CN110779265A - Remote monitoring system and method for air source heat pump defrosting based on fan current - Google Patents
Remote monitoring system and method for air source heat pump defrosting based on fan current Download PDFInfo
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- CN110779265A CN110779265A CN201911132292.3A CN201911132292A CN110779265A CN 110779265 A CN110779265 A CN 110779265A CN 201911132292 A CN201911132292 A CN 201911132292A CN 110779265 A CN110779265 A CN 110779265A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D21/00—Defrosting; Preventing frosting; Removing condensed or defrost water
- F25D21/02—Detecting the presence of frost or condensate
Abstract
The invention provides a remote monitoring system and a control method for air source heat pump defrosting based on fan current, wherein the system comprises the following steps: the current measuring device is used for measuring a current signal of a fan of the air source heat pump evaporator in real time; the data conversion module is used for filtering, demodulating, decomposing and reconstructing the current signal to obtain data information, then analyzing a time domain and a frequency domain by using a feature extraction algorithm to extract a feature data value, and the data diagnosis module is used for identifying, classifying and diagnosing frosting conditions of the feature data value by using a machine learning method; and the defrosting control module is used for carrying out defrosting control on the air source heat pump evaporator according to the frosting diagnosis condition. The invention judges the frosting condition of the outdoor heat exchanger of the air source heat pump by preprocessing the collected current data of the fan and extracting the characteristics and combining the data analysis such as machine learning, etc., so as to find and clear the fault as soon as possible and ensure that the air source heat pump unit operates in a high-efficiency state.
Description
Technical Field
The invention belongs to the technical field of heat pump defrosting, and particularly relates to a remote monitoring system and a remote monitoring method for air source heat pump defrosting based on fan current.
Background
The air source heat pump technology is an energy-saving and environment-friendly heating technology established based on the reverse Carnot cycle principle. The air source heat pump system obtains a low-temperature heat source through natural energy (air heat storage), and becomes a high-temperature heat source after the system efficiently collects heat and integrates, so as to supply heat or hot water. The air source heat pump has the advantages of wide application range, low operation cost, no environmental pollution and good energy-saving and emission-reducing effects, and is widely applied to the fields of chemical industry, heat energy, heating ventilation and the like.
Although the air source heat pump is widely applied to urban development in China, the frosting phenomenon exists when the outdoor heat exchanger of the air source heat pump operates in winter, so that the operating condition of the air source heat pump during heating in winter is not ideal. The air source heat pump outdoor heat exchanger frosts to reduce the air flow and reduce the heat supply performance of the unit. Along with the increase of the frost layer on the wall surface of the outdoor heat exchanger, the evaporation temperature of the outdoor heat exchanger is reduced, the heating capacity of a unit is reduced, the performance of a fan is attenuated, the input current is increased, the heat supply performance coefficient is reduced, and the compressor is stopped in severe cases, so that the unit cannot work normally. Because the input current can be changed under the frosting condition, the change of the fan current under the frosting condition is researched, and the frosting phenomenon is timely found and the defrosting is controlled by monitoring and diagnosing the current flowing through the fan in real time. The existing technology for controlling defrosting based on fan current only adopts macroscopic features (amplitude and sine frequency) to compare threshold values, so that the recognition rate is low, and the control effect is poor.
Disclosure of Invention
In order to solve the problems of low defrosting efficiency and poor defrosting effect caused by untimely defrosting and mistaken defrosting under the traditional condition, the invention provides a remote monitoring system and a monitoring method for air source heat pump defrosting based on fan current. The system analyzes the fan current signal by machine learning, realizes real-time monitoring of the running state of the heat exchanger outside the air source heat pump chamber by means of a remote monitoring platform, and if a frosting fault is found, the monitoring platform can quickly send out early warning and control the heat pump to defrost.
In order to achieve the purpose, the invention adopts the following technical scheme:
air source heat pump defrosting remote monitering system based on fan current includes: the current measuring device is used for measuring a current signal of a fan of the air source heat pump evaporator in real time; the current measuring device transmits the acquired current signal to the data acquisition module through the wireless transmission device; the data conversion module is used for filtering, demodulating, decomposing and reconstructing the data acquired by the data acquisition module to acquire data information, and then performing time domain and frequency domain analysis by using a feature extraction algorithm to extract a feature data value; the data diagnosis module is used for carrying out signal identification on the characteristic data value by using a machine learning method, classifying and diagnosing the frosting condition; and the defrosting control module is used for carrying out defrosting control on the air source heat pump evaporator according to the frosting diagnosis condition.
As one of the preferable schemes of the present invention, in the data conversion module, filtering and demodulation operations are performed by using Hilbert transform, and decomposition and reconstruction operations are performed by using wavelet packet analysis, frequency domain analysis, or CZT transform.
As one preferable aspect of the present invention, the feature extraction algorithm employs any one of wavelet packet decomposition, fast fourier transform, and principal component analysis.
As one of the preferable schemes of the invention, the machine learning method comprises an artificial neural network learning method, a deep learning method and a support vector machine learning method.
As one of preferable aspects of the present invention, the current measuring device includes a switching power supply and a current sensor; the current measuring device transmits the acquired current signals to the data acquisition module through the wireless signal transmitter and the wireless signal receiver; and the defrosting control module controls the defrosting of the air source heat pump evaporator through the control signal transmitter and the control signal receiver.
The defrosting control module carries out defrosting control according to remote diagnosis information or diagnosis frosting conditions of the data diagnosis module.
As one of the preferable schemes of the present invention, the remote monitoring module includes a remote diagnosis workstation, a data server and a display device, the remote diagnosis workstation includes an intelligent mobile terminal, and the display device is used for displaying the real-time monitoring information, the remote diagnosis information and the diagnosis frosting condition of the data diagnosis module.
The invention also provides a remote monitoring method for defrosting of the air source heat pump based on the current of the fan, which comprises the following steps:
s1, acquiring a current signal of a fan of an air source heat pump evaporator in real time;
s2, performing data preprocessing on the current signal: acquiring macroscopic characteristics, and sequentially performing filtering, demodulation, decomposition and reconstruction operations on the current signals to obtain data information;
s3, analyzing the time domain and the frequency domain of the data information by using a feature extraction algorithm, and extracting feature data values;
s4, performing signal identification on the characteristic data value by using a machine learning method, classifying and diagnosing the frosting condition;
and S5, performing defrosting control according to the frosting diagnosis.
As one preferable embodiment of the control method of the present invention, the method further includes: and S6, remotely monitoring the current signal and making remote diagnosis, and controlling the defrosting mode in real time by a user according to the diagnosed frosting condition or the remote diagnosis information.
Compared with the prior art, the invention has the beneficial effects that:
the invention starts from the current flowing through the fan, the collected current data is preprocessed through filtering, demodulation, decomposition, reconstruction and the like, the characteristic data value is extracted through a characteristic extraction algorithm, and then the frosting condition of the outdoor heat exchanger of the air source heat pump is judged by combining data analysis such as machine learning and the like, so that the faults can be found and eliminated early, the low efficiency of the heat pump in the frosting state is reduced, and the operation of the air source heat pump unit in the high-efficiency state is ensured. The wireless transmission device and the remote monitoring platform are utilized to realize real-time monitoring of the heat pump, so that the restriction of a monitoring place is eliminated, and the running state of the outdoor heat exchanger of the air source heat pump is mastered more flexibly and conveniently.
Drawings
FIG. 1 is a schematic diagram of a remote monitoring system according to the present invention;
fig. 2 is a schematic flow chart of the remote monitoring method according to the present invention.
Detailed Description
The technical solution of the present invention will be further explained below.
Example one
As shown in fig. 1, the air source heat pump defrosting remote monitoring system based on fan current in this embodiment includes an air source heat pump circulation system 11, a fan 12 of an air source heat pump evaporator, an ac power supply 13, a data acquisition module 14, a data conversion module 15, a data diagnosis module 16, a defrosting control module 17, a remote monitoring module 18, a current measurement device 21, a wireless signal transmitter 31, a wireless signal receiver 32, a control signal transmitter 33, and a control signal receiver 34.
The air source heat pump evaporator comprises a fan 12 used for increasing the air speed around the evaporator in an air source heat pump circulating system 11 to achieve forced convection, a current measuring device 21 connected to a lead between the fan 12 and an alternating current power supply 13 of the air source heat pump evaporator, a data acquisition module 14, a data conversion module 15 and a data diagnosis module 16 connected in sequence, the data acquisition module 14 and the current measuring device 21 transmit signals through a wireless transmission device, and the data diagnosis module 16 is connected with a defrosting control module 17 and a remote monitoring module 18 respectively, so that the system can automatically control defrosting by means of diagnosis information and can also control defrosting by means of manual intervention of a remote monitoring platform.
The current measuring device 21 comprises a switching power supply and a current sensor and is used for detecting the change of the current of the fan; the current sensor can adopt various types such as electromagnetic type, electronic type and the like, and the installation mode adopts an induction type installation or an access type installation method according to the different types of the sensor and the measured current.
The data acquisition module 14 automatically acquires non-electric quantity or electric quantity signals in analog and digital measurement units of a sensor or other devices to be measured, and sends the non-electric quantity or electric quantity signals to a processor for analysis and processing. The system mainly comprises a data acquisition card and can be accessed to a data acquisition system through buses such as PXI, serial ports, USB, Ethernet or wireless networks.
The data conversion module 15 performs data preprocessing and feature extraction on the acquired original data information, the data preprocessing performs filtering and demodulation operations such as Hilbert Transform and the like on the data while acquiring macroscopic features, and then decomposition and reconstruction operations such as wavelet packet analysis, frequency domain analysis or CZT Transform (Chirp-Z Transform) and the like are utilized to acquire useful data information, the feature extraction refers to performing time domain and frequency domain analysis on the data by utilizing a feature extraction algorithm, and the feature extraction algorithm can adopt methods such as wavelet packet decomposition, fast fourier Transform, principal component analysis and the like.
The data diagnosis module 16 performs signal processing on the extracted feature signals by using a machine learning intelligent learning method, and classifies data information to diagnose frosting faults, wherein the machine learning method comprises various learning methods such as an artificial neural network, deep learning, a support vector machine and the like.
The wireless transmission device is a device for transmitting data by utilizing a wireless technology, and the transmission mode can adopt WIFI, ZigBee, 433MHz, GPRS, Ethernet and the like. The wireless transmission device is composed of a wireless signal transmitter 31, a wireless signal receiver 32, a control signal transmitter 33 and a control signal receiver 34. The wireless signal transmitter 31 and the wireless signal receiver 32 are used for transmitting and receiving data signals of the current measuring device 21 for data transmission, and the control signal transmitter 33 and the control signal receiver 34 are used for transmitting and receiving defrosting instructions sent by the defrosting control module 17 for defrosting the air source heat pump evaporator.
The remote monitoring module 18 includes a remote diagnostic workstation, a data server and a display device for real-time display of diagnostic information and storage of data. The remote diagnosis workstation can adopt an intelligent mobile terminal, such as a smart phone, a tablet computer and the like. The display device is used for displaying real-time monitoring data, remote diagnosis information and diagnosis frosting conditions of the data diagnosis module, real-time monitoring of the air source heat pump evaporator is achieved, a user can conveniently and rapidly know heat pump information, and control over the heat pump is achieved.
The defrosting control module 17 may implement defrosting control according to the frosting diagnosis of the data diagnosis module, and control the heat pump to defrost if a frosting fault is diagnosed. Meanwhile, the defrosting control can also be performed according to a defrosting instruction made by a user at a remote diagnosis workstation of the remote monitoring module 18.
In the defrosting remote monitoring system, the current data of the fan is preprocessed through filtering, demodulation, decomposition, reconstruction and the like, the characteristic data value is extracted through a characteristic extraction algorithm, and then the frosting condition of the outdoor heat exchanger of the air source heat pump is judged by combining data analysis such as machine learning and the like, so that more accurate judgment data can be obtained, faults can be found and eliminated as soon as possible, the low efficiency of the heat pump in the frosting state is reduced, and the air source heat pump unit is ensured to operate in a high-efficiency state. The wireless transmission device and the remote monitoring platform are utilized to realize real-time monitoring of the heat pump, so that the restriction of a monitoring place is eliminated, and the running state of the outdoor heat exchanger of the air source heat pump is mastered more flexibly and conveniently.
Example two
As shown in fig. 2, the present embodiment provides a remote monitoring method for air source heat pump defrosting based on fan current, which at least includes the following steps:
s1, acquiring a current signal of a fan of an air source heat pump evaporator in real time;
s2, performing data preprocessing on the current signal: acquiring macroscopic characteristics, and sequentially performing filtering, demodulation, decomposition and reconstruction operations on the current signals to obtain data information;
s3, analyzing the time domain and the frequency domain of the data information by using a feature extraction algorithm, and extracting feature data values;
s4, performing signal identification on the characteristic data value by using a machine learning method, classifying and diagnosing the frosting condition;
and S5, performing defrosting control according to the frosting diagnosis.
And S6, remotely monitoring the current signal and making remote diagnosis, and controlling the defrosting mode in real time by a user according to the diagnosed frosting condition or the remote diagnosis information.
The defrosting control method can be realized by adopting the monitoring system and the system with the similar control principle, when the monitoring system is adopted, the specific operation of the air source heat pump defrosting remote monitoring method based on the fan current comprises the following steps:
the method comprises the following steps: measuring and sampling a current signal on a fan 12 flowing through an air source heat pump evaporator by a current measuring device 21;
step two: the wireless signal transmitter 31 transmits the acquired measurement data of the current measuring device 21 to the wireless signal receiver 32, and the data acquisition module 14 finally receives the data;
step three: the data acquisition module 14 transmits the acquired data to the data conversion module 15 to perform data preprocessing and feature extraction on the acquired raw data information. According to the invention, the macro characteristics are utilized, meanwhile, the filtering and demodulation operations such as Hilbert transformation and the like are carried out on the data, then the decomposition and reconstruction operations such as wavelet packet analysis, frequency domain analysis or CZT conversion (Chirp-ZTransform) and the like are utilized, so that useful data information is obtained, and further, the time domain and frequency domain analysis is carried out on the data by utilizing a characteristic extraction algorithm to extract characteristic data values.
Step four: the data diagnosis module 15 performs signal recognition on the extracted characteristic data values by using a machine learning intelligent learning method, and classifies data information to diagnose frosting conditions. The module performs defrost control through the defrost control module 17 according to the diagnostic information;
step five: the defrosting control module 17 uses the control signal transmitter 33 to transmit a defrosting instruction and the control signal receiver 34 to receive the defrosting instruction according to the diagnostic information transmitted by the data diagnostic module 15 or the remote monitoring module 18, so as to achieve the purpose of controlling defrosting.
Step six: the remote monitoring module 18 monitors the heat pump current data information in real time, displays the diagnosis information in real time, and controls the defrosting mode of the heat pump in real time according to the diagnosis information by a user.
In the process of testing the remote monitoring system and the remote monitoring method, accurate defrosting control effect can be achieved through accurate analysis of the data conversion module and the data diagnosis module and wireless transmission and electrical connection of the modules, and the air source heat pump unit is guaranteed to operate in a high-efficiency state.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.
Claims (10)
1. Air source heat pump defrosting remote monitering system based on fan current, its characterized in that includes:
the current measuring device is used for measuring a current signal of a fan of the air source heat pump evaporator in real time;
the current measuring device transmits the acquired current signal to the data acquisition module through the wireless transmission device;
the data conversion module is used for filtering, demodulating, decomposing and reconstructing the data acquired by the data acquisition module to acquire data information, then analyzing the time domain and the frequency domain by using a characteristic extraction algorithm to extract a characteristic data value,
the data diagnosis module is used for carrying out signal identification on the characteristic data value by using a machine learning method, classifying and diagnosing the frosting condition;
and the defrosting control module is used for carrying out defrosting control on the air source heat pump evaporator according to the frosting diagnosis condition.
2. The system of claim 1, wherein: in the data conversion module, Hilbert conversion is used for filtering and demodulating, and wavelet packet analysis, frequency domain analysis or CZT conversion is used for decomposing and reconstructing.
3. The control system of claim 2, wherein: the feature extraction algorithm adopts any one of wavelet packet decomposition, fast Fourier transform and principal component analysis.
4. The system of claim 3, wherein: the machine learning method comprises an artificial neural network learning method, a deep learning method and a support vector machine learning method.
5. The system of claim 4, wherein: the current measuring device comprises a switching power supply and a current sensor; the current measuring device transmits the acquired current signals to the data acquisition module through the wireless signal transmitter and the wireless signal receiver; and the defrosting control module controls the defrosting of the air source heat pump evaporator through the control signal transmitter and the control signal receiver.
6. The system of claim 5, wherein: the defrosting control module carries out defrosting control according to the remote diagnosis information or the diagnosis frosting condition of the data diagnosis module.
7. The system of claim 6, wherein: the remote monitoring module comprises a remote diagnosis workstation, a data server and a display device, the remote diagnosis workstation comprises an intelligent mobile terminal, and the display device is used for displaying the real-time monitoring information, the remote diagnosis information and the diagnosis frosting condition of the data diagnosis module.
8. The remote monitoring method for the defrosting of the air source heat pump based on the current of the fan is characterized in that: the method comprises the following steps:
s1, acquiring a current signal of a fan of an air source heat pump evaporator in real time;
s2, performing data preprocessing on the current signal: acquiring macroscopic characteristics, and sequentially performing filtering, demodulation, decomposition and reconstruction operations on the current signals to obtain data information;
s3, analyzing the time domain and the frequency domain of the data information by using a feature extraction algorithm, and extracting feature data values;
s4, performing signal identification on the characteristic data value by using a machine learning method, classifying and diagnosing the frosting condition;
and S5, performing defrosting control according to the frosting diagnosis.
9. The method of claim 8, wherein: further comprising the steps of:
and S6, remotely monitoring the current signal and making remote diagnosis, and controlling the defrosting mode in real time by a user according to the diagnosed frosting condition or the remote diagnosis information.
10. The method of claim 9, wherein: operating steps S1-S6 with the control system of claim 7.
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