CN108398982A - A kind of maximum power tracking method of photovoltaic array under local shadow - Google Patents

Abstract

The present invention relates to a kind of maximum power tracking methods of photovoltaic array under local shadow, include the following steps：S1：According to the characteristic of photovoltaic cell, the photovoltaic array model under the conditions of local shades is established；S2：Using the QPSO innovatory algorithms based on Bloch spherical surfaces to photovoltaic array model solution, output power is obtained；S3：Using the output power of acquisition as fitness function, by iterative search, the MPPT maximum power point tracking of photovoltaic array is realized.The diversity of particle can be still kept in the iteration later stage based on the QPSO innovatory algorithms of Bloch spherical surfaces, the probability for obtaining globally optimal solution can be improved, the present invention can avoid the oscillation near maximum power point when realizing photovoltaic array maximal power tracing, improve steady-state behaviour.

Description

A Method of Maximum Power Tracking for Photovoltaic Arrays in Partial Shading

technical field

The invention relates to the technical field of photovoltaic power generation, in particular to a method for maximum power tracking of a photovoltaic array under partial shadow.

Background technique

Energy plays an extremely important role in creating new opportunities and promoting economic growth, while the development of the world economy and population growth in turn contribute to the world's energy demand. The core problems of my country's energy structure are as follows: first, the energy structure is dominated by coal; second, the problem of oil safety is becoming more and more prominent; third, soot pollution has brought serious problems to the ecological environment. It can be seen that it is imperative to optimize the energy structure, slowly increase the proportion of green and renewable energy, and reduce the use of fossil energy.

Solar photovoltaic power generation is considered to be the most promising new energy technology in the world at present. All developed countries have invested huge sums of money to compete in research and development, actively promote the process of industrialization, and vigorously develop market applications. However, the photovoltaic power generation industry has also encountered many problems in the development: high cost of photovoltaic cells, low photoelectric conversion efficiency, and the harm of partial shading.

Maximum power point tracking is the most direct and effective method to reduce power generation costs and improve power generation efficiency. Most of the existing maximum power point tracking methods are applied on the premise that the photovoltaic array is evenly illuminated, while ignoring that in real life, the photovoltaic array has a high probability of being blocked. When the photovoltaic array is partially shaded, the traditional maximum power point tracking method is easy to fall into the local optimum and difficult to search for the global optimum.

The perturbation and observation method and the incremental conductance method are the earlier maximum power tracking methods used in photovoltaic power generation systems, and are called traditional maximum power tracking methods. The perturbation and observation method has a simple control idea and is more convenient to implement. It can realize the tracking of the maximum power point and improve the utilization efficiency of the system. However, since the perturbation and observation method only studies the two output powers of the photovoltaic cell before and after, and does not consider the impact of changes in external environmental conditions on the output power of the photovoltaic array before and after the two times, it is prone to "misjudgment" in the process of use. , "Misjudgment" increases the tracking time, reduces the output efficiency of the photovoltaic array, and leads to tracking failure in severe cases, so that the method cannot accurately track the maximum output power.

The conductance incremental method has high tracking accuracy and good control effect, and is not affected by the power-time curve. However, this method has higher requirements on the sensor, and the selection of the step size will also affect the performance of the algorithm, and "misjudgment" will also occur when the external environmental conditions change rapidly.

In recent years, with the continuous improvement of intelligent algorithms, particle swarm algorithm, genetic algorithm, fuzzy control algorithm and neural network algorithm have been introduced into the maximum power tracking control of photovoltaic power generation systems. The use of these algorithms effectively improves the accuracy of maximum power tracking and reduces energy loss. However, intelligent algorithms often have the disadvantages of many control parameters, complex control ideas, and high requirements for hardware, which restricts the engineering practice application of these algorithms to a certain extent, and as the operating environment of photovoltaic arrays becomes more and more complex, Due to the shading of buildings, trees or dust, the uneven light intensity on the surface of the photovoltaic array often occurs. At this time, the power-voltage (P-U) characteristic curve of the photovoltaic array will have multiple peaks. Like the traditional maximum power tracking method, some intelligent algorithms lack the ability of global optimization, and are only suitable for single-peak maximum power tracking systems. When tracking multi-peak systems, they will cause tracking failure. Therefore, it is very important to study a maximum power tracking method with global optimization characteristics to improve the efficiency of photovoltaic power generation.

In the application of photovoltaic arrays, PSO, QPSO and BQPSO algorithms are commonly used algorithms, but the three are prone to "premature" and local convergence problems due to the lack of particle diversity.

Contents of the invention

The object of the present invention is to provide a method for maximum power tracking of photovoltaic arrays under local shadows based on the improved QPSO algorithm of the Bloch sphere and to improve system stability in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A method for maximum power tracking of a photovoltaic array under partial shading, comprising the following steps:

(1) According to the characteristics of photovoltaic cells, a photovoltaic array model under partial shadow conditions is established;

(2) Using the improved QPSO algorithm (IBQPSO algorithm) based on the Bloch sphere to solve the photovoltaic array model to obtain the output power;

(3) Using the obtained output power as the fitness function, through iterative search, the maximum power point tracking of the photovoltaic array is realized.

In the described step (two), the QPSO improved algorithm based on the Bloch sphere specifically includes the following:

1) Set the algorithm parameters and initialize the particle population, initialize the phase of the particles, each particle contains information of three positions, the specific steps include:

101) In Bloch spherical coordinates, a point can be defined by two angles θ and To determine, the qubit is expressed in Bloch spherical coordinates as:

102) Using the Bloch spherical coordinate encoding of the qubit, the Bloch spherical coordinate of the i-th particle P _i in the population is:

In the formula, θ _ij = π×rand, rand is a random number in the [0, 1] interval; i = 1, 2Λ, m, m is the population size; n is the number of optimization variables; in the IBQPSO algorithm, each particle simultaneously occupies Three positions in the space represent three optimized solutions at the same time, namely X solution, Y solution and Z solution:

P _iz ＝(cosθ _i1 ,cosθ _i2 ,Λ,cosθ _in );

103) Record the Bloch coordinates of the j qubit on the i particle P _i as [x _ij , y _ij , z _ij ] ^T , j=1, 2, Λn, n is the number of optimization variables; optimization problem The value range of the jth dimension of the solution space is Then the transformation formula for mapping the unit space I ⁿ = [-1,1] ⁿ to the optimization problem solution space is:

104) End initialization and output initial particle information.

2) Calculate the fitness value of each particle;

3) Update itself and the global optimal phase according to the fitness value of the particle;

4) Using the adaptive quantum revolving gate, the two phase parameters θ and Make adjustments to update the particle's position and map it to the solution space;

The adaptive quantum revolving door is shown in the following formula:

Among them, the definition of the rotation angle α _i corresponding to the current iteration is:

In the formula, α _min is the minimum rotation angle, which is 0.01×pi; α _max is the maximum rotation angle, which is 0.5×pi; f _i refers to the fitness value of the current i-th particle; f _min is the minimum fitness value of contemporary particles ; f _max is the maximum fitness value in the contemporary particle; gen is the current iteration number; maxgen is the maximum iteration number set by the algorithm.

The update formula is as follows:

5) Calculate and evaluate the fitness value of each particle, update itself according to the fitness of the particle, select the individual optimal phase, and obtain the global optimal phase;

According to the fitness value of the particle, the initial position of the particle is judged, and the position of the particle is compared with the position of other particles; the particle with the highest fitness value is the individual optimal particle, and its phase is the individual optimal phase;

Compare the fitness value of the individual optimal particle with the four particles of the upper level. If the fitness value is greater than the highest fitness value of the upper level, the individual optimal particle is the global optimal particle, and its phase is the global optimal particle. Optimal phase; if the fitness value is less than the highest fitness value of the previous level, the global optimal particle is the particle with the highest fitness value of the previous level, and its phase is the global optimal phase.

6) Save the individual optimal phase and judge whether the maximum number of iterations is reached, if not reached, go to step 7), if reached, go to step 8);

7) Select the mutated particles with the mutation probability pa, and use the adaptive quantum revolving door to adjust the two phase parameters θ and Realize the variation of particles, calculate and evaluate the fitness value of the new population, go to step 4);

The adaptive quantum revolving door is shown in the following formula:

Among them, the definition of the rotation angle α _i corresponding to the current iteration is:

For each particle, according to the mutation probability, use the adaptive quantum revolving gate to adjust the two phase parameters θ and To achieve particle mutation, the particle variation formula is as follows:

The update formula is as follows:

8) Output the optimal solution.

In the maximum power tracking method of the photovoltaic array under partial shadow of the present invention, the position of the particle is used to represent the voltage of the photovoltaic array, and the power of the photovoltaic array is output through the photovoltaic array as the fitness function of the particle, and the photovoltaic array is found through continuous iterative search Maximum power to achieve the purpose of optimization.

Compared with the prior art, the present invention has the following advantages:

1. The method of the present invention is based on the Bloch spherical surface, and QPSO is improved. When applied to the maximum power point tracking of photovoltaic arrays, the maximum power point has a faster tracking speed, the dynamic response of the system is improved, and the maximum power point is avoided. nearby oscillations, improving the steady-state performance of the system;

2. The IBQPSO algorithm uses the Bloch sphere for particle encoding. Each particle represents three positions, and the three positions are updated synchronously during the iteration. Maintaining the diversity of particles can improve the ergodicity of the solution space, thereby increasing the probability of obtaining the global optimal solution;

3. The method of the present invention adopts the self-adaptive quantum revolving door to realize the update and variation of particles, and realizes a stable steady-state power output while avoiding falling into a local optimum. For changes in the environment, including local shadows and shadow mutations, it is stable The maximum power point can be found, which enhances the tracking ability of the system.

Description of drawings

Fig. 1 is the flowchart of the inventive method;

Fig. 2 is the flowchart of the QPSO improved algorithm based on the Bloch sphere;

Fig. 3 is the P-U characteristic curve of the photovoltaic array under different illumination conditions of the present embodiment, wherein, Fig. 3 (a) is the P-U characteristic curve of the photovoltaic array under the standard illumination, and Fig. 3 (b) is the shadow situation 1, 2, 3 The P-U characteristic curve of the photovoltaic array under , Fig. 3 (c) is the P-U characteristic curve of the photovoltaic array under the shadow conditions 4, 5 and 6;

Fig. 4 is the P-T curve simulation result figure of applying IBQPSO, PSO, QPSO and BQPSO algorithm under the standard illumination condition of photovoltaic array in the embodiment of the present invention;

Fig. 5 is the P-T curve result figure of applying IBQPSO, PSO, QPSO and BQPSO algorithm to the photovoltaic array in the embodiment of the present invention under six kinds of shadow conditions, wherein, Fig. 5 (a) is the P-T curve of the photovoltaic array under shadow situation 1, Figure 5(b) is the P-T curve of the photovoltaic array under the shadow situation 2, Figure 5(c) is the P-T curve of the photovoltaic array under the shadow situation 3, and Figure 5(d) is the P-T curve of the photovoltaic array under the shadow situation 4 , Fig. 5 (e) is the P-T curve of the photovoltaic array under the shading situation 5, and Fig. 5 (f) is the P-T curve of the photovoltaic array under the shading situation 6;

Fig. 6 is the P-T curve simulation result figure of applying IBQPSO, PSO, QPSO and BQPSO algorithm in the shadow sudden change situation of photovoltaic array in the embodiment of the present invention, wherein, Fig. 6 (a) is the P-T curve of the photovoltaic array under the situation 1, Fig. 6(b) is the P-T curve of the photovoltaic array in case 2, FIG. 6(c) is the P-T curve of the photovoltaic array in case 3, and FIG. 6(d) is the P-T curve of the photovoltaic array in case 4.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

Example

As shown in Figure 1, the present invention relates to a method for maximum power tracking of a photovoltaic array under partial shading, the method comprising the following steps:

(1) According to the characteristics of photovoltaic cells, a photovoltaic array model under partial shadow conditions is established;

(2) Using the improved QPSO algorithm (IBQPSO algorithm) based on the Bloch sphere to solve the photovoltaic array model to obtain the output power;

(3) Using the obtained output power as the fitness function, through iterative search, the maximum power point tracking of the photovoltaic array is realized.

As shown in Figure 2, the QPSO improved algorithm based on the Bloch sphere in the present invention specifically includes the following steps:

1) Set the parameters of the improved QPSO algorithm based on the Bloch sphere, initialize the particle population, and initialize the phase of the particles;

2) Calculate the fitness value of each particle;

3) Update itself and the global optimal phase according to the fitness value of the particle;

4) Using the adaptive quantum revolving gate, the two phase parameters θ and Make adjustments to update the particle's position and map it to the solution space;

5) Calculate and evaluate the fitness value of each particle, update itself according to the fitness of the particle, select the individual optimal phase, and select the global optimal phase;

6) Save the individual optimal phase and judge whether the maximum number of iterations is reached, if not reached, go to step 7), if reached, go to step 8);

7) Select the mutated particle, calculate the adaptive quantum revolving door, and perform step 4) after utilizing the adaptive quantum revolving door to carry out the adaptive particle variation;

8) Output the optimal solution, obtain the voltage change according to the mutated particle phase, and further obtain the output power.

The specific content of step 1) is:

A point in Bloch spherical coordinates can be defined by two angles θ and To determine, the qubit is expressed in Bloch spherical coordinates as:

The Bloch spherical coordinate encoding of qubit is adopted in the BQPSO algorithm, and the IBQPSO algorithm of the present invention adopts the same encoding method, namely:

In the formula, P _i is the Bloch spherical coordinate of the i-th particle in the population; θ _ij = π×rand, rand is a random number in the [0, 1] interval; i = 1, 2Λ, m, m is the population size; n is the number of optimization variables; in the IBQPSO algorithm, each particle simultaneously occupies Three positions in the space represent three optimized solutions at the same time, namely X solution, Y solution and Z solution:

P _iz ＝(cosθ _i1 ,cosθ _i2 ,Λ,cosθ _in )

Record the Bloch coordinates of the j qubit on the i particle P _i as [x _ij , y _ij , z _ij ] ^T , j=1, 2, Λn, n is the number of optimization variables; the optimization problem solution space The value range of the jth dimension of is Then the transformation formula for mapping from the unit space I ⁿ = [-1,1] ⁿ to the solution space of the optimization problem is:

End the initialization and output the initial particle information.

The specific content of step 4) is:

Using the adaptive quantum revolving gate, the two phase parameters θ and Make adjustments to update the particle's position and map it to the solution space;

The adaptive quantum revolving door is shown in the following formula:

The update formula is as follows:

Among them, α _i is the rotation angle corresponding to the current iteration.

The definition of the rotation angle α _i corresponding to the current iteration is:

In the formula, α _min is the minimum rotation angle, which is 0.01×pi; α _max is the maximum rotation angle, which is 0.5×pi; f _i refers to the fitness value of the current i-th particle; f _min is the minimum fitness value of contemporary particles ; f _max is the maximum fitness value in the contemporary particle; gen is the current iteration number; maxgen is the maximum iteration number set by the algorithm.

The specific content of step 5) is:

According to the fitness value of the particle, determine the initial position of the particle; compare the fitness value of the particle with the position of all other particles, the particle with the highest fitness value is the individual optimal particle, and its phase is the individual optimal phase;

Compare the fitness value of the individual optimal particle with the four particles of the upper level. If the fitness value is greater than the highest fitness value of the upper level, the individual optimal particle is the global optimal particle, and its phase is the global optimal particle. Optimal phase; if the fitness value is less than the highest fitness value of the previous level, the global optimal particle is the particle with the highest fitness value of the previous level, and its phase is the global optimal phase.

In order to prove the effectiveness and advantages of the present invention, this embodiment simulates the IBQPSO algorithm for photovoltaic arrays under different conditions, and compares the simulation results with the PSO, QPSO and BQPSO algorithms. In this embodiment, six shadow conditions are designed, and the simulation results of the six shadow conditions and standard lighting conditions are compared and analyzed.

Figure 3 is the power-voltage (P-U) characteristic curve of the photovoltaic array under partial shadow conditions. The photovoltaic array is in the standard situation, that is, the reference temperature and the reference light intensity, and the P-U characteristic curve is shown in Figure 3 (a). In real life, there is a high probability that the photovoltaic array will be shaded. Photovoltaic arrays will produce local shadow problems due to the surrounding shade of trees, dark clouds, houses, etc., the present invention designs six shadow situations, and analyzes the P-U characteristic curve of photovoltaic arrays under partial shadows:

1) Shadow situation 1: The shadow distribution is [3:2:1], where 1B, 1C, 1D irradiance is 800W/m ² , 2C, 2D irradiance is 600W/m ² , and 3D irradiance is 200W /m ² .

2) Shadow situation 2: The shadow distribution is [2:1:0], where the irradiance of 1C and 1D is 800W/m ² , and the irradiance of 2D is 600W/m ² .

3) Shadow situation 3: The shadow distribution is [2:0:0], where the irradiance of 1C and 1D is 800W/m ² , and the PU characteristic curves under shadow situations 1, 2 and 3 are shown in Figure 3(b) :

Analysis of Fig. 3(b) shows that when the light intensity of the photovoltaic cells in the photovoltaic array is different, the P-U characteristic curve of the photovoltaic array presents multiple peaks. The P-U characteristic curves under shaded cases 1, 2, and 3 exhibit four peaks, three peaks, and double peaks, respectively.

4) Shadow situation 4: The shadow distribution is [3:2:1], where the temperature of 1B, 1C, and 1D is 50°C, the temperature of 2C, 2D is 35°C, and the temperature of 3D is 15°C.

5) Shadow situation 5: The shadow distribution is [2:1:0], where the temperature of 1C and 1D is 50°C, and the temperature of 2D is 35°C.

6) Shadow situation 6: The shadow division is [2:0:0], where the temperature of 1C and 1D is 50℃, and the P-U characteristic curves under shadow situations 4, 5 and 6 are shown in Figure 3(c):

The analysis of Fig. 3(c) shows that when the temperature of the photovoltaic cells in the photovoltaic array is different, the P-U characteristic curve of the photovoltaic array will also show multiple peaks. Therefore, it is necessary to carry out the maximum power point tracking of photovoltaic array under the condition of partial shadow caused by different light intensity or different temperature.

Figure 4 is the P-T curve simulation results of the IBQPSO, PSO, QPSO and BQPSO algorithms under the standard illumination conditions of the photovoltaic array. The analysis of Figure 4 shows that the convergence speed of the PSO, QPSO and BQPSO algorithms is slow and the early oscillation is serious; the IBQPSO algorithm can Realize fast and stable steady-state power output, and significantly improve the efficiency of photovoltaic power generation.

Figure 5 is the simulation results of the IBQPSO, PSO, QPSO and BQPSO algorithms under six shadow conditions of the photovoltaic array. In real life, the probability of the photovoltaic array being blocked is very high. And temperature conditions, the P-U curve of the photovoltaic array presents a multi-peak characteristic.

The convergence times of the four methods under standard lighting conditions and six shade conditions are shown in Table 1:

Table 1 Simulation convergence time of four methods

Based on the analysis of the simulation results under standard lighting conditions, six shades and Table 1, it is concluded that the PSO, QPSO and BQPSO algorithms converge slowly and will fall into local optimum in multi-peak conditions. The IBQPSO algorithm has a fast convergence speed, can achieve stable steady-state power output, and significantly improve the efficiency of photovoltaic power generation.

For shadow mutation, this embodiment designs four mutation situations, as follows:

1) Standard lighting → shadow case 1 → shadow case 2 → shadow case 3

2) Shadow situation 3 → shadow situation 2 → shadow situation 1 → standard lighting

3) Standard Lighting→Shadow Situation 4→Shadow Situation 5→Shadow Situation 6

4) Shadow situation 6 → Shadow situation 5 → Shadow situation 4 → Standard lighting

Aiming at the above four mutation situations, IBQPSO, BQPSO, QPSO and PSO algorithms are used to simulate respectively, and the specific simulation results are shown in Figure 6. It can be seen from Figure 6 that the PSO algorithm has a slow convergence speed and severe oscillations at sudden changes in light intensity and temperature, and may fall into local optimum at the same time, so that the efficiency of photovoltaic power generation cannot be improved. Although the QPSO and BQPSO algorithms converge faster than the PSO algorithm, and the oscillation at the sudden change is smaller than the PSO algorithm, they may still fall into a local optimum, so that the efficiency of photovoltaic power generation cannot be improved. The IBQPSO algorithm has the fastest convergence speed and there is no oscillation near the maximum power point, which realizes a stable steady-state power output and significantly improves the efficiency of photovoltaic power generation.

In summary, the IBQPSO algorithm can quickly and efficiently converge to the global maximum power point no matter when the light intensity suddenly increases or decreases. Due to temperature changes, the P-U curve of the photovoltaic array will also show a multi-peak phenomenon. It is obtained through simulation that no matter the temperature suddenly rises or falls, it can quickly and efficiently converge to the global maximum power point. Therefore, the IBQPSO algorithm can quickly and efficiently track the global maximum power point under complex shading conditions, and significantly improve the efficiency of photovoltaic power generation.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any worker familiar with the technical field can easily think of various equivalents within the technical scope disclosed in the present invention. Modifications or replacements shall all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (8)

1. a kind of maximum power tracking method of photovoltaic array under local shadow, which is characterized in that include the following steps：

S1：According to the characteristic of photovoltaic cell, the photovoltaic array model under the conditions of local shades is established；

S2：Using the QPSO innovatory algorithms based on Bloch spherical surfaces to photovoltaic array model solution, output power is obtained；

S3：Using the output power of acquisition as fitness function, by iterative search, realize the maximum power point of photovoltaic array with Track.

2. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 1, which is characterized in that In the step S2, the QPSO innovatory algorithms based on Bloch spherical surfaces include the following steps：

1) parameter of the QPSO innovatory algorithms based on Bloch spherical surfaces is set, the phase of particle populations and particle is initialized；

2) fitness value of each particle is calculated；

3) itself and global optimum's phase are updated according to the fitness value of particle；

4) adaptive Quantum rotating gate is utilized, the phase parameter of quantum bit is adjusted, realizes the location updating of particle, and Map that solution space；

5) it calculates each particle fitness value and evaluates, itself is updated according to the fitness of particle, selects individual optimum angle, and select Go out global optimum's phase；

6) it preserves individual optimum angle and judges whether to reach maximum iteration, if not up to, going to step 7), if reaching, Go to step 8)；

7) particle is selected according to mutation probability, calculates adaptive Quantum rotating gate, carried out using adaptive Quantum rotating gate adaptive After particle variations, step 4) is executed；

8) optimal solution is exported.

3. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 2, which is characterized in that In the step 4) and step 7), phase parameter include two phase parameter θ and

4. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 3, which is characterized in that The particular content of the step 1) is：

101) by phase parameter θ andPoint of two angle-determinings under Bloch spherical coordinates, quantum bit Bloch spherical coordinates It is expressed as：

102) the Bloch spherical coordinates of quantum bit are used to encode, then i-th of particle P in population_iBloch spherical coordinates be：

In formula,θ_ij=π × rand, rand are the random number in [0,1] section；I=1,2 Λ, m, m are population Scale；N is the number of optimized variable；In IBQPSO algorithms, each particle takes up space three positions simultaneously, i.e., represents simultaneously Three optimization solutions, respectively X solutions, Y solutions, Z solutions：

P_iz=(cos θ_i1,cosθ_i2,Λ,cosθ_in)

103) remember i-th of particle P_iOn j-th of quantum bit Bloch coordinates be [x_ij,y_ij,z_ij]^T, j=1,2, Λ n, n are excellent Change the number of variable；The value range of the jth dimension of optimization problem solution space is [a_j,b_j], then by unit space Iⁿ=[- 1,1]ⁿ The transformation for mula for being mapped to optimization problem solution space is：

104) terminate initialization, export primary information.

5. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 4, which is characterized in that The particular content of the step 4) is：

Using adaptive Quantum rotating gate, two phase parameter θ to quantum bit andIt is adjusted, realizes the position of particle Update, and map that solution space；

Adaptive Quantum rotating gate U is shown below：

More new formula is shown below：

Wherein, α_iFor the corresponding rotation angle of current iteration.

6. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 5, which is characterized in that The corresponding rotation angle α of the current iteration_iDefinition be：

In formula, α_minIt is minimum rotation angle；α_maxIt is maximum rotation angle；f_iIt refer to the adaptive value of current i-th of particle；f_minIt is the present age Minimum adaptive value in particle；f_maxIt is the maximum adaptation value in contemporary particle；Gen is current iterations；Maxgen is to calculate The maximum iteration of method setting.

7. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 6, which is characterized in that The particular content of the step 5) is：

According to the fitness value of particle, the initial position of particle is judged, and by the position of the position of this particle and other particles It is compared；The highest particle of fitness value is individual optimal particle, and phase is individual optimum angle；By individual optimal particle It is compared with the fitness value of four particles of upper level, it, should if fitness value is more than the highest fitness value of upper level Individual optimal particle is global optimum's particle, and phase is global optimum's phase；If the highest that fitness value is less than upper level is suitable Angle value is answered, then global optimum's particle is upper level highest fitness value particle, and phase is global optimum's phase.

8. a kind of maximum power tracking method of photovoltaic array under local shadow according to claim 6, which is characterized in that The step 7) is identical as the corresponding rotation angle of current iteration in the adaptive Quantum rotating gate that step 4) uses.