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

Abstract

The application 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, which comprises the following steps: acquiring actual operation parameters of each collection time in the operation process of a water turbine, analyzing the difference degree between high-frequency information of each actual operation parameter in the operation process of the water turbine, and calculating the high-frequency coupling degree of each collection time according to the random change degree of the high-frequency information of each actual operation parameter; extracting abnormal rotating speeds, acquiring abnormal deviation degrees of the abnormal rotating speeds through the local water turbine rotating speed change discrete degree of each abnormal rotating speed, and then obtaining a rotating speed dead zone optimization value of the current collection time; judging whether to adjust the inner guide vane opening degree of the water turbine or not, and acquiring optimal control parameters of a PID controller through an intelligent optimization algorithm, so that the primary frequency modulation control of the water turbine is realized. The application can guarantee the stability of the water turbine frequency modulation control.

Description

Technical Field

[0001] This application relates to the field of frequency regulation control technology for hydro turbines, specifically to a primary frequency regulation control method for hydro turbines based on an intelligent optimization algorithm. Background Technology

[0002] When hydropower units are connected to the grid, the volatility and intermittency of hydropower renewable energy cause uncontrollable changes in the grid frequency, posing a serious threat to the stable operation of the grid. Primary frequency regulation of the turbine is an inherent frequency characteristic of the turbine governor. When the speed fluctuation of a grid-connected hydropower unit exceeds the specified dead zone, the turbine governor automatically increases or decreases the opening of the turbine's guide vanes to regulate the grid frequency and ensure the stable operation of the power system.

[0003] In the intelligent optimization process of primary frequency regulation control of a hydroelectric turbine, dynamic compensation of the turbine's speed dead zone is necessary to prevent the turbine from overreacting to non-critical speed fluctuations. This effectively filters out unnecessary adjustments in the turbine governor and optimizes the control parameters of the PID controller through intelligent optimization algorithms, thereby controlling and regulating the opening of the turbine's guide vanes to ensure the stability of the grid frequency during the operation of the hydroelectric unit. Currently, existing technologies use dynamic compensation to optimize the turbine's speed dead zone. However, these technologies do not fully consider the coupled changes in the actual operating parameters of the turbine, which can easily lead to the optimized speed dead zone being unsuitable for real-time frequency regulation requirements. This results in the turbine's primary frequency regulation failing to respond promptly and accurately to changes in the grid frequency, thus affecting the stability of the grid operation. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a primary frequency control method for hydro turbines based on an intelligent optimization algorithm, thereby resolving the existing issues.

[0005] The turbine primary frequency control method based on intelligent optimization algorithm in this application adopts the following technical solution:

[0006] One embodiment of this application provides a primary frequency control method for a hydro turbine based on an intelligent optimization algorithm, including the following steps:

[0007] Acquire the actual operating parameters at each sampling time during the operation of the water turbine, including water flow, turbine speed and turbine output power;

[0008] By analyzing the degree of difference between the high-frequency information of each actual operating parameter during the operation of the water turbine, as well as the degree of random change of the high-frequency information of each actual operating parameter, the high-frequency coupling degree at each acquisition time can be obtained.

[0009] According to the mutation of the high-frequency coupling degree in a short time before each collection time and the water turbine speed, an abnormal speed is extracted, the abnormal deviation degree of each abnormal speed is obtained through the local discrete degree of the water turbine speed change of each abnormal speed, the key fluctuation significance of each collection time is obtained in combination with the mutation point of the high-frequency coupling degree, and the speed dead zone optimization value of the current collection time is obtained according to the change degree of the key fluctuation significance.

[0010] The speed dead zone optimization value is used to determine whether the guide vane opening of the water turbine is adjusted, and the optimal control parameter of the PID controller is obtained through the intelligent optimization algorithm, so as to realize the primary frequency modulation control of the water turbine.

[0011] Preferably, the water flow, the water turbine speed and the output power of all collection times in a preset time length before each collection time are arranged in time sequence respectively to form a water flow sequence, a speed sequence and a power sequence of each collection time, and the moving average method is used for smoothing processing of the three sequences to obtain a water flow low-frequency sequence, a speed low-frequency sequence and a power low-frequency sequence, and the water flow high-frequency sequence, the speed high-frequency sequence and the power high-frequency sequence are obtained through difference operation respectively.

[0012] Preferably, the method for obtaining the high-frequency coupling degree of each collection time is as follows:

[0013] , wherein, is the high-frequency coupling degree of the tth collection time, is a normalization function, is the average value of the permutation entropy of the water flow high-frequency sequence, the speed high-frequency sequence and the power high-frequency sequence of the tth collection time, is the first difference degree of the tth collection time, is a constant to avoid the denominator being 0.

[0014] Preferably, the average value of the distance between any two sequences in the water flow high-frequency sequence, the speed high-frequency sequence and the power high-frequency sequence of each collection time is calculated as the first difference degree of each collection time.

[0015] Preferably, the extraction process of the abnormal speed is as follows:

[0016] The high-frequency coupling degrees of all collection times in a preset time length before each collection time are arranged in time sequence to form a high-frequency coupling sequence of each collection time, the position number of the mutation point in the high-frequency coupling sequence is extracted, which is recorded as the target number of each collection time, and the speed corresponding to each target number of each collection time in the speed sequence is taken as the abnormal speed of each collection time.

[0017] Preferably, the method for obtaining the abnormal deviation degree of each abnormal speed is as follows:

[0018] ; wherein, is an abnormal deviation degree of the i-th abnormal speed, is a dispersion degree of elements in a first-order difference sequence of the i-th abnormal speed sliding window sequence, is a number of speeds in the i-th abnormal speed sliding window sequence, and are the j-th and j-1-th speeds in the i-th abnormal speed sliding window sequence, respectively.

[0019] Preferably, a window is constructed with each abnormal speed in the speed sequence as the center, and all elements in the window form a sliding window sequence of each abnormal speed.

[0020] Preferably, the method for obtaining the key fluctuation significance of each collection time is as follows:

[0021] ; wherein, is the key fluctuation significance of the t-th collection time, is a number of all target serial numbers at the t-th collection time, is a high-frequency coupling degree corresponding to the i-th mutation point in the high-frequency coupling sequence at the t-th collection time, is an abnormal deviation degree of the i-th abnormal speed in the speed sequence at the t-th collection time, is a normalization function.

[0022] Preferably, the method for obtaining the speed dead zone optimization value is as follows:

[0023] ; wherein, is the speed dead zone optimization value at the current collection time, is a preset speed dead zone, and are the key fluctuation significances of the current collection time and the previous collection time thereof, respectively.

[0024] Preferably, if the speed fluctuation of the hydraulic turbine at the current collection time is less than or equal to the speed dead zone optimization value, it is determined that the guide vane opening of the hydraulic turbine does not need to be adjusted; otherwise, the guide vane opening of the hydraulic turbine needs to be adjusted.

[0025] The present application has at least the following beneficial effects:

[0026] The present application can better represent the key high-frequency operating parameter information of the hydraulic turbine by extracting the high-frequency features of the actual operating parameters of the turbine and measuring the high-frequency coupling of the actual operating parameters of the hydraulic turbine during operation, which helps to effectively dynamically compensate and optimize the speed dead zone, thereby avoiding the problem of excessive reaction of the hydroelectric generator set to non-key speed fluctuations.

[0027] Meanwhile, according to the high-frequency coupling characteristics of the actual operation parameters in the operation process of the water turbine, the application more accurately measures and analyzes the significant characteristics of the key speed fluctuation in the operation process of the water turbine, and through the change of the key fluctuation significance in the operation process of the water turbine, the speed dead zone of the water turbine is more accurately dynamically compensated and optimized, so that the speed dead zone after compensation and optimization is adapted to the real-time frequency modulation demand, which is beneficial to subsequent guarantee of the stability of the output power of the water turbine and the power grid frequency;

[0028] Further, the application more accurately determines whether the guide vane opening of the water turbine needs to be controlled and adjusted through the dynamically compensated and optimized speed dead zone optimization value, and optimizes the control parameters of the PID controller by using the intelligent optimization algorithm, so as to realize the control and adjustment of the output power of the water turbine, guarantee the stability of the output power of the water turbine and the power grid frequency, and avoid the problem that the water turbine cannot timely and accurately respond to the change of the power grid frequency, thereby affecting the stability of the power grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the application or the prior art, the drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0030] Figure 1 The step flow chart of the water turbine primary frequency modulation control method based on the intelligent optimization algorithm provided by the application. DETAILED DESCRIPTION

[0031] In order to further illustrate the technical means and effects adopted by the application to achieve the predetermined invention purpose, the specific implementation, structure, features and effects of the water turbine primary frequency modulation control method based on the intelligent optimization algorithm according to the application are described in detail as follows by combining the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, such as the term "comprise", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a circuit structure, article or apparatus that includes a list of elements not only includes those elements, but also includes other elements not expressly listed or inherent to such article or apparatus. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the article or apparatus including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs.

[0033] The specific scheme of the water turbine primary frequency modulation control method based on the intelligent optimization algorithm provided in the present application will be specifically described below in combination with the drawings.

[0034] The water turbine primary frequency modulation control method based on the intelligent optimization algorithm provided in an embodiment of the present application, specifically, please refer to Figure 1 , comprising the following steps:

[0035] Step 1: Obtain the actual operating parameters at each collection time during the operation of the water turbine, including water flow, water turbine speed and water turbine output power.

[0036] In order to avoid the problem that the hydroelectric generator set overreacts to non-critical speed fluctuations, thereby effectively filtering out unnecessary adjustment actions in the water turbine, it is necessary to more accurately dynamically compensate and optimize the speed dead zone of the water turbine.

[0037] In the present embodiment, the water flow, water turbine speed and water turbine output power during the operation of the water turbine are preferably obtained through the data acquisition unit of the water turbine speed regulation system, the data acquisition unit of the water turbine speed regulation system integrates multi-dimensional sensors, including water flow sensors, speed sensors and power sensors, which can realize real-time monitoring of the water flow, water turbine speed and water turbine output power during the operation of the water turbine. In the present embodiment, the collection frequency of the sensors is 1 kHz, and the implementer can adaptively set the collection frequency according to the actual accuracy requirement.

[0038] In order to accurately dynamically compensate and optimize the speed dead zone of the water turbine subsequently, the water flow, water turbine speed and water turbine output power within a preset time length before each collection time are arranged in time sequence, the preset time length is 1 second, to form a water flow sequence, a speed sequence and a power sequence of each collection time. In the present embodiment, the arranged sequences are respectively subjected to range normalization processing to eliminate the dimensional effect existing between different physical parameters, wherein the range normalization processing is a known technology and the specific process will not be described again.

[0039] Step 2: Obtain the high-frequency coupling degree of each collection moment by analyzing the difference degree between the high-frequency information of each actual operation parameter during the operation of the water turbine and the random variation degree of the high-frequency information of each actual operation parameter.

[0040] Since there is a close relationship among the water flow, the rotational speed of the water turbine and the output power of the water turbine during the operation of the water turbine, the coupling variation characteristics are generated among the three, and the existing technology adopts the dynamic compensation method to compensate and optimize the rotational speed dead zone of the water turbine, without fully considering the coupling variation of the actual operation parameters of the water turbine, which is easy to lead to the fact that the rotational speed dead zone after compensation and optimization is not suitable for real-time frequency modulation demand. Therefore, in order to more accurately dynamically compensate and optimize the rotational speed dead zone of the water turbine, it is necessary to accurately analyze the coupling variation among the actual operation parameters of the water turbine.

[0041] In order to more accurately analyze the coupling characteristics among the water flow, the rotational speed and the output power of the water turbine, the water flow sequence, the rotational speed sequence and the power sequence of each collection moment are respectively taken as the input of the moving average method (MA), and the water flow sequence, the rotational speed sequence and the power sequence are respectively smoothed by the moving average method to obtain the water flow low-frequency sequence, the rotational speed low-frequency sequence and the power low-frequency sequence. The moving average method is a known technology and will not be described in detail.

[0042] Due to the high-frequency variation characteristics of the actual operation parameters of the water turbine, the key high-frequency operation parameter information in the water turbine can be better represented, which is helpful for subsequent effective dynamic compensation and optimization of the rotational speed dead zone, thereby avoiding the problem of excessive reaction of the hydroelectric generator set to non-key rotational speed fluctuations.

[0043] Therefore, the difference between all corresponding elements at the same position of the water flow sequence and the water flow low-frequency sequence is obtained, and the result after the difference is recorded as the water flow high-frequency sequence, which reflects the high-frequency variation characteristics of the water flow in the water turbine. Similarly, the rotational speed low-frequency sequence and the power low-frequency sequence are processed by using the same acquisition method as the water flow high-frequency sequence to obtain the rotational speed high-frequency sequence and the power high-frequency sequence, which respectively reflect the high-frequency variation characteristics of the rotational speed and the output power in the water turbine.

[0044] Further, the average of the distance between any two of the water flow high frequency sequence, the rotation speed high frequency sequence and the power high frequency sequence at each collection time is calculated as the first difference degree at each collection time, and the distance between the sequences can be measured by DTW dynamic programming distance or Euclidean distance. In this embodiment, the distance is measured by DTW dynamic programming distance. If the high frequency information in the water flow high frequency sequence, the rotation speed high frequency sequence and the power high frequency sequence changes more chaotically, and the difference distance between the water flow high frequency sequence, the rotation speed high frequency sequence and the power high frequency sequence is smaller, the high frequency coupling characteristic of the actual operation parameters in the operation process of the hydraulic turbine is better reflected.

[0045] Based on the above analysis, the high frequency coupling degree at each collection time is calculated as follows:

[0046] In the formula, is the high frequency coupling degree at the tth collection time, is a normalization function, and in this embodiment, a range normalization method is used, is the average of the permutation entropy of the water flow high frequency sequence, the rotation speed high frequency sequence and the power high frequency sequence at the tth collection time, is the first difference degree at the tth collection time, is a constant to avoid a denominator of 0, and the value range is (0.01, 0.1). The influence on the calculation result is small and can be ignored. In this embodiment, the value is 0.05. The calculation of the permutation entropy is a known technology, and no further description is given.

[0047] The high frequency coupling degree reflects the high frequency coupling characteristic of the actual operation parameters in the operation process of the hydraulic turbine. The greater the high frequency coupling degree, the more likely the actual operation parameters in the operation process of the hydraulic turbine appear coupled high frequency changes, and the more likely the rotation speed of the hydraulic turbine appears critical rotation speed fluctuations. At this time, the rotation speed dead zone of the hydraulic turbine needs to be appropriately reduced, which helps the hydraulic turbine governor to respond to the critical rotation speed fluctuations in the hydraulic turbine in time and accurately, and ensures the stability of the power grid operation.

[0048] Step 3: According to the mutation of the high frequency coupling degree in a short time before each collection time, the rotation speed of the hydraulic turbine is extracted to extract abnormal rotation speeds. The abnormal deviation degree of each abnormal rotation speed is obtained through the local rotation speed change dispersion degree of the hydraulic turbine. Then, the key fluctuation significance of each collection time is obtained by combining the mutation point of the high frequency coupling degree. According to the change degree of the key fluctuation significance, the rotation speed dead zone optimization value of the current collection time is obtained.

[0049] Further, preferably, in the embodiment, the high-frequency coupling degrees of all the collection time points within one second before each collection time point are arranged in time sequence to obtain a high-frequency coupling sequence of each collection time point, which reflects the change of the high-frequency coupling characteristic of the actual operating parameter of the water turbine in the operation process. If the influence of the sudden high-frequency coupling fluctuation on the water turbine speed is greater in the operation process of the water turbine, the critical speed fluctuation of the water turbine is more likely to occur at this time, which affects the output power of the water turbine and the stability of the power grid.

[0050] Therefore, taking the high-frequency coupling sequence of the tth collection time point as an example, the high-frequency coupling sequence of the tth collection time point is taken as the input of the Bernaola Galvan segmentation algorithm, and the position sequence number of all the mutation points in the high-frequency coupling sequence is obtained by the Bernaola Galvan segmentation algorithm, which is recorded as all the target sequence numbers of the tth collection time point, which reflects the sudden high-frequency coupling fluctuation in the operation process of the water turbine. The Bernaola Galvan segmentation algorithm is a known technology and will not be described in detail.

[0051] Further, the elements at all the target sequence number positions in the speed sequence of the tth collection time point are marked, which are all the abnormal speeds of the tth collection time point. If the abnormal degree of the speed deviation in the local area of the abnormal speed is higher, and the high-frequency coupling of the mutation point in the high-frequency coupling sequence is more significant at this time, it means that the speed of the water turbine is more likely to have a critical speed fluctuation, which is more likely to affect the output power of the water turbine and the stability of the power grid.

[0052] Therefore, in the embodiment, a sliding window with a size of is set around each abnormal speed in the speed sequence of each collection time point, and a sequence composed of all the speeds in the sliding window is recorded as the sliding window sequence of each abnormal speed, wherein K is 49, so that the time length of the sliding window tends to be 0.05s, and if there is a missing value element in the sliding window, the missing value is completed by the mean filling method.

[0053] Based on the above analysis, the abnormal deviation degree of each abnormal speed in the speed sequence is calculated:

[0054] ; in the formula, is the abnormal deviation degree of the ith abnormal speed, is the dispersion degree of the elements in the first-order difference sequence of the ith abnormal speed sliding window sequence, is the number of speeds in the ith abnormal speed sliding window sequence, and are the jth and j-1th rotation speed in the ith abnormal rotation speed sliding window sequence respectively. The discrete degree measurement method can be variance, standard deviation or coefficient of variation, and the coefficient of variation is used in the embodiment to measure the discrete degree.

[0055] The abnormal deviation degree reflects the abnormal deviation degree of the rotation speed of the hydraulic turbine when affected by the sudden high-frequency coupling fluctuation of the hydraulic turbine. The greater the abnormal deviation degree, the more severe the influence of the sudden high-frequency coupling fluctuation of the hydraulic turbine on the rotation speed of the hydraulic turbine, and the more likely the hydraulic turbine is to have a critical rotation speed fluctuation.

[0056] Further, according to the sudden high-frequency coupling characteristics of the actual operation parameters in the hydraulic turbine and in combination with the abnormal deviation degree of the rotation speed of the hydraulic turbine when affected by the sudden high-frequency coupling fluctuation of the hydraulic turbine, the critical fluctuation significance at each collection time is calculated:

[0057] In the formula, is the critical fluctuation significance at the tth collection time, is the number of all target serial numbers at the tth collection time, is the high-frequency coupling degree corresponding to the ith mutation point in the high-frequency coupling sequence at the tth collection time, is the abnormal deviation degree of the ith abnormal rotation speed in the rotation speed sequence at the tth collection time.

[0058] The critical fluctuation significance reflects the significance characteristics of the critical rotation speed fluctuation in the operation process of the hydraulic turbine. The greater the critical fluctuation significance, the more significant the critical rotation speed fluctuation in the operation process of the hydraulic turbine. At this time, the rotation speed dead zone of the hydraulic turbine should be appropriately reduced, which helps the speed governor of the hydraulic turbine to respond to the critical rotation speed fluctuation in the hydraulic turbine in a timely and accurate manner, and improves the stability of the output power of the hydraulic turbine and the power grid frequency.

[0059] Further, the rotation speed dead zone of the hydraulic turbine is dynamically compensated and optimized according to the change of the critical fluctuation significance in the operation process of the hydraulic turbine. If the critical fluctuation significance in the operation process of the hydraulic turbine presents an upward trend, it indicates that the critical rotation speed fluctuation in the hydraulic turbine is relatively significant. At this time, the rotation speed dead zone of the hydraulic turbine should be appropriately reduced, so that the speed governor of the hydraulic turbine can respond to the critical rotation speed fluctuation in the hydraulic turbine in a timely and accurate manner.

[0060] On the contrary, if the critical fluctuation significance in the operation process of the hydraulic turbine presents a downward trend, it indicates that the critical rotation speed fluctuation in the hydraulic turbine is relatively low. In order to avoid the problem of excessive reaction of the hydraulic turbine to the non-critical rotation speed fluctuation, the rotation speed dead zone of the hydraulic turbine should be appropriately increased, so as to effectively filter out unnecessary adjustment actions of the speed governor of the hydraulic turbine.

[0061] Therefore, the rotational speed dead zone of the hydraulic turbine is dynamically compensated and optimized, and the rotational speed dead zone optimization value at the current collection time is calculated:

[0062] In the formula, is the rotational speed dead zone optimization value at the current collection time, is a preset rotational speed dead zone, and in the embodiment is 0.02%, and are the key fluctuation significance at the current collection time and the previous collection time, respectively.

[0063] Therefore, by the change of the key fluctuation significance during the operation of the hydraulic turbine, the rotational speed dead zone of the hydraulic turbine is dynamically compensated and optimized, and the rotational speed dead zone optimization value at the current collection time is calculated in real time, so that the compensated and optimized rotational speed dead zone is adapted to the real-time frequency regulation demand, and the stability of the output power of the hydraulic turbine and the power grid frequency is ensured.

[0064] Step 4: judging whether to adjust the guide vane opening of the hydraulic turbine according to the rotational speed dead zone optimization value, and obtaining the optimal control parameters of the PID controller through an intelligent optimization algorithm to realize the primary frequency regulation control of the hydraulic turbine.

[0065] Further, the rotational speed fluctuation of the hydraulic turbine during the operation of the hydraulic turbine is monitored in real time. It should be noted that the rotational speed fluctuation of the hydraulic turbine during the operation of the hydraulic turbine refers to the deviation change of the rotational speed of the hydraulic turbine at each collection time, and the specific calculation is the prior art, which is not described herein. In the embodiment, for example, if the rotational speed of the hydraulic turbine at the previous collection time is 40 and the rotational speed of the hydraulic turbine at the current collection time is 45, then the rotational speed fluctuation of the hydraulic turbine at the current collection time is .

[0066] Therefore, according to the rotational speed fluctuation of the hydraulic turbine and the rotational speed dead zone optimization value, the stability of the output power of the hydraulic turbine and the power grid frequency is determined, and then it is determined whether the guide vane opening of the hydraulic turbine needs to be adjusted. Specifically, if the rotational speed fluctuation of the hydraulic turbine at the current collection time is less than or equal to the rotational speed dead zone optimization value, it indicates that the output power of the hydraulic turbine and the power grid frequency are stable, and at this time, the guide vane opening of the hydraulic turbine does not need to be controlled and adjusted; if the rotational speed fluctuation at the current collection time is greater than the rotational speed dead zone optimization value, it indicates that the output power of the hydraulic turbine and the power grid frequency are unstable, and the guide vane opening of the hydraulic turbine needs to be controlled and adjusted.

[0067] Specifically, the guide vane opening of the water turbine is controlled and adjusted by a PID controller, and the control parameters of the PID controller are optimized by using a PSO particle swarm optimization algorithm. In the preferred embodiment, the range of the proportional parameter Kp is (2, 3), the range of the integral parameter Ki is (0, 1), the range of the differential parameter Kd is (0, 1), the number of particles is 500, the maximum number of iterations is 1000, the initial particle swarm is randomly generated according to the parameter range of the PID controller, the ITAE index value (ITAE index value is an error integral type target function used to evaluate the dynamic performance of the system in the field of automatic control) corresponding to each particle is calculated by using the Simulink simulation tool, 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 minimum ITAE index value, i.e. the optimal proportional parameter, the optimal integral parameter and the optimal differential parameter, is output. The PSO particle swarm optimization algorithm is a known technology, and the specific process will not be described here.

[0068] The optimal proportional parameter, the optimal integral parameter and the optimal differential parameter are used as the three parameters of the PID controller. If the speed fluctuation at the current collection time is greater than the speed dead zone optimization value, the PID controller calculates the control information according to the real-time output power of the water turbine, transmits the control signal to the water turbine governor, and the water turbine governor automatically increases or decreases the guide vane opening in the water turbine, thereby realizing the primary frequency modulation control of the water turbine and ensuring the stability of the output power of the water turbine and the power grid frequency.

[0069] It can be understood that the reference to "one embodiment" or "some embodiments" and the like in the description of the application means that a particular feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Therefore, the appearance of "in one embodiment", "in some embodiments", "in other some embodiments", "in other some embodiments" and the like in the specification does not necessarily refer to the same embodiment, but means "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous. At the same time, the size of the serial number of each step in the embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments in the specification.

[0071] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A primary frequency control method for a hydro turbine based on an intelligent optimization algorithm, characterized in that, Includes the following steps: Acquire the actual operating parameters at each sampling time during the operation of the water turbine, including water flow, turbine speed and turbine output power; By analyzing the degree of difference between the high-frequency information of each actual operating parameter during the operation of the water turbine, as well as the degree of random change of the high-frequency information of each actual operating parameter, the high-frequency coupling degree at each acquisition time can be obtained. Abnormal speeds are extracted by combining the abrupt changes in high-frequency coupling degree within a short period before each acquisition time with the turbine speed. The abnormal deviation of each abnormal speed is obtained by the dispersion of the turbine speed change in each abnormal speed. The abrupt change point of high-frequency coupling degree is then combined to obtain the key fluctuation significance at each acquisition time. Based on the degree of change of the key fluctuation significance, the speed dead zone optimization value at the current acquisition time is obtained. The optimal control parameters of the PID controller are obtained by using the speed dead zone optimization value to determine whether the opening of the guide vanes inside the turbine should be adjusted, and the optimal control parameters of the turbine are obtained by using an intelligent optimization algorithm to achieve primary frequency regulation control of the turbine.

2. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 1, characterized in that, The water flow, turbine speed, and output power of all data collected within a preset time period before each data collection time are arranged in chronological order to form the water flow sequence, speed sequence, and power sequence for each data collection time. The three sequences are smoothed using the moving average method to obtain the low-frequency water flow sequence, low-frequency speed sequence, and low-frequency power sequence. The high-frequency water flow sequence, high-frequency speed sequence, and high-frequency power sequence are obtained by subtraction.

3. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 2, characterized in that, The method for obtaining the high-frequency coupling degree at each acquisition time is as follows: In the formula, Let be the high-frequency coupling degree at the t-th acquisition time. For normalization function, Let be the mean of the permutation entropy of the high-frequency sequences of water flow, rotational speed, and power at the t-th acquisition time. The degree of difference at the t-th acquisition time is... To avoid constants with a denominator of 0.

4. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 3, characterized in that, The average distance between any two of the three sequences—water flow high-frequency sequence, rotational speed high-frequency sequence, and power high-frequency sequence—is calculated at each acquisition time and used as the first degree of difference at each acquisition time.

5. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 2, characterized in that, The process for extracting the abnormal rotation speed is as follows: The high-frequency coupling degrees of all acquisition times with a preset duration before each acquisition time are arranged in time sequence to form a high-frequency coupling sequence for each acquisition time. The position number of the mutation point in the high-frequency coupling sequence is extracted and recorded as the target number of each acquisition time. The rotation speed corresponding to all target numbers of each acquisition time in the rotation speed sequence is taken as the abnormal rotation speed of each acquisition time.

6. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 5, characterized in that, The method for obtaining the abnormal deviation of each abnormal speed is as follows: In the formula, Let be the degree of abnormal deviation of the i-th abnormal speed. Let represent the degree of dispersion of elements within the first-order difference sequence of the i-th abnormal speed sliding window sequence. Let i be the number of rotational speeds within the i-th abnormal rotational speed sliding window sequence. and These are the j-th and (j-1)-th speeds within the i-th abnormal speed sliding window sequence, respectively.

7. The turbine primary frequency control method based on intelligent optimization algorithm as described in 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 form a sliding window sequence for each abnormal speed.

8. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 7, characterized in that, The method for obtaining the significance of key fluctuations at each acquisition time is as follows: In the formula, Let be the significance of the key fluctuations at the t-th data collection time. Let be the number of all target indices at the t-th acquisition time. Let be the high-frequency coupling degree corresponding to the i-th abrupt change point in the high-frequency coupling sequence at the t-th acquisition time. Let be the abnormal deviation of the i-th abnormal rotational speed in the rotational speed sequence at the t-th acquisition time. This is the normalization function.

9. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 1, characterized in that, The method for obtaining the optimized value of the rotational speed dead zone is as follows: In the formula, This is the optimized value for the rotational speed dead zone at the current acquisition moment. The preset speed dead zone, and These represent the significance of key fluctuations between the current acquisition time and the previous acquisition time.

10. The turbine primary frequency control method based on intelligent optimization algorithm as described in claim 1, characterized in that, If the turbine speed fluctuation at the current acquisition time is less than or equal to the optimized dead zone value, it is determined that no adjustment of the turbine guide vane opening is required; otherwise, the turbine guide vane opening needs to be adjusted.

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