Hydroelectric generating set power feedback control method and system by utilizing PID (Proportion Integration Differentiation) technology

Abstract

The invention relates to the technical field of power feedback control, in particular to a hydroelectric generating set power feedback control method and system using a PID technology. The method specifically comprises the steps that a load abnormal change coefficient is constructed by analyzing the collaborative change degree between the output power of the hydroelectric generating set and power grid load data in the working process of the hydroelectric generating set and the random volatility of the power grid load data; a power regulation and control coefficient is constructed by analyzing the fluctuation of a residual error between the output power of the hydroelectric generating set and power grid load data and combining with a load abnormal change coefficient, and the power regulation and control coefficient is used as a feedback correction weight of a PID control feedback regulation mechanism, so that power feedback control in the working process of the hydroelectric generating set can be better realized. The problem that the output power of the hydroelectric generating set is difficult to accurately match the actual demand of the power grid due to neglecting the direct influence of the fluctuation of the power grid load on the power feedback regulation of the hydroelectric generating set is avoided, and the stability of the power system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power feedback control, and in particular to a method and system for power feedback control of a hydropower unit using PID technology. Background Art

[0002] In the hydropower system, hydropower stations, as important power generation facilities, undertake the tasks of peak load regulation and frequency regulation and maintaining real-time balance of power. Hydropower generator sets are usually one of the main frequency regulation plants in the power grid due to their rapid start-up and convenient regulation, responsible for real-time power balance regulation. However, in recent years, with the widespread commissioning of ultra-large capacity units and pumped storage units, hydropower units have shown a development trend of large capacity and complex structure, making the control of unit regulation systems more and more complex. Therefore, in order to ensure the safe and stable operation of the power system, it is particularly important to accurately, quickly and smoothly regulate the power of hydropower station units.

[0003] In the power regulation of hydropower units, the application of PID (Proportion Integration Differentiation) control technology has significant advantages. The PID controller can perform closed-loop control according to the difference between the set power and the actual power, and control the increase or decrease of the unit output by calculating the output pulse width, thereby achieving precise regulation of the unit power. However, in existing research, the control parameters of the power feedback regulation of hydropower units are generally based on the dynamic characteristics of the hydropower station itself. The direct impact of the volatility of the grid load on the power feedback regulation of hydropower units is often ignored, resulting in the difficulty of the output power of the hydropower unit to accurately match the actual needs of the grid, and unable to meet the high requirements of modern power systems for stability and flexibility. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a power feedback control method and system for a hydropower unit using PID technology. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a power feedback control method for a hydropower unit using PID technology, the method comprising the following steps: Collect the output power data of the hydropower units and the load data of the power grid at each moment; Based on the output power data and load data of each moment in the time period adjacent to the current moment, a power data sequence and a load data sequence at the current moment are constructed; based on the difference between the power data sequence and the load data sequence, and the difference between the actual value and the predicted value of the load data at the current moment, an abnormal load change coefficient at the current moment is constructed; Based on the difference between the elements at the same time in the power data sequence and the load data sequence, the residual data sequence at the current moment is constructed; based on the degree of data fluctuation in the neighborhood of each element in the residual data sequence, the residual entropy sequence at the current moment is constructed; based on the numerical change of the maximum value in the residual entropy sequence, and the time distance between the time corresponding to the maximum value and the current moment, combined with the load abnormal change coefficient, the power regulation coefficient at the current moment is constructed; Based on the difference between the output power of the hydropower unit at the current moment and the actual value of the grid load data, combined with the power regulation coefficient, the error feedback result at the current moment is constructed; based on the error feedback result, combined with the PID controller, the power of the hydropower unit is feedback controlled.

[0005] In one embodiment, the process of obtaining the load abnormality change coefficient at the current moment is: The load data sequence at the previous moment is used as the input of the prediction algorithm, and the output is the predicted value of the load data at the current moment; the absolute value of the difference between the actual value of the load data at the current moment and the predicted value is calculated; the metric distance between the power data sequence at the current moment and the load data sequence is calculated, and the load abnormal change coefficient at the current moment is determined in combination with the absolute value of the difference.

[0006] In one embodiment, the load abnormality change coefficient at the current moment is: the product of the absolute value of the difference and the measured distance.

[0007] In one embodiment, the process of obtaining the residual data sequence at the current moment is: Calculate the absolute value of the difference between the output power data and the load data at the same collection time in the power data sequence and the load data sequence at the current moment, and record it as the first absolute value of the difference; record the sequence composed of all the first absolute values ​​of the difference as the residual data sequence at the current moment.

[0008] In one embodiment, the process of obtaining the residual entropy sequence at the current moment is: For each element in the residual data sequence at the current moment, the information entropy of all elements in the neighborhood window of each element is calculated, recorded as the first information entropy of each element, and the sequence composed of the first information entropies of all elements in the residual data sequence is recorded as the residual entropy sequence at the current moment.

[0009] In one embodiment, the process of obtaining the power regulation coefficient at the current moment is: The residual entropy sequence at the current moment is used as the input of the peak search algorithm, and the output is the maximum values ​​in the residual entropy sequence at the current moment; the time distance between the moment corresponding to each maximum value and the current moment is calculated, and recorded as the first time distance of each maximum value; the residual data sequence at the current moment is used as the input of the straight line fitting algorithm, and the slope of the output fitting line is obtained; The power regulation coefficient of the hydropower unit at the current moment is constructed based on the load abnormal change coefficient at the current moment, the first information entropy, the first time distance and the slope.

[0010] In one embodiment, the expression of the power regulation coefficient of the hydropower unit at the current moment is: , where is the power regulation coefficient of the hydropower unit at the current moment; Indicates the abnormal load change coefficient at the current moment; is the slope of the fitting straight line; N represents the number of maximum values ​​in the residual entropy sequence at the current moment; The first information entropy representing the nth maximum value in the residual entropy sequence at the current moment; They respectively represent the first time distance of the nth maximum value in the residual entropy sequence at the current moment.

[0011] In one embodiment, the process of obtaining the error feedback result at the current moment is: The difference between the monitored output power of the hydropower unit at the current moment and the grid load data is taken as the error coefficient; the product of the normalized value of the power regulation coefficient of the hydropower unit at the current moment and the error coefficient is taken as the error feedback result at the current moment.

[0012] In one embodiment, the feedback control of the power of the hydropower unit is specifically: The error feedback result at the current moment is used as the steady-state error parameter in the PID controller of the hydropower unit at the current moment to perform feedback adjustment on the power of the hydropower unit.

[0013] In the second aspect, an embodiment of the present application also provides a hydropower unit power feedback control system using PID technology, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above methods when executing the computer program.

[0014] The embodiments of the present application have at least the following beneficial effects: The present application is based on the output power data and grid load data collected during the operation of the hydropower unit. First, by analyzing the degree of coordinated change between the output power of the hydropower unit and the grid load data during the operation of the hydropower unit and the random volatility of the grid load data, an abnormal load change coefficient is constructed. The consistency between the output power of the hydropower unit and the grid load data during the operation of the hydropower unit and the abnormal fluctuation degree of the grid load are considered, reflecting the matching degree between the output power of the hydropower unit and the grid load data at each moment during the operation of the hydropower unit and the severity of the grid load fluctuation; secondly, by analyzing the volatility of the residual between the output power of the hydropower unit and the grid load data and combining the abnormal load change coefficient, a power regulation coefficient is constructed, considering the abnormal fluctuation degree of the residual data. Therefore, according to the power regulation coefficient, the abnormal power regulation degree at each moment during the operation of the hydropower unit can be accurately reflected. Finally, the power regulation coefficient is used as the feedback correction weight of the PID control feedback regulation mechanism, so that the power feedback control during the operation of the hydropower unit can be better realized, avoiding the problem that the output power of the hydropower unit is difficult to accurately match the actual needs of the power grid due to ignoring the direct impact of the volatility of the grid load on the power feedback regulation of the hydropower unit, thereby improving the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0016] Figure 1 A flowchart of a method for controlling a hydropower unit power feedback using PID technology provided in one embodiment of the present application; Figure 2 Schematic diagram of the process of obtaining the residual entropy sequence at the current moment. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the power feedback control method and system of the hydropower unit using PID technology proposed in the present application, its specific implementation method, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The specific scheme of the hydropower unit power feedback control method and system using PID technology provided by the present application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the steps of a hydropower unit power feedback control method using PID technology provided by an embodiment of the present application, the method comprising the following steps: Step S1, collecting the output power data of the hydropower units and the load data of the power grid at each moment.

[0021] The hydropower unit adjusts the output power of the hydropower unit in a timely and rapid manner according to the grid dispatch requirements and grid frequency changes to meet the needs of grid active power balance and frequency regulation. Therefore, in this application, the target output power of the hydropower unit is the actual load data of the grid.

[0022] The output power data of the hydropower unit and the load data of the power grid are collected through the data center. Among them, for the collection of the output power data of the hydropower unit and the load data of the power grid, preferably, in the embodiment of the present application, the data collection time interval is set to 1 second. As other embodiments of the present application, the implementer can set the data collection time interval according to the actual situation.

[0023] In order to prevent data loss in the collected data, the collected output power data and load data are filled by the mean filling method. Furthermore, in order to eliminate the influence of the dimension, all output power data and load data are normalized by the Z-score normalization method. Among them, the mean filling method and the Z-score normalization method are both well-known technologies, and the specific process will not be repeated.

[0024] It should be noted that for data filling and normalization of output power data and load data, the present application only provides one data filling method and normalization method. There are many existing data filling methods and normalization methods. Implementers may also use other data filling algorithms and normalization methods to fill and normalize the output power data and load data. The present application does not make any specific restrictions.

[0025] Step S2, constructing a power data sequence and a load data sequence at the current moment based on the output power data and load data at each moment in the adjacent time period of the current moment; constructing the load abnormality change coefficient at the current moment based on the difference between the power data sequence and the load data sequence, and the difference between the actual value and the predicted value of the load data at the current moment.

[0026] As one of the widely distributed electricity production devices in modern power systems, hydropower units are responsible for energy supply, peak load regulation and frequency regulation in the power grid. Hydropower units should adjust the output power of hydropower units in a timely and rapid manner according to the grid dispatching requirements and grid load changes to meet the needs of grid active power balance and frequency regulation. Therefore, in order to improve the stability of grid operation, it is necessary to improve the regulation performance of hydropower units. However, in the medium and long-term operation of hydropower units, the output power is generally feedback-regulated based on the hydraulic mechanical characteristics as the basis for parameter setting, including but not limited to head height and guide vane opening, ignoring the direct impact of the volatility of the grid load on the power feedback regulation of the hydropower unit, resulting in the output power of the hydropower unit being difficult to accurately match the actual needs of the grid. Therefore, this application performs feedback control and regulation on the output power of the hydropower unit based on the volatility of the grid load.

[0027] In the power feedback regulation system of the hydropower unit, the actual output power of the hydropower unit always tries to keep consistent with the actual load of the power grid. However, since the actual load of the power grid is not a stable value, the actual load of the power grid is affected by the power consumption during the operation of the power grid and is in periodic fluctuations. This causes the change in the actual output power of the hydropower unit to always lag behind the change in the actual load of the power grid.

[0028] Based on the above analysis, the response delay between the output power and the grid load data of the hydropower unit during operation is obtained, specifically: (1) First, obtain the output power data and load data within T = 5 minutes before the current time t, and record the time series of the output power data as the power data series at the current time t , record the time series of the load data as the load data series at the current time t . It should be noted that the implementer can set the value of T according to the actual situation, and this application does not impose any specific restrictions. In addition, in order to ensure the rationality of data analysis, this application no longer analyzes the data at the initial start-up of the hydropower unit. The data at the initial start-up in this application refers to the data within 5 minutes after the start of the hydropower unit. As other embodiments of this application, the implementer can set the time length of the initial start-up according to the actual situation.

[0029] Furthermore, in order to analyze the dynamic relationship between the grid load data and the hydropower unit output power data in the T minutes before the current time t, the power data sequence at the current time t is calculated. With load data series The DTW distance between them. The DTW distance is a well-known technology and is not specifically limited in this application. The larger the DTW distance, the smaller the similarity between the grid load data and the output power of the hydropower unit, and the slower the response of the output power of the hydropower unit relative to the grid load data at the current time t.

[0030] (2) In the power system, since the electricity consumption of residents is usually relatively stable, the load data of the power grid collected by the large-scale panel will not change suddenly and drastically, so that the collected power grid load data has a certain degree of predictability. However, due to the randomness of user electricity consumption in the power system, the collected power grid load data shows a certain volatility. Therefore, this application further evaluates the abnormal fluctuation of the power grid load by analyzing the deviation between the predicted value and the actual value at the current time t through data prediction. Specifically: By obtaining the load data sequence at the current time t, the load data sequence at time t-1 is obtained. ; For the current time t, the load data sequence at time t-1 As the input of the EMA prediction algorithm (Exponential Moving Average Model), the load data of the power grid at the current time t is predicted, wherein, preferably, in the embodiment of the present application, the smoothing factor in the EMA prediction algorithm is set to 0.9. As other embodiments of the present application, the implementer can set the value of the smoothing factor according to the actual situation. The output is the predicted value of the power grid load data at the current time t.

[0031] It should be noted that for the prediction of the load data at the current moment, this application only provides a prediction method. There are many existing prediction methods, and implementers can also use other prediction algorithms to predict the load data at the current moment. This application does not make specific restrictions.

[0032] Furthermore, the absolute value of the difference between the actual value and the predicted value of the power grid load data at the current time t is calculated and recorded as , which reflects the abnormal fluctuation degree of power grid load. The larger the value of is, the more serious the grid load fluctuation is at the current time t.

[0033] (3) Based on the above analysis, construct the load abnormal change coefficient at the current time t , the expression is: In the formula, Indicates the load abnormal change coefficient at the current time t, , They represent the power data sequence and load data sequence at the current time t respectively, represents the function for calculating the DTW distance, represents the DTW distance between the power data sequence and the load data sequence at the current time t; It represents the absolute value of the difference between the actual value and the predicted value of the power grid load data at the current time t.

[0034] The larger the value, the greater the difference between the power data sequence and the load data sequence at the current time t, indicating that the dynamic change relationship between the output power data of the hydropower unit and the grid load data is less matched, and the slower the response of the output power of the hydropower unit relative to the grid load data at the current time t; the greater the difference between the predicted value and the actual value of the grid load data at the current time t, the calculated The larger the value of is, the greater the load abnormal change coefficient calculated at the current time t is. The larger the value of is, the more serious the grid load fluctuation is at the current time t.

[0035] Step S3, constructing the residual data sequence at the current moment based on the difference between the elements in the power data sequence and the load data sequence at the same moment; constructing the residual entropy sequence at the current moment based on the degree of data fluctuation in the neighborhood of each element in the residual data sequence; constructing the power control coefficient at the current moment based on the numerical change of the maximum value in the residual entropy sequence, and the time distance between the moment corresponding to the maximum value and the current moment, combined with the load abnormal change coefficient.

[0036] Under ideal conditions, as the power of the hydropower unit is continuously adjusted and controlled, the residual between the output power of the hydropower unit and the grid load data will gradually decrease until the output power of the hydropower unit is consistent with the grid load. However, in the power system, since the grid load is always in a changing state, and due to the slow responsiveness of the output power of the hydropower unit and the random volatility of the grid load data, the residual between the output power of the hydropower unit and the grid load data will be in a complex fluctuation phenomenon in practical applications. The more stable the residual is, the more accurately and timely the power regulation of the hydropower unit can respond to changes in the grid load, thereby maintaining the stable operation of the grid. When the residual fluctuates greatly, it indicates that there is a large uncertainty in the power regulation of the hydropower unit, which may have an adverse effect on the stable operation of the grid. Therefore, in order to further improve the flexibility and accuracy of the power regulation of the hydropower unit, this application further analyzes the residual between the output power of the hydropower unit and the grid load data.

[0037] First, calculate the absolute value of the difference between the output power data and the load data at the same collection time in the power data sequence and the load data sequence at the current time t, and record it as the first absolute value of the difference; record the sequence composed of all the first absolute values ​​of the difference in the power data sequence and the load data sequence at the current time t as the residual data sequence U of the power regulation of the hydropower unit at the current time t.

[0038] Further, in the residual data sequence U, a neighborhood window of each element is constructed with each element as the center. Preferably, in the embodiment of the present application, the neighborhood window size of the element is set to . As other embodiments of the present application, the implementer can set the neighborhood window size of the element according to the actual situation. Calculate the information entropy of all elements in the neighborhood window of each element, recorded as the first information entropy of each element, indicating the residual fluctuation degree of each element in the residual data sequence; record the sequence composed of the first information entropy of all elements in the residual data sequence U as the residual entropy sequence H at the current time t. It should be noted that if there are insufficient elements in the neighborhood window of each element, the missing elements in the neighborhood window are filled by polynomial interpolation. The information entropy calculation and polynomial interpolation method are both well-known technologies, and the specific process will not be repeated.

[0039] Furthermore, the residual entropy sequence H is used as the input of the automatic multi-scale peak search algorithm, and the output is each maximum value in the residual entropy sequence H; the time distance between the moment corresponding to each maximum value and the current moment t is calculated, and recorded as the first time distance of each maximum value. The closer to the current moment t, and the larger the first information entropy of the maximum value, the greater its influence on the power regulation of the hydropower unit at the current moment t. The automatic multi-scale peak search algorithm is a well-known technology, and the specific process will not be repeated.

[0040] Furthermore, in order to more clearly represent the changing trend of the residual data sequence U of the power regulation of the hydropower unit at the current time t, the data in the residual data sequence U are arranged in order from small to large to obtain a new sequence, and the new sequence is fitted by the least squares linear fitting algorithm, and the slope of the obtained fitting line is recorded as k, wherein the slope k of the fitting line reflects the changing trend of the power regulation of the hydropower unit, and the smaller the slope k, the more stable the power regulation of the hydropower unit is, and the more it can adapt to the change of the power grid load. The least squares linear fitting algorithm belongs to the known technology, and this application will not describe its implementation process in detail.

[0041] Based on the above analysis and combined with the load abnormal change coefficient, the power regulation coefficient of the hydropower unit at the current moment is constructed , the expression is: In the formula, represents the power regulation coefficient of the hydropower unit at the current time t, Indicates the load abnormal change coefficient at the current time t; represents the slope of the fitting straight line of the residual data sequence at the current time t; N represents the number of maximum values ​​in the residual entropy sequence at the current time t; The first information entropy representing the nth maximum value in the residual entropy sequence at the current time t; They respectively represent the first time distance of the nth maximum value in the residual entropy sequence at the current time t.

[0042] The more severe the grid load fluctuation at the current moment t, the greater the difference between the grid load data and the output power of the hydropower unit, and the greater the value of the calculated load abnormality change coefficient of the grid load data at the current moment t; and the greater the fluctuation degree of the residual data sequence at the current moment t, the greater the slope of the fitting straight line of the residual data sequence calculated at the current moment t, indicating that the power regulation of the hydropower unit is more unstable; secondly, the fluctuation of the residual data sequence at the current moment t will also cause obvious extreme points to appear in the residual entropy sequence, among which, the greater the first information entropy of the maximum value in the residual entropy sequence at the current moment t, and the closer the time distance of the maximum value to the current moment t, the calculated The larger the value is, the greater the fluctuation of the residual data at the current time t is, which makes the power control coefficient of the hydropower unit at the current time t calculated, indicating that the stability of the power regulation of the hydropower unit at the current time t is lower.

[0043] Step S4, based on the difference between the output power of the hydropower unit at the current moment and the actual value of the grid load data, combined with the power regulation coefficient, construct the error feedback result at the current moment; based on the error feedback result, combined with the PID controller, feedback control is performed on the power of the hydropower unit.

[0044] Power regulation coefficient Considering the coordinated variation degree between the output power of the hydropower unit and the grid load data during the operation of the hydropower unit and the random volatility of the grid load data, the power regulation coefficient It can accurately reflect the degree of abnormal power regulation during the operation of the hydropower unit. The power regulation coefficient calculated at the current time t The larger the value is, the worse the stability of the power regulation of the hydropower unit is.

[0045] Therefore, this application uses the PID feedback control mechanism according to the calculated power regulation coefficient Feedback control is performed on the output power of the hydropower unit during operation. The specific adjustment process is as follows: During the operation of the hydropower unit, the power regulation coefficient of the hydropower unit at each moment before the current moment is calculated by the above method, and the power regulation coefficient of the hydropower unit at all moments is normalized by the maximum and minimum value normalization method, so as to obtain the normalized value of the power regulation coefficient of the hydropower unit at the current moment t, and the normalized value is used as the feedback control weight at the current moment t, which is recorded as .

[0046] Furthermore, the difference between the output power of the hydropower unit at the current moment and the actual value of the grid load data is used as the error coefficient , so the error feedback result at the current time t during the operation of the hydropower unit is recorded as e, and the expression of e is: The error feedback result is used as the steady-state error parameter in the PID controller of the PLC regulation system in the hydropower unit at the current moment to perform feedback regulation on the power of the hydropower unit. The PID feedback control regulation is a well-known technology, and this application will not describe its implementation process in detail.

[0047] The schematic diagram of the acquisition process of the residual entropy sequence at the current moment is as follows Figure 2 shown.

[0048] Based on the same inventive concept as the above method, an embodiment of the present application also provides a hydropower unit power feedback control system using PID technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned hydropower unit power feedback control methods using PID technology are implemented.

[0049] In summary, the embodiment of the present application provides a power feedback control method for a hydropower unit using PID technology. The present application is based on the output power data and grid load data collected during the working process of the hydropower unit. First, by analyzing the degree of coordinated change between the output power of the hydropower unit and the grid load data during the working process of the hydropower unit and the random volatility of the grid load data, an abnormal load change coefficient is constructed. The consistency between the output power and the grid load data during the working process of the hydropower unit and the degree of abnormal fluctuation of the grid load are considered, and the matching degree between the output power of the hydropower unit and the grid load data at each moment during the working process of the hydropower unit and the severity of the grid load fluctuation are reflected. This time, by analyzing the volatility of the residual between the output power of the hydropower unit and the grid load data and combining the load abnormal change coefficient, a power control coefficient was constructed, taking into account the abnormal fluctuation degree of the residual data. Therefore, the power control coefficient can accurately reflect the abnormal power control degree at each moment in the working process of the hydropower unit. Finally, the power control coefficient is used as the feedback correction weight of the PID control feedback adjustment mechanism, so as to better realize the power feedback control in the working process of the hydropower unit, avoid the problem that the output power of the hydropower unit is difficult to accurately match the actual needs of the power grid due to ignoring the direct impact of the volatility of the grid load on the power feedback adjustment of the hydropower unit, and improve the stability of the power system.

[0050] 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. The above-mentioned specific embodiments of the present application are described. 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, multitasking and parallel processing are also possible or may be advantageous.

[0051] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0052] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A power feedback control method for a hydropower unit using PID technology, characterized in that: The method comprises the following steps: Collect the output power data of the hydropower units and the load data of the power grid at each moment; Based on the output power data and load data of each moment in the time period adjacent to the current moment, a power data sequence and a load data sequence at the current moment are constructed; based on the difference between the power data sequence and the load data sequence, and the difference between the actual value and the predicted value of the load data at the current moment, an abnormal load change coefficient at the current moment is constructed; Based on the difference between the elements at the same time in the power data sequence and the load data sequence, the residual data sequence at the current moment is constructed; based on the degree of data fluctuation in the neighborhood of each element in the residual data sequence, the residual entropy sequence at the current moment is constructed; based on the numerical change of the maximum value in the residual entropy sequence, and the time distance between the time corresponding to the maximum value and the current moment, combined with the load abnormal change coefficient, the power regulation coefficient at the current moment is constructed; Based on the difference between the output power of the hydropower unit at the current moment and the actual value of the grid load data, combined with the power regulation coefficient, the error feedback result at the current moment is constructed; based on the error feedback result, combined with the PID controller, the power of the hydropower unit is feedback controlled.

2. The power feedback control method of a hydropower unit using PID technology as claimed in claim 1, characterized in that: The process of obtaining the load abnormal change coefficient at the current moment is as follows: The load data sequence at the moment before the current moment is used as the input of the prediction algorithm, and the output is the predicted value of the load data at the current moment; the absolute value of the difference between the actual value of the load data at the current moment and the predicted value is calculated; The metric distance between the power data sequence and the load data sequence at the current moment is calculated, and the load abnormal change coefficient at the current moment is determined in combination with the absolute value of the difference.

3. The power feedback control method of a hydropower unit using PID technology as claimed in claim 2, characterized in that: The load abnormality variation coefficient at the current moment is: the product of the absolute value of the difference and the measured distance.

4. The power feedback control method of a hydropower unit using PID technology as claimed in claim 1, characterized in that: The process of obtaining the residual data sequence at the current moment is: Calculate the absolute value of the difference between the output power data and the load data at the same collection time in the power data sequence and the load data sequence at the current moment, and record it as the first absolute value of the difference; record the sequence composed of all the first absolute values ​​of the difference as the residual data sequence at the current moment.

5. The power feedback control method of a hydropower unit using PID technology as claimed in claim 1, characterized in that: The process of obtaining the residual entropy sequence at the current moment is: For each element in the residual data sequence at the current moment, the information entropy of all elements in the neighborhood window of each element is calculated, recorded as the first information entropy of each element, and the sequence composed of the first information entropies of all elements in the residual data sequence is recorded as the residual entropy sequence at the current moment.

6. The power feedback control method of a hydroelectric generator set using PID technology as claimed in claim 5, characterized in that: The process of obtaining the power regulation coefficient at the current moment is: The residual entropy sequence at the current moment is used as the input of the peak search algorithm, and the output is the maximum values ​​in the residual entropy sequence at the current moment; the time distance between the moment corresponding to each maximum value and the current moment is calculated, and recorded as the first time distance of each maximum value; the residual data sequence at the current moment is used as the input of the straight line fitting algorithm, and the slope of the output fitting line is obtained; The power regulation coefficient of the hydropower unit at the current moment is constructed based on the load abnormal change coefficient at the current moment, the first information entropy, the first time distance and the slope.

7. The power feedback control method of a hydroelectric generator set using PID technology as claimed in claim 6, characterized in that: The expression of the power regulation coefficient of the hydropower unit at the current moment is: , where is the power regulation coefficient of the hydropower unit at the current moment; Indicates the abnormal load change coefficient at the current moment; is the slope of the fitting straight line; N represents the number of maximum values ​​in the residual entropy sequence at the current moment; The first information entropy representing the nth maximum value in the residual entropy sequence at the current moment; They respectively represent the first time distance of the nth maximum value in the residual entropy sequence at the current moment.

8. The power feedback control method of a hydroelectric generator set using PID technology as claimed in claim 1, characterized in that: The process of obtaining the error feedback result at the current moment is: The difference between the monitored output power of the hydropower unit at the current moment and the grid load data is taken as the error coefficient; the product of the normalized value of the power regulation coefficient of the hydropower unit at the current moment and the error coefficient is taken as the error feedback result at the current moment.

9. The power feedback control method of a hydroelectric generator set using PID technology as claimed in claim 1, characterized in that: The feedback control of the power of the hydropower unit is specifically as follows: The error feedback result at the current moment is used as the steady-state error parameter in the PID controller of the hydropower unit at the current moment to perform feedback adjustment on the power of the hydropower unit.

10. A hydropower unit power feedback control system using PID technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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