A fan stall online monitoring and adjusting system

Abstract

This invention relates to the field of online monitoring technology for wind turbine stall, and more specifically to an online monitoring and adjustment system for wind turbine stall, comprising: a stall judgment module, which judges the stall of the primary air turbine by detecting the primary air turbine current, air pressure, and primary air duct air pressure; a blade adjustment module, which can adjust the blade opening of the primary air turbine, and adjusts the blade opening when stall is judged to have occurred; a peak shaving prediction module, which predicts the trend of deep peak shaving of the power plant based on historical data; and a pre-adjustment module, which adjusts the primary air system according to the predicted trend of deep peak shaving to prevent the primary air turbine from stalling due to deep peak shaving; and by pre-adjusting the primary air system according to the prediction of deep peak shaving, the system effectively avoids wind turbine stall caused by untimely blade feedback, thereby improving the stability of unit operation.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology for wind turbine stall, and more specifically to an online monitoring and adjustment system for wind turbine stall. Background Technology

[0002] In my country, wind power, hydropower, photovoltaic power and thermal power are the main sources of electricity connected to the grid. However, under special conditions such as low wind speeds, dry seasons, and rainy days, new energy sources cannot guarantee a stable output of electricity, and thermal power plants need to supplement the output of electricity through deep peak shaving.

[0003] When performing deep peak shaving according to grid demand, the primary air fan maintains a low air volume operation with a high pressure head when the unit is under low load. When the unit load rises or falls rapidly or is started or stopped, the primary air volume changes rapidly and significantly. If the fan blade opening adjustment feedback is not timely, it is easy to stall due to wind, which poses a safety hazard of fan stall.

[0004] Therefore, how to monitor and adjust primary air turbines is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides an online monitoring and adjustment system for wind turbine stall. By detecting the current and wind pressure of the primary wind turbine and the wind pressure of the primary air duct, the system realizes real-time monitoring of wind turbine stall. By predicting the power generation trend of the power plant and predicting the deep peak shaving time, the system pre-adjusts the blade opening of the primary wind turbine, which can effectively avoid the occurrence of wind turbine stall due to untimely blade feedback.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Preferably, the above-mentioned online monitoring and adjustment system for wind turbine stall includes:

[0008] The stall detection module detects the stall of the primary fan by monitoring the primary fan current, air pressure, and primary air duct air pressure.

[0009] The blade adjustment module can adjust the blade opening of the primary air fan. When stall is detected, the blade opening is adjusted.

[0010] The peak shaving prediction module predicts the trend of deep peak shaving in power plants based on historical data.

[0011] The pre-adjustment module adjusts the primary air system based on the predicted trend of deep peak shaving to prevent primary air turbine stall caused by deep peak shaving.

[0012] Compared with the prior art, the above embodiments provide a method for pre-adjusting the primary air system based on the prediction of deep peak shaving, which effectively avoids the occurrence of fan stall caused by untimely feedback of moving blades and improves the stability of unit operation.

[0013] Preferably, in the above-mentioned online monitoring and adjustment system for wind turbine stall, the stall judgment module includes:

[0014] The current deviation unit detects the current deviation of the parallel primary air fans;

[0015] The differential pressure unit detects the differential pressure of the parallel primary air fans;

[0016] The duct air pressure unit detects the air pressure in the primary air duct.

[0017] The judgment unit determines that the fan has stalled when the detected current deviation is greater than the current deviation threshold or the wind pressure difference is greater than the wind pressure difference threshold, and the wind pressure in the primary air duct is less than the wind pressure threshold, and generates a stall signal.

[0018] Preferably, in the above-mentioned online monitoring and adjustment system for wind turbine stall, the blade adjustment module includes:

[0019] The blade adjustment device, installed on the primary air fan, can adjust the opening degree of the blades of the primary air fan;

[0020] When the stall signal is generated, the automatic adjustment unit cancels the automatic control of the primary fan, adjusts the blade opening of the stall fan to the lowest value, and adjusts the opening of the normal fan to the highest value.

[0021] Preferably, in the above-mentioned online monitoring and adjustment system for wind turbine stall, the peak shaving prediction module includes:

[0022] The data acquisition unit obtains historical data on wind power, hydropower, photovoltaic power, and thermal power in the region through the power grid, forming a historical database;

[0023] The preprocessing unit preprocesses the data in the historical database, removes abnormal data from the historical data, and generates a trend chart of the electricity input to the grid.

[0024] The power prediction unit predicts the power supply trend of the power plant in the next 24 hours based on the current time and the power trend chart, and obtains the first predicted power chart.

[0025] The meteorological forecasting unit acquires meteorological forecast data, obtains the impact of meteorological changes on power peak regulation by comparing similar meteorological data in historical data, and corrects the first predicted power map to obtain the second predicted power map.

[0026] Preferably, in the above-mentioned online monitoring and adjustment system for wind turbine stall, the preprocessing unit includes:

[0027] The data is divided into daily units to obtain the data on changes in grid-connected electricity volume over a 24-hour period.

[0028] The data is categorized and statistically analyzed according to wind power, thermal power, hydropower, and photovoltaic power to form comparative data on the electricity connected to the grid for different types.

[0029] The data is statistically analyzed on a monthly basis to generate trend charts of changes in the electricity volume of various types connected to the grid.

[0030] Preferably, in the above-mentioned wind turbine stall online monitoring and adjustment system, the step of statistically analyzing the data on a monthly basis to generate trend charts of various types of grid-connected power includes:

[0031] Perform a unified analysis of data from the same months over many years;

[0032] The data is divided into multiple datasets, with each dataset measured in minutes.

[0033] Sort the data set according to the size of the data to obtain the median value of the data set;

[0034] After sorting the data set, all the data is divided into ten equal parts. The values ​​of the two parts with the larger median value are taken as the upper values, and the values ​​of the two parts with the smaller median value are taken as the lower values.

[0035] The upper limit value and the lower limit value are calculated using the upper value and the lower value, respectively, and the calculation formulas for the upper limit value and the lower limit value are: Q3 = Q1 + 1.5 * (Q1 - Q2), Q4 = Q2 - 1.5 * (Q1 - Q2); where Q1 is the upper value, Q2 is the lower value, Q3 is the upper limit value, and Q4 is the lower limit value.

[0036] Data in the data set that is greater than the upper limit or less than the lower limit is removed to obtain a valid data set. The valid value of the valid data set is then calculated using the following formula:

[0037]

[0038] Where c is the valid value, n is the number of data in the valid data set, and c i For each piece of data within the valid data set;

[0039] Based on the effective values, generate trend charts of the changes in the power supply for each type of grid connection.

[0040] Compared with the prior art, the above embodiments use a box plot data processing method to remove abnormal data in historical data, which helps to improve the accuracy of electricity prediction.

[0041] Preferably, in the above-mentioned online monitoring and adjustment system for wind turbine stall, the pre-adjustment module includes:

[0042] The load calculation unit calculates the unit load in the power plant based on the second predicted power output diagram, and generates a unit load change trend diagram.

[0043] The blade opening pre-adjustment unit, based on the correspondence between unit load and blade opening and the unit load change trend diagram, performs planned pre-adjustment of blade opening during the preset time period for deep peak shaving.

[0044] The damper pre-adjustment unit pre-adjusts the fan blades and simultaneously pre-adjusts the opening of the primary air main damper to maintain the primary air volume matching the unit load.

[0045] Preferably, in the above-mentioned online monitoring and adjustment system for wind turbine stall, the planned pre-adjustment includes:

[0046] The target value for pre-adjustment is set at 80% of the blade opening during deep peak shaving.

[0047] Calculate the difference in blade opening between the pre-conditioning target value and the blade opening at the start of the planned pre-conditioning;

[0048] The opening difference is divided equally according to the planned pre-adjustment time to obtain the unit adjustment opening, thus forming the pre-adjustment plan;

[0049] The opening of the moving blades is adjusted according to the pre-adjustment plan.

[0050] Compared with the prior art, the above embodiments provide a method for pre-adjusting the opening of the turbine blades, which can pre-adjust the opening before deep peak shaving and pre-adjust the opening of the primary air damper, thereby reducing the impact of the blade pre-adjustment on the primary air volume, reducing the occurrence of stall, and improving the stability of unit operation.

[0051] As can be seen from the above technical solution, compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] 1. Based on the prediction of deep peak shaving, the primary air system is pre-adjusted, which effectively avoids the occurrence of fan stall caused by untimely feedback of moving blades and improves the stability of unit operation.

[0053] 2. Using box plot data processing methods to remove abnormal data in historical data helps improve the accuracy of electricity prediction.

[0054] 3. Before performing deep peak shaving, the opening degree is pre-adjusted, and the opening degree of the primary air damper is also pre-adjusted to reduce the impact of the pre-adjustment of the moving blades on the primary air volume. This reduces the occurrence of stall and improves the stability of unit operation. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0056] Figure 1 The attached figure is a functional structure diagram of the system of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In this invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance; the term "multiple" refers to two or more unless otherwise explicitly defined. The terms "install," "connect," "link," and "fix" should be interpreted broadly. For example, "connect" can be a fixed connection, a detachable connection, or an integral connection; "link" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0059] In the description of this invention, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0060] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0061] like Figure 1 As shown in the figure, an embodiment of the present invention discloses an online monitoring and adjustment system for wind turbine stall, comprising:

[0062] The stall detection module detects the stall of the primary fan by monitoring the primary fan current, air pressure, and primary air duct air pressure.

[0063] The blade adjustment module can adjust the blade opening of the primary air fan. When stall is detected, the blade opening is adjusted.

[0064] The peak shaving prediction module predicts the trend of deep peak shaving in power plants based on historical data.

[0065] The pre-adjustment module adjusts the primary air system based on the predicted trend of deep peak shaving to prevent primary air turbine stall caused by deep peak shaving.

[0066] The beneficial effects of the above embodiments are as follows: based on the prediction of deep peak shaving, the primary air system is pre-adjusted, which effectively avoids the occurrence of fan stall caused by untimely feedback of moving blades and improves the stability of unit operation.

[0067] In one embodiment, a wind turbine stall online monitoring and adjustment system includes a stall judgment module, comprising:

[0068] The current deviation unit detects the current deviation of the parallel primary air fans; in this embodiment, there are two primary air fans connected in parallel.

[0069] The preferred current deviation unit is a current deviation detection circuit, which is a well-known prior art that can detect the current difference between two primary air blowers.

[0070] The differential pressure unit detects the differential pressure of the parallel primary air fans;

[0071] The wind pressure differential unit is preferably a fan pressure differential detection circuit, which is a well-known prior art that can detect the wind pressure of the primary fan.

[0072] The duct air pressure unit detects the air pressure in the primary air duct.

[0073] The preferred method for detecting the air pressure of the primary air duct and primary air fan is to use a pressure transmitter, which is existing technology.

[0074] The judgment unit determines that the fan has stalled when the detected current deviation is greater than the current deviation threshold or the wind pressure difference is greater than the wind pressure difference threshold, and the wind pressure in the primary air duct is less than the wind pressure threshold, and generates a stall signal.

[0075] The aforementioned decision-making unit is powered by the DCS system within the power plant.

[0076] In one embodiment, a wind turbine stall online monitoring and adjustment system includes a blade adjustment module, comprising:

[0077] The blade adjustment device, installed on the primary air fan, can adjust the opening degree of the blades of the primary air fan;

[0078] The automatic adjustment unit cancels the automatic control of the primary fan after a stall signal is generated, adjusts the blade opening of the stalled fan to the minimum value, and adjusts the opening of the normal fan to the maximum value.

[0079] The aforementioned blade adjustment device is existing technology and can adjust the blade between its maximum and minimum opening; the minimum and maximum values ​​are both preset values.

[0080] Specifically, the automatic adjustment unit adjusts the blade opening of the stalled fan to 20% when a stall occurs, and adjusts the blade opening of the other fan to 80% to maintain the air pressure in the primary air duct.

[0081] In one embodiment, a wind turbine stall online monitoring and adjustment system includes a peak-shaving prediction module, comprising:

[0082] The data acquisition unit obtains historical data on wind power, hydropower, photovoltaic power, and thermal power in the region through the power grid, forming a historical database;

[0083] The preprocessing unit preprocesses the data in the historical database, removes abnormal data from the historical data, and generates a trend chart of the electricity input to the grid.

[0084] The power prediction unit predicts the power supply trend of the power plant in the next 24 hours based on the current time and the power trend chart, and obtains the first predicted power chart.

[0085] The meteorological forecasting unit acquires meteorological forecast data, obtains the impact of meteorological changes on power peak regulation by comparing similar meteorological data in historical data, and corrects the first predicted power volume map to obtain the second predicted power volume map.

[0086] In the above embodiments, it should be noted that environmental conditions have a significant impact on the power generation of wind power and photovoltaic power. Based on meteorological forecasts, deep peak shaving can be predicted, which can improve the accuracy of deep peak shaving prediction.

[0087] When revising the forecast power map based on weather conditions, this is done every hour to prevent sudden weather changes.

[0088] In one embodiment, a wind turbine stall online monitoring and adjustment system includes a preprocessing unit comprising:

[0089] The data is divided into daily units to obtain the data on changes in grid-connected electricity volume over a 24-hour period.

[0090] The data is categorized and statistically analyzed according to wind power, thermal power, hydropower, and photovoltaic power to form comparative data on the electricity connected to the grid for different types.

[0091] The data is statistically analyzed on a monthly basis to generate trend charts of changes in the electricity volume of various types connected to the grid.

[0092] In one embodiment, a wind turbine stall online monitoring and adjustment system statistically analyzes data on a monthly basis to generate trend charts of various types of grid-connected power, including:

[0093] Perform a unified analysis of data from the same months over many years;

[0094] The data is divided into multiple datasets, with each dataset measured in minutes.

[0095] Sort the data set according to the size of the data to obtain the median value of the data set;

[0096] After sorting the data set, divide all the data into ten equal parts. Take the values ​​of the two parts with the largest median values ​​as the upper values ​​and take the values ​​of the two parts with the smallest median values ​​as the lower values.

[0097] The upper and lower limits are calculated using the upper and lower values, respectively. The formulas for calculating the upper and lower limits are: Q3 = Q1 + 1.5 * (Q1 - Q2), Q4 = Q2 - 1.5 * (Q1 - Q2); where Q1 is the upper value, Q2 is the lower value, Q3 is the upper limit, and Q4 is the lower limit.

[0098] Remove data points in the data set that are greater than the upper limit or less than the lower limit to obtain the valid data set. Calculate the valid value of the valid data set using the following formula:

[0099]

[0100] Where c is the valid value, and n is the number of data points in the valid data set. iFor each piece of data within the valid data set;

[0101] Generate trend charts of various types of grid-connected electricity volume based on the effective values.

[0102] The beneficial effects of the above embodiments are: using the box plot data processing method to remove abnormal data in historical data helps to improve the accuracy of electricity prediction.

[0103] In one embodiment, a wind turbine stall online monitoring and adjustment system includes a pre-adjustment module, comprising:

[0104] The load calculation unit calculates the load of the units in the power plant based on the second predicted power output diagram, and generates a load change trend diagram of the units.

[0105] The blade opening pre-adjustment unit, based on the correspondence between unit load and blade opening and the unit load change trend diagram, performs planned pre-adjustment of blade opening during the preset time period for deep peak shaving.

[0106] The damper pre-adjustment unit pre-adjusts the fan blades and simultaneously pre-adjusts the opening of the primary air main damper to maintain the primary air volume matching the unit load.

[0107] In one embodiment, a wind turbine stall online monitoring and adjustment system, with planned pre-adjustment, includes:

[0108] The target value for pre-adjustment is set at 80% of the blade opening during deep peak shaving.

[0109] Calculate the difference in blade opening between the pre-conditioning target value and the blade opening at the start of the planned pre-conditioning;

[0110] The opening difference is divided equally according to the planned pre-adjustment time to obtain the unit adjustment opening, thus forming the pre-adjustment plan;

[0111] The opening of the moving blades is adjusted according to the pre-adjustment plan.

[0112] The beneficial effects of the above embodiments are as follows: the opening degree is pre-adjusted before deep peak shaving, and the opening degree of the primary air damper is pre-adjusted at the same time, reducing the impact of the pre-adjustment of the moving blades on the primary air volume, thereby reducing the occurrence of stall and improving the stability of unit operation.

[0113] It should be noted that the above embodiments are merely illustrative examples of the division of functional modules. In practical applications, the functions described above can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are merely for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0114] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0115] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0116] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims and their equivalents, this invention is also intended to include these modifications and variations in the above description of the disclosed embodiments, enabling those skilled in the art to implement or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, this invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A wind turbine stall online monitoring and adjustment system, characterized in that, include: The stall detection module detects the stall of the primary fan by monitoring the primary fan current, air pressure, and primary air duct air pressure. The blade adjustment module can adjust the blade opening of the primary air fan. When stall is detected, the blade opening is adjusted. The peak shaving prediction module predicts the trend of deep peak shaving in power plants based on historical data. The pre-adjustment module adjusts the primary air system based on the predicted trend of deep peak shaving to prevent primary air turbine stall caused by deep peak shaving. The peak-shaving prediction module includes: The data acquisition unit obtains historical data on wind power, hydropower, photovoltaic power, and thermal power in the region through the power grid, forming a historical database; The preprocessing unit preprocesses the data in the historical database, removes abnormal data from the historical data, and generates a trend chart of the electricity input to the grid. The power prediction unit predicts the power supply trend of the power plant in the next 24 hours based on the current time and the power trend chart, and obtains the first predicted power chart. The meteorological forecasting unit acquires meteorological forecast data, obtains the impact of meteorological changes on power peak regulation by comparing similar meteorological data in historical data, and corrects the first predicted power map to obtain the second predicted power map. The pre-adjustment module includes: The load calculation unit calculates the unit load in the power plant based on the second predicted power output diagram, and generates a unit load change trend diagram. The blade opening pre-adjustment unit, based on the correspondence between unit load and blade opening and the unit load change trend diagram, performs planned pre-adjustment of blade opening during the preset time period for deep peak shaving. The damper pre-adjustment unit pre-adjusts the fan blades and simultaneously pre-adjusts the opening of the primary air main damper to maintain the primary air volume matching the unit load. The planned pre-adjustment includes: The target value for pre-adjustment is set at 80% of the blade opening during deep peak shaving. Calculate the difference in blade opening between the pre-conditioning target value and the blade opening at the start of the planned pre-conditioning; The opening difference is divided equally according to the planned pre-adjustment time to obtain the unit adjustment opening, thus forming the pre-adjustment plan; The opening of the moving blades is adjusted according to the pre-adjustment plan.

2. The online monitoring and adjustment system for wind turbine stall according to claim 1, characterized in that, The stall detection module includes: The current deviation unit detects the current deviation of the parallel primary air fans; The differential pressure unit detects the differential pressure of the parallel primary air fans; The duct air pressure unit detects the air pressure in the primary air duct. The judgment unit determines that the fan has stalled when the detected current deviation is greater than the current deviation threshold or the wind pressure difference is greater than the wind pressure difference threshold, and the wind pressure in the primary air duct is less than the wind pressure threshold, and generates a stall signal.

3. The online monitoring and adjustment system for wind turbine stall according to claim 2, characterized in that, The blade adjustment module includes: The blade adjustment device, installed on the primary air fan, can adjust the opening degree of the blades of the primary air fan; When the stall signal is generated, the automatic adjustment unit cancels the automatic control of the primary fan, adjusts the blade opening of the stall fan to the lowest value, and adjusts the opening of the normal fan to the highest value.

4. The online monitoring and adjustment system for wind turbine stall according to claim 1, characterized in that, The preprocessing unit includes: The data is divided into days to obtain the data on changes in grid-connected electricity volume over a 24-hour period. The data is categorized and statistically analyzed according to wind power, thermal power, hydropower, and photovoltaic power to form comparative data on the electricity connected to the grid for different types. The data is statistically analyzed on a monthly basis to generate trend charts of changes in the electricity volume of various types connected to the grid.

5. The online monitoring and adjustment system for wind turbine stall according to claim 4, characterized in that, The process of statistically analyzing the data on a monthly basis to generate trend charts of various types of grid-connected electricity includes: Perform a unified analysis of data from the same months over many years; The data is divided into multiple data sets, with each set measured in minutes. Sort the data set according to the size of the data to obtain the median value of the data set; After sorting the data set, all the data is divided into ten equal parts. The values ​​of the two parts with the larger median value are taken as the upper values, and the values ​​of the two parts with the smaller median value are taken as the lower values. The upper and lower limits are calculated using the upper and lower values, respectively, with the formula: Q3 = Q1 + 1.

5. (Q1-Q2), Q4=Q2-1.5 (Q1-Q2); where Q1 is the upper value, Q2 is the lower value, Q3 is the upper limit value, and Q4 is the lower limit value; Data in the data set that is greater than the upper limit or less than the lower limit is removed to obtain a valid data set. The valid value of the valid data set is then calculated using the following formula: Where c is the valid value, n is the number of data in the valid data set, and c i For each piece of data within the valid data set; Based on the effective values, generate trend charts of the changes in the power supply for each type of grid connection.

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