Intelligent heat pump control system based on PV / T light storage direct drive

Abstract

The invention discloses an intelligent heat pump control system based on PV / T optical storage direct drive, and particularly relates to the technical field of energy control, which realizes real-time acquisition and comprehensive analysis of PV / T assembly output power, irradiation signals and phase change material temperature change data by adopting a data acquisition module, a control processing module and a heat pump output module; the method comprises the following steps: performing quantitative evaluation on discontinuous fluctuation and thermal response interference in a signal by adopting a fuzzy logic algorithm, Hilbert-Huang transform and a local phase space reconstruction and chaos dynamics model so as to identify a thermal coupling risk of nonlinear abrupt change, and generating an adjustment instruction according to a difference value between a phase change response fluctuation frequency and a preset reference frequency; the heat pump output module adjusts the working medium flow and the compression ratio in real time according to the adjusting instruction, synchronous matching of the phase change heat storage device and the PV / T assembly is achieved, and the energy density and the temperature control precision of the system are effectively improved.

Description

Technical Field

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

[0002] Phase change materials, as efficient heat storage media, can improve the energy density and temperature control accuracy of PV / T direct-drive heat pump systems. PV / T refers to solar photovoltaic / thermal, which achieves dual energy utilization by capturing both electrical and thermal energy from 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 sudden thermal response phenomena, especially in the critical interval of phase change, where the heat transfer characteristics will change suddenly. This nonlinear mutation forms a coupling effect with the continuous and stable regulation output of the heat pump, which may lead to response delays, local thermal imbalance and system oscillation, thereby affecting the dynamic performance and stable operation of the system. Traditional control strategies based on linear models are difficult to effectively deal with such nonlinear 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 light storage direct drive to solve the problems raised in the above-mentioned background technology.

[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 light storage direct drive, including 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, irradiation signal and temperature change data of the phase change material of the PV / T module;

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

[0009] 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;

[0010] 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;

[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 nonlinear mutation;

[0012] 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;

[0013] 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.

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

[0015] 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;

[0016] 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.

[0017] In a preferred embodiment, 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, specifically including:

[0018] 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;

[0019] 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;

[0020] Whether entering the phase change critical zone is determined based on the fuzzy logic decision results.

[0021] In a preferred embodiment, 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, specifically including:

[0022] Performing preliminary preprocessing on the collected irradiation signal, including noise filtering and amplitude normalization, 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 separate multiple intrinsic mode functions, wherein the Hilbert-Huang transform sets the corresponding parameters according to the time-varying characteristics of the signal.

[0024] 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;

[0025] 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.

[0026] In a preferred embodiment, 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:

[0027] 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

[0028] The implementation method includes calculating each Standard deviation and coefficient of variation of the values.

[0029] In a preferred embodiment, 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, specifically including:

[0030] Performing digital filtering and amplitude normalization processing on the collected temperature change data of the phase change material 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 temperature changes;

[0032] 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;

[0033] 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.

[0034] In a preferred embodiment, 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, specifically:

[0035] 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;

[0036] 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.

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

[0038] 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 ;

[0039] 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.

[0040] In a preferred embodiment, 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 a clear indicator of positive or negative deviation is used to generate an adjustment instruction, which specifically includes:

[0041] 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;

[0042] 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;

[0043] 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;

[0044] 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.

[0045] In a preferred embodiment, the flow rate and compression ratio are adjusted according to the regulation instruction to achieve synchronous matching between the phase change heat storage device and the PV / T component, specifically including:

[0046] 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;

[0047] 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.

[0048] The technical effects and advantages of the heat pump intelligent control system based on PV / T light storage direct drive of the present invention are as follows:

[0049] 1. The present invention uses fuzzy logic algorithm, Hilbert-Huang transform, local phase space reconstruction and chaotic dynamics model to comprehensively analyze the output power of solar photovoltaic / thermal components, irradiation signals and temperature change data of phase change materials, realizes quantitative evaluation of discontinuous fluctuations in irradiation signals and thermal response interference phenomena in phase change processes, and accurately identifies the thermal coupling risk of nonlinear mutations. This technical solution enables the system to capture the dynamic changes of temperature and irradiation signals in real time, generate precise adjustment instructions, and adjust the flow rate and compression ratio of the heat pump output end in real time according to the instructions, thereby realizing the synchronous matching of heat energy transfer between the phase change heat storage device and the photovoltaic / thermal components, and significantly improving the overall energy density and temperature control accuracy of the system.

[0050] 2. Through multi-level nonlinear data processing and evaluation, the present invention overcomes the problems of response delay, local thermal imbalance and system oscillation that occur in traditional linear control methods when dealing with the sudden 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 This is a schematic diagram of the structure of a heat pump intelligent control system based on PV / T light storage direct drive of the present invention;

[0052] Figure 2 The flowchart is a control process module execution step of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example: Figure 1 A structural schematic diagram of a heat pump intelligent control system based on PV / T solar energy storage direct drive of the present invention is given, which 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, irradiation signal and temperature change data of the phase change material of the PV / T module.

[0056] Figure 2 A flowchart of the execution steps of the control processing module of the present invention is given, and the control processing module is used to execute the following steps:

[0057] 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;

[0058] 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;

[0059] 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;

[0060] 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, with clear indicators of positive or negative deviation, to generate adjustment instructions.

[0061] 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.

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

[0063] 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.

[0064] 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.

[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 component in real time. The output power sensor adopts a sensor element with high sampling rate and wide bandwidth characteristics. The analog signal collected by it accurately reflects the instantaneous value of the electric energy converted by the PV / T component; the irradiation signal sensor is used to detect and record the photoelectric signal related to the solar radiation intensity in the environment where the PV / T component is located. The irradiation signal sensor outputs the irradiation information of the sunlight in the environment in the form of an analog electrical signal through a built-in photosensitive element and a spectrum decomposition circuit; the temperature sensor is used to collect and record the analog signal reflecting the temperature change of the phase change material. The temperature sensor adopts a high-precision thermocouple or thermistor element, and the analog signal output by it has 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, which amplifies and pre-processes the analog signals collected by the output power sensor, the irradiation signal sensor and the temperature sensor, and converts the continuous analog signals into digital signals using analog-to-digital conversion technology. The digital signals have clear time stamps and are transmitted to the control processing module via wired or wireless means, thereby achieving sufficient and continuous collection of PV / T component output power, irradiation signals and phase change material temperature change data.

[0067] 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:

[0068] The existing fuzzy logic algorithm is used to fuzzify the output power data of the PV / T component to generate an output power membership value. At the same time, the preset ambient temperature threshold is fuzzified to generate an ambient temperature membership value.

[0069] In this process, the output power data is represented by the actual measured value, and its value range is determined by the actual output power. In order to ensure the accuracy of the data in the fuzzy processing, the pre-defined output power membership function is used. For example, the output power membership function can be expressed by the following formula: M1=f (P1); where P1 represents the actual collected PV / T module output power value, in watts; M1 represents the fuzzy membership value of the output power, which is limited to between 0 and 1; function f It is the preset output power membership function, and its design is determined based on experimental data and statistical laws.

[0070] At the same time, a known fuzzy logic algorithm is used to fuzzify the preset ambient temperature threshold and generate an ambient temperature membership value. In this step, the ambient temperature threshold is represented by a preset temperature value in degrees Celsius, and is fuzzy converted through a preset ambient temperature membership function, specifically expressed as: M2=f_b(T1); wherein T1 represents the preset ambient temperature threshold; M2 represents the fuzzy membership value of the ambient temperature, and its value range is 0 to 1; function f_b is a preset ambient temperature membership function, and its design is also determined based on the actual distribution of the ambient temperature and the results of statistical analysis.

[0071] According to the preset 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.

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

[0073] Whether entering the phase change critical zone is determined based on the fuzzy logic decision results.

[0074] The fuzzy logic decision result D is compared with a preset decision threshold to determine whether the PV / T component has entered the phase change critical region; specifically, if D is less than a preset decision threshold T0 (where T0 is a constant between 0 and 1), the PV / T component is determined to have entered the phase change critical region; otherwise, if D is greater than or equal to T0, the PV / T component is determined not to have entered the phase change critical region.

[0075] It is worth noting that the output power membership function f , ambient temperature membership function f_b and fuzzy reasoning function g_c are all pre-defined functions, whose definitions and parameter values ​​are determined by experimental data and statistical analysis methods, and remain consistent in system implementation.

[0076] 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:

[0077] The collected irradiation signal is pre-processed, including noise filtering and amplitude normalization, to obtain standardized irradiation signal data.

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

[0079] For the sake of clarity, let the original irradiation signal data be recorded as , whose unit is volt; normalization is performed using the normalization function , the normalization function is defined as: ;in, Represents the normalized irradiation signal data, and its value is limited to the interval [0, 1]; is the normalization function, and its specific form is: ;in, and Respectively represent the minimum and maximum values ​​measured during the calibration process.

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

[0081] Specifically, the empirical mode decomposition method is used to Decomposed into several intrinsic mode functions, the decomposition expression can be expressed as: ;in, represents the time variable; Indicates The intrinsic mode functions, 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 discontinuous fluctuation degree in the irradiation signal, the higher the degree of mutational change in the irradiation signal, which also means the greater the discontinuous fluctuation degree in the irradiation signal.

[0087] This step uses the Hilbert-Huang transform to decompose the radiation signal in the time-frequency domain, and combines digital filtering and amplitude normalization preprocessing to accurately separate the intrinsic mode functions in the signal, calculate the instantaneous frequency and amplitude of each intrinsic mode function, and then use statistical analysis methods to generate quantitative evaluation indicators to objectively reflect the sudden changes and discontinuous fluctuations in the radiation signal. Compared with the traditional method of only using Fourier transform or simple filtering, this step can capture the nonlinear and time-varying characteristics of the signal and provide higher-resolution dynamic analysis.

[0088] 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:

[0089] The collected temperature change data of the phase change material are digitally filtered and amplitude normalized to obtain standardized temperature data.

[0090] The collected phase change material temperature change data is pre-processed, including using a digital filter to filter out random noise in the signal to ensure the smoothness and stability of the signal, and converting the original temperature data into standardized temperature data through amplitude normalization processing to facilitate subsequent data analysis. The digital filter can select a suitable filtering range according to the noise characteristics of the signal, and the amplitude normalization processing is implemented through a normalization function, so that all temperature data are limited to a fixed range to provide a consistent analysis basis.

[0091] 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.

[0092] Specifically, let the time variable be , the preset time delay is recorded as and the embedding dimension is denoted as , then the mathematical expression for constructing the phase space vector is ;in, Indicates at time The corresponding multidimensional phase space vector; is the selected embedding dimension, whose value is predetermined according to the intrinsic dynamic characteristics of the phase change temperature data; The preset time delay and the embedding dimension together ensure that the phase space reconstruction can accurately capture the nonlinear dynamic information of temperature change; Represents normalized temperature data.

[0093] 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 high-dimensional space. Multiple phase space vectors are arranged in time series to form a complete multi-dimensional phase space trajectory. The phase space trajectory can capture the nonlinear dynamic characteristics of temperature changes and reflect the evolution path and internal laws of the system at different time points, such as local fluctuations, periodicity or chaotic behavior, thereby providing an accurate dynamic description for subsequent nonlinear analysis.

[0094] The chaotic dynamics model is used 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 chaos analysis function be , then the extracted dynamic parameters are recorded as ;in, It represents the parameter describing the dynamic complexity or local instability of temperature data obtained by the chaotic dynamics model. The parameter may be calculated based on the correlation dimension, local divergence rate or other nonlinear indicators. Function The implementation method may include but is not limited to the Grassberger-Procaccia algorithm or the 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] 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.

[0097] Suppose the evaluation index is ;in, The evaluation index of the degree of thermal response interference during phase change, and its value reflects the significance of the interference phenomenon during temperature change; function is a preset statistical mapping function, and its implementation method includes The calculation of standard deviation, coefficient of variation or other statistics is determined by experimental data. The numerical value can accurately quantify the interference characteristics of the thermal response of the phase change material during the phase change process, providing a quantitative basis for subsequent system control decisions. The larger the value is, the higher the degree of thermal response interference is during the phase change process, which reflects that the local instability or nonlinear interference phenomenon is more significant during the temperature change process.

[0098] This step uses the local phase space reconstruction method and chaotic dynamics model to perform nonlinear analysis on the temperature change data of phase change materials, and extracts parameters that describe the key dynamic characteristics of temperature evolution to quantitatively generate evaluation indicators of the degree of thermal response interference. Unlike traditional linear analysis methods or Fourier transform methods, this step can capture the nonlinear and time-varying characteristics implicit in 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, including:

[0100] Extract the non-continuous fluctuation degree of the irradiation signal and the degree of interference of thermal response during phase change .

[0101] 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 .

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

[0103] When the evaluation index of the discontinuous fluctuation degree in the irradiation signal is Greater than threshold And the evaluation index of the degree of thermal response interference during phase change Greater than threshold When , it is considered that there are significant discontinuous fluctuations in the irradiation signal, and at the same time, there is obvious interference in the thermal response during the phase change process, thus determining that there is a risk of thermal coupling with nonlinear mutations.

[0104] When the evaluation index of the discontinuous fluctuation degree 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 evaluation index of the degree of thermal response interference in the phase change process exceeds the standard, indicating that there is a local drastic fluctuation in temperature change. The superposition of the two will lead to abnormal heat energy transfer and imbalance in heat pump regulation, thus forming a nonlinear sudden thermal coupling risk, that is, obvious nonlinear interference between light-heat conversion and phase change heat storage process, endangering the stability of the system.

[0105] 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:

[0106] 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.

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

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

[0109] A comparison operation is performed on 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. The offset state is stored in the control processing module.

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

[0111] 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.

[0112] The mapping function is set to , then the generated offset index It can be expressed as: ; Among them, the mapping function According to experimental data and system requirements, it may include but is not limited to square mapping, exponential mapping, etc. of the offset to enhance or attenuate the frequency offset effects of different amplitudes. As the key data for quantifying the degree of deviation, it determines the generation process of the final adjustment instruction; Indicates the frequency offset.

[0113] 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.

[0114] Using the deviation indicator The collected raw data is analyzed again. The comprehensive analysis process includes multi-level logical operations, combining the raw data, frequency offset and offset index to identify the specific control parameters that need to be adjusted. For example, when the offset index is large, the heat pump flow and compression ratio may need to be adjusted to cope with significant fluctuation offsets. This analysis process is performed in the control processing module to ensure that the evaluation results accurately reflect the actual dynamic needs of the system.

[0115] According to the comprehensive analysis results, the preset rule base is used to generate adjustment instructions. The adjustment instructions include but are not limited to the adjustment parameters of the heat pump flow 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 thermal coupling risk of nonlinear mutations.

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

[0117] 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:

[0118] After receiving the adjustment instruction, the heat pump output module sends specific control signals to the flow regulation mechanism and the compressor control mechanism according to the built-in control algorithm to ensure that each unit works together 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.

[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 subsequent control signal generation.

[0120] Based on 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 regulation mechanism, and the other signal is used to instruct the compressor control mechanism to adjust the specific parameters of the compression ratio. After digital processing, each control signal is transmitted to its respective control unit through real-time communication.

[0121] The working medium flow regulating mechanism adopts an electric regulating valve or a variable frequency pump device to regulate 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.

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

[0123] The compressor control mechanism adjusts the compressor speed and gas compression ratio according to the control signal; the adjustment is achieved by changing the operating frequency and mechanical transmission ratio of the compressor's internal drive system, so that the compressor output state is consistent with the predetermined parameters, ensuring that the various thermal energy conversion units in the system work together and realizing real-time dynamic synchronous matching between the phase change heat storage device and the PV / T components.

[0124] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0125] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0126] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this 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 aforementioned method embodiments and will not be repeated here.

[0128] In the 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0129] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0131] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0132] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0133] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in 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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