A composite insulator state prediction method based on improved physical model of surface resistance and leakage current

Abstract

The present invention provides a composite insulator state prediction method based on a surface resistance-leakage current improved physical model, and relates to the field of power equipment monitoring and fault diagnosis. The present invention establishes an equivalent circuit model, takes into account dry-band discharge and wet contamination layer resistance, and reflects the state of the insulator in actual operation; estimates model parameters based on the measured leakage current waveform, obtains surface resistance estimation values, and can effectively evaluate the aging and contamination degree of the insulator; according to the leakage current monitoring and surface resistance estimation in different time periods, combined with the service life of the insulator, the surface resistance change trend is judged, and then according to the surface resistance change trend, the state of the composite insulator is divided into three categories: normal, warning and critical, and the future insulator operation state is warned; this method uses the leakage current waveform under the nominal voltage for analysis, is suitable for online state prediction, can correctly diagnose the state of the insulator, and provide protection for the safe operation of the power system.

Description

Technical Field

[0001] The invention relates to the field of power equipment monitoring and fault diagnosis, and in particular to a composite insulator state prediction method based on a surface resistance-leakage current improved physical model. Background Art

[0002] With the development of power systems, composite insulators are increasingly used, especially in areas with severe pollution. However, composite insulators are affected by environmental factors such as ultraviolet radiation, heat, humidity and pollution during operation, which can lead to performance degradation, aging and pollution. These problems may cause flashover accidents of insulators, posing a serious threat to the safe operation of power systems.

[0003] At present, the methods used for composite insulator condition monitoring mainly include the following methods:

[0004] (1) Leakage current method

[0005] The status of the insulator is analyzed by measuring the effective value and harmonic components of the leakage current. This method is significantly affected by environmental conditions in practical applications. For example, fluctuations in humidity and contamination levels can cause changes in leakage current characteristics, making it difficult to define a unified alarm threshold. In addition, the complexity of the leakage current signal and the randomness of the discharge process increase the difficulty of diagnosis.

[0006] (2) Ultraviolet / infrared imaging method

[0007] The ultraviolet discharge pulse or surface temperature of the insulator is monitored in a long-distance non-contact manner. These imaging methods can provide certain diagnostic information, but they are too sensitive to the external environment (such as light intensity and temperature changes), and the reliability of the monitoring results is low. In addition, due to data processing and equipment limitations, their real-time online monitoring capabilities are insufficient.

[0008] (3) Electric field distribution method

[0009] Online detection of electric field distribution requires high-precision electric field measurement sensors that are resistant to strong electromagnetic interference, and must also ensure safe compatibility with high-voltage equipment on the transmission line, which is technically difficult to achieve. In addition, when conducting electric field distribution detection, electric field measurement points need to be arranged at multiple locations on the insulator, and the measured electric field data needs to be analyzed and processed in a complex manner, which places high technical requirements on the detection personnel and the detection process takes a long time.

[0010] (4) Ultrasonic method

[0011] Due to the problems of coupling, attenuation and ultrasonic transducer performance, there has not been a major breakthrough in long-distance telemetry, and it is not suitable for on-site detection. It is mainly used for online detection in enterprise production and laboratory identification.

[0012] (5) Microwave method

[0013] Interference in the operating environment of the transmission line will seriously affect the accuracy of the microwave detection signal. It is also a technical challenge to effectively couple the microwave detection equipment with the insulator in operation without power outage. Microwave detection equipment is relatively complex and requires professional operators to debug and operate. At the same time, composite insulators of different types and structures have different response characteristics to microwaves, and it is necessary to establish complex signal analysis models for different insulators.

[0014] The above methods all have limitations to varying degrees, especially in complex environmental conditions, where they exhibit problems such as poor real-time performance, insufficient accuracy, and limited scope of application. Summary of the invention

[0015] Purpose of the invention: A composite insulator state prediction method based on the surface resistance-leakage current improved physical model is proposed, which analyzes the surface resistance through the leakage current signal, thus overcoming the shortcomings of the traditional method. This method not only improves the efficiency of online monitoring of the composite insulator state, but also reduces the judgment error caused by subjective human judgment, thereby effectively solving the above-mentioned problems existing in the prior art.

[0016] The present invention proposes a composite insulator state prediction method based on a surface resistance-leakage current improved physical model, comprising the following steps:

[0017] Collect the operation data of composite insulators, including aging time T, measured leakage current , operating voltage V s ;

[0018] Establishing an equivalent circuit model of composite insulators under the influence of field pollution; the equivalent circuit model is composed of a dry strip discharge model and a wet pollution resistance in series;

[0019] Based on the equivalent circuit model, a group of ordinary differential equations is constructed to obtain a surface resistance-leakage current improved physical model, and the leakage current is calculated using the output of the surface resistance-leakage current improved physical model ;

[0020] To minimize the measured leakage current Calculate the leakage current with The error establishes the objective function and uses the genetic algorithm to calculate the wet contamination resistance value R p ;

[0021] Calculate the wet contamination resistance R p The normalized value of , according to the normalized value The status categories of composite insulators are divided into normal, critical and warning;

[0022] Establish normalized values ​​within each scheduled monitoring period A fitting formula for the aging time T is used to deduce the predicted value of the surface resistance of the composite insulator changing with time;

[0023] The composite insulator state prediction result is given in advance according to the surface resistance prediction value.

[0024] In a further embodiment, the equivalent circuit model simulates the field operating environment of the composite insulator affected by pollution, and uses a dry-band discharge model to simulate the discharge event of the composite insulator in a humid air environment, wherein the dry-band discharge model includes a parallel discharge capacitor C d and the discharge resistor R d ; Use wet contamination resistance to simulate damp composite insulators.

[0025] In a further embodiment, the discharge resistor R d Based on the Cassie black box arc model, the differential equation of the Cassie black box arc model is as follows:

[0026]

[0027] Where g represents arc conductance; represents the time constant of the arc; u represents the arc voltage; Represents the characteristic voltage of the arc.

[0028] In a further embodiment, Kirchhoff's current law is applied at the node between the dry strip discharge model and the wet contamination resistor in the equivalent circuit model:

[0029]

[0030] In the formula, It represents the voltage value applied in the equivalent circuit; represents the wet contamination resistance value; g represents the arc conductivity; u represents the arc voltage.

[0031] In a further embodiment, the improved physical model of surface resistance-leakage current is obtained in the following manner: combining the differential equations of the Cassie black box arc model and Kirchhoff's current law to obtain a set of ordinary differential equations in the form of Ẋ=f(X):

[0032]

[0033]

[0034] Solve the ordinary differential equations to obtain the discharge voltage at time t , and then calculate the leakage current :

[0035]

[0036] In the formula, Represents the value of the applied voltage as a function of time in the equivalent circuit.

[0037] In a further embodiment, to minimize the measured leakage current Calculate the leakage current with The error establishes the objective function OF:

[0038]

[0039] Where N is the number of samples of the leakage current waveform; is the i-th leakage current sample tested; is the i-th leakage current sample generated by the equivalent circuit model.

[0040] In a further embodiment, based on the contamination resistance value R p , calculate its normalized value , where L represents the leakage distance;

[0041] According to the normalized value , determine a first boundary value and a second boundary value;

[0042] when > the second boundary value, the composite insulator is in a normal state;

[0043] When the first boundary value ≤ When ≤ the second boundary value, the composite insulator is in a warning state;

[0044] when When <the first boundary value, the composite insulator is in a critical state.

[0045] In a further embodiment, a normalized value is established within each predetermined monitoring period. The fitting formula with aging time T is as follows:

[0046]

[0047] Where, T is the aging time; a and b are constants obtained by curve fitting.

[0048] The present invention also proposes an electronic device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the composite insulator state prediction method based on the surface resistance-leakage current improved physical model is implemented.

[0049] The present invention also proposes a computer-readable storage medium, which stores at least one executable instruction. When the executable instruction is executed on an electronic device, the electronic device executes the composite insulator state prediction method based on the surface resistance-leakage current improved physical model.

[0050] Compared with the prior art, the present invention has at least the following beneficial effects:

[0051] The surface resistance of composite insulators is closely related to the contamination, moisture and aging of the insulator surface. The insulator state can be intuitively judged through measurement. It has a wide range of applications and is applicable to insulators of different types and operating environments. On this basis, an improved equivalent circuit model is used to consider the influence of dry-belt discharge, making the surface resistance estimation more accurate and able to reflect the comprehensive state of aging and contamination of the insulator surface.

[0052] In addition, based on the leakage current waveform characteristics, the present invention can realize online monitoring without additional equipment, reducing system complexity and cost. Through the classification boundary value, a clear classification of the insulator state is achieved, which helps to warn of possible flashover faults in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is the overall flow chart of the present invention.

[0054] Figure 2 Schematic diagram of the leakage current-surface resistance equivalent circuit model.

[0055] Figure 3 Schematic diagram of leakage current data in the embodiment.

[0056] Figure 4 Flowchart for estimating unknown parameters of equivalent circuit model by genetic algorithm.

[0057] Figure 5 It is the fitting curve of surface resistance and aging time in a certain period in the embodiment. DETAILED DESCRIPTION

[0058] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

[0059] Surface resistance is considered to be an effective parameter for evaluating the state of composite insulators. It has many significant advantages. It can directly reflect the surface state of the insulator. The surface resistance is closely related to the contamination, moisture and aging of the insulator surface. Through measurement, this information can be intuitively obtained to evaluate the insulation performance. Its physical meaning is clear and easy to understand, which is convenient for engineers to judge the state of the insulator based on the resistance value. It has a wide range of applications and can be applied to insulators of different types and operating environments. However, the existing methods need to obtain surface resistance data through low-voltage testing under laboratory conditions, and cannot be directly applied to online monitoring in actual operation. In addition, these methods usually ignore the impact of dry-belt discharge on surface resistance, resulting in incomplete diagnostic results.

[0060] To this end, the present invention proposes a new equivalent circuit model, which includes a dry strip discharge model in series with a wet contamination resistor; based on the leakage current signal, the surface resistance of the insulator is estimated and used as a state evaluation indicator; using the classification boundary value, the state of the composite insulator is divided into three categories: normal, warning and critical. In addition, based on the equivalent circuit model, the present invention also discloses a composite insulator state prediction method based on a surface resistance-leakage current improved physical model, which analyzes the surface resistance through the leakage current signal, thus overcoming the shortcomings of the traditional method. This method not only improves the efficiency of online monitoring of the composite insulator state, but also reduces the judgment error caused by subjective human judgment.

[0061] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0062] like Figure 1 As shown, the present invention proposes a composite insulator state prediction method based on a surface resistance-leakage current improved physical model, the steps are as follows:

[0063] Step 1: Collect the aging time T, leakage current data I(t), and operating voltage V of the composite insulator in operation s .

[0064] Step 2: Establish leakage current-surface resistance equivalent circuit model (see Figure 2 ), by solving the differential equations containing the equivalent circuit model, the surface resistance-leakage current improved physical model is obtained, and the leakage current is calculated using the surface resistance-leakage current improved physical model Expression (the u(t) term in the expression contains unknown parameters such as the arc time constant):

[0065] The leakage current-surface resistance equivalent circuit model takes into account the dry-band discharge on the insulator surface. The equivalent circuit model is composed of the dry-band discharge and the wet contamination resistance R pThe dry belt discharge model includes the discharge capacitor C d and the discharge resistor R d , the surface resistance and dry-band discharge intensity are affected by factors such as insulator aging and on-site pollution level.

[0066] Discharge resistor R d Modeling based on the Cassie arc model. The differential equation of the Cassie model is given in Equation 1.

[0067]

[0068] Where g is the arc conductance, u is the arc voltage, τ and u 0 are the time constant and characteristic voltage of the arc respectively.

[0069] Figure 2 Medium insulator surface wet contamination resistance R p Kirchhoff's current law is applied at node A between the dry belt discharge model, as shown in equation 2:

[0070]

[0071] Equation 2 and Equation 1 form a system of ordinary differential equations of the form Ẋ=f(X), namely:

[0072]

[0073]

[0074] The ordinary differential equations are solved numerically to calculate the discharge voltage u(t), and then the leakage current is calculated. :

[0075]

[0076] Step 3: Minimize the leakage current through genetic algorithm The difference between the measured leakage current I(t) and the model unknown parameters, including surface resistance The objective function is as follows:

[0077]

[0078] N is the number of samples of the leakage current waveform; is the i-th leakage current sample tested; is the i-th leakage current sample generated by the equivalent circuit model.

[0079] Step 4: In order to reduce the cost and computing resources of continuous monitoring, the present invention proposes to adopt the method of intermittent monitoring of leakage current. A fitting formula of surface resistance and aging time T can be established in each monitoring cycle as follows.

[0080]

[0081] Formula 7 gives the normalized surface resistance (R p / leakage distance) changes with the operating time of the insulator.

[0082] At this time, the surface resistance can be obtained only by the above fitting formula without monitoring the current. However, considering that changes in conditions such as surface contamination of the insulator will affect the model parameters over time, steps 1 to 4 are repeated at regular intervals to obtain a new fitting formula.

[0083] Step 5: Evaluate the operating status based on the solved normalized surface resistance.

[0084] Specifically, according to the estimated normalized surface resistance The states of composite insulators are classified into three categories: normal, warning and critical. The two boundary values ​​of the three states are A and B kΩ / cm. When the surface resistance is higher than B kΩ / cm, the insulator is in a normal state, at which the dry-band discharge intensity is low or zero, and there is no flashover risk. When the surface resistance is between AB kΩ / cm, the insulator is in a warning state, at which the dry-band discharge activity is obvious. When the surface resistance is lower than A kΩ / cm, the insulator is in a critical state, at which the insulator is close to flashover and the dry-band discharge is strong.

[0085] The present embodiment is described below by substituting specific scenario data.

[0086] First, collect the aging time T=803 / 365 (years) and leakage current data I(t) of the running composite insulator. Figure 3 As shown, the operating voltage Vs=110kV.

[0087] Then, an improved physical model of leakage current-surface resistance is established. By solving the differential equations containing the improved physical model, the calculated leakage current data is obtained. Expression (the u(t) term in the expression contains unknown parameters such as the arc time constant):

[0088]

[0089] Based on the measured leakage current data I(t) and the calculated leakage current data ; Minimize the leakage current by genetic algorithm The difference between the measured leakage current I(t) and the model unknown parameters are estimated, including surface resistance, discharge capacitance, discharge resistance, arc characteristic voltage and arc time constant. The complete parameter estimation algorithm is as follows Figure 4 shown.

[0090] The stopping criterion of the algorithm is met if the number of generations approaches the maximum value or the relative change in the objective function value is less than a given tolerance. Figure 3 The leakage current shown is input, and the normalized surface resistance is .

[0091] Step 4: Based on the above data and Figure 5 The monitoring results shown in (the present invention recommends that the leakage current be sampled at intervals of one day, and the maximum amplitude of the leakage current in one day is taken as the modeling data. The air humidity is often the highest in the morning when condensation occurs. At this time, the leakage current is the largest and most representative), and a fitting formula for the surface resistance and the aging time T in this period is established.

[0092] Since the degradation of composite insulators is a chronic process, we can take 36 days as a cycle to calculate the average value of the surface resistance. Then, based on the surface resistance values ​​of four cycles, we can fit the surface resistance change trend prediction curve. Figure 5 The calculation formula of the prediction curve obtained by fitting in the example is shown in (9):

[0093]

[0094] At this time, the surface resistance is obtained only through the above fitting formula, and the surface state of the composite insulator can be predicted without monitoring the current. For example, when T=949 / 365 years, the normalized surface resistance can be obtained as ; When T=1277 / 365 years, the normalized surface resistance is .

[0095] Then, according to the estimated normalized surface resistance The states of composite insulators are classified. The insulator states are divided into three categories: normal, warning and critical. Two boundary values ​​of the three states are proposed, which are 50 and 200 kΩ / cm. When the surface resistance is higher than 200 kΩ / cm, the insulator is in a normal state, at which the dry-band discharge intensity is low or zero, and there is no flashover risk. When the surface resistance is between 50-200 kΩ / cm, the insulator is in a warning state, at which the dry-band discharge activity is obvious. When the surface resistance is lower than 50 kΩ / cm, the insulator is in a critical state, at which the insulator is close to flashover and the dry-band discharge is strong.

[0096] Therefore, in this case, when the aging time is 949 days, , the insulator is in warning state. When the aging time is 1277 days, , the insulator is in a critical (dangerous) state. At this time, it is necessary to instruct the operation and maintenance personnel to check and handle it on site.

[0097] In summary, the present invention provides a composite insulator state prediction method based on the surface resistance-leakage current improved physical model. By establishing an equivalent circuit model and considering dry strip discharge and wet contamination resistance, the state of the insulator in actual operation can be more accurately reflected; the model parameters are estimated based on the measured leakage current waveform, and then the estimated value of the surface resistance is obtained, which can effectively evaluate the aging and contamination degree of the insulator; according to the estimated value of the surface resistance, two boundary values ​​of 50 and 200 kΩ / cm are proposed, and the state of the composite insulator is divided into three categories: normal, warning and critical; the logarithmic relationship equation between the flashover voltage and the normalized surface resistance is established, and the flashover voltage can be further estimated according to the estimated surface resistance; the method uses the leakage current waveform under the nominal voltage for analysis, does not require low voltage testing, and is suitable for online monitoring. The method can correctly diagnose the state of the insulator, guide the on-site refined operation and maintenance, and provide guarantee for the safe operation of the power system.

[0098] The technical process of the composite insulator state prediction method based on the surface resistance-leakage current improved physical model disclosed in the above embodiment can be implemented in whole or in part through software, hardware, firmware or any other combination.

[0099] When implemented in hardware, the above embodiments can run all or part of the working logic and calculation process on an electronic device after being compiled by software. The electronic device includes a processor, a memory, a communication interface and a communication bus. The processor, the memory and the communication interface communicate with each other through the communication bus. The memory is used to store at least one executable instruction, which enables the processor to execute the technical process of the composite insulator state prediction method based on the surface resistance-leakage current improved physical model disclosed in the above embodiments,

[0100] When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. If the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific hardware, software or firmware, or any combination of hardware, software, and firmware.

[0101] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to it in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A composite insulator state prediction method based on surface resistance-leakage current improved physical model, characterized in that: include: Collect the operation data of composite insulators, including aging time T, measured leakage current , operating voltage V s ; Establishing an equivalent circuit model of composite insulators under the influence of field pollution; the equivalent circuit model is composed of a dry strip discharge model and a wet pollution resistance in series; Based on the equivalent circuit model, a group of ordinary differential equations is constructed to obtain a surface resistance-leakage current improved physical model, and the leakage current is calculated using the output of the surface resistance-leakage current improved physical model ; To minimize the measured leakage current Calculate the leakage current with The error establishes the objective function and uses the genetic algorithm to calculate the wet contamination resistance value R p ; Calculate the wet contamination resistance R p The normalized value of , i.e. normalized surface resistance, according to which the state categories of composite insulators are divided into normal, critical and warning; A fitting formula of normalized surface resistance and aging time T is established in each predetermined monitoring cycle, and a predicted value of surface resistance of the composite insulator varying with time is deduced according to the fitting formula; The composite insulator state prediction result is given in advance according to the surface resistance prediction value.

2. A composite insulator state prediction method based on surface resistance-leakage current improved physical model according to claim 1, characterized in that: The equivalent circuit model simulates the on-site operating environment of the composite insulator affected by pollution, and uses a dry-belt discharge model to simulate the discharge event of the composite insulator in a humid air environment, wherein the dry-belt discharge model includes a parallel discharge capacitor C d and the discharge resistor R d ; Wet contamination resistance is used to simulate damp composite insulators.

3. The composite insulator state prediction method based on the surface resistance-leakage current improved physical model according to claim 2 is characterized in that: The discharge resistor R d Based on the Cassie black box arc model, the differential equation of the Cassie black box arc model is as follows: Where g represents arc conductance; represents the time constant of the arc; u represents the arc voltage; Represents the characteristic voltage of the arc.

4. The composite insulator state prediction method based on the surface resistance-leakage current improved physical model according to claim 2 is characterized in that: In the equivalent circuit model, Kirchhoff's current law is applied at the node between the dry strip discharge model and the wet contamination resistor: In the formula, It represents the voltage value applied in the equivalent circuit; represents the wet contamination resistance value; g represents the arc conductivity; u represents the arc voltage.

5. The composite insulator state prediction method based on the surface resistance-leakage current improved physical model according to claim 4, characterized in that: Combining the differential equation of the Cassie black box arc model with Kirchhoff's current law, we get the form The ordinary differential equations of : Solve the ordinary differential equations to obtain the discharge voltage at time t , and then calculate the leakage current : In the formula, Represents the value of the applied voltage as a function of time in the equivalent circuit.

6. A composite insulator state prediction method based on surface resistance-leakage current improved physical model according to claim 1 or 5, characterized in that: To minimize the measured leakage current Calculate the leakage current with The error establishes the objective function OF: Where N is the number of samples of the leakage current waveform; is the i-th leakage current sample tested; is the i-th leakage current sample generated by the equivalent circuit model.

7. The composite insulator state prediction method based on the surface resistance-leakage current improved physical model according to claim 1 is characterized in that: Based on the contamination resistance value R p , calculate its normalized value , where L represents the leakage distance; According to the normalized value , determine a first boundary value and a second boundary value; when > the second boundary value, the composite insulator is in a normal state; When the first boundary value ≤ When ≤ the second boundary value, the composite insulator is in a warning state; when When <the first boundary value, the composite insulator is in a critical state.

8. The composite insulator state prediction method based on the surface resistance-leakage current improved physical model according to claim 1, characterized in that: Establish normalized values ​​within each scheduled monitoring period That is, the fitting formula of normalized surface resistance and aging time T is as follows: Where T is the aging time; a and b are constants obtained by curve fitting.

9. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the composite insulator state prediction method based on the surface resistance-leakage current improved physical model as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one executable instruction, and when the executable instruction is executed on an electronic device, the electronic device executes the composite insulator state prediction method based on the surface resistance-leakage current improved physical model as described in any one of claims 1 to 8.

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