CN107193320A - A kind of local shades Photovoltaic array MPPT controls based on Molecule Motion Theory optimized algorithm - Google Patents

Abstract

The invention discloses a partial shadow photovoltaic array MPPT control method based on molecular kinetic theory optimization algorithm. The output current of the photovoltaic array is used as the particle of the algorithm, and the power of the photovoltaic array is used as the fitness function of the algorithm. The optimization algorithm is a new type of fast and global search technology, which efficiently and accurately finds the global maximum power point of the photovoltaic array, and then the algorithm outputs the voltage of the maximum power point and makes a difference with the actual output voltage of the photovoltaic array. The circuit is controlled to achieve maximum power point tracking. This algorithm can solve the problem that the MPPT control of photovoltaic arrays with multi-peak output characteristics under local shadows is easy to fall into the local maximum power point, and can accurately find the global maximum power point, and the tracking speed is fast and the steady-state oscillation is small.

Description

MPPT control of partially shadowed photovoltaic array based on molecular kinetic theory optimization algorithm

technical field

The invention relates to a method for controlling MPPT of a partially shaded photovoltaic array, in particular to an MPPT control method for partially shaded photovoltaic arrays based on molecular dynamic theory optimization algorithms.

Background technique

With the popularization of solar power generation, the operating environment of photovoltaic arrays has become more and more complex. Partial shading causes multiple extreme points in the output characteristic curve of photovoltaic arrays, which affects the output efficiency of photovoltaic arrays, and even damages them due to hot spots. PV. In order to make the photovoltaic system work at the maximum power point and improve system efficiency, it is necessary to use an effective multi-peak maximum power point tracking algorithm.

Traditional maximum power point tracking algorithms (such as fixed voltage method, perturbation and observation method, incremental conductance method, etc.) can better find the maximum power point under uniform illumination conditions. However, under partial shadow lighting conditions, the power-voltage curve of the photovoltaic array has multiple peak points, and the conventional maximum power point tracking algorithm cannot accurately track the maximum power point because it falls into a local extreme point. With the rise of intelligent control methods, intelligent control methods represented by particle swarm algorithm have become typical representatives of modern control theory, and are used in the multi-peak MPPT control problem of photovoltaic arrays under local shadows, but these algorithms have many random parameters. , the convergence speed is slow, and it may fall into a local extremum when it is not handled well, and oscillation will appear at the maximum power point.

Contents of the invention

Purpose of the present invention: To solve the problem of the existing control technology of photovoltaic array MPPT under partial shadow, to propose a partial shadow photovoltaic array MPPT control based on molecular dynamic theory optimization algorithm with few adjustment parameters, fast search speed, accurate and stable search results method.

The object of the present invention is achieved through the following technical solutions:

A partially shadowed photovoltaic array MPPT control based on molecular kinetic theory optimization algorithm, including the following steps:

Step 1: Collect the ambient temperature T and light intensity S of each photovoltaic array in real time;

Step 2: According to the current ambient temperature and light intensity of each photovoltaic array, iteratively search for the total maximum power point of the photovoltaic array by using the molecular kinetic theory optimization algorithm;

Step 3: The molecular kinetic theory optimization algorithm iteratively searches out the difference between the maximum power point voltage corresponding to the total maximum power point of the photovoltaic array and the actual output voltage of the photovoltaic array, and then sends it to the PWM module for generating PWM waves;

Step 4: Use the PWM wave generated by the PWM module to control the Boost circuit to make the photovoltaic array work stably at the maximum power point.

Further, in step 2, the method of iteratively searching for the total maximum power point of the photovoltaic array using the molecular kinetic theory optimization algorithm is:

Step 2.1: Input data such as ambient temperature T and light intensity S of each photovoltaic array, and set the number and value range of the population (control variable I) of the algorithm;

Step 2.2: Initialize the KMTOA algorithm, and randomly generate the initial velocity and initial position of the group individual within the constraint range;

Step 2.3: For the individuals in the population, calculate the fitness function according to the power of the photovoltaic array to obtain the corresponding fitness value (power and voltage) of each population individual;

Step 2.4: Select the optimal individual in the population according to the size of the power;

Step 2.5: Update the number of iterations k=k+1;

Step 2.6: According to the distance between the population individual and the optimal individual, judge its stress situation, and calculate the gravitational acceleration, repulsive acceleration and disturbance acceleration of the individual;

Step 2.7: Update the speed of the population individual according to the speed update formula of the algorithm, and update the position of the population individual according to the position update formula of the algorithm. If the individual speed exceeds the boundary, assign the boundary value to the current individual speed;

Step 2.8: Recalculate the fitness value of each individual, and judge whether the stop criterion is met according to the fitness value of each individual. If yes, go to step 2.9, otherwise go to step 2.5:;

Step 2.9: Output the optimal solution, and the algorithm ends.

Furthermore, what is used in the step 3 is PWM control technology, which is also called pulse width modulation technology, and the molecular kinetic theory optimization algorithm is iteratively searched for the maximum power point voltage corresponding to the total maximum power point of the photovoltaic array and The actual output voltage of the photovoltaic array is different, and the obtained deviation value is modulated with the carrier signal, and the PWM pulse wave (duty cycle signal) is output to control the subsequent boost chopper circuit;

Finally, the follow-up circuit using maximum power tracking control in step 4 is a boost chopper (Boost) circuit, which controls the duty cycle of the switching tube through PWM control technology, so that the equivalent resistance of the boost circuit is equal to the photovoltaic The internal resistance of the array under real-time environmental conditions, so as to realize the maximum output power control of the photovoltaic array.

Working principle: the MPPT control method based on molecular kinetic theory optimization algorithm used in the present invention, in the algorithm, the output current I of the photovoltaic array is used as the particle of the algorithm, and the mathematical model of the output power under the partial shadow of the photovoltaic array is used as the fitness function, By obtaining the light intensity and ambient temperature of each component in the photovoltaic array, the algorithm can iteratively search for the total maximum power and corresponding voltage of the photovoltaic array in real time and efficiently, and cooperate with the Boost circuit to make the photovoltaic array run stably at the maximum power point out. The algorithm concept of the present invention is simple, with few adjustment parameters, fast and accurate tracking speed, relatively stable voltage output and current output, almost negligible steady-state oscillation, and can effectively improve the power generation efficiency of the photovoltaic system.

Description of drawings

Fig. 1: is the schematic diagram of system circuit structure of the present invention;

Fig. 2: is the algorithm flowchart of the present invention;

detailed description

In order to clarify the features and advantages of the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings.

As shown in Fig. 1, the present invention realizes the MPPT control of the photovoltaic array under partial shadow based on the molecular dynamic theory optimization algorithm. At this time, the output of the photovoltaic array presents a multi-peak U-P characteristic curve. The present invention uses the molecular kinetic theory optimization algorithm as the MPPT control module, and the light intensity and ambient temperature information of the photovoltaic array are transmitted to the MPPT module, and the maximum power point voltage is output, which is consistent with the photovoltaic The deviation value is obtained by comparing the real-time voltage of the array, and combined with the PWM module and the Boost circuit, the real-time maximum power point tracking of the photovoltaic array is realized. In this embodiment, a simulation model is built under the MATLAB environment according to the method and working principle of the present invention.

Fig. 2 is a flow chart of the molecular kinetic theory optimization algorithm adopted in the present invention. Molecular kinetic theory optimization algorithm is based on the following rules existing between molecules: when When , the resultant force on the molecule is 0, and this position is the equilibrium position. when , the molecular resultant force is the repulsive force at this time, because the molecular repulsive force changes faster than the attractive force. when , the resultant molecular force is attraction at this time, also because the repulsive force changes faster than the attractive force. According to the rules of attraction and repulsion between molecules in molecular kinetic theory, the conditions that molecules must satisfy when they are subjected to attraction, repulsion and no force are proposed; for molecules without force, the individual can jump out of the local solution by simulating the molecular thermal motion.

when , the molecules are attracted. Other individuals in the population move towards the optimal individual, and its gravitational calculation formula is:

Among them: G is the gravitational constant, M _i and M _Best are the masses of individual Xi and the best individual X _Best respectively, and F _{i represents} the gravitational force on individual Xi _. According to Newton's theorem, the calculation formula of gravitational acceleration a _i of individual X _i can be known from the above formula:

when , the resultant force of the molecules acts as a repulsive force at this time. The population individual moves towards the optimal individual direction, and the repulsive force calculation formula is:

At this time, the calculation formula of the repulsive acceleration a _i is:

when At this time, the resultant force on the molecule is zero, and it is in an equilibrium position. The individual random disturbance acceleration is , where: a _ij is the acceleration of individual X _i in dimension j, are the upper and lower bounds of the jth dimension of the solution space, respectively. A is the vibration amplitude. In this paper, A=(1-0.9*t/G) is taken, where t is the current iteration number, G is the total iteration number; N(0,1) is a random number that obeys a normal distribution.

The speed update formula of individual _Xi in the group:

Individual X _i position update formula:

The process of searching and solving the maximum power point of photovoltaic array based on molecular kinetic theory optimization algorithm is shown in Figure 2:

Step 1: Initialize the KMTOA algorithm, set the population size of the algorithm and the algorithm termination condition, and randomly generate the initial velocity and initial position of the group individual within the range of the photovoltaic array current [0, I _sc ];

Step 2: Determine the objective function. In this algorithm, the output current I of the photovoltaic array is used as the variable of the algorithm, and the output power of the photovoltaic array is used as the adaptive value. The objective function of the algorithm is obtained by referring to the mathematical model of the photovoltaic array under partial shadow, and the calculation Individual fitness value, and select the best individual X _Best ;

In the example of the present invention, three photovoltaic modules are connected in series and irradiated with different intensities of light to simulate local shadow conditions, and the objective function is:

Among them: I _sc1 , I _sc2 , and I _sc3 are the short-circuit currents of each photovoltaic module under different light intensities, and I _sc3 <I _sc2 <I _sc1 , U(S ₁ , 2, 3 , I) respectively represent the short-circuit current of each photovoltaic module under different light intensities. The output voltage of each photovoltaic module under intensity.

Step 3: According to the distance between the population individual and the optimal individual, determine the individual's force-bearing mode, and calculate the gravitational acceleration, repulsive acceleration and disturbance acceleration respectively;

Step 4: According to the previous speed update formula and position update formula, calculate the speed of the individual and move the individual;

Step 5: Perform elite retention processing on the best individuals in the population;

Step 6: Judging whether the termination condition is satisfied, if not, return to step 2, otherwise, output the maximum power point voltage found by the solution.

Further, as shown in Figure 1, the maximum power point voltage output by the MPPT module based on the molecular kinetic theory optimization algorithm is subtracted from the output voltage of the photovoltaic array collected in real time to obtain a deviation value, which is modulated with the carrier signal of the PWM module. Output the PWM duty ratio control value, and then control the Boost circuit, so that the photovoltaic array can run stably at the maximum power.

In addition, since the output characteristics of the photovoltaic array will also change when the external environment changes, the molecular kinetic theory optimization algorithm should be restarted at this time to find a new global maximum power point. The start condition is as follows:

When the power output changes suddenly and satisfies the above formula, it can be considered that it is necessary to search for the maximum power again. In addition, when the environment changes slowly, the algorithm should also be able to track the change of the maximum power, so the molecular kinetic theory optimization algorithm can be set to restart naturally with time, and after a period of time, the algorithm will automatically restart to search for a new global maximum power point. If the above conditions are met, go back to step 1 of the algorithm, otherwise, continue the Boost circuit to maintain the maximum power point.

Claims (4)

1. it is a kind of based on Molecule Motion Theory optimized algorithm local shades Photovoltaic array MPPT control, it is characterised in that including with Lower step：

Step 1.1：The environment temperature T and intensity of illumination S of each Photovoltaic array are gathered in real time；

Step 1.2：According to the environment temperature and intensity of illumination of each current Photovoltaic array, changed using Molecule Motion Theory optimized algorithm In generation, searches out total maximum power point of Photovoltaic array；

Step 1.3：Molecule Motion Theory optimized algorithm iterative search is gone out to the corresponding maximum work of total maximum power point of Photovoltaic array Rate point voltage and the voltage of Photovoltaic array reality output make poor, PWM module are then fed into, for producing PWM ripples；

Step 1.4：The PWM ripples produced using PWM module control Boost circuit, make Photovoltaic array steady operation in maximum work On rate point.

2. the local shades Photovoltaic array MPPT controls according to claim 1 based on Molecule Motion Theory optimized algorithm, its It is characterised by：Go out total maximum power point of Photovoltaic array in the step 1.2 using Molecule Motion Theory optimized algorithm iterative search Method be：

Step 2.1：The data such as the environment temperature T and intensity of illumination S of each Photovoltaic array are inputted, the population of algorithm is set（Control Variable I）Quantity and span；

Step 2.2：KMTOA algorithms are initialized, randomly generated in restriction range colony individual initial velocity and Initial position；

Step 2.3：To the individual in colony, each population at individual correspondence is drawn according to the power calculation fitness function of Photovoltaic array Voltage and power（Adaptive value）；

Step 2.4：According to the size of power, the optimum individual in population is selected；

Step 2.5：Update iterations k=k+1；

Step 2.6：According to population at individual and optimum individual distance, its stressing conditions is judged, calculate gravitational acceleration, the reprimand of individual Power acceleration and disturbance acceleration；

Step 2.7：According to the speed of the speed of algorithm more new formula Population Regeneration individual, and according to the location updating formula of algorithm Boundary value, if individual speed crosses border, is assigned to current individual speed by the position of Population Regeneration individual；

Step 2.8：The adaptive value for calculating each individual is re-started, and symbol is judged whether according to each individual adaptive value size Stopping criterion is closed, if met, step 2.9 is turned to, otherwise turns to step 2.5；

Step 2.9：Optimal solution is exported, algorithm terminates.

3. the local shades Photovoltaic array MPPT controls according to claim 1 based on Molecule Motion Theory optimized algorithm, its It is characterised by：PWM control technologies are used in described step 1.3, the technology is also known as pulse width modulating technology, will divided The dynamic theoretical optimization algorithm iteration of son searches out the corresponding maximum power point voltage of total maximum power point of Photovoltaic array and photovoltaic is arranged The voltage of battle array reality output makees poor, and obtained deviation and carrier signal are modulated, and exports pwm pulse ripple（Dutycycle is believed Number）, follow-up boost chopper is controlled.

4. the local shades Photovoltaic array MPPT controls according to claim 1 based on Molecule Motion Theory optimized algorithm, its It is characterised by：The subsequent conditioning circuit of the maximal power tracing control used in described step 1.4 is boost chopper （Boost）, it is controlled by the dutycycle of PWM control technology switch tubes, the equivalent resistance of booster circuit is equal to photovoltaic Internal resistance of the array under real-time environmental conditions, so as to realize the peak power output control of photovoltaic array.