of the present application, the output voltage, the output current, and the output power are normalized and hyperbolic tangent transformed one by one to obtain a target voltage parameter, a target current parameter, and a target power parameter, including: Normalizing the output voltage with a preset rated voltage value to obtain a voltage normalized value; Normalizing the output current and a preset rated current value to obtain a current normalized value; Normalizing the output power and a preset rated power value to obtain a power normalized value; The voltage per unit value, the current per unit value, and the power per unit value are subjected to hyperbolic tangent transformation one by one to obtain the target voltage parameter, the target current parameter, and the target power parameter after conversion by the hyperbolic tangent function.

[0035] Specifically, as shown above, the voltage per unit value is the quotient of the actual output voltage divided by the rated voltage value, the current per unit value is the quotient of the actual output current divided by the rated current value, and the power per unit value is the quotient of the actual output power divided by the rated power value.

[0036] The target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature are input into the deep learning training model to obtain the target stability coefficient, the target tracking voltage range and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature. Figure 2 As shown in , F1 and F2 are the target stability coefficients, and Vpv_max and Vpv_min are the maximum voltage and the minimum voltage of a single MPPT tracking, respectively, which constitute the target tracking voltage range, and step is the target tracking step size.

[0037] It should be noted that Figure 2 C represents the deep learning algorithm, which is the deep learning training model mentioned in the embodiment of this application.

[0038] Furthermore, in order to better illustrate the calculation and determination method of the voltage reference value, the following is a specific introduction: First, the voltage reference value at the current moment is determined by the target tracking voltage range, the target tracking step and the voltage reference value at the previous moment. If the sum of the voltage reference value at the previous moment plus the current target tracking step is within the target tracking voltage range, then the sum of the voltage reference value at the previous moment plus the current target tracking step is used as the voltage reference value at the current moment; if it is not within the target tracking voltage range, it needs to be further explained in combination with the following embodiments: In step 130 , a voltage reference value of a voltage control loop is determined based on the photovoltaic output data, the target tracking data and a preset reference voltage.

[0039] Specifically, the voltage reference value of the voltage control loop can be determined by the output voltage in the photovoltaic output data, the target tracking voltage range and the target tracking step in the target tracking data combined with a preset reference voltage, wherein the preset reference voltage can be set as needed, or the voltage reference value given at the previous moment can be used as the preset reference voltage.

[0040] In one embodiment of the present application, determining a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage includes: Acquire the last output power of the photovoltaic array at the last moment based on the photovoltaic output data, and determine the disturbance attribute of the target tracking step length based on the last output power and the output power at the current moment; The voltage reference value is determined based on the disturbance property, the target tracking voltage range, and the reference voltage.

[0041] In one embodiment of the present application, the disturbance attribute includes a disturbance step and a disturbance step attribute, and determining the voltage reference value based on the disturbance attribute, the target tracking voltage range and the reference voltage includes: Determining whether the sum of the disturbance step length and the reference voltage is within the target tracking voltage range, and if so, taking the sum of the disturbance step length and the reference voltage as the voltage reference value; If the sum of the disturbance step length and the reference voltage is not within the target tracking voltage range, selecting a maximum value / minimum value in the target tracking voltage range as the voltage reference value based on the disturbance step length attribute; The unit of the disturbance step is voltage, and the disturbance step attribute is used to characterize the relationship between the target tracking step and the output voltage.

[0042] Specifically, the previous output power of the photovoltaic array at the previous moment is obtained based on the photovoltaic output data, and the disturbance attribute of the target tracking step is determined based on the previous output power and the output power at the current moment. That is to say, the disturbance attribute of the target tracking step can affect the output power of the current photovoltaic panel. If the disturbance attribute of the disturbance step is a positive value, then an increase in the value of the disturbance step will cause the output power of the photovoltaic panel to increase. If it is a negative value, it will cause the output power of the photovoltaic panel to decrease.

[0043] The perturbation step length represents the amplitude of the step length, while the perturbation step length attribute represents the positive or negative value of the step length. Therefore, if the sum of the perturbation step length and the reference voltage is not within the target tracking voltage range, the voltage reference value can be determined in combination with the perturbation step length attribute. First, if the value of the step length represented by the perturbation step length attribute is positive, then the maximum value in the target tracking voltage range can be selected. Figure 2 Vpv_max in the target tracking voltage range is used as the voltage reference value; on the contrary, if the step value represented by the disturbance step attribute is negative, then the minimum value in the target tracking voltage range can be selected. Figure 2 Vpv_min in is used as the voltage reference value.

[0044] In step 140, a first target control parameter associated with the target stability coefficient is searched in a preset coefficient-voltage control parameter mapping relationship table, and a second target control parameter associated with the target stability coefficient is searched in a preset coefficient-current control parameter mapping relationship table.

[0045] Specifically, the preset coefficient-voltage control parameter mapping relationship table is: Figure 2 In F2, the preset coefficient-current control parameter mapping relationship table is Figure 2 In F1, F1 and F2 are both debugged nonlinear lookup tables, and the corresponding target parameters can be directly found by the target stability coefficient, and then the target parameters are calculated through G1 and G2 to obtain the final first target control parameters and second target control parameters. G1 and G2 both represent linear interpolation algorithms.

[0046] In step 150 , a current reference value of a current control loop is determined based on the first target control parameter, the photovoltaic output data, and the voltage reference value.

[0047] Specifically, the first target control parameter and the second target control parameter are Figure 2 The controller parameters shown in , and based on the first target control parameter, the output voltage and the voltage reference value, a pid operation can be performed to obtain a current reference value.

[0048] In one embodiment of the present application, determining a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value includes: A pid operation is performed based on a voltage error value between the output voltage and the voltage reference value and the first target control parameter to obtain a current reference value of the current control loop.

[0049] The target stability coefficient is input as an input parameter to the deep learning algorithm C (i.e., the deep learning training model described in the present application). Through the linear interpolation algorithm, the debugged nonlinear voltage loop parameter table and current loop parameter table (i.e., the preset coefficient-voltage control parameter mapping relationship table and the preset coefficient-current control parameter mapping relationship table) are queried respectively to obtain the appropriate voltage loop Kp parameter and Ki parameter (i.e., the first target control parameter), as well as the Kp parameter and Ki parameter (i.e., the second target control parameter) of the current control loop. The voltage reference value (Vpv*), the actual output voltage value (Vpv), the Kp parameter and the Ki parameter of the voltage control loop are used together for PI operation ( Figure 2 H), and obtain the current reference value (iL*).

[0050] In step 160, a target output duty cycle of a pulse signal is determined based on the current reference value, the photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array reaches a target maximum output power.

[0051] Specifically, by performing PI operation on the current reference value, the output current in the photovoltaic output data and the second target control parameter, the target output duty cycle of the pulse signal can be determined, so that the inverter can control the output current of the photovoltaic array according to the sent pulse signal, and its output voltage is also controlled to the corresponding voltage reference value, so that the photovoltaic array reaches the target maximum output power.

[0052] In one embodiment of the present application, determining the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter includes: A pid operation is performed based on the current error value between the output current and the current reference value and the second target control parameter to obtain the target output duty cycle of the pulse signal, so that the inverter can control the output current and output voltage of the photovoltaic array according to the pulse signal so that the photovoltaic array reaches the target maximum output power.

[0053] Specifically, the current reference value (iL*), the actual output current value (iL), the Kp parameter and the Ki parameter of the current control loop are used together by PI calculation ( Figure 2 I), and obtain the output duty cycle d; according to the output duty cycle d, adjust the PWM (pulse signal) output duty cycle, and drive the power switch tube IGBT through the PWM (pulse signal) signal to adjust the output current of the photovoltaic array to adjust the output voltage of the photovoltaic array, and then adjust the output power of the photovoltaic array, so as to continuously approach the MPPT point, so that the photovoltaic array is not affected by some special situations such as drastic changes in light intensity, sudden drop or sudden rise in temperature, and can still continuously track the maximum output power according to its own control strategy.

[0054] In summary, the embodiment of the present application inputs photovoltaic output data and environmental data into a preset deep learning training model, and determines the voltage reference value of the voltage control loop according to the target tracking data output by the deep learning training model combined with the photovoltaic output data and the preset reference voltage. At the same time, the first target control parameter and the second target control parameter associated with it are determined by the target stability coefficient, and the current reference value of the current control loop is determined based on the first target control parameter, the photovoltaic output data and the voltage reference value.

[0055] Finally, the target output duty cycle of the pulse signal is determined according to the obtained current reference value, the obtained photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array reaches the target maximum output power. The MPPT control method provided by the present application can quickly predict the accurate voltage reference value and current reference value based on the target stability coefficient and target tracking data obtained by the deep learning training model, and finally control the current of the photovoltaic array based on the current reference value, so that the photovoltaic array can quickly reach the corresponding target voltage according to the current adjustment value, and then the photovoltaic array can quickly reach the maximum power point at the target voltage, thereby improving the tracking efficiency and accuracy of the maximum power point.

[0056] Figure 3 The block diagram of the MPPT control device 300 based on the photovoltaic array according to an embodiment of the present application is shown. According to the MPPT control device 300 based on the photovoltaic array according to an embodiment of the present application, the device 300 includes: a first acquisition unit 301, a second acquisition unit 302, a first determination unit 303, a search unit 304, a second determination unit 305, and a control unit 306.

[0057] A first acquisition unit 301 is used to acquire environmental data and photovoltaic output data of a photovoltaic array; A second acquisition unit 302 is used to acquire a target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and a preset deep learning training model; A first determining unit 303, configured to determine a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage; A search unit 304, configured to search a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping relationship table, and to search a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping relationship table; A second determining unit 305, configured to determine a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value; The control unit 306 is used to determine the target output duty cycle of the pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array reaches the target maximum output power.

[0058] As another aspect, the present application further provides a computer-readable storage medium on which a program product capable of implementing the method provided above in this specification is stored. In some possible implementations, various aspects of the present application may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Embodiment Method" section of this specification.

[0059] According to the program product for implementing the above method in the embodiment of the present application, it can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0060] The program product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0061] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0062] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0063] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0064] As another aspect, the present application also provides an electronic device capable of implementing the above method.

[0065] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0066] Refer to the following Figure 4 The electronic device 400 according to this embodiment of the present application is described. Figure 4 The electronic device 400 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0067] like Figure 4 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410).

[0068] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps described in the above “Example Method” section of this specification according to various exemplary implementations of the present application.

[0069] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0070] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0071] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller node, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0072] The electronic device 400 may also communicate with one or more external devices 1200 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 through a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0073] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0074] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0075] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be performed without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A MPPT control method based on a photovoltaic array, characterized in that: The method comprises: Obtain environmental data and photovoltaic output data of photovoltaic arrays; Acquire a target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and a preset deep learning training model; determining a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage; Searching for a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping relationship table, and searching for a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping relationship table; determining a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value; A target output duty cycle of the pulse signal is determined based on the current reference value, the photovoltaic output data and the second target control parameter, so that the inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal to enable the photovoltaic array to reach a target maximum output power.

2. The control method according to claim 1, characterized in that: The photovoltaic output data includes the output voltage, output current and output power of the photovoltaic array, the environmental data includes the illuminance and the air temperature, and the target tracking data includes the target tracking voltage range and the target tracking step size; the target stability coefficient and the target tracking data are obtained based on the photovoltaic output data, the environmental data and the preset deep learning training model, including: The output voltage, the output current, and the output power are normalized and subjected to hyperbolic tangent transformation one by one to obtain a target voltage parameter, a target current parameter, and a target power parameter; Inputting the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature into the deep learning training model, and obtaining the target stability coefficient, the target tracking voltage range and the target tracking step size associated with the target voltage parameter, the target current parameter, the target power parameter, the illuminance and the air temperature; The output power can be calculated based on the output voltage and the output current.

3. The control method according to claim 2, characterized in that: The determining of a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage includes: Acquire the last output power of the photovoltaic array at the last moment based on the photovoltaic output data, and determine the disturbance attribute of the target tracking step length based on the last output power and the output power at the current moment; The voltage reference value is determined based on the disturbance property, the target tracking voltage range, and the reference voltage.

4. The control method according to claim 3, characterized in that: The disturbance attribute includes a disturbance step and a disturbance step attribute, and determining the voltage reference value based on the disturbance attribute, the target tracking voltage range and the reference voltage includes: Determining whether the sum of the disturbance step length and the reference voltage is within the target tracking voltage range, and if so, taking the sum of the disturbance step length and the reference voltage as the voltage reference value; If the sum of the disturbance step length and the reference voltage is not within the target tracking voltage range, selecting a maximum value / minimum value in the target tracking voltage range as the voltage reference value based on the disturbance step length attribute; The unit of the disturbance step is voltage, and the disturbance step attribute is used to characterize the relationship between the target tracking step and the output voltage.

5. The control method according to claim 2, characterized in that: The output voltage, the output current, and the output power are normalized and subjected to hyperbolic tangent transformation one by one to obtain a target voltage parameter, a target current parameter, and a target power parameter, including: Normalizing the output voltage with a preset rated voltage value to obtain a voltage normalized value; Normalizing the output current and a preset rated current value to obtain a current normalized value; Normalizing the output power and a preset rated power value to obtain a power normalized value; The voltage per unit value, the current per unit value, and the power per unit value are subjected to hyperbolic tangent transformation one by one to obtain the target voltage parameter, the target current parameter, and the target power parameter after conversion by the hyperbolic tangent function.

6. The control method according to claim 4, characterized in that: The determining of a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value comprises: A pid operation is performed based on a voltage error value between the output voltage and the voltage reference value and the first target control parameter to obtain a current reference value of the current control loop.

7. The control method according to claim 6, characterized in that: The step of determining a target output duty cycle of a pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter comprises: A pid operation is performed based on the current error value between the output current and the current reference value and the second target control parameter to obtain the target output duty cycle of the pulse signal, so that the inverter can control the output current and output voltage of the photovoltaic array according to the pulse signal so that the photovoltaic array reaches the target maximum output power.

8. An MPPT control device based on a photovoltaic array, characterized in that: The device comprises: A first acquisition unit, used to acquire environmental data and photovoltaic output data of the photovoltaic array; A second acquisition unit, configured to acquire a target stability coefficient and target tracking data associated with the photovoltaic output data and the environmental data based on the photovoltaic output data, the environmental data and a preset deep learning training model; a first determining unit, configured to determine a voltage reference value of a voltage control loop based on the photovoltaic output data, the target tracking data and a preset reference voltage; A search unit, configured to search a first target control parameter associated with the target stability coefficient in a preset coefficient-voltage control parameter mapping relationship table, and to search a second target control parameter associated with the target stability coefficient in a preset coefficient-current control parameter mapping relationship table; a second determining unit, configured to determine a current reference value of a current control loop based on the first target control parameter, the photovoltaic output data and the voltage reference value; A control unit is used to determine a target output duty cycle of a pulse signal based on the current reference value, the photovoltaic output data and the second target control parameter, so that an inverter can control the photovoltaic output data of the photovoltaic array according to the pulse signal, so that the photovoltaic array reaches a target maximum output power.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed by the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to any one of claims 1 to 7.

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