A heat pump intelligent control system based on PV / T direct drive of energy storage

Abstract

The present invention discloses a heat pump intelligent control system based on PV / T direct drive for energy storage, which specifically relates to the field of energy control technology. By adopting three modules of data acquisition, control processing, and heat pump output, it realizes the real-time acquisition and comprehensive analysis of the output power of PV / T components, irradiation signals, and the temperature change data of phase change materials; by using fuzzy logic algorithms, Hilbert-Huang transform, and local phase space reconstruction and chaotic dynamics models, it quantitatively evaluates the non-continuous fluctuations and thermal response interferences in the signals, thereby identifying the thermal coupling risks of non-linear mutations, and generating adjustment instructions based on the difference between the phase change response fluctuation frequency and the preset reference frequency; the heat pump output module adjusts the working medium flow rate and compression ratio in real time according to the adjustment instructions, realizes the synchronous matching of the phase change heat storage device and PV / T components, and effectively improves the system energy density and temperature control accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy control, and more specifically, to a heat pump intelligent control system based on PV / T photovoltaic energy storage direct drive. Background Art

[0002] As an efficient heat storage medium, phase change materials can improve the energy density and temperature control accuracy of the PV / T photovoltaic energy storage direct drive heat pump system. PV / T refers to solar photovoltaic / thermal, which realizes dual energy utilization by simultaneously capturing electrical energy and thermal energy in sunlight. Compared with traditional hot water energy storage, phase change materials can store and release a large amount of thermal energy in a smaller volume, thus helping to improve the overall energy efficiency and compactness of the system.

[0003] However, during the phase change process, phase change materials often exhibit discontinuous and abrupt thermal response phenomena. Especially in the phase change critical interval, the heat transfer characteristics will undergo abrupt changes. This non-linear mutation forms a coupling effect with the continuous and stable regulation output of the heat pump, which may lead to problems such as response delay, local thermal balance disorder, and system oscillation, thus affecting the dynamic performance and stable operation of the system. Traditional control strategies based on linear models are difficult to effectively cope with such non-linear phenomena. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a heat pump intelligent control system based on PV / T photovoltaic energy storage direct drive to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A heat pump intelligent control system based on PV / T photovoltaic energy storage direct drive, comprising a data acquisition module, a control processing module, and a heat pump output module;

[0007] The data acquisition module is used to obtain the output power of the PV / T module, the irradiation signal, and the temperature change data of the phase change material;

[0008] The control processing module is used to perform the following steps:

[0009] Compare the output power with the ambient temperature threshold using a known fuzzy logic algorithm to determine whether to enter the phase change critical zone;

[0010] If entering the phase change critical zone, perform time-frequency domain decomposition on the irradiation signal through Hilbert-Huang transform to evaluate the degree of discontinuous fluctuation in the irradiation signal; perform non-linear analysis on the temperature change data of the phase change material through local phase space reconstruction and chaotic dynamics model to evaluate the degree of thermal response interference during the phase change process;

[0011] Based on the degree of discontinuous fluctuation in the irradiation signal and the degree of thermal response interference during the phase change process, identify whether there is a thermal coupling risk of non-linear mutation;

[0012] If there is a thermal coupling risk of non-linear mutation, based on the difference between the phase change response fluctuation frequency and the preset reference frequency, conduct a secondary in-depth evaluation of the collected data according to the clear indicators of positive or negative offset, and generate a regulation instruction;

[0013] The heat pump output module adjusts the flow rate and compression ratio according to the regulation instruction to achieve synchronous matching of the phase change heat storage device and the PV / T module.

[0014] In a preferred embodiment, obtaining the output power of the PV / T module, the irradiation signal, and the temperature change data of the phase change material specifically includes:

[0015] Collect the output power data of the PV / T module through an output power sensor, collect the irradiation signal data of the PV / T module through an irradiation signal sensor, and collect the temperature change data of the phase change material through a temperature sensor;

[0016] Convert the analog signals collected by the output power sensor, the irradiation signal sensor, and the temperature sensor into digital signals, and transfer the digital signals to the control processing module.

[0017] In a preferred embodiment, use a known fuzzy logic algorithm to compare the output power with the ambient temperature threshold to determine whether to enter the phase change critical zone, specifically including:

[0018] Use an existing fuzzy logic algorithm to perform fuzzy processing on the output power data of the PV / T module to generate an output power membership value, and at the same time perform fuzzy processing on the preset ambient temperature threshold to generate an ambient temperature membership value;

[0019] According to the preset membership function and fuzzy rules, compare the output power membership value and the ambient temperature membership value to generate a fuzzy logic decision result for judging whether the PV / T module enters the phase change critical zone;

[0020] Based on the fuzzy logic decision result, judge whether to enter the phase change critical zone.

[0021] In a preferred embodiment, perform time-frequency domain decomposition on the irradiation signal through Hilbert-Huang transform to evaluate the degree of discontinuous fluctuation in the irradiation signal, specifically including:

[0022] Perform preliminary preprocessing on the collected irradiation signal, including noise filtering and amplitude normalization processing, to obtain standardized irradiation signal data;

[0023] The Hilbert-Huang transform is used to decompose the standardized irradiation signal data in the time-frequency domain, and multiple intrinsic mode functions are separated. The Hilbert-Huang transform sets corresponding parameters according to the time-varying characteristics of the signal;

[0024] For each intrinsic mode function, the instantaneous frequency and amplitude are calculated according to the signal variation law, and frequency distribution data that accurately describes the dynamic characteristics of the signal is formed;

[0025] Based on the frequency distribution data, the degree of non-continuous fluctuation in the irradiation signal is evaluated, and an evaluation index representing the degree of non-continuous fluctuation in the irradiation signal is generated.

[0026] In a preferred embodiment, based on the frequency distribution data, the degree of non-continuous fluctuation in the irradiation signal is evaluated, and an evaluation index representing the degree of non-continuous fluctuation in the irradiation signal is generated. Specifically:

[0027] For all the obtained instantaneous frequency data of the intrinsic mode functions, the degree of non-continuous fluctuation existing in the normalized irradiation signal is evaluated, and an evaluation index used to represent the degree of non-continuous fluctuation is generated. Its calculation method uses statistical analysis to quantify the fluctuation characteristics of all the instantaneous frequency data of the intrinsic mode functions within a predetermined time window, expressed as: ; where represents the evaluation index of the degree of non-continuous fluctuation in the irradiation signal, is a statistical function, represents the number of the intrinsic mode function, is an integer and , is the total number of the extracted intrinsic mode functions, represents the th intrinsic mode function at time ;

[0028] The implementation of includes calculating the standard deviation and coefficient of variation of each value.

[0029] In a preferred embodiment, non-linear analysis is performed on the temperature change data of the phase change material through local phase space reconstruction and chaotic dynamics model to evaluate the degree of thermal response interference during the phase change process, specifically including:

[0030] The collected temperature change data of the phase change material is subjected to digital filtering and amplitude normalization processing to obtain standardized temperature data;

[0031] The local phase space reconstruction method is used to reconstruct the standardized temperature data into a multi-dimensional phase space trajectory to reflect the dynamic evolution characteristics of the temperature change;

[0032] Nonlinear analysis is performed on the reconstructed multi-dimensional phase space trajectory using a chaotic dynamics model to extract parameter data that can describe the key dynamic characteristics of temperature evolution;

[0033] Based on the extracted parameter data, quantitatively evaluate the degree of thermal response interference during the phase change process, and generate an evaluation index for describing the degree of thermal response interference during the phase change process.

[0034] In a preferred embodiment, the standardized temperature data is reconstructed into a multi-dimensional phase space trajectory using a local phase space reconstruction method to reflect the dynamic evolution characteristics of temperature changes. Specifically:

[0035] Let the time variable be denoted as , the preset time delay be denoted as and the embedding dimension be denoted as . The mathematical expression for constructing the phase space vector is ; where, represents the multi-dimensional phase space vector corresponding to the moment , represents the standardized temperature data;

[0036] The multi-dimensional phase space vector is the state point obtained by embedding the standardized temperature data into a high-dimensional space through local phase space reconstruction at the moment ; Multiple phase space vectors are arranged in sequence according to the time series to form a complete multi-dimensional phase space trajectory.

[0037] In a preferred embodiment, based on the degree of discontinuous fluctuation in the irradiation signal and the degree of thermal response interference during the phase change process, identify whether there is a risk of non-linear mutation in thermal coupling, specifically including:

[0038] Set the threshold corresponding to the evaluation index of the degree of discontinuous fluctuation in the irradiation signal, and set the threshold corresponding to the evaluation index of the degree of thermal response interference during the phase change process;

[0039] When the evaluation index of the degree of discontinuous fluctuation in the irradiation signal is greater than the threshold and the evaluation index of the degree of thermal response interference during the phase change process is greater than the threshold , it is determined that there is a risk of non-linear mutation in thermal coupling.

[0040] In a preferred embodiment, if there is a risk of non-linear mutation in thermal coupling, based on the difference between the phase change response fluctuation frequency and the preset reference frequency, perform a secondary in-depth evaluation of the collected data according to a clear index of positive or negative offset, and generate an adjustment instruction, specifically including:

[0041] Numerically compare the phase change response fluctuation frequency data with a preset reference frequency, calculate the difference between the two through subtraction operation, and mark this difference as the frequency offset;

[0042] Perform a comparison operation on the frequency offset. If the frequency offset is greater than zero, it is determined as a positive offset; otherwise, it is determined as a negative offset, and store the offset state in the control processing module;

[0043] According to the offset state, non-linearly transform the frequency offset using a preset mapping function to generate an evaluation coefficient matching the offset direction, and further form an offset index;

[0044] Combined with the offset index, conduct a secondary comprehensive analysis of the collected original data, generate specific adjustment instructions through multi-level logical operations, and transmit the adjustment instructions to the heat pump output module in real time.

[0045] In a preferred embodiment, adjust the flow rate and compression ratio according to the adjustment instructions to achieve synchronous matching between the phase change heat storage device and the PV / T module, specifically including:

[0046] After receiving the adjustment instructions, the heat pump output module issues specific control signals to the flow rate adjustment mechanism and the compressor control mechanism respectively according to the built-in control algorithm, ensuring that each unit works in coordination according to the adjustment instructions, so that the phase change heat storage device and the PV / T module maintain real-time dynamic synchronous matching during the heat energy transfer process;

[0047] The working medium flow rate adjustment mechanism regulates the flow rate of the working medium, and the adjustment of the compressor operating parameters is achieved by changing the compressor speed and the gas compression ratio.

[0048] The technical effects and advantages of a heat pump intelligent control system based on PV / T direct drive for energy storage of the present invention:

[0049] 1. By adopting the fuzzy logic algorithm, Hilbert-Huang transform, local phase space reconstruction, and chaotic dynamics model, the present invention comprehensively analyzes the output power of the solar photovoltaic / thermal module, the irradiation signal, and the temperature change data of the phase change material, realizes the quantitative evaluation of the non-continuous fluctuation in the irradiation signal and the thermal response interference phenomenon during the phase change process, and accurately identifies the thermal coupling risk of non-linear mutation. This technical solution enables the system to capture the dynamic changes of temperature and irradiation signals in real time, generate accurate adjustment instructions, and adjust the flow rate and compression ratio at the heat pump output end in real time according to the instructions, so as to achieve synchronous matching of heat energy transfer between the phase change heat storage device and the photovoltaic / thermal module, significantly improving the overall energy density and temperature control accuracy of the system.

[0050] 2. Through multi-level non-linear data processing and evaluation, the present invention overcomes problems such as response delay, local thermal balance disorder, and system oscillation that occur in traditional linear control methods when dealing with the abrupt thermal response of phase change materials, ensuring the stable operation of the system under low temperature and complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic structural diagram of a heat pump intelligent control system based on PV / T photovoltaic energy storage direct drive of the present invention;

[0052] Figure 2 It is a flowchart of the steps executed by the control processing module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment: Figure 1 A schematic structural diagram of a heat pump intelligent control system based on PV / T photovoltaic energy storage direct drive of the present invention is given. A heat pump intelligent control system based on PV / T photovoltaic energy storage direct drive includes a data acquisition module, a control processing module, and a heat pump output module.

[0055] The data acquisition module is used to obtain the output power of the PV / T module, the irradiance signal, and the temperature change data of the phase change material.

[0056] Figure 2 A flowchart of the steps executed by the control processing module of the present invention is given. The control processing module is used to execute the following steps:

[0057] Use the known fuzzy logic algorithm to compare the output power with the environmental temperature threshold to determine whether to enter the phase change critical region;

[0058] If entering the phase change critical region, perform time-frequency domain decomposition on the irradiance signal through Hilbert-Huang transform to evaluate the non-continuous fluctuation degree of the irradiance signal; perform non-linear analysis on the temperature change data of the phase change material through local phase space reconstruction and chaotic dynamics model to evaluate the thermal response interference degree during the phase change process;

[0059] Based on the non-continuous fluctuation degree of the irradiance signal and the thermal response interference degree during the phase change process, identify whether there is a risk of non-linear abrupt thermal coupling;

[0060] If there is a risk of non - linear mutation in thermal coupling, according to the difference between the phase - change response fluctuation frequency and the preset reference frequency, the collected data is evaluated in depth for the second time according to the clear index of positive or negative deviation to generate an adjustment instruction.

[0061] The heat - pump output module adjusts the flow rate and compression ratio according to the adjustment instruction to achieve the synchronous matching of the phase - change heat - storage device and the PV / T module.

[0062] Obtain the output power, irradiation signal and phase - change material temperature change data of the PV / T module, specifically including:

[0063] Collect the output power data of the PV / T module through the output - power sensor, collect the irradiation signal data of the PV / T module through the irradiation - signal sensor, and collect the phase - change material temperature change data through the temperature sensor.

[0064] Convert the analog signals collected by the output - power sensor, irradiation - signal sensor and temperature sensor into digital signals, and transmit the digital signals to the control and processing module.

[0065] Among them, the output - power sensor is used to collect and record the analog electrical signals related to the output power of the PV / T module in real time. The output - power sensor adopts a sensing element with high sampling rate and wide - band characteristics, and the analog signals it collects accurately reflect the instantaneous value of the electric energy converted by the PV / T module; the irradiation - signal sensor is used to detect and record the optoelectronic signals related to the solar irradiation intensity in the environment where the PV / T module is located. The irradiation - signal sensor outputs the irradiation information of sunlight in the environment in the form of analog electrical signals through the built - in photosensitive element and spectral decomposition circuit; the temperature sensor is used to collect and record the analog signals reflecting the temperature change of the phase - change material. The temperature sensor adopts a high - precision thermocouple or thermistor element, and the analog signals it outputs have a definite corresponding relationship with the temperature change of the phase - change material during the phase - change process;

[0066] In addition, the data - acquisition module also includes a signal - conversion device. The signal - conversion device amplifies, pre - processes the analog signals collected by the output - power sensor, irradiation - signal sensor and temperature sensor, and uses analog - to - digital conversion technology to convert the continuous analog signals into digital signals. The digital signals are marked with clear time stamps and are transmitted to the control and processing module by wired or wireless means, so as to realize the full and continuous acquisition of the output power, irradiation signal and phase - change material temperature change data of the PV / T module.

[0067] Use the known fuzzy - logic algorithm to compare the output power with the environmental temperature threshold to judge whether to enter the phase - change critical zone, specifically including:

[0068] Using the existing fuzzy logic algorithm, the output power data of the PV / T module is fuzzified to generate the membership value of the output power. At the same time, the preset environmental temperature threshold is fuzzified to generate the membership value of the environmental temperature.

[0069] In this process, the output power data is represented by the actual measured value, and its numerical range is determined by the actual output power. To ensure the accuracy of the data in the fuzzification process, a predefined output power membership function is adopted. For example, the output power membership function can be expressed by the following formula: M1 = (P1); where P1 represents the numerical value of the output power of the PV / T module actually collected, and its unit is watt; M1 represents the fuzzy membership value of the output power, and its value is limited between 0 and 1; the function is a preset output power membership function, and its design is determined based on experimental data and statistical laws.

[0070] At the same time, the preset environmental temperature threshold is fuzzified using a known fuzzy logic algorithm to generate the membership value of the environmental temperature; in this step, the environmental temperature threshold is represented by a preset temperature value, and its unit is degree Celsius, and it is fuzzified and converted through a preset environmental temperature membership function, which is specifically expressed as: M2 = f_b(T1); where T1 represents the preset environmental temperature threshold; M2 represents the fuzzy membership value of the environmental temperature, and its value range is from 0 to 1; the function f_b is a preset environmental temperature membership function, and its design is also determined based on the actual distribution of the environmental temperature and the results of statistical analysis.

[0071] According to the preset membership function and fuzzy rules, the membership value of the output power and the membership value of the environmental temperature are compared to generate a fuzzy logic decision result for judging whether the PV / T module enters the phase change critical region.

[0072] After obtaining the output power membership value M1 and the environmental temperature membership value M2, the control processing module compares M1 and M2 according to the preset membership function and fuzzy rules to generate a fuzzy logic decision result for judging whether the PV / T module enters the phase change critical region; this fuzzy logic decision process can be expressed by the following formula: D = g_c(M1, M2); where D represents the fuzzy logic decision result, and its numerical range is from 0 to 1; the function g_c is a fuzzy inference function based on the preset fuzzy rules, and its rule base includes but is not limited to fuzzy rules such as "if M1 is lower and M2 is higher, then D is lower", and this rule is implemented through fuzzy intersection, fuzzy union, and defuzzification processing inside the control processing module.

[0073] Based on the fuzzy logic decision result, it is judged whether to enter the phase change critical region.

[0074] Based on the fuzzy logic decision result D, it is compared with a preset decision threshold to determine whether the PV / T module enters the phase change critical region. Specifically, if D is less than the preset decision threshold T0 (where T0 is a constant between 0 and 1), it is determined that the PV / T module enters the phase change critical region; conversely, if D is greater than or equal to T0, it is determined that the PV / T module does not enter the phase change critical region.

[0075] It should be noted that the output power membership function fₐ, the ambient temperature membership function f_b, and the fuzzy inference function g_c are all pre-defined functions, and their definitions and parameter values are determined through experimental data and statistical analysis methods and remain consistent during system implementation.

[0076] The Hilbert-Huang transform is used to decompose the irradiance signal in the time-frequency domain to evaluate the degree of non-continuous fluctuation of the irradiance signal, specifically including:

[0077] The collected irradiance signal is preliminarily preprocessed, including noise filtering and amplitude normalization processing, to obtain standardized irradiance signal data.

[0078] The original irradiance signal collected by the irradiance signal sensor is preprocessed. The preprocessing includes filtering the random noise in the original signal using a digital filtering method and performing amplitude normalization processing to convert the original signal into standardized irradiance signal data.

[0079] For clear illustration, let the original irradiance signal data be denoted as , whose unit is volts; the normalization processing uses the normalization function , and this normalization function is defined as: ; where represents the normalized irradiance signal data, and its value is limited to the interval [0, 1]; is the normalization function, and its specific form is: ; where and represent the minimum and maximum values measured during the calibration process, respectively.

[0080] The Hilbert-Huang transform is used to decompose the standardized irradiance signal data in the time-frequency domain to separate out multiple intrinsic mode functions, where the Hilbert-Huang transform sets corresponding parameters according to the time-varying characteristics of the signal.

[0081] Specifically, through the empirical mode decomposition method, is decomposed into several intrinsic mode functions, and its decomposition expression can be represented as: ; where represents the time variable; represents the th intrinsic mode function, is an integer and ; is the total number of extracted intrinsic mode functions; The parameter settings used in the decomposition process are preset according to the time-varying characteristics of the irradiation signal to ensure that each intrinsic mode function can fully reflect the different frequency components in the signal.

[0082] The instantaneous frequency and amplitude of each inherent mode function are calculated according to the signal change law, and frequency distribution data that accurately describes the dynamic characteristics of the signal is formed.

[0083] For each separated intrinsic mode function , and use Hilbert transform to calculate its instantaneous parameters. Specifically, for each intrinsic mode function , first construct its analytical signal, extract the instantaneous phase, and then calculate the instantaneous frequency. The calculation formula is expressed as: ;in, Indicates The intrinsic mode function is in time The instantaneous frequency at represents the value obtained by Hilbert transform The instantaneous phase of Instantaneous phase versus time At the same time, the instantaneous amplitude of each intrinsic mode function is also calculated. The calculation process is similar to the calculation of the instantaneous frequency, and both are determined based on the change law of the local signal amplitude.

[0084] The degree of discontinuous fluctuation in the irradiation signal is evaluated based on the frequency distribution data, and an evaluation index characterizing the degree of discontinuous fluctuation in the irradiation signal is generated.

[0085] Obtain instantaneous frequency data of all natural mode functions , for the normalized irradiance signal The degree of discontinuous fluctuation in the data is evaluated and an evaluation index is generated to characterize the degree of discontinuous fluctuation. To this end, an evaluation index is defined and its calculation method is to use statistical analysis to The fluctuation characteristics within the predetermined time window are quantified, which can be specifically expressed as: ;in, An evaluation index that indicates the degree of discontinuous fluctuation in the irradiation signal. Its value is used to reflect the degree of mutational change in the irradiation signal. is a statistical function, and its implementation includes but is not limited to calculating each Standard deviation, coefficient of variation or other appropriate statistical parameter of the value; function The specific form and parameters of are pre-set based on experimental data.

[0086] The larger the evaluation index of the non - continuous fluctuation degree in the irradiation signal, the higher the degree of mutational change in the irradiation signal, and at the same time, it also means the larger the non - continuous fluctuation degree in the irradiation signal.

[0087] In this step, the time - frequency domain decomposition of the irradiation signal is realized by applying the Hilbert - Huang transform, and combined with digital filtering and amplitude normalization pre - processing, each intrinsic mode function in the signal is accurately separated, and the instantaneous frequency and amplitude of each intrinsic mode function are calculated. Then, statistical analysis methods are used to generate a quantitative evaluation index to objectively reflect the mutational change and non - continuous fluctuation degree in the irradiation signal. Compared with the traditional method that only uses Fourier transform or simple filtering processing, this step can capture the non - linear and time - varying characteristics of the signal and provide a higher - resolution dynamic analysis.

[0088] Non - linear analysis is carried out on the temperature change data of the phase - change material through local phase - space reconstruction and chaotic dynamics model to evaluate the degree of thermal response interference during the phase - change process, specifically including:

[0089] Digital filtering and amplitude normalization processing are carried out on the collected temperature change data of the phase - change material to obtain standardized temperature data.

[0090] Preliminary pre - processing is carried out on the collected temperature change data of the phase - change material, including filtering out random noise in the signal by using a digital filter to ensure the smoothness and stability of the signal. At the same time, through amplitude normalization processing, the original temperature data is converted into standardized temperature data for subsequent data analysis. The digital filter can select an appropriate filtering range according to the noise characteristics of the signal, and the amplitude normalization processing is realized through a normalization function to limit all temperature data within a fixed interval to provide a consistent analysis basis.

[0091] The local phase - space reconstruction method is used to reconstruct the standardized temperature data into a multi - dimensional phase - space trajectory to reflect the dynamic evolution characteristics of temperature changes.

[0092] Specifically, let the time variable be denoted as , the preset time delay be denoted as and the embedding dimension be denoted as , then the mathematical expression for constructing the phase - space vector is ; where, represents the multi - dimensional phase - space vector corresponding to the time ; is the selected embedding dimension, and its value is determined in advance according to the inherent dynamic characteristics of the phase - change temperature data; is the preset time delay, and the preset time delay and the embedding dimension together ensure that the phase - space reconstruction can accurately capture the non - linear dynamic information of temperature changes; represents the standardized temperature data.

[0093] Multidimensional phase space vector At time is the state point obtained by embedding the standardized temperature data into a high-dimensional space through local phase space reconstruction. Multiple phase space vectors are arranged in sequence according to the time series to form a complete multidimensional phase space trajectory. The phase space trajectory can capture the nonlinear dynamic characteristics of temperature changes, reflect the evolution path and internal laws of the system at different time points, such as local fluctuations, periodicity or chaotic behavior, etc., so as to provide an accurate dynamic description for subsequent nonlinear analysis.

[0094] Use the chaotic dynamics model to perform nonlinear analysis on the reconstructed multidimensional phase space trajectory, and extract parameter data that can describe the key dynamic characteristics of temperature evolution.

[0095] Specifically, let the chaotic analysis function be denoted as , then the extracted dynamic parameters are denoted as ; where represents the parameter describing the dynamic complexity or local instability of the temperature data obtained through the chaotic dynamics model, and this parameter may be calculated based on the correlation dimension, local divergence rate or other nonlinear indicators; the implementation method of the function may include but is not limited to the Grassberger-Procaccia algorithm or local Lyapunov exponent calculation, and its parameter settings are preset according to the experimental data and statistical analysis results to ensure that the extracted data fully reflects the key dynamic characteristics in the temperature evolution process.

[0096] Quantitatively evaluate the degree of thermal response interference during the phase change process based on the extracted parameter data, and generate an evaluation index for describing the degree of thermal response interference during the phase change process.

[0097] Let this evaluation index be denoted as ; where represents the evaluation index of the degree of thermal response interference during the phase change process, and the magnitude of its value reflects the significance of the interference phenomenon during the temperature change process; the function is a preset statistical mapping function, and its implementation method includes calculating the standard deviation, coefficient of variation or other statistics of , and the specific form is determined according to the experimental data. Through the value of this evaluation index , the interference characteristics of the thermal response of the phase change material during the phase change process can be accurately quantified, providing a quantitative basis for subsequent system control decisions. The larger the value, the higher the degree of thermal response interference during the phase change process, indicating that the local instability or nonlinear interference phenomenon during the temperature change process is more significant.

[0098] In this step, the local phase space reconstruction method and the chaotic dynamics model are used to perform nonlinear analysis on the temperature change data of the phase change material. By extracting the parameters that describe the key dynamic characteristics of the temperature evolution, an evaluation index of the thermal response interference degree is quantitatively generated. Different from the traditional linear analysis method or the Fourier transform method, this step can capture the nonlinear and time-varying characteristics hidden in the temperature data, reflecting the local instability and dynamic complexity of the system.

[0099] Based on the degree of discontinuous fluctuation in the irradiation signal and the degree of thermal response interference during the phase change process, identify whether there is a thermal coupling risk of nonlinear mutation, specifically including:

[0100] Extract the reflecting the degree of discontinuous fluctuation in the irradiation signal and the reflecting the degree of thermal response interference during the phase change process.

[0101] Set the threshold corresponding to the evaluation index of the degree of discontinuous fluctuation in the irradiation signal , and set the threshold corresponding to the evaluation index of the degree of thermal response interference during the phase change process .

[0102] Among them, the threshold is determined by the statistics of a large number of measured data and the analysis of the signal distribution characteristics, and is comprehensively set by error testing, probability model fitting and empirical parameter debugging to ensure that normal fluctuations and abnormal mutations can be accurately distinguished, so as to meet the requirements of the safe and stable operation of the system; the threshold is determined based on the statistical analysis of multiple groups of experimental data and the evaluation of local nonlinear indicators, combined with the chaotic parameter distribution and the calculation of local Lyapunov exponents, and is set after empirical debugging and optimization to ensure that the system can accurately identify the local instability of the temperature and ensure safe and reliable operation.

[0103] When the evaluation index of the degree of discontinuous fluctuation in the irradiation signal is greater than the threshold and the evaluation index of the degree of thermal response interference during the phase change process is greater than the threshold , it is considered that there is a significant discontinuous fluctuation in the irradiation signal, and at the same time, there is an obvious interference in the thermal response during the phase change process, so it is determined that there is a thermal coupling risk of nonlinear mutation.

[0104] When the evaluation index of the degree of discontinuous fluctuation in the irradiation signal exceeds the threshold, it indicates that there is a sudden change in the solar irradiation output; at the same time, the exceeding of the evaluation index of the degree of thermal response interference during the phase change process indicates that there is a local violent fluctuation in the temperature change. The superposition of the two will lead to abnormal heat energy transfer and imbalance of heat pump regulation, thus forming a thermal coupling risk of nonlinear mutation, that is, there is an obvious nonlinear interference between the photothermal conversion and the phase change heat storage process, endangering the stability of the system.

[0105] If there is a risk of non - linear mutation in thermal coupling, according to the difference between the phase - change response fluctuation frequency and the preset reference frequency, the collected data is evaluated in depth for the second time according to the clear index of positive or negative offset to generate an adjustment instruction, which specifically includes:

[0106] Compare the phase - change response fluctuation frequency data with the preset reference frequency, and calculate the difference between the two through subtraction operation. This difference is marked as the frequency offset.

[0107] The phase - change response fluctuation frequency data refers to the instantaneous frequency value at each moment calculated by using the Hilbert transform method after processing the temperature change data of the phase - change material through local phase - space reconstruction. This data can comprehensively reflect the local non - linear dynamic changes and oscillation characteristics of temperature during the phase - change process, and its numerical size and time - varying distribution accurately quantify the fluctuation of temperature response.

[0108] The preset reference frequency is the reference value of the normal phase - change temperature response frequency determined based on a large amount of experimental data and statistical analysis. This frequency reflects the stable operation state of the system, and its specific value is set according to the actual operating conditions of the system and is used as a key offset reference.

[0109] Perform a comparison operation on the frequency offset. If the frequency offset is greater than zero, it is determined as a positive offset; otherwise, it is determined as a negative offset, and the offset state is stored in the control processing module.

[0110] Judge the positive and negative values of the calculated frequency offset to determine the offset direction. If the frequency offset is greater than 0, it is determined as a positive offset; if the frequency offset is less than or equal to 0, it is determined as a negative offset. The offset state is stored in the control processing module and used as the basis for the adjustment strategy in the subsequent steps. This judgment process uses a simple comparison operation logic to ensure the accuracy of the offset state.

[0111] According to the offset state, use the preset mapping function to perform non - linear transformation on the frequency offset, generate an evaluation coefficient that matches the offset direction, and further form an offset index.

[0112] The mapping function is set as , then the generated offset index can be expressed as: ; where the mapping function is set according to experimental data and system requirements, and may include, but is not limited to, square mapping, exponential mapping, etc. of the offset amount to enhance or attenuate the influence of frequency offsets of different amplitudes. The offset index as the key data for quantifying the offset degree determines the generation process of the final adjustment instruction; represents the frequency offset.

[0113] Combined with the offset index, the collected raw data is comprehensively analyzed again. Specific adjustment instructions are generated through multi-level logical operations, and the adjustment instructions are transmitted to the heat pump output module in real time.

[0114] Utilize the offset index Conduct a second comprehensive analysis of the collected raw data. The comprehensive analysis process includes multi-level logical operations, combining the raw data, frequency offset, and offset index to identify specific control parameters that need to be adjusted. For example, when the offset index is large, it may be necessary to adjust the heat pump flow rate and compression ratio to cope with significant fluctuating offsets. This analysis process is carried out in the control processing module to ensure that the evaluation results accurately reflect the actual dynamic requirements of the system.

[0115] According to the results of the comprehensive analysis, adjustment instructions are generated using a preset rule base. The adjustment instructions include, but are not limited to, the adjustment parameters for the heat pump flow rate and compression ratio. The control processing module transmits the adjustment instructions to the heat pump output module in real time via wired or wireless means to ensure that the system can quickly respond to the risk of non-linear sudden thermal coupling.

[0116] The raw data here is the sensing data obtained in real time by the data acquisition module during the operation of the system, mainly including the output power of the PV / T module, irradiation signal, and phase change material temperature change data after preliminary digital filtering and amplitude normalization processing. These raw data truly reflect the immediate dynamic characteristics during system operation, retain the key physical information during sensing, and provide basic data for subsequent multi-level logical operations and in-depth comprehensive analysis using the offset index, ensuring the accuracy and reliability of the generation of adjustment instructions.

[0117] Adjust the flow rate and compression ratio according to the adjustment instructions to achieve synchronous matching between the phase change heat storage device and the PV / T module, specifically including:

[0118] After receiving the adjustment instructions, the heat pump output module issues specific control signals to the flow rate adjustment mechanism and the compressor control mechanism respectively according to the built-in control algorithm, ensuring that each unit works in coordination according to the adjustment instructions, so that the phase change heat storage device and the PV / T module maintain real-time dynamic synchronous matching during the heat energy transfer process.

[0119] The heat pump output module receives the adjustment instructions generated by the control processing module in real time through digital communication. The adjustment instructions clearly include specific numerical control parameters for the working medium flow rate and the compressor compression ratio. These parameters are determined after the aforementioned secondary in-depth evaluation and provide a basis for the subsequent generation of control signals.

[0120] According to the built-in control algorithm, the heat pump output module parses the received adjustment instructions into two independent control signals. One signal is used to indicate the control requirements of the working medium flow regulating mechanism, and the other signal is used to indicate the specific parameters for adjusting the compression ratio of the compressor control mechanism. After being digitally processed, each control signal is transmitted to its respective control unit through real-time communication.

[0121] The working medium flow regulating mechanism uses an electric control valve or a variable frequency pump device to regulate the flow of the working medium. The adjustment of the compressor operating parameters is achieved by changing the compressor speed and the gas compression ratio.

[0122] The working medium flow regulating mechanism uses an electric control valve or a variable frequency pump device to adjust the flow of the working medium according to the control signal. Its adjustment process includes changing the opening of the control valve or the speed of the variable frequency pump to ensure that the working medium is continuously and stably transmitted according to the predetermined parameters to meet the synchronous matching requirements of heat transfer.

[0123] The compressor control mechanism adjusts the speed and gas compression ratio of the compressor respectively according to the control signal. This adjustment is achieved by changing the operating frequency and mechanical transmission ratio of the internal drive system of the compressor, so that the output state of the compressor is consistent with the predetermined parameters, ensuring that each heat energy conversion unit in the system works in coordination to achieve real-time dynamic synchronous matching between the phase change heat storage device and the PV / T module.

[0124] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0125] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0126] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0127] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0128] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in an electrical, mechanical, or other form.

[0129] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module. It may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, in each embodiment of this application, each functional module can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0131] If the described function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0132] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0133] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A heat pump intelligent control system based on PV / T direct drive, characterized in that: It includes a data acquisition module, a control processing module and a heat pump output module; The data acquisition module is used to obtain the output power, irradiation signal and temperature change data of the phase change material of the PV / T module; The control processing module is used to perform the following steps: A known fuzzy logic algorithm is used to compare the output power with the ambient temperature threshold to determine whether the phase change critical region has been entered; If the critical phase change region is reached, the irradiation signal is decomposed in the time-frequency domain by Hilbert-Huang transform to evaluate the degree of discontinuous fluctuation in the irradiation signal; the temperature change data of the phase change material is nonlinearly analyzed by local phase space reconstruction and chaotic dynamics model to evaluate the degree of thermal response interference during the phase change process; Based on the degree of discontinuous fluctuation in the irradiation signal and the degree of thermal response interference during the phase change process, identify whether there is a thermal coupling risk of nonlinear mutation; If there is a risk of thermal coupling with nonlinear mutation, a secondary in-depth evaluation of the collected data is performed according to the difference between the phase change response fluctuation frequency and the preset reference frequency, and a clear indicator of positive or negative deviation is used to generate an adjustment instruction; The heat pump output module adjusts the flow rate and compression ratio according to the regulation instructions to achieve synchronous matching between the phase change heat storage device and the PV / T component.

2. According to claim 1, a heat pump intelligent control system based on PV / T light storage direct drive is characterized in that: Obtain the output power, irradiation signal and temperature change data of the PV / T module, including: The output power data of the PV / T module is collected through the output power sensor, the irradiation signal data of the PV / T module is collected through the irradiation signal sensor, and the temperature change data of the phase change material is collected through the temperature sensor; The analog signals collected by the output power sensor, the irradiation signal sensor and the temperature sensor are converted into digital signals, and the digital signals are transmitted to the control processing module.

3. According to claim 1, a heat pump intelligent control system based on PV / T light storage direct drive is characterized in that: A known fuzzy logic algorithm is used to compare the output power with the ambient temperature threshold to determine whether the phase change critical region has been entered, including: The existing fuzzy logic algorithm is used to fuzzify the output power data of the PV / T module to generate the output power membership value, and the preset ambient temperature threshold is fuzzified to generate the ambient temperature membership value; According to the pre-set membership function and fuzzy rules, the output power membership value is compared with the ambient temperature membership value, thereby generating a fuzzy logic decision result for judging whether the PV / T component enters the critical region of phase change; Whether entering the phase change critical zone is determined based on the fuzzy logic decision results.

4. According to claim 1, a heat pump intelligent control system based on PV / T light storage direct drive is characterized in that: The irradiation signal is decomposed in the time-frequency domain by Hilbert-Huang transform to evaluate the degree of discontinuous fluctuation in the irradiation signal, including: Performing preliminary preprocessing on the collected irradiation signal, including noise filtering and amplitude normalization, to obtain standardized irradiation signal data; The Hilbert-Huang transform is used to decompose the standardized irradiation signal data in the time-frequency domain and separate multiple intrinsic mode functions, wherein the Hilbert-Huang transform sets the corresponding parameters according to the time-varying characteristics of the signal. Calculate the instantaneous frequency and amplitude of each intrinsic mode function according to the signal change law, and form frequency distribution data that accurately describes the dynamic characteristics of the signal; The degree of discontinuous fluctuation in the irradiation signal is evaluated based on the frequency distribution data, and an evaluation index characterizing the degree of discontinuous fluctuation in the irradiation signal is generated.

5. According to claim 4, a heat pump intelligent control system based on PV / T light storage direct drive is characterized in that: The degree of discontinuous fluctuation in the irradiation signal is evaluated based on the frequency distribution data, and an evaluation index characterizing the degree of discontinuous fluctuation in the irradiation signal is generated, specifically: The instantaneous frequency data of all inherent mode functions are obtained to evaluate the degree of discontinuous fluctuation in the normalized irradiation signal, and an evaluation index for characterizing the degree of discontinuous fluctuation is generated. The calculation method uses a statistical analysis method to quantify the fluctuation characteristics of the instantaneous frequency data of all inherent mode functions within a predetermined time window, which is expressed as: ;in, An evaluation index that represents the degree of discontinuous fluctuation in the irradiation signal. is the statistical function, represents the number of the intrinsic mode function, is an integer and , is the total number of extracted intrinsic mode functions, Indicates The intrinsic mode function is in time The instantaneous frequency at The implementation method includes calculating each Standard deviation and coefficient of variation of the values.

6. According to claim 1, a heat pump intelligent control system based on PV / T light storage direct drive is characterized in that: The temperature change data of phase change materials are analyzed nonlinearly through local phase space reconstruction and chaotic dynamics model to evaluate the degree of thermal response interference during the phase change process, including: Performing digital filtering and amplitude normalization processing on the collected temperature change data of the phase change material to obtain standardized temperature data; The local phase space reconstruction method is used to reconstruct the standardized temperature data into a multi-dimensional phase space trajectory to reflect the dynamic evolution characteristics of temperature changes; The chaotic dynamics model is used to perform nonlinear analysis on the reconstructed multi-dimensional phase space trajectory to extract parameter data that can describe the key dynamic characteristics of temperature evolution; The degree of thermal response interference during the phase change process is quantitatively evaluated based on the extracted parameter data, and an evaluation index for describing the degree of thermal response interference during the phase change process is generated.

7. The heat pump intelligent control system based on PV / T light storage direct drive according to claim 6 is characterized in that: The local phase space reconstruction method is used to reconstruct the standardized temperature data into a multidimensional phase space trajectory to reflect the dynamic evolution characteristics of temperature changes, specifically: Let the time variable be , the preset time delay is recorded as and the embedding dimension is denoted as , the mathematical expression for constructing the phase space vector is ;in, Indicates at time The corresponding multidimensional phase space vector, Represents normalized temperature data; Multidimensional phase space vector It is at the moment Under the condition of local phase space reconstruction, the standardized temperature data is embedded into the state point obtained in the high-dimensional space; multiple phase space vectors are arranged in sequence according to the time series to form a complete multi-dimensional phase space trajectory.

8. The heat pump intelligent control system based on PV / T light storage direct drive according to claim 1 is characterized in that: Based on the degree of discontinuous fluctuation in the irradiation signal and the degree of thermal response interference during the phase change process, identify whether there is a thermal coupling risk of nonlinear mutation, including: Set the threshold corresponding to the evaluation index of the discontinuous fluctuation degree in the irradiation signal , set the threshold value corresponding to the evaluation index of the degree of thermal response interference during phase change ; When the evaluation index of the discontinuous fluctuation degree in the irradiation signal is greater than the threshold And the evaluation index of the degree of thermal response interference during the phase change process is greater than the threshold When , it is determined that there is a thermal coupling risk of nonlinear mutation.

9. The heat pump intelligent control system based on PV / T light storage direct drive according to claim 1 is characterized in that: If there is a risk of thermal coupling of nonlinear mutation, a secondary in-depth evaluation of the collected data is performed according to the difference between the phase change response fluctuation frequency and the preset reference frequency, and clear indicators of positive or negative deviation are used to generate adjustment instructions, including: The phase change response fluctuation frequency data is numerically compared with the preset reference frequency, and the difference between the two is calculated by subtraction operation, and the difference is marked as the frequency offset; Comparing and operating the frequency offset, if the frequency offset is greater than zero, it is determined to be a positive offset, otherwise it is determined to be a negative offset, and the offset state is stored in the control processing module; According to the offset state, a preset mapping function is used to perform nonlinear transformation on the frequency offset, generate an evaluation coefficient matching the offset direction, and further form an offset index; Combined with the offset index, the collected raw data is analyzed again, and specific adjustment instructions are generated through multi-level logical operations, and the adjustment instructions are transmitted to the heat pump output module in real time.

10. The heat pump intelligent control system based on PV / T light storage direct drive according to claim 1 is characterized in that: Adjust the flow rate and compression ratio according to the regulation instructions to achieve synchronous matching between the phase change thermal storage device and the PV / T component, specifically including: After receiving the adjustment instruction, the heat pump output module sends specific control signals to the flow adjustment mechanism and the compressor control mechanism respectively according to the built-in control algorithm to ensure that each unit works in coordination according to the adjustment instruction, so that the phase change heat storage device and the PV / T component maintain real-time dynamic synchronization matching during the heat energy transfer process; The working medium flow regulating mechanism regulates the flow of the working medium, and the adjustment of the compressor operating parameters is achieved by changing the compressor speed and the gas compression ratio.

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