阅读说明：本技术 基于风机电流的空气源热泵除霜远程监控系统及监控方法 (Remote monitoring system and method for air source heat pump defrosting based on fan current ) 是由 徐英杰 陈宁 于 2019-11-19 设计创作，主要内容包括：本发明提供一种基于风机电流的空气源热泵除霜远程监控系统及控制方法,系统包括：电流测量装置,实时测量空气源热泵蒸发器风机的电流信号；数据转换模块,对所述电流信号进行滤波、解调、分解和重构,获取数据信息,再利用特征提取算法进行时域和频域分析,提取特征数据值,数据诊断模块,利用机器学习方法对特征数据值进行信号识别,分类并诊断结霜情况；除霜控制模块,根据所述诊断结霜情况,对空气源热泵蒸发器进行除霜控制。本发明将采集到的风机电流数据通过数据预处理和特征提取,再结合机器学习等数据分析从而对空气源热泵的室外侧换热器结霜的情况加以判断,以便于及早地发现并清除故障,保证空气源热泵机组在高效状态下运行。(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.)

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.

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.