A start-up control method and system for a low-noise, energy-saving air conditioner

Abstract

This application relates to the field of start-up control technology for energy-saving air conditioners, specifically to a start-up control method and system for a low-noise energy-saving air conditioner. The method includes: synchronously acquiring data on the evaporator inlet and outlet temperature difference, compressor frequency, winding current, and ambient humidity of the energy-saving air conditioner; combining wavelet packet decomposition and mutual information to analyze the evaporator inlet and outlet temperature difference and compressor frequency, constructing the phase change response factor at each acquisition moment; combining Kalman filter prediction and fuzzy comprehensive evaluation to analyze the phase change response factor, ambient humidity, and predicted attenuation, evaluating the stagnation phase change margin at each acquisition moment; and dynamically adjusting refrigerant micro-injection by comparing the stagnation phase change margin with a preset judgment threshold. This application aims to achieve improved energy efficiency and ultra-low noise during the low-frequency start-up phase.

Description

Technical Field

[0001] This application relates to the field of start-up control technology for energy-saving air conditioners, specifically to a start-up control method and system for a low-noise energy-saving air conditioner. Background Technology

[0002] The development of start-up control technology for energy-efficient air conditioning equipment originated from traditional air conditioning systems. These systems suffered from high energy consumption, noise pollution, and insufficient operating efficiency during start-up and shutdown. Early energy-efficient air conditioning equipment primarily used variable frequency speed control technology to reduce starting current; however, limited by the precision of the control algorithm, it still suffered from significant energy efficiency fluctuations and insignificant noise reduction effects. In recent years, with breakthroughs in intelligent control theory and semiconductor technology, composite algorithms based on fuzzy control and model predictive control (MPC) have been gradually applied. By monitoring environmental parameters and equipment status in real time, the start-up curves of the compressor and fan are dynamically adjusted, reducing instantaneous power consumption during start-up and controlling noise. This intelligent start-up control method not only extends the service life of energy-efficient air conditioning equipment but also reduces the impact on the power grid through soft-start technology.

[0003] In energy-saving air conditioning equipment, the energy efficiency degradation of variable frequency compressors at low-frequency start-up is a key issue restricting their overall performance. To achieve soft start-up noise reduction and power surge suppression, the compressor needs to operate in the low-frequency range (such as 10-30Hz) for a long time. However, at this time, the refrigerant circulation velocity decreases, resulting in a significant decrease in the heat exchange efficiency between the evaporator and condenser. Due to the extended residence time of the refrigerant in the pipes, the two-phase flow heat exchange is insufficient, the evaporation temperature is forced to decrease, and the condensation temperature increases, causing the actual compression ratio of the compressor to deviate from the design conditions. The energy consumption per unit cooling capacity (COP) decreases compared to the rated frequency, and this contradiction is particularly prominent in high-temperature and high-humidity environments. Existing technologies mainly improve the low-flow-rate heat exchange performance by optimizing the heat exchanger fin structure (such as window-type fins). However, due to limitations in physical space and material properties, structural improvements are not sufficiently adaptable to wide-frequency heat exchange, and the refrigerant phase change characteristics are still difficult to match dynamic load requirements under low-flow conditions. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a start-up control method and system for a low-noise, energy-saving air conditioner, the specific technical solution of which is as follows:

[0005] In a first aspect, one embodiment of this application provides a start-up control method for a low-noise, energy-saving air conditioner, the method comprising the following steps:

[0006] The time-series data of the evaporator inlet and outlet temperature difference, compressor frequency, winding current and ambient humidity of the energy-saving air conditioner are collected synchronously at each collection time.

[0007] For the time series data at each acquisition time, the comprehensive temperature difference fluctuation energy of the evaporator inlet and outlet temperature difference in all frequency bands is calculated based on wavelet packet decomposition, and the mutual information value between the compressor frequency and the evaporator inlet and outlet temperature difference is analyzed in combination with mutual information analysis to construct the phase change response factor at each acquisition time.

[0008] Using the phase change response sequence and winding current time sequence as input, Kalman filtering is used to predict the predicted attenuation of energy consumption per unit cooling capacity within a preset time period in the future. Fuzzy comprehensive evaluation is used to evaluate the phase change response factor, ambient humidity and predicted attenuation to output the energy efficiency stability level, so as to assess the stagnation phase change margin at each acquisition time.

[0009] The refrigerant micro-injection is dynamically adjusted by comparing the stagnation phase change margin with the preset judgment threshold.

[0010] Preferably, the time-series data for each type of data at each acquisition time is the data after sorting all such data acquired at each acquisition time and before in ascending order of time.

[0011] Preferably, the comprehensive temperature difference fluctuation energy is the sum of the temperature difference fluctuation energies of the evaporator inlet and outlet temperature difference time series across all frequency bands.

[0012] Preferably, the phase change response factor is determined by the ratio of the mutual information value to the temperature difference fluctuation energy.

[0013] Preferably, in the process of using Kalman filtering for prediction, the state transition matrix predicted by Kalman filtering is set based on the compressor thermodynamic model.

[0014] Preferably, the evaluation object of the fuzzy comprehensive evaluation is three time series consisting of data obtained at each acquisition time and all acquisition times prior to the phase change response factor, ambient humidity, and predicted attenuation amount.

[0015] Preferably, the formula for calculating the laminar phase transition margin is:

[0016]

[0017] Where B is the slack phase transition margin at each acquisition time, and A is the phase transition response factor at each acquisition time. L represents the predicted decrease in energy consumption per unit of cooling capacity over a preset time period at each data acquisition moment, where L is the energy efficiency stability level at each data acquisition moment.

[0018] Preferably, during the dynamic adjustment of refrigerant micro-injection, if the stagnation phase change margin is less than a preset judgment threshold in the preset time period before the current moment, the intermittent injection mode is activated; otherwise, the intermittent injection mode is not activated.

[0019] Preferably, the wavelet packet decomposition uses a db4 wavelet basis and a 3-level decomposition depth.

[0020] Secondly, another embodiment of this application provides a start-up control system for a low-noise energy-saving air conditioner, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the start-up control method for a low-noise energy-saving air conditioner described in any one of the above claims.

[0021] This application has at least the following beneficial effects:

[0022] 1. To address the issues of reduced refrigerant flow rate during low-frequency startup leading to decreased evaporator heat exchange efficiency and insufficient two-phase flow, this paper combines wavelet packet decomposition and mutual information analysis to quantify the system's comprehensive ability to maintain efficient heat exchange under low-frequency operating conditions. It accurately identifies the real-time correlation between refrigerant phase change state and heat exchange efficiency, breaking through the bottleneck of abnormal evaporation / condensation temperatures and compression ratio imbalance caused by low flow rate in traditional control.

[0023] 2. To address the COP decay and high humidity interference issues caused by refrigerant retention during low-frequency startup, a Kalman filter is employed based on the phase change response sequence and current timing. Fuzzy comprehensive evaluation is used to quantify the overall margin of the system's resistance to energy efficiency decay and environmental disturbances, dynamically isolate the interference of high temperature and high humidity environments on energy efficiency, and provide real-time early warning of the risk of a sharp drop in COP.

[0024] 3. Based on the stagnant phase change margin B, a dynamic adjustment mechanism is designed. When continuously in a high temperature, high humidity and heavy load scenario, the piezoelectric micro-flow solenoid valve is triggered to inject atomized refrigerant droplets into the evaporator, completely avoiding the energy efficiency cliff caused by two-phase flow separation, and enabling the COP to quickly recover to the high efficiency range. By stabilizing the refrigerant flow, the evaporation temperature fluctuation is narrowed, and energy efficiency improvement and ultra-low noise are achieved simultaneously in the low-frequency start-up stage. Attached Figure Description

[0025] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a startup control method for a low-noise, energy-saving air conditioner, as provided in one embodiment of this application. Detailed Implementation

[0027] Example 1

[0028] One embodiment of this application provides a start-up control method for a low-noise, energy-saving air conditioner; see details below. Figure 1 The method includes the following steps:

[0029] Step 1: Synchronously collect time-series data of the evaporator inlet and outlet temperature difference, compressor frequency, winding current, and ambient humidity of the energy-saving air conditioner at each collection point.

[0030] High-precision temperature sensor arrays are symmetrically installed at the inlet and outlet pipe walls of the evaporator in the energy-saving air conditioner to collect real-time temperature difference time-series data on both sides of the evaporator, which is used to quantify the refrigerant phase change state and heat exchange efficiency. The compressor operating frequency time-series data is directly obtained through the built-in frequency detection circuit of the compressor drive module, and combined with the effective value of the winding current collected by the current sampling unit integrated in the compressor body, a dynamic power consumption characteristic model under low-frequency start-up conditions is constructed. At the same time, a digital humidity sensor is deployed at the air conditioner return air grid to continuously monitor the ambient humidity parameters.

[0031] The above four types of heterogeneous data were synchronously acquired by the multi-channel ADC of the energy-saving air conditioner main controller. The data acquisition frequency of the four data acquisition devices was set to 10Hz. After processing with sliding window filtering and timestamp alignment, a time-related dataset was formed. Specifically, for all acquisition times, all the same data acquired at each acquisition time and before it were sorted in ascending order of time to obtain the time sequence of each type of data at each acquisition time.

[0032] Step 2: For the time series data at each acquisition time, calculate the comprehensive temperature difference fluctuation energy of the evaporator inlet and outlet temperature difference in all frequency bands based on wavelet packet decomposition, and combine the mutual information analysis to analyze the mutual information value between the compressor frequency and the evaporator inlet and outlet temperature difference, so as to construct the phase change response factor at each acquisition time.

[0033] Because the refrigerant circulation velocity decreases during the low-frequency start-up phase of energy-saving air conditioning equipment, the heat exchange efficiency between the evaporator and condenser is significantly reduced, resulting in long refrigerant residence time and insufficient two-phase heat exchange. This leads to an abnormal decrease in evaporation temperature and an increase in condensation temperature, causing the actual compression ratio of the compressor to deviate from the design conditions, and a significant decrease in energy consumption per unit cooling capacity.

[0034] Therefore, using the time series of evaporator inlet and outlet temperature differences at any acquisition time as input, wavelet packet decomposition is used, with db4 wavelet basis and 3-level decomposition depth set, to output the comprehensive temperature difference fluctuation energy E of the evaporator inlet and outlet temperature differences in all frequency bands at the corresponding acquisition time. The comprehensive temperature difference fluctuation energy E is the sum of temperature difference fluctuation energy in all frequency bands. The lower the output E value, the more stable the refrigerant flow, the more sufficient the heat exchange, and the greater the energy saving potential.

[0035] Subsequently, using the compressor frequency timing sequence and the evaporator inlet and outlet temperature difference timing data as input, mutual information analysis (MI) is performed. In this embodiment, a time delay is set. The output compressor frequency and the temperature difference between the inlet and outlet of the evaporator are mutually related, with the output value C being the mutual information value. The higher the output value of C, the more sensitive the frequency regulation is to the heat exchange, and the stronger the system's dynamic energy efficiency regulation capability.

[0036] Wavelet packet decomposition and mutual information analysis are well-known techniques and will not be elaborated further.

[0037] Based on the above analysis, a phase change response factor A is constructed for each data acquisition moment to characterize the phase change response of the heat exchange in the energy-saving air conditioner. The calculation formula is as follows:

[0038]

[0039] E represents the comprehensive temperature difference fluctuation energy across all frequency bands of the evaporator inlet and outlet temperature difference at each sampling moment. It quantifies the energy magnitude in the frequency bands related to flow instability and heat exchange fluctuations in the temperature difference signal, directly reflecting the stability of the two-phase flow of refrigerant and the uniformity of heat exchange within the evaporator. The larger the value, the more unstable the refrigerant flow, the existence of fluctuations or pulsations, resulting in a more uneven and insufficient heat exchange process. C represents the mutual information value between the compressor frequency and the evaporator inlet and outlet temperature difference at each sampling moment. In the low-frequency start-up scenario of energy-saving air conditioners, the C value directly reflects the response speed and consistency of the evaporator heat exchange process to compressor frequency adjustment, i.e., dynamic controllability. The larger the value, the faster and more consistent the evaporator temperature difference can produce the expected change after changing the compressor frequency. This indicates that the dynamic energy efficiency adjustment capability of the energy-saving air conditioner is stronger, and the controller can effectively optimize the heat exchange state by adjusting the frequency to cope with load changes or improve heat exchange efficiency at low frequencies.

[0040] It should be understood that the phase change response factor A quantifies the comprehensive performance status of the evaporator heat exchange process of an energy-saving air conditioner under low-frequency start-up conditions. It identifies and characterizes the system's potential and ability to maintain efficient heat exchange under low-frequency and low-flow-rate conditions. The larger the value, the more stable the refrigerant flow in the evaporator and the more sufficient the heat exchange. At the same time, the more rapid and effective the energy-saving air conditioner's response to the compressor's frequency adjustment command.

[0041] Furthermore, the phase transition response factors calculated at each acquisition time and all previous acquisition times are sorted in ascending order of time to obtain the phase transition response sequence for each acquisition time.

[0042] Step 3: Using the phase change response sequence and winding current time sequence as input, Kalman filtering is used to predict the predicted attenuation of energy consumption per unit cooling capacity within a preset time period in the future. Fuzzy comprehensive evaluation is then used to evaluate the phase change response factor, ambient humidity, and predicted attenuation to output the energy efficiency stability level, so as to assess the slack phase change margin at each acquisition time.

[0043] When energy-saving air conditioning equipment operates in the low-frequency start-up range, the refrigerant circulation velocity decreases, resulting in a significant drop in the heat exchange efficiency between the evaporator and condenser. This leads to a prolonged refrigerant residence time and insufficient two-phase heat exchange, causing an abnormal decrease in evaporation temperature and an increase in condensation temperature. Consequently, the actual compression ratio of the compressor deviates significantly from the high-efficiency design conditions, ultimately resulting in a significant reduction in energy consumption per unit cooling capacity. This problem severely restricts the overall energy efficiency performance of energy-saving air conditioning equipment during the start-up phase and the achievement of energy-saving targets.

[0044] Therefore, for each data acquisition moment, using the phase change response sequence and winding current time sequence as inputs, Kalman filtering is used for prediction. Specifically, in this embodiment, the noise covariance Q = 0.01 and the observation noise covariance R = 0.05 in the Kalman filtering prediction process. The state transition matrix of the Kalman filtering prediction is set based on the compressor thermodynamic model, describing the trend of system state evolution over time. The output of the Kalman filtering prediction is the predicted decay of the COP per unit cooling capacity within a preset time period (three seconds in this embodiment) corresponding to each acquisition moment. , This characterizes the degree and trend of the system's energy efficiency deviating from its rated high-efficiency operating condition. A larger absolute value indicates a higher risk of energy efficiency degradation and a worse energy-saving effect. The output values ​​at each data acquisition moment and all previous data acquisition moments are considered. Sort the data in ascending order of time to obtain the predicted decay sequence for each acquisition time.

[0045] Subsequently, using the phase change response sequence, ambient humidity time series sequence, and predicted attenuation sequence at each acquisition time as input, fuzzy comprehensive evaluation is used. The universe of discourse for the phase change response factor, ambient humidity, and predicted attenuation is defined as [0,1], and the triangular membership functions are: poor [0,0.3], medium [0.2,0.7], and excellent [0.6,1]. By using the fuzzy comprehensive evaluation method, the energy efficiency stability level L corresponding to each acquisition time is output. The output L represents the degree of stability of the system in maintaining high-efficiency and energy-saving operation under the combined effect of the three input data. The higher the L value, the more stably the system can operate in the high-efficiency range.

[0046] The Kalman filter prediction process and the fuzzy comprehensive evaluation method are well-known techniques and will not be elaborated further.

[0047] Based on the above analysis, the laminar phase transition margin B at each acquisition time is constructed, and its calculation formula is as follows:

[0048]

[0049] Where A is the phase change response factor at each sampling time, which directly reflects the comprehensive ability of the energy-saving air conditioner to maintain efficient heat exchange. The larger the value, the more sufficient the two-phase flow heat exchange of the evaporator is and the greater the energy-saving potential. The predicted attenuation of unit cooling capacity energy consumption within a preset time period (three seconds in this embodiment) for each data collection moment quantifies the risk of the energy-saving air conditioner deviating from its rated high-efficiency operating condition during low-frequency startup. A larger value indicates an actual compression ratio mismatch, increased unit cooling capacity energy consumption, and more severe energy efficiency degradation. L is the energy efficiency stability level for each data collection moment, which assesses the robustness of the energy-saving air conditioner in maintaining high-efficiency operation under complex conditions. A larger value indicates that even with high ambient humidity or the risk of degradation, the energy-saving air conditioner can still maintain stable operation in the high-efficiency range through dynamic adjustment.

[0050] It should be understood that the sluggish phase change margin B quantifies the system's comprehensive ability to maintain efficient heat exchange under low-speed refrigerant circulation conditions. The higher the B value, the more energy-efficient the air conditioner can ensure sufficient heat exchange through stable two-phase refrigerant flow during low-flow-rate and low-noise start-up, while also responding quickly to frequency adjustment commands and resisting environmental humidity interference and energy efficiency degradation, ultimately minimizing energy consumption per unit cooling capacity.

[0051] Step 4: Compare the stagnation phase change margin with the preset judgment threshold and dynamically adjust the refrigerant micro-injection.

[0052] Because the refrigerant circulation speed decreases during the low-frequency start-up phase of energy-saving air conditioning equipment, the heat exchange efficiency between the evaporator and condenser decreases, resulting in insufficient two-phase flow heat exchange, abnormally low evaporation temperature and high condensation temperature. This causes the actual compression ratio of the compressor to deviate from the design conditions, ultimately resulting in a significant decrease in energy consumption per unit cooling capacity. At the same time, traditional slow-start strategies cannot dynamically respond to environmental changes and are difficult to balance energy efficiency and noise control.

[0053] Therefore, a dynamic adjustment mechanism needs to be designed based on the lagging phase transition margin B. This indicator quantifies the energy efficiency stability under low-frequency conditions in real time by integrating the phase transition response factor A, ambient humidity, and predicted attenuation. The specific dynamic adjustment mechanism design is as follows:

[0054] The stagnation phase transition margin at all acquisition moments is normalized. If the normalized stagnation phase transition margin B at the current moment is less than a preset judgment threshold (0.3 in this embodiment) within a preset time period (three seconds in this embodiment) before the current acquisition moment, then the system is in a continuous high-temperature, high-humidity, heavy-load scenario. At this time, the controller sends a pulse command to the piezoelectric micro-flow solenoid valve of the backup refrigerant circuit to activate the intermittent injection mode. Specifically, the high-pressure liquid refrigerant stored in this circuit is atomized into droplets through a micro-orifice nozzle and injected into the main pipe of the evaporator, narrowing the evaporation temperature fluctuation and allowing the COP to recover rapidly, completely avoiding the energy efficiency cliff caused by two-phase flow separation.

[0055] Conversely, if the stagnation phase transition margin at the current acquisition time, after normalization, is greater than or equal to the judgment threshold within a preset time period before the current acquisition time, then there is no continuous high temperature and high humidity heavy load scenario, and the intermittent injection mode is not activated at this time.

[0056] The intermittent injection mode of the evaporator is a technique known to those skilled in the art, and will not be described in detail in this embodiment.

[0057] Example 2

[0058] Having the same inventive concept as the aforementioned low-noise energy-saving air conditioner start-up control method, another embodiment of this application also provides a low-noise energy-saving air conditioner start-up control system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any one of the aforementioned low-noise energy-saving air conditioner start-up control methods.

[0059] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not invented in this application.

[0060] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A start-up control method for a low-noise, energy-saving air conditioner, characterized in that, The method includes the following steps: The time-series data of the evaporator inlet and outlet temperature difference, compressor frequency, winding current and ambient humidity of the energy-saving air conditioner are collected synchronously at each collection time. For the time series data at each acquisition time, the comprehensive temperature difference fluctuation energy of the evaporator inlet and outlet temperature difference in all frequency bands is calculated based on wavelet packet decomposition, and the mutual information value between the compressor frequency and the evaporator inlet and outlet temperature difference is analyzed in combination with mutual information analysis to construct the phase change response factor at each acquisition time. Among them, the time series data of each type of data at each acquisition time is the data after sorting all the data of that type acquired at each acquisition time and before in ascending order of time; the comprehensive temperature difference fluctuation energy is the sum of the temperature difference fluctuation energy of the evaporator inlet and outlet temperature difference time series in all frequency bands; the phase change response factor is determined by the ratio of the mutual information value to the temperature difference fluctuation energy. Using the phase change response sequence and winding current time sequence as input, Kalman filtering is used to predict the predicted attenuation of energy consumption per unit cooling capacity within a preset time period in the future. Fuzzy comprehensive evaluation is used to evaluate the phase change response factor, ambient humidity and predicted attenuation to output the energy efficiency stability level, so as to assess the stagnation phase change margin at each acquisition time. The evaluation object of the fuzzy comprehensive evaluation is three time series consisting of data obtained at each acquisition time and all acquisition times before the phase change response factor, ambient humidity, and predicted attenuation amount. The refrigerant micro-injection is dynamically adjusted by comparing the stagnation phase change margin with the preset judgment threshold. In the process of dynamically adjusting the refrigerant micro-injection, if the stagnation phase change margin is less than the preset judgment threshold in the preset time period before the current moment, the intermittent injection mode is activated; otherwise, the intermittent injection mode is not activated.

2. The start-up control method for a low-noise energy-saving air conditioner as described in claim 1, characterized in that, In the process of using Kalman filtering for prediction, the state transition matrix predicted by Kalman filtering is set based on the compressor thermodynamic model.

3. The start-up control method for a low-noise energy-saving air conditioner as described in claim 1, characterized in that, The formula for calculating the lagging phase transition margin is as follows: Where B is the slack phase transition margin at each acquisition time, and A is the phase transition response factor at each acquisition time. L represents the predicted decrease in energy consumption per unit of cooling capacity over a preset time period at each data acquisition moment, where L is the energy efficiency stability level at each data acquisition moment.

4. The start-up control method for a low-noise energy-saving air conditioner as described in claim 1, characterized in that, The wavelet packet decomposition uses the db4 wavelet basis and a 3-level decomposition depth.

5. A start-up control system for a low-noise, energy-saving air conditioner, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the start-up control method for a low-noise energy-saving air conditioner as described in any one of claims 1-4.

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