TW201506576A - Photovoltaic module array configuration method and system thereof - Google Patents

Abstract

A photovoltaic module array configuration method and system is disclosed and is applied for a photovoltaic module. As the result, the present invention tracks a maximum power point of the photovoltaic module array based on particle swarm optimization (PSO), and obtains the optimized configuration structure of the photovoltaic module array.

Description

Solar photovoltaic module array optimization configuration method and system thereof

The invention relates to a solar photovoltaic module array, in particular to a solar photovoltaic module array optimization configuration method and system thereof for constructing a shadow or fault condition by using a particle swarm algorithm.

Among the many renewable energy sources, solar energy accounts for the most important part. For example, considering the power generation efficiency of the solar photovoltaic module array, it must be set in a place with long sunshine hours and no shade. However, the shade of sunlight is inevitable, and the array of solar photovoltaic modules is placed outdoors for a long time, and may also be affected by the natural environment, such as typhoons, lightning strikes and long-term use, which may easily lead to solar photovoltaic module arrays. Deterioration and failure, once the situation of shading or failure occurs, the power output will be greatly reduced. Therefore, to improve the power generation efficiency of the solar photovoltaic module array, it is the primary task to solve the problem of shading and failure of the solar photovoltaic module array.

However, the conventional solar photovoltaic module array only has the function of fault detection. Although the cause of the fault can be immediately known, it is still necessary for the professional to perform further maintenance to restore the solar photovoltaic module array to normal operation.

In addition, if the solar photovoltaic module array is directly connected to the load of the power supply, the output power will be determined by the power of the load demand, and the power converted by the light energy cannot be effectively outputted. Therefore, there is another conventional solar photovoltaic module array in which the output power is maintained at the maximum power point under any working conditions, and the Maximum Power Point Tracking (MPPT) is used between the solar photovoltaic module array and the load. Connect to let you track the maximum power point in any working situation.

However, when the array of conventional solar photovoltaic modules is shaded or fails, the solar photovoltaic module array is affected and cannot work normally, which will cause a change in the current-voltage (IV) characteristic curve of the solar photovoltaic module array. Its power-voltage (PV) characteristic produces a bimodal or multi-peak phenomenon. In order to solve the above problems, more conventional techniques propose to improve the maximum power tracking method of the solar photovoltaic module array, so that it can avoid tracking the power extreme value falling in the region with multiple peaks, so that the maximum power tracking The device can track to the true maximum power point to reduce the impact of shading or malfunction on the solar module array. Although the above-mentioned further conventional methods can improve the power generation efficiency, since the solar photovoltaic module array still adopts a fixed connection mode, if the shading or the fault condition is too serious, the overall power generation performance will still be poor, so the above-mentioned maximum power tracking The improvement in technology is still limited.

Another conventional technology proposes to divide the solar photovoltaic module array into two parts: the fixed end and the adaptive library. When the solar photovoltaic module array at the fixed end is partially shaded, the array of solar photovoltaic modules adapted to the library is transmitted through the array switch. Connected to the fixed end, so by changing the solar module The characteristics of column connections reduce the negative effects of shading. Although this method can effectively improve the power generation efficiency, an additional number of voltage and current sensors and connection switches are needed to connect the solar photovoltaic module array of the adaptation library to the solar photovoltaic module array at the fixed end. However, if the output power is to be greatly increased, more solar photovoltaic modules must be installed in the adaptation library, which will impose a considerable burden on cost considerations.

In addition, there are also known techniques to change the solar photovoltaic module array that originally used a single set of maximum power trackers to use multiple sets of maximum power trackers, to avoid the influence of a few shaded or faulty solar photovoltaic modules on the solar photovoltaic mode. The power generation efficiency of the array. Although this method can effectively improve the power generation efficiency, if you want to minimize the power generation loss, you must switch each solar photovoltaic module to a set of maximum power tracker, and add additional sets of DC-DC converters. (DC-DC Converter), resulting in an increase in overall costs.

The invention provides a solar photovoltaic module array optimization configuration method and system thereof, which only requires a single group maximum power tracker, and the invention does not need to use too many array switches, thereby reducing the cost.

Therefore, one embodiment of the method aspect of the present invention provides a method for optimally configuring a solar photovoltaic module array, which is applied to a solar photovoltaic module array, and a solar photovoltaic module array optimization configuration method. Including recording an ideal maximum power of the solar photovoltaic module array under a preset condition; tracking the output maximum power of one of the solar photovoltaic module arrays; comparing the ideal maximum power and outputting maximum power; and determining the solar photovoltaic module Whether the array is shaded or faulty, and if the shade or fault occurs, the solar photovoltaic module array is optimally configured.

According to an embodiment of the invention, the step of tracking the maximum output power of the array of solar photovoltaic modules is performed by a Particle Swarm Optimization (PSO) operation. The above method further comprises: an initial step of initializing the relevant parameters of the particle swarm algorithm, and outputting the particle position of the particle swarm algorithm as one of the duty cycles of the solar photovoltaic module array. A calculating step is to detect an output voltage and an output current of the solar photovoltaic module array, and calculate an output power of one of the solar photovoltaic module arrays as one of the optimized objective function values of the particle swarm algorithm. In an alignment step, whether the output power is greater than one of the Particle Best Values of the particle swarm algorithm, and if the output power is greater than the individual optimal fitness value, the individual optimal fitness value is corrected, and the output power is compared. Whether it is larger than the best value of the population of the particle swarm algorithm (Globe Best Value), if the output power is greater than the optimal fitness value of the group, the output power is used to replace the optimal fitness value of the group. An evaluation step that updates the particle velocity and particle position according to the equation of the particle swarm algorithm. In a stack of steps, the repeating calculation step, the comparison step, and the evaluation step satisfy a maximum number of iterations. And a judging step of determining whether the shading or fault condition of the solar photovoltaic module array changes, and if the shading or fault condition changes, the initial step is performed again, and if the shading or the fault condition does not change, the judging step is maintained.

According to another embodiment of the present invention, the determining whether the solar photovoltaic module array is shaded or faulty, and if the shading or the fault occurs, the step of optimizing the arrangement of the solar photovoltaic module array is to adopt a particle. Group algorithm operation. The above steps further comprise an initial step of initializing the relevant parameters of the particle swarm algorithm and outputting the particle position of the particle swarm algorithm as a switch control signal of the solar photovoltaic module array. In a confirmation step, the step of waiting to track the maximum output power of the solar photovoltaic module array is completed. A calculating step is to detect an output voltage and an output current of the solar photovoltaic module array, and calculate an output power of one of the solar photovoltaic module arrays as one of the optimized objective function values of the particle swarm algorithm. A comparison step, whether the output power is greater than the individual optimal fitness value of the particle swarm algorithm, and if the output power is greater than the individual optimal fitness value, the individual optimal fitness value is corrected, and whether the output power is greater than the particle swarm calculus The optimal fitness value of one of the populations is such that if the output power is greater than the optimal fitness value of the population, the output power is used to replace the optimal fitness value of the population. An evaluation step that updates the particle velocity and particle position according to the equation of the particle swarm algorithm. In a stack of steps, the repeating calculation step, the comparison step, and the evaluation step satisfy a maximum number of iterations. And a judging step of determining whether the shading or fault condition of the solar photovoltaic module array changes, and if the shading or fault condition changes, the initial step is performed again, and if the shading or the fault condition does not change, the judging step is maintained.

According to still another embodiment of the present invention, the preset condition of the solar photovoltaic module array is a daylight intensity and a temperature.

One embodiment of a system aspect of the present invention provides a solar photovoltaic module array optimization configuration system applied to a solar photovoltaic module array, and the solar photovoltaic module array optimization configuration system includes one liter. A profiler, a maximum power tracker, at least one connection switch, and an optimized configuration controller. Boost converter electrically connected to solar photovoltaic Module array. The maximum power tracker is electrically connected to the boost converter and the solar photovoltaic module array, and the maximum power tracker tracks the maximum power point of one of the solar photovoltaic module arrays. The connection switch is electrically connected to the solar photovoltaic module array. The optimal configuration controller electrically connects the solar photovoltaic module array, the maximum power tracker and the connection switch, and optimizes the configuration controller to detect one output voltage, one output current and one output power of the solar photovoltaic module array, and the most The configuration controller compares the output power and the maximum power point and outputs a switch control signal corresponding to the control connection switch.

According to another embodiment of the present invention, the optimized configuration controller and the maximum power tracker can adopt a particle swarm algorithm to optimize the configuration and maximum power tracking of the solar photovoltaic module array.

According to still another embodiment of the present invention, the solar photovoltaic module array includes a plurality of solar photovoltaic modules, and the number of connection switches may be plural, and the connection switches respectively correspond to the solar photovoltaic modules. And the solar photovoltaic module can be arranged in all spans to form an array of solar photovoltaic modules. The solar photovoltaic module array optimization configuration system further comprises a constant AC converter, a power supply load and a transistor driving circuit, the direct AC converter is electrically connected to the boost converter, and the power supply load is electrically connected to the direct AC converter. The transistor driving circuit is electrically connected to the transistor and the maximum power tracker. The boost converter can include an input capacitor, an inductor, a transistor, a diode, and an output capacitor, wherein the input capacitor has a first end and a second end, and the first end of the input capacitor and The second end of the input capacitor is electrically connected to the solar photovoltaic module array. The inductor has a third end and a fourth end, and the third end of the inductor is electrically connected to the first end of the input capacitor. The transistor has a fifth end and a sixth end, and the transistor The fifth end of the transistor is electrically connected to the fourth end of the inductor, and the sixth end of the transistor is electrically connected to the second end of the input capacitor. The diode has a seventh end and an eighth end, and the seventh end of the diode is electrically connected to the fifth end of the transistor and the fourth end of the inductor. The output capacitor has a ninth end and a tenth end, and the ninth end of the output capacitor is electrically connected to the eighth end of the diode, and the tenth end of the output capacitor is electrically connected to the sixth end of the transistor and the input capacitor Second end.

Therefore, once the solar photovoltaic module array is partially shaded or fails, the optimal configuration controller of the particle swarm algorithm can be used to control the connection switch, thereby changing the connection mode of the solar photovoltaic module array to enhance The amount of electricity generated by the solar photovoltaic module array. In addition, the maximum power tracker also uses the particle swarm algorithm to perform the maximum power tracking of the solar photovoltaic module array, so as to avoid the situation that the maximum power tracker of the prior art is prone to the maximum power point misjudgment.

100‧‧‧Solar Photovoltaic Module Array

200‧‧‧Boost converter

300‧‧‧Max Power Tracker

400‧‧‧Connecting switch

500‧‧‧Optimized configuration controller

600‧‧‧Crystal drive circuit

700‧‧‧Direct AC Converter

800‧‧‧Power supply load

910~940‧‧‧Steps

920a~920k‧‧‧Steps

920m‧‧‧ steps

921f, 921g, 921h, 921j‧‧ steps

940a~940k‧‧‧Steps

940m‧‧‧ steps

941f, 941g, 941h, 941j‧‧ steps

f‧‧‧Fault solar photovoltaic module

p _best ‧ ‧ _best individual fitness value

g _best ‧‧‧ group best fit value

FIG. 1 is a schematic diagram of a solar photovoltaic module array optimization configuration system according to an embodiment of the present invention.

2 is a flow chart showing the operation of a solar photovoltaic module array optimization configuration system according to an embodiment of the present invention.

Figure 3 is a flow chart showing maximum power tracking in accordance with an embodiment of the present invention.

Figure 4 is a flow chart showing an optimized configuration in accordance with an embodiment of the present invention.

Figure 5 is a diagram showing a solar photovoltaic module according to an embodiment of the present invention. Schematic diagram of the column.

FIG. 6 is a schematic diagram showing a current-voltage characteristic curve of a solar photovoltaic module array optimization configuration system according to an embodiment of the present invention.

Figure 7 is a graph showing changes in population optimal fitness values in accordance with an embodiment of the present invention.

Figure 8 is a schematic illustration of an optimized array in accordance with an embodiment of the present invention.

Fig. 9 is a graph showing the power-voltage (P-V) characteristic before and after comparison in accordance with Fig. 8.

FIG. 10 is a schematic view showing an array of solar photovoltaic modules according to another embodiment of the present invention.

Figure 11 is a graph showing changes in population optimal fitness values in accordance with another embodiment of the present invention.

Figure 12 is a schematic illustration of an optimized array in accordance with another embodiment of the present invention.

Figure 13 is a graph showing the power-voltage (P-V) characteristics of the comparison before and after the configuration of Figure 12.

Figure 14 is a schematic view showing an array of solar photovoltaic modules according to still another embodiment of the present invention.

Figure 15 is a graph showing the variation of the optimal population adaptation value according to still another embodiment of the present invention.

Figure 16 is a schematic illustration of an optimized array in accordance with yet another embodiment of the present invention.

Figure 17 shows the power-voltage (P-V) characteristics of the comparison before and after the configuration of Figure 16. Graph.

Figure 18 is a schematic view showing an array of solar photovoltaic modules according to still another embodiment of the present invention.

Figure 19 is a graph showing changes in population optimal fitness values in accordance with yet another embodiment of the present invention.

Figure 20 is a schematic illustration of an optimized array in accordance with yet another embodiment of the present invention.

Figure 21 is a graph showing the power-voltage (P-V) characteristics of the comparison before and after the configuration of Figure 20.

Please refer to FIG. 1 , which is a schematic diagram of a solar photovoltaic module array optimization configuration system according to an embodiment of the present invention. The solar photovoltaic module array optimization configuration system is applied to a solar photovoltaic module array 100. The solar photovoltaic module array optimization configuration system comprises a boost converter 200, a maximum power tracker 300, and at least one connection. The switch 400, an optimized configuration controller 500, a transistor driving circuit 600, a constant AC converter 700, and a power supply load 800.

The solar photovoltaic module array 100 is formed by arranging a plurality of solar photovoltaic modules (not shown).

The boost converter 200 is electrically connected to the solar photovoltaic module array 100. In this embodiment, the boost converter 200 can include an input capacitor Cin, an inductor L, a transistor M, a diode D, and an output capacitor Cout, wherein the boost converter 200 circuit of the present embodiment Although the architecture is as shown in FIG. 1, the boost converter 200 is a conventional technique, so the above The architecture of the circuit can be varied by those skilled in the art without any limitation. The boost converter 200 performs a DC-DC conversion power supply such that the voltage output from the boost converter 200 is higher than the voltage of the input boost converter 200 of the solar photovoltaic module array 100.

The maximum power tracker 300 is electrically coupled to the boost converter 200 and the solar photovoltaic module array 100, and the maximum power tracker 300 employs a particle swarm algorithm to track the maximum power point of one of the solar photovoltaic module arrays 100.

The connection switch 400 is electrically connected to the solar photovoltaic module array 100 for respectively connecting the plurality of solar photovoltaic modules in the solar photovoltaic module array 100.

The optimal configuration controller 500 is electrically connected to the solar photovoltaic module array 100, the maximum power tracker 300, and the connection switch 400. The optimization configuration controller 500 detects an output voltage and an output current of the solar photovoltaic module array 100. An output power, and the optimized configuration controller 500 and the maximum power tracker 300 adopt a particle swarm algorithm, thereby comparing the output power of the solar photovoltaic module array 100 and the maximum power tracked by the maximum power tracker 300. Pointing and outputting a switch control signal corresponds to controlling the connection switch 400.

The transistor driving circuit 600 is electrically connected to the transistor M and the maximum power tracker 300, and the maximum power tracker 300 outputs a Pulse Width Modulation (PWM) signal, thereby controlling the transistor driving circuit 600.

Straight AC converter 700 is electrically connected to the boost converter 200. Convert the DC power source boosted by the boost converter 200 to an AC power source.

The power supply load 800 is electrically connected to the straight AC converter 700.

The solar photovoltaic module array optimization configuration system of the present embodiment controls the boost converter 200 through the maximum power tracker 300 so that the solar photovoltaic module array 100 can perform maximum power transfer. If the solar photovoltaic module array 100 is operating normally, the optimized configuration controller 500 will not operate and maintain its original connection mode. Conversely, when a shading or fault condition occurs, the optimization configuration controller 500 will detect the output voltage and current of the solar photovoltaic module array 100, and perform an optimized power calculation to calculate the solar photovoltaic module array 100. Optimizing the output power connection structure under shading or fault conditions, and sending a switch control signal to control the connection switch 400, so that the solar photovoltaic modules of the solar photovoltaic module array 100 are connected to the optimized connection. The architecture thereby enhances the output power of the solar photovoltaic module array 100 to reduce the effects of shading or failure on the output power of the solar photovoltaic module array 100.

Referring to FIG. 2, FIG. 3 and FIG. 4 simultaneously, FIG. 2 is a flow chart showing the operation of the solar photovoltaic module array optimization configuration system and a flow chart of maximum power tracking according to an embodiment of the present invention. Flow chart for optimizing the configuration. The operational flow of the solar photovoltaic module array optimization configuration system is as follows: Step 910, recording an ideal maximum power of the solar photovoltaic module array under a preset condition. Step 920, tracking one of the solar photovoltaic module arrays to output maximum power. In step 930, the ideal maximum power and the actual output maximum power are compared. Step 940, determining whether the solar photovoltaic module array is sent A shade or fault condition, if so, an optimal configuration of the solar module array.

For the step of tracking the maximum output power of one of the solar photovoltaic module arrays, please refer to FIG. 3, and the detailed steps are as follows: Step 920a, initialize the relevant parameters of the particle swarm algorithm. In step 920b, the particle of the particle swarm algorithm is set to i , where i =1, 2, 3...N, calculated from i =1. In step 920c, the particle i is output and the position of the particle i is set to be one of the duty cycles of the solar photovoltaic module array. Step 920d: detecting an output voltage of an array of solar photovoltaic modules and an output current. In step 920e, an output power of one of the solar photovoltaic module arrays is calculated, and the output power is one of the optimized objective function values of the particle swarm algorithm. Step 920f, comparing whether the current output power is greater than an individual optimal fitness value p _{best of the} particle swarm algorithm. Step 921f, if yes, correct the individual best fitness value p _best . Step 920g, if the output power is otherwise greater than the optimal fitness value g _best of one of the particle swarm algorithms. Step 921g, if yes, replace the optimal fitness value g _best with the output power. Step 920h, if otherwise confirm that all particles have completed the evaluation. In step 921h, if all the particles have not been evaluated, the evaluation of the next particle i = i +1 is performed. In step 920i, if all particles are evaluated, the particle velocity and particle position are updated according to the equation of the particle swarm algorithm. Step 920j, then confirm whether a maximum number of iterations has been met. Step 921j, if otherwise return to the setting of the particle i to operate again. Step 920k, if yes, output the optimal fitness value of the population. In step 920m, it is determined whether the shading or fault condition of the solar photovoltaic module array is changed. If yes, the steps of initializing the relevant parameters are re-executed, otherwise the step of determining the solar photovoltaic module array is maintained.

In the determination of whether the solar photovoltaic module array is shaded or faulty, if the step of optimizing the arrangement of the solar photovoltaic module array is as follows, please refer to FIG. 4, the detailed steps are as follows: Step 940a, initialize the particle swarm algorithm Related parameters. In step 940b, the particle j of the particle swarm algorithm is set, where j is a positive integer, and is calculated from j =1. In step 940c, the particle i is output and the position of the particle i is set as a switch control signal of the solar photovoltaic module array. In step 940d, an output voltage and an output current of one of the solar photovoltaic module arrays are detected. At step 940e, an output power of one of the solar photovoltaic module arrays is calculated, and the output power is one of the optimized objective function values of the particle swarm algorithm. Step 940f, comparing whether the current output power is greater than an individual optimal fitness value p _{best of the} particle swarm algorithm. Step 941f, if yes, correct the individual best fitness value p _best . Step 940g, if otherwise, the output power is greater than the optimal fitness value g _best of one of the particle swarm algorithms. Step 941g, if yes, replace the optimal fitness value g _best with the output power. Step 940h, if otherwise confirm that all particles have completed the assessment. In step 941h, if all the particles are not evaluated, the evaluation of the next particle j = j +1 is performed. In step 940i, if all particles are evaluated, the particle velocity and particle position are updated according to the equation of the particle swarm algorithm. At step 940j, it is then confirmed whether a maximum number of iterations has been satisfied. Step 941j, if otherwise return to the setting of the particle i , to perform the next iteration. Step 940k, if yes, output the optimal fitness value of the population. In step 940m, it is determined whether the shading or fault condition of the solar photovoltaic module array is changed, and if so, the steps of initializing the relevant parameters are re-executed, otherwise the steps of determining the solar photovoltaic module array are maintained.

The equation for the above particle swarm algorithm is: The relevant parameters of the particle swarm algorithm are: k and k +1 are time points. versus The velocity of particle j at time points k +1 and k , C ₁ and C ₂ are learning factors, and w is inertia weight. For the optimal value of individual particle j at time k , g _best is the optimal fitness value of the group. versus The position of particle j at k +1 and k , respectively, and rand (.) is a random real number between 0 and 1.

Referring to FIG. 5 and FIG. 6 simultaneously, a schematic diagram of a solar photovoltaic module array and an IV (current-voltage) characteristic diagram thereof according to an embodiment of the present invention are shown. In order to carry out the simulation experiment, the solar photovoltaic module array 100 in the embodiment adopts the connection switch 400 to adopt a four-series connection mode for the solar photovoltaic module, and the fifth embodiment shows that the solar photovoltaic module array has three pieces. The solar photovoltaic module is shaded by 30%. And for the solar photovoltaic module to plot the current and voltage in different shade ratios (as shown in Figure 6). In addition, please refer to Table 1, which lists the basic electrical parameters of the solar photovoltaic module, wherein the rated maximum output power (P _max ) is 27.87 watts (W), and the current at the maximum output power point (I _mpp ) is 1.63 amps (A). The maximum output power point voltage (V _mpp ) is 17.1 volts (V), the short circuit current (I _sc ) is 1.82 amps (A), and the open circuit voltage (V _oc ) is 21.6 volts (V).

Then, in this embodiment, the simulation configuration of the solar photovoltaic module array is simulated for different shade ratios in FIG. 6 by using the simulation software (Matlab). Table 2 lists the relevant parameter values of the particle swarm algorithm used in the optimal configuration simulation of the solar photovoltaic module array, where the number of particles is 4, the learning factor C ₁ is 1, the learning factor C ₂ is 2, and the inertia weight w is 0.8 and the number of iterations is 8.

Please refer to FIG. 7 , FIG. 8 and FIG. 9 , which are diagrams showing the optimal group value curve of the particle swarm algorithm of the solar photovoltaic module array in FIG. 5 , and the schematic diagram and configuration after optimization configuration. Power-voltage graph before and after comparison. Figure 8 shows the solar photovoltaic module array after particle swarm optimization, using the optimal configuration controller output control switch signal control connection switch, thereby obtaining the optimal connection mode of the solar photovoltaic module array after shading . According to Figure 9, the maximum output power before and after the optimal configuration of the solar photovoltaic module array is listed in Table 3. From Table 3, it can be clearly seen that the optimal configuration of the solar photovoltaic module array before and after optimization is optimized. After The maximum output power is 8.06W (approximately 3.22%) higher than the maximum output power before the optimized configuration.

Please refer to FIG. 10, which is a schematic diagram of a solar photovoltaic module array according to another embodiment of the present invention. The solar photovoltaic module array 100 in this embodiment is the same as the previous embodiment, and the solar photovoltaic module adopts a four-string triple connection mode, and the solar photovoltaic module array has three solar photovoltaic modules receiving 30%. Shading, while three other solar modules are shaded by 50%.

Please refer to FIG. 11 , FIG. 12 and FIG. 13 , which are diagrams showing the optimal group value curve of the particle swarm algorithm performed by the solar photovoltaic module array 100 of FIG. 10 , and the schematic diagram of the optimal configuration. Power-voltage graph for comparison before and after configuration. Figure 12 shows the solar photovoltaic module array after particle swarm optimization, using the optimal configuration controller output control switch signal control connection switch, thereby obtaining the optimal connection mode of the solar photovoltaic module array after shading . According to Figure 13, the maximum output power before and after the optimal configuration of the solar photovoltaic module array is listed in Table 4. From Table 4, it can be clearly seen that the optimal configuration of the solar photovoltaic module array before and after optimization is optimized. The maximum output power afterwards is 31.4 W (approximately 16.68%) higher than the maximum output power before the optimized configuration.

Table 4 Maximum output power ratio before and after optimal configuration of solar photovoltaic module array

Please refer to FIG. 14, which is a schematic diagram of a solar photovoltaic module array according to still another embodiment of the present invention. In the above embodiment, the solar photovoltaic module array 100 is connected to the solar photovoltaic module by a four-string triple connection method, and the solar photovoltaic module array has a faulty solar photovoltaic module f.

Please refer to FIG. 15 , FIG. 16 , and FIG. 17 , which are diagrams showing the optimal group value curve of the particle swarm algorithm performed by the solar photovoltaic module array 100 of FIG. 14 , and the schematic diagram of the optimal configuration. Power-voltage graph for comparison before and after configuration. Figure 16 shows the solar photovoltaic module array after particle swarm optimization, using the optimal configuration controller output control switch signal control connection switch, thereby obtaining the optimal connection mode of the solar photovoltaic module array after shading . According to Figure 17, the maximum output power before and after the optimal configuration of the solar photovoltaic module array is listed in Table 5. From Table 5, it can be clearly seen that the optimal configuration of the solar photovoltaic module array before and after optimization is optimized. The maximum output power afterwards is 9.51 W (approximately 4.12%) higher than the maximum output power before the optimized configuration.

Please refer to FIG. 18, which is a schematic diagram of a solar photovoltaic module array according to still another embodiment of the present invention. The solar photovoltaic module array in this embodiment is the same as the previous embodiment, and the solar photovoltaic module adopts a four-string triple connection mode, and the solar photovoltaic module array has two fault solar photovoltaic modules f.

Please refer to FIG. 19, FIG. 20 and FIG. 21, which are diagrams showing the optimal group value curve of the particle swarm algorithm of the solar photovoltaic module array in FIG. 18, and the schematic diagram and configuration after optimization configuration. Power-voltage graph before and after comparison. Figure 20 shows the solar photovoltaic module array after particle swarm optimization, using the optimal configuration controller output control switch signal control connection switch, thereby obtaining the optimal connection mode of the solar photovoltaic module array after shading . According to Figure 21, the maximum output power before and after the optimal configuration of the solar photovoltaic module array is listed in Table 6. From Table 6, it can be clearly seen that the optimal configuration of the solar photovoltaic module array before and after optimization is optimized. The maximum output power afterwards is 12.16 W (approximately 5.615%) higher than the maximum output power before the optimized configuration.

According to the above embodiment, the solar photovoltaic module array optimization method and system thereof of the present embodiment provide a solar photovoltaic module array that can arbitrarily adjust the connection mode of the solar photovoltaic module. When the solar photovoltaic module array is partially shaded or faulty, not only the maximum power tracker tracks the correct value of the maximum output power of the solar photovoltaic module array through the particle swarm algorithm, but also optimizes the configuration controller through the particle swarm calculus. The method controls the connection switch to optimize the configuration of the solar photovoltaic module array. Since the particle swarm algorithm is an algorithm developed by observing the predation behavior of bird populations, it has a decentralized search and memory, so that all particles can produce a group effect and gradually approach the global optimal value, thus reducing solar photovoltaic The array of modules is affected by shading or failure. In this way, the solar photovoltaic module array can operate at the maximum power point and improve the overall power generation performance of the solar photovoltaic module array.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention, and the present invention can be modified and modified without departing from the spirit and scope of the present invention. The scope is subject to the definition of the scope of the patent application attached.

100‧‧‧Solar Photovoltaic Module Array

200‧‧‧Boost converter

300‧‧‧Max Power Tracker

400‧‧‧Connecting switch

500‧‧‧Optimized configuration controller

600‧‧‧Crystal drive circuit

700‧‧‧Direct AC Converter

800‧‧‧Power supply load

Claims (15)

A solar photovoltaic module array optimization configuration method is applied to a solar photovoltaic module array, and the solar photovoltaic module array optimization configuration method comprises: recording the solar photovoltaic module array under a preset condition An ideal maximum power; tracking the maximum output power of one of the solar photovoltaic module arrays; comparing the ideal maximum power with the maximum output power; and determining whether the solar photovoltaic module array is shaded or faulty, if shading occurs In the case of a fault, the solar photovoltaic module array is optimally configured. The solar photovoltaic module array optimization method of claim 1, wherein the step of tracking the maximum output power of the solar photovoltaic module array is performed by a particle swarm algorithm. The method for optimizing the configuration of the solar photovoltaic module array of claim 2, wherein the step of tracking the maximum output power of the solar photovoltaic module array further comprises: an initial step of initializing the relevant parameters of the particle swarm algorithm and outputting The particle position of the particle swarm algorithm is a duty cycle of the solar photovoltaic module array; a calculation step of detecting an output voltage and an output current of the solar photovoltaic module array, and calculating the solar photovoltaic module array An output power is One of the particle swarm optimization algorithms optimizes the objective function value; a comparison step, whether the output power is greater than an individual optimal fitness value of the particle swarm algorithm, and if the output power is greater than the individual optimal fitness value, the correction is performed. The optimal fitness value of the individual, and whether the output power is greater than a population optimal fitness value of the particle swarm algorithm, and if the output power is greater than the optimal fitness value of the group, the optimal power of the group is replaced by the output power. Value; an evaluation step of updating the particle velocity and the particle position according to the equation of the particle swarm algorithm; repeating the calculation step, the comparison step, and the evaluating step satisfying a maximum iteration number; and a determining step Determining whether the shading or fault condition of the solar photovoltaic module array changes, if the shading or fault condition changes, the initial step is repeated, and the judging step is maintained if the shading or fault condition does not change. The solar photovoltaic module array optimization method of claim 1, wherein determining whether the solar photovoltaic module array is shaded or faulty, and if the shading or fault occurs, the solar photovoltaic module array is optimal. The steps of the configuration are based on a particle swarm algorithm. The solar photovoltaic module array optimization method of claim 4, wherein determining whether the solar photovoltaic module array is shaded or faulty, if yes, the step of optimizing the solar photovoltaic module array further comprises An initial step of initializing the relevant parameters of the particle swarm algorithm and outputting the particle position of the particle swarm algorithm as one of the solar photovoltaic module arrays a switching control signal; a step of confirming, waiting to track the maximum output power of the solar photovoltaic module array; a calculating step of detecting an output voltage of the solar photovoltaic module array and an output current, and calculating the sun The output power of one of the photoelectric module arrays is one of the optimized particle function values of the particle swarm algorithm; a comparison step, whether the output power is greater than the individual optimal fitness value of the particle swarm algorithm, if the output If the power is greater than the optimal fitness value of the individual, the optimal fitness value of the individual is corrected, and the output power is greater than the optimal fitness value of the population of the particle swarm algorithm, and if the output power is greater than the optimal fitness value of the group, Replacing the optimum fitness value of the group with the output power; an evaluation step of updating the particle velocity and the particle position according to the equation of the particle swarm algorithm; repeating the calculation step, the comparison step, and the evaluation step are satisfied in a stack of steps a maximum number of iterations; and a judging step of determining whether the shading or fault condition of the array of solar photovoltaic modules changes, if shading or Fault situation occurs the change in the initial step of re, or if shade does not change the fault condition determination step is maintained. The solar photovoltaic module array optimization configuration method of claim 1, wherein the preset condition is a daylight intensity and a temperature. A solar photovoltaic module array optimization configuration system is applied to a solar photovoltaic module array, and the solar photovoltaic module array optimization configuration system The system includes: a boost converter electrically connected to the solar photovoltaic module array; a maximum power tracker electrically connected to the boost converter and the solar photovoltaic module array, the maximum power tracker tracking the a maximum power point of one of the solar photovoltaic module arrays; at least one connection switch electrically connected to the solar photovoltaic module array; and an optimized configuration controller electrically connected to the solar photovoltaic module array, the maximum power tracking And the connection switch, the optimization configuration controller detects an output voltage, an output current, and an output power of the solar photovoltaic module array, and the optimized configuration controller compares the output power and the maximum power point And outputting a switch control signal correspondingly controlling the connection switch. The solar photovoltaic module array optimization configuration system of claim 7, wherein the optimized configuration controller adopts a particle swarm algorithm to optimize the solar photovoltaic module array. The solar photovoltaic module array optimization configuration system of claim 7, wherein the maximum power tracker adopts a particle swarm algorithm to perform maximum power tracking of the solar photovoltaic module array. The solar photovoltaic module array optimization configuration system of claim 7, wherein the solar photovoltaic module array comprises a plurality of solar photovoltaic modules, and the number of the connection switches is plural, and the connection switches respectively connect the solar photovoltaics Module. The solar photovoltaic module array optimization configuration system of claim 10, wherein the solar photovoltaic modules are all arranged in a span. The solar photovoltaic module array optimization configuration system of claim 11 further includes a constant AC converter electrically connected to the boost converter. The solar photovoltaic module array optimization configuration system of claim 12 further includes a power supply load electrically connected to the direct current converter. The solar photovoltaic module array optimization configuration system of claim 13, wherein the boost converter comprises: an input capacitor having a first end and a second end, the first end and the first The second end is electrically connected to the solar photovoltaic module array; an inductor having a third end and a fourth end, the third end electrically connected to the first end of the input capacitor; a transistor, the The transistor has a fifth end and a sixth end, the fifth end is electrically connected to the fourth end of the inductor, and the sixth end is electrically connected to the second end of the input capacitor; a diode, the second The pole body has a seventh end and an eighth end, the seventh end is electrically connected to the fifth end of the transistor and the fourth end of the inductor; and an output capacitor having a ninth end and a tenth end, the ninth end is electrically connected to the eighth end of the diode, and the tenth end is electrically connected to The sixth end of the transistor and the second end of the input capacitor. The solar photovoltaic module array optimization configuration system of claim 14 further includes a transistor driving circuit electrically connected to the transistor and the maximum power tracker.