Photovoltaic global maximum power tracking method based on hyperbolic model

Abstract

This invention discloses a photovoltaic (PV) global maximum power point tracking (MPPT) method based on a hyperbolic model. The method includes establishing a fitting model of the PV output curve using real-time sampled data; and dynamically combining the online fitting model with a perturbation-observation method using adaptive weight coefficients. Compared to existing PV MPPT methods, the method provided by this invention offers faster tracking speed, effectively narrows the search range and voltage trajectory during the search process, and is suitable for localized shading conditions with frequently changing environments, thus improving the overall performance of the PV system.

Description

Technical Field

[0001] This invention belongs to the field of control technology within power electronics. Specifically, it relates to a photovoltaic global maximum power point tracking method based on a hyperbolic model. Background Technology

[0002] In photovoltaic (PV) power generation systems, PV cells convert solar energy into clean, pollution-free electricity. With changes in sunlight and temperature conditions, the output of PV cells exhibits nonlinear and fluctuating characteristics. In practical applications, multiple PV modules are typically connected in series to form PV arrays with higher voltage levels. Under partial shading conditions, the output of the PV array exhibits multiple local power peaks. Therefore, researching maximum power point tracking (MPPT) methods under multi-peak conditions is a key issue in achieving efficient PV power conversion.

[0003] Classic MPPT algorithms, including perturbation observation and incremental conductance methods, can only converge to local peak points, and are therefore not suitable for some shading conditions. Some global MPPT algorithms based on metaheuristic optimization algorithms search for the optimal power value by distributing the search particles throughout the search space [1]. Such algorithms have the advantage of not needing to know the photovoltaic parameter information in advance, but their response speed and convergence performance depend on parameter adjustment and require trial and error. In addition, each particle is evaluated in each iteration, which makes the tracking time of the photovoltaic maximum power point long. Scanning-type maximum power tracking algorithms can converge to the power peak point quickly, but rapid charging and discharging will cause damage and aging of components [2]. On the other hand, the output power needs to be continuously sampled at high frequency during the scanning process. Some algorithms improve the convergence speed of power tracking by improving the classic MPPT algorithm and narrowing the search range of the operating point [3], [4]. In addition, the composite MPPT method that combines artificial intelligence and mathematical models can present better comprehensive performance [5], [6]. However, the existing MPPT algorithms still have shortcomings in terms of search speed, convergence performance, sensor requirements and controller computation.

[0004] Reference patent:

[0005] [1] Xia Xiangzi, Zhang Chun, Zhou Yuan. Maximum power point tracking method for output power of photovoltaic array under partial shading [P]. Anhui Province: CN202410912229.6, 2024-10-11.

[0006] [2] Ye Jianying, Wan Wei, Shu Yizhan, et al. A maximum power tracking device and method for photovoltaic power generation system [P]. Fujian Province: CN202311808939.6, 2024-03-15.

[0007] [3] Xiong Yuansheng, Liu Chunyuan, Zhang Huanle, et al. A method for maximum power point tracking of photovoltaic modules with multiple peaks [P]. Zhejiang Province: CN202311648410.2, 2024-02-06.

[0008] [4] Yun Ping. A global maximum power point tracking method, power optimizer and photovoltaic inverter [P]. Anhui Province: CN202111386502.9, 2024-04-12.

[0009] [5] Yin Haoran, Song Bo, Huang Bo. A photovoltaic power generation system and method for improving maximum power point tracking efficiency [P]. Shaanxi Province: CN202410884672.7, 2024-09-20.

[0010] [6] Xiao Yiping, Shen Zongtao, Shu Jun, et al. A method and system for multi-peak maximum power point tracking control of photovoltaic array [P]. Hubei Province: CN202311450529.9, 2024-05-10. Summary of the Invention

[0011] To address the aforementioned shortcomings in existing technologies, this invention provides a photovoltaic global maximum power point tracking method based on a hyperbolic model. This method enables rapid power point tracking, narrows the voltage trajectory and range during the search process, is suitable for complex operating conditions with frequent environmental changes, and improves the overall performance of photovoltaic systems.

[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0013] A photovoltaic global maximum power point tracking method based on a hyperbolic model includes the following steps:

[0014] S1. Determine whether the change in photovoltaic output power exceeds the power threshold. If not, continue to use the perturbation observation method to maintain steady-state operation. If yes, start global maximum power point tracking and go to S2.

[0015] S2. Determine the starting search direction based on the initial photovoltaic voltage and the voltage threshold. If the initial photovoltaic voltage is less than the threshold, first perform a downward search in the direction of decreasing voltage, and then return to the starting point to perform an upward search; if the initial photovoltaic voltage is greater than the threshold, first perform an upward search in the direction of increasing voltage, and then return to the starting point to perform a downward search.

[0016] After both the S3 and ascending and descending searches are completed, the system maintains steady-state operation at the recorded global maximum power point.

[0017] In S2, during the ascending or descending search process, if the photovoltaic voltage is detected to be less than the voltage boundary threshold, or the photovoltaic current is detected to be less than the current boundary threshold, the search in the current direction is completed. Voltage boundary threshold V minand current boundary threshold I min Determined by the following formula:

[0018]

[0019] Among them, P GMPP It is the maximum power recorded during the search process, I GMPP,STC,array It is the maximum power point current of the photovoltaic array under standard illumination conditions, V oc,STC,array It is the open-circuit voltage of the photovoltaic array under standard illumination conditions.

[0020] In S2, the descent search algorithm iteratively executes the local maximum power point tracking and short-circuit current estimation algorithms, while the ascending search algorithm iteratively executes the region segmentation point voltage estimation and local maximum power point current estimation algorithms.

[0021] The algorithm for local maximum power point tracking includes the following steps:

[0022] The photovoltaic output hyperbolic model parameters are calculated based on real-time sampling data. When the voltage and current data in the sampling window are (V1, I1), (V2, I2), and (V3, I3), respectively, the photovoltaic output resistances R1, R2, and R3 corresponding to the sampling data are calculated and determined by the following formula:

[0023]

[0024] The model parameters k1, k2, and k3 are calculated based on the photovoltaic sampling data and determined by the following formula:

[0025]

[0026] Therefore, a hyperbolic model of photovoltaic output is established, and the relationship between the photovoltaic output equivalent resistance and voltage is described by the following equation:

[0027]

[0028] Next, estimate the local maximum power point voltage V. LMPP,est It is determined by the following formula:

[0029]

[0030] The reference voltage V for the local maximum power point tracking algorithm ref_LMPPT Determined by the following formula:

[0031] V ref_LMPPT =(1-α)V po +αV LMPP,est

[0032] Where V poTo obtain the reference value of the photovoltaic voltage after perturbation using the classical perturbation observation method, α is a weighting coefficient that dynamically combines perturbation observation and model estimation.

[0033] The weighting coefficient α is adjusted according to the change in photovoltaic output power and is determined by the following formula:

[0034]

[0035] Where T po It is the perturbation period, T, in the perturbation-observation algorithm. α It is the adjustment period of the weighting coefficients, α prev It is the weighting coefficient of the previous adjustment cycle, and P is the photovoltaic output power. prev It is the historical value of the output power in the previous adjustment cycle.

[0036] In the algorithm for estimating the short-circuit current, the reference current I ref_sc Determined by the following formula:

[0037]

[0038] Among them, I ref I is the current reference value. mpp I is the local maximum power point current in the current region. sc,STC I represents the short-circuit current of a photovoltaic module under standard illumination conditions. MPP,STC This represents the maximum power point current of a photovoltaic system under standard illumination conditions.

[0039] In the algorithm for estimating the voltage at the region segmentation point, the reference voltage V ref_SDP Determined by the following formula:

[0040]

[0041] In the algorithm for estimating the local maximum power point current, the reference current I... ref_MPP Determined by the following formula:

[0042]

[0043] Among them, I sc This represents the short-circuit current of the photovoltaic module corresponding to the current search area.

[0044] The photovoltaic global maximum power point tracking method based on a hyperbolic model disclosed in this application has the following advantages compared with the prior art:

[0045] 1. This invention reduces the search space and voltage trajectory in the photovoltaic maximum power point tracking process, enabling rapid tracking of the global maximum power point under partial shading conditions and reducing energy loss in photovoltaic power generation;

[0046] 2. This invention enables the photovoltaic system to operate stably at the global maximum power point, reduces steady-state power oscillations, achieves efficient conversion of photovoltaic power generation, and improves the stability of the photovoltaic system's energy output;

[0047] 3. By improving the maximum power point tracking speed of photovoltaics and suppressing the steady-state waveform of photovoltaic power, this invention can reduce the energy disturbance of the photovoltaic system to the power grid, enabling the power system to resume normal operation more quickly when operating conditions change, realizing reliable support of the photovoltaic unit for the system load, and improving the stability of the power grid and the power quality. Attached Figure Description

[0048] To more clearly illustrate the embodiments and technical solutions of the present invention, the accompanying drawings used will be briefly described below.

[0049] Figure 1 This is a flowchart illustrating the steps of the photovoltaic global maximum power point tracking method based on the hyperbolic model of the present invention.

[0050] Figure 2 This is a flowchart of the photovoltaic global maximum power point tracking method based on the hyperbolic model of the present invention.

[0051] Figure 3 This is a schematic diagram of a photovoltaic power generation system.

[0052] Figure 4 This is a schematic diagram of the global search process under partial shading conditions.

[0053] Figure 5 The PV output characteristic curves are shown under different local shading conditions.

[0054] Figure 6 The waveform is the experimental waveform when the photovoltaic operating point starts from the open-circuit voltage under PSC1.

[0055] Figure 7 Experimental waveforms when switching from PSC1 to PSC2 for partial shading conditions.

[0056] Figure 8 Experimental waveforms when switching from PSC2 to PSC3 for partial shading conditions.

[0057] Figure 9 Experimental waveforms when switching from PSC3 to PSC4 for partial shading conditions. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and tables. It should be understood that the description of the embodiments of the present invention herein is not intended to limit the scope of protection of the present invention.

[0059] This invention provides a photovoltaic global maximum power point tracking method based on a hyperbolic model. Figure 1 This is a flowchart illustrating the steps of the photovoltaic global maximum power point tracking method based on the hyperbolic model of the present invention, including the following steps:

[0060] S1. Determine whether the change in photovoltaic output power exceeds the power threshold. If not, continue to use the perturbation observation method to maintain steady-state operation. If yes, start global maximum power point tracking and go to S2.

[0061] S2. Determine the starting search direction based on the initial photovoltaic voltage and the voltage threshold. If the initial photovoltaic voltage is less than the threshold, first perform a downward search in the direction of decreasing voltage, and then return to the starting point to perform an upward search; if the initial photovoltaic voltage is greater than the threshold, first perform an upward search in the direction of increasing voltage, and then return to the starting point to perform a downward search.

[0062] After both the S3 and ascending and descending searches are completed, the system maintains steady-state operation at the recorded global maximum power point.

[0063] Figure 2 This is a flowchart of the photovoltaic global maximum power point tracking method based on the hyperbolic model of the present invention. Figure 3 This is a schematic diagram of a photovoltaic power generation system, including a photovoltaic array, a DC-DC converter circuit, a DC bus, loads, and a controller. The photovoltaic array converts solar energy into electrical energy, and the DC-DC converter circuit, under the control of the drive signal generated by the controller, converts the photovoltaic power to the output side to power the loads on the DC bus.

[0064] Figure 4 This is a schematic diagram of the global search process under partial shading conditions. Point A in the diagram is the starting photovoltaic operating point. In this embodiment, the starting voltage threshold is designed to be 0.5V. oc,array , where V oc,array Let be the open-circuit voltage of the photovoltaic array under standard illumination. Furthermore, at point A, the difference between the current photovoltaic output power and the photovoltaic power of the previous sampling period is detected to be greater than the power threshold.

[0065] Start global maximum power tracking from point A using S1.

[0066] Since the initial photovoltaic voltage is less than the threshold, the search first proceeds downwards in the direction of decreasing voltage, and then returns to the starting point to perform an upward search.

[0067] During the search process, the photovoltaic voltage and current are sampled in real time to calculate the photovoltaic output power P. GMPP Thus, the voltage boundary threshold V is calculated. min and current boundary threshold I min It is determined by the following formula:

[0068]

[0069] Short-circuit current estimation is performed from point A. The sampled current at point A is substituted into equation (2) as the local maximum power point current to calculate the estimated short-circuit current, which is determined by the following formula:

[0070]

[0071] This estimated value is used as the reference current, and the current is tracked to point B through current closed-loop control.

[0072] Next, local maximum power point tracking (MPPT) begins at point B. Based on the perturbation-observation algorithm, the photovoltaic output voltage and current are continuously sampled, and the sampled data (V1, I1), (V2, I2), and (V3, I3) are continuously updated by moving the sampling window. Substituting the sampled data into equation (4), the photovoltaic output resistances R1, R2, and R3 corresponding to the sampled data are calculated and determined by the following formula:

[0073]

[0074] The model parameters k1, k2, and k3 are calculated based on the photovoltaic sampling data and determined by the following formula:

[0075]

[0076] Therefore, a hyperbolic model of photovoltaic output is established, and the relationship between the photovoltaic output equivalent resistance and voltage is described by the following equation:

[0077]

[0078] Next, estimate the local maximum power point voltage V. LMPP,est It is determined by the following formula:

[0079]

[0080] The estimated local maximum power point voltage V LMPP,est The voltage reference value V obtained by the perturbation observation method po Substituting into equation (7), the reference voltage V is used as the reference value for voltage closed-loop control. ref_LMPPT Determined by the following formula:

[0081] V ref_LMPPT =(1-α)V po +αV LMPP,est (7)

[0082] In the above formula, α is the weighting coefficient for online adjustment. In the local maximum power point tracking algorithm, α is determined by the following formula based on the change in photovoltaic output power during each adjustment cycle.

[0083]

[0084] Where T po It is the perturbation period, T, in the perturbation-observation algorithm. α It is the adjustment period of the weighting coefficients, α prev It is the weighting coefficient of the previous adjustment cycle, and P is the photovoltaic output power. prev It is the historical value of the output power in the previous adjustment cycle.

[0085] In the Local Maximum Power Tracking (LMP) algorithm, the voltage reference value is updated in each voltage disturbance cycle, thereby gradually bringing the operating point closer to the local maximum power point within the current search area. During the tracking process from point B to the local maximum power point, the recorded power peak value increases, thus the voltage boundary threshold V calculated by equation (1) becomes larger. min It also gradually increases. When the photovoltaic operating point is searched to point B', the photovoltaic voltage is detected to be less than the voltage boundary threshold V. min If the descent search process ends, the photovoltaic operating point returns to the starting operating point divided by point A, also denoted as point C.

[0086] According to S2, the photovoltaic operating point is searched upwards from point C in the direction of increasing voltage. The current boundary threshold I calculated by the equation is then used. min Substituting into the photovoltaic output hyperbolic model, the estimated value of the voltage at the region segmentation point is obtained by solving equation (9), which is used as the reference voltage, V. ref_SDP It is determined by the following formula.

[0087]

[0088] Under voltage closed-loop control, the photovoltaic operating point reaches point D, which is near the region segmentation point. Then, the output current at point D is sampled as the short-circuit current of the photovoltaic module corresponding to this search region. Substituting this into equation (10), the local maximum power point current is calculated and used as a current reference value, I. ref_MPP Determined by the following formula:

[0089]

[0090] The photovoltaic operating point is tracked to the local maximum power point at point E through current closed-loop control.

[0091] Since the current at point E is still greater than the current boundary threshold I min Therefore, the algorithm for estimating the region segmentation point is repeated. The photovoltaic output hyperbolic model and current boundary threshold I established in this region are calculated using equation (9). min If the voltage at the intersection point is equal to the voltage at point F, then the photovoltaic operating point will track to point F.

[0092] The photovoltaic operating point samples the current at point F and substitutes it into equation (10) to estimate the current at the local maximum power point F' on the right, which is used as the current reference value. However, since the calculated current reference value is detected to be less than the current boundary threshold, the ascending search process ends. The photovoltaic operating point stops moving in the direction of increasing voltage at point F and returns to the power peak point recorded during the global search process, i.e., point E.

[0093] After the photovoltaic operating point returns to point E, a local maximum power point tracking (MPPT) algorithm is performed to ensure steady-state operation of the photovoltaic system at the global maximum power point. Based on S1, the fluctuation of the current photovoltaic output power relative to the power value recorded at the start of steady-state operation is detected during each voltage disturbance cycle. If the power fluctuation exceeds a preset threshold, the hyperbolic model-based global maximum power point tracking algorithm is restarted; if the power fluctuation is less than the preset threshold, steady-state operation continues.

[0094] To more clearly demonstrate the implementation effect of the photovoltaic global maximum power point tracking method based on a hyperbolic model provided by this invention, experimental studies were conducted on the photovoltaic maximum power point tracking effect under different shading conditions. Four shading conditions, PSC1-PSC4, were used in the experiments. Figure 5 The PV output characteristic curves are shown under different local shading conditions.

[0095] Figure 6 The figure shows the experimental waveforms when the photovoltaic (PV) operating point starts from the open-circuit voltage under PSC1. Since the starting operating point is at the open-circuit voltage, only the descent search algorithm is executed. The PV operating point searches from the 120V open-circuit voltage in the direction of decreasing voltage, completing the global search when the voltage drops to 50V. The PV operating point tracks and converges to the global maximum power point at 53V, with an output power of 195W and a search time of approximately 0.65s. During the maximum power point tracking (MPPT) process, the weight coefficient α is automatically adjusted within the range of 0 to 1, realizing a dynamic combination of the PV output hyperbolic model and the perturbation-observation method, thereby significantly improving the tracking speed. After the global search is completed, the PV operating point stabilizes at the global maximum power point. As can be seen from the figure, after entering steady state, the value of the weight coefficient α eventually stabilizes at 1, effectively suppressing the power oscillations caused by the traditional perturbation-observation method and achieving stable and efficient PV power output.

[0096] Figure 7The waveforms are from the experimental setup when switching from PSC1 to PSC2 under partial shading conditions. Under this condition, the global maximum power point (GMP) changes from the second local power peak point to the third local power peak point. Before the environmental condition switch, the photovoltaic (PV) operating point is stable at the global maximum power operating point of PSC1. After the environmental condition switch, the controller detects that the PV output power fluctuation exceeds the threshold and initiates the global maximum power point tracking (GMPT) algorithm. As shown in the figure, the PV operating point first descends during the search, then ascends, and finally converges to the global maximum power point. The search time is 0.5 s, the steady-state tracking efficiency is 99.62%, the voltage trajectory during the search process is 42 V, the voltage fluctuation range is 26.9 V, and the energy loss is 21.28 J.

[0097] Figure 8 The waveforms are from the experiment when switching from PSC2 to PSC3 under partial shading conditions. Under this condition, the global maximum power point changes from the third local power peak point to the fourth. The controller first performs an ascending search algorithm, then a descending search algorithm, converging to the global maximum power point after a response time of 0.63 s. In steady-state operation, the power tracking accuracy reaches 99.67%. The voltage trajectory during the search process is 136.1 V, with a voltage fluctuation of 51.46 V and an energy loss of 23.63 J.

[0098] Figure 9 The figure shows the experimental waveforms when switching from PSC3 to PSC4 under partial shading conditions. Under this condition, the global maximum power point changes from the fourth local power peak point to the first local power peak point. As can be seen from the figure, after completing the downward search, the photovoltaic operating point quickly reaches the search boundary threshold, thus converging to the global maximum power point. The search process takes 0.75 s, and the tracking efficiency in steady state is 99.72%. The voltage trajectory and voltage fluctuation amplitude during the search process are 51.9 V and 50.6 V, respectively, with an energy loss of 23.5 J.

[0099] The above embodiments demonstrate that the photovoltaic global maximum power point tracking method based on a hyperbolic model provided by this invention is feasible and effective. It can achieve rapid global maximum power point tracking under partial shading conditions and effectively shorten the photovoltaic voltage trajectory and fluctuation amplitude during the search process. Therefore, the photovoltaic global maximum power point tracking method based on a hyperbolic model can alleviate the photovoltaic energy waste caused by frequent changes in the lighting environment in practical application scenarios, as well as the problem of photovoltaic output power disturbance affecting system stability and power quality, thereby further improving the overall performance of photovoltaic power generation systems.

[0100] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A photovoltaic global maximum power tracking method based on hyperbolic model, characterized in that, Includes the following steps: S1. Determine whether the change in photovoltaic output power exceeds the power threshold. If not, continue to use the perturbation observation method to maintain steady-state operation. If yes, start global maximum power point tracking and go to S2. S2. Determine the starting search direction based on the initial photovoltaic voltage and the voltage threshold. If the initial photovoltaic voltage is less than the voltage threshold, first perform a downward search in the direction of decreasing voltage, and then return to the starting point to perform an upward search. If the initial photovoltaic voltage is greater than the voltage threshold, first perform an upward search in the direction of increasing voltage, and then return to the starting point to perform a downward search. After both the S3 and ascending and descending searches are completed, the system maintains steady-state operation at the recorded global maximum power point.

2. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 1, characterized in that: In S2, if during the up or down search process, the photovoltaic voltage is detected to be less than a voltage boundary threshold, or the photovoltaic current is detected to be less than a current boundary threshold, the search in the current direction is completed, the voltage boundary threshold V min and the current boundary threshold I min is determined by the following equation: in, P GMPP It is the maximum power recorded during the search process. I GMPP,STC,array It is the maximum power point current of the photovoltaic array under standard illumination conditions. V oc,STC,array It is the open-circuit voltage of the photovoltaic array under standard illumination conditions.

3. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 1, characterized in that: In S2, the descent search algorithm iteratively executes the local maximum power point tracking (MMP) and short-circuit current estimation algorithms, while the ascending search algorithm iteratively executes the region segmentation point voltage estimation and local maximum power point current estimation algorithms. The local maximum power point tracking includes: establishing a photovoltaic output hyperbolic model, describing the relationship between the photovoltaic output equivalent resistance and voltage as follows: in, R The equivalent resistance of photovoltaic output. V Photovoltaic voltage, k 1, k 2, k 3 represents the model parameters.

4. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 3, characterized in that: The algorithm for local maximum power point tracking includes the following steps: The parameters of the photovoltaic output hyperbolic model are calculated based on real-time sampling data, when the voltage and current data in the sampling window are respectively ( V 1, I 1), V 2, I 2) and ( V 3, I 3) Calculate the photovoltaic output resistance corresponding to the sampled data. R 1, R 2, R 3. Determined by the following formula: Model parameters were calculated based on photovoltaic sampling data. k 1, k 2, k 3. Determined by the following formula: Thus, a hyperbolic model of photovoltaic output is established; Next, estimate the voltage at the local maximum power point. V LMPP,est It is calculated by the following formula; The reference voltage for the local maximum power point tracking algorithm V ref_LMPPT Determined by the following formula: in V po To obtain a reference value for the perturbed photovoltaic voltage using the classic perturbation-observation method, α The weighting coefficients are dynamically combined with perturbation observations and model estimates.

5. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 3, characterized in that: In the algorithm for estimating the short-circuit current, the reference current... I ref_sc Determined by the following formula: in, I MPP This represents the local maximum power point current in the current region. I sc,STC This refers to the short-circuit current of a photovoltaic module under standard illumination conditions. I MPP,STC This represents the maximum power point current of a photovoltaic system under standard illumination conditions.

6. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 3, characterized in that: In the algorithm for estimating the voltage at the region segmentation point, the reference voltage... V ref_SDP Determined by the following formula: in, I min This is the current boundary threshold calculated from the sampled data.

7. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 3, characterized in that: In the algorithm for estimating the local maximum power point current, the reference current... I ref_MPP Determined by the following formula: in, I sc This represents the short-circuit current of the photovoltaic module corresponding to the current search area. I sc,STC This refers to the short-circuit current of a photovoltaic module under standard illumination conditions. I MPP,STC This represents the maximum power point current of a photovoltaic system under standard illumination conditions.

8. The photovoltaic global maximum power point tracking method based on a hyperbolic model according to claim 4, characterized in that: Weight coefficients α Adjustments are made based on changes in photovoltaic output power, determined by the following formula: in T po It refers to the perturbation period in the perturbation-observation algorithm. T α It is the adjustment period of the weighting coefficients. α prev It is the weighting coefficient of the previous adjustment cycle. P It is the output power of photovoltaics. P prev It is the historical value of the output power in the previous adjustment cycle.

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