TWI902272B - Photovoltaic module system and maximum power tracking method - Google Patents

Abstract

A maximum power tracking method is proposed. The maximum power tracking method is configured to track the maximum output power of a photovoltaic module array. The maximum power tracking method includes using a controller to execute an improved bat algorithm, and searching for an optimal solution among multiple bats in the improved bat algorithm to obtain maximum output power. The controller outputs a pulse width modulation signal with a corresponding duty cycle based on the optimal solution, and transmits the pulse width modulation signal to a boost converter, and drives the boost converter to provide maximum output power to the load. Thus, the maximum power tracking method of the present disclosure can improve the tracking efficiency of the maximum output power while maintaining the tracking stability.

Description

Methods for tracking the maximum output power of solar photovoltaic module systems.

This disclosure relates to a method for tracking maximum output power, and more particularly to a solar photovoltaic module system using a modified bat algorithm and a method for tracking maximum output power.

Solar energy is an ideal alternative energy source because it is not limited by geographical conditions. To improve the power generation efficiency of solar photovoltaic power generation systems, maximum power point tracking (MPPT) controllers are generally used for maximum power tracking. Commonly used MPPT techniques include voltage feedback, constant voltage method, power feedback method, perturbation and observation (P&O) method, and incremental conductance method (INC). However, these traditional methods may not be able to track the global maximum power point when the solar photovoltaic module array experiences partial shading or a fault, and may only be able to track a local maximum power point.

In view of this, there is currently a lack of a tracking method on the market that can avoid tracking only the local maximum power and quickly track the maximum power of the entire solar photovoltaic module array. Therefore, relevant industry players are looking for solutions.

The purpose of this disclosure is to provide a method for tracking the maximum output power of a solar photovoltaic module system. It employs an improved bat algorithm to enhance the tracking efficiency of the maximum output power while maintaining stability.

According to one embodiment of the present invention, a method for tracking maximum output power is provided for tracking the maximum output power of a solar photovoltaic module array and providing the maximum output power to a load through a boost converter. The maximum output power tracking method includes: executing a modified bat algorithm by a controller, and searching for an optimal solution among a plurality of bats in the modified bat algorithm to obtain the maximum output power. The modified bat algorithm includes: presetting a number of bats, a maximum number of iterations, a pulse frequency range, and a maximum pulse rate. Initializing the bats' position, pulse frequency, flight speed, number of iterations, and pulse emission rate. A power-voltage characteristic curve is obtained from a solar photovoltaic module array. A slope is calculated based on the iteration number and the power-voltage characteristic curve, and an optimal position and its corresponding fitness are calculated based on the slope. The iteration number is updated, and an update random number is generated. It is determined whether the update random number is greater than a current pulse emissivity, resulting in an update method selection result. Based on the update method selection result, one of a first update method and a second update method is selected to update the optimal position and fitness. The optimal position and fitness are then considered the best solution. The controller outputs a pulse width modulation signal corresponding to one duty cycle based on the optimal solution, and sends the pulse width modulation signal to the boost converter to drive the boost converter to provide maximum output power to the load.

A solar photovoltaic module system is provided according to one embodiment of the present invention for providing a maximum output power to a load. The solar photovoltaic module system includes a solar photovoltaic module array, a controller, and a boost converter. The controller is electrically connected to the solar photovoltaic module array and is configured to execute a modified bat algorithm, searching for an optimal solution among a complex number of bats to obtain the maximum output power. The modified bat algorithm includes: presetting a number of bats, a maximum number of iterations, a pulse frequency range, and a maximum pulse rate; initializing a position, a pulse frequency, a flight speed, a number of iterations, and a pulse emission rate of these bats. A power-voltage characteristic curve is obtained from a solar photovoltaic module array. A slope is calculated based on the iteration number and the power-voltage characteristic curve, and an optimal position and its corresponding fitness are calculated based on the slope. The iteration number is updated, and an update random number is generated. It is determined whether the update random number is greater than a current pulse emissivity, resulting in an update method selection result. Based on the update method selection result, one of a first update method and a second update method is selected to update the optimal position and fitness. The optimal position and fitness are then considered the best solution. The boost converter is electrically connected to a solar photovoltaic module array and a controller to obtain a pulse width modulation signal corresponding to one duty cycle from the controller, and provides maximum output power to the load driven by the pulse width modulation signal.

Referring to Figures 1 through 3, Figure 1 is a schematic diagram of a solar photovoltaic module system 100 according to a first embodiment of the present invention; Figure 2 is a block diagram illustrating the steps of a maximum output power tracking method 200 according to a second embodiment of the present invention; and Figure 3 is a flowchart illustrating the steps of the modified bat algorithm according to Figure 2. The solar photovoltaic module system 100 is configured to implement the maximum output power tracking method 200 to track the maximum output power of a solar photovoltaic module array 110 and provide the maximum output power to a load R_{_Load} . It should be noted that the maximum output power tracking method 200 of the present invention is not limited to implementation through the solar photovoltaic module system 100 of the present invention.

A solar photovoltaic module system 100 includes a solar photovoltaic module array 110, a controller 120, and a boost converter 130. The controller 120 is electrically connected to the solar photovoltaic module array 110, and the boost converter 130 is electrically connected to both the solar photovoltaic module array 110 and the controller 120. The boost converter 130 includes a drive circuit 131. The controller 120 receives current from the solar photovoltaic module array 110. and voltage The maximum output power is calculated, and a pulse width modulation (PWM) signal corresponding to one duty cycle is output to the driver circuit 131, thereby changing the power of the solar photovoltaic module array 110 and providing the maximum output power to the load _RLoad . The solar photovoltaic module array 110 can be composed of a plurality of solar photovoltaic modules. In the first embodiment, the solar photovoltaic module array 110 adopts the SWM20W solar photovoltaic module manufactured by MPPTSUN Co. Ltd.; the controller 120 can be a digital signal processor (DSP) of model TMS320F2809; the boost converter 130 can be a DC/DC boost converter, but the present disclosure is not limited to the above. In the first embodiment, the controller 120 simulates and obtains the power-voltage (PV) characteristic curve of the solar photovoltaic module system 100 using MatLab/Simulink software.

Referring to Figures 1 and 2, the maximum output power tracking method 200 includes steps S1 and S2. In step S1, the controller 120 executes a modified bat algorithm and searches for an optimal solution among complex bats to obtain the maximum output power. Furthermore, step S1 includes setting a preset starting tracking voltage, which is 0.8 times the maximum power point voltage under a standard test condition (STC). Specifically, the standard test condition is a solar irradiance of 1000 W/ ^m² , a temperature of 25°C, and an air mass index (AM) of 1.5. In step S2, the controller 120 outputs a PWM signal with the corresponding duty cycle according to the optimal solution and sends the PWM signal to the boost converter 130 to drive the boost converter 130 to provide the maximum output power to the load _RLoad .

Referring to Figures 1 to 3, the improved bat algorithm executed by controller 120 in step S1 includes steps S10, S20, S30, S40, S50, S60, S70, and S80. In step S10, a number of bats N, a maximum iteration count Iter_max, a pulse frequency range, and a maximum pulse rate are preset. In step S20, each bat's position x , pulse frequency F , flight speed v , iteration count t , pulse emission rate r , and loudness A are initialized. Specifically, the iteration count t is set to 0, the pulse emission rate r is set to 0, and the loudness A is set to be greater than 0.5 and less than or equal to 1. In step S30, the PV characteristic curve is obtained from the solar photovoltaic module array 110, and a slope m is calculated based on the iteration number t and the PV characteristic curve. Based on the slope m, an optimal position x _best and its corresponding fitness are calculated and recorded (i.e., the optimal fitness of each bat corresponding to the optimal position x _best is calculated).

In step S40, the iteration number t is updated, and an update random number rand is generated. It is determined whether the update random number rand is greater than the current pulse emission rate _ri , and an update method selection result is generated. Based on the update method selection result, one of a first update method and a second update method is selected to update the optimal position xbest and _fitness , and _the optimal position xbest and fitness are taken as the optimal solution.

Specifically, step S40 includes steps S41, S42 , S43, S44, and S45. In step S41, the iteration number t is updated, set to t = t + 1, and the i- th bat is set to the first bat. In step S42, it is determined whether the updated random number rand is greater than the current pulse emission rate _ri . In step S43, the controller 120 executes the first update method, updating the position x of the i -th bat with a first update formula, where the first update formula is as follows: .

Where t is the number of iterations, and Let represent the position of the i- th bat in the series at iteration number t and t -1. It is a random value. Slope; random value Greater than or equal to -0.25 and less than or equal to 0.

In step S44, controller 120 executes the second update method, updating the pulse frequency F and flight speed v of the i - th bat, updating the position x using a second update formula, and finally updating the loudness A and pulse emission rate r of the i - th bat, wherein the second update formula is as follows: .

in, and Let i be the position of the i - th bat among the bats. This refers to flight speed.

In addition, the pulse frequency F , flight speed v , loudness A and pulse emission rate r are obtained by the following equations (1), (2), (3) and (4), respectively. (1); (2); (3); (4).

in A random number in the range [0,1]. and These represent the maximum and minimum pulse frequencies, respectively. For flight speed, and For volume, It is a constant that is uniformly distributed within [0,1]. It is a constant greater than zero.

In step S45, the position x after updating using either the first or second update method is the _best position x . It should be noted that when the update method selection result in step S42 is "yes", the first update method in step S43 is executed; while when the update method selection result in step S42 is "no", the second update method in step S44 is executed.

In step S50, update the i- th bat, making it the i = i + 1-th bat. In step S60, determine if i is greater than the preset number of bats N and generate a count judgment result. If the count judgment result is "yes", continue to step S70; if the count judgment result is "no", repeat step S42 of step S40. In step S70, determine if the iteration number t has reached the maximum iteration number Iter_max and generate an iteration judgment result, providing the maximum output power based on the iteration judgment result. When the iteration judgment result is "no", step S80 is executed, _{the optimal position xbest} is output and used as the optimal solution as the maximum output power of the solar photovoltaic module array 110. When the iteration judgment result is "yes", step S41 of step S40 is repeated to determine again whether the updated random number rand is greater than the current pulse emission rate r _i .

<Experimental Example>

The first, second, and third experimental examples used a modified bat algorithm based on the maximum output power tracking method 200 to track the maximum output power of the solar photovoltaic module array 110. The shading ratio conditions set during the test are shown in Table 1 below. In Table 1, the symbols "+" and "//" represent series and parallel connections, respectively. The modified bat algorithm is divided into modified bat algorithm-1 and modified bat algorithm-2. The difference between the two is that modified bat algorithm-2, in addition to adjusting the step using the slope m calculated by the PV characteristic curve, also sets a starting tracking voltage for tracking. Furthermore, to facilitate comparison, the same delay time was set for each update iteration to lengthen the actual tracking time. The following will explain the maximum output power tracking comparison between the first to third experimental examples and the first to third comparative examples. The first to third comparative examples use the traditional bat algorithm, and the first comparative example and the first experimental example have the same series-parallel configuration of solar photovoltaic modules and the same shading situation. The second comparative example and the second experimental example also have the same series-parallel configuration of solar photovoltaic modules and the same shading situation, and so on. Table 1. Number of peaks in PV characteristic curves under different shading conditions Experimental examples Number of peaks in PV characteristic curve Shading situation of 4 strings of 3 connected configurations one The second peak (the maximum power point is on the right) (0% shade + 40% shade + 0% shade + 0% shade) // (0% shade + 0% shade + 0% shade + 0% shade) // (0% shade + 0% shade + 0% shade + 0% shade) two Three peaks (maximum power point in the middle). (90% shade + 0% shade + 0% shade + 30% shade) // (0% shade + 0% shade + 0% shade + 0% shade) // (0% shade + 0% shade + 0% shade + 0% shade) three Four peaks (maximum power point is at the second peak). (0% shade + 80% shade + 50% shade + 10% shade) // (0% shade + 80% shade + 50% shade + 10% shade) // (0% shade + 80% shade + 50% shade + 10% shade)

Referring to Figures 1, 4A, and 4B, Figure 4A shows the P-V characteristic curve of the solar photovoltaic module array 110 in the first experimental example; and Figure 4B is a schematic diagram comparing the maximum output power tracking simulation results of the first experimental example and the first comparative example according to Figure 4A. As shown in Figure 4A, when the solar photovoltaic module array 110 is shaded by 40%, the number of peaks in the P-V characteristic curve is two, and the maximum output power is located on the right side and is 210.13W. As shown in Figure 4B, all three methods can track the global maximum power point, but the improved bat algorithm-2 (which adjusts the pace based on the slope of the P-V characteristic curve and fixes the initial tracking voltage) can significantly escape the local maximum power point of 187W faster than the other two methods. Therefore, it can track the global maximum power point of 210.13W in a very short time. Thus, it is clear that this method has the fastest dynamic response and the best steady-state response performance among the three methods.

Referring to Figures 1, 5A, and 5B, Figure 5A shows the P-V characteristic curve of the solar photovoltaic module array 110 in the second experimental example; and Figure 5B is a schematic diagram comparing the maximum output power tracking simulation results of the second experimental example and the second comparative example according to Figure 5A. Figure 5A shows that when the two modules in the solar photovoltaic module array 110 are shaded by 90% and 30% respectively, the P-V characteristic curve has three peaks, with the maximum output power located in the middle at 178.93W. Figure 5B shows that all three methods can track the maximum power point across the entire range under standard test conditions. However, the improved bat algorithm-2 (which adjusts the pace based on the slope of the P-V characteristic curve and fixes the initial tracking voltage) significantly escapes the local maximum power point of 121.5W faster than the other two algorithms, quickly tracks to the global maximum power point, and stably maintains the highest output power of the solar photovoltaic module array 110.

Referring to Figures 1, 6A, and 6B, Figure 6A shows the PV characteristic curve of the solar photovoltaic module array 110 in the third experimental example; and Figure 6B is a schematic diagram comparing the maximum output power tracking simulation results of the third experimental example and the third comparative example according to Figure 6A. Figure 6A shows that when each module in the solar photovoltaic module array 110 is subjected to 80%, 50%, and 10% shading respectively, the PV characteristic curve has four peaks, with the maximum output power located at the second peak from left to right, at 106.64 W. Figure 6B shows that all three methods can track the true maximum power point of 106.6 W when the solar irradiance changes to 500 W/ ^m² . However, the traditional bat algorithm gets stuck at the local maximum power point of 51.5W for a relatively long time and oscillates excessively near the maximum power point, causing it to remain stuck there and unable to escape in time. In contrast, the improved bat algorithms -1 (adjusting the pace based on the slope of the PV characteristic curve) and -2 (adjusting the pace based on the slope of the PV characteristic curve and using a fixed starting tracking voltage) get stuck at the local maximum power point for a shorter time and can escape very quickly. Simulation results clearly show that the improved bat algorithm -2 has the fastest dynamic response and the best steady-state response performance among the three methods.

In summary, the solar photovoltaic module system and maximum output power tracking method disclosed herein, through an improved bat algorithm, adjusts the pace by adjusting the slope of the P-V characteristic curve and fixes the starting tracking voltage, which can shorten the tracking time for maximum output power and avoid the problem of the solar photovoltaic module array getting stuck in the regional maximum power due to shading or failure, thereby maintaining tracking stability.

Although the contents of this disclosure have been disclosed above in an embodied manner, they are not intended to limit the contents of this disclosure. Anyone skilled in the art may make various modifications and alterations without departing from the spirit and scope of this disclosure. Therefore, the scope of protection of this disclosure shall be determined by the scope of the appended patent application.

100: Solar photovoltaic module system; 110: Solar photovoltaic module array; 120: Controller; 130: Boost converter; 131: Drive circuit; 200: Maximum output power tracking method A. , : Resonance , constants Random values F , _Fmax , _Fmin ; Pulse frequency Iter_max; Maximum number of iterations. Current m, : Slope N: Number of bats r : Pulse emission rate r _i : Current pulse emission rate rand: Update random number R _Load : Load S1, S2, S10, S20, S30, S40, S41, S42, S43, S44, S45, S50, S60, S70, S80: Step t : Number of iterations v , Flight speed x , :position x _best :optimal position

Figure 1 is a schematic diagram of a solar photovoltaic module system according to the first embodiment of the present invention; Figure 2 is a block diagram illustrating the steps of the maximum output power tracking method according to the second embodiment of the present invention; Figure 3 is a flowchart illustrating the steps of the improved bat algorithm according to Figure 2; Figure 4A is a P-V characteristic curve of the solar photovoltaic module array of the first experimental example; Figure 4B is a schematic diagram comparing the maximum output power tracking simulation results of the first experimental example and the first comparative example according to Figure 4A; Figure 5A is a P-V characteristic curve of the solar photovoltaic module array of the second experimental example; Figure 5B is a schematic diagram comparing the maximum output power tracking simulation results of the second experimental example and the second comparative example in Figure 5A; Figure 6A is a P-V characteristic curve of the solar photovoltaic module array of the third experimental example; and Figure 6B is a schematic diagram comparing the maximum output power tracking simulation results of the third experimental example and the third comparative example in Figure 6A.

200: Method for tracking maximum output power

S1, S2: Steps

Claims (10)

A method for tracking maximum output power of a solar photovoltaic module array and supplying the maximum output power to a load via a boost converter, the method comprising: executing a modified bat algorithm by a controller, and adjusting the step size in the modified bat algorithm by a slope of a power-voltage characteristic curve, or searching for an optimal solution among a plurality of bats by the slope and a fixed initial tracking voltage to obtain the maximum output power; the modified bat algorithm comprising: presetting a number of bats, a maximum number of iterations, a pulse frequency range, and a maximum pulse rate; Initialize the bats' position, pulse frequency, flight speed, number of iterations, and pulse emission rate; obtain the power-voltage characteristic curve from the solar photovoltaic module array, calculate the slope based on the number of iterations and the power-voltage characteristic curve, and calculate an optimal position and its corresponding fitness based on the slope; update the number of iterations, generate an update random number, determine whether the update random number is greater than a current pulse emission rate to generate an update method selection result, and select one of a first update method and a second update method based on the update method selection result to update the optimal position and the fitness, and use the optimal position and the fitness as the optimal solution; and The controller outputs a pulse width modulation signal corresponding to one duty cycle based on the optimal solution, and transmits the pulse width modulation signal to the boost converter to drive the boost converter to provide the maximum output power to the load. The maximum output power tracking method as described in claim 1, wherein when the update method selection result is yes, the controller executes the first update method, updates the position of the i- th bat among the bats with a first update formula, and takes that position as the optimal position, wherein the first update formula is: Where t is the number of iterations, and Let be the position of the i- th bat among these bats at iteration number t and t -1. It is a random value. Let be the slope; where the random value is greater than or equal to -0.25 and less than or equal to 0. The maximum output power tracking method as described in claim 1, wherein, when the update method selection result is negative, the controller executes the second update method, updates the pulse frequency and flight speed of the i -th bat among the bats, updates the position with a second update formula, and takes that position as the optimal position, wherein the second update formula is: Where t is the number of iterations, and Let be the position of the i- th bat among these bats at iteration number t and t -1. This is the flight speed. The maximum output power tracking method as described in claim 1, wherein the modified bat algorithm executed by the controller further includes: setting the initial tracking voltage; wherein the initial tracking voltage is 0.8 times a maximum power point voltage under a standard test condition. The maximum output power tracking method as described in claim 1, wherein the improved bat algorithm executed by the controller further comprises: determining whether the number of iterations has reached the maximum number of iterations to generate an iteration determination result, and providing the maximum output power based on the iteration determination result; wherein, when the iteration determination result is negative, taking the optimal solution as the maximum output power of the solar photovoltaic module array; wherein, when the iteration determination result is positive, repeatedly determining whether the updated random number is greater than the current pulse emission rate. A solar photovoltaic module system for providing a maximum output power to a load, the solar photovoltaic module system comprising: a solar photovoltaic module array; and a controller electrically connected to the solar photovoltaic module array, the controller being configured to execute a modified bat algorithm, wherein the modified bat algorithm adjusts the step size by a slope of a power-voltage characteristic curve, or searches for an optimal solution among a plurality of bats by the slope and a fixed initial tracking voltage to obtain the maximum output power, the modified bat algorithm comprising: a preset number of bats, a maximum number of iterations, a pulse frequency range, and a maximum pulse rate; Initialize the bats' position, pulse frequency, flight speed, number of iterations, and pulse emission rate; obtain the power-voltage characteristic curve from the solar photovoltaic module array, calculate the slope based on the number of iterations and the power-voltage characteristic curve, and calculate an optimal position and its corresponding fitness based on the slope; update the number of iterations, generate an update random number, determine whether the update random number is greater than a current pulse emission rate to generate an update method selection result, and select one of a first update method and a second update method based on the update method selection result to update the optimal position and the fitness, and use the optimal position and the fitness as the optimal solution; and A boost converter is electrically connected to the solar photovoltaic module array and the controller to obtain a pulse width modulation signal with a corresponding duty cycle from the controller and to provide the maximum output power to the load driven by the pulse width modulation signal. As described in claim 6, in the solar photovoltaic module system, when the update method selection result is yes, the controller executes the first update method, updates the position of the i- th bat among the bats with a first update formula, and takes that position as the optimal position, wherein the first update formula is: Where t is the number of iterations, and Let be the position of the i- th bat among these bats at iteration number t and t -1. It is a random value. Let be the slope; where the random value is greater than or equal to -0.25 and less than or equal to 0. As described in claim 6, in the solar photovoltaic module system, when the update method selection result is negative, the controller executes the second update method, updates the pulse frequency and flight speed of the i - th bat among the bats, updates the position using a second update formula, and takes that position as the optimal position, wherein the second update formula is: Where t is the number of iterations, and Let be the position of the i- th bat among these bats at iteration number t and t -1. This is the flight speed. The solar photovoltaic module system as described in claim 6, wherein the modified bat algorithm executed by the controller further includes: setting the initial tracking voltage; wherein the initial tracking voltage is 0.8 times a maximum power point voltage under a standard test condition. The solar photovoltaic module system as described in claim 6, wherein the modified bat algorithm executed by the controller further comprises: determining whether the number of iterations has reached the maximum number of iterations to generate an iteration determination result, and providing the maximum output power based on the iteration determination result; wherein, when the iteration determination result is negative, taking the optimal solution as the maximum output power of the solar photovoltaic module array; wherein, when the iteration determination result is positive, repeatedly determining whether the updated random number is greater than the current pulse emission rate.