Online non-power-cut detection method and system for insulation of auxiliary motor of phase modifier

Abstract

The invention belongs to the technical field of electric power automatic auxiliary detection, and provides a phase modifier auxiliary motor insulation online non-power-failure detection method and system. A first auxiliary adjustment feature is obtained according to a first machine room environment parameter collected by a phase modifier environment collection module and a first aging parameter collected by a phase modifier insulation aging module, and a phase modifier auxiliary detection model is constructed according to the first auxiliary adjustment feature and a corresponding phase modifier first control method. And inputting a second auxiliary adjustment feature obtained by processing the motor to be subjected to insulation detection into the phase modifier auxiliary detection model to obtain a second control method of the phase modifier of the motor to be subjected to insulation detection, controlling working parameters of the phase modifier according to the second control method of the phase modifier, and assisting on-line insulation non-power-failure detection of the motor. And subsequent detection and adjustment of insulation detection parameters of the motor are carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of power automation auxiliary detection, and particularly relates to a synchronous condenser auxiliary motor insulation on-line power-off detection method and system. Background Art

[0002] With the continuous increase of the scale of DC power transmission, the power quality problem becomes more prominent, and the demand of the power grid system for reactive power increases day by day. As a dedicated reactive power compensation source, the synchronous condenser has the advantages of continuously controllable output reactive power, flexible adjustment, high precision, and zero-difference adjustment.

[0003] A synchronous condenser is a synchronous motor without mechanical load under special working conditions. By changing the magnitude of its excitation current, it can be controlled to absorb reactive power from the power system or output reactive power. Under the condition of no compensation, the rotor excitation winding current is the rated no-load excitation current, and at this time, the terminal voltage of the machine is the same as the grid voltage. Also, since the rotor has no mechanical load, there is almost no current in the stator armature winding at this time, so the synchronous condenser does not emit reactive power. When the system voltage is low, it operates over-excited and becomes a reactive power source, which can provide reactive power to the system and raise the system voltage; when the system voltage is high, it operates under-excited and becomes a reactive power load, absorbing reactive power from the system and lowering the system voltage. In this way, the synchronous condenser injects capacitive or inductive reactive current into the power grid, which is also called emitting capacitive or inductive reactive power.

[0004] The threat to the safe operation of large motors mainly comes from their insulation systems, which are determined by the insulation structure of the motors, the operating environment of the insulation, and the operating conditions. Traditional methods often use a high-voltage insulation tester or a megohmmeter for regular inspections during non-working periods. Motor equipment is also often equipped with a motor safety control cabinet, which has protection functions such as open-phase overcurrent. However, these detection and protection devices cannot display the insulation data of the motor operating state in real time, cannot reflect the changes in the actual insulation state of the motor, and thus cannot track the internal contamination phenomenon and insulation values of many important motor equipment in real time. As a result, preventive maintenance and replacement work cannot be effectively arranged, the safe operation of the motor cannot be fully guaranteed, the equipment cannot operate to its maximum life, and the motor cannot be fully utilized.

[0005] It can be seen that the prior art has the following problems:

[0006] First, during the on-line power-off detection of the motor insulation, there is a lack of characteristic processing of the data of the synchronous condenser, and the synchronous condenser cannot be continuously controlled and adjusted flexibly in an automated manner to assist in the detection. Secondly, there is no setting of a synchronous condenser parameter setting model for different on-line power-off detection environments of the motor insulation, and the big data intelligent control algorithm is not fully utilized to accurately control the synchronous condenser, so as to achieve the auxiliary accurate detection of the on-line power-off of the motor insulation under the variable adjustment of the synchronous condenser parameters. Summary of the Invention

[0007] To solve the above technical problems, the present invention proposes a method and system for assisting the on-line power-off detection of motor insulation by a synchronous condenser.

[0008] In the first aspect of the present invention, a method for assisting the on-line power-off detection of motor insulation by a synchronous condenser is provided. The method includes:

[0009] S1. Obtain the first insulation detection parameters of the motor, the first computer room environment parameters and the first aging parameters of the synchronous condenser, and obtain the first control method of the synchronous condenser;

[0010] S2. Process the first insulation detection parameters of the motor, the first computer room environment parameters and the first aging parameters of the synchronous condenser to obtain the first auxiliary adjustment feature;

[0011] S3. Construct a synchronous condenser assisted detection model according to the first auxiliary adjustment feature and the first control method of the corresponding synchronous condenser;

[0012] S4. Collect the second insulation detection parameters of the motor, the second computer room environment parameters and the second aging parameters of the synchronous condenser, and process them to obtain the second auxiliary adjustment feature;

[0013] S5. Input the obtained second auxiliary adjustment feature into the synchronous condenser assisted detection model to obtain the second control method of the synchronous condenser, and assist and control the change of the working parameters of the synchronous condenser according to the second control method, and assist the on-line power-off detection of the motor insulation by adjusting the working parameters of the synchronous condenser.

[0014] Further, the first insulation detection parameters are obtained by processing the first insulation resistance, the first voltage, the first leakage current, the first dielectric loss factor of the motor and the first temperature parameter at which the motor is located;

[0015] The second insulation detection parameters are obtained by processing the second insulation resistance, the second voltage, the second leakage current, the second dielectric loss factor of the motor and the second temperature parameter at which the motor is located.

[0016] Further, the first computer room environment parameters include the first environmental temperature, the first humidity and the first wind speed in the computer room where the synchronous condenser is located;

[0017] The second computer room environment parameter quantity includes the second environmental temperature, the second humidity, and the second wind speed of the computer room where the synchronous condenser is located.

[0018] Further, the first aging parameter is obtained by processing the maximum temperature data of the first rotor of the synchronous condenser, the first maximum vibration frequency at the end of the stator winding, and the resistance value of the first insulation resistance of the synchronous condenser;

[0019] The second aging parameter is obtained by processing the maximum temperature data of the second rotor of the synchronous condenser, the second maximum vibration frequency at the end of the stator winding, and the resistance value of the second insulation resistance of the synchronous condenser.

[0020] Further, the synchronous condenser auxiliary detection model includes a classifier based on the Fisher criterion, a support vector machine improved based on the motor insulation detection parameters and the synchronous condenser aging parameters, and a neural network model.

[0021] Further, the first auxiliary adjustment feature is obtained by splicing the first insulation detection parameter of the motor, the first computer room environment parameter of the synchronous condenser, and the first aging parameter vector, and the second auxiliary adjustment feature is obtained by splicing the second insulation detection parameter of the motor, the second computer room environment parameter of the synchronous condenser, and the second aging parameter vector.

[0022] There is also provided a synchronous condenser auxiliary motor insulation on-line power-off detection system, including a motor insulation on-line monitoring module, a synchronous condenser environmental parameter acquisition module, a synchronous condenser insulation aging module, a synchronous condenser auxiliary adjustment feature processing module, a synchronous condenser auxiliary detection model construction module, and a synchronous condenser control and management module, characterized in that:

[0023] The motor insulation on-line monitoring module: is used to collect the first insulation detection parameter and the second insulation detection parameter of the motor;

[0024] The synchronous condenser environmental parameter acquisition module: is connected to the Internet and is used to collect the first computer room environment parameter and the second computer room environment parameter of the synchronous condenser computer room;

[0025] The synchronous condenser insulation aging module: is used to collect the first aging parameter and the second aging parameter of the synchronous condenser;

[0026] The synchronous condenser auxiliary adjustment feature processing module: receives the first insulation detection parameter of the motor, the first computer room environment parameter of the synchronous condenser, and the first aging parameter feature to process and obtain the first auxiliary adjustment feature, and is also used to receive the second insulation detection parameter of the motor, the second computer room environment parameter of the synchronous condenser, and the second aging parameter feature to process and obtain the second auxiliary adjustment feature;

[0027] The synchronous condenser auxiliary detection model construction module: receives the first auxiliary adjustment feature transmitted by the synchronous condenser auxiliary adjustment feature processing module and the first control method of the synchronous condenser stored in the synchronous condenser control and management module, and constructs a synchronous condenser auxiliary detection model according to the first auxiliary adjustment feature and the first control method of the synchronous condenser;

[0028] The synchronous condenser control and management module: stores the first control method of the synchronous condenser, also receives the second auxiliary adjustment feature of the synchronous condenser auxiliary adjustment feature processing module, and invokes the synchronous condenser auxiliary detection model to process the second auxiliary adjustment feature to obtain the second control method of the synchronous condenser, and assists and controls the change of the working parameters of the synchronous condenser according to the second control method of the synchronous condenser.

[0029] Further, the synchronous condenser auxiliary detection model includes a classifier based on the Fisher criterion, a support vector machine improved based on the motor insulation detection parameters and the synchronous condenser aging parameters, and a neural network model.

[0030] First, when the motor insulation is detected online without power interruption, the present invention characteristically processes the synchronous condenser data, performs automatic continuous control and flexible adjustment on the synchronous condenser to assist in the detection. Secondly, a synchronous condenser auxiliary detection model for different motor insulation online power-off detection environments is set, and the big data intelligent control algorithm is fully utilized to perform precise control of the synchronous condenser, so as to realize the auxiliary motor insulation online power-off precise detection under the variable adjustment of the synchronous condenser parameters. Through the effective processing of the relevant parameter data of the synchronous condenser, and through the construction of a support vector machine improved based on the motor insulation detection parameters and the synchronous condenser aging parameters, on the basis of the traditional machine model, changes are made to improve the accuracy of the machine learning model in insulation monitoring, not only greatly reducing the manual detection and correction time, but also improving the accuracy of the motor insulation online monitoring.

[0031] More embodiments and improvement effects of the present invention will be further introduced in combination with the drawings and specific embodiments. Brief Description of the Drawings

[0032] Figure 1 is a flowchart of a method for auxiliary motor insulation online power-off detection by a synchronous condenser according to the present invention;

[0033] Figure 2 is a schematic diagram of a system for auxiliary motor insulation online power-off detection by a synchronous condenser according to the present invention;

[0034] Figure 3 is a schematic diagram of the distribution of synchronous condensers in the power grid according to the present invention;

[0035] Figure 4 is a schematic diagram of the principle of the support vector machine according to the present invention;

[0036] Figure 5 It is a schematic structural diagram of the electronic device according to an embodiment of the present invention. Specific embodiments

[0037] Next, in combination with the accompanying drawings and specific embodiments, the invention will be further described.

[0038] In the first aspect of the present invention, a method and system for on-line non-power-off detection of the insulation of a synchronous condenser-assisted motor are provided. The synchronous condensers in this embodiment are distributed in the power grid as Figure 3 shown.

[0039] In the first aspect of the present invention, a method for on-line non-power-off detection of the insulation of a synchronous condenser-assisted motor is provided. As shown in the attached Figure 1 drawing, the method includes:

[0040] S1. Obtain the first insulation detection parameter of the motor, the first machine room environment parameter and the first aging parameter of the synchronous condenser, and obtain the first control method of the corresponding synchronous condenser;

[0041] S2. Process the first insulation detection parameter of the motor, the first machine room environment parameter and the first aging parameter of the synchronous condenser to obtain a first auxiliary adjustment feature;

[0042] S3. Construct a synchronous condenser-assisted detection model according to the first auxiliary adjustment feature and the first control method of the corresponding synchronous condenser;

[0043] S4. Collect the second insulation detection parameter of the motor, the second machine room environment parameter and the second aging parameter of the synchronous condenser, and process them to obtain a second auxiliary adjustment feature;

[0044] S5. Input the obtained second auxiliary adjustment feature into the synchronous condenser-assisted detection model to obtain the second control method of the synchronous condenser. According to the second control method, assist and control the change of the working parameters of the synchronous condenser, assist in the on-line non-power-off detection of the motor insulation, and adjust the motor insulation detection parameters detected subsequently.

[0045] In this embodiment, corresponding calculations are performed according to the influence of the motor insulation parameters on the working parameters of the synchronous condenser, so as to perform model adaptability feature selection on the feature vectors input into the model.

[0046] Further, the first insulation detection parameter of the motor is obtained by processing the first insulation resistance, the first voltage, the first leakage current, the first dielectric loss factor of the motor, and the first temperature parameter at which the motor is located;

[0047] The second insulation detection parameter is obtained by processing the second insulation resistance, second voltage, second leakage current, second dielectric loss factor of the motor and the second temperature parameter at which the motor is located. The processing formula is:

[0048]

[0049] In the formula, P im is the first insulation detection parameter or the second insulation detection parameter of the motor, δ is the first dielectric loss factor or the second dielectric loss factor, which is less than 0.5, E m is the first temperature parameter or the second temperature parameter at which the motor is located, I c is the first leakage current or the second leakage current of the tiny current of the motor insulation layer, U is the first operating voltage or the second voltage of the motor, R i is the first insulation resistance or the second insulation resistance of the motor winding to the ground.

[0050] Further, the first machine room environment parameter of the synchronous condenser includes the first ambient temperature, first humidity and first wind speed of the machine room where the synchronous condenser is located;

[0051] The second machine room environment parameter includes the second ambient temperature, second humidity and second wind speed of the machine room where the synchronous condenser is located.

[0052] In this embodiment, corresponding calculations are performed according to the influence of the aging parameters of the synchronous condenser on the operating parameters of the synchronous condenser, so as to perform model adaptive feature selection on the feature vectors input into the model.

[0053] Further, the first aging parameter is obtained by processing the first maximum temperature data of the first rotor of the synchronous condenser, the first maximum vibration frequency at the end of the stator winding and the resistance value of the first insulation resistance of the synchronous condenser;

[0054] The second aging parameter is obtained by processing the second maximum temperature data of the second rotor of the synchronous condenser, the second maximum vibration frequency at the end of the stator winding and the resistance value of the second insulation resistance of the synchronous condenser.

[0055] The processing formula is:

[0056]

[0057] In the formula, P am is the first aging parameter or the second aging parameter of the synchronous condenser, R mt is the first maximum temperature data or the second maximum temperature data of the first rotor of the synchronous condenser, S mv is the first maximum vibration frequency or the second maximum vibration frequency at the end of the stator winding, R sc is the resistance value of the first insulation resistance or the second insulation resistance of the synchronous condenser.

[0058] Further, the synchronous condenser auxiliary detection model includes a classifier based on the Fisher criterion, a support vector machine improved based on motor insulation detection parameters and synchronous condenser aging parameters, and a neural network model.

[0059] Further, the calculation formula of the support vector machine improved based on motor insulation detection parameters and synchronous condenser aging parameters is as follows:

[0060]

[0061] where ω and b are the normal vector and intercept of the hyperplane respectively, S c is the first auxiliary adjustment feature or the second auxiliary adjustment feature, P am is the first aging parameter or the second aging parameter of the synchronous condenser, P im is the first insulation detection parameter or the second insulation detection parameter of the motor, P(S c ) is the first control method or the second control method of the synchronous condenser.

[0062] Further, S c is obtained by splicing the motor insulation detection parameters, the synchronous condenser machine room environment parameters and the synchronous condenser aging parameter vector. The splicing can be divided into horizontal splicing and vertical splicing. In the embodiment, the vector feature combination method of horizontal splicing is adopted.

[0063] There is also provided a synchronous condenser auxiliary motor insulation on-line power-off detection system, as shown in the appendix Figure 2 , including a motor insulation on-line monitoring module, a synchronous condenser ring sampling module, a synchronous condenser insulation aging module, a synchronous condenser auxiliary adjustment feature processing module, a synchronous condenser auxiliary detection model construction module and a synchronous condenser control management module, characterized in that:

[0064] The motor insulation on-line monitoring module: is used to collect the first insulation detection parameter and the second insulation detection parameter of the motor;

[0065] The synchronous condenser ring sampling module: is connected to the Internet and is used to collect the first machine room environment parameter and the second machine room environment parameter of the synchronous condenser machine room;

[0066] The synchronous condenser insulation aging module: is used to collect the first aging parameter and the second aging parameter of the synchronous condenser;

[0067] The synchronous condenser auxiliary adjustment feature processing module: receives the first insulation detection parameter of the motor, the first machine room environment parameter of the synchronous condenser and the first aging parameter to perform feature processing to obtain the first auxiliary adjustment feature, and is also used to receive the second insulation detection parameter of the motor, the second machine room environment parameter of the synchronous condenser and the second aging parameter to perform feature processing to obtain the second auxiliary adjustment feature;

[0068] The synchronous condenser auxiliary detection model construction module: receives the first auxiliary adjustment feature transmitted by the synchronous condenser auxiliary adjustment feature processing module and the first control method of the synchronous condenser stored in the synchronous condenser control and management module, and constructs a synchronous condenser auxiliary detection model according to the first auxiliary adjustment feature and the first control method of the synchronous condenser;

[0069] The synchronous condenser control and management module: stores the first control method of the synchronous condenser, also receives the second auxiliary adjustment feature of the synchronous condenser auxiliary adjustment feature processing module, and calls the synchronous condenser auxiliary detection model to process the second auxiliary adjustment feature to obtain the second control method of the synchronous condenser, and assists and controls the change of the working parameters of the synchronous condenser according to the second control method of the synchronous condenser.

[0070] Further, the synchronous condenser auxiliary detection model includes a classifier based on the Fisher criterion, a support vector machine improved based on the motor insulation detection parameters and the synchronous condenser aging parameters, and a neural network model. In this embodiment, the model principle of the support vector machine is referred to in the appendix Figure 4 as shown.

[0071] Further, the calculation formula of the support vector machine improved based on the motor insulation detection parameters and the synchronous condenser aging parameters is as follows:

[0072]

[0073] where ω and b are the normal vector and intercept of the hyperplane respectively, S c is the first auxiliary adjustment feature or the second auxiliary adjustment feature, P am is the first aging parameter or the second aging parameter of the synchronous condenser, P im is the first insulation detection parameter or the second insulation detection parameter of the motor, P(S c ) is the first control method or the second control method of the synchronous condenser.

[0074] In this embodiment, the control method of the synchronous condenser is set according to the value of P(S c ). Among them, the setting of the synchronous condenser parameters in the synchronous condenser control method includes the speed, excitation current and power factor of the synchronous condenser. If the value of P(S c ) is less than 0, the speed of the synchronous condenser is set to 3000 revolutions, the excitation current is 300 A, and the power factor is 0.89. If the value of P(S c ) is equal to 0 or greater than 0, the speed, excitation current and power factor of the synchronous condenser are set by those skilled in the art, which will not be elaborated here.

[0075] First, when the present invention conducts on-line power-off detection of the motor insulation, it characteristically processes the data of the synchronous condenser, and conducts automated continuous control and flexible adjustment on the synchronous condenser to assist the detection. Secondly, it sets up a synchronous condenser-assisted detection model for different on-line power-off detection environments of the motor insulation, and makes full use of the big data intelligent control algorithm to precisely control the synchronous condenser, so as to realize the accurate detection of the on-line power-off of the auxiliary motor insulation under the variable adjustment of the synchronous condenser parameters. Through the effective processing of the relevant parameter data of the synchronous condenser, and through the construction of a support vector machine improved based on the motor insulation detection parameters and the synchronous condenser aging parameters, on the basis of the traditional machine model, changes are made to improve the accuracy of the machine learning model in insulation monitoring, not only greatly reducing the manual detection and correction time, but also improving the accuracy of the on-line monitoring of the motor insulation.

[0076] Of course, it can be understood that each embodiment of the present invention can achieve one of the effects alone, and the combination of multiple embodiments of the present invention can achieve all the above effects. However, it is not required that each embodiment of the present invention achieve all the above advantages and effects, because each embodiment of the present invention can constitute an independent technical solution and make one or more contributions to the prior art.

[0077] For the part of the module structure not specifically defined in the present invention, it shall be subject to the content recorded in the prior art. The prior art mentioned in the foregoing background art part and the specific embodiment part of the present invention can be used as a part of the present invention to understand the meaning of some technical features or parameters. The protection scope of the present invention shall be subject to the content actually recorded in the claims.

Claims

1. A method for detecting the insulation of a phase-converting auxiliary motor without power outage, characterized in that: The method comprises: S1. Obtaining a first insulation detection parameter of the motor, a first machine room environment parameter and a first aging parameter of the phase shifter, and obtaining a first control method of the phase shifter; S2. The first insulation detection parameter of the motor, the first machine room environment parameter of the phase regulator and the first aging parameter are processed to obtain a first auxiliary adjustment feature; S3. Constructing a phase tunner auxiliary detection model according to the first auxiliary adjustment feature and the first control method corresponding to the phase tunner; S4. Collect the second insulation detection parameters of the motor, the second machine room environment parameters of the phase regulator and the second aging parameters, and process them to obtain the second auxiliary adjustment characteristics; S5. Input the processed second auxiliary adjustment feature into the phase modulator auxiliary detection model to obtain the second control method of the phase modulator, assist and control the change of the phase modulator working parameters according to the second control method, and assist the online non-stop detection of motor insulation by adjusting the phase modulator working parameters.

2. The method for detecting the insulation of a phase-converter auxiliary motor without power outage as claimed in claim 1, characterized in that: The first insulation detection parameter is obtained by processing the first insulation resistance, the first voltage, the first leakage current, the first dielectric loss factor and the first temperature parameter of the motor; The second insulation detection parameter is obtained by processing a second insulation resistance, a second voltage, a second leakage current, a second dielectric loss factor and a second temperature parameter of the motor.

3. A method for detecting the insulation of a phase-converting auxiliary motor without power outage as claimed in claim 2, characterized in that: The first machine room environmental parameters include a first ambient temperature, a first humidity and a first wind speed of the machine room where the phase regulator is located; The second machine room environmental parameter comprises a second ambient temperature, a second humidity and a second wind speed of the machine room where the phase regulator is located.

4. The method for detecting the insulation of a phase-converter auxiliary motor without power outage as claimed in claim 1, characterized in that: The first aging parameter is obtained by processing the maximum temperature data of the first rotor of the phase regulator, the first maximum vibration frequency of the stator winding end and the first insulation resistance value of the phase regulator; The second aging parameter is obtained by processing the second maximum temperature data of the phase regulator rotor, the second maximum vibration frequency of the stator winding end and the second insulation resistance value of the phase regulator.

5. A method for detecting the insulation of a phase-converting auxiliary motor without power outage as claimed in claim 4, characterized in that: The phase modulator auxiliary detection model includes a classifier based on Fisher criterion, a support vector machine based on the improvement of motor insulation detection parameters and phase modulator aging parameters, and a neural network model.

6. A method for detecting the insulation of a phase-converter auxiliary motor without power outage as claimed in claim 5, characterized in that: The first auxiliary adjustment feature is obtained by splicing the first insulation detection parameter of the motor, the first machine room environment parameter of the phase regulator, and the first aging parameter vector, and the second auxiliary adjustment feature is obtained by splicing the second insulation detection parameter of the motor, the second machine room environment parameter of the phase regulator, and the second aging parameter vector.

7. A phase-converter auxiliary motor insulation online non-stop detection system, comprising a motor insulation online monitoring module, a phase-converter environmental sampling module, a phase-converter insulation aging module, a phase-converter auxiliary adjustment feature processing module, a phase-converter auxiliary detection model building module and a phase-converter control management module, characterized in that: The motor insulation online monitoring module is used to collect the first insulation detection parameter and the second insulation detection parameter of the motor; The phase condenser environmental acquisition module is connected to the Internet to collect the first and second environment parameters of the phase condenser room; The phase regulator insulation aging module is used to collect the first aging parameter and the second aging parameter of the phase regulator; The phase regulator auxiliary adjustment feature processing module is used for receiving the first insulation detection parameter of the motor, the first machine room environment parameter of the phase regulator, and the first aging parameter feature processing to obtain a first auxiliary adjustment feature, and is also used for receiving the second insulation detection parameter of the motor, the second machine room environment parameter of the phase regulator, and the second aging parameter feature processing to obtain a second auxiliary adjustment feature; The phase tunner auxiliary detection model construction module receives the first auxiliary adjustment feature transmitted by the phase tunner auxiliary adjustment feature processing module and the first control method of the phase tunner stored in the phase tunner control management module, and constructs the phase tunner auxiliary detection model according to the first auxiliary adjustment feature and the first control method of the phase tunner; The phase modulator control management module: stores the first control method of the phase modulator, also receives the second auxiliary adjustment feature of the phase modulator auxiliary adjustment feature processing module, and calls the phase modulator auxiliary detection model to process the second auxiliary adjustment feature to obtain the second control method of the phase modulator, and assists and controls the change of the phase modulator working parameters according to the second control method of the phase modulator.

8. The phase-converter auxiliary motor insulation online non-stop detection system as claimed in claim 7, characterized in that: The phase modulator auxiliary detection model includes a classifier based on Fisher criterion, a support vector machine based on the improvement of motor insulation detection parameters and phase modulator aging parameters, and a neural network model.

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