CN110737302A - MPPT control method based on photovoltaic power generation system resistance matching - Google Patents
MPPT control method based on photovoltaic power generation system resistance matching Download PDFInfo
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- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
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Abstract
The invention discloses an MPPT control method based on photovoltaic power generation system resistance matching, wherein a fuzzy MPPT controller samples a voltage value and a current value in a photovoltaic array power generation process in real time through a voltage sampling and current sampling circuit, generates a PWM control signal through calculation and processing of an MPPT fuzzy control algorithm on the fuzzy MPPT controller, controls a Boost booster circuit to work through the PWM control signal, adjusts the duty ratio of a direct current chopper circuit, realizes control on load impedance matching, and enables the internal resistance of a photovoltaic power generation system circuit to be matched with the load impedance at the fastest speed.
Description
Technical Field
The invention relates to the field of photoelectric power generation, in particular to MPPT control methods based on photovoltaic power generation system resistance matching.
Background
The power output of the photovoltaic array is in a nonlinear relation with voltage and current, however, under the conditions of determined illumination intensity and temperature, Maximum power points exist in the output of the photovoltaic array, and thus Maximum Power Point Tracking (MPPT) needs to be performed under different conditions by adopting dynamic control.
The MPPT control is essentially optimization processes, the position relation of a current working point and a peak point is determined by measuring voltage, current and power, comparing the change conditions of the voltage, the current and the peak power, and finally the current (or the voltage) is controlled to swing back and forth in a fixed range of near the peak power.
The MPPT control method adopting the fuzzy control algorithm can also control the circuits of the photovoltaic array power generation system to start power generation under the conditions of weaker illumination intensity, smaller circuit series resistance and reduced control circuit self consumption, so that the photovoltaic power generation system can be realized to sleep early and late, the power generation time of the photovoltaic power generation system can be prolonged, and the power generation capacity of the photovoltaic power generation system can be improved.
Disclosure of Invention
The invention relates to MPPT control methods based on photovoltaic power generation system resistance matching, which are designed for solving the technical problems.
The technical scheme adopted by the invention for solving the technical problems is as follows:
MPPT control method based on photovoltaic power generation system resistance matching comprises a photovoltaic power generation system, a fuzzy MPPT controller, a voltage sampling, current sampling, driving and Boost circuit, wherein the voltage sampling and current sampling circuit is connected with the output end of a photovoltaic array, the fuzzy MPPT controller samples the voltage value and the current value in the photovoltaic array power generation process in real time through the voltage sampling and current sampling circuit, and generates a PWM (pulse width modulation) control signal through calculation and processing of an MPPT fuzzy control algorithm on the fuzzy MPPT controller, the PWM control signal controls the Boost circuit to work, the duty ratio of a direct current chopper circuit is adjusted, control over load impedance is achieved, the load impedance is enabled to be close to the internal resistance value of a power supply at the fastest speed, when the load impedance is equal to the internal resistance value of the power supply, the load can obtain the maximum power, and the photovoltaic cell works at the maximum power point, so that the maximum power tracking control is achieved.
According to the MPPT control method based on photovoltaic power generation system resistance matching, the Boost circuit is formed by an -pole driving circuit and comprises a switching tube Q, a capacitor C, an inductor L and a diode VD, a fuzzy MPPT controller is connected with a base electrode of the switching tube Q through the driving circuit, the output voltage of a photovoltaic array is in a range of 200V-1000V, the fuzzy MPPT controller controls a power generation system to normally work, and the photovoltaic power generation system is enabled to output near the maximum power.
According to the MPPT control method based on photovoltaic power generation system resistance matching, a Boost voltage boosting circuit is a voltage boosting circuit formed by a diode driving circuit and comprises switching tubes Q1 and Q2, capacitors C1, C2, C3 and C4, inductors L1 and L2, and diodes VD1 and VD 2; the emitter of the switching tube Q1 is connected with the collector of the switching tube Q2 in series, the fuzzy MPPT controller is connected with the bases of the switching tube Q1 and the switching tube Q2 through a driving circuit respectively, the output voltage of the photovoltaic array is within the range of 200V-1000V, the fuzzy MPPT controller controls the power generation system to work normally, and the photovoltaic power generation system is enabled to output near the maximum power.
According to the MPPT control method based on photovoltaic power generation system resistance matching, a fuzzy MPPT controller is industrial personal computers provided with MPPT fuzzy control algorithms.
The MPPT fuzzy control algorithm of the MPPT control method based on photovoltaic power generation system resistance matching is that processes of continuous measurement and continuous adjustment are carried out to achieve the optimal process, the setting value of a controllable parameter is continuously changed in the operation process, so that the current working point gradually approaches to a peak power point, and the photovoltaic system operates at the peak power point, wherein the controllable parameter is input variable deviation E and input variable deviation change rate A.
In the MPPT control method based on photovoltaic power generation system resistance matching, the input variable deviation E and the input variable deviation change rate A are calculated; inputting the last step quantity A (n-1); outputting the step length A (n); linguistic variables E and a are defined as 8 and 6 fuzzy subsets, respectively, namely:
E＝{NB，NM，NS，N0，P0，PS，PM，PB)
A＝{NB，NM，NS，PS，PM，PB}
in the formula: NB, NM, NS, N0, P0, PS, PM, PB represent fuzzy concepts of negative large, negative medium, negative zero, positive small, medium, positive large, etc., respectively, and their domains are specified in 14 and 12 levels, namely:
E＝{-6，-5，-4，-3，-2，-1，-0，+0，+1，+2，+3，+4，+5，+6)
A＝{-6，-5，-4，-3，-2，-1，+1，+2，+3，+4，+5，+6}
e (n) in the fuzzy self-optimizing controller represents the actual value of the difference between the output power at the nth moment and the output power at the (n-1) th moment; e (n) indicates that this difference corresponds to a value in the ambiguity set theory domain; a (n) represents an actual value of the step at the nth time; a (A)n) indicates that this step value corresponds to a value in the ambiguity set theory domain; ke、KaAre quantization factors respectively;
analyzing a characteristic curve between the output P and the duty ratio D of the photovoltaic cell, and considering the influence of external environmental factors on the output power of the photovoltaic cell, adjusting the actual simulation result to obtain a final control rule;
the fuzzy logic controller adopts a gravity center method for calculation, and the calculation formula is as follows:
in the formula: u (ai) is the membership of the ith fuzzy output; a is the ith fuzzy output.
According to the MPPT control method based on photovoltaic power generation system resistance matching, the time interval between the nth time and the (n-1) th time is an integer value between 1 and 1000 microseconds.
Principle of operation
1 mathematical model and output electrical characteristics of photovoltaic cell
The photovoltaic cell is equivalent to a large-area equivalent diode with an extremely thin PN section parallel to the light receiving surface, the I-V characteristic is closely related to the illumination intensity and the temperature of the photovoltaic cell, and the principle of the equivalent circuit of the photovoltaic cell adopting crystalline silicon as a material is shown in figure 1:
in fig. 1: u shapejIs a PN junction voltage, IdIs the saturation current of the photovoltaic cell in the absence of light,
ideal solar cells due to series resistance RsVery small, shunt resistance RshIt is very large, so it can be ignored when calculating the ideal circuit.
The I-V relation is obtained by the equivalent circuit of the solar photovoltaic cell in the figure 1, and the expression is as follows:
in the formula: i is the output current of the photovoltaic cell; i is0Is the reverse saturation current of the PN junction; i isphIs a photo-generated current; v is the output voltage of the photovoltaic cell; q is unit charge (1.6X 10)-19C) (ii) a k is Boltzmann constant, k is 1.38 × 10-23J/K, T is the thermodynamic temperature, N is the constant of the curve of the N junction, is between 1 and 2 (N is 1 when the positive bias is large and N is 2 when the positive bias is small), Rs、RshIs the intrinsic resistance of the photovoltaic cell itself.
When the load is changed from 0 to infinity, the output characteristic curve of the solar cell as shown in FIG. 2 can be obtained, and the load resistance is adjusted to a certain value RMWhen the voltage is zero, points M are obtained on the curve, and the product of the corresponding working voltage and the working current is maximum, namely Pm＝ImVm. This M point is now defined as the maximum power output point (MPP).
As can be seen from the mathematical model of the photovoltaic cell, the photovoltaic cell is influenced by temperature and illumination intensity, and the output current and the voltage of the photovoltaic cell have obvious nonlinear characteristics.
2 photovoltaic array mathematical model
Therefore, a mathematical model of the photovoltaic array is obtained based on a model of the photovoltaic cells, and it is generally assumed that all the series-parallel photovoltaic cells have the same characteristic parameters, the connection resistance between the photovoltaic cells is ignored, and they are assumed to have ideal consistency.
Obtaining an I-V equation of the photovoltaic array according to the I-V relation of the photovoltaic cell:
therefore, the silicon solar cell engineering mathematical model only uses 4 electrical parameters under standard test conditions provided by the photovoltaic cell manufacturer: short-circuit current IscOpen circuit voltage VocOutput current I at maximum power pointmOutput voltage V at maximum power pointmThen the new illumination intensity and the new I under the battery temperature at any moment can be calculatedsc′、Voc′、Im′、Vm', so that new lighting conditions and new electrical characteristics at the battery temperature can be obtained:
Voc′＝Voc(1-cΔT)ln(e+bΔS)
Vm′＝Vm(1-cΔT)ln(e+bΔS)
in the formula: t ═ Tair+KS，ΔT＝T-Tref，
a. b, c typical values: a 0.00255/deg.C, b 0.55/deg.C, c 0.00285/deg.C; srefFor reference illumination intensity, 1000W/m2；T ref25 ℃ for reference cell temperature; t isairIs ambient temperature in units of; s is the illumination intensity with the unit of W/m2(ii) a T is the temperature of the solar cell and the unit is; delta S is the difference between the actual illumination intensity and the reference illumination intensity, W/m2(ii) a Δ T is the difference between the actual battery temperature and the reference battery temperature; k is the temperature coefficient of the solar cell when the illumination changes, and adopts the typical value of 0.03 ℃ and W/m2。
The variation range of the illumination intensity on the ground is 0-1000W/m2The variation range of the battery temperature is 10-70 ℃. Therefore, the illumination intensity is 0 to 1000W/m2And simulating the maximum power point in a changing environment with the battery temperature of 10-70 ℃.
The simulation shows that: in the starting process of the photovoltaic power generation system, the series resistance R of the systems are continuously enlarged and tend to be a stable change value, so that the photovoltaic power generation system can be designed according to the requirementsWhen the circuit is optimally matched and configured by the inherent characteristic resistors of all the equipment of the photovoltaic power generation unit, the simulation optimization technologies such as self-adaptive neural fuzzy inference and BP neural network algorithm are applied, so that the photovoltaic power generation unit can be started and operated under the condition of extremely weak light: the voltage is greater than the voltage threshold, the electric energy converted from solar energy is greater than the circuit self-consumption, the sleep early and late is realized, the solar energy is utilized to the maximum extent, the power generation time is prolonged, the efficiency of the photovoltaic power generation system is improved, and the power generation capacity of the photovoltaic power generation system is improved.
3 maximum power point tracking of photovoltaic systems
The Maximum Power Point Tracking (MPPT) system for photovoltaic power generation has the advantages that when the load is equal to the internal resistance of a power supply, the load can obtain the maximum power, but the internal resistance of a photovoltaic cell is influenced by the sunlight intensity and the ambient temperature and can change along with the change of the external environment, so the optimal load size cannot be determined, direct current chopper circuits are added between the photovoltaic cell and the load, the duty ratio of the direct current chopper circuits is adjusted, the equivalent load size can be adjusted, the photovoltaic cell is controlled to work at the maximum power point, and the maximum power tracking control is realized.
In a photovoltaic power generation system, a direct current chopper circuit mainly adjusts the output voltage to meet the system requirement, and then adjusts the working voltage of a photovoltaic array to realize maximum power tracking.
The direct current chopper circuit (DC-DC converter) can convert direct current voltage and current into voltage and current with different amplitudes by adjusting the switching tube, and the Boost booster circuit can keep working in an inductive current continuous state, so that the conversion efficiency is slightly influenced by a duty ratio, and the Boost booster circuit is mainly used as a maximum power tracking main circuit.
For a resistive load, the intersection of its load line with the I-V curve determines the operating point of the photovoltaic power generation system. Different loads determine different operating points. Therefore, under the conditions of different temperatures and different sunshine intensities, when the maximum power point drifts, the photovoltaic power generation system can work at the maximum power point again by adjusting the load.
Regarding the maximum power tracking algorithm of the photovoltaic power generation system, many documents have proposed various methods such as a voltage feedback method, a disturbance observation method, a power feedback method, a straight line approximation method, an actual measurement method and an incremental conductance method, fig. 4 is -like photovoltaic power generation system structure, a duty ratio disturbance method is adopted, and an MPPT controller adjusts an input/output relationship by adjusting a duty ratio D of a PWM signal, so as to achieve an impedance matching function.
However, under the condition of complex environmental changes of the photovoltaic module, the methods cannot track immediately and react quickly. The conventional method can only converge to the local highest operating point, but is not the true highest point of the P-V curve.
The MPPT is based on fuzzy control and is processes of continuous measurement and continuous adjustment to achieve the optimization, an accurate mathematical model of the photovoltaic array is not needed to be known, and the setting value of a controllable parameter is continuously changed in the operation process, so that the current working point is gradually close to the peak power point, and the photovoltaic system operates at the peak power point.
4 MPPT implementation based on fuzzy control
4.1 fuzzy control philosophy
Fuzzy control is based on fuzzy logic, which is closer to human thinking and language expression than traditional logic systems, and is often superior to conventional control in complex systems, especially where qualitative imprecision and uncertain information exists.
The fuzzy control is kinds of computer digital control technology, it is based on fuzzy set theory, fuzzy linguistic variable and fuzzy logic reasoning, and the deviation e and deviation change rate delta e of the controlled object are input variables, the controlled quantity is output variable, the fuzzy quantification and its algorithm structure of the input/output variables and control rule are obtained by fuzzy logic reasoning control, the applied process is that the logic controller firstly makes the fuzzy reasoning and fuzzy decision of the collected control information through the linguistic control rule, thus the fuzzy set of the control quantity is obtained, then the precise quantity of the output control is obtained by fuzzy judgment, and then it is used in the controlled object, finally the controlled object achieves the expected control effect, the fuzzy control system working flow is shown in figure 5.
The fuzzy control system controls the process control according to the output error and the error change, and it first converts the actual measured precision quantity error e and the error change Δ e into fuzzy quantity through fuzzy processing, and defines the error and the error change as:
e＝yr—yk
Δe＝ek—ek-1
in the formula: y isrAnd ykRespectively representing the process outputs of the set value and the k moment; e.g. of the typekIs the output error at time k. These quantities are used to calculate fuzzy control rules, which are then converted into precise quantities to control the process.
4.2 design of fuzzy controller
The design of the fuzzy logic controller mainly comprises the following items:
(1) determining input variables and output variables of a fuzzy controller;
(2) generalizing and summarizing the control rules of the fuzzy controller;
(3) determining fuzzification and defuzzification methods;
(4) a domain of discourse is selected and the relevant parameters are determined.
The maximum working point of the solar cell is mainly influenced by the ambient temperature and the sunshine intensity under the condition of constant load , while the internal resistance of the solar cell is mainly influenced by the working point of the load.
4.3Boost voltage-boosting circuit
The fuzzy logic control and the PWM jointly generate pulses for a driver control switch, so that the driver control switch is controlled, and the output voltage expression is as follows:
in the formula: t issFor the control period of the switching tube, toffD is the duty ratio of the conduction of the switching tube T for the continuous turn-off time of the switch in each control period. The circuit topology is as shown in fig. 8.
Under different sunshine intensities and environment temperatures, the maximum power points of the solar photovoltaic power generation system are different, maximum power point tracking is achieved by using a fuzzy self-optimization method, a target function is taken as the output power of a photovoltaic array, and the controllable quantity is used for controlling the duty ratio of a PWM signal of a Boost circuit.
4.4 input/output quantity fuzzy subsets and domains of discourse
Input variables of the fuzzy controller: an input variable deviation E and an input variable deviation change rate A; inputting the last step quantity A (n-1); and outputting the step size A (n). Linguistic variables E and a are defined as 8 and 6 fuzzy subsets, respectively, namely:
E＝{NB，NM，NS，N0，P0，PS，PM，PB)
A＝{NB，NM，NS，PS，PM，PB}
in the formula: NB, NM, NS, N0, P0, PS, PM, PB represent fuzzy concepts of negative large, negative medium, negative zero, positive small, medium, positive large, etc., respectively, and their domains are specified in 14 and 12 levels, namely:
E＝{-6，-5，-4，-3，-2，-1，-0，+0，+1，+2，+3，+4，+5，+6)
A＝{-6，-5，-4，-3，-2，-1，+1，+2，+3，+4，+5，+6}
4.5MPPT fuzzy control algorithm
In fig. 6, e (n) represents an actual value of the difference between the output power at the nth time and the output power at the n-1 st time; e (n) indicates that this difference corresponds to a value in the ambiguity set theory domain; a (n) represents an actual value of the step at the nth time; a (n) indicates that this step value corresponds to a value in the ambiguity set theory domain; ke、KaRespectively, quantization factors.
The final control rule obtained by adjusting the actual simulation result by analyzing the characteristic curve between the photovoltaic cell output P and the duty ratio D and considering the influence of the external environmental factors on the photovoltaic cell output power is shown in table 1.
Table 1: MPPT fuzzy control rule
4.6 deblurring method
The fuzzy logic controller adopts a gravity center method for calculation, and the calculation formula is as follows:
in the formula: u (ai) is the membership of the ith fuzzy output; a is the ith fuzzy output.
Through experimental simulation, the MPPT output power result is shown in fig. 7.
When the duty ratio is controlled through MPPT fuzzy control, the maximum power point can be quickly tracked, the nonlinearity and time lag of a photovoltaic array are effectively overcome, the maximum power point can be quickly tracked, and the maximum power point is kept in the state. The fuzzy control can also realize off-line design, save the internal storage space of the microcomputer and improve the working speed.
The intelligent string inverter selected and matched through test simulation optimization of the photovoltaic array power generation system adopts a bipolar topological structure, the output voltage of a component passes through a BOOST direct-current booster circuit, and when the direct-current input voltage is low, the BOOST can meet the requirement of bus capacitance, so that the MPPT working voltage range of the photovoltaic array power generation system is 200V-1000V, and the MPPT working voltage range is V-1000V in contrast, the centralized inverter adopts a single-stage topological structure, and as shown in figure 10, the relation curve diagram of the working start-stop time and the voltage of the photovoltaic power generation system and the existing photovoltaic power generation system is embodied, wherein T2 is the time of the string inverter, the sleep is early and late, and T1 is the time of the centralized inverter.
The invention has the advantages that through photovoltaic array simulation and fuzzy control, the circuit of the photovoltaic array power generation system can start power generation under the conditions of extremely weak illumination intensity, small circuit series resistance and small circuit self-consumption, thereby realizing the purpose that the photovoltaic power generation system sleeps early and late, prolonging the power generation time of the photovoltaic power generation system and improving the power generation capacity of the photovoltaic power generation system. And enabling the current working point to gradually approach to the peak power point through an MPPT fuzzy control algorithm, and enabling the photovoltaic system to operate at the peak power point.
Drawings
FIG. 1 is a schematic diagram of an equivalent circuit of a photovoltaic cell;
FIG. 2 is a graph of solar cells I-V and P-V;
FIG. 3 is a schematic diagram of an equivalent circuit of a photovoltaic array;
FIG. 4 is a functional block diagram of a photovoltaic power generation system, generally designated ;
FIG. 5 is a flow chart of a fuzzy control system;
FIG. 6 is a block diagram of a fuzzy self-optimizing controller;
FIG. 7 is a tracking waveform diagram of the fuzzy control output power P;
FIG. 8 is a schematic diagram of a Boost circuit topology of the photovoltaic power generation system of the present invention;
fig. 9 is a schematic diagram of a Boost two-stage driving circuit of the photovoltaic power generation system of the present invention;
FIG. 10 is a graph showing the relationship between the on-off time and the voltage of the photovoltaic power generation system of the present invention and the existing photovoltaic power generation system.
Detailed Description
The invention is further illustrated in the following description with reference to the figures and examples.
As shown in fig. 8 and 9, MPPT control methods based on photovoltaic power generation system attribute matching include a photovoltaic power generation system, a fuzzy MPPT controller, a voltage sampling, current sampling, driving and Boost voltage-boosting circuit, where the voltage sampling and current sampling circuit is connected to an output end of a photovoltaic array, the fuzzy MPPT controller samples a voltage value and a current value in a power generation process of the photovoltaic array in real time through the voltage sampling and current sampling circuit, and generates a PWM (pulse width modulation) control signal through calculation and processing of a fuzzy control algorithm on the fuzzy MPPT controller, and the PWM control signal controls the Boost voltage-boosting circuit to operate, adjusts a duty ratio of a dc chopper circuit, thereby realizing control of load impedance attribute matching, so that load impedance is the fastest close to an internal resistance value of a power supply, and when the load impedance is equal to the internal resistance value of the power supply, a load can obtain maximum power, and a photovoltaic cell operates at a maximum power point, thereby realizing maximum power tracking control.
As shown in fig. 8, in the MPPT control method based on photovoltaic power generation system resistance matching, the Boost circuit is a Boost circuit formed by an -pole driving circuit, and includes a switching tube Q, a capacitor C, an inductor L and a diode VD, the fuzzy MPPT controller is connected to the base of the switching tube Q through the driving circuit, so that the output voltage of the photovoltaic array is within a range of 200V-1000V, and the fuzzy MPPT controller controls the power generation system to normally work, thereby ensuring that the photovoltaic power generation system outputs near the maximum power.
As shown in fig. 9, in the MPPT control method based on resistance matching of the photovoltaic power generation system, the Boost circuit is a Boost circuit formed by a diode driving circuit, and includes switching tubes Q1 and Q2, capacitors C1, C2, C3 and C4, inductors L1 and L2, and diodes VD1 and VD 2; the emitter of the switching tube Q1 is connected with the collector of the switching tube Q2 in series, the fuzzy MPPT controller is connected with the bases of the switching tube Q1 and the switching tube Q2 through a driving circuit respectively, the output voltage of the photovoltaic array is within the range of 200V-1000V, the fuzzy MPPT controller controls the power generation system to work normally, and the photovoltaic power generation system is enabled to output near the maximum power.
According to the MPPT control method based on photovoltaic power generation system resistance matching, a fuzzy MPPT controller is industrial personal computers provided with MPPT fuzzy control algorithms.
The MPPT fuzzy control algorithm of the MPPT control method based on photovoltaic power generation system resistance matching is that processes of continuous measurement and continuous adjustment are carried out to achieve the optimal process, the setting value of a controllable parameter is continuously changed in the operation process, so that the current working point gradually approaches to a peak power point, and the photovoltaic system operates at the peak power point, wherein the controllable parameter is input variable deviation E and input variable deviation change rate A.
In the MPPT control method based on photovoltaic power generation system resistance matching, the input variable deviation E and the input variable deviation change rate A are calculated; inputting the last step quantity A (n-1); outputting the step length A (n); linguistic variables E and a are defined as 8 and 6 fuzzy subsets, respectively, namely:
E＝{NB，NM，NS，N0，P0，PS，PM，PB)
A＝{NB，NM，NS，PS，PM，PB}
in the formula: NB, NM, NS, N0, P0, PS, PM, PB represent fuzzy concepts of negative large, negative medium, negative zero, positive small, medium, positive large, etc., respectively, and their domains are specified in 14 and 12 levels, namely:
E＝{-6，-5，-4，-3，-2，-1，-0，+0，+1，+2，+3，+4，+5，+6)
A＝{-6，-5，-4，-3，-2，-1，+1，+2，+3，+4，+5，+6}
e (n) in the fuzzy self-optimizing controller represents the actual value of the difference between the output power at the nth moment and the output power at the (n-1) th moment; e (n) indicates that this difference corresponds to a value in the ambiguity set theory domain; a (n) represents an actual value of the step at the nth time; a (n) indicates that this step value corresponds to a value in the ambiguity set theory domain; ke、KaAre quantization factors respectively;
analyzing a characteristic curve between the output P and the duty ratio D of the photovoltaic cell, and considering the influence of external environmental factors on the output power of the photovoltaic cell, adjusting the actual simulation result to obtain a final control rule;
the fuzzy logic controller adopts a gravity center method for calculation, and the calculation formula is as follows:
in the formula: u (ai) is the membership of the ith fuzzy output; a is the ith fuzzy output.
According to the MPPT control method based on photovoltaic power generation system attribute resistance matching, the time interval between the nth time and the n-1 th time is an integer value between 1 microsecond and 1000 microseconds.
The present invention is not limited to the above-mentioned preferred embodiments, and any other products similar or identical to the present invention, which can be obtained by anyone based on the teaching of the present invention, fall within the protection scope of the present invention.
Claims (7)
- The MPPT control method based on photovoltaic power generation system resistance matching is characterized in that the voltage sampling and current sampling circuit is connected with the output end of a photovoltaic array, the fuzzy MPPT controller samples the voltage value and the current value in the photovoltaic array power generation process in real time through the voltage sampling and current sampling circuit, and then the PWM control signal generated by calculation and processing of an MPPT fuzzy control algorithm on the fuzzy MPPT controller controls the Boost circuit to work through the PWM control signal, the duty ratio of a direct current chopper circuit is adjusted, the control of load impedance matching is achieved, the internal resistance of a photovoltaic power generation system circuit is enabled to be matched with the load impedance fastest, when the load impedance is matched with the internal resistance of a power supply, the load can obtain the maximum power, and the photovoltaic cell works at the maximum power point, so that the maximum power tracking control is achieved.
- 2. The MPPT control method based on photovoltaic power generation system resistance matching according to claim 1 is characterized in that the Boost circuit is a voltage boosting circuit formed by an -pole driving circuit and comprises a switching tube Q, a capacitor C, an inductor L and a diode VD, a fuzzy MPPT controller is connected with a base electrode of the switching tube Q through the driving circuit, the output voltage of a photovoltaic array is within a range of 200V-1000V, the fuzzy MPPT controller controls the power generation system to normally work, and the photovoltaic power generation system is enabled to output near the maximum power.
- 3. The MPPT control method based on photovoltaic power generation system resistance matching according to claim 1, characterized in that: the Boost circuit is a Boost circuit formed by a two-pole drive circuit and comprises switching tubes Q1 and Q2, capacitors C1, C2, C3 and C4, inductors L1 and L2, and diodes VD1 and VD 2; the emitter of the switching tube Q1 is connected with the collector of the switching tube Q2 in series, the fuzzy MPPT controller is connected with the bases of the switching tube Q1 and the switching tube Q2 through a driving circuit respectively, the output voltage of the photovoltaic array is within the range of 200V-1000V, the fuzzy MPPT controller controls the power generation system to work normally, and the photovoltaic power generation system is enabled to output near the maximum power.
- 4. The MPPT control method based on the photovoltaic power generation system resistance matching of claim 1 is characterized in that the fuzzy MPPT controller is industrial personal computers provided with MPPT fuzzy control algorithms.
- 5. The MPPT control method based on photovoltaic power generation system resistance matching according to claim 1 or 5, characterized in that the MPPT fuzzy control algorithm is processes of continuous measurement and continuous adjustment to achieve the optimum, the setting value of the controllable parameter is continuously changed in the operation process, the current working point gradually approaches to the peak power point, the photovoltaic system is operated at the peak power point, and the controllable parameter is the input variable deviation E and the input variable deviation change rate A.
- 6. The MPPT control method based on photovoltaic power generation system resistance matching of claim 5, characterized in that: the input variable deviation E and the input variable deviation rate of change a; inputting the last step quantity A (n-1); outputting the step length A (n); linguistic variables E and a are defined as 8 and 6 fuzzy subsets, respectively, namely:E＝{NB，NM，NS，N0，P0，PS，PM，PB)A＝{NB，NM，NS，PS，PM，PB}in the formula: NB, NM, NS, N0, P0, PS, PM, PB represent fuzzy concepts of negative large, negative medium, negative zero, positive small, medium, positive large, etc., respectively, and their domains are specified in 14 and 12 levels, namely:E＝{-6，-5，-4，-3，-2，-1，-0，+0，+1，+2，+3，+4，+5，+6)A＝{-6，-5，-4，-3，-2，-1，+1，+2，+3，+4，+5，+6}e (n) in the fuzzy self-optimizing controller represents the actual value of the difference between the output power at the nth moment and the output power at the (n-1) th moment; e (n) indicates that this difference corresponds to a value in the ambiguity set theory domain; a (n) represents an actual value of the step at the nth time; a (n) indicates that this step value corresponds to a value in the ambiguity set theory domain; ke、KaAre quantization factors respectively;analyzing a characteristic curve between the output P and the duty ratio D of the photovoltaic cell, and considering the influence of external environmental factors on the output power of the photovoltaic cell, adjusting the actual simulation result to obtain a final control rule;the fuzzy logic controller adopts a gravity center method for calculation, and the calculation formula is as follows:
in the formula: u (ai) is the membership of the ith fuzzy output; a is the ith fuzzy output. - 7. The MPPT control method based on photovoltaic power generation system attribute matching of claim 6, characterized in that: the time interval between the nth time and the nth-1 time is an integer value between 1 and 1000 microseconds.
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