A decision-making aid method for the startup process of giant hydropower units

Abstract

The present invention relates to the technical field of hydropower station automation systems, and in particular to a method for assisting decision-making in the startup process of a giant hydropower unit. Three data sequences are constructed for the motors in the water supply system, high-pressure oil system, thrust bearing external circulation system, and water-guide bearing external circulation system, respectively. The equivalent operating time and the single average equivalent operating time of the motors are calculated to construct an equivalent operating time sequence. The next startup time of the motor is predicted, and abnormal and normal areas of the startup time are distinguished. After analyzing the abnormal startup times of all motors in the auxiliary system, the motor to be started next is determined. The motors for limited startup of all auxiliary systems are determined, and the corresponding motors are started when the startup process reaches the point where startup is required. The present invention can intelligently control the reasonable operation of auxiliary motors, automatically balance motor usage time, and reduce safety risks.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower station automation systems, and in particular to a startup process auxiliary decision-making method for a giant hydropower unit. Background Art

[0002] Automated startup and shutdown processes ensure the safe and efficient operation of hydropower units. During grid operation, hydropower units utilize automated startup and shutdown processes to achieve peak load regulation, frequency regulation, and emergency backup. The startup and shutdown processes of large hydropower units automate the technical water supply and oil systems. The core components of these auxiliary systems are motors. These processes automatically control the starting and stopping of the motors according to specific steps and sequences to ensure unit safety. To ensure reliable unit operation, the auxiliary system motors utilize dual redundancy: one primary and one backup. Under normal circumstances, the startup and shutdown processes utilize fixed equipment and fail to intelligently select motors based on actual usage. After extended operation, the operating time and health status of the primary and backup motors differ significantly, increasing the risk of failure of the primary motor. If the motors fail to start and stop smoothly, startup and shutdown operations will be impacted, further compromising unit safety.

[0003] In practice, the current startup process cannot automatically select the optimal motor for startup, and manual switching is generally required during operation. This invention provides a decision-making assistance technology for large hydropower units. During startup, the startup process automatically selects the appropriate motor for startup based on the usage of the auxiliary motors, reducing power operation risks. Summary of the Invention

[0004] The present invention provides a method for assisting decision-making in the startup process of a giant hydropower unit. By predicting, analyzing, and comparing the status of the motors, it automatically selects the motors that should be prioritized for startup and shutdown, thereby reducing operational risks. Specifically, it includes the following steps:

[0005] Step 1: Construct three sequence data for the motors of the technical water supply system, high-pressure oil system, thrust bearing external circulation system, and water-guide bearing external circulation system: the motor start time sequence T1, the motor stop time sequence T2, and the motor running time sequence T3, and then count and clean the sequence data:

[0006] The starting time sequence of the motor is T1, T1=(t 11 ,t 21 ,t 31 ,···,t 1m1 ), the number of data in the sequence m1 = the number of times the motor is started, and the data value t1 = the start time. The start time is the time from when the motor receives the start command to when the equipment is fully put into operation.

[0007] The motor stop time sequence T2, T2 = (t 21 ,t 22 ,t 23 ,···,t 2m2 ), the number of data in the sequence m2 = the number of times the motor stops, the data value t2 = the stop time, and the stop time is the time from when the motor receives the stop command to when the equipment completely stops operating.

[0008] The motor's operating time sequence is T3, T3=(t 31 ,t 32 ,t 33 ,···,t 3m3 ), the number of data in the sequence m3 = the number of times the auxiliary motor runs, the data value t3 = the running time, and the running time is the time from the equipment being "fully put into operation" to "receiving the stop command".

[0009] Only the data of normal start and stop are counted, and the data of fault stop are not counted. Every time the motor is started, if the motor does not stop due to fault, T1 will add a new data; every time the motor stops normally, T2 and T3 will add a new data. Yes, m1 = m2 = m3 = m;

[0010] Step 2: Based on step 1, calculate the equivalent running time T of the motor eq and single average equivalent running time T eq / , construct the equivalent running time series T4;

[0011] Specifically, it includes the following sub-steps.

[0012] (1) Calculation of equivalent running time T eq Calculation. First, convert each motor start and stop time into running time, and then add up all the running times, as follows:

[0013]

[0014] t 1i , t 2i and t 3i are the starting time of the motor when it is started for the i-th time, the stopping time when it is stopped for the i-th time, and the running time when it is run for the i-th time; m1, m2 and m3 are the total number of starts, stops and runs respectively, and K is the starting multiple;

[0015] (2) Construct the motor equivalent operation time series T4, the number of data in the series m = the number of times the auxiliary motor operates, the data value T eq =The sum of the historical equivalent running time of each run.

[0016] T4=(T eq1 ,Teq2 ,T eq3 ,···,T eqm )

[0017] (3) Every time T1, T2, and T3 are updated, a new T is calculated. eq and T eq / , T4 adds a new data.

[0018] Step 3: Based on steps 1 and 2, a machine learning method, including but not limited to a support vector machine, is used to predict the next startup time of the motor. When predicting the startup time, T1, T2, and T4 are used as inputs, and the next startup time is used as the output.

[0019] Step 4: Distinguish between abnormal and normal areas in the startup time. First, calculate the maximum, minimum, and median of the startup time, and use the 80 / 20 rule to determine the normal and abnormal areas. Input the result of step 3 to determine whether the predicted value is in the abnormal area.

[0020] Step 5. After analyzing the startup time anomalies for all auxiliary system motors, determine the next motor to be started. Let the two auxiliary system motors be designated as Motor 1 and Motor 2. Use the following process to determine the motor to be controlled by the next automatic startup process.

[0021] (1) If the predicted start-up time of motor 1 and motor 2 are both in the abnormal range or the normal range.

[0022] a. If the equivalent running time of motor 1 minus the equivalent running time of motor 2 is greater than or equal to the average single equivalent running time of motor 2, select motor 2 as the motor to be used for the next start and stop;

[0023] b. If the equivalent running time of motor 2 - the equivalent running time of motor 1 ≥ the average single equivalent running time of motor 1, select motor 1 as the motor to be used for the next start and stop;

[0024] c. If |Equivalent running time of motor 1 - Equivalent running time of motor 2| < the average single equivalent running time of motor 2, and the equivalent running time of motor 1 - Equivalent running time of motor 2| < the average single equivalent running time of motor 1, select the motor with fewer running times as the motor to be used for the next start and stop;

[0025] (2) If one of the predicted values ​​is within the abnormal range:

[0026] a. If the predicted value of Motor 1 is within the abnormal range, such as if the equivalent operating time of Motor 2 minus the equivalent operating time of Motor 1 is ≥ 2 times the average equivalent operating time of Motor 1, Motor 1 is selected as the motor to be used for the next start and stop. Otherwise, Motor 2 is selected as the motor to be used for the next start and stop.

[0027] b. If the predicted value of Motor 2 is in the abnormal range, such as the equivalent operating time of Motor 1 minus the equivalent operating time of Motor 2 ≥ 2 times the average single equivalent operating time of Motor 2, Motor 2 is selected as the motor to be used for the next start and stop. Otherwise, Motor 1 is selected as the motor to be used for the next start and stop.

[0028] Step 6. Follow steps 2 to 5 to determine the motors for limited start-up of all auxiliary systems.

[0029] Step 7: When the startup process reaches the point where the technical water supply pump, high-pressure oil pump, upper guide bearing, lower guide bearing, thrust bearing, and water guide bearing external circulation oil pump need to be started, if the auxiliary system has only one motor, start that motor directly. If there are two motors, start the corresponding motor according to the result of step 5.

[0030] Compared with the prior art, the present invention has the following technical effects:

[0031] 1) The start-up and shutdown process can intelligently control the reasonable operation of the auxiliary motor. When automatically starting and stopping, it can automatically balance the use time of the motor to reduce safety risks.

[0032] 2) No additional detection devices are required. Decisions can be made automatically by simply counting and calculating the start time, stop time, and running time of the auxiliary motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the present invention. Specific implementation methods

[0034] In the following, we will present example embodiments of the present invention in the form of diagrams. However, it should be emphasized that the invention has many different feasible implementation forms and is not limited to the examples described here. The purpose of providing these examples is to fully and thoroughly disclose the present invention and to fully convey the wide range of applicability of the invention to those skilled in the relevant professional fields. The terms used in the embodiments shown in the diagrams do not limit the present invention. Figure 1 The flowchart shown further describes the present invention, which is a method for assisting decision-making in the startup process of a giant hydropower unit, comprising the following steps:

[0035] Step 1: Construct three sequence data for the motors of the technical water supply system, high-pressure oil system, thrust bearing external circulation system, and water-guide bearing external circulation system: the motor start time sequence T1, the motor stop time sequence T2, and the motor running time sequence T3, which are respectively expressed as T1=(t 11 ,t 21 ,t 31 ,···,t 1m1 ), T2=(t 21 ,t 22 ,t 23 ,···,t 2m2 ), T3=(t 31 ,t 32 ,t 33 ,···,t 3m3 ), statistics and clean up the data of normal startup, and do not count the operation records caused by failure. It can be seen that: m1=m2=m3=m

[0036] Step 2: Calculate the equivalent running time T of the motor eq and single average equivalent running time T eq / , and the equivalent running time series T4.

[0037] T1, T2, T 23 Each time it is updated, T is calculated according to the following formula: eq , T eq / , and update T4 at the same time.

[0038]

[0039] T4=(T eq1 ,T eq2 ,T eq3 ,···,T eqm )

[0040] Step 3: Use T1, T2 and T4 to make sample X. Assume that the mth data in T1, T2 and T4 are t 1m , t 2m and t 3m , let X m =[t 1m , t 2m , t 3m ] is used as the input variable, and the support vector machine SVM is used to predict the next starting time t of the motor. 1(m+1) , in this example, the RBF kernel function is used for calculation.

[0041] Step 4. Find the maximum value T of T1 1max and the minimum value T 1min, median T 1med =(T 1max +T 1min ) / 2, based on the median, according to the 80-20 principle, the interval 1(T 1med -4(T 1med -T 1min ) / 5,T 1med +4(T 1max -T 1med ) / 5) as the normal interval, interval 2 (T 1min ,T 1med -4(T 1med -T 1min ) / 5) and the interval 3(4(T 1max -T 1med ) / 5, T 1max ) is the abnormal interval. As predicted in step 3, t 1(m+1) If it is in interval 1, it is judged as a normal value; if it is in interval 2 or interval 3, it is judged as an abnormal value.

[0042] Step 5: Assuming the auxiliary system motors are represented by motor 1 and motor 2, determine the motor to be controlled by the next automatic startup process according to the following process:

[0043] (1) If the predicted start-up times of motor 1 and motor 2 are both in the same range.

[0044] a. Condition 1: If the equivalent running time of motor 1 minus the equivalent running time of motor 2 is greater than or equal to the single average equivalent running time of motor 2, motor 2 is selected as the motor to be used for the next start and stop;

[0045] b. Condition 2: If the equivalent operating time of motor 2 minus the equivalent operating time of motor 1 is greater than or equal to the single average equivalent operating time of motor 1, motor 1 is selected as the motor to be used for the next start and stop;

[0046] c. Condition 3: If |Equivalent running time of motor 1 - Equivalent running time of motor 2| < the average single equivalent running time of motor 2, and the equivalent running time of motor 1 - Equivalent running time of motor 2| < the average single equivalent running time of motor 1, the motor with the fewer running times will be selected as the motor to be used for the next start and stop;

[0047] (2) If one of the predicted values ​​is within the abnormal range:

[0048] a. The predicted value of Motor 1 is within the abnormal range. Condition 4: If the equivalent operating time of Motor 2 minus the equivalent operating time of Motor 1 is greater than or equal to 2 times the average single equivalent operating time of Motor 1, Motor 1 is selected as the motor to be used for the next start and stop. Otherwise, Motor 2 is selected as the motor to be used for the next start and stop.

[0049] b. The predicted value of Motor 2 is in the abnormal range; Condition 5: If the equivalent operating time of Motor 1 minus the equivalent operating time of Motor 2 is ≥ 2 times the average single equivalent operating time of Motor 2, Motor 2 is selected as the motor to be used for the next start and stop; otherwise, Motor 1 is selected as the motor to be used for the next start and stop.

[0050] Step 6: When the start / stop process reaches the stage of starting the technical water supply pump, high-pressure oil pump, thrust external circulation oil pump, and water-guide bearing external circulation oil pump, start the corresponding motors according to the result of step 5.

Claims

1. A method for assisting decision-making in the startup process of a giant hydropower unit, characterized in that: By predicting, analyzing, and comparing motor status, the system automatically selects motors that are prioritized for startup and shutdown, reducing operational risks. This includes the following steps: Step 1: Construct three sequence data for the motors of the technical water supply system, high-pressure oil system, thrust bearing external circulation system, and water-guide bearing external circulation system: the motor start time sequence T1, the motor stop time sequence T2, and the motor running time sequence T3, and then count and clean the sequence data: The starting time sequence of the motor is T1, T1=(t 11 ,t 21 ,t 31 ,···,t 1m1 ), the number of data in the sequence m1 = the number of times the motor is started, and the data value t1 = the start time, which is the time from when the motor receives the start command to when the equipment is fully put into operation; The motor stop time sequence T2, T2 = (t 21 ,t 22 ,t 23 ,···,t 2m2 ), the number of data in the sequence m2 = the number of times the motor stops, the data value t2 = the stop time, the stop time is the time from when the motor receives the stop command to when the equipment completely stops; The motor's operating time sequence is T3, T3=(t 31 ,t 32 ,t 33 ,···,t 3m3 ), the number of data in the sequence m3 = the number of times the auxiliary motor runs, and the data value t3 = the running time, which is the time from "full operation" to "receiving the stop command" of the equipment; Only the data of normal start and stop are counted, and the data of fault stop are not counted. Each time the motor is started, if the motor does not stop due to fault, a new data is added to T1. Each time the motor stops normally, a new data is added to T2 and T3. So, m1=m2=m3=m. Step 2: Based on step 1, calculate the equivalent running time T of the motor eq and single average equivalent running time T eq / , construct the equivalent running time series T4; Step 3: Based on steps 1 and 2, predict the next starting time of the motor; When predicting the startup time, T1, T2, and T4 are used as inputs, and the next startup time is used as the output; Step 4: Distinguish between abnormal and normal areas in the startup time. First, calculate the maximum, minimum, and median of the startup time, and use the 80 / 20 rule to determine the normal and abnormal areas. Input the result of step 3 to determine whether the predicted value is in the abnormal area. Step 5: After completing the abnormal analysis of the start-up time of all motors in the auxiliary system, determine the motor that should be started next time; let the two motors of the auxiliary system be represented as motor 1 and motor 2 respectively, and determine the motor to be controlled by the next automatic start-up process; Step 6: Determine the motors for limited start of all auxiliary systems according to steps 2 to 5; Step 7. When the startup process reaches the stage where the technical water supply pump, high-pressure oil pump, upper guide bearing, lower guide bearing, thrust bearing and water guide bearing external circulation oil pump need to be started, if the auxiliary system has only one motor, start the motor directly; if there are two motors, start the corresponding motor according to the result of step 5.

2. The startup process auxiliary decision-making method for a giant hydropower unit according to claim 1 is characterized in that: Step 2 specifically includes the following sub-steps: (1) Calculation of equivalent running time T eq Calculation: First, convert each motor start and stop time into running time, and then add up all the running times, as follows: t 1i , t 2i and t 3i They are the starting time when the motor starts for the i-th time, the stopping time when the motor stops for the i-th time, and the running time when the motor runs for the i-th time; m1, m2 and m3 are the total number of starts, stops and runs respectively, and K is the start multiple; (2) Construct the motor equivalent operation time series T4, the number of data in the series m = the number of times the auxiliary motor operates, the data value T eq = the sum of the historical equivalent running time of each run; T4=(The q1 ,The q2 ,The q3 ,···,The qm ) (3) Every time T1, T2, and T3 are updated, a new T is calculated. eq and T eq / , T4 adds a new data.

3. The startup process auxiliary decision-making method for a giant hydropower unit according to claim 1 is characterized in that: Step 3 uses the support vector machine machine learning method to predict the next start-up time of the motor.

4. The startup process auxiliary decision-making method for a giant hydropower unit according to claim 1 is characterized in that: In step 5, the motor to be controlled by the next automatic startup process is determined according to the following process: (1) If the predicted start-up time of motor 1 and motor 2 is both in the abnormal range or the normal range; a. If the equivalent running time of motor 1 minus the equivalent running time of motor 2 is greater than or equal to the average single equivalent running time of motor 2, select motor 2 as the motor to be used for the next start and stop; b. If the equivalent running time of motor 2 - the equivalent running time of motor 1 ≥ the average single equivalent running time of motor 1, select motor 1 as the motor to be used for the next start and stop; c. If |Equivalent running time of motor 1 - Equivalent running time of motor 2| < the average single equivalent running time of motor 2, and the equivalent running time of motor 1 - Equivalent running time of motor 2| < the average single equivalent running time of motor 1, select the motor with fewer running times as the motor to be used for the next start and stop; (2) If one of the predicted values ​​is within the abnormal range: a. If the predicted value of motor 1 is in the abnormal range, such as the equivalent operating time of motor 2 minus the equivalent operating time of motor 1 ≥ 2 times the average equivalent operating time of motor 1, motor 1 will be selected as the motor to be used for the next start and stop; otherwise, motor 2 will be selected as the motor to be used for the next start and stop; b. If the predicted value of Motor 2 is in the abnormal range, such as the equivalent operating time of Motor 1 minus the equivalent operating time of Motor 2 ≥ 2 times the average single equivalent operating time of Motor 2, Motor 2 is selected as the motor to be used for the next start and stop. Otherwise, Motor 1 is selected as the motor to be used for the next start and stop.

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