CN112162589B - Maximum power point tracking control method based on conductance incremental method and particle swarm optimization - Google Patents

Maximum power point tracking control method based on conductance incremental method and particle swarm optimization Download PDF

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CN112162589B
CN112162589B CN202010894879.4A CN202010894879A CN112162589B CN 112162589 B CN112162589 B CN 112162589B CN 202010894879 A CN202010894879 A CN 202010894879A CN 112162589 B CN112162589 B CN 112162589B
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power point
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孟凡英
雷茂杰
许坦奇
刘正新
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Shanghai Institute of Microsystem and Information Technology of CAS
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    • 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
    • G—PHYSICS
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
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    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Abstract

The invention relates toAnd a maximum power point tracking control method based on a conductance increment method and a particle swarm algorithm, which comprises the following steps: a quick start stage: increasing a period of dead time during starting, and stopping the DC-DC circuit from working in the dead time of the photovoltaic system; step length changing tracking stage: judging the disturbance direction according to the positive and negative of dP/dU, and judging the disturbance step length according to the size of | dP/dU |; wherein dP is the output power variation, and dU is the output voltage variation; and (3) a dynamic particle swarm algorithm stabilization phase: entering dynamic particle swarm optimization when the value of dP/dU is smaller than the starting threshold value of the dynamic particle swarm algorithm, wherein the found optimal value is the optimal output voltage UbestWhen the fitness value is met or the maximum iteration number is reached, outputting the optimal output voltage Ubest. The invention can greatly improve the tracking time, the tracking precision and the response speed.

Description

Maximum power point tracking control method based on conductance incremental method and particle swarm optimization
Technical Field
The invention relates to the technical field of maximum power point tracking of photovoltaic systems, in particular to a maximum power point tracking control method based on a conductance incremental method and a particle swarm algorithm.
Background
With the increasing exhaustion of traditional fossil energy, clean and environment-friendly solar energy becomes an important component in the energy field. The solar cell can convert solar energy into electric energy, and when irradiance and temperature change, the output voltage and the output current of the solar cell change, so that nonlinear output characteristics are presented. In order to improve the conversion efficiency of the photovoltaic module, the photovoltaic module is always operated at the maximum power point through a corresponding control strategy, the process is the maximum power point tracking (MPPT for short), the MPPT technology becomes an important component of the photovoltaic system, and is very important for increasing the conversion efficiency of the photovoltaic module. The conventional MPPT method comprises a constant voltage method, a disturbance observation method and a conductance increment method, and is limited by a fixed disturbance step length and cannot meet the requirements of tracking time, tracking precision and response speed. The constant voltage method is simple to control and low in implementation cost, but influences of temperature on output characteristics of the solar cell are ignored, and dynamic tracking cannot be achieved. The disturbance observation method is simple and easy to implement, but due to step length limitation, the disturbance observation method can oscillate near the maximum power point, the power loss of the system is large, and the tracking accuracy is poor. The conductance incremental method and the disturbance observation method are similar and can dynamically track, but are also limited by a fixed disturbance step length, and cannot simultaneously meet the requirements of tracking time, tracking precision and corresponding speed.
Disclosure of Invention
The invention aims to provide a maximum power point tracking control method based on a conductance incremental method and a particle swarm algorithm, which can greatly improve tracking time, tracking precision and response speed.
The technical scheme adopted by the invention for solving the technical problems is as follows: the maximum power point tracking control method based on the conductance increment method and the particle swarm algorithm comprises the following steps:
(1) a quick start stage: increasing a period of dead time during starting, and stopping the DC-DC circuit from working in the dead time of the photovoltaic system;
(2) step length changing tracking stage: judging the disturbance direction according to the positive and negative of dP/dU, and judging the disturbance step length according to the size of | dP/dU |; wherein dP is the output power variation, and dU is the output voltage variation;
(3) and (3) a dynamic particle swarm algorithm stabilization phase: entering dynamic particle swarm optimization when the value of dP/dU is smaller than the starting threshold value of the dynamic particle swarm algorithm, wherein the found optimal value is the optimal output voltage UbestWhen the fitness value is met or the maximum iteration number is reached, outputting the optimal output voltage Ubest。
When the disturbance direction is judged according to the positive and negative of dP/dU in the step (2), when dP/dU is greater than 0, forward disturbance is given to increase the equivalent resistance, when dP/dU is less than 0, reverse disturbance is given to reduce the equivalent resistance, and when dP/dU is equal to 0, disturbance is not carried out.
In the step (2), the disturbance step length is judged according to the size of | dP/dU |, when | dP/dU | > epsilon, the disturbance step length is alpha, when | dP/dU | < epsilon, the disturbance step length is beta, wherein epsilon is a step length selection threshold, and alpha is larger than beta.
When the dynamic particle swarm is optimized in the step (3), the irradiance and the temperature sensitivity particles are used as sensitive particles, and when the sensitive particles are suddenly changed, the particle swarm is updated; the particle swarm fitness value is
Figure BDA0002658129920000021
P is the power of a load end, t is the whole working time, and the adaptive value of the sensitive particles is fitness ═ dP/dU |; the speed and position updating formula of the particle swarm algorithm is as follows:
Figure BDA0002658129920000022
wherein the content of the first and second substances,
Figure BDA0002658129920000023
indicating the position of the particle at the k-th iteration,
Figure BDA0002658129920000024
denotes the velocity of the particle at the kth iteration, ω is the inertial weight, c1And c2Is an acceleration factor, r1And r2Is [0,1 ]]The random number of (a) is set,
Figure BDA0002658129920000025
representing the individual optima at the k-th iteration,
Figure BDA0002658129920000026
the optimal value of the group in the k iteration is shown, the inertia weight omega determines the speed of the particle swarm, the search speed of the particle swarm is adjusted by adjusting the inertia weight, and the inertia weight is adjusted in a mode that
Figure BDA0002658129920000027
Wherein, ω ismaxIs the maximum inertial weight, ωminIs the minimum inertial weight, kmaxFor the maximum number of iterations, ω (k) is the inertial weight at the kth iteration.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: according to the invention, the dead time, the dynamic particle swarm algorithm and the variable-step conductance incremental method are combined with each other, the photovoltaic system is quickly started after being stopped for a period of time in the starting stage, the variable-step tracking is utilized to quickly track the photovoltaic system to the vicinity of the maximum power point, and finally the photovoltaic system is further stabilized at the maximum power point through the dynamic particle swarm algorithm, so that the tracking time, the tracking precision and the response speed are greatly improved, and the purpose of increasing the conversion efficiency of the photovoltaic module is finally realized. The invention has very sensitive adaptability under the condition of severe change of irradiance and temperature, and can be used for photovoltaic systems in areas with severe change of environmental conditions.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a prior art conductance delta method MPPT power output curve at different irradiance;
FIG. 3 is a graph of power output for the present invention at different irradiance;
FIG. 4 is a graph of prior art conductance delta MPPT power output at different temperatures;
fig. 5 is a graph of power output at different temperatures according to the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a maximum power point tracking control method based on a conductance incremental method and a particle swarm algorithm. As shown in fig. 1, the specific steps are as follows:
step 1, accelerating the starting speed by dead time:
the duty ratio of the photovoltaic module is increased when dP/dU is greater than 0 in the initial stage, the voltage of the photovoltaic module can be prevented from rising due to the existence of the inductor in the DC-DC circuit, after the duty ratio is added, the starting speed of the whole photovoltaic module is slower, and the DC-DC circuit can consume power, so that the starting speed is slow directly.
The larger the phase-joining duty cycle of the startup, the slower the photovoltaic system starts up, and the greater the power lost. Therefore, a dead time is added in the improved conductance incremental method, and the DC-DC circuit stops working in the dead time, so that the photovoltaic module is quickly started, and the power loss is reduced.
Step 2, variable step length tracking:
the photovoltaic module has at the maximum power point: dP/dU is 0.
Judging the direction of disturbance according to the positive and negative of dP/dU, when dP/dU is greater than 0, the photovoltaic module works on the left side of the maximum power point, and the equivalent resistance should be increased by the positive disturbance, so that the working point is shifted to the right side of the maximum power point. When dP/dU is less than 0, the photovoltaic cell works at the right side of the maximum power point, and the equivalent resistance is reduced by the reverse disturbance, so that the working point is shifted to the left of the maximum power point. When dP/dU is 0, the photovoltaic cell works at the maximum power point and does not disturb.
In order to better select the size of a disturbance step, a variable-step conductance incremental method is designed for fast tracking, the size of the disturbance step is determined according to the value of dP/dU, and a large-step disturbance factor alpha, a small-step disturbance factor beta and a step selection threshold epsilon are introduced into the variable-step conductance incremental method.
After the photovoltaic system is quickly started, dP/dU quickly rises, in the period, the | dP/dU | is larger than epsilon, the large-step tracking area is entered, and the disturbance step length is alpha;
after the large-step tracking, dP/dU rapidly decreases, and in the period, | dP/dU | < epsilon enters a small-step tracking area, and the disturbance step is beta.
After variable step tracking, as the disturbance step is not 0, dP/dU cannot be 0, dP/dU can oscillate near the zero point, the variable step tracking method cannot be stabilized at the maximum power point, and the variable step response speed is slow when irradiance and temperature change suddenly.
Step 3, accurately optimizing by a dynamic particle swarm algorithm:
in order to solve the problem of variable step length, a self-adaptive particle swarm algorithm is introduced for accurate optimization.
After variable-step-length tracking, the | dP/dU | can be continuously reduced, a dynamic particle swarm algorithm starting threshold i (i < epsilon) is added, and when the | dP/dU | is less than i, a dynamic particle swarm algorithm optimizing stage can be started;
the sensitive particles are irradiance and temperature sensitive particles, when the irradiance and the temperature are suddenly changed, the dP/dU is also suddenly changed, and the particle swarm is updated by the sudden change;
the speed and position updating formula of the particle swarm algorithm is as follows:
Figure BDA0002658129920000041
wherein the content of the first and second substances,
Figure BDA0002658129920000042
indicating the position of the particle at the k-th iteration,
Figure BDA0002658129920000043
denotes the velocity of the particle at the kth iteration, ω is the inertial weight, c1And c2Is an acceleration factor, r1And r2Is [0,1 ]]The random number of (a) is set,
Figure BDA0002658129920000044
representing the individual optima at the k-th iteration,
Figure BDA0002658129920000045
representing the population optimum at the k-th iteration.
The change in the inertial parameters is:
to find the global optimumPreferably, the local optimization is avoided, the inertia weight ω in the algorithm is automatically changed on the basis of the particle swarm algorithm, the inertia weight determines the speed of the particle swarm, the search speed of the particle swarm is adjusted by adjusting the inertia weight, and the formula for adjusting the inertia weight ω is as follows:
Figure BDA0002658129920000046
wherein, ω ismaxIs the maximum inertial weight, ωminIs the minimum inertial weight, kmaxFor the maximum number of iterations, ω (k) is the inertial weight at the kth iteration. In the present embodiment,. omega.max＝1，ωmin＝0.3。
Each particle represents the energy output as a whole in a duty cycle, fitness function.
The particle swarm fitness value is as follows:
Figure BDA0002658129920000047
p is the load end power, and t is the whole working time.
The adaptive value of the sensitive particle is fitness ═ dP/dU |. And introducing an abrupt change threshold eta of | dP/dU |, and when the temperature and the irradiance send abrupt changes | dP/dU | > eta, the particle swarm can be initialized again, the range of the particle swarm is changed, and the optimal solution is searched more quickly.
Therefore, the sensitive particles are introduced into the dynamic particle swarm optimization, the adaptability value of the sensitive particles changes according to the suddenly changing environment, the change trend of the sensitive particles determines the range of the next search space, the search range can be dynamically changed, the search range is reduced, and therefore the efficiency of the particle swarm optimization is improved. In order to find out the global optimum and avoid falling into the local optimum, the inertial weight omega in the particle swarm optimization is automatically changed on the basis of the particle swarm optimization, the inertial weight determines the speed of the particle swarm, and the searching speed of the particle swarm is adjusted by adjusting the inertial weight.
And 4, outputting the optimal duty ratio found by the dynamic particle swarm optimization as the duty ratio of PWM.
FIGS. 2 and 3 show temperature-dependent (T ═ 25 ℃ C.) irradiationThe temperature is changed once every 0.2 seconds and is sequentially 800W/m2、1000W/m2、1200W/m2、1000W/m2、800W/m2The power output profiles of the conventional MPPT and the present invention are as follows. FIGS. 4 and 5 show constant irradiance (1000W/m)2) The temperature changes once every 0.2 seconds, and the traditional MPPT and the power output curve chart of the invention are sequentially at 15 ℃, 25 ℃, 45 ℃, 25 ℃ and 15 ℃, and the output power is the load end power.
As shown in fig. 2, at the stage of 0-0.2 seconds, the tracking time of the conventional MPPT is about 0.16 seconds, the tracking capabilities of two MPPTs under the condition of continuous increase of irradiance are tested within 0.2-0.6 seconds, the tracking times of the conventional algorithm are respectively 0.16 seconds and 0.11 seconds, and the tracking capabilities of two MPPTs under the condition of continuous decrease of irradiance are tested within 0.6-1.0 seconds, so that it can be seen that the response speed of the conventional algorithm is slow, the tracking time is long, the two-time stabilization time is 0.10 seconds and 0.12 seconds, the optimal duty ratio and the maximum power point can not be accurately found and stabilized under the condition of irradiance change, and the power loss is increased.
As shown in FIG. 3, in the stage of 0-0.2 seconds, the tracking time of the improved algorithm is about 0.04 seconds, the tracking time efficiency is improved by 75% compared with the traditional algorithm, and the improved algorithm has stable output power and less fluctuation due to fixed duty ratio and no fluctuation when the maximum power point is reached because of the accurate optimization of the dynamic particle swarm algorithm. The tracking capability of two MPPT under the condition of continuous rising of irradiance is tested within 0.2-0.6 seconds, and the improved algorithm has the tracking time of about 0.03 second and the response speed of 0.02 second is faster. The tracking capability of two MPPT under the condition that the irradiance continuously drops is tested within 0.6-1.0 second, the improved algorithm can be rapidly stabilized due to the addition of variable step length and dynamic particle swarm algorithm, the tracking time is about 0.03s and 0.01s, the tracking precision and the response speed are greatly improved, the optimal duty ratio can be accurately found under the condition that the irradiance changes, the MPPT is stabilized at the maximum power point, and the power loss is reduced.
Compared with the traditional MPPT algorithm, the improved MPPT algorithm has the advantages that the fast tracking capability and the adaptive capability are realized under the condition of irradiance change, the output power of the photovoltaic module is increased, and the generating capacity is improved. In the simulation time of 1s, the total power generation amount of the photovoltaic module using the improved MPPT is 303.8J, the total power generation amount of the photovoltaic module using the traditional MPPT is 286.8J, and the total power generation amount of the photovoltaic module is improved by 5.9% by using the improved MPPT.
As shown in fig. 4, the conventional algorithm traces for about 0.2s during the 0-0.2 second phase. The tracking ability of two MPPT under the condition of continuous temperature rise is tested within 0.2-0.6 seconds, and the tracking time of the traditional algorithm is respectively 0.17 second and 0.07 second. The tracking capability of two MPPT under the condition of continuous temperature reduction is tested in the stage of 0.6-1.0 second, and it can be seen that the traditional algorithm is slow in response speed and long in tracking time, the two-time stabilization time is 0.10 second and 0.05 second, the optimal duty ratio cannot be accurately found and the MPPT is stabilized at the maximum power point under the condition of temperature change, and the power loss is increased.
As shown in FIG. 5, in the stage of 0-0.2 seconds, the tracking time of the improved algorithm is 0.040 seconds, the tracking time efficiency is improved by 75%, and when the maximum power point is reached, the duty ratio is fixed, the fluctuation-free output power is more stable and the fluctuation is smaller due to the accurate optimization of the particle swarm optimization in winter. The tracking ability of two MPPT under the condition of continuous temperature rise is tested within 0.2-0.6 seconds, the tracking time of the improved algorithm is about 0.03 second, and the response speed of the improved algorithm is faster than 0.03 second. The tracking capabilities of two MPPT under the condition of continuous temperature decrease are tested in the stage of 0.6-1.0 second, the tracking time of the improved algorithm is about 0.03 second and 0.02 second due to the addition of the variable step size and dynamic particle swarm algorithm under the condition, the response speed is higher, the tracking accuracy and the response speed can be rapidly and stably improved, the optimal duty ratio can be accurately found under the condition of temperature change, the optimal duty ratio is stabilized at the maximum power point, and the power loss is reduced.

Claims (4)

1. A maximum power point tracking control method based on a conductance increment method and a particle swarm algorithm is characterized by comprising the following steps:
(1) a quick start stage: increasing a period of dead time during starting, and stopping the DC-DC circuit from working in the dead time of the photovoltaic system;
(2) step length changing tracking stage: judging the disturbance direction according to the positive and negative of dP/dU, and judging the disturbance step length according to the size of | dP/dU |; wherein dP is the output power variation, and dU is the output voltage variation;
(3) and (3) a dynamic particle swarm algorithm stabilization phase: entering dynamic particle swarm optimization when the value of dP/dU is smaller than the starting threshold value of the dynamic particle swarm algorithm, wherein the found optimal value is the optimal output voltage UbestWhen the fitness value is met or the maximum iteration number is reached, outputting the optimal output voltage Ubest(ii) a When the dynamic particle swarm is optimized, irradiance and temperature sensitive particles are used as sensitive particles, when the sensitive particles suddenly change, the particle swarm is updated, and the adaptive value of the sensitive particles is fitness ═ dP/dU |.
2. The method for maximum power point tracking control based on the conductance increment method and the particle swarm optimization according to claim 1, wherein in the step (2), when the disturbance direction is determined according to the positive and negative values of dP/dU, a forward disturbance is given to increase the equivalent resistance when dP/dU >0, a reverse disturbance is given to decrease the equivalent resistance when dP/dU <0, and no disturbance is given when dP/dU is 0.
3. The method for maximum power point tracking control based on the conductance incremental method and the particle swarm optimization according to claim 1, wherein the perturbation step length is determined by the magnitude of | dP/dU | in the step (2), and when | dP/dU | > epsilon, the perturbation step length is α, and when | dP/dU | < epsilon, the perturbation step length is β, where epsilon is a step length selection threshold, and α > β.
4. The method for maximum power point tracking control based on conductance increment method and particle swarm optimization according to claim 1, wherein the fitness value of the particle swarm in the step (3) is
Figure FDA0003218768610000011
P is the power of the load end, and t is the whole working time; speed and bit of the particle swarm algorithmThe update formula is:
Figure FDA0003218768610000012
wherein the content of the first and second substances,
Figure FDA0003218768610000013
indicating the position of the particle at the k-th iteration,
Figure FDA0003218768610000014
denotes the velocity of the particle at the kth iteration, ω is the inertial weight, c1And c2Is an acceleration factor, r1And r2Is [0,1 ]]The random number of (a) is set,
Figure FDA0003218768610000015
representing the individual optima at the k-th iteration,
Figure FDA0003218768610000016
the optimal value of the group in the k iteration is shown, the inertia weight omega determines the speed of the particle swarm, the search speed of the particle swarm is adjusted by adjusting the inertia weight, and the inertia weight is adjusted in a mode that
Figure FDA0003218768610000017
Wherein, ω ismaxIs the maximum inertial weight, ωminIs the minimum inertial weight, kmaxFor the maximum number of iterations, ω (k) is the inertial weight at the kth iteration.
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