CN114323691A - Gas circuit fault diagnosis device and method for compressed air energy storage system - Google Patents
Gas circuit fault diagnosis device and method for compressed air energy storage system Download PDFInfo
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Abstract
The invention discloses a gas circuit fault diagnosis device and method for a compressed air energy storage system, which comprises the following steps: collecting the ambient temperature and pressure, the exhaust temperature and pressure of an expansion machine and the inlet and outlet pressure of an air storage tank when an energy storage system to be tested operates, and performing wavelet denoising processing on the obtained air path signals; inputting the denoised signal into a pre-trained fault recognition module of a support vector machine to obtain the running state of the energy storage device; if the fault in the energy storage process is identified, determining the performance degradation degree of the multi-stage compressor and the heat exchanger; if the energy release process fault is identified, determining the performance degradation degree of the multistage expansion machine and the heat exchanger; if the standby process fault is identified, the standby process diagnosis module compares and analyzes the outlet temperature and the outlet pressure of the gas storage/heat storage tank with the baseline allowable range, and judges whether the gas storage tank has the faults of gas leakage or heat leakage of the heat storage tank and the like.
Description
Technical Field
The invention mainly relates to a device and a method for diagnosing gas circuit faults of a compressed air energy storage system, and particularly provides a fault identification module, an energy storage and release process fault evaluation module and a standby process fault diagnosis module based on a support vector machine.
Background
As a novel large-scale clean energy storage technology, the compressed air energy storage system can effectively solve the problem of serious wind and light abandonment of renewable energy sources. However, after the compressed air energy storage system operates for a long time, gas path components such as a compressor, an expander, a heat exchanger and the like are prone to corrosion, scaling, corrosion and the like to cause performance degradation, and meanwhile, gas leakage and the like of a gas storage tank or a pipeline may exist in a standby stage, and the gas path faults seriously affect the safe and efficient operation of the whole energy storage system. Meanwhile, the compressed air energy storage system has a complex structure, and relates to a multi-stage compressor, a multi-stage expander and a plurality of heat exchangers, so that rapid gas path fault diagnosis of the compressed air energy storage system is very challenging. More seriously, no effective diagnostic method is available at present. Therefore, the method is of great importance for gas circuit fault diagnosis of advanced compressed air energy storage. The present invention is set forth in this context.
Disclosure of Invention
The invention aims to solve the problems of safety and economy of the conventional compressed air energy storage system caused by the failure of a gas circuit component. A gas circuit fault diagnosis device and method of a compressed air energy storage system are provided, which are characterized in that: the fault identification module judges whether the gas path component in the energy storage process or the energy release process has a fault through a pre-trained support vector machine; the fault evaluation module of the energy storage/release process determines the performance degradation degree of the gas circuit component by combining the extended Kalman filter with the nonlinear models established in the respective processes; the standby stage fault diagnosis module establishes an air storage tank outlet pressure baseline and a heat storage tank outlet temperature baseline through a self-association neural network, and is used for judging whether an air storage tank in the standby stage is in air leakage or not and whether a heat (cold) leakage fault exists in a heat storage (cold) water tank or not.
The invention aims to solve the problems of safety and economy of the conventional compressed air energy storage system caused by the failure of a gas circuit component. Further provides a gas circuit fault diagnosis device and method of the compressed air energy storage system.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a gas path fault diagnosis device of a compressed air energy storage system comprises an energy storage unit, an energy release unit, a gas storage tank and a heat exchange unit, wherein the energy storage unit comprises an air compressor unit and an interstage cooling heat exchanger, the energy release unit comprises an expansion unit and an interstage warming heat exchanger, the heat exchange unit comprises a hot water storage tank and a cold water storage tank, each interstage cooling heat exchanger, each interstage warming heat exchanger, each hot water storage tank and each cold water storage tank form a loop through a pipeline, the hot water storage tank is used for storing compression heat generated in the process of compressing and storing energy, the cold water storage tank is used for storing expansion cold generated in the process of expanding and releasing energy, the gas path fault diagnosis device comprises a fault identification module based on a support vector machine, an energy storage/energy release process fault evaluation module and a standby process fault diagnosis module, and is characterized in that,
the fault identification module trains a support vector machine model through a machine learning algorithm of data with energy storage process faults and energy release process fault category labels, learns the nonlinear mapping relation between the fault data and the fault category labels, and the trained support vector machine model can automatically identify different process faults;
the energy storage/energy release process fault evaluation module comprises an energy storage process fault evaluation module and an energy release process evaluation module, wherein the energy storage process fault evaluation module tracks the decline degree of the gas path component by combining the nonlinear model information and the measurement information of the energy storage process through an extended Kalman filter; the energy release process fault evaluation module tracks the decline degree of the gas path component by combining the nonlinear model information and the measurement information of the energy release process through an extended Kalman filter;
the standby process fault diagnosis module comprises a self-association neural network AANN diagnosis model, wherein the self-association neural network AANN diagnosis model is used for determining outlet temperature and pressure baselines of the air storage tank, the hot water storage tank and the cold water storage tank when the compressed air energy storage system normally operates in a standby stage according to different working conditions, and determining a normal range of the standby stage of the health component according to the baselines.
Preferably, the energy storage process fault assessment module relates to each stage of compressor and interstage cooling heat exchanger.
Preferably, the energy release process fault assessment module involves each stage of expander and interstage temperature rising heat exchanger.
Preferably, in the standby process fault diagnosis module, when the outlet pressure of the gas storage tank is monitored to be greatly lower than the baseline allowable range, it can be judged that gas leakage exists in the gas storage tank; when the outlet temperature of the hot water storage tank and/or the cold water storage tank is monitored to be greatly lower than the temperature baseline allowable range, the hot water storage tank and/or the cold water storage tank can be judged to have the heat leakage problem.
Another object of the present invention is to provide a method for diagnosing a gas circuit fault of a compressed air energy storage system based on the above device, which is characterized in that the method at least comprises the following steps,
SS1, collecting energy storage gas path signals such as the ambient temperature and pressure of a compressed air energy storage system to be detected during operation, the exhaust temperature and the exhaust pressure of each stage of expansion machine, the inlet and outlet pressure of a gas storage tank and the like, and performing wavelet denoising processing on each energy storage gas path signal;
SS2, inputting the denoised energy storage gas path signal into a pre-trained fault recognition module of the support vector machine to obtain the running state of the energy storage system;
SS3, if the fault of the gas circuit component in the energy storage process is identified, determining the performance degradation degree of the multi-stage compressor and the interstage cooling heat exchanger by means of the established energy storage process fault evaluation module;
SS4, if the fault of the air circuit component in the energy release process is identified, determining the performance degradation degree of the multistage expansion machine and the interstage temperature rise heat exchanger through the established energy release process fault evaluation module;
and SS5, if the standby process fault is identified, judging the fault position by adopting the temperature and pressure baseline model allowable range established by the self-association neural network.
Preferably, the energy storage process fault evaluation module is composed of an energy storage process nonlinear model and an extended kalman filter, the extended kalman filter combines energy storage system measurement information and nonlinear model information under the same working condition, and after time updating and measurement updating, when a residual error reaches a smaller range, the change condition of gas circuit component performance parameters (efficiency flow of the multistage compressor and heat exchange effectiveness of the heat exchanger) in the energy storage process can be determined, wherein the measurement parameters y mainly include temperature pressure at an inlet and an outlet of each stage of compressor and fluid temperature at an inlet and an outlet of each stage of cooling heat exchanger.
Preferably, the energy release process fault evaluation module is composed of an energy release process nonlinear model and an extended kalman filter, and the variation condition of the performance parameters (the efficiency flow of the multistage expander and the heat exchange effectiveness of the heat exchanger) of the air circuit component in the energy release process is determined by combining the information of the energy release process nonlinear model with the monitored measurement information of the temperature and the pressure of the inlet and the outlet of the multistage expander, the fluid temperature of the inlet and the outlet of each interstage heating heat exchanger and the like through the extended kalman filter, wherein the measurement parameters y mainly include the temperature and the pressure of the inlet and the outlet of each stage expander and the fluid temperature of the inlet and the outlet of each interstage heating heat exchanger.
Preferably, the auto-associative neural network model is based on the outlet pressure and temperature baseline allowable range of the air storage tank, the hot water storage tank and the cold water storage tank in the standby stage under the normal state, and when the outlet pressure of the air storage tank deviates from the baseline allowable range, the air storage tank is judged to have air leakage fault; when the temperature of the outlets of the hot water storage tank and the cold water storage tank deviates from the baseline allowable range, the hot water storage tank and the cold water storage tank can be judged to have heat leakage faults.
Compared with the prior art, the gas circuit fault diagnosis device and method of the compressed air energy storage system have the remarkable advantages that: the identification module based on the support vector machine isolates the gas circuit fault in the processes of energy storage, energy release and standby, and the diagnosis speed of the fault is improved. The fault evaluation module of the energy storage/release process based on the extended Kalman filter fully combines nonlinear system information and measurement information of different processes to further determine the decline degree of the gas circuit fault and realize the quantitative diagnosis of the compressed air energy storage gas circuit fault; and establishing a component outlet pressure and temperature baseline in a standby stage based on the self-association neural network to judge whether leakage faults occur in the air storage tank and the cold (hot) water storage tank. The whole gas circuit diagnosis model provides early fault early warning for the compressed air energy storage system, reasonably arranges a maintenance plan and ensures the safety and the economy of the system.
Drawings
FIG. 1 is a schematic view of a gas path fault diagnosis method of a compressed air energy storage system according to the present invention;
FIG. 2 is a block diagram of a gas path fault evaluation module in the energy storage/release process;
FIG. 3 is a schematic diagram of standby process fault diagnosis;
description of reference numerals:
the system comprises a low-pressure compressor 1, an interstage cooling heat exchanger 2, a high-pressure compressor 3, an interstage cooling heat exchanger 4, an interstage heating heat exchanger 5, a high-pressure expander 6, an interstage heating heat exchanger 7, a low-pressure expander 8, a hot water storage tank 9, a gas storage tank 10 and a cold water storage tank 11.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments, which are part of the present invention, are not all embodiments, and are intended to be illustrative of the present invention and should not be construed as limiting the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 3, the gas path fault diagnosis device of the compressed air energy storage system comprises an energy storage unit, an energy release unit, a gas storage tank 10 and a heat exchange unit, wherein the energy storage unit comprises an air compressor unit (comprising a low-pressure compressor 1 and a high-pressure compressor 3) and interstage cooling heat exchangers 2 and 4, the energy release unit comprises an expansion unit (comprising a high-pressure expander 6 and a low-pressure expander 8) and interstage warming heat exchangers 5 and 7, the heat exchange unit comprises a hot water storage tank 9 and a cold water storage tank 11, the interstage cooling heat exchangers 2 and 4 and the interstage warming heat exchangers 5 and 7, the hot water storage tank 9 and the cold water storage tank 11 form a loop through a pipeline, the hot water storage tank 9 is used for storing compression heat generated in the compression energy storage process, the cold water storage tank 11 is used for storing expansion cold generated in the expansion energy release process, and the gas path fault diagnosis device comprises a fault identification module a A based on a support vector machine, An energy storage/release process fault evaluation module B and a standby process fault diagnosis module C, wherein,
the fault identification module A trains a support vector machine model through data with fault class labels in the energy storage process and the energy release process, learns the nonlinear mapping relation between fault data and the fault class labels, and the trained support vector machine model can automatically identify different process faults;
the energy storage/release process fault evaluation module B comprises an energy storage process fault evaluation module and an energy release process evaluation module, wherein the energy storage process fault evaluation module tracks the recession degree of the gas circuit component by combining the nonlinear model information and the measurement information of the energy storage process through an extended Kalman filter, and the energy storage process involves a low-pressure compressor 1, a high-pressure compressor 3 and interstage cooling heat exchangers 2 and 4; the energy release process fault evaluation module tracks the decline degree of the gas circuit component by combining the nonlinear model information and the measurement information of the energy release process through an extended Kalman filter, and the energy release process relates to a high-pressure expansion machine 6, a low-pressure expansion machine 8 and interstage temperature rise heat exchangers 5 and 7;
the fault diagnosis module C in the standby process is mainly composed of an auto-associative neural network AANN diagnosis model, the model can obtain the outlet temperature and pressure baselines of the air storage tank, the hot water storage tank and the cold water storage tank when the compressed air energy storage system device normally operates in the standby stage under different working conditions, and the normal range of the standby stage of the health component is determined according to the baselines. When the outlet pressure of the gas storage tank monitored by the sensor is greatly lower than the baseline allowable range, the gas storage tank can be judged to have gas leakage; when the outlet temperatures of the hot water storage tank and the cold water storage tank monitored by the sensors are greatly lower than the temperature baseline allowable range, the hot water storage tank and the cold water storage tank can be judged to have the heat leakage problem.
When the device is used for diagnosing the gas circuit fault of the compressed air energy storage system, the method mainly comprises the following steps:
SS1, collecting the ambient temperature and pressure of the energy storage system to be tested during operation, the exhaust temperature and pressure of the expansion machine and the inlet and outlet pressure of the gas storage tank, and performing wavelet denoising processing on the gas path signal;
SS2, inputting the denoised signal into a pre-trained fault recognition module of the support vector machine to obtain the running state of the energy storage system;
and SS3, if the fault of the gas circuit component in the energy storage process is identified, determining the performance degradation degree of the multi-stage compressor and the heat exchanger by means of the established energy storage process fault evaluation module. The evaluation module consists of an energy storage process nonlinear model and an extended Kalman filter, wherein the extended Kalman filter is combined with energy storage system measurement information y and nonlinear model information under the same working condition u
After time updating and measurement updating, when the residual error e reaches a smaller range, the change condition of the performance parameter p (the efficiency flow of the multistage compressor and the heat exchange effectiveness of the heat exchanger) of the gas path component in the energy storage process can be determined. Wherein the measurement parameters y mainly comprise the temperature and pressure of the inlet and outlet of the low-pressure compressor 1 and the high-pressure compressor 3 and the temperature of the fluid of the inlet and outlet of the heat exchangers 2 and 4;
and SS4, if the fault of the air path component in the energy release process is identified, determining the performance degradation degree of the high-pressure expansion machine 6, the low-pressure expansion machine 8 and the heat exchangers 5 and 7 through the established energy release process fault evaluation module. The evaluation module combines the nonlinear model information of the energy release process through an extended Kalman filter
And determining the change condition of the performance parameter p (the efficiency flow of the multi-stage expander and the heat exchange effectiveness of the heat exchanger) of the air path component in the energy release process according to the monitored measurement information y such as the temperature and the pressure of the inlet and the outlet of the multi-stage expander, the temperature of the fluid at the inlet and the outlet of the heat exchanger and the like. The fault identification module based on the support vector machine, which is provided by the invention, has wide application value as well as the fault evaluation module in the energy storage and release process.
And SS5, if the standby process fault is identified, judging the fault position by adopting the temperature and pressure baseline model allowable range established by the self-association neural network. Particularly, the self-association neural network model can obtain the outlet pressure and temperature baseline allowable range of the air storage tank/hot water storage tank/cold water storage tank in the standby stage under the normal state. When the outlet pressure of the air storage tank 10 deviates from the baseline allowable range, the air storage tank can be judged to have air leakage fault; when the outlet temperatures of the heat storage (cold) water tanks 9 and 11 deviate from the baseline allowable range, the heat storage (cold) water tank can be judged to have heat leakage fault.
The object of the present invention is fully effectively achieved by the above embodiments. Those skilled in the art will appreciate that the present invention includes, but is not limited to, what is described in the accompanying drawings and the foregoing detailed description. While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications within the spirit and scope of the appended claims.
The invention has not been described in detail and is part of the common general knowledge of a person skilled in the art.
Claims (8)
1. A gas path fault diagnosis device of a compressed air energy storage system comprises an energy storage unit, an energy release unit, a gas storage tank and a heat exchange unit, wherein the energy storage unit comprises an air compressor unit and an interstage cooling heat exchanger, the energy release unit comprises an expansion unit and an interstage warming heat exchanger, the heat exchange unit comprises a hot water storage tank and a cold water storage tank, each interstage cooling heat exchanger, each interstage warming heat exchanger, each hot water storage tank and each cold water storage tank form a loop through a pipeline, the hot water storage tank is used for storing compression heat generated in the process of compressing and storing energy, the cold water storage tank is used for storing expansion cold generated in the process of expanding and releasing energy, the gas path fault diagnosis device comprises a fault identification module based on a support vector machine, an energy storage/energy release process fault evaluation module and a standby process fault diagnosis module, and is characterized in that,
the fault identification module trains a support vector machine model through a machine learning algorithm of data with energy storage process faults and energy release process fault category labels, learns the nonlinear mapping relation between the fault data and the fault category labels, and the trained support vector machine model can automatically identify different process faults;
the energy storage/energy release process fault evaluation module comprises an energy storage process fault evaluation module and an energy release process evaluation module, wherein the energy storage process fault evaluation module tracks the decline degree of the gas path component by combining the nonlinear model information and the measurement information of the energy storage process through an extended Kalman filter; the energy release process fault evaluation module tracks the decline degree of the gas path component by combining the nonlinear model information and the measurement information of the energy release process through an extended Kalman filter;
the standby process fault diagnosis module comprises a self-association neural network AANN diagnosis model, wherein the self-association neural network AANN diagnosis model is used for determining outlet temperature and pressure baselines of the air storage tank, the hot water storage tank and the cold water storage tank when the compressed air energy storage system normally operates in a standby stage according to different working conditions, and determining a normal range of the standby stage of the health component according to the baselines.
2. The gas circuit fault diagnosis device according to the preceding claim, wherein the energy storage process fault assessment module involves compressors at each stage and an interstage cooling heat exchanger.
3. The gas circuit fault diagnosis device according to the preceding claim, wherein the energy release process fault assessment module involves expander stages and an interstage temperature rising heat exchanger.
4. The gas circuit fault diagnosis device according to the previous claim, wherein in the standby process fault diagnosis module, when the outlet pressure of the gas storage tank is monitored to be substantially lower than the baseline allowable range, it can be determined that there is gas leakage in the gas storage tank: when the outlet temperature of the hot water storage tank and/or the cold water storage tank is monitored to be greatly lower than the temperature baseline allowable range, the hot water storage tank and/or the cold water storage tank can be judged to have the heat leakage problem.
5. A method for diagnosing a gas circuit fault of a compressed air energy storage system based on the device of any one of claims 1 to 4, characterized by at least comprising the following steps,
SS1, collecting energy storage gas path signals such as the ambient temperature and pressure of a compressed air energy storage system to be detected during operation, the exhaust temperature and the exhaust pressure of each stage of expansion machine, the inlet and outlet pressure of a gas storage tank and the like, and performing wavelet denoising processing on each energy storage gas path signal;
SS2, inputting the denoised energy storage gas path signal into a pre-trained fault recognition module of the support vector machine to obtain the running state of the energy storage system;
SS3, if the fault of the gas circuit component in the energy storage process is identified, determining the performance degradation degree of the multi-stage compressor and the interstage cooling heat exchanger by means of the established energy storage process fault evaluation module;
SS4, if the fault of the air circuit component in the energy release process is identified, determining the performance degradation degree of the multistage expansion machine and the interstage temperature rise heat exchanger through the established energy release process fault evaluation module;
and SS5, if the standby process fault is identified, judging the fault position by adopting the temperature and pressure baseline model allowable range established by the self-association neural network.
6. The method as claimed in the preceding claim, wherein the energy storage process fault assessment module is composed of an energy storage process nonlinear model and an extended kalman filter, the extended kalman filter combines energy storage system measurement information and nonlinear model information under the same working condition, and after time updating and measurement updating, when a residual error reaches a smaller range, the change condition of gas circuit component performance parameters (efficiency flow of the multistage compressor and heat exchange effectiveness of the heat exchanger) in the energy storage process can be determined, wherein the measurement parameters mainly include inlet and outlet temperature pressures of the compressors at all stages and inlet and outlet fluid temperatures of the cooling heat exchangers between all stages.
7. The method as claimed in the preceding claim, wherein the energy release process fault assessment module is composed of an energy release process nonlinear model and an extended kalman filter, and the variation of the performance parameters of the gas path components (the efficiency flow of the multistage expander and the heat exchange effectiveness of the heat exchanger) in the energy release process is determined by combining the information of the nonlinear model of the energy release process with the monitored measurement information of the temperature and the pressure of the inlet and the outlet of the multistage expander and the fluid temperature of the inlet and the outlet of the inter-stage heating heat exchanger, wherein the measurement parameters y are mainly the temperature and the pressure of the inlet and the outlet of the expander and the fluid temperature of the inlet and the outlet of the inter-stage heating heat exchanger.
8. The method of the preceding claims, wherein the auto-associative neural network model is based on the outlet pressure and temperature baseline allowable range of the air storage tank, the hot water storage tank and the cold water storage tank in the normal state in the standby stage, and when the outlet pressure of the air storage tank deviates from the baseline allowable range, the air storage tank is judged to have an air leakage fault; when the temperature of the outlets of the hot water storage tank and the cold water storage tank deviates from the baseline allowable range, the hot water storage tank and the cold water storage tank can be judged to have heat leakage faults.
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| CN116204779A (en) * | 2023-04-19 | 2023-06-02 | 中能建数字科技集团有限公司 | Method, system and readable storage medium for judging operation state of energy storage salt cavern |
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