Buck-boost converter circuit for photovoltaic maximum power point tracking

Abstract

The application discloses a four-switch Buck-Boost converter circuit for photovoltaic maximum power point tracking, which comprises a main circuit module, an input voltage detection module, an output voltage detection module, an output current detection module, a control output module and a driving amplification circuit module. The control output module comprises a micro control unit, and the micro control unit is provided with an ADC sampling interface and a PWM output interface. The main circuit module processes the input voltage of a photovoltaic panel and outputs direct current to charge a battery. The input voltage detection module, the output voltage detection module and the output current detection module sample the input voltage, the output voltage and the output current of the main circuit module and transmit the sampling values to the micro control unit. The micro control unit judges the mode of the main circuit module according to the sampling values, outputs a PWM signal through the PWM output interface by improving a maximum power point tracking algorithm, and controls the conduction and turn-off of the switch tube of the main circuit module through the driving amplification circuit module.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photovoltaic and switching power supply, in particular, especially to a three-mode four-switch Buck-Boost converter circuit for photovoltaic maximum power point tracking. BACKGROUND

[0002] The traditional photovoltaic input circuit is mainly Buck circuit, Boost circuit or Buck-Boost circuit. This puts forward the requirement of photovoltaic panel input voltage, but in fact, because of the changes of light intensity, temperature, humidity, etc., the photovoltaic panel input voltage fluctuates within a certain range. The Buck circuit, Boost circuit or Buck-Boost circuit cannot fully meet the voltage range of the photovoltaic panel input, so a Buck-Boost converter circuit with wide voltage input range for photovoltaic maximum power point tracking is proposed.

[0003] The traditional four-switch Buck-Boost converter (FSBB) circuit has Buck and Boost modes, can freely switch between the two modes, and because MOSFET tubes are used instead of diodes, the circuit loss is reduced and the efficiency is improved. However, the switching from Buck mode to Boost mode is completed instantaneously, which will cause irreversible loss to the switch tube device, so a Buck-Boost transition mode is added here to smooth the duty cycle switching process, and an improved switching duty cycle method is used to further improve the circuit conversion efficiency.

[0004] The maximum power point tracking algorithm is one of the important factors affecting the photovoltaic conversion efficiency. The use of advanced maximum power point tracking (MPPT) algorithm can greatly improve the photovoltaic conversion efficiency. The traditional maximum power point tracking algorithm is mainly based on incremental conductance method and perturbation and observation method. However, the above methods have the disadvantages of empirical step size setting, poor stability, low output efficiency, low applicability, easy to lose maximum power point under shading conditions, etc. Therefore, a new improved intelligent maximum power point tracking algorithm is proposed and innovatively applied to the four-switch Buck-Boost converter circuit. SUMMARY

[0005] The Buck-Boost converter circuit for photovoltaic maximum power point tracking can significantly increase the input voltage range of the photovoltaic panel and reduce power loss, improves the adaptability, stability, reliability, precision and efficiency of the circuit by using the improved duty cycle switching method of the transition mode Buck-Boost and the improved maximum power point tracking algorithm, and meets the increasing performance requirements of the photovoltaic conversion circuit.

[0006] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0007] A Buck-Boost converter circuit for photovoltaic maximum power point tracking, comprising a main circuit module, an input voltage detection module, an output voltage detection module, an output current detection module, a control output module and a drive amplification circuit module, the control output module comprising a micro control unit, the micro control unit having an ADC sampling interface and a PWM output interface;

[0008] The input ends of the main circuit module and the input voltage detection module are connected with the photovoltaic panel, the output end of the main circuit module is connected with the input ends of the output voltage detection module and the output current detection module and the battery, and the output ends of the input voltage detection module, the output voltage detection module and the output current detection module are connected with the ADC sampling interface;

[0009] The main circuit module is used for processing the 15-130V input voltage of the photovoltaic panel and outputting direct current to charge the battery; the input voltage detection module, the output voltage detection module and the output current detection module are used for sampling the input voltage, the output voltage and the output current of the main circuit module and transmitting the sampling values to the micro control unit; the micro control unit is used for judging the mode of the main circuit module according to the sampling values, outputting the PWM control signal from the PWM output interface through the improved maximum power point tracking algorithm, and controlling the conduction and turn-off of the switch tube of the main circuit module through the drive amplification circuit module;

[0010] The main circuit module comprises a first switch tube, a second switch tube, a third switch tube, a fourth switch tube, a first diode, a second diode, a third diode, a fourth diode, a first parasitic capacitor, a second parasitic capacitor, a third parasitic capacitor, a fourth parasitic capacitor and a charge and discharge inductor;

[0011] The control end of the first switch tube inputs the first PWM control signal, the first end of the first switch tube is connected with the positive poles of the first diode and the first parasitic capacitor, and the second end of the first switch tube is connected with the negative poles of the first diode and the first parasitic capacitor;

[0012] The control end of the second switch tube inputs a second PWM control signal, the first end of the second switch tube is connected with the positive pole of the second diode and the second parasitic capacitor, and the second end of the second switch tube is connected with the negative pole of the second diode and the second parasitic capacitor;

[0013] The control end of the third switch tube inputs a third PWM control signal, the first end of the third switch tube is connected with the positive pole of the third diode and the third parasitic capacitor, and the second end of the third switch tube is connected with the negative pole of the third diode and the third parasitic capacitor;

[0014] The control end of the fourth switch tube inputs a fourth PWM control signal, the first end of the fourth switch tube is connected with the positive pole of the fourth diode and the fourth parasitic capacitor, and the second end of the fourth switch tube is connected with the negative pole of the fourth diode and the fourth parasitic capacitor.

[0015] Further, the input voltage detection module adopts an operational amplifier TP2412 to perform differential sampling, so that the input voltage is reduced by 50 times, and when the maximum input voltage is 130V, the maximum output voltage of the differential sampling circuit is 2.6V.

[0016] Further, the output voltage detection module adopts an operational amplifier TP2412 to perform differential sampling, so that the input voltage is reduced by 20 times, and when the maximum output voltage is 55V, the maximum output voltage of the differential sampling circuit is 2.75V.

[0017] Further, the output current detection module is connected in parallel with two 2mR / 2W current detection resistors, the maximum output current is 63A, and the power reaches 3000W through the current detection resistors; the INA280 current detection amplifier measures the voltage drop on the current detection resistor in a wide common mode range of 2.7V to 120V, and inputs the sampling value to the micro control unit.

[0018] Further, the Buck-Boost converter circuit has three working modes, which are:

[0019] Let V i be the input voltage, V o be the output nominal voltage, when V i is less than V o -ΔV h , the circuit is in Boost mode; when V i is greater than V o +ΔVh, the circuit is in Buck mode; when V i is between V o -ΔV h and V o +ΔV h , the circuit is in Buck-Boost mode; ΔV his half of the output voltage range of the Buck-Boost mode;

[0020] The Buck-Boost mode of the Buck-Boost converter circuit is between the Buck mode and the Boost mode, and the intermediate process is the Buck-Boost mode when the circuit transits from the Buck mode to the Boost mode;

[0021] The Buck-Boost mode range depends on the voltage gain M, and is expressed as:

[0022]

[0023] In the formula, D1 is the duty ratio of the first switch tube; D2 is the duty ratio of the fourth switch tube;

[0024] The relationship of D1 and D2 is expressed as:

[0025]

[0026] Let D1 and D2 satisfy the linear relationship:

[0027] D1=D2+0.95;

[0028] Thus, the jump process of the working point is eliminated, and the voltage gain M is:

[0029]

[0030] Further, the optimized particle swarm algorithm BSPSO is used for maximum power point tracking under local shadow condition, and specifically:

[0031] Firstly, chaos initialization based on reverse strategy is performed, improved Tent mapping is used for initialization, and quasi-reverse individual is used for initialization; secondly, evolution mutation stage is performed, a mutation factor is introduced, and the range of global optimal solution is further expanded; finally, optimization convergence stage is entered, the speed term is removed to enter low latitude optimization, and the convergence speed is accelerated until the global optimal solution is converged; voltage and current output by the photovoltaic panel are taken as independent variables, power is taken as a target function, and duty ratio is output to control maximum output power.

[0032] Further, the chaos initialization process based on reverse strategy of the optimized particle swarm algorithm BSPSO includes:

[0033] The simplified particle swarm PSO algorithm is a global optimization algorithm based on swarm intelligence, and the updating formula during the working process of the particle is:

[0034] v n i = wv n-1 i + c1r1(p ni -x n i )+c2r2(g n -x n i );

[0035] x n+1 i =x n i +v n i ;

[0036] In the formula, v n i Let v be the velocity of particle i in the nth iteration; w be the inertial weight; v n-1 i Let be the velocity of particle i in the (n-1)th iteration; c1 is the individual learning factor; r1 and r2 are random numbers from 0 to 1; p n i c1 is the optimal position of particle i after n iterations; c2 is the group learning factor; g n The optimal position of all particles after n iterations; x n i Let x be the position of particle i during the nth iteration; n+1 i Let i be the position of particle i during the (n+1)th iteration.

[0037] Particle swarm chaos initialization using improved Tent mapping:

[0038]

[0039] In the formula, x i Let x be the initial position of particle i; i ～ The position of particle i after Tent mapping;

[0040] The condition for this chaotic mapping to hold is that it is a full mapping within [0,1], and it depends on s and q, where q takes the value of a discrete point set:

[0041] q=2πN N∈(-∞,0)∪(0,+∞),N∈Z;

[0042] The possible values ​​of s:

[0043]

[0044] If a quasi-reverse strategy is used to initialize individual particles in the particle swarm, then the chaotic initialization based on the reverse strategy is as follows:

[0045]

[0046] where x i is the position of particle i after mapping; x mid is the center of feasible region, and rand represents a random function of (0, 1); sin represents the sin function.

[0047] Further, the evolution and mutation stage process of the optimized particle swarm algorithm BSPSO includes:

[0048] Although the use of chaos initialization particle swarm based on the reverse strategy can accelerate the convergence speed, the similarity of the particle swarm increases in the optimization process, and it is easy to fall into local optimization. In order to jump out of local optimization and improve the diversity of the particle swarm, the optimization idea of the beetle algorithm is used for improvement;

[0049] The beetle perceives the food through the antennae on both sides of the head without knowing the specific position of the food. Because the distance between the antennae and the food position is different, the concentration of the perceived smell is also different. The beetle can move towards the direction of the food according to the concentration difference of the perceived smell, and reach the position of the food after multiple iterations. The mathematical expression is:

[0050]

[0051] where x is a random unit vector, indicating the direction of the beetle's horn at any time; rands represents a random vector of (-1, 1); Dim represents the spatial dimension; x t+1 represents the centroid position of the beetle after n iterations at t+1; x l , x r are the positions of the left and right antennae of the beetle; x t represents the centroid position of the beetle after n iterations at t; f function is the objective function; δt is the iteration step; sign is the sign function;

[0052] In order to improve the global optimization of the particle swarm, the formula of the particle swarm algorithm is improved:

[0053] v n i = wv n-1 i + c1r1(p n i - x n i ) + c2r2(g n - x n i ) + c3r3(p n i - g n );

[0054] where c3 is the mutation factor; r3 is a random number between 0 and 1.

[0055] In the formula, the current particle and the optimal position of the group are introduced, the positions of the two horns are regarded as the individual local optimum and the group local optimum, and the distance between the two horns represents the distance between the individual and the group optimal position.

[0056] Further, the optimization convergence stage process of the optimized particle swarm optimization BSPSO algorithm includes:

[0057] After iteration, when the best position found by the particle swarm individual is better than the best position obtained by the group in the last iteration, the particle swarm in the vicinity of the individual enters the optimization convergence state, the iteration algorithm is converted, the simplified particle swarm PSO algorithm is adopted, the speed term is removed, and the convergence speed is accelerated.

[0058] If only the motion of the i-th particle is considered, the particle swarm update formula is

[0059]

[0060] which can be changed to:

[0061]

[0062] x(n+1)=x(n)+v(n);

[0063] After iteration of the above formula, the following is obtained:

[0064]

[0065] The formula is a classical second-order differential equation without a speed term;

[0066] At this time, it can be changed into a first-order differential equation:

[0067]

[0068] And the particle swarm optimization equation without a speed term is simplified as:

[0069] x n i =wx n-1 i +c1r1(p n i -x n i )+c2r2(g n -x n i );

[0070] After removing the speed term, the particle swarm equation is reduced from a second-order differential equation to a first-order differential equation, which simplifies the iteration process and facilitates the analysis and control of the particle swarm evolution process.

[0071] Further, the adaptive parameter process of the optimized particle swarm algorithm BSPSO comprises:

[0072] The inertia weight w is updated by using a nonlinear strategy:

[0073]

[0074] In the formula, w1 is the maximum value of the inertia weight w; w2 is the minimum value of the inertia weight w; h is a nonlinear coefficient; k m is the maximum number of iterations; k is the current iteration number;

[0075] The individual learning factor c1 and the group learning factor c2 are updated by using a trigonometric function formula:

[0076]

[0077] The mutation factor c3 is updated by using a nonlinear strategy:

[0078]

[0079] In the formula, c α = 0.9, and c β = 0.45.

[0080] Compared with the prior art, the present application has the following advantages and beneficial effects.

[0081] (1) The present application increases the input voltage range of the photovoltaic panel, and by using a four-switch Buck-Boost converter circuit instead of a traditional Buck circuit, Boost circuit or Buck-Boost circuit, the input voltage range of the photovoltaic panel is expanded to 15-130V.

[0082] (2) The present application uses MOSFET switching tubes instead of traditional diodes, and by PWM controlling the turn-on and turn-off of the MOSFET switching tubes, there is no turn-on voltage drop and turn-on loss of the diodes, and the circuit power conversion efficiency is improved.

[0083] (3) The micro control unit of the present application uses a dsPIC chip with higher precision and reliability, has high performance and flexible peripherals, and has strong performance in asynchronous event processing capability, precise simulation and the like, and the precision and reliability of the circuit are improved.

[0084] (4) The present application adds a transition mode Buck-Boost mode between the Buck and Boost modes, smoothes the transition process, and the duty ratio switching is stable without jumping, reduces the device loss, and improves the circuit stability.

[0085] (5) The intelligent improved particle swarm algorithm is adopted to replace the conductance increment method and the disturbance observation method, so that the maximum power point tracking effect of the photovoltaic panel under the shadow state is further improved, the output power is higher, and the maximum power point tracking effect of the circuit is improved. BRIEF DESCRIPTION OF DRAWINGS

[0086] Figure 1 It is a structural schematic diagram of a Buck-Boost converter circuit for photovoltaic maximum power point tracking.

[0087] Figure 2 It is a sampling circuit diagram of an input voltage detection module.

[0088] Figure 3 It is a sampling circuit diagram of an output voltage detection module.

[0089] Figure 4 It is a sampling circuit diagram of an output current detection module.

[0090] Figure 5 It is a three-mode schematic diagram of a four-switch Buck-Boost converter circuit.

[0091] Figure 6 It is a duty cycle switching schematic diagram of the three-mode of the four-switch Buck-Boost converter circuit.

[0092] Figure 7 It is a Buck-Boost mode duty cycle switching continuous strategy schematic diagram of the four-switch Buck-Boost converter circuit.

[0093] Figure 8 It is a P-V curve schematic diagram of a photovoltaic cell under a shadow state.

[0094] Figure 9 It is a flowchart of an optimized particle swarm algorithm.

[0095] Figure 10 It is a simulation schematic diagram of the optimized particle swarm algorithm.

[0096] Figure 11 It is a simulation experiment result diagram of the optimized particle swarm algorithm. DETAILED DESCRIPTION

[0097] The Buck-Boost converter circuit for photovoltaic maximum power point tracking of the present application will be further described below in combination with the drawings and specific embodiments.

[0098] Please refer to Figure 1The application discloses a Buck-Boost converter circuit for photovoltaic maximum power point tracking, which comprises a main circuit module, an input voltage detection module, an output voltage detection module, an output current detection module, a control output module and a driving amplification circuit module.

[0099] The micro control unit adopts a dsPIC33CK128MP205 chip, and the dsPIC is a device which combines the features of a single-chip microcomputer with the capability structure of a digital signal processor (DSP), has high performance, flexible peripherals and a complete software and hardware tool ecosystem, and has strong performance in asynchronous event processing capability, precise simulation, common development environment and peripheral components.

[0100] The input ends of the main circuit module and the input voltage detection module are connected with a photovoltaic panel, the output end of the main circuit module is connected with the input ends of the output voltage detection module and the output current detection module and a battery, and the output ends of the input voltage detection module, the output voltage detection module and the output current detection module are connected with an ADC sampling interface.

[0101] The main circuit module is used for processing a 15-130V input voltage of the photovoltaic panel and outputting direct current to charge the battery, the input voltage detection module, the output voltage detection module and the output current detection module are used for sampling the input voltage, the output voltage and the output current of the main circuit module and transmitting the sampling values to the micro control unit, and the micro control unit is used for judging the mode of the main circuit module according to the sampling values, outputting a PWM control signal from a PWM output interface through an improved maximum power point tracking algorithm, and controlling the conduction and shutdown of a switch tube of the main circuit module through the driving amplification circuit module.

[0102] The main circuit module comprises a first switch tube Q1, a second switch tube Q2, a third switch tube Q3, a fourth switch tube Q4, a first diode, a second diode, a third diode, a fourth diode, a first parasitic capacitor, a second parasitic capacitor, a third parasitic capacitor, a fourth parasitic capacitor and a charge-discharge inductor L.

[0103] The control end of the first switch tube Q1 inputs a first PWM control signal PWM1, the first end of the first switch tube Q1 is connected with the positive poles of the first diode and the first parasitic capacitor, and the second end of the first switch tube Q1 is connected with the negative poles of the first diode and the first parasitic capacitor.

[0104] The control end of the second switch tube Q2 inputs a second PWM control signal PWM2, the first end of the second switch tube Q2 is connected with the positive pole of the second diode and the second parasitic capacitor, and the second end of the second switch tube Q2 is connected with the negative pole of the second diode and the second parasitic capacitor.

[0105] The control end of the third switch tube Q3 inputs a third PWM control signal PWM3, the first end of the third switch tube Q3 is connected with the positive pole of the third diode and the third parasitic capacitor, and the second end of the third switch tube Q3 is connected with the negative pole of the third diode and the third parasitic capacitor.

[0106] The control end of the fourth switch tube Q4 inputs a fourth PWM control signal PWM4, the first end of the fourth switch tube Q4 is connected with the positive pole of the fourth diode and the fourth parasitic capacitor, and the second end of the fourth switch tube Q4 is connected with the negative pole of the fourth diode and the fourth parasitic capacitor.

[0107] As shown in Figure 2 , the input voltage detection module adopts an operational amplifier TP2412 to perform differential sampling, and the input voltage is reduced by 50 times. When the maximum input voltage is 130V, the maximum voltage output by the differential sampling circuit is 2.6V. At the same time, in order to protect the micro control unit, a diode is added at the ADC sampling interface of the micro control unit to play a clamping protection role, and the purpose is to make the voltage input to the ADC sampling interface not exceed 3.3V.

[0108] As shown in Figure 3 , the output voltage detection module adopts an operational amplifier TP2412 to perform differential sampling, and the input voltage is reduced by 20 times. When the maximum output voltage is 55V, the maximum voltage output by the differential sampling circuit is 2.75V. At the same time, in order to protect the micro control unit, a diode is added at the ADC sampling interface of the micro control unit to play a clamping protection role, and the purpose is to make the voltage input to the ADC sampling interface not exceed 3.3V.

[0109] As shown in Figure 4 , the output current detection module is connected in parallel with two 2mR / 2W current sensing resistors. Through the two current sensing resistors, the maximum current at the output end is 63A, and the power can reach 3000W. Through the INA280 current detection amplifier, the voltage drop on the current sensing resistor can be measured in a wide common mode range of 2.7V to 120V, and the sampling value can be input to the micro control unit.

[0110] As shown in Figure 5 , the four-switch Buck-Boost converter circuit presents three working modes. It is assumed that V i is the input end voltage, V o is the output end nominal voltage, when V i is less than V o -ΔV hWhen V is in Boost mode, the circuit is in Boost mode; when V is in Boost mode, the circuit is in Boost mode. i Greater than V o When V = +ΔVh, the circuit is in Buck mode; when V = +ΔVh, the circuit is in Buck mode. i In V o -ΔV h and V o +ΔV h During this period, the circuit is in Buck-Boost mode; ΔV h It is half the output voltage range of Buck-Boost mode; typically V. o *5% (duty cycle), here we take V o If it is 50, then V o -ΔV h It is 47.5, V o +ΔV h It is 52.5.

[0111] like Figure 6 The diagram shown illustrates the mode switching of a four-switch Buck-Boost converter circuit. The Buck-Boost mode in this circuit was introduced to address voltage fluctuations caused by frequent switching between dual-mode Buck and Boost modes. Buck-Boost mode lies between Buck and Boost modes; the transition from Buck to Boost mode is a Buck-Boost process. The range of Buck-Boost mode depends on the voltage gain M, expressed as:

[0112]

[0113] When the input voltage V i When the voltage is less than 47.5V and D1 is less than 95%, the circuit is in Buck mode. The fourth switch Q4 is not turned on, and its duty cycle D2 is 0. At this time, the voltage gain M is equal to the duty cycle D1 of the first switch Q1. When the input voltage V... i When the voltage is greater than 52.5V and the duty cycle D2 of the fourth switch Q4 is greater than 5%, the circuit is in Boost mode. The second switch Q2 is not turned on, D1 is 1, and the voltage gain M is 1 / (1-D2). When the output voltage range is (47.5V, 52.5V), the circuit is in Buck-Boost mode. In this mode, within one cycle, the first switch Q1 and the fourth switch Q4 turn on first, the inductor stores energy, and the inductor current rises. Then the second switch Q2 and the third switch Q3 turn on, the inductor L discharges, and the inductor current decreases.

[0114] In the ideal mode, without considering the influence of dead zone, the starting process of the four-switch Buck-Boost converter circuit is first in the Buck mode, then switched to the Buck-Boost mode, and finally to the Boost mode without sampling the input voltage. The change of duty cycles D1 and D2 in the Buck-Boost mode is always the focus of research. The relationship between the two can be expressed as:

[0115]

[0116] As can be seen from the formula, there is a linear relationship between the duty cycles D1 and D2. As shown in FIG. 1, the change range of the duty cycles D1 and D2 in the Buck-Boost mode is a quadrilateral region surrounded by two straight lines with a slope of -0.95 and -1.05, so it can be known that the switching mode of the duty cycles D1 and D2 is various, which determines the various strategies of the Buck-Boost mode switching. Figure 6

[0117] As shown in FIG. 1, the present application proposes a new switching strategy of the duty cycles in the Buck-Boost transition mode, which eliminates the jumping process of the working point. The duty cycle is switched along the OA segment to point A in the Buck mode, at this time the duty cycles D1 and D2 are (0.95, 0), then the circuit is switched to the Buck-Boost mode, the duty cycle is switched along the AD segment to point D, at this time the duty cycles D1 and D2 are (1, 0.05), at this time the circuit is switched to the Boost mode, and the duty cycle is switched along the DE segment, so that each segment of the duty cycle is continuous without jumping, which ensures the continuity of the switching of the duty cycle and improves the stability of the circuit. Figure 7

[0118] The AD segment is analyzed, and the linear relationship between the duty cycles D1 and D2 in the AD segment is satisfied:

[0119] D1=D2+0.95;

[0120] In the formula, D1 is the duty cycle of the first switch Q1; D2 is the duty cycle of the fourth switch Q4.

[0121] The voltage gain M is:

[0122]

[0123] The range of the duty cycle D1 is (0.95, 1), and the range of the voltage gain M is (0.95, 1.05).

[0124] As shown in FIG. 1, the present application proposes a new switching strategy of the duty cycles in the Buck-Boost transition mode, which eliminates the jumping process of the working point. The duty cycle is switched along the OA segment to point A in the Buck mode, at this time the duty cycles D1 and D2 are (0.95, 0), then the circuit is switched to the Buck-Boost mode, the duty cycle is switched along the AD segment to point D, at this time the duty cycles D1 and D2 are (1, 0.05), at this time the circuit is switched to the Boost mode, and the duty cycle is switched along the DE segment, so that each segment of the duty cycle is continuous without jumping, which ensures the continuity of the switching of the duty cycle and improves the stability of the circuit. Figure 8 ​​As shown, under local shadow condition, the working voltage and current of shaded assembly are reduced, resulting in multiple local extreme points of P-V curve of photovoltaic cell. The power model of photovoltaic cell is built, and three photovoltaic panels in series are adopted. When the light intensity of series photovoltaic panels is 1000 W / m2, 800 W / m2 and 600 W / m2 respectively, and the environmental temperature is 25℃, the P-V curve is as shown in the figure. Figure 8 At this time, the P-V curve has multiple peaks, and the conventional optimization algorithm cannot meet the multi-peak optimization requirement.

[0125] As shown in the figure, Figure 9 The application adopts the optimized particle swarm algorithm BSPSO (Behavior Strategies Particle Swarm Optimization) to perform maximum power point tracking under local shadow condition. Firstly, the chaos initialization based on reverse strategy is performed, and the improved Tent mapping and quasi-reverse individual are adopted for initialization; then the evolution mutation stage is performed, the mutation factor is introduced, and the range of global optimal solution is further expanded; finally, the optimization convergence stage is entered, the speed term is removed to enter the low-latitude optimization, the convergence speed is accelerated, and the global optimal solution is converged until the global optimal solution is converged; the voltage and current output by the photovoltaic panel are taken as independent variables, the power is taken as target function, and the output duty cycle is controlled to output maximum power.

[0126] (1) Particle swarm algorithm

[0127] The simplified particle swarm PSO (Particle Swarm Optimization) algorithm is a global optimization algorithm based on group intelligence, and the updating formula during particle operation is:

[0128] v n i = wv n-1 i + c1r1 (p n i - x n i ) + c2r2 (g n - x n i );

[0129] x n+1 i = x n i + v n i ;

[0130] In the formula, v n i is the speed of particle i at the n th iteration; w is the inertia weight; v n-1 iis the velocity of particle i after the n-1th iteration; c1 is an individual learning factor; r1 and r2 are random numbers between 0 and 1; p n i is the optimal position after the nth iteration of particle i; c2 is a group learning factor; g n is the optimal position after the nth iteration of all particles; x n i is the position of particle i after the n-1th iteration; x n+1 i is the position of particle i after the nth iteration.

[0131] (2) Chaos initialization

[0132] Chaos is an unstable phenomenon spontaneously generated by a deterministic system, and a chaotic sequence presents regularity, randomness and ergodicity. The PSO population initialization combined with a chaos algorithm has better diversity and convergence. The chaos algorithm mainly generates a new chaotic sequence through nonlinear iteration of a chaotic mapping in the [0, 1] interval. There are various chaotic mappings, such as Logistic mapping, Tent mapping, Circle mapping and Cubic mapping.

[0133] The improved Tent mapping is used for chaos initialization of the particle swarm in the application:

[0134]

[0135] In the formula, x i is the initial position of particle i; x i ～ is the position of particle i after Tent mapping.

[0136] The condition for the chaotic mapping to be established is that it is a full mapping in [0, 1], which is related to s and q, wherein the value of q is a discrete point set:

[0137] q = 2πn n ∈ (-∞, 0) ∪ (0, +∞), n ∈ Z;

[0138] The value of s:

[0139]

[0140] (3) Reverse strategy

[0141] According to the probabilistic principle, each randomly generated candidate solution has a 50% probability of moving away from or moving closer to the optimal solution compared with its reverse solution, and the reverse solution can also be the optimal solution. Selecting the individual closer to the two as the initial population member will accelerate the convergence to a certain extent.

[0142] The quasi-reverse strategy is used for initialization of the particle swarm individual in the application, and the corresponding standard reverse individual position is:

[0143] x i * =2x mid -x i ;

[0144] In the formula, x i * Let x be the position of particle i after passing through the standard inverse individual mapping; mid Center of feasible region; x i Let be the initial position of particle i.

[0145] The quasi-reverse individual position is represented as:

[0146] x i = rand(x mid ,x i * );

[0147] In the formula, x i ' represents the position of particle i after quasi-reverse individual mapping; rand represents a random function of (0, 1).

[0148] The position of the quasi-reflective reverse individual is represented as:

[0149] x i = rand(x i ,x mid );

[0150] In the formula, x i " is the position of particle i after being mapped by the quasi-reflection reverse individual".

[0151] The chaotic initialization based on the inverse strategy is then:

[0152]

[0153] In the formula, x i ^ represents the position of particle i after mapping; sin represents the sin function.

[0154] (4) Evolutionary variation

[0155] While using a chaotic initialization of the particle swarm based on a reverse strategy can accelerate convergence to some extent, the similarity of the particle swarm increases during the optimization process, making it prone to getting trapped in local optimization. To escape local optimization and improve the diversity of the particle swarm, an improvement based on the beetle algorithm optimization concept is proposed.

[0156] Longhorn beetles sense the location of food using their antennae on either side of their heads, even when they don't know its exact location. The concentration of the odor detected varies depending on the distance of the antennae from the food. Based on this difference in odor concentration, the beetle moves towards the food, iterating multiple times until it reaches its destination. The mathematical expression for this is:

[0157]

[0158] In the formula, Let X be a random unit vector representing the orientation of the beetle's whiskers at any given time; rands represents a random vector in the range (-1, 1); Dim represents the spatial dimension; X t+1 X represents the centroid position of the longhorn beetle after n iterations at time t+1; l X r The positions of the left and right antennae of the longhorn beetle; X t Let f represent the centroid position of the bullhead after n iterations at time t; f is the objective function; δt is the iteration step size; and sign is the sign function.

[0159] To improve the global optimization of particle swarm optimization, the particle swarm optimization algorithm formula is improved as follows:

[0160] v n i =wv n-1 i +c1r1(p n i -x n i )+c2r2(g n -x n i )+c3r3(p n i -g n );

[0161] In the formula, c3 is the variation factor; r3 is a random number between 0 and 1;

[0162] The formula introduces the current optimal position of the particle and the group, and regards the positions of the two whiskers as the local optimum of the individual and the local optimum of the group. The distance between the two whiskers represents the distance between the optimal positions of the individual and the group.

[0163] (5) Optimize convergence

[0164] After iteration, when the optimal position found by an individual particle swarm is better than the optimal position obtained by the swarm in the previous iteration, the particle swarm in the vicinity of that individual enters the optimization convergence state. The iterative algorithm is then changed to a simplified particle swarm optimization (PSO) algorithm, which removes the velocity term and accelerates the convergence speed.

[0165] If we only consider the motion of the i-th particle, for the particle swarm update formula, let

[0166]

[0167] then can be changed to:

[0168]

[0169] x(n+1) = x(n) + v(n) ;

[0170] After iteration of the above formula, we get:

[0171]

[0172] The formula is a classical second-order differential equation without velocity term.

[0173] At this time, the deformation is a first-order differential equation:

[0174]

[0175] And the particle swarm optimization equation without velocity term is simplified to:

[0176] x i n = wx i n-1 + c1r1(p i n - x i n ) + c2r2(g n - x i n ) ;

[0177] It can be seen that after removing the velocity term, the particle swarm equation is reduced from a second-order differential equation to a first-order differential equation, which simplifies the iteration process and facilitates the analysis and control of the particle swarm evolution process. The convergence accuracy and convergence speed of the simplified particle swarm algorithm (SPSO, Simple Particle Swarm Optimization) are significantly improved, so using SPSO algorithm in the convergence stage of particle swarm optimization can significantly improve the convergence speed.

[0178] (6) Adaptive parameters

[0179] The values of the three important parameters in the particle swarm algorithm, the inertia weight w, the individual learning factor c1, and the group learning factor c2, have a great influence on the maximum power point tracking process.

[0180] When the inertia weight w is small, the local optimization ability of the particle is improved, and the algorithm converges faster; when the inertia weight w is large, the global optimization ability is improved. For the individual learning factor c1 and the group learning factor c2, the individual learning is more important in the initial stage of the algorithm, and the group learning is more important in the later stage.

[0181] The inertia weight w is updated by using a nonlinear strategy:

[0182]

[0183] In the formula, w1 is the maximum value of the inertia weight w; w2 is the minimum value of the inertia weight w; t is a nonlinear coefficient; k m is the maximum number of iterations; and k is the current iteration number.

[0184] The individual learning factor c1 and the group learning factor c2 are updated by using a trigonometric function formula:

[0185]

[0186] The mutation factor c3 is updated by using a nonlinear strategy:

[0187]

[0188] In the formula, c α = 0.9, and c β = 0.45.

[0189] The flowchart of the optimization algorithm is shown in Figure 9 .

[0190] As shown in Figure 10 , when the local shadow condition occurs, the light intensity suddenly changes, and the P-V curve has multiple local extreme points. In order to verify the global optimization of the BSPSO algorithm, the light intensity of the third photovoltaic panel is changed from 600 W / m 2 to 400 W / m 2 at 0.5 s, and the ability of the algorithm to jump out of the local extreme point and perform global optimization is tested.

[0191] The initial stable time of the BSPSO algorithm is 0.032 s, the initial stable power is 4255 W, the second stable time is 0.517 s, and the second stable power is 2208 W, and the power fluctuation is 787 W. The BSPSO algorithm has a 72% improvement in speed in tracking the maximum power point compared with the PSO algorithm. At the same time, the BSPSO algorithm can jump out of the local optimal point. Under the condition of disturbance, the BSPSO algorithm has a decrease in the recovery stable state time and the fluctuation amplitude, and the algorithm has a relatively obvious improvement.

[0192] The programmable power supply, the four-switch Buck-Boost converter circuit board, the battery, and the load instrument are connected, the maximum power point is set to 77 V, 14.3 A, and 1100 W, the shadow state is simulated at 16 s, the light intensity is reduced by half, the normal state is restored at 26 s, and then the algorithm is tested and optimized, as shown in Figure 11As shown, the real-time tracking state of voltage, current, power and maximum power point tracking efficiency, it can be known that the lowest efficiency is above 90%, due to the sudden drop of light intensity, the efficiency is reduced, the efficiency of other time periods is above 98%, the average efficiency is above 96%, the algorithm has good optimization.

[0193] In summary, the present application has the following advantages and beneficial effects.

[0194] (1) The present application increases the input voltage range of photovoltaic panel, by adopting four-switch Buck-Boost converter circuit instead of traditional Buck circuit, Boost circuit or Buck-Boost circuit, the input voltage range of photovoltaic panel is expanded to 15-130V.

[0195] (2) The present application adopts MOSFET switch tube instead of traditional diode, through PWM control of MOSFET switch tube conduction and turn-off, there is no diode conduction voltage drop and conduction loss, the circuit power conversion efficiency is improved.

[0196] (3) The micro control unit of the present application adopts higher precision and reliability dsPIC chip, has high performance and flexible peripherals, has strong performance in asynchronous event processing ability, precise simulation and other aspects, improves the precision and reliability of the circuit.

[0197] (4) The present application increases the transition mode Buck-Boost mode between Buck and Boost mode, smoothens the transition process, the duty ratio switching is stable without jump, reduces the device loss, and improves the circuit stability.

[0198] (5) The present application adopts intelligent improved particle swarm algorithm instead of conductance increment method and disturbance observation method, further improves the maximum power point tracking effect of photovoltaic panel under shading state, the output power is higher, and the maximum power point tracking effect of the circuit is improved.

[0199] The above description is a detailed description of the preferred embodiment of the present application, but the embodiment is not used to limit the scope of the patent application of the present application, any equivalent changes or modifications made under the technical spirit disclosed by the present application should belong to the patent scope covered by the present application.

Claims

1. A Buck-Boost converter circuit for photovoltaic maximum power point tracking, characterized by, The main circuit module, the input voltage detection module, the output voltage detection module, the output current detection module, the control output module and the driving amplifier circuit module are included, the control output module includes a micro control unit, and the micro control unit has an ADC sampling interface and a PWM output interface; The input ends of the main circuit module and the input voltage detection module are connected with the photovoltaic panel, the output ends of the main circuit module are connected with the input ends of the output voltage detection module and the output current detection module and the battery, and the output ends of the input voltage detection module, the output voltage detection module and the output current detection module are connected with the ADC sampling interface; The main circuit module is used for processing the 15-130V input voltage of the photovoltaic panel and outputting the direct current to charge the battery, the input voltage detection module, the output voltage detection module and the output current detection module are used for sampling the input voltage, the output voltage and the output current of the main circuit module and transmitting the sampling values to the micro control unit, the micro control unit is used for judging the mode of the main circuit module according to the sampling values, outputting the PWM control signal through the PWM output interface, and controlling the conduction and the shutdown of the switch tube of the main circuit module through the driving amplifier circuit module by improving the maximum power point tracking algorithm; The main circuit module includes a first switch tube, a second switch tube, a third switch tube, a fourth switch tube, a first diode, a second diode, a third diode, a fourth diode, a first parasitic capacitor, a second parasitic capacitor, a third parasitic capacitor, a fourth parasitic capacitor and a charge and discharge inductor; The control end of the first switch tube inputs the first PWM control signal, the first end of the first switch tube is connected with the positive pole of the first diode and the first parasitic capacitor, and the second end of the first switch tube is connected with the negative pole of the first diode and the first parasitic capacitor; The control end of the second switch tube inputs the second PWM control signal, the first end of the second switch tube is connected with the positive pole of the second diode and the second parasitic capacitor, and the second end of the second switch tube is connected with the negative pole of the second diode and the second parasitic capacitor; The control end of the third switch tube inputs the third PWM control signal, the first end of the third switch tube is connected with the positive pole of the third diode and the third parasitic capacitor, and the second end of the third switch tube is connected with the negative pole of the third diode and the third parasitic capacitor; The control end of the fourth switch tube inputs the fourth PWM control signal, the first end of the fourth switch tube is connected with the positive pole of the fourth diode and the fourth parasitic capacitor, and the second end of the fourth switch tube is connected with the negative pole of the fourth diode and the fourth parasitic capacitor. The BSPSO is used for tracking the maximum power point under the local shadow condition, specifically: Firstly, the chaos initialization based on the reverse strategy is carried out, the improved Tent mapping is adopted to initialize the quasi-reverse individual; then, the evolution mutation stage is carried out, the mutation factor is introduced to further expand the range of the global optimal solution; finally, the optimization convergence stage is entered, the speed term is removed to enter the low-latitude optimization, the convergence speed is accelerated, and the global optimal solution is converged until the global optimal solution is converged; the voltage and the current output by the photovoltaic panel are taken as the independent variables, the power is taken as the target function, and the PWM duty ratio is output to control the maximum output power.

2. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 1, characterized in that, The input voltage detection module adopts operational amplifier TP2412 to carry out differential sampling, and the input voltage is reduced by 50 times. When the maximum input voltage is 130V, the maximum output voltage of the differential sampling circuit is 2.6V.

3. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 1, characterized in that, The output voltage detection module adopts operational amplifier TP2412 to carry out differential sampling, and the input voltage is reduced by 20 times. When the maximum output voltage is 55V, the maximum output voltage of the differential sampling circuit is 2.75V.

4. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 1, characterized in that, The output current detection module is connected in parallel with two 2mR / 2W current detection resistors. The maximum output current is 63A, and the power reaches 3000W. The voltage drop on the current detection resistor is measured by the INA280 current detection amplifier in a wide common mode range of 2.7V to 120V, and the sampling value is input to the micro control unit.

5. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 1, characterized by, The Buck-Boost converter circuit has three working modes, which are: Let V i be the input voltage, V o the nominal output voltage, when V i is less than V o -ΔV h the circuit is in Boost mode; when V i is greater than V o +ΔV h the circuit is in Buck mode; when V i is between V o -ΔV h and V o +ΔV h the circuit is in Buck-Boost mode; ΔV h is half the range of output voltage in Buck-Boost mode. The Buck-Boost mode of the Buck-Boost converter circuit is between the Buck mode and the Boost mode. When the circuit transits from the Buck mode to the Boost mode, the intermediate process is the Buck-Boost mode; The Buck-Boost mode range depends on the voltage gain M, which is represented as: In the formula, D1 is the duty cycle of the first switch tube; D2 is the duty cycle of the fourth switch tube; The relationship between D1 and D2 is represented as: Let D1 and D2 satisfy the linear relationship: D1=D2+0.95; Thus, the voltage gain M is:

6. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 1, characterized by, The chaos initialization process of the optimized particle swarm algorithm BSPSO based on the reverse strategy includes: The simplified particle swarm optimization algorithm PSO is a global optimization algorithm based on swarm intelligence. The update formula during the particle working process is: v n i = wv n-1 i + c1r1(p n i - x n i + c2r2(g n - x n i ); x n+1 i = x n i + v n i ; where v n i is the velocity of particle i at iteration n; w is the inertia weight; v n-1 i is the velocity of particle i at iteration n-1; c1 is the individual learning factor; r1, r2 are random numbers between 0 and 1; p n i is the best position of particle i after n iterations; c2 is the group learning factor; g n is the best position of all particles after n iterations; x n i is the position of particle i at iteration n; x n+1 i is the position of particle i at iteration n+1; The improved Tent mapping is used for particle swarm chaos initialization: where x i is the initial position of particle i; x i ～ is the position of particle i after the Tent mapping. The condition for this chaos mapping to hold is that it is a full mapping in [0, 1], which is related to s and q, where the value of q is a discrete point set: q=2πN N∈(-∞,0)∪(0,+∞),N∈Z; The value of s is: The particle swarm individual initialization using the quasi-reverse strategy is: where x i is the mapped position of particle i; x mid is the center of the feasible region, and rand denotes a random function of (0, 1); sin denotes the sin function.

7. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 6, characterized in that, The evolution mutation stage process of the optimized particle swarm algorithm BSPSO includes: Although the particle swarm initialized based on the reverse strategy can speed up the convergence speed, the similarity of the particle swarm increases during the optimization process, which easily falls into local optimization. In order to jump out of local optimization and improve the diversity of the particle swarm, the optimization idea of the beetle algorithm is used for improvement; The beetle can sense the smell concentration difference by the two antennae on its head, and can move towards the food direction according to the smell concentration difference. After multiple iterations, it reaches the food position. The mathematical expression is: where, is a random unit vector, indicating the orientation of the beetle's antennae at any time; randsis a random vector in (-1, 1); Dim represents the spatial dimension; X t+1 represents the centroid position of the beetle after n iterations at time t+1; X l , X r represents the position of the beetle's left and right antennae; X t represents the centroid position of the beetle after n iterations at time t; f function is the objective function; δt is the iteration step size; sign is the sign function; In order to improve the global optimization of the particle swarm, the particle swarm algorithm formula is improved: v n i = wv n-1 i + clrl(p n i - x n i ) + c2r2(g n - x n i + c3r3(p n i - g n ); In the formula, c3 is the mutation factor; r3 is a random number between 0 and 1. In the formula, the current particle and the optimal position of the group are introduced, the positions of the two horns are regarded as the individual local optimum and the group local optimum, and the distance between the two horns represents the distance between the individual and the group optimal position.

8. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 7, characterized in that, The optimization convergence stage process of the BSPSO optimization particle swarm algorithm includes: After iteration, when the best position found by the particle swarm individual is better than the best position obtained by the group in the last iteration, the particle swarm in the vicinity of the individual enters the optimization convergence state, the iteration algorithm is converted, the simplified particle swarm PSO algorithm is adopted, the speed term is removed, and the convergence speed is accelerated; After removing the speed term, the particle swarm equation is reduced from a second-order differential equation to a first-order differential equation, the iteration process is simplified, and the particle swarm evolution process is facilitated for analysis and control.

9. The Buck-Boost converter circuit for photovoltaic maximum power point tracking according to claim 1, characterized by, The adaptive parameter process of the BSPSO optimization particle swarm algorithm includes: The inertia weight w is updated using a nonlinear strategy: In the formula, w1 is the maximum value of the inertia weight w; w2 is the minimum value of the inertia weight w; h is a nonlinear coefficient; k m is the maximum number of iterations set; k is the current iteration number; The individual learning factor c1 and the group learning factor c2 are updated using a trigonometric function formula: The mutation factor c3 is updated using a nonlinear strategy: In the formula, c α =0.9, c β =0.45.

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