Photovoltaic hydrogen production system MPPT control method based on improved whale-conductance increment method

Abstract

The invention discloses a photovoltaic hydrogen production system MPPT control method based on an improved whale-conductance increment method, and the method comprises the steps: building a photovoltaic hydrogen production system with a photovoltaic array which is composed of a plurality of photovoltaic panels connected in series and in parallel; an output circuit of the photovoltaic array is connected with the PEM electrolytic bath through the boost DC-DC converter, and the PEM electrolytic bath decomposes water molecules into hydrogen and oxygen by using direct current; a duty ratio D is dynamically optimized and generated by the improved whale-conductance increment method, and starting and stopping of a power switch tube IGBT in the boost DC-DC converter are controlled by the duty ratio D, so that the photovoltaic array voltage regulation performance of the photovoltaic hydrogen production system is optimized. According to the MPPT control method for the photovoltaic hydrogen production system, the whale-conductance increment method is improved, so that the maximum power point can be successfully tracked under dynamic change illumination, and the electrolytic efficiency of the photovoltaic hydrogen production system is improved.

Description

Technical Field

[0001] The invention relates to the field of renewable energy technology and power electronics technology, and in particular to an MPPT control method for a photovoltaic hydrogen production system based on an improved whale-conductance increment method. Background Art

[0002] As climate change and energy security issues become increasingly severe, energy transformation has become an urgent task. Solar energy, as a clean, safe and renewable energy source, is receiving more and more attention. However, due to the uneven distribution of solar energy resources in various places and the limitations of geographical conditions, the actual utilization rate of photovoltaic power generation has not reached the ideal level. According to statistics from the Energy Bureau, as of the end of 2022, the actual utilization rate of photovoltaic power generation in many regions with rich solar energy resources was between 80.0% and 91.1%, indicating that a large amount of photovoltaic power generation has not been effectively utilized. The introduction of hydrogen energy storage can significantly improve the energy utilization rate of photovoltaic systems and effectively absorb abandoned light energy. In hydrogen production technology, proton exchange membrane (PEM) electrolyzers are favored for their good volatility adaptability and compatibility with renewable energy sources such as solar energy.

[0003] At present, the research focus in the field of photovoltaic power generation and hydrogen production is on how to quickly and accurately find the maximum power point to maximize energy utilization and improve system stability. Under uniform illumination conditions, the traditional maximum power point tracking (MPPT) algorithm can quickly find the global maximum power point; however, under uneven illumination, the traditional MPPT algorithm is relatively simple in control principle and is prone to fall into a local optimal state, making it difficult to quickly and accurately capture the global maximum power point.

[0004] As an emerging intelligent optimization method, the whale algorithm is combined with the traditional conductance increment method. Although the algorithm is simple and easy to implement, it still has the following defects:

[0005] (1) When the light intensity changes rapidly, the whale optimization algorithm may experience oscillation, resulting in system instability and slow convergence;

[0006] (2) When the light intensity changes rapidly, the conductivity increment method is prone to fall into the local optimum under complex working conditions due to its simple control principle, resulting in power loss.

[0007] Both situations cause power loss in the system and reduce hydrogen production efficiency. Therefore, it is particularly important to conduct in-depth research and optimization on these aspects to improve the performance and stability of the system and provide more reliable and efficient solutions to solve practical problems. Summary of the invention

[0008] The purpose of the present invention is to provide an MPPT control method for a photovoltaic hydrogen production system based on an improved whale-conductance increment method, which is used to solve the technical problems of the traditional whale-conductance increment algorithm in the background technology, such as the slow convergence speed affecting the hydrogen production efficiency and the instability of the photovoltaic hydrogen production system when the light intensity changes rapidly.

[0009] The present invention solves the above problems through the following technical solutions:

[0010] The MPPT control method of photovoltaic hydrogen production system based on the improved whale-conductance increment method includes:

[0011] Establish a photovoltaic hydrogen production system with a photovoltaic array, which consists of a number of photovoltaic panels connected in series and parallel;

[0012] The output circuit of the photovoltaic array is connected to the PEM electrolyzer through a step-up DC-DC converter. The PEM electrolyzer uses direct current to decompose water molecules into hydrogen and oxygen.

[0013] The duty cycle D is dynamically optimized and generated by the improved whale-conductance increment method. The duty cycle D controls the start and stop of the power switch tube IGBT in the boost DC-DC converter circuit to optimize the photovoltaic array voltage regulation performance of the photovoltaic hydrogen production system.

[0014] As a further improvement of the present invention, the improved whale-conductance increment method inputs the instantaneous output current I and instantaneous output voltage U of the photovoltaic array, and calculates the instantaneous output power P=IU, and dynamically adjusts the duty cycle D through the output power change rate ΔP=P(t)-P(t-1) of the photovoltaic array, where t is the number of iterations.

[0015] As a further improvement of the present invention, the control method further includes:

[0016] Establishing a photovoltaic cell equivalent model, thereby obtaining the instantaneous output current I, instantaneous output voltage U, and instantaneous output power P=IU of the photovoltaic array according to the established photovoltaic cell equivalent model;

[0017] The photovoltaic cell equivalent model at least includes an ideal diode, a photocurrent source, a series resistor and a parallel resistor. The photocurrent source is connected in parallel with the ideal diode and the parallel resistor respectively. Finally, the entire structure is connected in series with the series resistor and the load.

[0018] As a further improvement of the present invention, the instantaneous output current I of the photovoltaic array is obtained according to the established photovoltaic cell equivalent model, and the formula is:

[0019]

[0020] Where n is the total number of photovoltaic panels; I is the instantaneous output current of the photovoltaic array; Iph is the current of the photocurrent source; I0 is the reverse saturation current of the ideal diode; q is the electron charge; U is the instantaneous output voltage of the photovoltaic array; R s is the resistance of the series resistance of the photovoltaic cell equivalent model; m is the characteristic fitting parameter of the ideal diode; K is the Boltzmann constant; T is the working environment temperature of the photovoltaic panel; R sh is the resistance of the parallel resistor of the photovoltaic cell equivalent model;

[0021] Let the resistance R of the series resistor in the photovoltaic cell equivalent model be s =0, the resistance R of the parallel resistor of the photovoltaic cell equivalent model sh =∞; due to the resistance R of the series resistor in the photovoltaic cell equivalent model s =0, the resistance of the parallel resistor R sh =∞, It tends to 0, so it can be ignored. Then the general equation of the instantaneous output current I of the photovoltaic array is simplified to:

[0022]

[0023] As a further improvement of the present invention, the equivalent circuit of the boost DC-DC converter is specifically:

[0024] A periodically working power switch tube IGBT is connected in series with an inductor L, and then connected in parallel with a load R and a capacitor C2. Capacitor C2 is used to smooth the output voltage. Capacitor C2 realizes the smoothing of the output voltage of the photovoltaic array and the suppression of voltage ripple. Ideal diode D5 prevents current backflow. The voltage regulation performance is optimized by controlling the start and stop of the power switch tube IGBT in the boost DC-DC converter.

[0025] As a further improvement of the present invention, the improved whale-conductance increment method includes an improved whale algorithm and an improved conductance increment method; including:

[0026] Initialize the duty cycle D, collect the instantaneous output current I and instantaneous output voltage U of the photovoltaic array, calculate the instantaneous output power P = IU, and iterate, enable the improved whale algorithm to use nonlinear dynamic adjustment factors for dynamic search, and introduce nonlinear adaptive weight strategy to jump out of local optimality and introduce screening disturbance mechanism to accelerate convergence speed for preliminary optimization;

[0027] After the iteration is completed, the output power change rate ΔP of the photovoltaic array is determined:

[0028] When the output power change rate ΔP of the photovoltaic array is greater than or equal to the preset value Z, the improved whale algorithm is restarted;

[0029] When the output power change rate ΔP of the photovoltaic array is less than the preset value Z, the improved conductivity increment method is used for precise optimization to dynamically adjust and output the duty cycle D to control the start and stop of the power switch tube IGBT;

[0030] When the output power change rate ΔP of the photovoltaic array is 0, the instantaneous output power of the photovoltaic array reaches the maximum power point. At this time, the duty cycle D is directly output to control the start and stop of the power switch tube IGBT.

[0031] As a further improvement of the present invention, the improved whale algorithm adopts a nonlinear dynamic adjustment factor to perform dynamic search; the specific method is:

[0032] The formula of the nonlinear dynamic adjustment factor is defined as follows:

[0033]

[0034] Among them, t is the number of iterations, t_max is the maximum number of iterations, and a1 is the nonlinear dynamic adjustment factor;

[0035] Taking the first-order and second-order derivatives of the nonlinear dynamic adjustment factor a1, we get:

[0036]

[0037] The first-order derivative is used to analyze the curve change trend of the nonlinear dynamic adjustment factor a1, and the second-order derivative is used to analyze the concave and convex changes of the curve of the nonlinear dynamic adjustment factor a1, so as to understand how the nonlinear dynamic adjustment factor a1 can enhance the search capability through numerical changes.

[0038] As a further improvement of the present invention, the improved whale algorithm introduces a nonlinear adaptive weight strategy; the specific expression is:

[0039]

[0040] Where t is the number of iterations, t_max is the maximum number of iterations, and w(t) is the nonlinear adaptive weight;

[0041] The improved whale algorithm updates the shrinking and spiral hunting methods, and the specific expression is:

[0042]

[0043] Where X(t+1) represents the position vector of the current solution at t+1 iterations; X(t) represents the position vector of the current solution at t iterations; A, D1, D2, D3 represent coefficient vectors, A∈[0,2]; l, r3 represent random numbers between 0 and 1; b represents the direction of the control spiral, and its value is a constant 1; X rand(t) represents the position of a random humpback whale individual.

[0044] As a further improvement of the present invention, the improved whale algorithm introduces a screening disturbance mechanism, and the specific expression is:

[0045]

[0046] Among them, X(t+1) represents the position vector of the current solution of t+1 iterations; X(t) represents the position vector of the current solution of t iterations; f represents the fitness value corresponding to the corresponding whale individual; f med represents the median of the fitness value; rand() represents a random number uniformly distributed between 0 and 1; X1 and X2 represent the positions of the humpback whale individuals with the smallest and largest fitness values ​​in the top 50% of the fitness values, respectively.

[0047] As a further improvement of the present invention, the improved conductance increment method is used to perform precise optimization to dynamically adjust and output the duty cycle D to control the start and stop of the power switch tube IGBT; the specific method is:

[0048] Calculate the current change rate dI and voltage change rate dU between two adjacent iterations;

[0049] The instantaneous output power of the photovoltaic cell is P = IU,

[0050] When dU≠0, the voltage and duty cycle D are adjusted by judging the relationship between dI / dU and -I / U:

[0051]

[0052] When dU=0, the voltage and duty cycle D are adjusted by judging dI;

[0053]

[0054] The duty cycle D is dynamically adjusted and outputted through the current change rate dI and the voltage change rate dU.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0056] (1) The MPPT control method for a photovoltaic hydrogen production system of the present invention improves the accuracy and response speed of maximum power point tracking (MPPT) by improving the whale-conductance increment method and utilizing the powerful optimization capability of the improved whale algorithm, thereby overcoming the problems of slow initial search speed and insufficient search accuracy of the linear convergence factor in the later stage, and adopts a nonlinear dynamic adjustment factor, which has a larger value in the early stage to accelerate global exploration, and is reduced to a smaller value in the later stage to deepen the accuracy of local search.

[0057] (2) The MPPT control method of the photovoltaic hydrogen production system of the present invention introduces a nonlinear adaptive weight strategy by improving the whale-conductance increment method and using the improved whale algorithm, which overcomes the problem that the original WOA-INC algorithm is prone to fall into the local optimum in multi-objective optimization, and can dynamically adjust the weight value to adapt to the optimization needs of different stages. In addition, in the process of contraction and encirclement, the nonlinear adaptive weight strategy can better guide the whale group to move closer to the optimal solution, while maintaining sufficient diversity to avoid premature convergence to the local optimum.

[0058] (3) The MPPT control method for the photovoltaic hydrogen production system of the present invention introduces a screening disturbance mechanism by improving the whale-conductance increment method and using the improved whale algorithm. To address the problem of slow convergence in the late stage of the algorithm, the output power of photovoltaic cells is sorted by fitness at the end of the iteration, and the positions of the first 50% of high-fitness humpback whales remain unchanged. At the same time, the last 50% of low-fitness individuals are randomly updated between these leading groups, thereby significantly accelerating the late optimization process, improving the overall performance and optimization efficiency of the algorithm, and ensuring that the algorithm continues to operate efficiently during the convergence stage.

[0059] (4) The MPPT control method of the photovoltaic hydrogen production system of the present invention improves the whale-conductance increment method and uses the improved conductance increment method to further accurately optimize. Based on the relationship between the rate of change of the output voltage and the rate of change of the current of the photovoltaic cell, the two rates of change are monitored in real time and the working point is adjusted to achieve accurate tracking of the maximum power point, ensuring that the system always operates at the highest efficiency state.

[0060] (5) The MPPT control method of the photovoltaic hydrogen production system of the present invention can successfully track the maximum power point under dynamically changing light by improving the whale-conductance increment method, thereby improving the electrolysis efficiency of the photovoltaic hydrogen production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A circuit diagram of a photovoltaic cell equivalent model of the present invention;

[0062] Figure 2 A topological structure diagram of the photovoltaic array of the present invention connected to a boost DC-DC converter;

[0063] Figure 3 It is a schematic diagram of the charging phase of the boost DC-DC converter of the present invention;

[0064] Figure 4 It is a schematic diagram of the freewheeling phase of the boost DC-DC converter of the present invention;

[0065] Figure 5 The output of the photovoltaic array cell of the present invention is P -U characteristic curve diagram;

[0066] Figure 6 It is a comparative schematic diagram of the linear convergence factor and the nonlinear convergence factor of the present invention;

[0067] Figure 7 It is a schematic diagram of a nonlinear adaptive weight strategy curve of the present invention;

[0068] Figure 8 It is a schematic flow chart of the MPPT control method of the photovoltaic hydrogen production system based on the improved whale-conductance increment method of the present invention;

[0069] Fig. 9 It is a schematic diagram of the output power curve of the photovoltaic array under uniform illumination of the present invention;

[0070] Fig.10 It is a schematic diagram of the photovoltaic array output power curve under dynamically changing illumination of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below in conjunction with the drawings in the preferred embodiments of the present application. In the drawings, the same or similar reference numerals throughout represent the same or similar parts or parts with the same or similar functions. The described embodiments are part of the embodiments of the present application, not all of the embodiments. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limitations on the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0072] The following will be combined Figure 1-10 , the MPPT control method of the photovoltaic hydrogen production system based on the improved whale-conductance increment method involved in the embodiment of the present application is described in detail. It is worth noting that the following embodiments are only used to explain the present application and do not constitute a limitation of the present application.

[0073] Embodiment 1:

[0074] The MPPT control method of photovoltaic hydrogen production system based on the improved whale-conductance increment method includes the following specific steps:

[0075] Step 1: Establish a photovoltaic hydrogen production system with a photovoltaic array. The photovoltaic array generates electricity in series by connecting K photovoltaic panels in series with the number of panels connected in series and the number of panels connected in parallel. The output circuit of the photovoltaic array is connected to a PEM electrolyzer through a boost DC-DC converter. The PEM electrolyzer uses direct current to decompose water molecules into hydrogen and oxygen.

[0076] In a preferred embodiment, the output circuit of the photovoltaic array is connected to a PEM electrolyzer via a boost DC-DC converter. The PEM electrolyzer uses direct current to decompose water molecules into hydrogen and oxygen. The specific method is:

[0077] 1.1. Connect the output circuit of the photovoltaic array through a boost DC-DC converter, and optimize the voltage regulation performance of the photovoltaic array by controlling the start and stop of the switch tube IGBT of the boost DC-DC converter.

[0078] The equivalent circuit of a boost DC-DC converter is as follows: Figure 2 As shown, it can be described as: an ideal switch IGBT working periodically is connected in series with an inductor L, and then connected in parallel with a load R and a capacitor C2. Capacitor C2 is used to smooth the output voltage, where capacitor C1 realizes the smoothing of the output voltage of the photovoltaic array and the voltage ripple, and diode D5 prevents current backflow. The voltage regulation performance is optimized by controlling the start and stop of the switch tube IGBT in the boost DC-DC converter.

[0079] The power switch tube IGBT controls its on and off time according to the logic signal in a working cycle. The inductor will be connected to loops ① and ② at different time periods, so as to store and release energy in this conversion process, ensuring that the output voltage is stable and always higher than the input voltage. The input and output voltage of the boost DC-DC converter circuit is:

[0080] The input and output voltage relationship of the boost DC-DC circuit is:

[0081]

[0082] Where: D is the duty cycle, switch on time T on The ratio of the total switching cycle time T; U in The circuit input voltage of the boost DC-DC converter; U out The output voltage of the boost DC-DC converter circuit.

[0083] The working process of a boost DC-DC converter in one cycle can be divided into two stages:

[0084] The first stage is the charging stage. The principle is as follows Figure 3 As shown, at this time, the logic signal is high, the power switch tube IGBT is turned on, one end of the inductor is grounded, and the voltage V at the LX node LX is zero (ignoring the on-resistance of the IGBT), the diode D5 is in a reverse cut-off state, and the filter capacitor C2 continues to supply power to Uout to ensure that the output voltage remains stable.

[0085] The second stage is the freewheeling stage. The principle is as follows Figure 4 As shown, at this time, the logic signal jumps from high level to low level, the power switch tube IGBT is turned off, one end of the inductor is connected to the positive end of the diode, and the diode D5 is turned on (there is a conduction voltage drop V D5 ), the voltage U at the LX node L x=Uout+U D , at this time the inductance L and the input voltage U in Together they power the load and charge the filter capacitor to ensure that the output voltage U out Always stable.

[0086] 1.2. The start and stop of the power switch tube IGBT in the boost DC-DC converter circuit is controlled by the duty cycle D. The duty cycle can be generated by the improved whale-conductance increment method.

[0087] In practical applications, power switch tube IGBT (insulated gate bipolar transistor) is used as a switching device to control the flow of current. The control adjustment of duty cycle D is achieved through PWM (pulse width modulation) signal. The high level duration of PWM signal determines the conduction time of power switch tube IGBT. Therefore, duty cycle D determines the conduction time of power switch tube IGBT in a switching cycle. Specifically:

[0088] When the duty cycle D needs to be increased, the high level time of the PWM signal is lengthened and the conduction time of the power switch tube IGBT is prolonged, thereby increasing the output voltage and current.

[0089] When the duty cycle D needs to be reduced, the high level time of the PWM signal is shortened, and the conduction time of the power switch tube IGBT is shortened, thereby reducing the output voltage and current.

[0090] 1.3. The output end of the boost DC-DC converter circuit is connected to a PEM electrolyzer. The PEM electrolyzer uses direct current to decompose water molecules into hydrogen and oxygen. The anode produces oxygen and the cathode produces hydrogen.

[0091] Anode: H2O-2e - →0.5O2+2H + ;

[0092] Cathode: 2H + +2e - →H2;

[0093] 1.4. The output voltage of the PEM electrolyzer is composed of the open circuit voltage, activation overvoltage, diffusion overpotential and ohmic overpotential, which are expressed in the following formula:

[0094] V el =V oc +V act +V diff +Vohm

[0095] The open circuit voltage can be obtained by the Nernst equation:

[0096] Where: E0 is the standard electromotive force of the PEM electrolyzer; R is the gas constant; T is the working temperature of the electrolyzer; F is the Faraday constant; P H2 , P O2 is the partial pressure of hydrogen and oxygen; a H2O is the activity of water. The calculation formula of E0 is: E0 = 1.229-0.9×10 -3 (T-298);

[0097] The activation overpotential is caused by the activation loss during the electrochemical reaction, which is affected by many factors including operating temperature, catalyst and reactants. The electrode surface reaction of the activation overvoltage uses the Butler-Volmer equation, which is expressed as follows:

[0098] Where: T a 、T c is the operating temperature of the anode and cathode of the electrolytic cell; a a 、a c is the charge transfer coefficient between the anode and cathode; j is the current density; j 0,a 、j 0,c is the current density at the anode and cathode.

[0099] The diffusion overpotential is formed due to the fact that the transfer of reactants and products in the porous electrodes in the PEM electrolyzer is hindered, especially under high current density conditions. The diffusion overpotential combines the Nernst equation and Fick's law, and the expression is as follows:

[0100] Where: C O2 , C H2 ——The concentration of oxygen and hydrogen at the interface between the membrane and the electrode; C O2,0 , C H2,0 ——Corresponding reference value.

[0101] The ohmic overpotential is formed by the impedance of the internal devices of the electrolytic cell, mainly the membrane resistance, and other resistances are ignored. The expression is as follows:

[0102] Where: m ——proton exchange membrane thickness, 200um; λ——proton exchange membrane water content, 20.

[0103] The mathematical models of the above open circuit voltage, activation overvoltage, diffusion overpotential and ohmic overpotential are combined to obtain the overall mathematical model of the output voltage of the PEM electrolyzer, thereby building a simulation model of the output voltage of the PEM electrolyzer.

[0104] 1.5. Electrolysis efficiency η of electrolytic cell el That is, the water splitting efficiency under stable temperature and pressure conditions, which is determined by the current efficiency η el(i) and voltage efficiency η el(v) Influence.

[0105]

[0106] Where: U tn ——The minimum voltage at which water electrolysis occurs.

[0107] Step 2: Establish a photovoltaic cell equivalent model, thereby obtaining the instantaneous output current I, instantaneous output voltage U, and instantaneous output power P=IU of the photovoltaic array according to the established photovoltaic cell equivalent model;

[0108] like Figure 1 As shown, the photovoltaic cell equivalent model is mainly composed of four main components:

[0109] An ideal diode D4, a photocurrent source Iph, a series resistor Rs and a parallel resistor Rsh, these components together constitute the working principle and electrical characteristics of the photovoltaic cell. The ideal diode represents the PN junction inside the photovoltaic cell, simulating the change of current with light intensity and temperature under the photovoltaic effect. The photocurrent source Iph is an ideal current source, which represents the maximum photocurrent under specific lighting conditions. The series resistor Rs simulates the resistance inside the battery, and the parallel resistor Rsh simulates the non-ideal current leakage path, such as tiny cracks or pollution on the surface of the battery. The equivalent model of the entire photovoltaic cell can be described as: the photocurrent source Iph is connected in parallel with the ideal diode D4 and the parallel resistor Rsh respectively, and finally the entire structure is connected in series with the series resistor Rs and the load RL, I, I d , Ish are the currents passing through the series resistor Rs, the ideal diode D4 and the parallel resistor Rsh respectively.

[0110] According to the established photovoltaic cell equivalent model, the instantaneous output current I of the photovoltaic array is obtained, and the formula is:

[0111]

[0112] Let the resistance R of the series resistor in the photovoltaic cell equivalent model be s =0, the resistance R of the parallel resistor of the photovoltaic cell equivalent model sh =∞; due to the resistance R of the series resistor in the photovoltaic cell equivalent model s= 0 and the resistance R of the parallel resistor sh =∞, It tends to 0, so it can be ignored. Then the general equation of the instantaneous output current I of the photovoltaic array is simplified to:

[0113]

[0114] Where n is the total number of photovoltaic panels; I is the instantaneous output current of the photovoltaic array; I ph is the current of the photocurrent source; I0 is the reverse saturation current of the ideal diode; q is the electron charge; U is the instantaneous output voltage of the photovoltaic array; R s is the resistance of the series resistance of the photovoltaic cell equivalent model; m is the characteristic fitting parameter of the ideal diode; K is the Boltzmann constant; T is the working environment temperature of the photovoltaic panel; R sh is the resistance of the parallel resistor of the photovoltaic cell equivalent model.

[0115] Step 3: Dynamically optimize and generate the duty cycle D by the improved whale-conductance increment method, and use the duty cycle D to control the start and stop of the power switch tube IGBT in the equivalent circuit of the boost DC-DC converter to optimize the photovoltaic array voltage regulation performance of the photovoltaic hydrogen production system.

[0116] By improving the whale-conductance increment method to dynamically optimize the duty cycle D, when the instantaneous change rate of the photovoltaic array output power is 0, the maximum power point is found and the duty cycle D is kept unchanged, so that the photovoltaic array output power reaches the maximum power point.

[0117] Specifically, the improved whale-conductance increment method inputs the instantaneous output current I, instantaneous output voltage U, and instantaneous output power P=IU of the photovoltaic array, and dynamically adjusts the duty cycle D by the output power change rate ΔP=P(t)-P(t-1) of the photovoltaic array; specifically includes:

[0118] Initialize the duty cycle D, collect the instantaneous output current I and instantaneous output voltage U of the photovoltaic array, calculate the instantaneous output power P = IU, and iterate, enable the improved whale algorithm to use nonlinear dynamic adjustment factors for dynamic search, and introduce nonlinear adaptive weight strategy to jump out of local optimal and introduce screening perturbation mechanism to accelerate convergence for preliminary optimization; Among them, the main goal of the preliminary optimization stage is to quickly find the approximate maximum power point range. This stage usually uses a relatively rough method to quickly search for the maximum power point.

[0119] After the iteration is completed, the output power change rate ΔP of the photovoltaic array is determined:

[0120] When the output power change rate ΔP of the photovoltaic array is greater than or equal to the preset value Z, the improved whale algorithm is restarted, and the duty cycle D is not output in this process;

[0121] When the output power change rate ΔP of the photovoltaic array is less than the preset value Z, after completing the iterative optimization of the improved whale algorithm, the improved conductance increment method is enabled for precise optimization to dynamically adjust and output the duty cycle D to control the start and stop of the power switch tube IGBT; this stage uses the preliminary optimization to quickly find the approximate position of the maximum power point, and then starts the precise optimization stage. It will adjust the duty cycle more finely to achieve a more accurate maximum power point. This stage usually uses a more efficient algorithm.

[0122] When the output power change rate ΔP of the photovoltaic array is 0, the instantaneous output power of the photovoltaic array reaches the maximum power point. At this time, the duty cycle D is directly output to control the start and stop of the power switch tube IGBT.

[0123] In this embodiment, the preset value Z=0.08 is a constant.

[0124] Embodiment 2:

[0125] This study conducted simulation experiments on MATLAB / Simulink to verify the feasibility of the MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method. Figure 8 The parameters of the solar module used are shown in Table 1 (five 3*3 photovoltaic cells are connected in series).

[0126] Table 1:

[0127] Series Connection 3 in parallel 3 Open circuit voltage 36.3v Short circuit current 7.84A Maximum power point voltage 29V Maximum power point current 7.35A

[0128] The boost circuit parameter settings are shown in Table 2:

[0129] Table 2:

[0130] Components Numeric inductance 3mh Capacitor 1 10uF Capacitor 2 80uF DC load 45Ω Switching frequency 20khz

[0131] When a PV array is under uniform illumination, its output power-voltage (PU) curve shows a clear peak, which makes it easy for traditional maximum power point tracking (MPPT) algorithms to find this maximum power point (MPP). However, under dynamically changing illumination conditions, the PU curve may have multiple peaks, increasing the difficulty of finding the global maximum power point. In this multi-peak situation, the traditional MPPT algorithm is prone to lock on a local maximum rather than the global maximum power point due to its simple control mechanism. This causes the system to fail to reach its maximum possible power output, resulting in power loss.

[0132] like Figure 5As shown in the figure, the PU characteristic curve of the photovoltaic array output under different lighting conditions is shown, in which the multi-peak characteristics under non-uniform lighting conditions can be seen. In order to solve this problem, more advanced MPPT algorithms are needed, which can effectively track the global maximum power point under complex lighting conditions, reduce power loss and improve the overall efficiency of the system.

[0133] In the traditional whale-conductance increment method, WOA imitates the hunting behavior of humpback whales, including siege, spiral hunting and random search, to approach the optimal solution. Among them, the whale group alternately uses contraction siege and spiral hunting paths with a probability of 50% to approach the target, simulating the behavior of sieging prey.

[0134] In order to overcome the problems of long tracking time and large output fluctuation of the traditional whale conductance increment (WOA-INC) algorithm, this embodiment provides an improved whale-conductance increment method (PEWOA-INC), which specifically includes: an improved whale algorithm and an improved conductance increment method, wherein:

[0135] The improved whale algorithm uses a nonlinear dynamic adjustment factor for dynamic search;

[0136] And introduce nonlinear adaptive weight strategy to jump out of local optimum and introduce screening disturbance mechanism to speed up convergence for preliminary optimization;

[0137] After the improved whale algorithm completes the iterative optimization, the improved conductance increment method is used for precise optimization.

[0138] The improved whale-conductance increment method introduces three key improvements:

[0139] (1) Use nonlinear dynamic adjustment factors for dynamic search;

[0140] In order to optimize the ability of the traditional WOA-INC algorithm in solving nonlinear problems, the convergence factor of the traditional algorithm is upgraded to overcome the problems of slow initial search speed and insufficient search accuracy in the later stage due to the linear convergence factor. Specifically, a nonlinear dynamic adjustment factor is adopted, which has a large value in the early stage to accelerate global exploration, and is reduced to a small value in the later stage to deepen the accuracy of local search, such as Figure 6 As shown. The formula of the nonlinear dynamic adjustment factor is defined as follows:

[0141]

[0142] Among them, t is the number of iterations, t_max is the maximum number of iterations, 2, -5, 3 are parameters that control the nonlinear decline rate, and a1 is the nonlinear dynamic adjustment factor;

[0143] The nonlinear dynamic adjustment factor a1 is first-order and second-order derivatives are taken. The first-order derivative is used to analyze the curve change trend of the nonlinear dynamic adjustment factor a1. The second-order derivative is used to analyze the curve concave and convex changes of the nonlinear dynamic adjustment factor a1, so as to understand how the nonlinear dynamic adjustment factor a1 can enhance the search capability through numerical changes. The first-order and second-order derivative formulas are:

[0144]

[0145] The coefficient of -30 after the first-order derivative (da1 / dt) means that as the number of iterations t increases, the search range will gradually narrow, which helps to find the optimal solution faster, and then gradually narrow the search range to find the optimal solution.

[0146] The second-order derivative (da1 2 / d 2 t) we can know that: when When 2 a1 / dt 2 =0, and Right now Therefore, within the maximum range of iterations (mid-range of iterations), the nonlinear convergence factor a1 curve has concave and convex changes:

[0147] If in (x,y) when d 2 a1 / dt 2 <0, the nonlinear convergence factor a1 is convex on (x, y), that is, When the nonlinear convergence factor a1 is The upper curve is convex;

[0148] If in (x,y) when d 2 a1 / dt 2 > 0, the nonlinear convergence factor a1 curve on (x, y) is concave, that is, When the nonlinear convergence factor a1 is The upper curve is concave.

[0149] In this way, the algorithm can use a larger a1 to quickly explore the solution space in the early stage of the search, and as a1 decreases in the later stage, it can search the potential optimal solution area more carefully, thereby significantly improving the search efficiency when solving nonlinear problems.

[0150] (2) Introducing nonlinear adaptive weight strategy;

[0151] In order to overcome the problem that the original WOA-INC algorithm is prone to fall into local optimality in multi-objective optimization, a nonlinear adaptive weight strategy is introduced:

[0152]

[0153] Where t is the number of iterations, t_max is the maximum number of iterations, and w(t) is the nonlinear adaptive weight.

[0154] The nonlinear adaptive weight strategy is as follows Figure 7 As shown in the figure, the weight value can be adjusted dynamically to meet the optimization needs of different stages. In the early stage of the algorithm, the weight value is large, which helps to promote global search and transition and expand the search range; in the later stage, the weight value gradually decreases to ensure that the details of the search process are refined and avoid the decline of accuracy in the later stage. This strategy can adaptively adjust the search vector and span according to the characteristics of the function and the current state of the algorithm, thereby greatly improving the efficiency and accuracy of the algorithm. This dynamic weight adjustment is not only conducive to macro exploration in complex problems, but also can be optimized at the micro level, effectively avoiding the situation where the algorithm falls into the local optimal solution.

[0155] Corresponding updates have also been made to the contraction hunting and spiral hunting methods. In the contraction hunting process, the nonlinear adaptive weight strategy can better guide the whale group to move closer to the optimal solution while maintaining sufficient diversity to avoid premature convergence to the local optimum. In the spiral hunting method, this strategy can help whales approach the optimal solution more efficiently, especially in the later stages of the algorithm, by reducing the weight value to enhance the local search ability, thereby ensuring the search accuracy.

[0156] The improved whale algorithm has been updated for the shrinking encirclement and spiral attack methods. The specific expressions are:

[0157]

[0158] Where X(t+1) represents the position vector of the current solution at t+1 iterations; X(t) represents the position vector of the current solution at t iterations; A, D1, D2, D3 represent coefficient vectors, A∈[0,2]; l, r3 represent random numbers between 0 and 1; b represents the direction of the control spiral, and its value is a constant 1; X rand (t) represents the position of a random humpback whale individual.

[0159] By introducing the nonlinear adaptive weight strategy, not only can the performance of the original WOA-INC algorithm in multi-objective optimization be improved, but also the search efficiency and accuracy of the shrinking encirclement and spiral hunting methods can be effectively improved, making the algorithm more robust and efficient in solving complex problems.

[0160] (3) Introducing a screening perturbation mechanism;

[0161] In order to solve the problem of slowing down the convergence of the algorithm in the later stage, the output power of photovoltaic cells is sorted by fitness at the end of the iteration, the positions of the first 50% of humpback whales with high fitness are locked unchanged, and the last 50% of low fitness individuals are randomly updated between these leading groups. This can significantly accelerate the optimization process in the later stage, improve the overall performance and optimization efficiency of the algorithm, and ensure that the algorithm continues to operate efficiently in the convergence stage. The specific expression is:

[0162]

[0163] Among them, X(t+1) represents the position vector of the current solution of t+1 iterations; X(t) represents the position vector of the current solution of t iterations; f represents the fitness value corresponding to the corresponding whale individual; f med represents the median of the fitness value; rand() represents a random number uniformly distributed between 0 and 1; X1 and X2 represent the positions of the humpback whale individuals with the smallest and largest fitness values ​​in the top 50% of the fitness values, respectively.

[0164] The improved whale algorithm has shown significant advantages in the field of complex multi-objective optimization. It is not only fast but also stable, providing strong support for solving complex problems in practical engineering and scientific research.

[0165] (4) After the improved whale algorithm completes the iterative optimization, the improved conductance increment method is used for further precise optimization;

[0166] Compared with the perturbation observation method and the constant voltage method, the conductivity increment method shows higher accuracy in tracking performance. This method is based on the relationship between the rate of change of the output voltage and the rate of change of the current of the photovoltaic cell. By real-time monitoring of these two rates of change and adjusting the operating point, it can achieve accurate tracking of the maximum power point and ensure that the system always operates at the highest efficiency.

[0167] The specific method is:

[0168] Calculate the current change rate dI and voltage change rate dU between two adjacent iterations;

[0169] dI=I(t)-I(t-1);

[0170] dU=U(t)-U(t-1);

[0171] The instantaneous output power of the photovoltaic cell is P = IU. Differentiating the voltage on both sides of the equal sign gives dP / dU = I + U * dI / dU, where dP is the power change rate △P between two adjacent iterations. When dP / dU = 0, the photovoltaic cell operates at the maximum power point.

[0172] That is, when dU≠0, dP=0, dI / dU=-I / U, and the photovoltaic cell operates at the maximum power point;

[0173] The voltage and duty cycle D can be adjusted by judging the relationship between dI / dU and -I / U:

[0174]

[0175] Among them, I(t) represents the current when the iteration number is t; I(t-1) represents the current when the iteration number is t-1; U(t) represents the voltage when the iteration number is t; U(t-1) represents the voltage when the iteration number is t-1; I represents the current; dP represents the power change rate between two adjacent iterations.

[0176] When dU=0, the duty cycle D is adjusted and output by judging dI;

[0177]

[0178] The duty cycle D is dynamically adjusted and outputted through the current change rate dI or the voltage change rate dU.

[0179] In this embodiment, specifically, the MPPT control method of the photovoltaic hydrogen production system based on the improved whale-conductance increment method is as follows: Figure 8 As shown, the specific steps include:

[0180] Step A, initialize the duty cycle D, calculate the whale fitness and the whale optimal solution, and record the individual position of the optimal solution to generate nonlinear dynamic adjustment factors a1, A, coefficient vector C, and randomly generate three random numbers r1, r2 and r3, r1, r2 and r3 are all ∈ [0,1];

[0181] The coefficient calculation formula is as follows:

[0182]

[0183] Where a1 represents the nonlinear dynamic adjustment factor; r1 and r2 represent random numbers between 0 and 1; t represents the number of iterations; t_max is the maximum number of iterations; A, C, D1, D2, and D3 represent coefficient vectors, A∈[0,2]; X * represents the position vector of the current optimal solution; X represents the position vector of the current solution; X rand (t) represents the position of a random humpback whale individual.

[0184] Step B, collecting the instantaneous output current I and instantaneous output voltage U of the photovoltaic array, and calculating the instantaneous output power P=IU;

[0185] Step C, iterative start, using the improved whale algorithm to perform preliminary optimization: determine the value of the random number r3; use the following formula to achieve:

[0186]

[0187] Where X(t+1) represents the position vector of the current solution at t+1 iterations; X(t) represents the position vector of the current solution at t iterations; A, D1, D2, D3 represent coefficient vectors, A∈[0,2]; l, r3 represent random numbers between 0 and 1; b represents the direction of the control spiral, with a value of constant 1, X rand (t) represents the position of a random humpback whale individual.

[0188] If the random number r3 ≥ 0.5, the spiral attack of the improved whale algorithm is used;

[0189] If the random number r3 is less than 0.5, the value of |A| is determined; if |A|≥1, the random search of the improved whale algorithm is adopted; if -1<A<1, the contraction roundup of the improved whale algorithm is adopted;

[0190] Step D: At the end of the iteration, the improved whale algorithm screening perturbation mechanism is enabled to speed up the convergence speed; this is achieved using the following formula:

[0191]

[0192] Among them, X(t+1) represents the position vector of the current solution of t+1 iterations; X(t) represents the position vector of the current solution of t iterations; f represents the fitness value corresponding to the corresponding whale individual; f med represents the median of the fitness value; rand() represents a random number uniformly distributed between 0 and 1; X1 and X2 represent the positions of the humpback whale individuals with the smallest and largest fitness values ​​in the top 50% of the fitness values, respectively.

[0193] Step E: After the iteration is completed, the output power change rate △P is determined;

[0194] If the output power change rate △P≥Z, in this embodiment, Z=0.08, and return to step A to restart the improved whale algorithm;

[0195] If the output power change rate △P<Z, the improved conductivity increment method is used and the process goes to step F;

[0196] If the output power change rate △P=0, directly go to step G;

[0197] Step F, after the improved conductivity increment method is enabled, the change rates of voltage and current are checked, and the duty cycle D is outputted accordingly;

[0198] Calculate the current change rate dI and voltage change rate dU between two adjacent iterations;

[0199] dI=I(t)-I(t-1);

[0200] dU=U(t)-U(t-1);

[0201] The instantaneous output power of the photovoltaic cell is P = IU. Differentiate the voltage on both sides of the equal sign to obtain dP / dU = I + U * dI / dU, where dP is the power change rate △P between two adjacent iterations, and U is the photovoltaic output voltage monitored in real time; when dP / dU = 0, the photovoltaic cell operates at the maximum power point;

[0202] When dU≠0, dP=0, dI / dU=-I / U;

[0203] The voltage and duty cycle can be adjusted by judging the relationship between dI / dU and -I / U:

[0204]

[0205] Among them, I(t) represents the current when the iteration number is t; I(t-1) represents the current when the iteration number is t-1; U(t) represents the voltage when the iteration number is t; U(t-1) represents the voltage when the iteration number is t-1; I represents the current; dP represents the power change rate between two adjacent iterations.

[0206] When dU=0, the duty cycle D is adjusted and output by judging dI;

[0207] If dI = 0, it means that the photovoltaic cell is operating at the maximum power point and directly outputs the duty cycle D;

[0208] If dI>0, increase the duty cycle D;

[0209] If dI<0, reduce the duty cycle D;

[0210] Step G: Output duty cycle D to control the start and stop of the power switch tube IGBT switch tube.

[0211] Simulation Results

[0212] In order to verify the performance of the proposed control algorithm, tests were carried out under static lighting conditions and dynamically changing conditions.

[0213] 1) Performance test of the proposed algorithm under static uniform illumination

[0214] The uniform illumination condition is set to: 1000W·m -2 、1000W·m -2 、1000W·m -2 、1000W·m -2 、1000W·m -2 The simulation results are as follows: Fig. 9 And as shown in Table 3.

[0215] Table 3 Algorithm performance comparison under uniform illumination

[0216]

[0217] according to Fig. 9 According to the data in Table 3, under uniform illumination conditions, the INC algorithm can find the maximum power point within 0.01 seconds, with a tracking accuracy of 99.92%, an error of only 0.08%, and an electrolysis efficiency of 93.33%. In contrast, although the WOA-INC algorithm can achieve the same tracking accuracy and error, the time consumption has increased to 0.19 seconds, and the electrolysis efficiency has slightly increased to 94.62%. The most outstanding is the PEWOA-INC algorithm, which not only finds the maximum power point within 0.08 seconds, but also has a higher tracking accuracy of 99.97%, a reduced error of 0.03%, and a maximum electrolysis efficiency of 94.92%, and the output curve has a small fluctuation.

[0218] From these results, it can be seen that under uniform illumination conditions, the INC algorithm excels with its fast convergence speed and high stability. However, compared with the WOA-INC algorithm, the PEWOA-INC algorithm exhibits faster tracking speed, higher stability, and better electrolysis efficiency. Therefore, the PEWOA-INC algorithm effectively tracks the maximum power point of the photovoltaic cell under uniform illumination conditions, proving its effectiveness in finding the maximum power point under static uniform illumination conditions.

[0219] 2) Performance test of the proposed algorithm under dynamically changing lighting

[0220] Dynamically changing light conditions are set to: 1000W·m -2 、1000W·m -2 、800W·m -2 , 700W·m -2 , 700W·m -2 , and suddenly changes to: 1000W·m at 0.3s -2 、1000W·m -2 , 900W·m -2 、800W·m -2 、800W·m -2 The simulation results are as follows: Fig.10 And as shown in Table 4.

[0221] Table 4 Comparison of algorithm performance under dynamic changes

[0222]

[0223]

[0224] From the above simulation results, under dynamic conditions, the INC algorithm fell into the local optimal solution and failed to successfully track the maximum power point. Its electrolysis efficiency before and after the step was 64.61% and 80.43%, respectively. In contrast, the WOA-INC algorithm was able to find the maximum power point within 0.16 seconds, with tracking accuracies of 99.88% and 99.90%, respectively, and errors of 0.12% and 0.10%, respectively. The electrolysis efficiency was 76.42% and 83.86%, respectively. The PEWOA-INC algorithm performed even better. It found the maximum power point within 0.08 seconds and 0.10 seconds, with tracking accuracies of 99.94% and 99.95%, respectively, and errors reduced to 0.06% and 0.05%, respectively. The electrolysis efficiency was also 76.42% and 83.86%.

[0225] Through comparative analysis, it is found that under dynamic changing conditions, the INC algorithm fails to effectively track the maximum power point. Compared with the WOA-INC algorithm, the PEWOA-INC algorithm can not only track the maximum power point faster and more stably, but also has higher electrolysis efficiency. Therefore, under dynamic changing conditions, the PEWOA-INC algorithm can effectively track the maximum power point of photovoltaic cells and improve the electrolysis efficiency, thus verifying the effectiveness of the PEWOA-INC algorithm in finding the maximum power point under dynamic light changing conditions.

[0226] Although the present invention is described herein with reference to the illustrative embodiments of the present invention, the above embodiments are only preferred embodiments of the present invention, and the embodiments of the present invention are not limited to the above embodiments. It should be understood that those skilled in the art can design many other modifications and embodiments, which will fall within the scope and spirit of the principles disclosed in this application.

Claims

1. The MPPT control method of photovoltaic hydrogen production system based on the improved whale-conductance increment method is characterized in that: include: Establish a photovoltaic hydrogen production system with a photovoltaic array, which consists of a number of photovoltaic panels connected in series and parallel; The output circuit of the photovoltaic array is connected to the PEM electrolyzer through a step-up DC-DC converter. The PEM electrolyzer uses direct current to decompose water molecules into hydrogen and oxygen. The duty cycle D is dynamically optimized and generated by the improved whale-conductance increment method. The duty cycle D controls the start and stop of the power switch tube IGBT in the boost DC-DC converter circuit to optimize the photovoltaic array voltage regulation performance of the photovoltaic hydrogen production system.

2. According to claim 1, the MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method is characterized in that: The improved whale-conductance increment method takes the instantaneous output current I and instantaneous output voltage U of the photovoltaic array as input, and calculates the instantaneous output power P=IU. The duty cycle D is dynamically adjusted by the output power change rate ΔP=P(t)-P(t-1) of the photovoltaic array, where t is the number of iterations.

3. According to claim 2, the MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method is characterized in that: The control method further comprises: Establishing a photovoltaic cell equivalent model, thereby obtaining the instantaneous output current I, instantaneous output voltage U, and instantaneous output power P=IU of the photovoltaic array according to the established photovoltaic cell equivalent model; The photovoltaic cell equivalent model at least includes an ideal diode, a photocurrent source, a series resistor and a parallel resistor. The photocurrent source is connected in parallel with the ideal diode and the parallel resistor respectively. Finally, the entire structure is connected in series with the series resistor and the load.

4. According to claim 3, the MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method is characterized in that: According to the established photovoltaic cell equivalent model, the instantaneous output current I of the photovoltaic array is obtained, and the formula is: Where n is the total number of photovoltaic panels; I is the instantaneous output current of the photovoltaic array; I ph is the current of the photocurrent source; I0 is the reverse saturation current of the ideal diode; q is the electron charge; U is the instantaneous output voltage of the photovoltaic array; R s is the resistance of the series resistance of the photovoltaic cell equivalent model; m is the characteristic fitting parameter of the ideal diode; K is the Boltzmann constant; T is the working environment temperature of the photovoltaic panel; R sh is the resistance of the parallel resistor of the photovoltaic cell equivalent model; Let the resistance R of the series resistor in the photovoltaic cell equivalent model be s =0, the resistance R of the parallel resistor of the photovoltaic cell equivalent model sh =∞; due to the resistance R of the series resistor in the photovoltaic cell equivalent model s =0, the resistance of the parallel resistor R sh =∞, It tends to 0, so it can be ignored. Then the general equation of the instantaneous output current I of the photovoltaic array is simplified to:

5. According to claim 3, the MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method is characterized in that: The equivalent circuit of the boost DC-DC converter is specifically: A periodically working power switch tube IGBT is connected in series with an inductor L, and then connected in parallel with a load R and a capacitor C2. Capacitor C2 is used to smooth the output voltage. Capacitor C2 realizes the smoothing of the output voltage of the photovoltaic array and the suppression of voltage ripple. The ideal diode prevents current backflow. The voltage regulation performance is optimized by controlling the start and stop of the power switch tube IGBT in the boost DC-DC converter.

6. According to claim 2, the MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method is characterized in that: The improved whale-conductance increment method includes an improved whale algorithm and an improved conductance increment method; including: Initialize the duty cycle D, collect the instantaneous output current I and instantaneous output voltage U of the photovoltaic array, calculate the instantaneous output power P = IU, and iterate, enable the improved whale algorithm to use nonlinear dynamic adjustment factors for dynamic search, and introduce nonlinear adaptive weight strategy to jump out of local optimality and introduce screening disturbance mechanism to accelerate convergence speed for preliminary optimization; After the iteration is completed, the output power change rate ΔP of the photovoltaic array is determined: When the output power change rate ΔP of the photovoltaic array is greater than or equal to the preset value Z, the improved whale algorithm is restarted; When the output power change rate ΔP of the photovoltaic array is less than the preset value Z, the improved conductivity increment method is used for precise optimization to dynamically adjust and output the duty cycle D to control the start and stop of the power switch tube IGBT; When the output power change rate ΔP of the photovoltaic array is 0, the instantaneous output power of the photovoltaic array reaches the maximum power point. At this time, the duty cycle D is directly output to control the start and stop of the power switch tube IGBT.

7. The MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method according to claim 6 is characterized in that: The improved whale algorithm adopts a nonlinear dynamic adjustment factor to perform dynamic search; the specific method is: The formula of the nonlinear dynamic adjustment factor is defined as follows: Among them, t is the number of iterations, t_max is the maximum number of iterations, and a1 is the nonlinear dynamic adjustment factor; Taking the first-order and second-order derivatives of the nonlinear dynamic adjustment factor a1, we get: The first-order derivative is used to analyze the curve change trend of the nonlinear dynamic adjustment factor a1, and the second-order derivative is used to analyze the concave and convex changes of the curve of the nonlinear dynamic adjustment factor a1, so as to understand how the nonlinear dynamic adjustment factor a1 can enhance the search capability through numerical changes.

8. The MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method according to claim 6 is characterized in that: The improved whale algorithm introduces a nonlinear adaptive weight strategy; the specific expression is: Where t is the number of iterations, t_max is the maximum number of iterations, and w(t) is the nonlinear adaptive weight; The improved whale algorithm updates the shrinking and spiral hunting methods, and the specific expression is: Where X(t+1) represents the position vector of the current solution at t+1 iterations; X(t) represents the position vector of the current solution at t iterations; A, D1, D2, D3 represent coefficient vectors, A∈[0,2]; l, r3 represent random numbers between 0 and 1; b represents the direction of the control spiral, and its value is a constant 1; X rand (t) represents the position of a random humpback whale individual.

9. The MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method according to claim 6 is characterized in that: The improved whale algorithm introduces a screening disturbance mechanism, and the specific expression is: Among them, X(t+1) represents the position vector of the current solution at t+1 iterations; X(t) represents the position vector of the current solution at t iterations; f represents the fitness value corresponding to the corresponding whale individual; f med represents the median of the fitness value; rand() represents a random number uniformly distributed between 0 and 1; X1 and X2 represent the positions of the whales with the smallest and largest fitness values ​​in the top 50% of the fitness values, respectively.

10. The MPPT control method for photovoltaic hydrogen production system based on the improved whale-conductance increment method according to claim 6 is characterized in that: The improved conductance increment method is used to perform precise optimization to dynamically adjust and output the duty cycle D to control the start and stop of the power switch tube IGBT; the specific method is: Calculate the current change rate dI and voltage change rate dU between two adjacent iterations; The instantaneous output power of the photovoltaic cell is P = IU, When dU≠0, the voltage and duty cycle D are adjusted by judging the relationship between dI / dU and -I / U: When dU=0, the voltage and duty cycle D are adjusted by judging dI; The duty cycle D is dynamically adjusted and outputted through the current change rate dI and the voltage change rate dU.

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