Water turbine primary frequency modulation control method based on intelligent optimization algorithm

Abstract

The invention relates to the technical field of water turbine frequency modulation control, in particular to a water turbine primary frequency modulation control method based on an intelligent optimization algorithm, and the method comprises the steps: obtaining actual operation parameters of all collection moments in the operation process of a water turbine, and analyzing the difference degree between the high-frequency information of all actual operation parameters in the operation process of the water turbine; calculating the high-frequency coupling degree at each acquisition moment according to the random change degree of the high-frequency information of each actual operation parameter; according to the method, abnormal rotating speeds are extracted, the abnormal deviation degree of each abnormal rotating speed is obtained through the local water turbine rotating speed change dispersion degree of each abnormal rotating speed, then a rotating speed dead zone optimization value at the current collection moment is obtained, whether the opening degree of a guide vane in the water turbine is adjusted or not is judged, and optimal control parameters of a PID controller are obtained through an intelligent optimization algorithm. Therefore, primary frequency modulation control of the water turbine is realized. The stability of water turbine frequency modulation control can be guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of hydraulic turbine frequency regulation control, and in particular to a hydraulic turbine primary frequency regulation control method based on an intelligent optimization algorithm. Background Art

[0002] When hydropower units are connected to the grid, the volatility and intermittent nature of hydropower, a renewable energy source, can cause uncontrollable fluctuations in the grid frequency, posing a serious threat to the grid's stable operation. Turbine primary frequency regulation is a natural frequency characteristic of the turbine governor. When the speed of a grid-connected hydropower unit fluctuates beyond the specified deadband, the turbine governor automatically increases or decreases the turbine's internal guide vane opening, thereby regulating the grid frequency and ensuring stable operation of the power system.

[0003] In the intelligent optimization process of the primary frequency regulation control of the turbine, it is necessary to dynamically compensate for the turbine's speed dead zone to avoid the turbine's overreaction to non-critical speed fluctuations, thereby effectively filtering out unnecessary adjustment actions in the turbine governor. The control parameters of the PID controller are optimized through an intelligent optimization algorithm to control and adjust the guide vane opening in the turbine, thereby ensuring the stability of the grid frequency when the hydropower unit is connected to the grid. At present, the existing technology uses dynamic compensation to optimize the turbine's speed dead zone. However, the existing technology does not fully consider the coupling changes in the actual operating parameters of the turbine, which easily leads to the speed dead zone after compensation optimization being unsuitable for real-time frequency regulation requirements, making it impossible for the turbine's primary frequency regulation to respond to changes in the grid frequency in a timely and accurate manner, thereby affecting the stability of the grid operation. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a turbine primary frequency regulation control method based on an intelligent optimization algorithm to solve the existing problems.

[0005] The hydraulic turbine primary frequency regulation control method based on the intelligent optimization algorithm of this application adopts the following technical solutions: One embodiment of the present application provides a method for controlling primary frequency regulation of a hydraulic turbine based on an intelligent optimization algorithm, comprising the following steps: Obtain the actual operating parameters of the turbine at each sampling moment during operation, including water flow, turbine speed, and turbine output power; By analyzing the difference between the high-frequency information of each actual operating parameter during the operation of the turbine and the random change degree of the high-frequency information of each actual operating parameter, the high-frequency coupling degree at each acquisition moment is obtained; The abnormal speed is extracted based on the sudden change of high-frequency coupling in the short period before each acquisition moment and the turbine speed. The abnormal deviation of each abnormal speed is obtained by the discrete degree of turbine speed change at each abnormal speed. The key fluctuation significance at each acquisition moment is obtained by combining the mutation point of high-frequency coupling. The speed dead zone optimization value at the current acquisition moment is obtained based on the degree of change of the key fluctuation significance. The speed dead zone optimization value is used to determine whether the guide vane opening of the turbine should be adjusted, and the optimal control parameters of the PID controller are obtained through an intelligent optimization algorithm to achieve primary frequency regulation control of the turbine.

[0006] Preferably, the water flow, turbine speed and output power at all sampling moments within a preset time period before each sampling moment are arranged in time sequence to form a water flow sequence, a speed sequence and a power sequence at each sampling moment. The three sequences are smoothed using a moving average method to obtain a low-frequency sequence for water flow, a low-frequency sequence for speed and a low-frequency sequence for power. After performing difference operations, high-frequency sequences for water flow, high-frequency sequences for speed and high-frequency sequences for power are obtained.

[0007] Preferably, the method for obtaining the high-frequency coupling degree at each acquisition moment is: Where, is the high-frequency coupling degree at the tth acquisition moment, is the normalization function, is the mean of the permutation entropy of the high-frequency sequence of water flow, speed and power at the t-th acquisition moment, is the first difference degree at the t-th acquisition moment, To avoid constants with denominators equal to 0.

[0008] Preferably, the average value of the distance between any two sequences of the water flow high frequency sequence, the rotation speed high frequency sequence and the power high frequency sequence at each collection moment is calculated as the first difference degree at each collection moment.

[0009] Preferably, the abnormal rotation speed extraction process is: The high-frequency coupling degrees of all acquisition moments within a preset time period before each acquisition moment are arranged in time sequence to form a high-frequency coupling sequence for each acquisition moment. The position sequence number of the mutation point in the high-frequency coupling sequence is extracted and recorded as the target sequence number for each acquisition moment. The speed corresponding to all the target sequences at each acquisition moment in the speed sequence is taken as the abnormal speed at each acquisition moment.

[0010] Preferably, the method for obtaining the abnormal deviation of each abnormal speed is: Where, is the abnormal deviation of the ith abnormal speed, is the discrete degree of the elements in the first-order difference sequence of the i-th abnormal speed sliding window sequence, is the number of speeds in the i-th abnormal speed sliding window sequence, and They are the jth and j-1th speeds in the i-th abnormal speed sliding window sequence respectively.

[0011] Preferably, a window is constructed with each abnormal speed in the speed sequence as the center, and all elements in the window constitute a sliding window sequence of each abnormal speed.

[0012] Preferably, the method for obtaining the key fluctuation significance at each collection moment is: Where, is the key fluctuation significance at the t-th collection moment, is the number of all target serial numbers at the tth acquisition moment, is the high-frequency coupling degree corresponding to the i-th mutation point in the high-frequency coupling sequence at the t-th acquisition moment, is the abnormal deviation of the ith abnormal speed in the speed sequence at the tth acquisition moment, is the normalization function.

[0013] Preferably, the method for obtaining the speed dead zone optimization value is: Where, is the speed dead zone optimization value at the current acquisition moment, is the preset speed dead zone, and are the key fluctuation significance of the current collection moment and the previous collection moment respectively.

[0014] Preferably, if the speed fluctuation of the turbine at the current collection moment is less than or equal to the speed dead zone optimization value, it is determined that the guide vane opening of the turbine does not need to be adjusted; otherwise, the guide vane opening of the turbine needs to be adjusted.

[0015] This application has at least the following beneficial effects: This application extracts the high-frequency characteristics of the turbine's actual operating parameters and measures the high-frequency coupling of the actual operating parameters during turbine operation using the high-frequency characteristics. This can better represent the critical high-frequency operating parameter information within the turbine, facilitating the subsequent effective dynamic compensation optimization of the speed dead zone, thereby avoiding the problem of the hydropower unit overreacting to non-critical speed fluctuations. At the same time, the present application more accurately measures and analyzes the significant characteristics of key speed fluctuations during the operation of the turbine based on the high-frequency coupling characteristics of the actual operating parameters during the operation of the turbine. It also more accurately and dynamically compensates and optimizes the speed dead zone of the turbine based on the changes in the significance of key fluctuations during the operation of the turbine. This allows the speed dead zone after compensation optimization to adapt to the real-time frequency regulation requirements, which is beneficial to subsequently ensuring the stability of the turbine output power and the grid frequency. Furthermore, the present application uses the speed dead zone optimization value after dynamic compensation optimization to more accurately judge whether it is necessary to control and adjust the guide vane opening in the turbine, and uses an intelligent optimization algorithm to optimize the control parameters of the PID controller to achieve control and adjustment of the output power of the turbine, ensuring the stability of the turbine output power and the grid frequency, and avoiding the problem that the turbine cannot respond to changes in the grid frequency in a timely and accurate manner, thereby affecting the stability of the grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 This is a flowchart of the steps of the turbine primary frequency regulation control method based on the intelligent optimization algorithm provided in this application. DETAILED DESCRIPTION

[0018] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the hydraulic turbine primary frequency regulation control method based on the intelligent optimization algorithm proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0019] Unless otherwise defined, terms such as "comprises," "comprising," or any other variants thereof are intended to encompass non-exclusive inclusion, such that a circuit structure, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or device comprising the element. In addition, the term "and\or" as used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains.

[0020] The specific scheme of the turbine primary frequency regulation control method based on the intelligent optimization algorithm provided by the present application is described in detail below with reference to the accompanying drawings.

[0021] An embodiment of the present application provides a method for controlling primary frequency regulation of a hydraulic turbine based on an intelligent optimization algorithm. For details, please refer to Figure 1 , including the following steps: Step 1: Obtain the actual operating parameters of the turbine at each sampling moment during operation, including water flow, turbine speed, and turbine output power.

[0022] In order to avoid the problem of hydropower units overreacting to non-critical speed fluctuations and effectively filter out unnecessary adjustment actions in the turbine, it is necessary to more accurately dynamically compensate and optimize the speed dead zone of the turbine.

[0023] In this embodiment, the water flow, turbine speed and output power of the turbine during the operation of the turbine are preferably obtained through the data acquisition unit of the turbine speed control system. The data acquisition unit of the turbine speed control system integrates multi-dimensional sensors, including a water flow sensor, a speed sensor and a power sensor, which can realize real-time monitoring of the water flow, turbine speed and output power of the turbine during the operation of the turbine. In this embodiment, the acquisition frequency of the sensor is 1kHz, and the implementer can adaptively set the acquisition frequency according to the actual accuracy requirements.

[0024] In order to accurately and dynamically compensate for the turbine speed dead zone in the future, the water flow, turbine speed, and turbine output power within a preset time period before each acquisition moment are arranged in chronological order. The preset time period is 1 second, forming a water flow sequence, a speed sequence, and a power sequence at each acquisition moment. In this embodiment, each sequence after arrangement is subjected to range normalization processing to eliminate the dimensional effect between different physical parameters. The range normalization processing is a well-known technology, and the specific process will not be repeated here.

[0025] Step 2: By analyzing the difference between the high-frequency information of each actual operating parameter during the operation of the turbine and the random change degree of the high-frequency information of each actual operating parameter, the high-frequency coupling degree at each acquisition moment is obtained.

[0026] Due to the close relationship between the water flow rate, turbine speed, and turbine output power during turbine operation, coupled variations occur between the three. Existing technologies use dynamic compensation to optimize the turbine speed dead zone, but this method fails to fully account for the coupled variations in the turbine's actual operating parameters. This can easily lead to the compensated speed dead zone being unsuitable for real-time frequency regulation. Therefore, in order to more accurately dynamically optimize the turbine speed dead zone, it is necessary to accurately analyze the coupled variations between the turbine's actual operating parameters.

[0027] In order to more accurately analyze the coupling characteristics between the water flow, speed and output power of the turbine, the water flow sequence, speed sequence and power sequence at each acquisition moment are used as the input of the moving average method (MA), respectively. The water flow sequence, speed sequence and power sequence are smoothed by the moving average method to obtain a low-frequency sequence of water flow, a low-frequency sequence of speed and a low-frequency sequence of power, respectively. The moving average method is a well-known technology and will not be described in detail.

[0028] Due to the high-frequency variation characteristics of the actual operating parameters of the turbine, it can better represent the key high-frequency operating parameter information in the turbine, which helps to effectively and dynamically compensate and optimize the speed dead zone in the future, thereby avoiding the problem of hydropower units overreacting to non-critical speed fluctuations.

[0029] Therefore, the difference between the water flow sequence and its water flow low-frequency sequence is made for all the elements at the same position, and the result of the difference is recorded as the water flow high-frequency sequence, which reflects the high-frequency change characteristics of the water flow in the turbine; similarly, the speed low-frequency sequence and power low-frequency sequence are processed using the same acquisition method as the water flow high-frequency sequence to obtain the speed high-frequency sequence and power high-frequency sequence, which respectively reflect the high-frequency change characteristics of the speed and output power in the turbine.

[0030] Furthermore, the average value of the distance between any two of the three sequences of the water flow high-frequency sequence, the speed high-frequency sequence, and the power high-frequency sequence at each acquisition moment is calculated as the first difference degree at each acquisition moment. The distance between the sequences can be measured by DTW dynamic programming distance or Euclidean distance. In this implementation, DTW dynamic programming distance is used to measure the distance. If the high-frequency information changes in the water flow high-frequency sequence, the speed high-frequency sequence, and the power high-frequency sequence are more chaotic, and the difference distance between the water flow high-frequency sequence, the speed high-frequency sequence, and the power high-frequency sequence is smaller, the high-frequency coupling characteristics of the actual operating parameters during the operation of the turbine can be more reflected.

[0031] Based on the above analysis, the high-frequency coupling degree at each acquisition moment is calculated: Where, is the high-frequency coupling degree at the tth acquisition moment, is a normalization function. In this embodiment, the range normalization method is adopted. is the mean of the permutation entropy of the high-frequency sequence of water flow, speed and power at the t-th acquisition moment, is the first difference degree at the t-th acquisition moment, To avoid a constant with a denominator of 0, the value range is (0.01, 0.1), which has a negligible effect on the calculation result. In this embodiment, the value is 0.05. The calculation of permutation entropy is a well-known technique and will not be described in detail.

[0032] Among them, the high-frequency coupling degree reflects the high-frequency coupling characteristics of the actual operating parameters during the operation of the turbine. The larger the high-frequency coupling degree, the more likely the actual operating parameters are to have coupled high-frequency changes during the operation of the turbine, making it more likely that the turbine speed will have critical speed fluctuations. At this time, it is necessary to appropriately reduce the turbine speed dead zone, which will help the turbine speed governor to respond to critical speed fluctuations in the turbine in a timely and accurate manner, thereby ensuring the stability of the power grid operation.

[0033] Step 3: Based on the mutation of the high-frequency coupling degree in the short period before each acquisition moment and the turbine speed, the abnormal speed is extracted. The abnormal deviation of each abnormal speed is obtained by the discrete degree of the turbine speed change at each abnormal speed. The key fluctuation significance at each acquisition moment is obtained by combining the mutation point of the high-frequency coupling degree. Based on the degree of change of the key fluctuation significance, the speed dead zone optimization value at the current acquisition moment is obtained.

[0034] Furthermore, preferably, in this embodiment, the high-frequency coupling degrees of all acquisition moments within one second before each acquisition moment are arranged in chronological order to obtain a high-frequency coupling sequence at each acquisition moment, which reflects the changes in the high-frequency coupling characteristics of the actual operating parameters during the operation of the turbine. If the sudden high-frequency coupling fluctuation during the operation of the turbine has a greater impact on the turbine speed, then the turbine is more likely to produce critical speed fluctuations, affecting the turbine output power and the stability of the power grid operation.

[0035] Therefore, taking the high-frequency coupling sequence at the t-th acquisition moment as an example, the high-frequency coupling sequence at the t-th acquisition moment is used as the input of the Bernaola Galvan segmentation algorithm. The position sequence numbers of all mutation points in the high-frequency coupling sequence are obtained through the Bernaola Galvan segmentation algorithm, which are recorded as all target sequence numbers at the t-th acquisition moment, reflecting the sudden high-frequency coupling fluctuations during the operation of the turbine. The Bernaola Galvan segmentation algorithm is a well-known technology and will not be described in detail.

[0036] Furthermore, all elements at the target sequence position in the speed sequence at the t-th acquisition moment are marked as all abnormal speeds at the t-th acquisition moment. If the degree of speed deviation in the local area of ​​abnormal speed in the speed sequence is higher, and the high-frequency coupling of the mutation point in the high-frequency coupling sequence is more significant, it means that the turbine speed is more likely to experience critical speed fluctuations, which is more likely to affect the turbine output power and the stability of the power grid operation.

[0037] Therefore, in this embodiment, each abnormal speed in the speed sequence at each acquisition moment is taken as the center, and the The sliding window of size is used, and the sequence of all speeds in the sliding window is recorded as the sliding window sequence of each abnormal speed. Among them, the value of K is 49, which makes the time length of the sliding window close to 0.05s. If there are missing value elements in the sliding window, the missing values ​​are filled by the mean filling method.

[0038] Based on the above analysis, the abnormal deviation of each abnormal speed in the speed sequence is calculated: Where, is the abnormal deviation of the ith abnormal speed, is the discrete degree of the elements in the first-order difference sequence of the i-th abnormal speed sliding window sequence, is the number of speeds in the i-th abnormal speed sliding window sequence, and The jth and j-1th speeds in the i-th abnormal speed sliding window sequence are respectively. The dispersion degree can be measured by variance, standard deviation or coefficient of variation. In this embodiment, the coefficient of variation is used to measure the dispersion degree.

[0039] Among them, the deviation abnormality reflects the degree of abnormal deviation of the turbine speed when it is affected by the sudden high-frequency coupling fluctuation of the turbine. The larger the abnormal deviation, the more severe the impact of the sudden high-frequency coupling fluctuation on the turbine speed, making it more likely that the turbine speed will experience critical speed fluctuations.

[0040] Furthermore, based on the sudden high-frequency coupling characteristics of the actual operating parameters in the turbine and the degree of abnormal deviation of the turbine speed when it is affected by the sudden high-frequency coupling fluctuations of the turbine, the key fluctuation significance at each acquisition moment is calculated: Where, is the key fluctuation significance at the t-th collection moment, is the number of all target serial numbers at the tth acquisition moment, is the high-frequency coupling degree corresponding to the i-th mutation point in the high-frequency coupling sequence at the t-th acquisition moment, is the abnormal deviation of the i-th abnormal speed in the speed sequence at the t-th acquisition moment.

[0041] Among them, the key fluctuation significance reflects the significance characteristics of the key speed fluctuations during the operation of the turbine. The greater the key fluctuation significance, the more significant the key speed fluctuations during the operation of the turbine. At this time, it is necessary to appropriately reduce the speed dead zone of the turbine, which helps the turbine speed governor to respond to the key speed fluctuations in the turbine in a timely and accurate manner, thereby improving the turbine output power and the stability of the grid frequency.

[0042] Furthermore, the turbine speed dead zone is dynamically compensated and optimized based on the changes in the significance of key fluctuations during turbine operation. If the significance of key fluctuations during turbine operation shows an upward trend, it indicates that the key speed fluctuations in the turbine are more significant. In this case, the turbine speed dead zone should be appropriately reduced so that the turbine governor can respond to the key speed fluctuations in the turbine in a timely and accurate manner. On the contrary, if the significance of key fluctuations shows a downward trend during the operation of the turbine, it means that the significance of key speed fluctuations in the turbine is low. In order to avoid the problem of the turbine overreacting to non-critical speed fluctuations, the speed dead zone of the turbine should be appropriately increased at this time, thereby effectively filtering out unnecessary adjustment actions in the turbine governor.

[0043] Therefore, the speed dead zone of the turbine is dynamically compensated and optimized, and the speed dead zone optimization value at the current acquisition moment is calculated: Where, is the speed dead zone optimization value at the current acquisition moment, is the preset speed dead zone. In this embodiment The value is 0.02%, and are the key fluctuation significance of the current collection moment and the previous collection moment respectively.

[0044] Therefore, the speed dead zone of the turbine is dynamically compensated and optimized based on the changes in the significance of key fluctuations during the operation of the turbine, and the optimized speed dead zone value at the current acquisition moment is calculated in real time, so that the speed dead zone after compensation and optimization can adapt to the real-time frequency regulation requirements and ensure the stability of the turbine output power and the grid frequency.

[0045] Step 4: Determine whether to adjust the guide vane opening of the turbine through the speed dead zone optimization value, and obtain the optimal control parameters of the PID controller through the intelligent optimization algorithm to achieve primary frequency regulation control of the turbine.

[0046] Furthermore, the speed fluctuation of the turbine during operation is monitored in real time. It should be noted that the speed fluctuation of the turbine during operation refers to the deviation change of the turbine speed at each sampling moment. The specific calculation is a prior art and will not be described in detail in this embodiment. In this embodiment, for example, if the turbine speed at the previous sampling moment is 40 and the turbine speed at the current sampling moment is 45, then the speed fluctuation of the turbine at the current sampling moment is .

[0047] Therefore, based on the turbine speed fluctuation and the optimized speed deadband value, the turbine's output power and grid frequency stability are determined, and the need for adjustment of the turbine's internal guide vane opening is determined. Specifically, if the turbine speed fluctuation at the current acquisition time is less than or equal to the optimized speed deadband value, the turbine's output power and grid frequency are stable, and no control or adjustment of the turbine's internal guide vane opening is required. If the speed fluctuation at the current acquisition time is greater than the optimized speed deadband value, the turbine's output power and grid frequency are unstable, and control or adjustment of the turbine's internal guide vane opening is required.

[0048] Specifically, the guide vane opening of the turbine is controlled and adjusted by a PID controller, wherein the control parameters of the PID controller are optimized by using a PSO particle swarm optimization algorithm. In this embodiment, preferably, the range of the proportional parameter Kp is set to (2, 3), the range of the integral parameter Ki is set to (0, 1), the range of the differential parameter Kd is set to (0, 1), the number of particles is 500, and the maximum number of iterations is 1000. An initial particle swarm is randomly generated according to the parameter range of the PID controller, and the ITAE index value corresponding to each particle is calculated by using a Simulink simulation tool (the ITAE index value is an error integral type objective function used to evaluate the dynamic performance of the system in the field of automation control), and the individual and global optimal solutions are updated. When the maximum number of iterations is reached, the control parameter combination of the PID controller with the smallest ITAE index value is output, that is, the optimal proportional parameter, the optimal integral parameter, and the optimal differential parameter. The PSO particle swarm optimization algorithm is a well-known technology, and the specific process is not repeated here.

[0049] The optimal proportional parameter, optimal integral parameter, and optimal differential parameter are used as the three parameters of the PID controller. If the speed fluctuation at the current acquisition moment is greater than the optimized value of the speed dead zone, the PID controller calculates the control information through the real-time output power of the turbine and transmits the control signal to the turbine speed governor. The turbine speed governor will automatically increase or decrease the guide vane opening in the turbine, thereby realizing primary frequency regulation control of the turbine and ensuring the stability of the turbine output power and the grid frequency.

[0050] It is understood that references to "one embodiment" or "some embodiments" in the present specification mean that one or more embodiments of the present application include a particular feature, structure, or characteristic described in conjunction with that embodiment. Thus, if "in one embodiment," "in some embodiments," "in other embodiments," or "in other embodiments" appear in different places in this specification, they do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0051] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above description is of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. At the same time, the size of the sequence number of each step in the embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0052] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A hydraulic turbine primary frequency regulation control method based on an intelligent optimization algorithm, characterized in that: The following steps are involved: Obtain the actual operating parameters of the turbine at each sampling moment during operation, including water flow, turbine speed, and turbine output power; By analyzing the difference between the high-frequency information of each actual operating parameter during the operation of the turbine and the random change degree of the high-frequency information of each actual operating parameter, the high-frequency coupling degree at each acquisition moment is obtained; The abnormal speed is extracted based on the sudden change of high-frequency coupling in the short period before each acquisition moment and the turbine speed. The abnormal deviation of each abnormal speed is obtained by the discrete degree of turbine speed change at each abnormal speed. The key fluctuation significance at each acquisition moment is obtained by combining the mutation point of high-frequency coupling. The speed dead zone optimization value at the current acquisition moment is obtained based on the degree of change of the key fluctuation significance. The speed dead zone optimization value is used to determine whether the guide vane opening of the turbine should be adjusted, and the optimal control parameters of the PID controller are obtained through an intelligent optimization algorithm to achieve primary frequency regulation control of the turbine.

2. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 1, characterized in that: The water flow, turbine speed and output power at all sampling moments within a preset time period before each sampling moment are arranged in time sequence to form the water flow sequence, speed sequence and power sequence at each sampling moment. The three sequences are smoothed using the moving average method to obtain a low-frequency sequence for water flow, a low-frequency sequence for speed and a low-frequency sequence for power. The high-frequency sequence for water flow, a high-frequency sequence for speed and a high-frequency sequence for power are obtained by performing difference operations.

3. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 2, characterized in that: The method for obtaining the high-frequency coupling degree at each acquisition moment is: Where, is the high-frequency coupling degree at the tth acquisition moment, is the normalization function, is the mean of the permutation entropy of the high-frequency sequence of water flow, speed and power at the t-th acquisition moment, is the first difference degree at the t-th acquisition moment, To avoid constants with denominators equal to 0.

4. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 3, characterized in that: The average value of the distance between any two sequences of the water flow high frequency sequence, the speed high frequency sequence and the power high frequency sequence at each collection moment is calculated as the first difference degree at each collection moment.

5. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 2, characterized in that: The extraction process of the abnormal rotation speed is as follows: The high-frequency coupling degrees of all acquisition moments within a preset time period before each acquisition moment are arranged in time sequence to form a high-frequency coupling sequence for each acquisition moment. The position sequence number of the mutation point in the high-frequency coupling sequence is extracted and recorded as the target sequence number for each acquisition moment. The speed corresponding to all the target sequences at each acquisition moment in the speed sequence is taken as the abnormal speed at each acquisition moment.

6. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 5, characterized in that: The method for obtaining the abnormal deviation of each abnormal speed is as follows: Where, is the abnormal deviation of the ith abnormal speed, is the discrete degree of the elements in the first-order difference sequence of the i-th abnormal speed sliding window sequence, is the number of speeds in the i-th abnormal speed sliding window sequence, and They are the jth and j-1th speeds in the i-th abnormal speed sliding window sequence respectively.

7. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 6, characterized in that: A window is constructed with each abnormal speed in the speed sequence as the center, and all elements in the window constitute a sliding window sequence of each abnormal speed.

8. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 7, characterized in that: The method for obtaining the key fluctuation significance at each collection moment is: Where, is the key fluctuation significance at the t-th collection moment, is the number of all target serial numbers at the tth acquisition moment, is the high-frequency coupling degree corresponding to the i-th mutation point in the high-frequency coupling sequence at the t-th acquisition moment, is the abnormal deviation of the ith abnormal speed in the speed sequence at the tth acquisition moment, is the normalization function.

9. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 1, characterized in that: The method for obtaining the speed dead zone optimization value is: Where, is the speed dead zone optimization value at the current acquisition moment, is the preset speed dead zone, and are the key fluctuation significance of the current collection moment and the previous collection moment respectively.

10. The hydraulic turbine primary frequency regulation control method based on intelligent optimization algorithm according to claim 1, characterized in that: If the speed fluctuation of the turbine at the current acquisition moment is less than or equal to the speed dead zone optimization value, it is determined that the guide vane opening of the turbine does not need to be adjusted; otherwise, the guide vane opening of the turbine needs to be adjusted.

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