TW202110077A - Power generation efficiency abnormity judging method of solar apparatus judging whether a sunlight value received by a plurality of solar power generation apparatuses on a solar field is greater than a standard sunlight intensity - Google Patents

Abstract

This invention provides a power generation efficiency abnormity judging method of a solar apparatus, which includes the following steps: judging whether the sunlight value received by a plurality of solar power generation apparatuses on a solar field is greater than a standard sunlight intensity; acquiring the power generation ratio information of the plurality of solar power generation apparatuses at each time point; comparing whether the difference between the power generation ratio information of each of solar power generation apparatuses at each time point and the standard power generation ratio information is greater than a power generation ratio threshold; if the difference between the power generation ratio information and the standard power generation ratio information is greater than the threshold, judging that each of the corresponding solar power generation apparatuses is abnormal. According to this invention, a more precise judgment standard may be obtained by integrating the historical power generation ratio information into the standard power generation ratio information by using a nonlinear regression algorithm, so that higher precision is achieved when it is used to judge whether the power generation efficiencies of the solar power generation apparatuses are abnormal.

Description

Method for judging abnormal power generation efficiency of solar device

An abnormality judgment method, in particular, refers to a judgment method for judging whether there is an abnormality in the power generation efficiency of a solar device.

Nowadays, the awareness of environmental protection is increasing, and the proportion of green power is increasing. Among them, solar power is an important trend in future power development. Therefore, governments of various countries have offered many subsidies, such as subsidizing the cost of installing solar panels on the roof of homes. Method, it can be seen that solar power generation has become a national-level power generation mode.

In addition to installing solar panels on the roofs of their own buildings, many private power companies also build solar power generation sites in open areas or on the roofs of buildings to increase the amount of green power generation. Secondly, they can also use solar panels to generate electricity. It is also a business model for power companies to sell electricity back to the country for profit.

In order to improve power generation efficiency and production capacity, a large number of solar power generation modules will be used for power generation in the solar power generation field. Since each solar field is located in a different location, the power generation efficiency of solar power modules will vary depending on the angle of the sun, local climate, temperature, and terrain. Solar power modules may be caused by environmental factors, such as dust accumulation. , Shade shade makes the power generation abnormal. Because the location of the case is often remote, if the remote monitoring of the solar power module is abnormal, it is often necessary to send staff to the case to remove the obstacles. The inconvenience of transportation makes the round trip time-consuming, labor-intensive, and costly in manpower and time. Increase with it. At this stage, there is no effective remote monitoring method to determine the abnormal state of the solar power generation module, which makes the time to remove the obstacles quite lengthy, which in turn affects the efficiency of solar power generation.

In order to improve the accuracy of judging abnormal power generation of solar devices, the present invention proposes a method for judging abnormal power generation performance of solar devices. The historical power generation ratio information is integrated into the reference power generation ratio information by a nonlinear regression algorithm, which can more accurately determine solar power generation. Whether the device is abnormal during power generation.

In order to achieve the above objective, the method for judging abnormality in the power generation performance of the solar device of the present invention includes the following steps: Judge whether the daily sunshine value received by a plurality of solar power generation devices in a solar field is greater than a standard sunshine intensity; Obtain a power generation ratio information of the plurality of solar power generation devices in the solar energy field at various points in time; Comparing whether the difference between the power generation ratio information of the plurality of solar power generation devices at each time point and a reference power generation ratio information is greater than a power generation ratio threshold; Wherein, if the difference between the power generation ratio information and the reference power generation ratio information is greater than the threshold value, it is determined that the corresponding solar power generation device is abnormal.

The present invention first obtains the multiple historical power generation ratio information of the solar field, and calculates the reference power generation ratio information by using the complex historical power generation ratio information with a non-linear regression algorithm. The reference power generation ratio information will be quite accurate, and then compare The power generation ratio information and the reference power generation ratio information can accurately confirm whether the power generation status of the solar power generation device is abnormal, and avoid the situation that the power generation status is temporarily affected by environmental factors but is misjudged as abnormal power generation.

The present invention can also judge the abnormality of the solar power generation device by comparing the average power generation anomalous ratio of the solar power generation devices of the same specification. The reason for the abnormality of the solar power generation device can be learned during remote monitoring, and the relative solution can be studied. Measures can reduce the time and labor cost for staff to go back and forth to the case to confirm and eliminate abnormal factors.

The present invention is a method for judging an abnormality in the power generation performance of a solar device for judging whether the power generation status of a plurality of solar power generation devices 80 in a solar field is abnormal. The solar power generation device 80 is a solar panel or other solar power generation equipment. Please refer to Figure 1 and Figure 2. The method includes the following steps:

S11: Determine whether the daily sunshine value received by the plural solar power generation devices 80 of a solar field is greater than a standard sunshine intensity.

S12: Obtain a power generation ratio information of the plurality of solar power generation devices 80 at various time points.

S13: Compare whether the difference between the power generation ratio information and a reference power generation ratio information of each solar power generation device 80 at each time point is greater than a power generation ratio threshold; if the difference between the power generation ratio information and the reference power generation ratio information is greater than the threshold , It is determined that the corresponding solar power generation device 80 is abnormal.

In step S11, since the insolation value will be affected by natural factors such as clouds and the sun angle, it is first to measure the insolation value received by each solar power generation device 80 by using insolation meters and other equipment, and determine whether the insolation value is greater than the insolation value. Standard sunshine intensity; if the detection time is at night or rainy weather causes insufficient sunshine, and the sunshine value is less than the standard sunshine intensity, there is no need to further check whether the solar power generation device 80 is abnormal; if the sunshine value is greater than or equal to the standard sunshine intensity, execute Next step.

It is particularly emphasized that Irradiance is defined as the insolation power per unit area, generally in W/m ² or mW/m ² , and the standard test condition for solar cells is 1000 W/m ² . The standard solar intensity of the present invention is all 1000W/m ² as an illustrative example.

Please further refer to FIG. 3, in step S11, a noise filtering step S111 may be further included. Because there may be birds staying on the sunshield for a short time, or debris is blown up by the wind and temporarily covered on the sunshield, the sunshine value is greater than the standard sunshine intensity for most of the time, and a small part of the time is less than the standard sunshine intensity. In this way, although the actual sunshine value is quite sufficient, the error caused by the environment may make the sunshine value received by the solar power generation device 80 smaller than the standard sunshine intensity, causing the system to misjudge and fail to execute the next step. Therefore, in step S111, a data processing module 90 that is electrically connected to each solar power generation device 80 determines whether the difference between the insolation value and the standard insolation intensity is greater than an error value. If so, the insolation value is too large due to the error. It is regarded as noise and filtered out; if not, it is judged that the sunshine value falls within a reasonable range and can be regarded as normal data.

In step S12, the data processing module 90 detects the power generation ratio information of each solar power generation device 80 at each time point, wherein the power generation ratio information can be calculated from a plurality of different power generation data, wherein the power generation data It may include, but is not limited to, the _{DC power generation P DC of} each solar power generation device 80, the rated power P _{0 of} a power generation device, the sunshine value G ₁ and the standard sunshine intensity G ₀ (1000W/m ² ), which means, At each time point, a set of DC generating power P _DC , a set of rated power P _{0 of the} generating device, a set of the sunshine value G ₁ and a set of the standard sunshine intensity G ₀ (1000W/m ² ) will be generated. In this step, it can be determined by After the data processing module 90 integrates the power generation data, the power generation ratio information corresponding to each time point can be obtained. The calculation method of the power generation ratio information is:

Array Ratio = (P _DC / P ₀ )/(G ₁ / G ₀ ), where Array Ratio is power generation ratio information (RA).

Referring to FIG. 3, in step S13, the data processing module 90 can compare whether the difference between the power generation ratio information and the reference power generation ratio information is greater than the power generation ratio threshold, wherein the reference power generation ratio information can be obtained through Obtained from the following steps:

S131: Obtain plural historical power generation ratio information of each solar power generation device 80.

S132: Calculate the reference power generation ratio information based on the plural historical power generation ratio information. In this step, nonlinear regression algorithm or K-Nearest Neighbor Classification Algorithm (K-Nearest Neighbor Classification Algorithm, KNN) can be used to calculate the complex historical power generation ratio information into the benchmark power generation ratio information. First, we will introduce the use of nonlinear regression algorithm. Calculation method:

Please further refer to FIG. 4, in step S131, first obtain the plurality of historical power generation data of the solar field at each time point in the past, where the plurality of historical power generation data may include a historical DC power generation P _{DC at each time point in the past} , A historical power generation device rated power P ₀ , a historical sunshine value G ₁ and the standard sunshine intensity G ₀ (1000W/m ² ) and other data, from the historical DC generation power P _DC , the historical power generation device rated power P ₀ , The historical sunshine value G ₁ and the standard sunshine intensity G ₀ (1000W/m ² ) can be used to obtain the complex historical power generation ratio information 10 through the power generation ratio information calculation formula.

In step S132, the complex historical power generation ratio information 10 is calculated into the reference power generation ratio information 30 through a nonlinear regression algorithm. The nonlinear regression algorithm is executed by the following equation:

_{y i = a 0 + a 1} x + a 2 x 2 + ... + a n x n + ε, where y _i is the power ratio of the history information (RA _P), where _{a 0, a 1, a 2} ... a n The regression coefficients are obtained from each piece of power generation ratio information and the insolation value. The independent variable x is the insolation value recorded by the solar power plant, and the dependent variable y is calculated by the data processing module 90; ε represents Error is a compensation item made to make the regression line closer to the actual data.

It is hereby explained that when the detection system performs a nonlinear regression model, it is necessary to properly pre-process the data and use recent historical data (for example, the historical DC power generation P _DC , the historical sunshine value G ₁ ) As training data, a quadratic regression equation is used to classify that the solar field can generate expected power generation ratio information (RA) under specific sunlight, and this result is used as a judgment basis for judging whether the solar power generation device 80 is abnormal.

As shown in Figure 5, first obtain the multiple historical power generation ratio information 10 (the blank circle in Figure 5), and calculate each historical power generation ratio information 10 through the above equation to obtain the reference power generation ratio information, where the reference power generation ratio The information can be presented as a regression quadratic curve 30, where the regression quadratic curve 30 represents the performance of the corresponding solar power generation device 80. By obtaining the regression quadratic curve 30, the difference between each power generation ratio information and the regression quadratic curve 30 can be further compared. When the difference between the power generation ratio information and the regression quadratic curve 30 is greater, It means that the corresponding solar power generation device 80 is more likely to produce abnormal power generation.

In addition, in order to make the regression quadratic curve 30 more accurate, it is also possible to filter out all historical power generation ratio information 10 that differs greatly from the regression quadratic curve 30, and only extract the information 10 that is different from the regression quadratic curve 30. The historical power generation ratio information 10 with a small difference in the quadratic curve 30 is used as the complex training information 20 to perform a nonlinear regression operation, and the regression quadratic curve 30 can be modified to obtain a more accurate result.

Next, an example of using the nearest neighbor algorithm is introduced:

For the newly installed solar farm, since there is no historical power generation data in the past, during the initial period of operation of the solar farm (for example, five days, seven days), the power generation data during this period of time is collected as the multiple historical power generation data. Next, the abnormal categories represented by each historical power generation data are classified into groups. For example, each historical power generation data can be classified into inverter failure, fuse burnout, solar power generation device 80 being shaded, and thermal drop voltage. Different conditions such as abnormalities. Finally, compare the subsequently collected power generation data with the complex historical power generation data. If the power generation data and the complex historical power generation data have a small difference, it is determined that the power generation status of the solar farm is normal; if the power generation data is the same as the complex historical power generation data If the difference is large, it is judged that the power generation status of the solar farm is abnormal. Furthermore, the power generation data can be used to determine what kind of historical power generation data is in the interval to determine the abnormality type of the solar power generation device 80.

In addition to the above-mentioned method of judging the abnormality of the solar power generation device 80, the present invention can further judge the abnormal state. The steps of judging the abnormal state will be described below.

S14: Compare the average power generation heteroproportionality of the solar power generation devices 80 of the same specification. When it is determined that the solar power generation device 80 is abnormal, the average anomalous ratio of the current or voltage of the solar power generation device 80 and other normally operating solar power generation devices 80 located in the same solar field is further compared. Please refer to Figure 5, the calculation method of the average anomaly ratio is as follows:

；Average power generation anomaly ratio =

；

Where _xi ^- is the power generation data of the abnormal solar power generation device 80; x _i is the power generation data of the normal solar power generation device 80; T is the total number of abnormalities.

By Equation average iso-positive ratio above, respectively, by the power generation data (x _i ^-) abnormality solar power generating device 80 of the set of normal solar power generation apparatus generating data (x _i) of 80 of the set to obtain an abnormal curve tr1 and A normal curve tr2, wherein the abnormal curve tr1 represents a curve of the power generation current of the solar power generation device 80 over time, and the normal curve tr2 represents a time change curve of the power generation current of the solar power generation device 80 in a normal state. Taking the average current anomaly ratio as an example, as shown in Figure 5, at around 12 o'clock, the difference between the abnormal curve tr1 and the normal curve tr2 is not large, which means that the solar power generation device 80 is in normal power generation conditions at this time; Between 12 o'clock and 14 o'clock, the difference between the abnormal curve tr1 and the normal curve tr2 is relatively large, which means that the solar power generation device 80 has abnormal power generation conditions at this time. By comparing the difference between the abnormal curve tr1 and the normal curve tr2, and the trajectory of the distribution of the abnormal curve tr1 and the normal curve tr2, it is possible to determine the abnormal type of the solar power generation device 80 (step S15).

Please refer to Figure 6. It should be noted that the actual test is performed through the above judgment method, and the percentage value of the judgment accuracy rate as shown in Figure 6 can be obtained. The actual experimental data is tested for a total of 53 weeks. W01 represents the first week. The results are presented as unprocessed N, processed P, category error F, and no abnormal T. In the first stage, the above judgment method has not been introduced, and only the existing judgment method is used for judgment. Therefore, it can be seen that there are more results of category error F found after judgment. In the second stage, when the above judgment method is introduced, it can be seen that the judgment result of category error F is slightly reduced. In the third stage, because more data are obtained, the regression quadratic curve 30 and the average anomaly ratio are more accurate after multiple data corrections. Therefore, in the third stage, the judgment result of the category error F can be seen The sharp drop in the number indicates that the accuracy of this method in judging abnormal categories has been significantly improved.

The following further explains the broken line graph of the average anomalous ratio of voltage and current corresponding to each abnormal category.

Please refer to Figures 7A and 7B, which are the broken line graphs of inverter faults. Figure 7A is the broken line graph of abnormal voltage and Figure 7B is the broken line graph of abnormal current.

Please refer to Figures 8A and 8B, which are the broken line graphs of the fuse burned out, in which Figure 8A is the abnormal voltage line graph, and Figure 8B is the abnormal current line graph.

Please refer to FIGS. 9A and 9B, which are the broken line graphs of the solar power generation device 80 under shading, in which FIG. 9A is a broken line graph of abnormal voltage, and FIG. 9B is a broken line graph of abnormal current.

Please refer to FIGS. 10A and 10B, which are the line graphs of the heat drop, where FIG. 10A is the abnormal voltage line graph, and FIG. 10B is the abnormal current line graph.

Referring to FIG. 11A, the present invention can further automate the step of judging the abnormality of the solar power generation device 80. As shown in FIG. 11A, the complex voltage and current information 40 that the solar power generation device 80 generates at different times and is judged incorrectly by the conventional judgment method is extracted, and the complex voltage and current information is manually classified into four different abnormalities. Types, as shown in FIG. 11B, respectively have a plurality of first voltage and current information 40A, a plurality of second voltage and current information 50, a plurality of third voltage and current information 60, and a plurality of fourth voltage and current information 70. Then set the information of each abnormal type to an information center point, which can be the point closest to the voltage and current information. Finally, the complex voltage and current information and its corresponding abnormal type are repeatedly calculated, so that the complex voltage and current information can obtain the correct corresponding abnormal type result.

The present invention can also calculate current and voltage information thresholds corresponding to various abnormal types based on historical data. The method is to use Agglomerative Hierarchical Clustering and Decision Tree algorithm to summarize the above-mentioned complex voltage and current information, and find features between the complex voltage and current information and the information center point to which it belongs. According to the characteristic value, iterative calculations to find nodes with different values. For example, the output voltage threshold corresponding to inverter failure is VR>1.1V; the output voltage threshold corresponding to fuse burning is VR>0.2V; The output voltage threshold corresponding to the shading phenomenon is 0.1V>VR>0.9V; the output voltage threshold corresponding to the thermal drop is VR>1.1V.

10: Historical power generation ratio information 20: Training information 30: Regression quadratic curve 40: Voltage and current information 40A: First voltage and current information 50: Second voltage and current information 60: Third voltage and current information 70: Fourth voltage and current information 80: Solar power generation device 90: data processing module tr1: abnormal curve tr2: normal curve

Figure 1: The flow chart of the first step of the present invention. Figure 2: Block diagram of the circuit of the present invention. Figure 3: A flowchart of the second step of the present invention. Figure 4: A schematic diagram of the present invention using historical power generation ratio information to obtain a regression quadratic curve. Figure 5: A broken line diagram of the abnormal device curve and the normal device curve of the present invention. Fig. 6: The bar graph of the proportion of errors in the judgment category corresponding to each time in the present invention. Fig. 7A: An abnormal broken line diagram of the inverter fault voltage of the present invention. Fig. 7B: An abnormal broken line diagram of the inverter fault current of the present invention. Fig. 8A: The broken line diagram of the abnormal voltage when the fuse is burnt out according to the present invention. Fig. 8B: The broken line diagram of the abnormal fuse burnout current of the present invention. Fig. 9A: A broken line diagram of abnormal shading voltage of the solar power generation device of the present invention. Fig. 9B: A broken line diagram of the solar power generation device of the present invention subjected to an abnormal shading current. Fig. 10A: The abnormal line graph of the thermal drop voltage of the present invention. Fig. 10B: Line graph of abnormal heat drop current of the present invention. Fig. 11A: The voltage and current information distribution diagram of the judgment error of the present invention. Fig. 11B: The voltage and current information distribution diagram of each category of the present invention.

Claims (10)

A method for judging the abnormality of the power generation performance of a solar device includes the following steps: Judge whether the daily sunshine value received by a plurality of solar power generation devices in a solar field is greater than a standard sunshine intensity; Obtain a power generation ratio information of the plurality of solar power generation devices at various points in time; Compare whether the difference between the power generation ratio information of each solar power generation device at each time point and a reference power generation ratio information is greater than a power generation ratio threshold; Wherein, if the difference between the power generation ratio information and the reference power generation ratio information is greater than the threshold value, it is determined that the corresponding solar power generation devices are abnormal. According to the method for judging the abnormality of the power generation performance of the solar device according to claim 1, in the step of comparing whether the difference between the power generation ratio information of each solar power generation device at each time point and a reference power generation ratio information is greater than a power generation ratio threshold value, It also includes the following steps: Obtain multiple historical power generation ratio information of each solar power generation device; The reference power generation ratio information is calculated based on the plural historical power generation ratio information. According to the method for judging the abnormality of the power generation efficiency of the solar device described in claim 2, the reference power generation ratio information is calculated based on the complex historical power generation ratio information by a nonlinear regression algorithm. According to the method for judging the abnormality of the power generation efficiency of the solar device described in claim 2, the reference power generation ratio information is calculated by the nearest neighbor algorithm based on the complex historical power generation ratio information. As described in claim 3 or 4, the method for judging abnormal solar power generation performance, in the step of judging whether the solar radiation value received by a plurality of solar power generation devices in a solar field is greater than a standard solar radiation intensity, further includes a filtering of impurities The step of filtering noise is to determine whether the difference between the insolation value and the standard intensities of insolation is greater than an error value; if it is, the insolation value is regarded as noise and filtered out; if not, the insolation value depends on It is normal data. According to the method for judging anomalous power generation performance of a solar device according to claim 5, the power generation ratio information is calculated from a variety of different power generation data, wherein the power generation data includes a DC power generation, a power generation device rated power, the sunshine value, and The standard sunshine intensity. According to the method for judging abnormal power generation performance of solar devices according to claim 6, in the step of obtaining the multiple historical power generation ratio information of each solar power generation device, the multiple historical power generation data includes a historical DC power generation at each time point in the past, a Historical power generation device rated power, a historical sunshine value and the standard sunshine intensity. According to the method for judging abnormal power generation performance of solar devices as described in claim 7, in the step of calculating the reference power generation ratio information by using the complex historical power generation ratio information with a nonlinear regression algorithm, the nonlinear regression algorithm is executed by the following equation: _{y i = a 0 + a 1} x + a 2 x 2 + ... + a n x n + ε, where y _i is the historical power ratio information _{(RA P), a 0,} a 1, a 2 ... a n is the regression The coefficient, ε is the error. The method for judging anomalous power generation performance of a solar device as described in claim 8, further includes the following steps: Compare the average power generation anomalous ratio between each solar power generation device and the normal operation solar power generation device. According to the method for judging anomalous power generation efficiency of solar devices described in claim 9, in the step of comparing the average power generation anomalous ratios of solar power generation devices of the same specification, the average power generation anomaly ratio is calculated as follows: Average power generation anomaly ratio =

; Among them, x _i ^- is the power generation data of the abnormal solar power generation device; x _i is the power generation data of the normal solar power device; T is the total number of abnormalities.

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