JP4676176B2 - Control method of photovoltaic power generation system - Google Patents

Description

  The present invention relates to a photovoltaic power generation system that controls so that the output power value from a solar cell becomes a substantially maximum value, and in particular, a plurality of maximum values of output power that are generated due to uneven solar radiation on the panel surface of the solar cell. Of these, it relates to a technology that accurately follows the maximum power.

  FIG. 6 is a block diagram of a conventional photovoltaic power generation system. In the figure, SC1 to SC3 are three solar cells connected in parallel, PT is an output voltage detection circuit for detecting the output voltage of the solar cell, CT is an output current detection circuit for detecting the output current of the solar cell, and CC is A controller that performs MPPT control (Maximum Power Point Tracking) (hereinafter referred to as hill-climbing method) using a hill-climbing method by a microprocessor. The microprocessor CC has an AD for reading the output voltage value and output current value of a solar cell. Built-in converter. IN is a DC / AC inverter circuit (or DC / DC inverter circuit) that converts a direct current output from a solar cell into a voltage, AD is a load, and SP is a commercial power supply. Here, the microprocessor of the controller CC calculates the output power from the solar cell SC by multiplying the output voltage and output current of the solar cell SC, and stores the voltage and power value in the memory. Further, the controller CC can control the output voltage from the solar cell SC by controlling the output of the DC / AC inverter circuit IN via the pulse width control circuit PWM.

  FIG. 7 is a flowchart for explaining the hill-climbing method of the conventional photovoltaic power generation system, and the operation will be described with reference to this flowchart and the diagram for searching for the maximum value in FIG.

  A solar cell output set value corresponding to a predetermined operating point A of the power-voltage characteristic shown in FIG. 8 is set as an initial value (step S1). Using this initial value as the first solar cell output set value, the inverter is operated to set the first solar cell output voltage V1 (step S2). The first solar cell output current I1 output in accordance with the first solar cell output set value is measured (step T3). The microprocessor of the controller CC calculates the first solar cell output power W1 by multiplying the first solar cell output voltage V1 and the first solar cell output current I1, and stores the first solar cell in the memory. The values of the output voltage V1 and the first solar cell output power W1 are stored (step S4).

  Next, a second solar cell output set value that is higher than the first solar cell output set value by a predetermined amount is set to operate the inverter, and the second solar cell is changed from the first solar cell output voltage V1 shown in FIG. The battery output voltage is increased to V2 (step S5).

  The second solar cell output current I2 output according to the second solar cell output set value is measured (step T6). The microprocessor of the controller CC calculates the second solar cell output power W2 by multiplying the second solar cell output voltage V2 and the second solar cell output current I2, and stores the second solar cell in the memory. The values of the output voltage V2 and the second solar cell output power W2 are stored (step S7).

  Subsequently, a third solar cell output set value that is a predetermined amount lower than the second solar cell output set value is set and the inverter is operated, so that the third solar cell is changed from the first solar cell output voltage V1 shown in FIG. The voltage is lowered to the battery output voltage V3 (step S8).

  The third solar cell output current I3 output in accordance with the third solar cell output set value is measured (step T9). The microprocessor of the controller CC calculates the third solar cell output power W3 by multiplying the third solar cell output voltage V3 and the third solar cell output current I3, and stores the third solar cell in the memory. The values of the output voltage V3 and the third solar cell output power W3 are stored (step S10).

  The solar cell output voltages V1, V2, and V3 shown in FIG. 8 stored as described above are compared with the output power values W1, W2, and W3 at (V3 <V1 <V2), and the output power value is the largest. The operating point A is moved to the point and the series of operations is repeated to follow the maximum power point shown in FIG. As a prior document disclosing the above technique, for example, there is Patent Document 1.

JP 2001-325031 A

  In the conventional hill-climbing method described above, the operating point A shown in FIG. 8 is set at a predetermined location, and the output power at this operating point A is obtained. Next, the operating point A is forcibly moved to a certain higher solar cell output voltage, and the output power at the operating point is obtained. Next, move to the lower side, and calculate the output power in the same way. The output power at the three operating points thus obtained is compared, the operating point A is moved to a point where the power is high, and this series of operations is repeated to follow the maximum power point.

  However, when the solar radiation changes and a shadow is generated on a part of the panel, large and small peaks having two maximum values shown in FIG. 8 are generated. In such a case, if the operating point is set to B and the maximum power point is tracked by the hill climbing method, the second maximum value is determined as the maximum power point, but the operating point is set to A and the maximum power point is tracked. Then, the first maximum value stagnates as the maximum power point, and it becomes impossible to follow the second maximum value, which is the true maximum power point.

  Further, when the solar radiation state of the solar cell changes in a complicated manner and shadows are generated at several places on the panel, a plurality of maximum values (not shown) are generated, making it more difficult to follow the maximum power point. In this case, the solar power generation system cannot be operated with the maximum power.

  Then, this invention is providing the genetic algorithm control (GA) which is a control method of the photovoltaic power generation system which can solve the said subject.

  In order to solve the above-described problem, the first invention controls the inverter so that the output voltage or output current from the solar cell is substantially equal to a predetermined solar cell output set value, and follows the change in the solar radiation state. In the control method of the solar power generation system for controlling the solar cell output set value to an appropriate value so that the output power value from the solar cell becomes a substantially maximum value, the solar cell output set value is regarded as a gene and A GA controller based on a genetic algorithm using the output power value from the battery as a gene evaluation value is provided, and in the first step, a plurality of genes are randomly or previously determined from the set range of the solar cell output set value. Extracting based on conditions to form an initial population of the first generation, and operating the inverter sequentially according to each solar cell output set value corresponding to each gene of the initial population The output power value from the solar cell is stored as an evaluation value of each gene, and then the genes of the initial population are input to the GA controller, selected according to the evaluation value of each gene, and crossed and mutated to obtain a predetermined number of genes. Genes are output to form a second generation population, and in the second step, the inverter is sequentially operated according to each solar cell output set value corresponding to each gene of the second generation population, and output from the operating solar cells. The power value is stored as an evaluation value of each gene, and then the second generation gene is input to the GA controller, selected according to the evaluation value of each gene, and crossover / mutation is performed to output a predetermined number of genes. A third generation population is formed, and thereafter the operation of the second step is repeated so that the generation of the gene population is updated one after another so that the output power value from the solar cell becomes a substantially maximum value. A method of controlling a photovoltaic power generation system characterized by control.

  According to a second aspect of the present invention, in the method for controlling a solar power generation system according to claim 1, the probability of the mutation is 3% to 5%.

  According to the first invention, the genetic algorithm processing of the present invention is performed even if the solar radiation state of the solar cell changes and a shadow is generated on a part of the panel and a plurality of maximum values are generated in the output power of the solar cell. The maximum power maximum value can be tracked regardless of the occurrence of the plurality of maximum values, and the output power value from the solar cell can be controlled at a substantially maximum value.

  According to the second invention, even when the solar radiation state changes during the maximum power maximum value tracking and the plurality of maximum values (mountain shapes) of the output power of the solar cell change, each of the genes forming the gene population By sequentially operating the inverter according to the solar cell output set value and setting the mutation probability of the genetic algorithm to the optimum value, the tracking of the maximum power is started again according to the shape change of the maximum value, and the latest Following the maximum power, the output power value from the solar cell can be controlled at a substantially maximum value.

[Embodiment 1]
FIG. 1 is a block diagram of a photovoltaic power generation system according to an embodiment of the present invention. In this figure, the same reference numerals as those in the block diagram of the conventional solar power generation system shown in FIG.

  In the block diagram of the photovoltaic power generation system shown in FIG. 1, a controller CC is a controller that performs genetic algorithm control (GA) and is a microprocessor. Here, the microprocessor of the controller CC calculates the output power of the solar cell SC by multiplying the output voltage and the output current of the solar cell SC, and stores the output voltage and the output power value in the memory. In addition, the controller CC controls the output voltage from the solar cell SC by controlling the output of the DC / AC inverter IN via the pulse width control circuit PWM.

  Next, genetic algorithm control (GA) will be described. The controller CC is a GA controller, regards a solar cell output set value (not shown) as a gene, stores an output power value from the solar cell as a gene evaluation value, and sets a plurality of values from a set range of the solar cell output set value. Genes are generated randomly or based on predetermined conditions to form a population, and the above genes are selected, crossed and mutated to generate a predetermined number of genes to form a second generation population, and the above operations By repeating the above, the next generation population is formed one after another, and the maximum power value is followed based on the gene value of the population.

  FIG. 2 is a flowchart for explaining the operation of the first embodiment of the present invention. The operation will be described with reference to this flowchart and a diagram in which the genes of the group in FIG. 3 converge by the genetic algorithm and follow the maximum value.

  A plurality of (for example, 10) genes are extracted at random or based on predetermined conditions from the set range of the solar cell output set value (step T1). Then, the counter i is set to 1 (step T2).

  A solar cell output set value corresponding to a predetermined gene among the plurality of genes is set as a first solar cell output set value, and an inverter is operated according to the first solar cell output set value to thereby output a first solar cell output. The voltage V1 is set (step T3). The first solar cell output current I1 output according to the first solar cell output set value is measured (step T4). The microprocessor of the controller CC calculates the first solar cell output power W1 by multiplying the first solar cell output voltage V1 and the first solar cell output current I1, and stores it in the memory (step T5). . Then, 1 is added to the counter i. (Step T6)

  Next, it is determined whether all n (for example, 10) output powers of the solar cell output set values have been measured. If No, the process returns to step T3, and a predetermined number of the remaining plurality of genes is determined. The solar cell output set value corresponding to the gene is set as the second solar cell output set value, and the inverter is operated according to the second solar cell output set value to set the second solar cell output voltage V2 (step T3). ). The second solar cell output current I2 output according to the second solar cell output set value is measured (step T4). The microprocessor of the controller CC calculates the second solar cell output power W2 by multiplying the second solar cell output voltage V2 and the second solar cell output current I2, and stores it in the memory (step T4). Subsequently, 1 is added to the counter i (step T6).

  Thereafter, the same operation is repeated, the nth solar cell output current In corresponding to the nth solar cell output set value is measured, the nth solar cell output power Wn is calculated and stored in the memory, and FIG. An evaluation value for the first generation initial population shown in A) is determined (step T7).

  Next, in the initial population, two genes are randomly extracted, the one having the larger evaluation value (solar cell output power) is selected, and the above selection is repeated to select two genes (step T8).

  The evaluation values of the two selected genes are converted into binary character strings and crossed at an arbitrary point having a predetermined probability to generate two new genes (step T9).

  The crossed gene is mutated by intentionally changing a part of the character string with a predetermined low probability (step T10).

  When the number of selections and crossovers is n / 2 (for example, 10/2) or less, the process returns to step T8 (step T11). Then, from the initial population, two genes are randomly extracted again and the one with the larger evaluation value (solar cell output power) is selected, and the above selection is repeated to newly select two genes (step) T8).

  The evaluation values of the two selected genes are converted into binary character strings and crossed at an arbitrary point having a predetermined probability to generate two new genes (step T9).

  The crossed gene is mutated by intentionally changing a part of the character string with a predetermined low probability (step T10).

  When the number of selections, crossovers, and mutations is n / 2 (for example, 10/2), the process proceeds to step 12, and a second generation population shown in FIG. 3B is formed by the generated genes.

  Thereafter, the generation of the gene population is renewed one after another by repeating the above operation, and the nth generation population is formed in FIG. 3C. The genes of the nth generation population are converged to the vicinity of the maximum value. Yes.

  When a plurality of maximum values are tracked using the genetic algorithm described above, it is possible to avoid the maximum value of a small mountain that is not the maximum power and to follow the maximum value of a large mountain.

[Embodiment 2]
If the solar radiation state (shadow state) changes during tracking of the maximum power using the genetic algorithm control GA, and the shape of a plurality of local maximum values changes as shown in FIG. The nth generation group also moves according to the change in the shape of the value.

  Thereafter, the operation of the flowchart shown in FIG. 2 is repeated. At this time, it is possible to prevent all the genes of the group from being homogeneous by the mutation operation, and to prevent an extreme value solution that cannot be adapted to the change in the solar radiation state, and FIG. 4 (B) and FIG. As shown in (C), the generation of the gene population is renewed, and the generations of the gene population are renewed one after another to the (n + 1) th generation population and the (n + 2) generation population, and in the vicinity of the maximum output power from the solar cell, Converge genes.

  FIG. 5 is a diagram illustrating the relationship between the probability of mutation, the follow-up time of maximum power, and power loss. From FIG. 5, when the probability of mutation is 2% or less, the follow-up time to the vicinity of the maximum output power of the solar cell becomes longer, and the follow-up performance becomes worse with respect to changes in the solar radiation state.

  On the contrary, when the mutation probability is 6% or more, the followability is improved, but the number of fluctuations of the solar cell output set value increases according to the mutation probability, and the fluctuation rate of the output power of the inverter is large. Thus, the output power value from the solar cell cannot be controlled at a substantially maximum value. From the above, it is considered that the mutation probability is 3% to 5% as an appropriate value.

1 is a block diagram of a photovoltaic power generation system according to an embodiment of the present invention. It is a flowchart explaining the operation | movement of Embodiment 1 of this invention. It is a figure in which a population converges by a genetic algorithm and follows a maximum value. It is the 2nd figure where a population converges by a genetic algorithm and follows a maximum value. It is a figure which shows the relationship between the probability of a mutation, the follow-up time of electric power, and electric power loss. It is a block diagram of the solar power generation system of a prior art. It is a flowchart explaining operation | movement of a prior art. It is a figure which searches for a local maximum by a prior art.

Explanation of symbols

AD load CC controller CT current detection circuit IN inverter circuit PT voltage detection circuit PWM pulse width control circuit SP system power supply SC1 solar cell SC2 solar cell SC3 solar cell

Claims (2)

  The inverter is controlled so that the output voltage or output current from the solar cell becomes substantially equal to a predetermined solar cell output set value, and the output power value from the solar cell becomes a substantially maximum value following the change in the solar radiation state. In the control method of the photovoltaic power generation system for controlling the solar cell output set value to an appropriate value as described above, the solar cell output set value is regarded as a gene and the output power value from the solar cell is used as a gene evaluation value. A GA controller based on an algorithm is provided, and in the first step, a plurality of genes are randomly or based on a predetermined condition to form a first generation initial population from the set range of the solar cell output set value. The inverter is sequentially operated according to each solar cell output set value corresponding to each gene of this initial population, and the output power value from the operating solar cell is evaluated as the evaluation value of each gene. Then, the genes of the initial population are input to the GA controller, selected according to the evaluation value of each gene, and crossover / mutated to output a predetermined number of genes to form a second generation population. In the second step, the inverter is sequentially operated according to each solar cell output setting value corresponding to each gene of the second generation population, and the output power value from the operating solar cell is stored as an evaluation value of each gene. Subsequently, the second generation gene is input to the GA controller, selected according to the evaluation value of each gene, crossed and mutated, and a predetermined number of genes are output to form a third generation population. A photovoltaic power generation system characterized in that the generation of gene populations is renewed one after another by repeating the operation of the step so that the output power value from the solar cell becomes a substantially maximum value. Control method. The method of controlling a solar power generation system according to claim 1, wherein the probability of the mutation is 3% to 5%.

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