Hydro-generator shutdown diagnosis method and device based on ultrahigh frequency partial discharge monitoring

Abstract

The invention provides a hydro-generator shutdown diagnosis method and device based on ultrahigh frequency partial discharge monitoring, and relates to the technical field of partial discharge monitoring. The method comprises the following steps: acquiring an ultrahigh frequency partial discharge signal collected from a hydro-generator in a current monitoring period; discretizing the ultrahigh frequency partial discharge signal to obtain a partial discharge pulse sequence; judging whether the amplitudes of partial discharge pulse sequences of a plurality of sub-periods are greater than all maximum partial discharge amplitudes in a threshold group in the current monitoring period or not; if yes, a diagnosis result that the hydraulic generator needs to be shut down is generated, and otherwise, the next step is executed; and performing clustering analysis on the partial discharge pulse sequence in the current monitoring period to identify whether a typical partial discharge event occurs in the current monitoring period, if so, generating a diagnosis result that the hydraulic generator needs to be shut down, and otherwise, entering the next monitoring period. According to the method, rapid and accurate hydro-generator shutdown diagnosis is realized.

Description

Technical Field

[0001] The present application belongs to the technical field of partial discharge monitoring, and specifically relates to a method for diagnosing a shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring, a device for diagnosing a shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring, a computer device, and a machine-readable storage medium. Background Art

[0002] As the core equipment of hydropower stations, the health of the insulation system of hydropower generators is directly related to the safety and stability of the entire power system. With the continuous expansion of hydropower installed capacity, higher requirements are put forward for the monitoring and evaluation of the insulation status of hydropower generators. In actual production, the detection of partial discharge (PD) signals is an important means to reflect the insulation status of large-scale power equipment. The mainstream PD test equipment in the market mainly adopts low-frequency PD test technology with a frequency range of 10kHz~500kHz. When applied to hydropower generators, the monitored test data fluctuates greatly due to the influence of interference signals during the operation stage of the generator, resulting in difficulties in identifying PD patterns. In order to overcome the above-mentioned defects of low-frequency PD test technology, many scholars have carried out research on ultra-high frequency PD test technology for hydropower generators. The frequency range of ultra-high frequency PD is 300MHz~3GHz, and existing research has shown that compared with low-frequency PD test technology, ultra-high frequency PD test technology can avoid some external interference, and the stability of test results is better than that of low-frequency PD test technology.

[0003] In the actual production process of hydro-turbine generators, the following three factors must be considered in the handling of partial discharge events: the first aspect is the determination of the partial discharge level and severity, which is related to whether the power plant needs to shut down to avoid serious damage to the equipment; the second aspect is the location of the partial discharge source. Accurately determining the location of the partial discharge is conducive to the formulation of subsequent repair strategies; the third aspect is the processing time of the partial discharge event. The processing time of the partial discharge event is related to the power plant's operating plan and resource income, and is a very important economic consideration.

[0004] Taking all the above factors into consideration, once a partial discharge event occurs, it is necessary to quickly assess the severity of the partial discharge and its impact on the normal operation of the generator to determine whether it needs to be shut down for processing. Once shut down, it will affect the power supply and may cause great economic losses, so it is necessary to weigh the cost of shutdown and the necessity of equipment maintenance. If you encounter peak power supply periods, you also need to consider whether there is enough backup power generation capacity to make up for the power supply gap during the shutdown period. If the severity of the partial discharge event is not accurately judged, it will lead to failure to shut down in time, which will cause huge losses.

[0005] In summary, it is very important to quickly and accurately assess the severity of partial discharge. Summary of the invention

[0006] The purpose of the embodiments of the present application is to provide a method for diagnosing shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring, a device for diagnosing shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring, a computer device and a machine-readable storage medium, so as to realize fast and accurate shutdown diagnosis of a hydro-turbine generator.

[0007] To achieve the above-mentioned purpose, the first aspect of the present application provides a shutdown diagnosis method of a hydro-generator based on ultra-high frequency partial discharge monitoring, comprising: Acquire multiple ultra-high frequency partial discharge signals collected from the hydro-generator during the current monitoring period; Discretizing the ultra-high frequency partial discharge signal to obtain a partial discharge pulse sequence; Determine whether there are multiple sub-periods of partial discharge pulse sequences with amplitudes greater than all maximum partial discharge amplitudes in the threshold group within the current monitoring period; if so, generate a diagnosis result that the hydro-generator needs to be shut down, otherwise execute the next step, wherein the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library, and the threshold group is composed of each maximum partial discharge amplitude corresponding to each of the typical partial discharge events; A cluster analysis is performed on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnostic result indicating that the turbine generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

[0008] In a specific embodiment of the present application, cluster analysis is performed on the partial discharge pulse sequence in the current monitoring period to identify whether a typical partial discharge event has occurred in the current monitoring period, including: Convert each partial discharge pulse sequence in the current monitoring period into the frequency domain, and obtain each partial discharge spectrum in one-to-one correspondence; For any partial discharge spectrum, extract a low power ratio and a high power ratio of the partial discharge spectrum, and form sample points by the low power ratio and the high power ratio; Perform spectral clustering analysis on each sample point to obtain the center of each cluster; For any cluster center, if the difference between the amplitude of the partial discharge pulse sequence corresponding to the cluster center and the maximum partial discharge amplitude of a typical partial discharge event in the partial discharge case library is within a first preset range, it is determined that the typical partial discharge event has occurred in the current monitoring period; Among them, the low power ratio represents the ratio of the sum of the square of the modulus values ​​of each frequency point in the partial discharge spectrum within a preset low frequency interval to the sum of the square of the modulus values ​​of all frequency points in the preset frequency range in the partial discharge spectrum, and the high power ratio represents the ratio of the sum of the square of the modulus values ​​of each frequency point in the preset high frequency interval of the partial discharge spectrum to the sum of the square of the modulus values ​​of all frequency points in the preset frequency range in the partial discharge spectrum. The preset low frequency interval and the preset high frequency interval are both within the preset frequency range, the lower boundary of the preset frequency range is not lower than the minimum frequency in the partial discharge spectrum, and the upper boundary of the preset frequency range is not higher than the maximum frequency in the partial discharge spectrum.

[0009] In a specific embodiment of the present application, the partial discharge case library includes at least one of a first type of typical partial discharge event, a second type of typical partial discharge event, a third type of typical partial discharge event, a fourth type of typical partial discharge event, a fifth type of typical partial discharge event, a sixth type of typical partial discharge event, a seventh type of typical partial discharge event, and an eighth type of typical partial discharge event; Among them, the first type of typical partial discharge event is a partial discharge event generated when the humidity of the environment in which the hydro-turbine generator is located is greater than a first threshold value; the second type of typical partial discharge event is a partial discharge event generated when the temperature of the environment in which the hydro-turbine generator is located is greater than a second threshold value; the third type of typical partial discharge event is a partial discharge event generated when the electromagnetic interference intensity of the environment in which the hydro-turbine generator is located is greater than a third threshold value; the fourth type of typical partial discharge event is a partial discharge event generated after aging of the insulating material in the hydro-turbine generator; the fifth type of typical partial discharge event is a partial discharge event generated by the hydro-turbine generator under mechanical stress; the sixth type of typical partial discharge event is a partial discharge event generated after an insulation defect occurs in the hydro-turbine generator; the seventh type of typical partial discharge event is a partial discharge event generated when the hydro-turbine generator is extremely controlled; the eighth type of typical partial discharge event is a partial discharge event generated when the hydro-turbine generator is under a composite fault condition, and the composite fault condition includes more than one of the following: the humidity of the environment in which the hydro-turbine generator is located is greater than the first threshold value, the temperature of the environment in which the hydro-turbine generator is located is greater than the second threshold value, and the hydro-turbine generator is subjected to mechanical stress.

[0010] In a specific embodiment of the present application, cluster analysis is performed on the partial discharge pulse sequence in the current monitoring period to identify whether a typical partial discharge event has occurred in the current monitoring period, and further includes: A numerical optimization algorithm is used to find the optimal preset low-frequency interval, preset high-frequency interval and preset frequency range to trigger the execution of any partial discharge spectrum, extract the low power ratio and high power ratio of the partial discharge spectrum, and form sample points with the low power ratio and the high power ratio.

[0011] In a specific embodiment of the present application, a numerical optimization algorithm is used to find the optimal preset low frequency interval, preset high frequency interval and preset frequency range, including: The optimization objectives of the optimization problem are to maximize the minimum value of the Euclidean distance between cluster centers in spectral clustering, to maximize the minimum value of the Mahalanobis distance between cluster centers in spectral clustering, and to maximize the minimum value of the product of the Euclidean distance and the Mahalanobis distance between cluster centers in spectral clustering. Setting decision variables for the optimization problem, wherein the decision variables include a first difference, a second difference, a third difference, a fourth difference, and a fifth difference; Setting constraints of the optimization problem, wherein the constraints are set according to the value ranges of the upper and lower boundaries of the preset low-frequency interval and the upper and lower boundaries of the preset high-frequency interval; Find the optimal solution to the optimization problem and obtain the optimal decision variables; Calculating the optimal preset low-frequency interval, preset high-frequency interval and preset frequency range according to the optimal decision variables; Among them, the first difference is the difference between the lower boundary of the preset low-frequency interval and the lower boundary of the preset variation range, the second difference is the difference between the upper boundary of the preset low-frequency interval and the lower boundary of the preset low-frequency interval, the third difference is the difference between the lower boundary of the preset high-frequency interval and the lower boundary of the preset low-frequency interval, the upper boundary of the preset high-frequency interval is the maximum value of the sum of the upper boundary of the preset low-frequency interval and the fourth difference, and the lower boundary of the preset high-frequency interval and the sum of the fourth difference, the fifth difference is the difference between the upper boundary of the preset frequency range and the upper boundary of the preset high-frequency interval, and the preset variation range is the variation range when each element in the decision variable is updated.

[0012] In a specific embodiment of the present application, the constraint conditions are:

[0013]

[0014]

[0015] ; in, Indicates the lower boundary of the preset low-frequency range. Indicates the upper boundary of the preset low-frequency range. Indicates the lower boundary of the preset high-frequency range, Indicates the upper boundary of the preset high-frequency range. Indicates the upper boundary of the preset frequency range.

[0016] In a specific embodiment of the present application, the numerical optimization algorithm is a hill climbing algorithm.

[0017] The second aspect of the present application provides a hydro-generator shutdown diagnosis device based on ultra-high frequency partial discharge monitoring, comprising: A first acquisition module is used to acquire multiple ultra-high frequency partial discharge signals collected from the hydro-generator in the current monitoring period; A discretization processing module, used for discretizing the ultra-high frequency partial discharge signal to obtain a partial discharge pulse sequence; The first diagnostic module is used to determine whether there are multiple sub-periods of partial discharge pulse sequences with amplitudes greater than all maximum partial discharge amplitudes in the threshold group within the current monitoring period; if so, a diagnostic result that the hydro-generator needs to be shut down is generated, otherwise the next step is executed, wherein the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library, and the threshold group is composed of each maximum partial discharge amplitude corresponding to each of the typical partial discharge events; The second diagnostic module is used to perform cluster analysis on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnostic result indicating that the turbine generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

[0018] A third aspect of the present application provides a computer device, comprising: a memory configured to store instructions; and The processor is configured to call the instructions from the memory and implement the hydro-generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring according to the first aspect of the present application when executing the instructions.

[0019] The fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the method for diagnosing shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring according to the first aspect of the present application.

[0020] In the above technical solution, a partial discharge case library is constructed and hierarchical shutdown diagnosis of the hydro-turbine generator is performed based on it. The shutdown conditions include the existence of multiple sub-cycles of partial discharge pulse sequences with amplitudes greater than all maximum partial discharge amplitudes in the threshold group, and the identification of any typical partial discharge events in the partial discharge case library through a clustering algorithm. The hierarchical shutdown diagnosis improves the accuracy of the diagnosis. At the same time, only typical partial discharge events are used as the basis for the judgment of shutdown diagnosis, and combined with the clustering algorithm, thereby improving the efficiency of shutdown diagnosis. Based on this, the above technical solution realizes fast and accurate shutdown diagnosis of hydro-turbine generators. Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 A schematic diagram of a process flow of a hydro-generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring according to an embodiment of the present application is schematically shown; Figure 2 A block diagram schematically shows a component of a hydro-generator shutdown diagnosis device based on ultra-high frequency partial discharge monitoring according to an embodiment of the present application; Figure 3 The structural block diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0022] The specific implementation of the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present application, and is not used to limit the embodiment of the present application.

[0023] If there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0024] In the prior art, partial discharge monitoring of hydro-turbine generators mostly adopts qualitative judgment methods. Specifically, in one qualitative judgment implementation process, the waveform characteristics of the partial discharge signal, such as pulse amplitude, duration, repetition frequency, etc., are observed by an oscilloscope. In another qualitative judgment implementation process, a spectrum analyzer is used to perform spectrum analysis on the partial discharge signal to identify signal characteristics within a specific frequency range. However, the above qualitative analysis method cannot be used as a basis for fault shutdown. Even if the partial discharge data shows an increasing trend, it is difficult to determine whether to shut down. Further offline partial discharge diagnosis is required to determine the severity of the partial discharge. The delay in this diagnostic process may delay the diagnosis of some serious faults, or cause some small partial discharge faults to further deteriorate and evolve into a major overhaul that requires a comprehensive replacement of equipment, which not only increases the economic burden, but also may prolong the maintenance time, thereby affecting the stability of the power supply and causing irreparable economic losses. For example, the corona at the end develops into the slot. If it is not intervened in time, it may cause insulation breakdown and cause serious faults in the slot. The sudden occurrence of such a fault will not only damage the generator, but may also trigger a chain failure of the power grid, resulting in a large-scale power outage, causing severe damage to social order and economic development. At the same time, the potential outbreak of serious faults poses a great threat to the safety of workers. Serious partial discharge problems that are not identified in time may suddenly worsen during operation and cause casualties.

[0025] In order to overcome the above problems, the embodiment of the present application provides a method for diagnosing shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring. The method performs shutdown diagnosis of a hydro-turbine generator in the following manner: Acquire multiple ultra-high frequency partial discharge signals collected from the hydro-generator during the current monitoring period; Discretize the acquired ultra-high frequency partial discharge signal to obtain a partial discharge pulse sequence; Determine whether there are multiple sub-periods of partial discharge pulse sequences within the current monitoring period whose amplitudes are greater than all maximum partial discharge amplitudes in the threshold group; if so, generate a diagnosis result that the hydro-generator needs to be shut down; otherwise, execute the next step, wherein the threshold group is composed of each maximum partial discharge amplitude, and the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library; A cluster analysis is performed on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnostic result indicating that the turbine generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

[0026] Specifically, in this application, each typical partial discharge event in the partial discharge case library is a partial discharge event that occurs when the hydro-turbine generator is in extreme conditions. Here, it should be understood that the typical partial discharge event is different from the ordinary partial discharge event, that is, the severity of the fault of the hydro-turbine generator caused by the typical partial discharge event is higher than the severity of the fault of the hydro-turbine generator caused by the ordinary partial discharge event. The monitoring period is divided into multiple sub-periods.

[0027] The above embodiments realize hierarchical shutdown diagnosis of hydro-turbine generators, all based on a pre-built partial discharge case library, which includes multiple typical partial discharge events. The first is the first-level shutdown diagnosis: if the amplitude of the partial discharge pulse sequence in multiple sub-cycles in the current monitoring cycle is greater than all the maximum partial discharge amplitudes in the threshold group, a diagnosis result that the hydro-turbine generator needs to be shut down is generated. The second is the second-level shutdown diagnosis: by clustering the partial discharge pulse sequence in the current monitoring cycle, if a typical partial discharge event is identified in the current monitoring cycle, a diagnosis result that the hydro-turbine generator needs to be shut down is generated. Combined with the actual project, it can be seen that not all partial discharge events will cause serious damage to the hydro-turbine generator. In this application, only partial discharge events that cause serious damage to the unit and threaten the safety of the unit are identified, and then a diagnosis result that the unit needs to be shut down is made. The entire shutdown diagnosis process does not involve offline partial discharge tests, and a cluster analysis method is introduced, thereby improving the efficiency of shutdown diagnosis. At the same time, the hierarchical shutdown diagnosis method also has a high accuracy, which reduces unnecessary downtime while ensuring the safe operation of the unit. For other common partial discharge events that are not judged as typical partial discharge events, the partial discharge source can be located and repair plans can be formulated according to the type of common partial discharge event, thereby achieving a reasonable allocation of unit diagnostic resources, thereby improving the diagnostic efficiency of partial discharge faults of hydro-turbine generators. The optimization of unit operation and maintenance management also correspondingly improves the overall reliability and economy of the power system.

[0028] As an optional embodiment of the present application, the partial discharge case library includes at least one of the first type of typical partial discharge events, the second type of typical partial discharge events, the third type of typical partial discharge events, the fourth type of typical partial discharge events, the fifth type of typical partial discharge events, the sixth type of typical partial discharge events, the seventh type of typical partial discharge events, and the eighth type of typical partial discharge events. In a specific embodiment, the partial discharge case library includes the above-mentioned first type of typical partial discharge events to the eighth type of typical partial discharge events.

[0029] In the present application, the first type of typical partial discharge event is a partial discharge event generated when the humidity of the environment in which the hydro-turbine generator is located is greater than a first threshold value. The second type of typical partial discharge event is a partial discharge event generated when the temperature of the environment in which the hydro-turbine generator is located is greater than a second threshold value. The third type of typical partial discharge event is a partial discharge event generated when the electromagnetic interference intensity of the environment in which the hydro-turbine generator is located is greater than a third threshold value. The fourth type of typical partial discharge event is a partial discharge event generated after aging of the insulating material in the hydro-turbine generator. The fifth type of typical partial discharge event is a partial discharge event generated by the hydro-turbine generator under mechanical stress. The sixth type of typical partial discharge event is a partial discharge event generated after an insulation defect occurs in the hydro-turbine generator. The seventh type of typical partial discharge event is a partial discharge event generated when the hydro-turbine generator is extremely manipulated. The eighth type of typical partial discharge event is a partial discharge event generated when the hydro-turbine generator is under a composite fault condition, and the composite fault condition includes more than one of the humidity of the environment in which the hydro-turbine generator is located is greater than the first threshold value, the temperature of the environment in which the hydro-turbine generator is located is greater than the second threshold value, and the hydro-turbine generator is subjected to mechanical stress.

[0030] As an example, the first type of typical partial discharge event is a partial discharge case under extreme humidity conditions, specifically referring to: simulating partial discharge under high humidity environment, because moisture can significantly change the performance of insulating materials and increase the probability of partial discharge. Through the partial discharge simulation under such conditions, the maximum partial discharge amplitude or the maximum partial discharge amount under such conditions is obtained. The second type of typical partial discharge event is a partial discharge case under high temperature environment, specifically referring to: partial discharge simulation under conditions higher than the normal operating temperature, because high temperature may cause accelerated aging of insulating materials, thereby inducing partial discharge. Through the partial discharge simulation under such conditions, the maximum partial discharge amplitude or the maximum partial discharge amount under such conditions is obtained. The third type of typical partial discharge event is a partial discharge case under strong electromagnetic interference environment, specifically referring to: simulating partial discharge in an environment with strong electromagnetic interference. In specific applications, the complex electromagnetic interference environment in the actual power system is simulated to obtain the maximum partial discharge amplitude or the maximum partial discharge amount under such conditions. The fourth type of typical partial discharge event is the partial discharge case after the aging of the insulation material, which specifically refers to: using the aged insulation material or accelerated aging experiment to simulate the long-term operation effect of the turbine generator, investigating the influence of insulation aging on the partial discharge, and obtaining the maximum partial discharge amplitude or the maximum partial discharge amount under this condition. The fifth type of typical partial discharge event is the partial discharge case caused by the turbine generator under mechanical stress, which specifically refers to: during the partial discharge test of the turbine generator, mechanical vibration or impact is applied to the turbine generator, because mechanical stress or impact may cause damage to the insulation structure, thereby generating partial discharge. By investigating the partial discharge caused by such insulation structure damage, the maximum partial discharge amplitude or the maximum partial discharge amount under this condition is obtained. The sixth type of typical partial discharge event is the partial discharge case caused by different types of insulation defects, which specifically refers to: constructing insulation defect models with different types and sizes, such as bubbles, cracks, impurities, etc., investigating the influence of these different insulation defects on partial discharge, and obtaining the maximum partial discharge amplitude or the maximum partial discharge amount under this condition. The seventh type of typical partial discharge event is the partial discharge case of the hydro-turbine generator under extreme operating conditions, which specifically refers to: simulating the extreme operating conditions of the hydro-turbine generator, such as rapid voltage increase, rapid voltage reduction and overload, etc., because these extreme operating conditions may lead to the aggravation of the partial discharge phenomenon, and through the partial discharge simulation under these extreme operating conditions, the maximum partial discharge amplitude or maximum partial discharge amount under such conditions is obtained. The eighth type of typical partial discharge event is the partial discharge case under compound fault conditions, which specifically refers to: conducting partial discharge simulation tests on the hydro-turbine generator under multiple extreme conditions at the same time, such as the simultaneous existence of high temperature, high humidity and mechanical stress, examining the comprehensive impact of compound fault conditions on partial discharge, and obtaining the maximum partial discharge amplitude or maximum partial discharge amount under such conditions.

[0031] In this application, the core premise of the shutdown diagnosis of the hydro-turbine generator is how to visualize the partial discharge events that may cause serious damage to the hydro-turbine generator, that is, how to define typical partial discharge events. As in the above embodiment, various extreme conditions are defined to examine the partial discharge events under these extreme conditions, and typical partial discharge events and the maximum partial discharge amplitudes corresponding to each typical partial discharge event are constructed. Compared with the traditional partial discharge case library, the partial discharge case library constructed by multi-dimensional extreme condition definitions is a case library containing various typical partial discharge events, excluding ordinary partial discharge events that will not cause serious damage to the hydro-turbine generator. Therefore, according to the pre-defined typical partial discharge events, the efficiency of shutdown diagnosis is taken into account while maintaining the safe operation of the unit.

[0032] Figure 1 The flowchart of the method for diagnosing the shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring according to an embodiment of the present application is schematically shown. Figure 1 As shown, the hydro-turbine generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring provided in the embodiment of the present application includes steps 102 to 108. It can be understood that the above-mentioned hydro-turbine generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring may include all steps from step 102 to step 108, or may only include some of the steps.

[0033] Step 102, obtaining a plurality of ultra-high frequency partial discharge signals collected from the hydro-generator in the current monitoring period.

[0034] Step 104, preprocessing the acquired ultra-high frequency partial discharge signal. The preprocessing includes filtering, discretization and normalization, etc. Among them, the partial discharge pulse sequence is obtained by discretization.

[0035] Step 106, determining whether there are multiple sub-periods of partial discharge pulse sequences in the current monitoring period whose amplitudes are greater than all maximum partial discharge amplitudes in the threshold group; if so, generating a diagnosis result that the hydro-generator needs to be shut down, otherwise executing step 108.

[0036] Specifically, in the present application, the threshold group is composed of each maximum partial discharge amplitude, and the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library. The typical partial discharge event is a partial discharge event that occurs when the turbine generator is in extreme conditions.

[0037] Step 108, cluster analysis is performed on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnosis result indicating that the hydro-generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

[0038] As an optional embodiment of the present application, whether a typical partial discharge event occurs in the current monitoring period is identified by spectral clustering analysis, specifically, the following steps are included: Step 202, converting each partial discharge pulse sequence in the current monitoring period into the frequency domain, and obtaining each partial discharge spectrum in one-to-one correspondence; Step 204, for any partial discharge spectrum, extract the low power ratio and the high power ratio of the partial discharge spectrum, and form a sample point by the low power ratio and the high power ratio; Step 206, performing spectral clustering analysis on each sample point to obtain each cluster center; Step 208: for any cluster center, if the difference between the amplitude of the PD pulse sequence corresponding to the cluster center and the maximum PD amplitude of a typical PD event in the PD case library is within a first preset range, it is determined that the typical PD event has occurred in the current monitoring period.

[0039] Specifically, the low power ratio indicates the ratio of the sum of the squared modulus values ​​of each frequency point in the preset low frequency interval in the partial discharge spectrum to the sum of the squared modulus values ​​of all frequency points in the preset frequency range in the partial discharge spectrum. The high power ratio indicates the ratio of the sum of the squared modulus values ​​of each frequency point in the preset high frequency interval in the partial discharge spectrum to the sum of the squared modulus values ​​of all frequency points in the preset frequency range in the partial discharge spectrum. The preset low frequency interval and the preset high frequency interval are both within the above-mentioned preset frequency range, the lower boundary of the preset frequency range is not lower than the minimum frequency in the partial discharge spectrum, the upper boundary of the preset frequency range is not higher than the maximum frequency in the partial discharge spectrum, and the upper boundary frequency of the preset low frequency interval is lower than the upper boundary frequency of the preset high frequency interval.

[0040] It should be understood that the first preset range needs to be set and adaptively modified according to the actual application scenario, and the present application does not specifically limit the value of the first preset range. A sample point is composed of a low power ratio and a high power ratio, specifically referring to: in spectral clustering, the high power ratio and the low power ratio of a partial discharge pulse sequence are both represented by a two-dimensional graph.

[0041] It is known that the PD pulse sequence can be converted into the frequency domain using Fourier transform.

[0042] As an example, the low power ratio is expressed by: (Formula 1); In formula 1, PRL Indicates low power ratio, Indicates the preset low frequency range. is the lower boundary of the preset low-frequency range, is the upper boundary of the preset low-frequency range. Indicates the mid-frequency point of the partial discharge spectrum The modulus value, Indicates the upper boundary of the preset frequency range, that is, the maximum value in the preset frequency range. Indicates the lower boundary of the preset frequency range, that is, the minimum value in the preset frequency range.

[0043] As an example, the high power ratio is expressed by:

[0044] In formula 2, PRH Indicates high power ratio, Indicates the preset high frequency range, is the lower boundary of the preset high-frequency range, It is the upper boundary of the preset high frequency range.

[0045] It can be seen that, as in the above embodiment, in order to achieve accurate spectral clustering of each partial discharge pulse sequence, the preset low-frequency interval, the preset high-frequency interval and the preset frequency range need to be accurately divided according to the partial discharge spectrum, and inaccurate division may lead to overlap between the corresponding areas of each cluster center obtained by spectral clustering.

[0046] To overcome the above defects, in an improved embodiment of the present application, step 203 is further included between step 202 and step 204: Step 203: using a numerical optimization algorithm to find the optimal preset low frequency interval, preset high frequency interval and preset frequency range.

[0047] By optimizing the preset low-frequency interval, the preset high-frequency interval and the preset frequency range, the overlap between the clusters is avoided, thereby improving the accuracy of identifying typical partial discharge events.

[0048] As a preferred embodiment, the numerical optimization algorithm adopts a hill climbing algorithm.

[0049] As an example, using a numerical optimization algorithm to find the optimal preset low frequency interval, preset high frequency interval and preset frequency range specifically includes the following steps: The optimization objectives of the optimization problem are to maximize the minimum value of the Euclidean distance between cluster centers in spectral clustering, to maximize the minimum value of the Mahalanobis distance between cluster centers in spectral clustering, and to maximize the minimum value of the product of the Euclidean distance and the Mahalanobis distance between cluster centers in spectral clustering. Setting decision variables of the optimization problem, the decision variables including a first difference, a second difference, a third difference, a fourth difference and a fifth difference; Setting constraints for the optimization problem, where the constraints are set according to a value range of an upper boundary of a preset low-frequency interval, a lower boundary of a preset low-frequency interval, an upper boundary of a preset high-frequency interval, and a lower boundary of a preset high-frequency interval; Find the optimal solution to the optimization problem and obtain the optimal decision variables; The optimal preset low-frequency interval, the optimal preset high-frequency interval and the optimal preset frequency range are calculated according to the optimal decision variables.

[0050] Specifically, the first difference is the difference between the lower boundary of the preset low-frequency interval and the lower boundary of the preset variation range, the preset variation range is the variation range when each element in the decision variable is updated, the second difference is the difference between the upper boundary of the preset low-frequency interval and the lower boundary of the preset low-frequency interval, the third difference is the difference between the lower boundary of the preset high-frequency interval and the lower boundary of the preset low-frequency interval, the upper boundary of the preset high-frequency interval is the maximum value of the sum of the upper boundary of the preset low-frequency interval and the fourth difference, the lower boundary of the preset high-frequency interval and the sum of the fourth difference, and the fifth difference is the difference between the upper boundary of the preset frequency range and the upper boundary of the preset high-frequency interval.

[0051] As an example, the constraints of the above optimization problem can be expressed as follows:

[0052]

[0053]

[0054] (Formula 3); As an example, the decision variables of the above optimization problem can be expressed as follows: (Formula 4); In formula 4, represents the decision variable, 、 、 、 and All represent elements of decision variables, among which, represents the first difference, represents the second difference, represents the third difference, represents the fourth difference, Represents the fifth difference.

[0055] The relationship expressions between each decision variable element and the preset low frequency interval, the preset high frequency interval and the preset frequency range are as follows:

[0056]

[0057]

[0058]

[0059] (Formula 5); In formula five, is the lower boundary of the preset variation range, which can be expressed as , The upper boundary of the preset variation range.

[0060] As an example, the preset change range is the dynamic change range when the decision variable element is updated, which can be: , and wait.

[0061] As an example, the optimization objective of the optimization problem can be expressed as follows: ； (Formula 6); ; in, represents the first optimization objective, represents the second optimization objective, represents the third optimization objective, Max () represents the maximum value function, Represents the Euclidean distance between cluster centers ED The minimum value of Represents the Mahalanobis distance between cluster centers MD The minimum value of and are exponential factors reflecting the relative importance of each distance.

[0062] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0063] Corresponding to the above-mentioned embodiment, the hydro-generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring, Figure 2 The block diagram of the component of the hydro-generator shutdown diagnosis device based on ultra-high frequency partial discharge monitoring according to an embodiment of the present application is schematically shown. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0064] like Figure 2 As shown, the hydro-generator shutdown diagnosis device 400 based on ultra-high frequency partial discharge monitoring includes: A first acquisition module 410 is used to acquire a plurality of ultra-high frequency partial discharge signals collected from the hydro-generator in a current monitoring period; A discretization processing module 420, used for discretizing the ultra-high frequency partial discharge signal to obtain a partial discharge pulse sequence; The first diagnostic module 430 is used to determine whether there are multiple sub-periods of partial discharge pulse sequences with amplitudes greater than all maximum partial discharge amplitudes in the threshold group within the current monitoring period; if so, a diagnostic result that the hydro-generator needs to be shut down is generated, otherwise the next step is executed, wherein the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library, and the threshold group is composed of each maximum partial discharge amplitude corresponding to each of the typical partial discharge events; The second diagnostic module 440 is used to perform cluster analysis on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnostic result indicating that the turbine generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

[0065] As an embodiment of the present application, a hydro-turbine generator shutdown diagnosis device 400 based on ultra-high frequency partial discharge monitoring can be implemented as follows: Figure 1 The illustrated embodiments and other related method embodiments.

[0066] The process of each module of the hydro-turbine generator shutdown diagnosis device 400 based on ultra-high frequency partial discharge monitoring provided in the embodiment of the present application realizing its respective function can be specifically referred to the aforementioned Figure 1 The description of the illustrated embodiment and other related method embodiments will not be repeated here.

[0067] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0068] Figure 3 A schematic diagram of a computer device according to an embodiment of the present application is shown. In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a shutdown diagnosis method of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0069] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0070] In one embodiment, the hydro-turbine generator shutdown diagnosis device 400 based on ultra-high frequency partial discharge monitoring provided by the present application can be implemented in the form of a computer program. The computer program can be used in the following manner: Figure 3 The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the hydro-generator shutdown diagnosis device 400 based on ultra-high frequency partial discharge monitoring. The computer program composed of various program modules enables the processor to execute the steps of the hydro-generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring in various embodiments of the present application described in this specification.

[0071] In one embodiment, the present application also provides a machine-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for diagnosing shutdown of a hydro-turbine generator based on ultra-high frequency partial discharge monitoring in the above embodiment is implemented.

[0072] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0073] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program codes.

[0074] It should also be noted that the terms "include", "comprising" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring, characterized in that: include: Acquire multiple ultra-high frequency partial discharge signals collected from the hydro-generator during the current monitoring period; Discretizing the ultra-high frequency partial discharge signal to obtain a partial discharge pulse sequence; Determine whether there are multiple sub-periods of partial discharge pulse sequences with amplitudes greater than all maximum partial discharge amplitudes in the threshold group within the current monitoring period; if so, generate a diagnosis result that the hydro-generator needs to be shut down, otherwise execute the next step, wherein the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library, and the threshold group is composed of each maximum partial discharge amplitude corresponding to each of the typical partial discharge events; A cluster analysis is performed on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnostic result indicating that the turbine generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

2. The method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring according to claim 1 is characterized in that: Perform cluster analysis on the PD pulse sequence in the current monitoring period to identify whether a typical PD event has occurred in the current monitoring period, including: Convert each partial discharge pulse sequence in the current monitoring period into the frequency domain, and obtain each partial discharge spectrum in one-to-one correspondence; For any partial discharge spectrum, extract a low power ratio and a high power ratio of the partial discharge spectrum, and form sample points by the low power ratio and the high power ratio; Perform spectral clustering analysis on each sample point to obtain the center of each cluster; For any cluster center, if the difference between the amplitude of the partial discharge pulse sequence corresponding to the cluster center and the maximum partial discharge amplitude of a typical partial discharge event in the partial discharge case library is within a first preset range, it is determined that the typical partial discharge event has occurred in the current monitoring period; Among them, the low power ratio represents the ratio of the sum of the square of the modulus values ​​of each frequency point in the partial discharge spectrum within a preset low frequency interval to the sum of the square of the modulus values ​​of all frequency points in the preset frequency range in the partial discharge spectrum, and the high power ratio represents the ratio of the sum of the square of the modulus values ​​of each frequency point in the preset high frequency interval of the partial discharge spectrum to the sum of the square of the modulus values ​​of all frequency points in the preset frequency range in the partial discharge spectrum. The preset low frequency interval and the preset high frequency interval are both within the preset frequency range, the lower boundary of the preset frequency range is not lower than the minimum frequency in the partial discharge spectrum, and the upper boundary of the preset frequency range is not higher than the maximum frequency in the partial discharge spectrum.

3. The method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring according to claim 1 is characterized in that: The partial discharge case library includes at least one of a first type of typical partial discharge event, a second type of typical partial discharge event, a third type of typical partial discharge event, a fourth type of typical partial discharge event, a fifth type of typical partial discharge event, a sixth type of typical partial discharge event, a seventh type of typical partial discharge event, and an eighth type of typical partial discharge event; Among them, the first type of typical partial discharge event is a partial discharge event generated when the humidity of the environment in which the hydro-turbine generator is located is greater than a first threshold value; the second type of typical partial discharge event is a partial discharge event generated when the temperature of the environment in which the hydro-turbine generator is located is greater than a second threshold value; the third type of typical partial discharge event is a partial discharge event generated when the electromagnetic interference intensity of the environment in which the hydro-turbine generator is located is greater than a third threshold value; the fourth type of typical partial discharge event is a partial discharge event generated after aging of the insulating material in the hydro-turbine generator; the fifth type of typical partial discharge event is a partial discharge event generated by the hydro-turbine generator under mechanical stress; the sixth type of typical partial discharge event is a partial discharge event generated after an insulation defect occurs in the hydro-turbine generator; the seventh type of typical partial discharge event is a partial discharge event generated when the hydro-turbine generator is extremely controlled; the eighth type of typical partial discharge event is a partial discharge event generated when the hydro-turbine generator is under a composite fault condition, and the composite fault condition includes more than one of the following: the humidity of the environment in which the hydro-turbine generator is located is greater than the first threshold value, the temperature of the environment in which the hydro-turbine generator is located is greater than the second threshold value, and the hydro-turbine generator is subjected to mechanical stress.

4. The method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring according to claim 2 is characterized in that: Cluster analysis is performed on the partial discharge pulse sequence in the current monitoring period to identify whether a typical partial discharge event has occurred in the current monitoring period, including: A numerical optimization algorithm is used to find the optimal preset low-frequency interval, preset high-frequency interval and preset frequency range to trigger the execution of any partial discharge spectrum, extract the low power ratio and high power ratio of the partial discharge spectrum, and form sample points with the low power ratio and the high power ratio.

5. The method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring according to claim 4 is characterized in that: The numerical optimization algorithm is used to find the optimal preset low frequency interval, preset high frequency interval and preset frequency range, including: The optimization objectives of the optimization problem are to maximize the minimum value of the Euclidean distance between cluster centers in spectral clustering, to maximize the minimum value of the Mahalanobis distance between cluster centers in spectral clustering, and to maximize the minimum value of the product of the Euclidean distance and the Mahalanobis distance between cluster centers in spectral clustering. Setting decision variables for the optimization problem, wherein the decision variables include a first difference, a second difference, a third difference, a fourth difference, and a fifth difference; Setting constraints of the optimization problem, wherein the constraints are set according to the value ranges of the upper and lower boundaries of the preset low-frequency interval and the upper and lower boundaries of the preset high-frequency interval; Find the optimal solution to the optimization problem and obtain the optimal decision variables; Calculating the optimal preset low-frequency interval, preset high-frequency interval and preset frequency range according to the optimal decision variables; Among them, the first difference is the difference between the lower boundary of the preset low-frequency interval and the lower boundary of the preset variation range, the second difference is the difference between the upper boundary of the preset low-frequency interval and the lower boundary of the preset low-frequency interval, the third difference is the difference between the lower boundary of the preset high-frequency interval and the lower boundary of the preset low-frequency interval, the upper boundary of the preset high-frequency interval is the maximum value of the sum of the upper boundary of the preset low-frequency interval and the fourth difference, and the lower boundary of the preset high-frequency interval and the sum of the fourth difference, the fifth difference is the difference between the upper boundary of the preset frequency range and the upper boundary of the preset high-frequency interval, and the preset variation range is the variation range when each element in the decision variable is updated.

6. The method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring according to claim 5 is characterized in that: The constraints are: ； in, Indicates the lower boundary of the preset low-frequency range. Indicates the upper boundary of the preset low-frequency range. Indicates the lower boundary of the preset high-frequency range, Indicates the upper boundary of the preset high-frequency range. Indicates the upper boundary of the preset frequency range.

7. The method for diagnosing shutdown of a hydro-generator based on ultra-high frequency partial discharge monitoring according to any one of claims 4 to 6, characterized in that: The numerical optimization algorithm is a hill climbing algorithm.

8. A hydro-generator shutdown diagnosis device based on ultra-high frequency partial discharge monitoring, characterized in that: include: A first acquisition module is used to acquire multiple ultra-high frequency partial discharge signals collected from the hydro-generator in the current monitoring period; A discretization processing module, used for discretizing the ultra-high frequency partial discharge signal to obtain a partial discharge pulse sequence; The first diagnostic module is used to determine whether there are multiple sub-periods of partial discharge pulse sequences with amplitudes greater than all maximum partial discharge amplitudes in the threshold group within the current monitoring period; if so, a diagnostic result that the hydro-generator needs to be shut down is generated, otherwise the next step is executed, wherein the maximum partial discharge amplitude is the maximum partial discharge amplitude of a typical partial discharge event in a pre-constructed partial discharge case library, and the threshold group is composed of each maximum partial discharge amplitude corresponding to each of the typical partial discharge events; The second diagnostic module is used to perform cluster analysis on the partial discharge pulse sequence in the current monitoring cycle to identify whether a typical partial discharge event has occurred in the current monitoring cycle. If so, a diagnostic result indicating that the turbine generator needs to be shut down is generated, otherwise the next monitoring cycle is entered.

9. A computer device, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instruction from the memory and to implement the hydro-generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring according to any one of claims 1 to 7 when executing the instruction.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing the machine to execute the hydro-generator shutdown diagnosis method based on ultra-high frequency partial discharge monitoring according to any one of claims 1 to 7.

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