An automatic protection method and system for a micro-wind power generation unit

Abstract

The present invention relates to the field of generator set monitoring, and particularly to an automatic protection method and system for a micro-wind generator set. The method includes: obtaining the parameters of each micro-wind generator set at each moment; taking the micro-wind generator set to be detected as the target micro-wind generator set, and selecting any micro-wind generator set as the reference generator set of the target micro-wind generator set; dividing multiple moments into multiple time periods, and drawing a first curve of the target micro-wind generator set regarding wind speed and output power in each time period and a second curve of the reference generator set; constructing a fault evaluation function for the target micro-wind generator set, obtaining the maximum value of the fault evaluation value according to the fault evaluation function, and in response to the maximum value being greater than a preset threshold, issuing a warning prompt. The present invention can accurately monitor the micro-wind generator set, effectively avoid the further acceleration of the fault of the reference generator set, and achieve the effect of automatic protection of the micro-wind generator set.

Description

Technical Field

[0001] The present invention relates to the field of generator set monitoring, and particularly to an automatic protection method and system for a micro-wind generator set. Background Art

[0002] A micro-wind generator set is a small-scale wind power generation technology designed to generate electricity using relatively low wind speeds, and is typically used for power supply in households, agriculture, or small commercial sites. The micro-wind generator set is characterized by a small power generation capacity, but its advantage lies in its relatively simple equipment and flexible installation, making it suitable for areas with less abundant wind resources. Micro-wind generator sets are usually installed in clusters, that is, multiple micro-wind generator sets are installed within a certain range of the site for convenient equipment management and maintenance. However, during the operation of the micro-wind generator set, it is necessary to monitor the operating state of the micro-wind generator set to keep it in an excellent operating state.

[0003] Chinese Patent with Publication No. CN109869286B discloses a method for monitoring the vibration state of a wind turbine generator set, including the following steps: arranging multiple groups of sensor groups along the vertical direction on the inner wall of the wind power tower. Each group of sensor groups includes at least four low-frequency sensors evenly arranged along the same circumference to collect the low-frequency vibration signals of the wind turbine generator set in the natural state; performing an initial estimation of the natural excitation condition mode of the wind turbine generator set based on the collected low-frequency vibration signals, and obtaining the transfer function by performing Fourier transform on the low-frequency vibration signals. Taking the finite data of the transfer function at each modal frequency and fitting to obtain the condition modal parameters, and calculating each order of condition modes under the natural excitation of the wind turbine generator set; when the deviation between the measured modal frequency of a certain order and the operating frequency of the wind turbine generator set is within 10%, a fault warning is given.

[0004] The above-mentioned related technical solutions monitor the vibration signals of the wind turbine generator to achieve the monitoring of the wind turbine generator set. However, during the operation of the micro-wind generator set, based on the operating characteristics of the micro-wind generator set, when a fault occurs in the micro-wind generator set, its vibration amplitude is small and difficult to measure, resulting in a low accuracy of the monitoring results. In addition, when a fault occurs in the micro-wind generator set, it does not always generate vibration. Only when the rotating shaft and bearings are abnormal, the micro-wind generator set will generate vibration. When the internal circuit components are aged or faulty, the micro-wind generator set will not generate vibration. Therefore, relying on monitoring vibration signals to monitor the micro-wind generator set results in a low accuracy of the monitoring results and cannot accurately reflect the operating state of the micro-wind generator set. Summary of the Invention

[0005] In order to solve the problem of low accuracy of the monitoring results caused by monitoring the micro-wind generator set through vibration signals in the prior art, the present invention provides an automatic protection method and system for a micro-wind generator set.

[0006] In a first aspect, the present invention provides an automatic protection method for a micro-wind power generation unit, adopting the following technical solution:

[0007] Obtain the parameters of each micro-wind power generation unit at each moment, as well as the parameters of the reference power generation unit at each moment, where the parameters include wind speed, rotational speed, and output power;

[0008] Take the micro-wind power generation unit to be detected as the target micro-wind power generation unit, and select any micro-wind power generation unit as the reference power generation unit for the target micro-wind power generation unit;

[0009] Divide multiple moments into multiple time periods, and draw a first curve of the target micro-wind power generation unit regarding wind speed and output power in each time period, as well as a second curve of the reference power generation unit regarding wind speed and output power in each time period;

[0010] Construct a fault evaluation function for the target micro-wind power generation unit, obtain the maximum value of the fault evaluation value according to the fault evaluation function, and issue a warning prompt in response to the maximum value being greater than a preset threshold;

[0011] Among them, the expression of the fault evaluation function is:

[0012]

[0013] In the formula, w represents the fault evaluation value of the target micro-wind power generation unit, 、 、 represent the wind speeds corresponding to the first, the k-th, and the a-th data points respectively, k represents the k-th data point, a represents the total number of data points, g(n) is the first curve of the target micro-wind power generation unit in the time period, is the second curve of the reference power generation unit in the time period, tanh represents the normalization function, and n represents the wind speed.

[0014] By selecting the reference power generation unit, comparing the target micro-wind power generation unit with the reference power generation unit, and constructing a fault evaluation function, it is possible to accurately monitor the micro-wind power generation unit, effectively avoid the further acceleration of the fault of the reference power generation unit, realize the automatic protection of the micro-wind power generation unit, and improve the accuracy of the monitoring results.

[0015] Preferably, the selection method of the reference power generation unit is:

[0016] Obtain the rotational speeds of each micro-wind power generation unit at different moments in the time period, and construct a rotational speed sequence according to the obtained multiple rotational speeds;

[0017] Calculate the reference coefficients between the target micro-wind power generation unit and each of the remaining micro-wind power generation units;

[0018] Take the micro-wind turbine corresponding to the maximum value in the reference coefficient as the reference generator set of the target micro-wind turbine within the corresponding time period;

[0019] Among them, the expression of the reference coefficient is:

[0020]

[0021] In the formula, is the reference coefficient, is the rotational speed sequence of the target micro-wind turbine from the i-th to the j-th moment within the time period, is the rotational speed sequence of the other one micro-wind turbine from the i-th to the j-th moment within the time period, mean is the mean function, and PPMCC is the Pearson correlation coefficient.

[0022] By calculating the reference coefficient and using the reference coefficient to select the reference generator set, the similarity between the reference generator set and the target micro-wind turbine is quantified, and the accuracy of the selection of the reference generator set is improved.

[0023] Preferably, the method for selecting the reference generator set is:

[0024] Calculate the distance between each micro-wind turbine and the target micro-wind turbine, and take the micro-wind turbine corresponding to the minimum value as the reference generator set.

[0025] Preferably, the method for dividing multiple moments into multiple time periods is:

[0026] Sort the wind speeds at each moment in chronological order, perform ordered sample clustering on the wind speeds to obtain multiple clustering clusters, and the data points within each clustering cluster correspond to a time period.

[0027] Classify each moment to obtain multiple time periods, which is convenient for analyzing the data within the time period and improves the accuracy of data analysis.

[0028] Preferably, the method for drawing the first curve of the target micro-wind turbine regarding wind speed and output power in each time period is: construct a three-dimensional coordinate system with wind speed as the x-axis, time as the y-axis, and output power as the z-axis, map the parameters into the three-dimensional coordinate system, project the parameters onto the plane formed by the x-axis and the z-axis to obtain multiple data points, and fit the multiple data points to obtain the first curve.

[0029] By fitting the data points to obtain the first curve, it is convenient for analyzing the parameters and can understand the operating conditions of the micro-wind turbine.

[0030] Preferably, use the spline curve fitting method or the least squares method to fit the multiple data points to obtain the first curve.

[0031] Preferably, the method further includes:

[0032] Calculate the fault deepening speed of the target micro-wind generating set, and the expression is as follows:

[0033]

[0034] In the formula, u is the fault deepening speed, g(n) is the first curve of the target micro-wind generating set within the time period, is the second curve of the reference generating set within the time period, represents the wind speed corresponding to the th data point, represents the wind speed corresponding to the th data point, and n represents the wind speed.

[0035] By calculating the fault deepening speed, the trend of fault acceleration can be understood. By taking corresponding measures for the micro-wind generating set in a timely manner according to the trend of fault acceleration, the micro-wind generating set can be effectively protected.

[0036] In a second aspect, the present invention provides an automatic protection system for a micro-wind generating set, and the following technical solution is adopted:

[0037] An automatic protection system for a micro-wind generating set includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, an automatic protection method for a micro-wind generating set according to the above is implemented.

[0038] The present invention has the following technical effects:

[0039] By selecting a reference generating set, comparing the target micro-wind generating set with the reference generating set, and constructing a fault evaluation function, the micro-wind generating set can be accurately monitored, effectively avoiding the further acceleration of the fault of the reference generating set, realizing the automatic protection of the micro-wind generating set, and improving the accuracy of the monitoring result. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0041] Figure 1 is a flowchart of an automatic protection method for a micro-wind generating set according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] It should be understood that when terms such as "first" and "second" are used in the claims, the description, and the drawings of the present invention, they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0044] An embodiment of the present invention discloses an automatic protection method for a micro-wind power generation set. Referring to Figure 1 , the method includes the following steps:

[0045] S1: Obtain the parameters of each micro-wind power generation set at each moment, as well as the parameters of the reference power generation set at each moment. The parameters include wind speed, rotation speed, and output power.

[0046] For each micro-wind power generation set in the area, each moment corresponds to a wind speed data, a rotation speed data of the rotating shaft, and an output power data of the micro-wind power generation set. The data acquisition interval is 5 seconds, so as to further obtain the parameters at multiple moments.

[0047] S2: Take the micro-wind power generation set to be detected as the target micro-wind power generation set, and select any micro-wind power generation set as the reference power generation set of the target micro-wind power generation set.

[0048] In one embodiment, the method for selecting the reference power generation set is as follows: Obtain the rotation speeds of each micro-wind power generation set at different moments within a time period, and construct a rotation speed sequence based on the obtained multiple rotation speeds; calculate the reference coefficients between the target micro-wind power generation set and each of the other micro-wind power generation sets; take the micro-wind power generation set corresponding to the maximum value in the reference coefficients as the reference power generation set of the target micro-wind power generation set within the corresponding time period.

[0049] Among them, the expression of the reference coefficient is:

[0050]

[0051] In the formula, is the reference coefficient, is the rotation speed sequence of the target micro-wind power generation set from the i-th moment to the j-th moment within the time period, is the rotational speed sequence of the remaining one small wind turbine from the $i$-th to the $j$-th moment within a time period, mean is the mean function, and PPMCC is the Pearson correlation coefficient.

[0052] Among them, represents the average similarity of the rotational speeds of two small wind turbines. The larger its value, the more similar the rotational speeds of the two small wind turbines are; represents the overall similarity of the rotational speeds of two small wind turbines. The larger its value, the more similar the overall rotational speeds of the two small wind turbines are; Therefore, the larger the reference coefficient, the more similar the rotational speeds of the two small wind turbines are.

[0053] In a group of small wind turbines, the spatial positions of each small wind turbine are different. Therefore, the influence of wind force on each small wind turbine will also vary. Therefore, within a certain time period, find a small wind turbine with a similar force-receiving situation to the target small wind turbine as a reference generator set. The influence degree of wind force on a small wind turbine can be measured by its rotational speed. When the rotational speeds of two small wind turbines are similar, the influence of wind force on them is also relatively approximate. It can be understood that the larger the reference coefficient, the more similar the rotational speeds of the two small wind turbines are, and the more similar the situations of the two small wind turbines affected by wind force are.

[0054] Exemplarily, there are four groups of small wind turbines a, b, c, and d. Taking small wind turbine a as the target small wind turbine, the reference coefficient between group a and group b is 0.75, the reference coefficient between group a and group c is 0.82, and the reference coefficient between group a and group d is 0.78. Among them, the reference coefficient between group a and group c is the largest. Therefore, group c is used as the reference generator set for group a.

[0055] In one embodiment, in a group of small wind turbines, the two small wind turbines with the closest distance are affected by wind force relatively approximately. Therefore, the small wind turbine closest to the target small wind turbine is used as the reference generator set.

[0056] S3: Divide multiple moments into multiple time periods, and draw a first curve of the target small wind turbine regarding wind speed and output power in each time period, and a second curve of the reference generator set regarding wind speed and output power in each time period.

[0057] Within a day, the wind speeds in different time periods are different. Therefore, divide multiple moments into multiple time periods according to wind speed. The division method is: sort the wind speeds of each moment in chronological order, and perform ordered sample clustering on the wind speeds to obtain multiple clustering clusters. Each clustering cluster contains multiple wind speed data. Furthermore, the data points within each clustering cluster correspond to a time period.

[0058] The method for plotting the first curve of the target small wind turbine regarding wind speed and output power in each time period is as follows: construct a three-dimensional coordinate system with wind speed as the x-axis, time as the y-axis, and output power as the z-axis, map the parameters into the three-dimensional coordinate system, project the parameters onto the plane formed by the x-axis and z-axis to obtain multiple data points, and use the spline curve fitting method or the least squares method to fit the multiple data points to obtain the first curve. Thus, it can be known that the first curve reflects the relationship between the wind speed and output power of the target small wind turbine.

[0059] Similarly, plot the second curve of the reference generator set regarding wind speed and output power in each time period. The second curve reflects the relationship between the wind speed and output power of the reference generator set.

[0060] S4: Construct a fault evaluation function for the target small wind turbine, obtain the maximum value of the fault evaluation value according to the fault evaluation function, and issue a warning prompt in response to the maximum value being greater than the preset threshold.

[0061] The expression of the fault evaluation function is:

[0062]

[0063] In the formula, w represents the fault evaluation value of the target small wind turbine, 、 、 respectively represent the wind speeds corresponding to the first, the k-th, and the a-th data points. k represents the k-th data point, and a represents the total number of data points (the total number of data points in the first curve or the second curve). g(n) is the first curve of the target small wind turbine in the time period, is the second curve of the reference generator set in the time period. tanh represents the normalization function, and n represents the wind speed.

[0064] Among them, represents the degree of difference in performance between the target small wind turbine and the reference generator set when the wind speed is from to . The larger its value, the greater the difference between the two. Similarly, represents the degree of difference in performance between the target small wind turbine and the reference generator set when the wind speed is from to When it comes to the degree of difference in performance between the target micro wind turbine generator set and the reference generator set, the larger its value, the greater the difference between the two. The difference here can be understood as the difference in the power generation capacity of the micro wind turbine generator set. During the operation of the micro wind turbine generator set, the target micro wind turbine generator set and the reference generator set are similarly affected by the wind. Therefore, under normal circumstances, the output powers of the target micro wind turbine generator set and the reference generator set should also be similar. In other words, under normal circumstances, the first curve and the second curve are similar and their difference is small; conversely, when the target micro wind turbine generator set fails, the difference in output power between the target micro wind turbine generator set and the reference generator set is large, and the difference between the corresponding first curve and the second curve is also large.

[0065] In the first curve and the second curve, different wind speeds are iteratively selected to obtain different fault evaluation values. When the maximum value of the fault evaluation value is greater than the preset threshold, it indicates that the target micro wind turbine generator set has failed, and the target micro wind turbine generator set needs to be repaired in a timely manner. At the same time, the wind speed corresponding to the kth data point (the data point in the first curve) when the fault evaluation value is the maximum value is used as the fault node, and the fault node indicates that the target micro wind turbine generator set has failed when the wind speed is nearby. The threshold is set manually according to the actual situation. Exemplarily, the threshold is 0.5.

[0066] Exemplarily, within the current time period of a day, at the 20th data point within the first curve the fault evaluation value of the target micro wind turbine generator set is 0.6, indicating that the target micro wind turbine generator set has failed during the current time period. At this time, an interval (6.5, 7.0) is constructed with as the center, and parameters with wind speeds within the interval (6.5, 7.0) are searched for within the time period. If the wind speeds at the 50th, 60th, and 65th moments within this time period are within the interval, it indicates that the target micro wind turbine generator set has failed at the 50th, 60th, or 65th moment. At this time, the 50th moment is used as the time point corresponding to the fault node.

[0067] S5: Calculate the fault deepening speed of the target micro wind turbine generator set.

[0068] The expression for the fault deepening speed is:

[0069]

[0070] In the formula, u is the fault deepening speed, g(n) is the first curve of the target micro wind turbine generator set within the time period, is the second curve of the reference generator set within the time period, represents the th data point (the data point in the first curve) corresponding wind speed, represents the wind speed corresponding to the th data point (data point in the first curve), and n represents the wind speed.

[0071] The fault deepening speed represents the speed at which the target micro wind turbine generator is damaged. The larger its value, the faster the target micro wind turbine generator is damaged. When the fault deepening speed is greater than the preset deepening threshold, it indicates that the target micro wind turbine generator is in a state of rapid damage at this time. During the calculation process, the value of L is iteratively selected in ascending order.

[0072] Exemplarily, within the current time period of a day, the target micro wind turbine generator fails at the 50th moment (corresponding to the 20th data point in the first curve ). The fault deepening speed is greater than the preset threshold at the 30th data point in the first curve, that is, at the point, the fault deepening speed is greater than the preset threshold, the time point corresponding to the wind speed of is the 100th moment, indicating that the target micro wind turbine generator is accelerating damage at the 100th moment. At this time, before the 100+(100 - 50)=150th moment, stop the operation of the target micro wind turbine generator to avoid damage caused by the continuous operation of the target micro wind turbine generator. It can be understood that, corresponds to corresponds to and corresponds to .

[0073] An embodiment of the present invention also discloses an automatic protection system for a micro wind turbine generator, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, an automatic protection method for a micro wind turbine generator according to the present invention is implemented.

[0074] The above system further includes other components well known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be elaborated here.

[0075] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high bandwidth memory (HBM), a hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0076] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, changes, and alternative approaches will occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0077] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A method for automatically protecting a micro-wind generator set, characterized in that: Includes steps: Obtain the parameters of each breeze generator set at each time, as well as the parameters of the reference generator set at each time, the parameters including wind speed, rotation speed and output power; The breeze generator set to be detected is taken as the target breeze generator set, and any breeze generator set is selected as the reference generator set of the target breeze generator set; Divide the multiple moments into multiple time periods, draw a first curve about wind speed and output power of the target breeze generator set in each time period, and draw a second curve about wind speed and output power of the reference generator set in each time period; Constructing a fault evaluation function of the target micro-wind generator set, obtaining a maximum value of the fault evaluation value according to the fault evaluation function, and issuing an early warning prompt in response to the maximum value being greater than a preset threshold value; The method for selecting the reference generator set is as follows: Obtain the speed of each breeze generator set at different times within a time period, and construct a speed sequence based on the obtained multiple speeds; Calculate the reference coefficient between the target micro-wind generator set and each of the remaining micro-wind generator sets; The micro-wind generator set corresponding to the maximum value of the reference coefficient is used as the reference generator set of the target micro-wind generator set in the corresponding time period; The expression of the reference coefficient is: In the formula, is the reference coefficient, is the speed sequence of the target breeze generator set from the i-th to the j-th moment in the time period, is the speed sequence of the other breeze generator set from the i-th to the j-th moment in the time period, mean is the mean function, and PPMCC is the Pearson correlation coefficient; The expression of the fault evaluation function is: Where w represents the fault evaluation value of the target wind turbine generator set, , , represents the wind speed corresponding to the first, kth and ath data points, k represents the kth data point, a represents the total number of data points, g(n) is the first curve of the target breeze generator set in the time period, The second curve of the reference generator set during the time period, tanh represents the normalized function, and n represents the wind speed.

2. The method for automatically protecting a micro-wind generator set according to claim 1, characterized in that: The selection method of the reference generator set is: The distance between each breeze generator set and the target breeze generator set is calculated, and the breeze generator set corresponding to the minimum value is used as the reference generator set.

3. The method for automatically protecting a micro-wind generator set according to claim 1, characterized in that: The method of dividing multiple moments into multiple time periods is: The wind speeds at each moment are sorted in chronological order, and the wind speed samples are clustered in an ordered manner to obtain multiple clusters, and the data points in each cluster correspond to a time period.

4. The method for automatically protecting a micro-wind generator set according to claim 1, characterized in that: The method for drawing the first curve of wind speed and output power of the target breeze generator set in each time period is as follows: construct a three-dimensional coordinate system with wind speed as the x-axis, time as the y-axis, and output power as the z-axis, map the parameters into the three-dimensional coordinate system, project the parameters into the plane formed by the x-axis and the z-axis to obtain multiple data points, and fit the multiple data points to obtain the first curve.

5. The method for automatically protecting a micro-wind generator set according to claim 1, characterized in that: A first curve is obtained by fitting multiple data points using a spline curve fitting method or a least squares method.

6. The method for automatically protecting a micro-wind generator set according to claim 1, characterized in that: The method also includes: Calculate the fault deepening speed of the target wind turbine generator set, the expression is: Where u is the fault deepening speed, g(n) is the first curve of the target wind turbine generator set in the time period, is the second curve of the reference generator set in the time period, k represents the kth data point, L represents a positive integer, Indicates The wind speed corresponding to the data point is Indicates The wind speed corresponding to the data point, n represents the wind speed.

7. An automatic protection system for a micro-wind generator set, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an automatic protection method for a micro wind generator set according to any one of claims 1 to 6 is implemented.

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