CN111649449B - Air conditioner fault sensing method based on user side ubiquitous power Internet of things - Google Patents

Air conditioner fault sensing method based on user side ubiquitous power Internet of things Download PDF

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CN111649449B
CN111649449B CN202010352627.9A CN202010352627A CN111649449B CN 111649449 B CN111649449 B CN 111649449B CN 202010352627 A CN202010352627 A CN 202010352627A CN 111649449 B CN111649449 B CN 111649449B
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air conditioner
formula
fault
power
data
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CN111649449A (en
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何光宇
郭歌
宁道龙
陈良宇
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Shanghai Shangta Software Development Co ltd
Shanghai Jiaotong University
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Shanghai Jiaotong University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/32—Responding to malfunctions or emergencies
    • F24F11/38—Failure diagnosis
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/56—Remote control
    • F24F11/58—Remote control using Internet communication
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/89—Arrangement or mounting of control or safety devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F24F2110/12—Temperature of the outside air

Abstract

The invention discloses a perception system and a perception method of an air conditioner fault perception method in the technical field of air conditioner fault perception, the sensing system consists of a single air conditioner, an intelligent socket, an air temperature sensor, an energy information gateway and a cloud server, wherein the single air conditioner is in circuit connection with the intelligent socket through a plug, the air temperature sensor collects indoor and outdoor air temperatures, the invention is designed aiming at the fault sensing method of the single air conditioner, the corresponding sensor is not required to be arranged in the air conditioner, and the sensing system is suitable for the single air conditioner of a family, considers the practical condition of the ubiquitous power Internet of things at the user side, has less sensor quantity and low required collection frequency, further reduces the data quantity through a sudden change storage mechanism of data, the method has low requirements on hardware and a data storage side and low cost, and realizes online real-time perception of air conditioner faults instead of offline fault analysis based on historical data.

Description

Air conditioner fault sensing method based on user side ubiquitous power Internet of things
Technical Field
The invention relates to the technical field of air conditioner fault perception, in particular to an air conditioner fault perception method based on a user side ubiquitous power Internet of things.
Background
The fault detection of the electric appliance can reduce the operation and maintenance cost of the equipment and improve the operation efficiency of the equipment, and is one of important means for realizing lean management of the ubiquitous power internet of things. In recent years, domestic electric load structures are changed greatly, and air conditioners are electric appliances with the largest power consumption ratio. Along with the increase of the operation age, the air conditioner is easy to have the problems of dirt blockage, equipment aging, control failure and the like, so that the abnormal operation of the air conditioner is caused, the operation efficiency of the air conditioner is greatly reduced, the energy consumption is obviously increased, and the service life is reduced. Therefore, if the fault can be identified accurately in time, the power utilization efficiency and the power utilization experience of the user can be greatly improved.
At present, the perception of air conditioner faults is mainly divided into two types: model-based fault awareness and operational data-based fault awareness. And establishing a physical and mathematical model of the air conditioning system based on the fault perception of the model, and judging the fault condition of the air conditioner by comparing the difference between the predicted value and the measured value of the model. In part of the prior art, mathematical statistics and residual error characteristic analysis are carried out on an energy conservation relational expression, and sensor fault diagnosis documents are carried out on a water chilling unit system. In the prior art, a building central air conditioning sensor is modeled through a conservation relation between mass and energy, a state variable capable of judging the fault condition of an air conditioning system is determined, and fault sensing is carried out by utilizing the residual error of the state variable.
The fault perception based on the air conditioner operation data is based on professional knowledge in the air conditioner field, and the preprocessed air conditioner operation data is analyzed by combining mathematical algorithms (classification, regression, clustering and the like). In some documents, a Principal Component Analysis (PCA) is used to complete the sensor fault diagnosis of a chiller. However, PCA requires data to be highly linear and normally distributed, which limits its application in fault detection. There is also a document that introduces a Support Vector Machine (SVM) to sense a chiller thermal fault. However, because SVMs are binary classifiers, modeling needs to be based on a certain amount of fault data, but in actual operation, the known fault data is very little. In the literature, data are decomposed by using a wavelet transform method, and sensor faults of a water chilling unit are sensed by combining PCA (principal component analysis), but the requirement on the data acquisition frequency of the sensor is high.
Existing research has two limitations. On the one hand, the research object is mainly a central air conditioning system of a large building such as a water chilling unit, and a plurality of sensors of different types are required to be arranged in the air conditioning system to measure a plurality of refrigeration professional physical quantities such as fresh air ratio, air enthalpy value, refrigerant water delivery temperature, refrigerant water flow and the like. The sensor is high in purchase cost and needs to be installed at a corresponding position in the air conditioning system, so that the sensor belongs to an intrusive fault monitoring method and is difficult to realize in a single air conditioner. On the other hand, for a fault perception method adopting signal analysis such as wavelet transformation, the required operation data frequency is high, the dimensionality is large, and the requirements on the acquisition frequency of the sensor and the data storage capacity are high. Therefore, the basic condition of the current power internet of things ubiquitous on the user side cannot provide support for large-scale landing application of the method.
Disclosure of Invention
The invention aims to provide an air conditioner fault sensing method based on a user side ubiquitous power internet of things, and aims to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a perception system of an air conditioner fault perception method is composed of a single air conditioner, an intelligent socket, an air temperature sensor, an energy information gateway and a cloud server;
the single air conditioner is in circuit connection with the intelligent socket through the plug, and the intelligent socket collects power consumption data of the single air conditioner and can perform on-off control on the single air conditioner;
the air sensor collects the indoor and outdoor air temperature;
the intelligent socket and the air temperature sensor are connected with the energy information gateway through wireless communication;
the energy information gateway is a local data center of a family, performs data storage and edge calculation, and is connected with the cloud server through the internet to perform data communication.
Preferably, the air temperature sensor is a non-invasive sensor and does not need to be installed inside the electric equipment.
Preferably, the data acquisition frequency and the dimensionality in the perception system are low, mainly in the order of seconds.
Preferably, the energy information gateway adopts a mutation storage mechanism, that is, data storage is performed only when the change degree of data reported by the sensor exceeds a certain threshold, and the mutation storage mechanism greatly reduces the data storage pressure of the energy information gateway.
An air conditioner fault perception method based on a physical model comprises the following steps:
s1, constructing an air conditioner approximate physical model;
and S2, performing online fault perception based on the air conditioner approximate physical model.
In S1, for thermodynamic modeling of the single air conditioner, a first-order equivalent thermal parameter model, i.e., a first-order ETP model, is often used in domestic and foreign research:
Figure BDA0002472391200000031
in the formula TiThe temperature of a room where an indoor unit of the air conditioner is located; t isoThe outdoor temperature is corresponding to the outdoor unit of the air conditioner; q is the refrigerating capacity of the air conditioner; c is the equivalent heat capacity of the room where the indoor unit of the air conditioner is located; r is the equivalent thermal resistance of the room;
an iso-simplified expression of formula (1) is formula (2):
Figure BDA0002472391200000032
in the formula To t+1Is the outdoor temperature at time t; t isi tAnd Ti t+1Indoor temperatures at time t and time t +1, respectively; e ═ eΔt/RCWherein Δ t is the calculation time interval;
when the value of the calculation time interval delta t is small, the change of the room temperature is very small and can be approximately considered to be constant; the following approximation then holds:
Figure BDA0002472391200000033
by substituting formula (3) for formula (2), it is possible to obtain:
Figure BDA0002472391200000034
since 1- ε ≠ 0, there is:
Figure BDA0002472391200000035
namely:
Figure BDA0002472391200000041
for the inverter air conditioner, the refrigerating power and the electric power both increase linearly with the increase of the frequency, and the relationship [10] is as follows:
Q＝af+b (7)
P＝cf+d (8)
wherein Q represents the refrigeration power of the air conditioner; f represents the working frequency of the air conditioner compressor; p represents electric power of the air conditioner; a. b, c and d represent constant coefficients;
the air-conditioning electric power P and the refrigeration power Q are in a linear functional relation according to the formula (7) and the formula (8):
Q＝mP+n (9)
in the formula, m and n are constant coefficients respectively;
substituting formula (9) for formula (6), defining indoor and outdoor temperature difference delta T ═ To t-Ti tIt can be derived that:
Figure BDA0002472391200000042
wherein M, N represents a constant coefficient;
the equation (10) may be referred to as an approximately first-order ETP model, and it can be seen that the ratio of the electric power to the indoor and outdoor temperature difference is inversely proportional to the indoor and outdoor temperature difference.
In S2, using the normal operation data of the air conditioner and offline regressing the first-order ETP approximation model of the air conditioner to obtain the first-order ETP model with the determined parameters, so as to perform online fault sensing, calculating the predicted value of the air conditioner power during normal operation according to the model, comparing the predicted value of the air conditioner power with the measured value of the air conditioner power, and performing online fault sensing according to the ratio:
εP＝|Ppre-Ptest|/Ppre×100％ (11)
in the formula PpreIs the predicted value of the air conditioner power; ptestAnalyzing the threshold delta of the relative error for the measured value of the air conditioner power, if epsilonp>Delta, judging that the air conditioner has a fault; otherwise, judging the normal operation of the air conditioner.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is designed aiming at the fault perception method of the single air conditioner, and the corresponding sensor is not required to be arranged in the air conditioner, thus being suitable for the household single air conditioner.
2. The invention considers the practical condition of the ubiquitous power Internet of things at the user side, has less sensors and low required acquisition frequency, further reduces the data volume by a data mutation storage mechanism, has lower requirements on hardware and a data storage side and has low cost.
3. The invention realizes the online real-time perception of the air conditioner fault instead of offline fault analysis based on historical data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is an architecture diagram of an air conditioner fault sensing system;
fig. 2 is a logic diagram for sensing air conditioner fault based on an approximate first-order ETP model.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention provides a technical scheme that: a perception system of an air conditioner fault perception method is composed of a single air conditioner, an intelligent socket, an air temperature sensor, an energy information gateway and a cloud server;
the single air conditioner is in circuit connection with the intelligent socket through the plug, and the intelligent socket collects power consumption data of the single air conditioner and can perform on-off control on the single air conditioner;
the air sensor collects the indoor and outdoor air temperature;
the intelligent socket and the air temperature sensor are connected with the energy information gateway through wireless communication;
the energy information gateway is a local data center of a family, performs data storage and edge calculation, and is connected with the cloud server through the internet to perform data communication.
Further, the air temperature sensor is a non-invasive sensor and does not need to be installed inside the electric equipment.
Furthermore, the data acquisition frequency and dimensionality in the sensing system are low, and are mainly in the second level.
Furthermore, the energy information gateway adopts a mutation storage mechanism, that is, data storage is performed only when the change degree of data reported by the sensor exceeds a certain threshold, and the mutation storage mechanism greatly reduces the data storage pressure of the energy information gateway, and in the research, the threshold is set to be 1%.
In the data acquisition link, the intelligent socket and the indoor and outdoor temperature sensors perform synchronous data acquisition at an interval of 1s and transmit data to the energy information gateway on the local side.
An air conditioner fault perception method based on a physical model comprises the following steps:
s1, constructing an air conditioner approximate physical model;
and S2, performing online fault perception based on the air conditioner approximate physical model.
In S1, for thermodynamic modeling of the single air conditioner, a first-order equivalent thermal parameter model, i.e., a first-order ETP model, is often used in domestic and foreign research:
Figure BDA0002472391200000061
in the formula TiThe temperature of a room where an indoor unit of the air conditioner is located; t isoThe outdoor temperature is corresponding to the outdoor unit of the air conditioner; q is the refrigerating capacity of the air conditioner; c is the equivalent heat capacity of the room where the indoor unit of the air conditioner is located; r is the equivalent thermal resistance of the room;
an iso-simplified expression of formula (1) is formula (2):
Figure BDA0002472391200000071
in the formula To t+1Is the outdoor temperature at time t; t isi tAnd Ti t+1Indoor temperatures at time t and time t +1, respectively; e ═ eΔt/RCWherein Δ t is the calculation time interval;
when the value of the calculation time interval delta t is small, the change of the room temperature is very small and can be approximately considered to be constant; the following approximation then holds:
Figure BDA0002472391200000072
by substituting formula (3) for formula (2), it is possible to obtain:
Figure BDA0002472391200000073
since 1- ε ≠ 0, there is:
Figure BDA0002472391200000074
namely:
Figure BDA0002472391200000075
for the inverter air conditioner, the refrigerating power and the electric power both increase linearly with the increase of the frequency, and the relationship [10] is as follows:
Q＝af+b (7)
P＝cf+d (8)
wherein Q represents the refrigeration power of the air conditioner; f represents the working frequency of the air conditioner compressor; p represents electric power of the air conditioner; a. b, c and d represent constant coefficients;
the air-conditioning electric power P and the refrigeration power Q are in a linear functional relation according to the formula (7) and the formula (8):
Q＝mP+n (9)
in the formula, m and n are constant coefficients respectively;
substituting formula (9) for formula (6), defining indoor and outdoor temperature difference delta T ═ To t-Ti tIt can be derived that:
Figure BDA0002472391200000081
wherein M, N represents a constant coefficient;
the equation (10) may be referred to as an approximately first-order ETP model, and it can be seen that the ratio of the electric power to the indoor and outdoor temperature difference is inversely proportional to the indoor and outdoor temperature difference.
During specific implementation, a first-order ETP model with determined parameters is obtained by utilizing normal operation data of the air conditioner and performing offline regression on the first-order ETP approximate model of the air conditioner, so that online fault perception can be performed, an air conditioner power predicted value during normal operation is calculated according to the model, the air conditioner power predicted value is compared with an air conditioner power measured value, and online fault perception is performed according to the ratio:
εP＝|Ppre-Ptest|/Ppre×100％ (11)
in the formula PpreIs the predicted value of the air conditioner power; ptestAnalyzing the threshold delta of the relative error for the measured value of the air conditioner power, if epsilonp>Delta, judging that the air conditioner has a fault; otherwise, judging the normal operation of the air conditioner.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (6)

1. An air conditioner fault perception method based on a physical model is characterized by comprising the following steps:
s1, constructing an air conditioner approximate physical model;
s2, performing online fault perception based on an air conditioner approximate physical model;
regarding S1, for thermodynamic modeling of single air conditioners, a first-order equivalent thermal parameter model, i.e., a first-order ETP model, is often used in domestic and foreign research:
Figure FDA0003293772450000011
in the formula TiThe temperature of a room where an indoor unit of the air conditioner is located; t isoThe outdoor temperature is corresponding to the outdoor unit of the air conditioner; q is the refrigerating capacity of the air conditioner; c is the equivalent heat capacity of the room where the indoor unit of the air conditioner is located; r is the equivalent thermal resistance of the room;
an iso-simplified expression of formula (1) is formula (2):
Figure FDA0003293772450000012
in the formula To t+1Is the outdoor temperature at time t; t isi tAnd Ti t+1Indoor temperatures at time t and time t +1, respectively; e ═ eΔt/RCWherein Δ t is the calculation time interval;
when the value of the calculation time interval delta t is small, the change of the room temperature is very small and can be approximately considered to be constant; the following approximation then holds:
Figure FDA0003293772450000013
by substituting formula (3) for formula (2), it is possible to obtain:
Figure FDA0003293772450000014
since 1- ε ≠ 0, there is:
Figure FDA0003293772450000015
namely:
Figure FDA0003293772450000021
for inverter air conditioners, the refrigerating power and the electric power both increase linearly with increasing frequency, with the following relationship:
Q＝af+b (7)
P＝cf+d (8)
wherein Q represents the refrigeration power of the air conditioner; f represents the working frequency of the air conditioner compressor; p represents electric power of the air conditioner; a. b, c and d represent constant coefficients;
the air-conditioning electric power P and the refrigeration power Q are in a linear functional relation according to the formula (7) and the formula (8):
Q＝mP+n (9)
in the formula, m and n are constant coefficients respectively;
substituting formula (9) for formula (6), defining indoor and outdoor temperature difference delta T ═ To t-Ti tIt can be derived that:
Figure FDA0003293772450000022
wherein M, N represents a constant coefficient;
the equation (10) may be referred to as an approximately first-order ETP model, and it can be seen that the ratio of the electric power to the indoor and outdoor temperature difference is inversely proportional to the indoor and outdoor temperature difference.
2. The method according to claim 1, wherein in S2, the normal operation data of the air conditioner is used to perform offline regression on the first-order ETP approximation model of the air conditioner to obtain the first-order ETP model with determined parameters, so as to perform online fault sensing, the predicted value of the air conditioner power during normal operation is calculated according to the model, the predicted value of the air conditioner power is compared with the measured value of the air conditioner power, and online fault sensing is performed according to the ratio:
εP＝|Ppre-Ptest|/Ppre×100％ (11)
in the formula PpreIs the predicted value of the air conditioner power; ptestAnalyzing the threshold delta of the relative error for the measured value of the air conditioner power, if epsilonp>Delta, judging that the air conditioner has a fault; otherwise, judging the normal operation of the air conditioner.
3. The air conditioner fault sensing method based on the physical model as claimed in claim 1, wherein the sensing system for performing online fault sensing in S2 is composed of a single air conditioner, a smart socket, an air temperature sensor, an energy information gateway and a cloud server;
the single air conditioner is in circuit connection with the intelligent socket through the plug, and the intelligent socket collects power consumption data of the single air conditioner and can perform on-off control on the single air conditioner;
the air sensor collects the indoor and outdoor air temperature;
the intelligent socket and the air temperature sensor are connected with the energy information gateway through wireless communication;
the energy information gateway is a local data center of a family, performs data storage and edge calculation, and is connected with the cloud server through the internet to perform data communication.
4. The air conditioner fault perception method based on the physical model as claimed in claim 3, wherein: the air temperature sensor is a non-invasive sensor and does not need to be installed inside electric equipment.
5. The air conditioner fault perception method based on the physical model as claimed in claim 3, wherein: the data acquisition frequency and dimensionality in the sensing system are low, and are mainly in the second level.
6. The air conditioner fault perception method based on the physical model as claimed in claim 3, wherein: the energy information gateway adopts a sudden change storage mechanism, namely, data storage is carried out when the change degree of data reported by the sensor exceeds a certain threshold value.
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CN108006894A (en) * 2017-12-12 2018-05-08 湖南省计量检测研究院 A kind of Portable type central air-conditioning energy efficiency monitoring method for early warning
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