A method for expanding fault samples of excitation system in pumped storage power station

Abstract

The present invention discloses a method for expanding fault samples of an excitation system of a pumped-storage power station. The method performs fixed-point quadratic fitting on characteristic quantity data of a small-power motor test group and characteristic quantity data of a large-power motor under normal operation of the excitation system of the pumped-storage power station to obtain a mapping matrix, obtains small motor test sample data under specific fault conditions to form a fault sample matrix, proposes a calculation method for a correlation judgment factor and a monotonicity judgment factor, divides characteristic quantities in different fault types into irrelevant characteristic quantities, stable characteristic quantities and follow-up characteristic quantities, and then forms a fault mapping matrix by defining different mapping coefficient calculation methods for different types of characteristic quantities. The method performs matrix operations on the fault sample matrix using a mapping function and the fault mapping matrix to achieve fault sample expansion, thereby effectively solving the problem of a small number and low quality of fault samples in the excitation system of the pumped-storage power station.

Description

Technical Field

[0001] The invention belongs to the technical field of fault monitoring of hydropower stations, and in particular relates to a method for expanding fault samples of an excitation system of a pumped storage power station. Background Art

[0002] The excitation system of a pumped-storage power plant is crucial for its normal operation. It provides the generator with the necessary magnetic field to maintain stable operation. Excitation system failures can occur due to power supply voltage fluctuations, rectifier component damage, rotating component failures, and control loop issues. If the type of excitation system failure is not detected and repaired in a timely manner, the normal operation of the pumped-storage power plant will be affected.

[0003] The current method for distinguishing fault types mainly adopts threshold analysis. By collecting and monitoring data such as stator voltage, stator current, power, rotor voltage, rotor current and speed of the excitation system of pumped storage power stations, the thresholds of various characteristic quantities are determined, and the normal and abnormal states of the system are distinguished. Combined with artificial intelligence, state analysis is performed to determine the fault type of the equipment and make timely repairs.

[0004] Threshold analysis requires that the threshold sensitivity be set within an appropriate range. Too low a sensitivity will fail to detect faults, while too high a sensitivity will result in excessive false alarms of various fault types. Consequently, threshold determination requires a large number of samples and relies heavily on the quality of the sample data. When the sample data is inaccurate or incomplete, the results of the threshold analysis may be biased, affecting the identification of the fault type. For large units, however, the number of samples of various fault types in the excitation system is small. In such cases, threshold analysis methods struggle to accurately detect the fault type in a timely manner. Summary of the Invention

[0005] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the existing technology and provide a method, device, electronic equipment and storable medium for expanding fault samples of the excitation system of a pumped storage power station. By collecting operating data of motors of different power under normal and fault conditions, the test sample data of small power motors is used to expand the fault sample data of the excitation system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a method for expanding fault samples of an excitation system of a pumped-storage power station, which is applied to the excitation system of a pumped-storage power station. The excitation system of a pumped-storage power station includes: an excitation power supply and an excitation winding for providing a magnetic field for a motor; an armature power supply for providing a constant DC voltage for the motor; and a motor carried by the excitation system, including a stator, a rotor, and a measuring device. The stator measures stator voltage, stator current, and reactive power by means of an external voltmeter, an ammeter, and a var meter. The rotor measures rotor voltage and rotor current by means of a voltage sensor and a current sensor. The measuring device includes a force sensor for measuring torque and a tachometer for measuring speed. The fault sample expansion method includes the following steps:

[0008] S1. Obtain the characteristic quantity data of the small power motor test group and the characteristic quantity data of the high power motor under normal operation of the excitation system:

[0009] Through the voltmeter, ammeter, voltage sensor, current sensor, tachometer and reactive meter, the characteristic quantities of the motor carried by the excitation system are obtained, including: stator voltage , stator current , rotor voltage , rotor current and speed , reactive power ;

[0010] Motors with rated power of 5kW, 10kW, 5nkW, ..., 95kW are selected as the low-power motor test group. n is the corresponding number of the small-power motor sample, n=1, 2, ..., 19;

[0011] Under normal operation of the excitation system, when the excitation system of the pumped storage power station is equipped with a 5kW motor, the motor operation data is recorded, that is, the stator voltage time series data of the excitation system under the operation of the 5kW motor is obtained. , stator current timing data , rotor voltage timing data , rotor current timing data , speed timing data and reactive power time series data , forming a sample time series array of the small power motor numbered 1 under normal operating conditions of the excitation system ;

[0012] The same process is performed on the other motors in the low-power motor test group, and the second sample timing array corresponding to the motor with a rated power of 10kW is obtained in turn. ;

[0013] The motor with a rated power of 5nkW corresponds to the Sample time series array ;

[0014] Equipped with the 19th sample timing array corresponding to the rated power of 95kW ;

[0015] The above 19 sample time series arrays are the characteristic value time series arrays of the small power motor test group under the normal operation of the excitation system;

[0016] Under the condition of normal operation of the pumped storage excitation system, the high-power motors actually put into use in the pumped storage power station are monitored and recorded. If the rated power of the high-power motors actually put into use in the pumped storage power station is , and its corresponding feature array number is , get the timing array corresponding to the high-power motor actually put into use in the pumped storage power station ;

[0017] S2. Calculate the standard values ​​of the characteristic quantities of the low-power motor test group and the high-power motor:

[0018] Select a time period with no missing values ​​in each feature time series array , is the time corresponding to the starting data point of the selected time segment, To select the moment corresponding to the last data point of a time segment, the average value of each characteristic value of all low-power motor test samples in the segment is calculated as the standard value. The specific steps are as follows:

[0019] The sample time series array numbered 1 For example, the stator voltage in the time period The stator voltage is obtained by taking the internal average value

[0020] (1)

[0021] Similarly, find the mean of the stator voltage, stator current, reactive power, rotor voltage, rotor current, and speed of the sample time series array numbered 1 、 、 、 、 、 The specific calculation formula is

[0022] (2)

[0023] The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following is used to obtain the characteristic value standard value of the small power motor test sample numbered 1 when the excitation system is operating normally. , The subscript 0 indicates that it is the standard value;

[0024] Similarly, for the sample time series array numbered n

[0025] Do the same (3)

[0026] The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following is used to obtain the characteristic value standard value of the nth small power motor test sample when the excitation system is operating normally. , The subscript 0 indicates that it is the standard value;

[0027] The above processing was performed on 19 small power motor test samples and high power motor samples to obtain the characteristic value standard value of the small power motor test group when the excitation system is normal. ,in , and the standard value of the characteristic quantity of high-power motors during normal operation of the excitation system .

[0028] In the above step S2, the standard values ​​of the characteristic quantities of the 19 small-power motor test samples when the excitation system is operating normally are used to fit the various functions in the subsequent step S3. The standard values ​​of the characteristic quantities of the high-power motor when the excitation system is operating normally are used as fixed points of the fitting function to play a fitting and constraint role.

[0029] S3. Fitting to obtain the mapping matrix:

[0030] Obtain the stator voltage standard value of the small power motor test group from the characteristic quantity standard value in step S2 And the stator voltage standard value of high-power motor , define equivalent numbers is a positive real number less than or equal to 20, and the stator voltage standard value is calculated by the least squares binomial over-value fitting method About Equivalent Numbers The stator voltage function , with equivalent numbers as The standard value of the stator voltage of a high-power motor is As the constraint condition, the function Perform fitting and take the stator voltage function , the fitting formula is as follows:

[0031] (4)

[0032] in are the coefficients of the quadratic term and the linear term to be fitted, respectively. are all constants, is the equivalent number corresponding to the small power motor sample, , Corresponding number for high power motor, Obtained by step S13;

[0033] Use the equivalent number x as the sample number n, that is, x=1,2,…,19 and its corresponding stator voltage standard value The quadratic term coefficient and the linear term coefficient are fitted and solved. The specific steps of fitting and solving are as follows:

[0034] Calculate the average value of the difference between the sample number and the corresponding number of the high-power motor, and use the result to calculate the average value of the difference between the sample number and the corresponding number of the high-power motor. express,

[0035]

[0036] Calculate the average of the squares of the differences between the sample numbers and the corresponding numbers of the high-power motors. The result is represented by b.

[0037] Calculate the average value of the cube of the difference between the sample number and the corresponding number of the high-power motor, and the result is represented by c.

[0038]

[0039] Calculate the average value of the fourth power of the difference between the sample number and the corresponding number of the high-power motor, and the result is represented by d.

[0040]

[0041] Calculate the average value of the stator voltage standard value corresponding to n=1,2,…,19, and the result is represented by e.

[0042]

[0043] Calculate the average value of the product of the difference between the sample number and the corresponding number of the high-power motor and the corresponding stator voltage standard value. The result is represented by f.

[0044]

[0045] Calculate the average value of the product of the square of the difference between the sample number and the corresponding number of the high-power motor and the corresponding stator voltage standard value. The result is expressed as g.

[0046] Then the coefficient of the quadratic term is

[0047] The coefficient of the first-order term is

[0048] Substitute the calculated quadratic term coefficient and linear term coefficient into the stator voltage function ;

[0049] The stator current, reactive power, rotor voltage, rotor current and speed are fitted with binomial over-fixed value using the same method to obtain the stator current function. , rotor voltage function , rotor current function , speed function and reactive function , define a sixth-order diagonal matrix as the mapping matrix , write the above function into the mapping matrix , diag represents the operation of constructing the elements in the brackets into a diagonal matrix;

[0050] Furthermore, any positive real number x less than or equal to 20 is substituted into the mapping matrix to obtain the rated power The measured mean value of each characteristic quantity of the motor in the normal operation state of the excitation system , The rated power is The measured mean value of the stator voltage of the motor in the normal operating state of the excitation system, The rated power is The measured mean value of the stator current of the motor in the normal operating state of the excitation system, The rated power is The measured mean value of the motor rotor voltage in the normal operating state of the excitation system, The rated power is The measured mean value of the motor rotor current in the normal operating state of the excitation system, The rated power is The measured average value of the motor speed in the normal operation state of the excitation system, The rated power is The measured mean value of the motor's reactive power in normal operating conditions of the excitation system.

[0051] S4. Obtaining small motor test sample data under specific fault conditions to form a fault sample matrix;

[0052] The excitation system under different fault conditions has different equipment problems, and the detection value of the characteristic quantity will also change differently. For specific fault conditions, by changing the motor, the changes in the characteristic quantity data when the motors with different power are used are studied to obtain the regularity at the data level. The specific steps are as follows:

[0053] Since the failure rate of the excitation system of the large motor put into operation is low, abnormal data is difficult to obtain, and the cost of fault testing is high, the small power motor test group in step S1 is selected to perform a specific fault type test, and the fault time period is recorded as , n is the corresponding number of the small power motor sample, n=1,2,…,19, the values ​​of each characteristic quantity of the small power motor numbered n during the excitation system fault are monitored, and the characteristic quantity of the small power motor numbered n is obtained Time series of fault data , The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of ;

[0054] Get the characteristic value of the low-power motor numbered n in step S2 The mean As a standard value;

[0055] Select time period Characteristic quantity of the small power motor with internal number n Failure data Average and standard value The one with the smallest absolute value of the difference is recorded as ,Right now satisfy (5)

[0056] The fault time period is recorded as , the number of fault data in this time period is m, calculate the mean value of fault data in this time period

[0057] (6)

[0058] The abnormal value of the characteristic quantity obtained from the fault experiment is selected as (7);

[0059] Take in turn is the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed , calculate the stator voltage of the small power motor numbered n , stator current , reactive power , rotor voltage , rotor current , speed Outliers under specific fault conditions 、 、 、 、 、 , the subscript b indicates that it is an outlier;

[0060] Define a sixth-order diagonal matrix is the fault sample matrix, and the abnormal value of the characteristic quantity under the above specific fault conditions is written into the matrix to obtain the fault sample matrix ;

[0061] Repeat the above operations for 19 low-power motors in sequence to obtain the fault sample matrix of the 19 low-power motors under specific fault conditions;

[0062] In this step S4, the fault sample matrix The six diagonal elements represent the six characteristic abnormal values ​​of the small power test motor numbered n under specific fault conditions. The fault sample matrix is ​​obtained from the fault test of the small power test motor group, reflecting the abnormal values ​​of the six characteristic values ​​of the small power motor test group under specific fault conditions.

[0063] S5. Calculate the correlation judgment factor to distinguish relevant feature quantities from irrelevant feature quantities:

[0064] Obtain the abnormal values ​​of each characteristic quantity in 19 low-power motors under a specific fault type from the fault sample matrix in step S4 , , , , , ;

[0065] Define feature quantities Correlation judgment factor : (8)

[0066] in, is the abnormal value of the characteristic quantity in 19 small power motors, is the characteristic quantity of the small power motor numbered n under normal operating conditions of the excitation system The standard value of The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following;

[0067] If the correlation factor , it is considered that the characteristic quantity is not a representation of the fault type and is defined as an irrelevant characteristic quantity of the fault type; if , it is believed that the characteristic quantity is the characterization of the fault type and can reflect the fault state, and is defined as the relevant characteristic quantity of the fault type.

[0068] S6. Calculate the monotonicity judgment factor to distinguish between stable feature quantities and follow-up feature quantities:

[0069] Define the monotonicity judgment factor , used to classify the relevant feature quantities, wherein the relevant feature quantities are those that meet the correlation judgment factor in step S5 The characteristic quantity of the monotonicity judgment factor is calculated as follows:

[0070] (9)

[0071] in, Indicates the characteristic value of the small power motor numbered n The abnormal value in the fault test, is the average abnormal value of the characteristic quantities of 19 small power motors in the fault test, and the calculation formula is: (10)

[0072] like , it is considered that the relevant characteristic quantity is a stable characteristic quantity, that is, the relevant characteristic quantity does not change with the change of motor power. If , it is considered that the relevant feature quantity is a follow-up feature quantity, that is, the relevant feature quantity changes with the change of motor power, that is,

[0073] (10).

[0074] S7. Calculate the fault mapping matrix:

[0075] Defining Fault Mapping Coefficients , represents the characteristic quantity of the small power motor test group The mapping relationship between the fault data obtained in the fault test and the characteristic value of the high-power motor actually put into operation under the corresponding fault conditions;

[0076] Corresponding feature quantity The standard value of The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the two, defining the fault mapping coefficient The value is obtained as follows:

[0077] For irrelevant feature quantities, fault mapping coefficient The value is 0;

[0078] For the stable feature quantity in the relevant feature quantity, the fault mapping coefficient The value is 1;

[0079] For the follow-up feature in the relevant feature, it is assumed that the fault mapping coefficient The change of conforms to the linear law, and the least square method is used to calculate its mapping coefficient, that is,

[0080] (11)

[0081] Define a sixth-order diagonal matrix is the fault mapping matrix, The values ​​are selected from the stator voltage in sequence , stator current , rotor voltage , rotor current , speed , reactive power , 6 characteristic quantities are calculated 、 、 、 、 、 The mapping coefficient 、 、 、 、 、 , the fault mapping matrix is ​​composed of the above mapping coefficients , the expression is as follows ;

[0082] In this step S7, the fault mapping matrix It represents the mapping relationship between the fault sample matrix of the low-power motor in step S4 and the fault sample matrix of the high-power motor put into actual operation under the corresponding fault condition.

[0083] S8. Expand the fault samples using the mapping function and the fault mapping matrix:

[0084] Use the fault sample matrix obtained in step S4 , and the mapping matrix obtained in step S3 and the fault mapping matrix obtained in step S7 Perform matrix operations to obtain the expanded sample matrix , the above fault sample matrix and the mapping matrix is the function matrix, the fault mapping matrix is a constant coefficient matrix, the specific process is:

[0085] Select a small power motor for the excitation system specific fault test, where the small power motor is any motor with a power less than 100kW. Obtain the fault sample matrix according to the method in step S4. , the rated power of the motor used is , then the fault sample matrix subscript number is ;

[0086] Take the equivalent number x as the number , substitute into the mapping matrix get , take the equivalent number x as the rated power of the pumped storage power station actually in operation The corresponding number of high-power motor , substitute into the mapping matrix get , perform the following matrix operations

[0087] (12)

[0088] The non-zero elements in the equation are the relevant characteristic quantities under this fault condition, and their values ​​can be used as the actual power put into operation in the pumped storage system. The fault test data of high-power motors is used to expand the fault samples.

[0089] By performing multiple fault tests on low-power motors, a large number of high-quality fault samples can be obtained, effectively improving the accuracy and sensitivity of threshold analysis.

[0090] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0091] 1. The present invention proposes a correlation judgment factor. By calculating the degree of change of characteristic quantities under the fault conditions of the excitation system and the normal operating conditions of the excitation system, and by setting a certain threshold, the correlation judgment factor is greater than 1 when the degree of change of the characteristic quantity is large, and the correlation judgment factor is less than or equal to 1 when the degree of change of the characteristic quantity is small. The correlation judgment factor is used to distinguish relevant characteristic quantities from irrelevant characteristic quantities in different fault types. Irrelevant characteristic quantities cannot reflect the operating conditions of the excitation system. The correlation judgment factor can effectively screen out characteristic quantities that can reflect specific fault states.

[0092] 2. The present invention proposes a monotonicity judgment factor. By calculating the degree of change of relevant characteristic quantities when equipped with motors of different powers in the case of an excitation system failure, a certain threshold is set to ensure that the monotonicity judgment factor is greater than 1 when the degree of change is large, and the monotonicity judgment factor is less than or equal to 1 when the degree of change is small. In this way, stable characteristic quantities that do not change significantly when the motor power changes and follow-up characteristic quantities that change significantly with the change of the motor power are screened out, and the change characteristics of relevant characteristic quantities under specific fault conditions can be identified.

[0093] 3. The present invention uses the correlation judgment factor and the monotonicity judgment factor to calculate the fault mapping matrix, takes the fault mapping coefficient corresponding to the irrelevant feature quantity as 0, takes the fault mapping coefficient corresponding to the stable feature quantity as 1, and fits the fault mapping coefficient corresponding to the follow-up feature quantity according to its change law, so that when the fault mapping matrix and the fault sample matrix are operated, the feature quantities with different change characteristics obtained from the small-power motor fault test are transformed as required to obtain the fault samples corresponding to the required power motor, wherein the fault mapping matrix can effectively screen out the feature quantities related to the specific fault type, which is simple and efficient. At the same time, the characteristics of each feature quantity of the fault type can be directly seen from the fault mapping matrix, and the feature quantities with monitoring significance can be screened out through the fault mapping matrix, simplifying the fault monitoring program.

[0094] 4. The present invention adopts a matrix operation method, which can quickly and effectively transform the test samples of various fault types and various low-power motors into fault samples corresponding to the required power motor, thereby achieving the purpose of expanding the sample.

[0095] 5. The present invention utilizes the changing law of various characteristic quantities when the power of the motor carried by the excitation system changes from small to large, adopts a small-power motor to conduct experiments, and processes the test results to obtain fault samples of the high-power motor, which can expand the effective samples while saving test costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0097] Figure 1 This is a structural block diagram of the excitation system of a pumped storage power station in Example 1 of the present invention;

[0098] Figure 2 The present invention discloses a flow chart of a method for expanding fault samples of an excitation system of a pumped storage power station. DETAILED DESCRIPTION

[0099] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0100] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0101] Example 1

[0102] like Figure 2 As shown, this embodiment discloses a method for expanding fault samples of an excitation system of a pumped storage power station, and the steps are as follows:

[0103] S1. Obtain the characteristic quantity data of the small power motor test group and the characteristic quantity data of the high power motor under normal operation of the excitation system:

[0104] Assuming that the power of the high-power motor actually put into operation in the pumped storage power station is 1 MW, in this embodiment, expanding the high-power motor fault sample includes the following steps:

[0105] S1. Obtain the characteristic quantity data of the small power motor test group and the characteristic quantity data of the high power motor under normal operation of the excitation system:

[0106] S11. Obtain the characteristic quantities of the motor carried by the excitation system through the voltmeter, ammeter, voltage sensor, current sensor, tachometer and reactive meter, including: stator voltage , stator current , rotor voltage , rotor current and speed , reactive power ;

[0107] S12. Select motors with rated power of 5 kW, 10 kW, 5 nkW, ..., 95 kW in descending order as the low-power motor test group, where n is the corresponding number of the small-power motor sample, n = 1, 2, ..., 19;

[0108] Under normal operation of the excitation system, when the pumped storage power station excitation system is equipped with a 5nkW motor, the motor operation data is recorded to obtain the stator voltage time series data of the excitation system under the operation of the 5nkW motor. , stator current timing data , rotor voltage timing data , rotor current timing data , speed timing data and reactive power time series data , forming a sample time series array of the small power motor numbered n under normal operating conditions of the excitation system ;

[0109] S13. Under the condition of normal operation of the pumped storage excitation system, the high-power motors actually put into use in the pumped storage power station are monitored and recorded. The characteristic quantity array number corresponding to the high-power motors actually put into use in the pumped storage power station is , get the timing array corresponding to the high-power motor actually put into use in the pumped storage power station .

[0110] S2. Calculate the standard values ​​of characteristic quantities of the low-power motor test group and the high-power motor;

[0111] Select a time period with no missing values ​​in each feature time series array , is the time corresponding to the starting data point of the selected time segment, To select the moment corresponding to the last data point of a time segment, calculate the average value of each characteristic value of all low-power motor test samples in the segment as the standard value. The specific steps are as follows:

[0112] The sample time series array numbered n For example, the stator voltage in the time period The stator voltage mean is obtained by taking the internal mean value :

[0113] Similarly, find the mean of stator voltage, stator current, reactive power, rotor voltage, rotor current, and speed of the sample time series array numbered n , , , , , The specific calculation formula is

[0114] The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following is used to obtain the characteristic value standard value of the small power motor test sample numbered n when the excitation system is operating normally , The subscript 0 indicates that it is the standard value;

[0115] The above processing was performed on 19 small power motor test samples and high power motor samples to obtain the characteristic value standard value of the small power motor test group when the excitation system is normal. ,in , and the standard value of the characteristic quantity of a high-power motor with a rated power of 1MW during normal operation of the excitation system ,The simulation results are shown in Table 1.

[0116] Table 1. Standard values ​​of characteristic quantities for the small power motor test group and the high power motor

[0117]

[0118] S3, fitting to obtain mapping matrix;

[0119] Obtain the stator voltage standard value of the small power motor test group from the characteristic quantity standard value in S2 And the stator voltage standard value of high-power motor , define equivalent numbers is a positive real number less than or equal to 20, and the stator voltage standard value is calculated by the least squares binomial over-value fitting method About Equivalent Numbers The stator voltage function , with equivalent numbers as The standard value of the stator voltage of a high-power motor is As the constraint condition, the function Perform fitting and take the stator voltage function , the fitting formula is as follows:

[0120]

[0121] in are the coefficients of the quadratic term and the linear term to be fitted, are all constants, is the equivalent number corresponding to the small power motor sample, , Corresponding numbers for high-power motors;

[0122] Use the equivalent number x as the sample number n, that is, x=1,2,…,19 and its corresponding stator voltage standard value Fit and solve the coefficients of the quadratic term and the linear term.

[0123] Substitute the calculated quadratic term coefficient and linear term coefficient into the stator voltage function ,get ;

[0124] The stator current, reactive power, rotor voltage, rotor current and speed are fitted with binomial over-fixed value using the same method to obtain the stator current function. ;

[0125] Rotor voltage function ;

[0126] Rotor current function ;

[0127] Speed ​​function ;

[0128] Reactive function , define a sixth-order diagonal matrix as the mapping matrix , write the above function into the mapping matrix , diag represents the operation of constructing the elements in the brackets into a diagonal matrix;

[0129] S4. Obtaining test sample data of a small-power motor under specific fault conditions to form a fault sample matrix;

[0130] Select the small power motor test group in step S1 to perform a specific fault type test, and record the fault time period as , n is the corresponding number of the small power motor sample, n=1,2,…,19, the values ​​of each characteristic quantity of the small power motor numbered n during the excitation system fault are monitored, and the characteristic quantity of the small power motor numbered n is obtained Time series of failure data , The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the ;

[0131] Get the characteristic value of the low-power motor numbered n in step S2 The mean As a standard value;

[0132] Select time period Characteristic quantity of the small power motor with internal number n Failure data Average and standard value The one with the smallest absolute value of the difference is recorded as ,Right now satisfy

[0133]

[0134] Record fault time period The number of fault data in the time period is m, and the mean value of the fault data in this time period is calculated as:

[0135]

[0136] The abnormal value of the characteristic quantity obtained from the fault experiment is selected as

[0137] Take in turn is the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed , calculate the stator voltage of the small power motor numbered n , stator current , reactive power , rotor voltage , rotor current , speed Outliers under specific fault conditions 、 、 、 、 、 , the subscript b indicates that the value is an outlier;

[0138] For example, it is assumed that abnormal values ​​are obtained in a certain fault test as shown in Table 2.

[0139] Table 2. Abnormal values ​​obtained in a specific fault test

[0140]

[0141] Define a sixth-order diagonal matrix is the fault sample matrix, and the abnormal value of the characteristic quantity under the above specific fault conditions is written into the matrix to obtain the fault sample matrix ;

[0142] The above operations are repeated for 19 low-power motors in sequence to obtain the fault sample matrix of the 19 low-power motors under specific fault conditions.

[0143] S5. Calculate the correlation judgment factor to distinguish relevant feature quantities from irrelevant feature quantities;

[0144] From the fault sample matrix Obtain abnormal values ​​of 6 characteristic quantities in 19 low-power motors under specific fault types , , , , , ;

[0145] Define feature quantities Correlation judgment factor :

[0146] in, is the characteristic quantity In the abnormal values ​​of 19 small power motors, is the characteristic quantity of the small power motor numbered n under normal operating conditions of the excitation system The standard value of The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following;

[0147] If the correlation factor , it is considered that the characteristic quantity is not a representation of the fault type and is defined as an irrelevant characteristic quantity of the fault type; if , it is believed that the characteristic quantity is the characterization of the fault type and can reflect the fault state, and is defined as the relevant characteristic quantity of the fault type.

[0148] S6. Calculate the monotonicity judgment factor to distinguish between stable characteristic quantities and follow-up characteristic quantities;

[0149] Define the monotonicity judgment factor , used to classify the relevant feature quantities, wherein the relevant feature quantities are those that meet the correlation judgment factor in step S5 The characteristic quantity of the monotonicity judgment factor is calculated as follows:

[0150] in, Indicates the characteristic value of the small power motor numbered n The abnormal value in the fault test, is the average abnormal value of the characteristic quantities of 19 low-power motors in the fault test, and the calculation formula is:

[0151] like , it is considered that the relevant characteristic quantity is a stable characteristic quantity, that is, the relevant characteristic quantity does not change with the change of motor power. If , it is considered that the relevant feature quantity is a follow-up feature quantity, that is, the relevant feature quantity changes with the change of motor power.

[0152] S7. Calculate the fault mapping matrix;

[0153] Defining Fault Mapping Coefficients , represents the characteristic quantity of the small power motor test group The mapping relationship between the fault data obtained from the fault test and the characteristic value under the corresponding fault condition of the high-power motor actually put into operation;

[0154] Corresponding feature quantity The standard value of The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the two, defining the fault mapping coefficient The value is obtained as follows:

[0155] For irrelevant feature quantities, fault mapping coefficient The value is 0;

[0156] For the stable feature quantity in the relevant feature quantity, the fault mapping coefficient The value is 1;

[0157] For the follow-up feature in the relevant feature, it is assumed that the fault mapping coefficient The change of conforms to the linear law, and the least square method is used to calculate its mapping coefficient, that is,

[0158]

[0159] Define a sixth-order diagonal matrix is the fault mapping matrix, The values ​​are selected from the stator voltage in sequence , stator current , rotor voltage , rotor current , speed , reactive power , and 6 characteristic quantities are calculated respectively The mapping coefficient 、 、 、 、 、 , the fault mapping matrix is ​​composed of the above mapping coefficients , the expression is as follows .

[0160] Calculated .

[0161] S8. Expand the fault samples using the mapping matrix and the fault mapping matrix.

[0162] Using the fault sample matrix , mapping matrix and fault mapping matrix Perform matrix operations to obtain the expanded sample matrix , the above fault sample matrix and the mapping matrix is the function matrix, the fault mapping matrix is a constant coefficient matrix, specifically:

[0163] Select any low-power motor to perform a specific fault test on the excitation system, where the low-power motor is a motor with a power less than 100kW. Obtain the fault sample matrix according to the method in step S4. , the rated power of the motor used is , then the fault sample matrix subscript number is ;

[0164] For example, Corresponding fault sample matrix Expand the fault sample, take the equivalent number x as number 1, and substitute it into the mapping matrix get , take the equivalent number x as 200, substitute it into the mapping matrix get , perform the following matrix operations: , The non-zero elements in are the relevant characteristic quantities under this fault condition, and the actual power put into operation in the pumped storage system is The fault test data of high-power motors is used to expand the fault samples.

[0165] Calculated

[0166] The results show that the non-zero elements in the matrix, stator voltage, rotor voltage, rotor current, and reactive power, are relevant characteristic quantities of this fault and have monitoring significance. In this fault case, the stator voltage fault abnormal value is 380V, the rotor voltage fault abnormal value is 300V, the rotor current fault abnormal value is 300A, and the reactive power fault abnormal value is 1090.4kVar. The expanded fault sample information is shown in Table 3.

[0167] Table 3. Expanded fault sample information

[0168]

[0169] The above abnormal values ​​can be used to formulate early warning thresholds during the monitoring process. Performing the above operation multiple times can obtain fault samples of multiple high-power motors.

[0170] Taking reactive power as an example, the fault data in Table 2 are expanded to obtain fault samples using the above method, as shown in Table 4.

[0171] Table 4. Reactive power abnormal values ​​of expanded fault samples

[0172]

[0173] As can be seen from Table 4, different reactive fault abnormal values ​​are obtained when performing specific fault tests on different small-power motors. It is difficult to directly obtain useful information from the reactive fault abnormal values ​​of small-power motors. The reactive fault abnormal values ​​of small-power motors are converted by the method of this patent to obtain the reactive abnormal values ​​of high-power motors actually put into operation in pumped-storage power stations. The values ​​tend to be stable and are closer to the reactive values ​​of high-power motors actually put into operation in specific fault tests, which is more accurate and intuitive.

[0174] To sum up, the fault sample expansion method disclosed in the present invention can identify the type of characteristic quantity, screen out characteristic quantities with detection significance, and calculate the corresponding characteristic quantity abnormality values ​​of the high-power motor in the fault test based on the characteristic quantity abnormality values ​​obtained in the fault test of different small-power motors. Through multiple small-motor specific fault tests, the fault samples of the high-power motor can be expanded without destroying the high-power motor, thereby reducing the fault test cost and simplifying the detection process while retaining the detection accuracy.

[0175] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0176] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for expanding fault samples of an excitation system of a pumped storage power station, characterized in that: The fault sample expansion method comprises the following steps: S1. Acquire characteristic quantity data of a small-power motor test group and characteristic quantity data of a large-power motor under normal operation of the excitation system of a pumped storage power station; S2. Calculate the standard values ​​of characteristic quantities of the low-power motor test group and the high-power motor; S3. Fitting to obtain the mapping matrix. The process is as follows: Obtain the stator voltage standard value of the small power motor test group from the characteristic quantity standard value obtained in step S2 And the stator voltage standard value of high-power motor , define equivalent numbers is a positive real number less than or equal to 20, and the stator voltage standard value is calculated by the least squares binomial over-value fitting method About Equivalent Numbers The stator voltage function , with equivalent numbers as The standard value of the stator voltage of a high-power motor is As the constraint condition, the function Perform fitting and take the stator voltage function , the fitting formula is as follows: in are the coefficients of the quadratic term and the linear term to be fitted, are all constants, is the equivalent number corresponding to the small power motor sample, , Corresponding numbers for high-power motors; Use the equivalent number x as the sample number n, that is, x=1,2,…,19 and its corresponding stator voltage standard value Fit and solve the quadratic term coefficient and the linear term coefficient, and substitute the calculated quadratic term coefficient and the linear term coefficient into the stator voltage function ; The stator current, reactive power, rotor voltage, rotor current and speed are fitted with binomial over-fixed value using the same method to obtain the stator current function. , rotor voltage function , rotor current function , speed function and reactive function , define a sixth-order diagonal matrix as the mapping matrix , write the above function into the mapping matrix , diag represents the operation of constructing the elements in the brackets into a diagonal matrix; Substituting any positive real number x less than or equal to 20 into the mapping matrix, the rated power can be obtained as The measured mean value matrix of each characteristic quantity of the motor in the normal operation state of the excitation system , The rated power is The measured mean value of the stator voltage of the motor in the normal operating state of the excitation system, The rated power is The measured mean value of the stator current of the motor in the normal operating state of the excitation system, The rated power is The measured mean value of the motor rotor voltage in the normal operating state of the excitation system, The rated power is The measured mean value of the motor rotor current in the normal operating state of the excitation system, The rated power is The measured average value of the motor speed in the normal operation state of the excitation system, The rated power is The measured mean value of the motor’s reactive power in the normal operating state of the excitation system; S4. Obtaining test sample data of a small-power motor under specific fault conditions to form a fault sample matrix; S5. Calculate the correlation judgment factor to distinguish relevant feature quantities from irrelevant feature quantities; S6. Calculate the monotonicity judgment factor to distinguish between stable characteristic quantities and follow-up characteristic quantities; S7. Calculate the fault mapping matrix. The process is as follows: Defining Fault Mapping Coefficients , represents the characteristic quantity of the small power motor test group The mapping relationship between the fault data obtained from the fault test and the characteristic value under the corresponding fault condition of the high-power motor actually put into operation; Corresponding feature quantity The standard value of The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the two, defining the fault mapping coefficient The value is obtained as follows: For irrelevant feature quantities, fault mapping coefficient The value is 0; For the stable feature quantity in the relevant feature quantity, the fault mapping coefficient The value is 1; For the follow-up feature in the relevant feature, it is assumed that the fault mapping coefficient The change of conforms to the linear law, and the least square method is used to calculate its mapping coefficient, that is, , define a sixth-order diagonal matrix is the fault mapping matrix, The values ​​are selected from the stator voltage , stator current , rotor voltage , rotor current , speed , reactive power , and 6 characteristic quantities are calculated respectively The mapping coefficient 、 、 、 、 、 , the fault mapping matrix is ​​composed of the above mapping coefficients , the expression is as follows ; S8. Expand the fault samples using the mapping matrix and the fault mapping matrix.

2. A method for expanding fault samples of an excitation system of a pumped storage power station according to claim 1, characterized in that: The process of step S1 is as follows: S11. Obtain the characteristic quantities of the motor carried by the excitation system through the voltmeter, ammeter, voltage sensor, current sensor, tachometer and reactive meter, including: stator voltage , stator current , rotor voltage , rotor current and speed , reactive power ; S12. Select motors with rated power of 5 kW, 10 kW, 5 nkW, ..., 95 kW in descending order as the low-power motor test group, where n is the corresponding number of the small-power motor sample, n = 1, 2, ..., 19; Under normal operation of the excitation system, when the pumped storage power station excitation system is equipped with a 5nkW motor, the motor operation data is recorded to obtain the stator voltage time series data of the excitation system under the operation of the 5nkW motor. , stator current timing data , rotor voltage timing data , rotor current timing data , speed timing data and reactive power time series data , forming a sample time series array of the small power motor numbered n under normal operating conditions of the excitation system ; S13. Under the normal operation of the pumped storage excitation system, monitor and record the high-power motors actually put into use in the pumped storage power station. If the rated power of the high-power motors actually put into use in the pumped storage power station is , and its corresponding feature array number is , get the timing array corresponding to the high-power motor actually put into use in the pumped storage power station .

3. A method for expanding fault samples of an excitation system of a pumped storage power station according to claim 2, characterized in that: The process of step S2 is as follows: Select a time period with no missing values ​​in each feature time series array , is the time corresponding to the starting data point of the selected time segment, To select the moment corresponding to the last data point of a time segment, calculate the average value of each characteristic value of all low-power motor test samples in the segment as the standard value. The specific steps are as follows: The sample time series array numbered n For example, the stator voltage in the time period The stator voltage mean is obtained by taking the internal mean value Similarly, find the mean of stator voltage, stator current, reactive power, rotor voltage, rotor current, and speed of the sample time series array numbered n , , , , , The specific calculation formula is The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following is used to obtain the characteristic value standard value of the small power motor test sample numbered n when the excitation system is operating normally , The subscript 0 indicates that it is the standard value; The above processing was performed on 19 small power motor test samples and high power motor samples to obtain the characteristic value standard value of the small power motor test group when the excitation system is normal. ,in , and the standard value of the characteristic quantity of high-power motors during normal operation of the excitation system .

4. A method for expanding fault samples of an excitation system of a pumped storage power station according to claim 3, characterized in that: The process of step S4 is as follows: Select the small power motor test group in step S1 to perform a specific fault type test, and record the fault time period as , n is the corresponding number of the small power motor sample, n=1,2,…,19, the values ​​of each characteristic quantity of the small power motor numbered n during the excitation system fault are monitored, and the characteristic quantity of the small power motor numbered n is obtained Time series of failure data , The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the ; Get the characteristic value of the low-power motor numbered n in step S2 The mean As a standard value; Select time period Characteristic quantity of the small power motor with internal number n Failure data Average and standard value The one with the smallest absolute value of the difference is recorded as ,Right now satisfy Record fault time period The number of fault data in the time period is m, and the mean value of the fault data in this time period is calculated as: The abnormal value of the characteristic quantity obtained from the fault experiment is selected as Take in turn is the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed , calculate the stator voltage of the small power motor numbered n , stator current , reactive power , rotor voltage , rotor current , speed Outliers under specific fault conditions 、 、 、 、 、 , the subscript b indicates that the value is an outlier; Define a sixth-order diagonal matrix is the fault sample matrix, and the abnormal value of the characteristic quantity under the above specific fault conditions is written into the matrix to obtain the fault sample matrix ; The above operations are repeated for 19 low-power motors in sequence to obtain the fault sample matrix of the 19 low-power motors under specific fault conditions.

5. A method for expanding fault samples of an excitation system of a pumped storage power station according to claim 4, characterized in that: The process of step S5 is as follows: From the fault sample matrix Obtain abnormal values ​​of 6 characteristic quantities in 19 low-power motors under specific fault types , , , , , ; Define feature quantities Correlation judgment factor : in, is the characteristic quantity In the abnormal values ​​of 19 small power motors, is the characteristic quantity of the small power motor numbered n under normal operating conditions of the excitation system The standard value of The value is selected from the stator voltage , stator current , reactive power , rotor voltage , rotor current , speed One of the following; If the correlation factor , it is considered that the characteristic quantity is not a representation of the fault type and is defined as an irrelevant characteristic quantity of the fault type; if , it is believed that the characteristic quantity is the characterization of the fault type and can reflect the fault state, and is defined as the relevant characteristic quantity of the fault type.

6. A method for expanding fault samples of an excitation system of a pumped storage power station according to claim 5, characterized in that: The process of step S6 is as follows: Define the monotonicity judgment factor , used to classify the relevant feature quantities, wherein the relevant feature quantities are those that meet the correlation judgment factor in step S5 The characteristic quantity of the monotonicity judgment factor is calculated as follows: in, Indicates the characteristic value of the small power motor numbered n Outliers in the fault test, is the average abnormal value of the characteristic quantities of 19 low-power motors in the fault test, and the calculation formula is: ; like , it is considered that the relevant characteristic quantity is a stable characteristic quantity, that is, the relevant characteristic quantity does not change with the rated power of the motor. If , it is considered that the relevant characteristic quantity is a follow-up characteristic quantity, that is, the relevant characteristic quantity changes with the rated power of the motor.

7. A method for expanding fault samples of an excitation system of a pumped storage power station according to claim 6, characterized in that: The process of step S8 is as follows: Using the fault sample matrix , mapping matrix and fault mapping matrix Perform matrix operations to obtain the expanded sample matrix , the above fault sample matrix and the mapping matrix is the function matrix, the fault mapping matrix is a constant coefficient matrix, specifically: Select a small power motor for the excitation system specific fault test, where the small power motor is a motor with a power less than 100kW. Obtain the fault sample matrix according to the method in step S4. , the rated power of the motor used is , then the subscript number of the fault sample matrix is ; Take the equivalent number x as the number , substitute into the mapping matrix get , take the equivalent number x as the rated power of the pumped storage power station actually in operation The corresponding number of high-power motor , substitute into the mapping matrix get , perform the following matrix operations: , The non-zero elements in are the relevant characteristic quantities under this fault condition, and the actual power put into operation in the pumped storage system is The fault test data of high-power motors is used to expand the fault samples.

Images