CN116225145B - Composite tracking method for maximum power point of photovoltaic system - Google Patents
Composite tracking method for maximum power point of photovoltaic system Download PDFInfo
- Publication number
- CN116225145B CN116225145B CN202310335333.9A CN202310335333A CN116225145B CN 116225145 B CN116225145 B CN 116225145B CN 202310335333 A CN202310335333 A CN 202310335333A CN 116225145 B CN116225145 B CN 116225145B
- Authority
- CN
- China
- Prior art keywords
- optimal solution
- algorithm
- particle
- maximum power
- photovoltaic system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 239000002131 composite material Substances 0.000 title claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 102
- 239000002245 particle Substances 0.000 claims abstract description 96
- 230000033001 locomotion Effects 0.000 claims abstract description 49
- 150000001875 compounds Chemical class 0.000 claims abstract description 16
- 238000005286 illumination Methods 0.000 claims description 18
- 230000010355 oscillation Effects 0.000 claims description 15
- 238000013459 approach Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 7
- 125000003275 alpha amino acid group Chemical group 0.000 claims description 6
- 239000003990 capacitor Substances 0.000 claims description 6
- 230000005684 electric field Effects 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 6
- 230000003044 adaptive effect Effects 0.000 claims description 5
- 239000003574 free electron Substances 0.000 claims description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 239000004020 conductor Substances 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 230000026058 directional locomotion Effects 0.000 claims description 3
- 239000002184 metal Substances 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 3
- 238000004088 simulation Methods 0.000 description 23
- 210000004027 cell Anatomy 0.000 description 20
- 238000010586 diagram Methods 0.000 description 15
- 108010038016 Mannose-1-phosphate guanylyltransferase Proteins 0.000 description 9
- 238000005457 optimization Methods 0.000 description 6
- 230000003068 static effect Effects 0.000 description 6
- 210000001783 ELP Anatomy 0.000 description 4
- 230000006399 behavior Effects 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 2
- 238000006386 neutralization reaction Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000544061 Cuculus canorus Species 0.000 description 1
- 230000001580 bacterial effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 229910021419 crystalline silicon Inorganic materials 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000002431 foraging effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
- G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
- G05F1/66—Regulating electric power
- G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Automation & Control Theory (AREA)
- Photovoltaic Devices (AREA)
- Control Of Electrical Variables (AREA)
Abstract
The invention discloses a compound tracking method of a photovoltaic system maximum power point, which is characterized in that a boost chopper circuit is connected with a photovoltaic cell and a load to realize MPPT control, a self-adaptive thermal motion coefficient is introduced into an RDPSO random drift particle swarm algorithm, a global optimal solution is calculated based on the ARDPSO self-adaptive random drift particle swarm algorithm, and meanwhile, the boost chopper circuit is used for designing an STMC terminal sliding mode controller to correct the global optimal solution of the algorithm, and finally the photovoltaic system maximum power point is output, so that compound tracking of the photovoltaic system maximum power point is realized. The invention can effectively improve the power supply reliability of the photovoltaic system and improve the energy utilization rate of the photovoltaic system.
Description
Technical Field
The invention belongs to the technical field of photovoltaic energy, and particularly relates to a compound tracking method of a maximum power point of a photovoltaic system.
Background
According to the future energy demand and the requirement of realizing carbon neutralization, the duty ratio of non-carbon energy is gradually increased, and the construction of a multifunctional complementary novel energy supply system has important strategic significance. The new energy source represented by the photovoltaic energy source is a main force for realizing carbon neutralization. The photovoltaic energy is widely utilized due to the characteristics of cleanness, reproducibility, inexhaustible use and the like. In the photovoltaic power generation process, how to improve the power generation efficiency of a photovoltaic system to the greatest extent and reduce the cost is the key point of the research in the field of photovoltaic power generation. Photovoltaic system maximum power point tracking (Maximum Power Point Tracking, MPPT) is a technique widely studied and applied at present.
At present, MPPT methods are mainly divided into two main categories: traditional MPPT algorithm and intelligent optimization algorithm. Conventional MPPT algorithms include conductivity delta (Incremental Conductance, INC), disturbance observer (Perturb and Observe, P & O), hill Climbing (HC), and the like. The algorithms have the advantages of simple structure and easy application, but have the defects of poor convergence speed, fixed step length, low optimizing precision and the like. In practical application, after the photovoltaic array is partially shielded by cloud cover, building, flying bird and the like, the output P-U characteristic curve of the photovoltaic array can show a multi-peak phenomenon. Under such conditions, conventional MPPT algorithms tend to sink to local maximum power points, resulting in low energy conversion efficiency of the photovoltaic system.
The realization of multimodal MPPT by intelligent optimization algorithms has become a domestic research hotspot. Common intelligent optimization algorithms include particle swarm algorithm, differential evolution algorithm, bacterial foraging algorithm, cuckoo algorithm and the like. The prior literature proposes an improved particle swarm optimization algorithm, which reduces power oscillation to a certain extent, but still has the problem of longer convergence time; there is also literature that proposes an adaptive genetic algorithm that, although tracking around MPPT, system steady-state oscillations are excessive and the genetic algorithm is complex; in addition, there is a literature that proposes a hybrid algorithm combining P & O and fuzzy control methods, which uses P & O in the early tracking stage, and easily falls into a local maximum power point. In addition, an MPPT control strategy based on a goblet-sea squirt algorithm is proposed, so that the output efficiency of a photovoltaic system is improved, but the convergence speed is too long.
Disclosure of Invention
The invention aims to provide a compound tracking method of a maximum power point of a photovoltaic system, which combines an adaptive random drift particle swarm Algorithm (ARDPSO) and Terminal Sliding Mode Control (TSMC) to rapidly and accurately track the maximum power point of the photovoltaic system.
According to the technical scheme, the method for compositely tracking the maximum power point of the photovoltaic system comprises the steps of connecting a photovoltaic cell and a load through a boost chopper circuit to realize MPPT control, introducing a self-adaptive thermal motion coefficient into an RDPSO random drift particle swarm algorithm to form an ARDPSO self-adaptive random drift particle swarm algorithm, calculating a global optimal solution based on the ARDPSO self-adaptive random drift particle swarm algorithm, and simultaneously, correcting the global optimal solution of the algorithm through a STMC terminal sliding mode controller designed by the boost chopper circuit, and finally outputting the maximum power point of the photovoltaic system to realize compositely tracking the maximum power point of the photovoltaic system.
The present invention is also characterized in that,
the method is implemented according to the following steps:
step 1, setting ARDPSO algorithm parameters, and randomly initializing the position and the speed of particles;
step 2, detecting illumination and temperature parameters, and calculating a photovoltaic array power value;
step 3, calculating the fitness value of each particle, and calculating a global optimal solution and an individual optimal solution;
step 4, updating the global optimal solution and the individual optimal solution, and updating the thermal motion coefficient of the ARDPSO algorithm and the speed and the position of each particle;
step 5, after updating in the step 4, judging whether the iteration times of the ARDPSO algorithm are met, if not, repeating the step 3-5 according to the updated ARDPSO algorithm thermal motion coefficient, and if so, outputting a global optimal solution;
and 6, designing a terminal sliding mode controller according to the boost chopper circuit, controlling the on-off of a switching tube of the boost chopper circuit through a terminal sliding mode control rate, reducing power oscillation in the iteration process of the step 3-5, correcting a global optimal solution, wherein the corrected global optimal solution is the maximum power point of the photovoltaic system, and tracking is completed.
And 5, after outputting the global optimal solution, judging whether restarting to track the maximum power point of the photovoltaic system if the external environment is suddenly changed, if so, repeating the step 2-5, and if not, outputting the global optimal solution according to the step 5.
The restarting condition for restarting and tracking the maximum power point of the photovoltaic system is as follows
In the formula (13): p1 is the maximum power value output by the photovoltaic array before external environment mutation; and P2 is the maximum power value output by the photovoltaic array after the external environment is suddenly changed.
The step 3 is specifically as follows:
and calculating the power value of the position of each particle according to the P=UI, taking the power value as the fitness value of the particle, taking the fitness value of each particle as the individual optimal solution of the particle, and taking the maximum fitness value of all particles as the global optimal solution.
Step 4, updating the global optimal solution and the individual optimal solution specifically comprises the following steps:
comparing the global optimal solution calculated in the step 3 with the individual optimal solution and the value calculated in the last iteration, and if the global optimal solution calculated in the step is larger than the value calculated in the last iteration, replacing the global optimal solution with the calculated value; if the value is smaller than the last value, the replacement is not performed;
and 4, updating the thermal motion coefficient of the ARDPSO algorithm, wherein the speed and the position of each particle are specifically as follows:
the RDPSO algorithm combines the motion trail of the standard PSO algorithm and the motion model of free electrons in the metal conductor in an external electric field, wherein the motion of electrons is superposition of thermal motion, namely random motion, and drift motion, namely directional motion, caused by the electric field, so that the velocity of electrons comprises two components, namely
V＝VR+VD(7)
In the formula (7), VR and VD are random speed and drift speed respectively;
in the RDPSO algorithm, the velocity of each particle is expressed as
In formula (8):is the random component of the velocity of the ith particle at the kth iteration; />Is the velocity drift component of the ith particle at the kth iteration;
in RDPSO algorithmGlobal search behavior for implementing particles, +.>For achieving a local search behaviour of the particles, thus re-writing the velocity of the particles to,
wherein,
in the formula (9), alpha is a thermal motion coefficient; c (C) k The average optimal position of the whole particle swarm in the kth iteration is the average optimal position; m is the number of particles;the current position of the ith particle at the kth iteration; />For random number sequences subject to a standard normal distribution, i.e.Beta is a drift coefficient; />Is a local attractor coordinate that indicates that if each particle in the population converges to its respective attraction point, the entire population is converged; />Is a random number uniformly distributed between 0 and 1;
the adaptive thermal motion coefficient is introduced into the RDPSO algorithm, and the expression is that,
in the formula (10): alpha max 、α min Maximum and minimum thermal motion coefficients; f is the current fitness value of the particle; fmin is the minimum fitness value in all the current particles; f (f) avg The average fitness value of all the particles at present; the maximum value and the minimum value of the thermal motion coefficient are respectively alpha max ＝0.9、α min ＝0.3；
The iterative formulas of the speed and the position in the ARDPSO algorithm are respectively,
in the formulae (11) to (12),the current position of the ith particle at the kth iteration; />The current position of the ith particle at the kth+1th iteration; />The speed of the ith particle at the kth+1th iteration;
the thermal motion coefficients and particle velocities and positions of the ARDPSO algorithm are updated according to equations (10) - (12).
Step 6 is implemented according to the following steps:
step 6.1, establishing a mathematical model of the Boost circuit
The Boost circuit comprises a series inductor L, an input end filter capacitor C1, an output end parallel capacitor C2 and a switching tube Q, and the mathematical model of the Boost circuit is as follows:
in the formula (14), R is a load, IL is current flowing through an inductor, and U is load terminal voltage; d is the duty cycle;
in the formula (15), the amino acid sequence of the compound,
in the formula (15), the amino acid sequence of the compound,is a state vector; f (x) and g (x) are coefficient matrixes;
step 6.2, design of terminal sliding mode controller
The reference value of the maximum power point voltage of the photovoltaic array is set as Uref, and the actual output voltage value of the photovoltaic array is set as U pv Error e=u ref -U pv Selecting a sliding mode surface function as
In the formula (17), c is more than 0, r is more than or equal to 0 and less than or equal to 2;
obtained by combining the formula (14) and the formula (18),
in order to ensure that the system can approach the sliding mode surface from any state in a limited time, an index approach law is selected to improve the approach speed, reduce the jitter, the index approach law is as follows,
in the formula (20): epsilon >0, k >0;
the slip form control rate expression of the terminal obtained by the combined type (19) and the formula (20) is
Step 6.3, controlling the on-off of a switching tube of the boost chopper circuit
And (3) comparing the control signal d calculated in the formula (21) with a standard triangular wave signal to generate a PWM signal, connecting the PWM signal to a switching tube of a boost chopper circuit, controlling the on-off of the switching tube, correcting the global optimal solution obtained in the step (5), wherein the corrected global optimal solution is the maximum power point of the photovoltaic system, and tracking is completed.
The beneficial effects of the invention are as follows:
the invention provides a composite tracking method of a maximum power point of a photovoltaic system, which is characterized in that a free electron motion model is introduced into an ARDPSO-TSMC composite tracking method, the capability of getting rid of local optimization of particles is enhanced, and meanwhile, a self-adaptive mechanism is added, so that the thermal motion coefficient of the algorithm is synchronously adjusted in the iterative optimization process; in order to further improve optimizing precision and tracking speed, TSMC and ARDPSO algorithm are combined, so that the convergence speed of the algorithm is improved, power oscillation in the ARDPSO algorithm tracking process is reduced through TSMC control, and stability of power output is guaranteed; under the conditions of no shading, static partial shading and dynamic partial shading, the algorithm provided by the invention is superior to a PSO algorithm and an ARDPSO algorithm in the aspects of early convergence speed and later power oscillation, the power supply reliability of the photovoltaic system is effectively improved, and the energy utilization rate of the photovoltaic system is improved.
Drawings
Fig. 1 is a photovoltaic cell equivalent circuit diagram;
FIG. 2 is a graph of photovoltaic array illumination intensity;
FIG. 3 is an I-U characteristic diagram;
FIG. 4 is a P-U characteristic diagram;
FIG. 5 is a flowchart of the ARDPSO algorithm in the method for composite tracking of the maximum power point of the photovoltaic system of the present invention;
FIG. 6 is a Boost equivalent circuit diagram;
fig. 7 is a photovoltaic MPPT control system diagram;
FIG. 8 is a graph of simulation results without shading, wherein FIG. 8 (a) is a P-U characteristic diagram and FIG. 8 (b) is a power trace waveform diagram;
FIG. 9 is a graph of simulation results for a static partial shading case, wherein FIG. 9 (a) is a P-U characteristic diagram and FIG. 9 (b) is a power trace waveform diagram;
fig. 10 is a diagram of simulation results in the case of dynamic partial shading, in which fig. 10 (a) is a P-U characteristic diagram and fig. 10 (b) is a power trace waveform diagram.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
According to the method for compositely tracking the maximum power point of the photovoltaic system, the boost chopper circuit is connected with the photovoltaic cell and the load to realize MPPT control, the self-adaptive thermal motion coefficient is introduced into the RDPSO random drift particle swarm algorithm to form the ARDPSO self-adaptive random drift particle swarm algorithm, the global optimal solution is calculated based on the ARDPSO self-adaptive random drift particle swarm algorithm, meanwhile, the boost chopper circuit is used for designing the STMC terminal sliding mode controller to correct the global optimal solution of the algorithm, the maximum power point of the photovoltaic system is finally output, and compositely tracking of the maximum power point of the photovoltaic system is realized.
The equivalent circuit of the photovoltaic cell is shown in fig. 1, and the output current equation of the photovoltaic cell can be obtained from fig. 1,
in the formula (1), I is the output current of the photovoltaic cell; i ph The photo-generated current Id is the reverse saturation leakage current flowing through the diode; u (U) ph Is the output voltage of the photovoltaic cell; rs is the equivalent series resistance of the photovoltaic cell; rsh is the equivalent parallel resistance of the photovoltaic cell; q is electron quantity, and its value is 1.6X10 -19 C, performing operation; a is a fitting constant, and the value of the A is between 1 and 2; k is Boltzmann constant, which is 1.38X10 -23 J/K; t is absolute temperature.
Wherein I is ph And Id are affected by environmental changes, and need to be determined according to specific illumination intensity and temperature, and the calculation formulas are respectively as follows,
in the formulae (2) to (3): isco is the standard illumination intensity and standard temperature (s=1000W/m 2 T=25℃; ht is a temperature coefficient having a value of 6.4X10 -4 The method comprises the steps of carrying out a first treatment on the surface of the T is absolute temperature; tref is the standard battery temperature; s is illumination intensity; sref is the standard illumination intensity; a1 and b1 are constants, and their values are 1.336×10 respectively 4 、235。
The photovoltaic array is an important component of a grid-connected photovoltaic system, and is formed by serial-parallel connection of a certain number of crystalline silicon photovoltaic cells, the output characteristic equation is as follows,
in the formula (4): ns and N p The series-parallel numbers of the photovoltaic cells are respectively.
The output P-U characteristic of a photovoltaic array at standard illumination intensity and standard temperature has only one peak, but a "hot spot effect" is produced when the surface of the photovoltaic array is locally shaded. The hot spot effect can cause the temperature of the photovoltaic cell to be too high to be damaged, and a bypass diode is generally connected with the photovoltaic cell in parallel to avoid the damage caused by the hot spot effect. But shunt diodes in parallel cause the photovoltaic array to multi-peak in its output P-U characteristic under partial shadows.
The output characteristics of the photovoltaic array under uniform illumination intensity and partial shading were analyzed at standard temperature using 3 photovoltaic cells in series as an example. And respectively constructing models in Matlab/simulink for 3 groups of different shading conditions in FIG. 2 for simulation. The parameters of the photovoltaic cell are as follows: open circuit voltage uoc=43.6v, short circuit current isc=8.35a, voltage um=35v at maximum power point, current im=7.6a. Fig. 3 and 4 are respectively output I-U, P-U characteristics of the photovoltaic array.
The method for compositely tracking the maximum power point of the photovoltaic system is implemented according to the following steps as shown in fig. 5:
step 1, setting ARDPSO algorithm parameters including the number of particles, the iteration times, the maximum value and the minimum value of the thermal motion coefficient, the drift coefficient and randomly initializing the positions and the speeds of the particles.
The number of particles is set to be 30, the iteration number is 50, the maximum value and the minimum value of the thermal motion coefficient are respectively 0.9 and 0.3, and the drift coefficient is 1.45.
Step 2, detecting illumination and temperature parameters, and calculating a photovoltaic array power value;
step 3, calculating the fitness value of each particle, and calculating a global optimal solution and an individual optimal solution;
and calculating the power value of the position of each particle according to the P=UI, taking the power value as the fitness value of the particle, taking the fitness value of each particle as the individual optimal solution of the particle, and taking the maximum fitness value of all particles as the global optimal solution.
Step 4, updating the global optimal solution and the individual optimal solution, and updating the thermal motion coefficient of the ARDPSO algorithm and the speed and the position of each particle;
the updating of the global optimal solution and the individual optimal solution is specifically as follows:
comparing the global optimal solution calculated in the step 3 with the individual optimal solution and the value calculated in the last iteration, and if the global optimal solution calculated in the step is larger than the value calculated in the last iteration, replacing the global optimal solution with the calculated value; if the value is smaller than the last value, the replacement is not performed;
the updating of the thermal motion coefficient of the ARDPSO algorithm and the speed and position of each particle are specifically:
the RDPSO algorithm combines the motion trail of the standard PSO algorithm and the motion model of free electrons in the metal conductor in an external electric field, wherein the motion of electrons is superposition of thermal motion, namely random motion, and drift motion, namely directional motion, caused by the electric field, so that the velocity of electrons comprises two components, namely
V＝VR+VD(7)
In the formula (7), VR and VD are random speed and drift speed respectively;
in the RDPSO algorithm, the velocity of each particle is expressed as
In formula (8):is the random component of the velocity of the ith particle at the kth iteration; />Is the velocity drift component of the ith particle at the kth iteration;
in RDPSO algorithmGlobal search behavior for implementing particles, +.>For achieving a local search behaviour of the particles, thus re-writing the velocity of the particles to,
wherein,
in the formula (9), alpha is a thermal motion coefficient; c (C) k The average optimal position of the whole particle swarm in the kth iteration is the average optimal position; m is the number of particles;the current position of the ith particle at the kth iteration; />For random number sequences subject to a standard normal distribution, i.e.Beta is a drift coefficient; />Is a local attractor coordinate that indicates that if each particle in the population converges to its respective attraction point, the entire population is converged; />Is a random number uniformly distributed between 0 and 1;
the adaptive thermal motion coefficient is introduced into the RDPSO algorithm, and the expression is that,
in the formula (10): alpha max 、α min Maximum and minimum thermal motion coefficients; f is the current fitness value of the particle; fmin is the minimum fitness value in all the current particles; f (f) avg The average fitness value of all the particles at present; the maximum value and the minimum value of the thermal motion coefficient are respectively alpha max ＝0 . 9、α min ＝0 . 3。
The iterative formulas of the speed and the position in the ARDPSO algorithm are respectively,
in the formulae (11) to (12),the current position of the ith particle at the kth iteration; />The current position of the ith particle at the kth+1th iteration; />Is the speed of the ith particle at the k+1th iteration.
The thermal motion coefficients and particle velocities and positions of the ARDPSO algorithm are updated according to equations (10) - (12).
Step 5, after updating in the step 4, judging whether the iteration times of the ARDPSO algorithm are met, if not, repeating the step 3-5, and if so, outputting a global optimal solution;
and 5, after outputting the global optimal solution, if the external environment is suddenly changed, the output power of the photovoltaic system is also changed, whether the maximum power point of the photovoltaic system needs to be restarted or not is judged, if the restarting is needed, the steps 2-5 are repeatedly executed, and if the restarting is not needed, the global optimal solution is output according to the step 5, and the maximum power point of the photovoltaic system is tracked.
The restarting condition of restarting and tracking the maximum power point of the photovoltaic system is as follows:
in the formula (13): p1 is the maximum power value output by the photovoltaic array before external environment mutation; and P2 is the maximum power value output by the photovoltaic array after the external environment is suddenly changed.
And 6, designing a terminal sliding mode controller according to the boost chopper circuit, controlling a switching tube of the boost chopper circuit through a terminal sliding mode control rate, further reducing power oscillation in the iteration process of the step 3-5, correcting a global optimal solution, wherein the corrected global optimal solution is the maximum power point of the photovoltaic system, and tracking is completed.
Step 6.1, establishing a mathematical model of the Boost circuit
The Boost circuit comprises a series inductor L, an input end filter capacitor C1, an output end parallel capacitor C2 and a switching tube Q, and the mathematical model of the Boost circuit is as follows:
in the formula (14), R is a load, IL is current flowing through an inductor, and U is load terminal voltage; d is the duty cycle;
in the formula (15), the amino acid sequence of the compound,
in the formula (15), the amino acid sequence of the compound,is a state vector; f (x) and g (x) are coefficient matrixes;
step 6.2, design of terminal sliding mode controller
The reference value of the maximum power point voltage of the photovoltaic array is set as Uref, and the actual output voltage value of the photovoltaic array is set as U pv Error e=u ref -U pv Selecting a sliding mode surface function as
In the formula (17), c is more than 0, r is more than or equal to 0 and less than or equal to 2;
obtained by combining the formula (14) and the formula (18),
in order to ensure that the system can approach the sliding mode surface from any state in a limited time, an index approach law is selected to improve the approach speed, reduce the jitter, the index approach law is as follows,
in the formula (20): epsilon >0, k >0;
the slip form control rate expression of the terminal obtained by the combined type (19) and the formula (20) is
To verify the stability of the terminal sliding mode controller constructed as above, a positive definite function is constructed according to the Lyapunov stability theory
Deriving V and substituting it into (17) to obtain
Due to epsilon>0，k>0, soMeets the Lyapunov stability criterion;
step 6.3, controlling the on-off of a switching tube of the boost chopper circuit
And (3) comparing the control signal d calculated in the formula (21) with a standard triangular wave signal to generate a PWM signal, connecting the PWM signal to a switching tube of a boost chopper circuit, controlling the on-off of the switching tube, correcting the global optimal solution obtained in the step (5), wherein the corrected global optimal solution is the maximum power point of the photovoltaic system, and tracking is completed.
Simulation verification
In order to verify the performance improvement of the ARDPSO-TSMC algorithm, the composite tracking method, the PSO algorithm and the ARDPSO algorithm are used for constructing a simulation model in Matlab/simulink, and the comparison analysis is carried out under the conditions of no shading, static partial shading and dynamic partial shading. The model structure of the simulation system is shown in fig. 7. The photovoltaic array is formed by connecting 3 independent photovoltaic cell elements in series. The Boost circuit simulation parameters were set as follows: c (C) 1 ＝100μF，C 2 =500 μf, l=30 mh, r=46 Ω, and the switching frequency is 50kHz. The ARDPSO-TSMC algorithm parameters are set as follows: particle number n=30, iteration number m=50, thermal motion coefficient α max ＝0.9、α min The drift coefficient β=1.45, epsilon=130, k=1000, c=2, r=2, =0.3. The PSO algorithm parameters are set as follows: particle number n=50, iteration number m=100, learning factor c 1 ＝c 2 =0.5, inertial weight ω=0.7.
1) Simulation without shading
The photovoltaic array is usually installed in a fully illuminated, non-shadowed environment, so that the output P-U characteristic of the photovoltaic array has only one extreme point under uniform illumination. The illumination intensity of 3 photovoltaic cells is set to be 1000W/m in the simulation model 2 The temperature is 25 ℃ and the system simulation time is 0.5s. The simulation results are shown in fig. 8. Wherein fig. 8 (a) is an output P-U characteristic curve of the photovoltaic array, and fig. 8 (b) is a power tracking waveform diagram of three algorithms.
From fig. 8 (a), it can be seen that the power at the maximum power point is 798W. Analysis of FIG. 8 (b) shows that all three methods can track around the maximum power point and settle at 0.05 s. The average power values tracked by PSO, ARDPSO and the compound tracking method of the invention are 797.55W, 797.75W and 797.85W respectively. Compared with a PSO algorithm and an ARDPSO algorithm, the power oscillation after the maximum power point is tracked by the compound tracking method is smaller, and the compound tracking method has better stability. The simulation results are shown in table 1. Where power generation efficiency = average output power/power at maximum power point.
Table 1 comparison of simulation results
2) Simulation in static partial shading
In the case of static partial shading, the output P-U characteristic of the photovoltaic array transitions from a single peak to multiple peaks. Setting the illumination intensity of 3 photovoltaic cells in a simulation model to be 1000W/m respectively 2 、800W/m 2 、600W/m 2 The temperature is 25 ℃ and the system simulation time is 0.5s. The simulation results are shown in fig. 9. Wherein fig. 9 (a) is an output P-U characteristic curve of the photovoltaic array, and fig. 9 (b) is a power tracking waveform diagram of three methods.
As can be seen from FIG. 9 (a), there are two LMPP, one GMPP, on the P-U characteristic. The power values of the two LMPP are 266W and 445.5W respectively, and the power value of the GMPP is 518.7W. As can be seen from analysis of fig. 9 (b), all three methods can track around the maximum power point, wherein the PSO algorithm needs 0.1s and the ardpso algorithm needs 0.09s, and the composite tracking method of the present invention needs 0.07s. Compared with other algorithms, the convergence speed of the compound tracking method is faster. The average power values tracked by PSO, ARDPSO and the compound tracking method of the invention are 514.39W, 517.94W and 518.6W respectively. It is readily apparent from fig. 9 (b) that the PSO algorithm has a larger power oscillation in tracking GMPP, whereas the ARDPSO algorithm has a significantly reduced power oscillation compared to the PSO algorithm, but still has a small amplitude of power oscillation. Compared with an ARDPSO algorithm, the composite tracking method further reduces power fluctuation, is more stable and has smoother tracking process. The comparison values of the simulation results of the three methods are shown in table 2.
Table 2 comparison of simulation results
3) Simulation in dynamic partial shading
In order to test the capability of an algorithm to track the GMPP of the photovoltaic system in a dynamic environment, the illumination intensity of 3 photovoltaic cells at t=0s is set to be 1000W/m respectively 2 、800W/m 2 、600W/m 2 When t=0.25 s, the illumination intensity of 3 photovoltaic cells is respectively changed into 900W/m 2 、700W/m 2 、500W/m 2 The photovoltaic array temperatures were 25 ℃. The system simulation time was 0.5s. The simulation results are shown in fig. 10. Wherein fig. 10 (a) is an output P-U characteristic curve of the photovoltaic array after the illumination intensity is suddenly changed, and fig. 10 (b) is a power tracking waveform diagram of three methods.
As can be seen from fig. 10 (a), there are still two LMPP, one GMPP, on the output P-U characteristic of the photovoltaic array after the illumination intensity is suddenly changed. The power values of the two LMPP are 235W and 383.9W respectively, and the power value of the GMPP is 426.6W. Analysis of FIG. 10 (b) shows that after a sudden change in illumination intensity, all three algorithms can trace around the new GMPP, with the PSO algorithm requiring 0.05s, the ARDPSO algorithm requiring 0.03s, and the ARDPSO-TSMC algorithm requiring 0.02s. The average power values tracked by the PSO, ARDPSO, ARDPSO-TSMC algorithm are 514.39W, 517.94W and 518.6W, respectively. After the new GMPP is tracked and stabilized, the power fluctuation range of the PSO algorithm is large and there is significant power oscillation, while the power fluctuation range of the ARDPSO algorithm is significantly reduced compared to the PSO algorithm, but the power oscillation phenomenon still exists. The ARDPSO-TSMC algorithm has faster dynamic response capability than the first two algorithms, and the power fluctuation range after GMPP is tracked is smaller, so that the power oscillation is basically eliminated.
According to the above, under the conditions of no shading, static partial shading and dynamic partial shading, the compound tracking method of the maximum power point of the photovoltaic system is superior to the PSO algorithm and the ARDPSO algorithm in the aspects of early convergence speed and later power oscillation, so that the power supply reliability of the photovoltaic system is effectively improved, and the energy utilization rate of the photovoltaic system is improved.
Claims (5)
1. The method is characterized in that an ARDPSO self-adaptive random drift particle swarm algorithm is formed by introducing a self-adaptive thermal motion coefficient into an RDPSO random drift particle swarm algorithm, a global optimal solution is calculated based on the ARDPSO self-adaptive random drift particle swarm algorithm, meanwhile, the global optimal solution of the algorithm is corrected by a TSMC terminal sliding mode controller designed through the boost chopper circuit, and finally, the maximum power point of the photovoltaic system is output, so that the composite tracking of the maximum power point of the photovoltaic system is realized;
the method is implemented according to the following steps:
step 1, setting ARDPSO algorithm parameters, and initializing particle positions and speeds;
step 2, detecting illumination and temperature parameters, and calculating a photovoltaic array power value;
step 3, calculating the fitness value of each particle, and calculating a global optimal solution and an individual optimal solution;
step 4, updating the global optimal solution and the individual optimal solution, and updating the thermal motion coefficient of the ARDPSO algorithm and the speed and the position of each particle;
step 4, updating the global optimal solution and the individual optimal solution specifically comprises the following steps:
comparing the global optimal solution calculated in the step 3 with the individual optimal solution and the value calculated in the last iteration, and if the global optimal solution calculated in the step is larger than the value calculated in the last iteration, replacing the global optimal solution with the calculated value; if the value is smaller than the last value, the replacement is not performed;
and 4, updating the thermal motion coefficient of the ARDPSO algorithm, wherein the speed and the position of each particle are specifically as follows: the RDPSO algorithm combines the motion trail of the standard PSO algorithm and the motion model of free electrons in the metal conductor in an external electric field, wherein the motion of electrons is superposition of thermal motion, namely random motion, and drift motion, namely directional motion, caused by the electric field, so that the velocity of electrons comprises two components, namely
V＝VR+VD (7)
In the formula (7), VR and VD are random speed and drift speed respectively;
in the RDPSO algorithm, the velocity of each particle is expressed as
In formula (8):is the random component of the velocity of the ith particle at the kth iteration; />Is the velocity drift component of the ith particle at the kth iteration;
in RDPSO algorithmGlobal search behavior for implementing particles, +.>For achieving a local search behaviour of the particles, thus re-writing the velocity of the particles to,
wherein,
in the formula (9), alpha is a thermal motion coefficient; c (C) k The average optimal position of the whole particle swarm in the kth iteration is the average optimal position; m is a granuleNumber of children;the current position of the ith particle at the kth iteration; />For random number sequences subject to a standard normal distribution, i.e.Beta is a drift coefficient; />Is a local attractor coordinate that indicates that if each particle in the population converges to its respective attraction point, the entire population is converged; />Is a random number uniformly distributed between 0 and 1;
the adaptive thermal motion coefficient is introduced into the RDPSO algorithm, and the expression is that,
in the formula (10): alpha max 、α min Maximum and minimum thermal motion coefficients; f is the current fitness value of the particle; f (f) min The minimum fitness value in all the current particles is used; f (f) avg The average fitness value of all the particles at present;
the iterative formulas of the speed and the position in the ARDPSO algorithm are respectively,
in the formulae (11) to (12),the current position of the ith particle at the kth iteration; />The current position of the ith particle at the kth+1th iteration; v (V) i k+1 The speed of the ith particle at the kth+1th iteration;
updating the thermal motion coefficient and the particle velocity and the position of the ARDPSO algorithm according to the formulas (10) - (12);
step 5, after updating in the step 4, judging whether the iteration times of the ARDPSO algorithm are met, if not, repeating the step 3-5, and if so, outputting a global optimal solution, and tracking the maximum power point of the photovoltaic system;
step 6, designing a terminal sliding mode controller according to the boost chopper circuit, controlling a switching tube of the boost chopper circuit through a terminal sliding mode control rate, further reducing power oscillation in the iteration process of the step 3-5, correcting a global optimal solution, wherein the corrected global optimal solution is the maximum power point of the photovoltaic system, and tracking is completed;
step 6.1, establishing a mathematical model of the Boost circuit
The Boost circuit comprises a series inductor L and an input end filter capacitor C 1 Output end parallel capacitor C 2 The mathematical model of the Boost circuit for switching the transistor Q is as follows:
in the formula (14), R is a load, I L U is the load end voltage for the current flowing through the inductor; d is the duty cycle;
in the formula (15), the amino acid sequence of the compound,
in the formula (15), the amino acid sequence of the compound,is a state vector; f (x) and g (x) are coefficient matrixes;
step 6.2, design of terminal sliding mode controller
Let the reference value of the maximum power point voltage of the photovoltaic array be U ref The actual output voltage value of the photovoltaic array is U pv Error e=u ref -U pv Selecting a sliding mode surface function as
In the formula (17), c is more than 0, r is more than or equal to 0 and less than or equal to 2;
obtained by combining the formula (14) and the formula (18),
in order to ensure that the system can approach the sliding mode surface from any state in a limited time, an index approach law is selected to improve the approach speed, reduce the jitter, the index approach law is as follows,
in the formula (20): epsilon >0, k >0;
the slip form control rate expression of the terminal obtained by the combined type (19) and the formula (20) is
Step 6.3, controlling the on-off of a switching tube of the boost chopper circuit
And (3) comparing the control signal d calculated in the formula (21) with a standard triangular wave signal to generate a PWM signal, connecting the PWM signal to a switching tube of a boost chopper circuit, controlling the on-off of the switching tube, correcting the global optimal solution obtained in the step (5), wherein the corrected global optimal solution is the maximum power point of the photovoltaic system, and tracking is completed.
2. The method for compositely tracking the maximum power point of the photovoltaic system according to claim 1, wherein after the global optimal solution is output in the step 5, if the external environment is suddenly changed, judging whether the maximum power point of the photovoltaic system needs to be restarted, if the maximum power point of the photovoltaic system needs to be restarted, repeating the steps 2-5, and if the maximum power point of the photovoltaic system does not need to be restarted, outputting the global optimal solution according to the step 5.
3. The method for compositely tracking the maximum power point of the photovoltaic system according to claim 2, wherein the restarting condition for restarting and tracking the maximum power point of the photovoltaic system is as follows
In the formula (13): p (P) 1 Outputting a maximum power value for the photovoltaic array before external environment mutation; p (P) 2 And outputting the maximum power value for the photovoltaic array after the external environment is suddenly changed.
4. The method for composite tracking of maximum power point of photovoltaic system according to claim 1, wherein said step 3 is specifically,
and calculating the power value of the position of each particle according to the P=UI, taking the power value as the fitness value of the particle, taking the fitness value of each particle as the individual optimal solution of the particle, and taking the maximum fitness value of all particles as the global optimal solution.
5. The method for composite tracking of maximum power point of photovoltaic system according to claim 1, wherein the maximum and minimum values of the thermal motion coefficient are respectively α max ＝0.9、α min ＝0.3。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310335333.9A CN116225145B (en) | 2023-03-30 | 2023-03-30 | Composite tracking method for maximum power point of photovoltaic system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310335333.9A CN116225145B (en) | 2023-03-30 | 2023-03-30 | Composite tracking method for maximum power point of photovoltaic system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116225145A CN116225145A (en) | 2023-06-06 |
| CN116225145B true CN116225145B (en) | 2023-12-29 |
Family
ID=86573229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310335333.9A Active CN116225145B (en) | 2023-03-30 | 2023-03-30 | Composite tracking method for maximum power point of photovoltaic system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116225145B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117458578B (en) * | 2023-07-27 | 2024-08-16 | 陕西理工大学 | Light storage direct current micro-grid bus voltage improved supercoiled sliding mode active disturbance rejection control method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105375522A (en) * | 2015-11-30 | 2016-03-02 | 河海大学常州校区 | Control method of photovoltaic grid-connected inverter |
| CN106875001A (en) * | 2017-03-22 | 2017-06-20 | 浙江大学 | Random drift particle group optimizing method with von Neumann structure |
| CN112859891A (en) * | 2021-01-29 | 2021-05-28 | 西北工业大学 | AUV course angle control method for optimizing self-adaptive sliding mode control parameters based on particle swarm optimization |
| CN113359938A (en) * | 2021-06-23 | 2021-09-07 | 安徽工业大学 | Photovoltaic system maximum power point tracking method based on nonsingular rapid terminal sliding mode control |
-
2023
- 2023-03-30 CN CN202310335333.9A patent/CN116225145B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105375522A (en) * | 2015-11-30 | 2016-03-02 | 河海大学常州校区 | Control method of photovoltaic grid-connected inverter |
| CN106875001A (en) * | 2017-03-22 | 2017-06-20 | 浙江大学 | Random drift particle group optimizing method with von Neumann structure |
| CN112859891A (en) * | 2021-01-29 | 2021-05-28 | 西北工业大学 | AUV course angle control method for optimizing self-adaptive sliding mode control parameters based on particle swarm optimization |
| CN113359938A (en) * | 2021-06-23 | 2021-09-07 | 安徽工业大学 | Photovoltaic system maximum power point tracking method based on nonsingular rapid terminal sliding mode control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116225145A (en) | 2023-06-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Farzaneh et al. | A new maximum power point tracking based on modified firefly algorithm for PV system under partial shading conditions | |
| Ocran et al. | Artificial neural network maximum power point tracker for solar electric vehicle | |
| Ali | Improved design of artificial neural network for MPPT of grid-connected PV systems | |
| Pant et al. | Comparative study of MPPT techniques for solar photovoltaic system | |
| Ahmed et al. | MPPT Control for Photovoltaic System using hybrid method under variant weather condition | |
| Ahmed et al. | PSO-SMC controller based GMPPT technique for photovoltaic panel under partial shading effect | |
| CN116225145B (en) | Composite tracking method for maximum power point of photovoltaic system | |
| de Dieu Nguimfack-Ndongmo et al. | Nonlinear neuro-adaptive MPPT controller and voltage stabilization of PV Systems under real environmental conditions | |
| Ali et al. | Optimization of PV model using fuzzy-neural network for DC-DC converter systems | |
| TWI721863B (en) | Photovoltaic apparatus and maximum power point tracking method thereof | |
| Jabbar et al. | A modified P&O-MPPT technique using fuzzy logic controller for PV systems | |
| Priya et al. | Design of adaptive perturb and observe-fuzzy MPPT controller for high voltage gain multilevel boost converter | |
| El-Khatib et al. | Efficient MPPT control for a photovoltaic system using artificial neural networks | |
| Zou et al. | Research on MPPT algorithm based on variable step size conductance increment+ PI method | |
| Chen et al. | MPPT control strategy of photovoltaic based on improved particle swarm optimization | |
| Ahmed et al. | FL-SMC based MPPT for PV module Under uniform meteorological conditions | |
| CN114967821A (en) | Photovoltaic power generation system maximum power tracking control method based on reinforcement learning | |
| Naick et al. | Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation. | |
| Sun et al. | Composite MPPT control algorithm with partial shading on PV arrays | |
| Li et al. | A comparison study on the performance of different MPPT control strategies in DC microgrids with photovoltaic systems | |
| Abbas et al. | A comparative study between the most used MPPT methods and particle swarm optimization method for a standalone PV system under fast change in irradiance level | |
| CN117458578B (en) | Light storage direct current micro-grid bus voltage improved supercoiled sliding mode active disturbance rejection control method | |
| Hamdi et al. | MPPT of a PV panel based on fuzzy logic and sliding mode controller | |
| Duan et al. | Application of improved radial basis function neural network method in global MPPT for PV array | |
| Lamzouri et al. | Optimal BISMC controller with evolutionary algorithm for MPPT based photovoltaic system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240328 Address after: 230000 Room 203, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee after: Hefei Jiuzhou Longteng scientific and technological achievement transformation Co.,Ltd. Country or region after: China Address before: 723000 No. 505 East Street, Hantai District, Shaanxi, Hanzhoung Patentee before: Shaanxi University of Technology Country or region before: China |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240423 Address after: Unit 2803, No. 893 Haicang Avenue, Haicang District, Xiamen City, Fujian Province, 361000, China Patentee after: Beitai Zhiyun (Fujian) Technology Co.,Ltd. Country or region after: China Address before: 230000 Room 203, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee before: Hefei Jiuzhou Longteng scientific and technological achievement transformation Co.,Ltd. Country or region before: China |
|
| TR01 | Transfer of patent right |