Photovoltaic direct-drive intelligent conversion air conditioner control method

Abstract

The application relates to the field of air conditioners, and particularly discloses a photovoltaic direct-drive intelligent conversion air conditioner control method, which comprises the following steps: collecting waveform features of photovoltaic direct-current electricity in real time through a sine wave positioning detection unit and generating an electric energy quality evaluation result based on waveform distortion rate analysis; performing collaborative analysis on the electric energy quality evaluation result and current operation state parameters of an air conditioner system to generate a photovoltaic power supply feasibility judgment result; based on the judgment result, performing collaborative control on a photovoltaic direct-drive control unit and an MPPT board to realize undisturbed switching and power distribution of photovoltaic electric energy and commercial power; and dynamically adjusting the compressor operation frequency according to the power distribution result to match the compressor power with the real-time available photovoltaic power. The application omits a traditional inverter device, reduces system cost, improves electric energy utilization efficiency, effectively solves the problems of unstable operation of a photovoltaic air conditioner system and low energy utilization rate through intelligent switching and load matching mechanisms.

Description

Technical Field

[0001] This invention relates to the field of air conditioning technology, and in particular to a photovoltaic direct-drive intelligent conversion air conditioning control method. Background Technology

[0002] With the increasing demands for air conditioning energy efficiency and the promotion of renewable energy applications, photovoltaic direct-drive air conditioning technology has received widespread attention in recent years. Traditional photovoltaic air conditioning systems typically adopt a "photovoltaic-inverter-air conditioner" architecture, where the DC power generated by photovoltaics must first be converted to AC power by an inverter, and then converted back to DC power by the rectifier circuit inside the air conditioner to drive the compressor. This DC-AC-DC conversion process has significant technical drawbacks: First, inverter devices are expensive, significantly increasing the initial investment cost of the system; second, multi-stage conversion leads to power losses as high as 10% to 15%, reducing the overall energy efficiency of the system; third, traditional solutions lack effective identification and response mechanisms for fluctuations in the quality of photovoltaic power, which can easily cause voltage instability when the light intensity changes, leading to frequent protection shutdowns of the air conditioning system.

[0003] In existing technologies, some improved solutions attempt to achieve switching between photovoltaic (PV) and mains power through simple light intensity detection. However, this method only considers light parameters and fails to deeply analyze the waveform quality of PV DC power, making it unable to effectively identify power quality issues such as harmonic interference and voltage spikes. Furthermore, traditional control strategies lack a coordinated optimization mechanism between air conditioning load and power supply capacity. When PV power is insufficient, they often directly switch to mains power instead of adjusting the compressor load to match available PV power, resulting in low PV energy utilization. During the switching process, the lack of precise phase synchronization and power gradient control easily leads to large current surges, affecting system stability and equipment lifespan.

[0004] Therefore, there is an urgent need in this field for a photovoltaic direct-drive air conditioning control method that can directly utilize photovoltaic DC power, realize intelligent power quality management, and seamlessly switch power supplies, in order to solve the problems of high cost, low efficiency, and poor stability in the existing technology. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a photovoltaic direct-drive intelligent conversion air conditioning control method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A photovoltaic direct-drive intelligent conversion air conditioning control method includes real-time acquisition of the waveform characteristics of photovoltaic DC power by a sine wave positioning detection unit, and generation of power quality assessment results based on waveform distortion rate analysis; the sine wave positioning detection unit includes a nanochip group for performing high-frequency sampling, waveform analysis and power quality assessment, and transmitting the power quality assessment results to the photovoltaic direct-drive control unit in real time;

[0008] The power quality assessment results are analyzed in conjunction with the current operating status parameters of the air conditioning system to generate a feasibility assessment result for photovoltaic power supply.

[0009] Based on the feasibility assessment of photovoltaic power supply, the photovoltaic direct drive control unit and the MPPT board work together to achieve seamless switching and power distribution between photovoltaic power and mains power.

[0010] Based on the power allocation results, the operating frequency of the air conditioning system compressor is dynamically adjusted to match the compressor power with the real-time available photovoltaic power, thereby achieving adaptive optimization of the air conditioning system load and power supply capacity.

[0011] As a further technical solution of the present invention, the step of acquiring the waveform characteristics of photovoltaic DC power in real time through a sinusoidal wave positioning and detection unit, and generating power quality assessment results based on waveform distortion rate analysis, specifically includes:

[0012] Voltage waveform data is acquired in real time at a sampling frequency of not less than 10kHz by a high-frequency sampling circuit set in the photovoltaic DC circuit.

[0013] Fourier transform is performed on the collected voltage waveform data to extract the fundamental component and each harmonic component, and the total harmonic distortion rate is calculated.

[0014] Analyze the fluctuation amplitude of the voltage waveform per unit time and count the number of times the voltage sudden change exceeds the set threshold;

[0015] The waveform smoothness index is calculated using a sliding time window algorithm to evaluate the stability of the waveform in the time domain.

[0016] The total harmonic distortion rate, voltage fluctuation amplitude statistics, and waveform smoothness index are input into a preset power quality assessment model, and the power quality level assessment result is output.

[0017] As a further technical solution of the present invention, the calculation of total harmonic distortion rate, the number of times the voltage surge exceeds a set threshold, and the calculation of waveform smoothness index through a sliding time window algorithm specifically include:

[0018] For the collected voltage waveform data Perform Fourier transform to extract the fundamental component. With each harmonic component Calculate the total harmonic distortion (THD): ,in: It is the amplitude of the fundamental component; It is the first The amplitude of the second harmonic component;

[0019] Analyze the voltage waveform per unit time The fluctuation range within the range, and the statistical voltage change exceeding the set threshold. Number of times : ,in: It is a unit of time Number of sampling points within; It is the set threshold for voltage surge. It is the change in voltage value between two consecutive sampling points; It is an indicator function, when the condition is met. The value is 1 if the condition is met, and 0 otherwise.

[0020] Waveform smoothness index calculated using a sliding time window algorithm. Evaluate the stability of the waveform in the time domain; let the sliding time window length be... The waveform smoothness index Defined as: ,in: It is the number of sampling points within the sliding time window; It is the maximum voltage change within the window.

[0021] As a further technical solution of the present invention, the total harmonic distortion rate, voltage fluctuation amplitude statistics, and waveform smoothness index are input into a preset power quality assessment model, and the power quality level assessment result is output, specifically including:

[0022] Total Harmonic Distortion Voltage fluctuation amplitude statistics Waveform smoothness index They were each normalized separately to convert them into a unified evaluation score. , and ;

[0023] The three evaluation scores are weighted and calculated to generate a weighted comprehensive evaluation value. : ,in: ;

[0024] Weighted comprehensive evaluation value The final power quality level assessment result is determined by comparing it with five preset quality level thresholds: when The power quality level is excellent; when The power quality level is good; when The power quality level is medium; when The power quality level is poor; when The power quality level is unqualified.

[0025] As a further technical solution of the present invention, the power quality assessment results are analyzed in conjunction with the current operating status parameters of the air conditioning system to generate a photovoltaic power supply feasibility judgment result, specifically including:

[0026] Obtain the power quality level from the power quality assessment results, and simultaneously collect the operating status parameters of the air conditioning system in real time, including the compressor load rate. (Percentage), Temperature difference between indoor and outdoor heat exchangers (°C), System operating frequency ( );

[0027] Based on the power quality level, determine the corresponding threshold range of air conditioning system operating parameters;

[0028] The actual operating parameters of the current air conditioning system are compared and analyzed with the threshold range. If all operating parameters are within the allowable range: ,in: , , , , , The minimum and maximum values ​​of the compressor load rate, indoor and outdoor heat exchanger temperature difference, and system operating frequency threshold range are used to generate a judgment result that allows the use of photovoltaic power supply; if any operating parameter exceeds the allowable range, a judgment result that restricts the use of photovoltaic power supply is generated.

[0029] When the ambient temperature Temperatures exceeding 35°C and temperature difference between indoor and outdoor heat exchangers Less than 5℃, that is When this happens, a protective judgment strategy is activated, raising the threshold requirements for operating parameters, and only generating a judgment result allowing the use of photovoltaic power supply when the power quality level is excellent or good.

[0030] As a further technical solution of the present invention, the process of collaboratively analyzing the power quality assessment results with the current operating status parameters of the air conditioning system also adopts a dynamic adjustment mechanism to optimize the operating efficiency of the air conditioning system and the reliability of photovoltaic power supply, specifically including:

[0031] Collect historical operation datasets, including power quality level, compressor load rate, indoor and outdoor heat exchanger temperature difference, system operating frequency, and corresponding system energy efficiency ratio and operation stability indicators;

[0032] A multiple linear regression model was established with power quality level, ambient temperature, and system running time as independent variables and the optimal threshold range of each operating parameter as the dependent variable.

[0033] The linear regression model is retrained weekly, and the regression coefficients are updated so that the model can adapt to the impact of seasonal changes and equipment aging on system operation.

[0034] The real-time collected power quality level, ambient temperature, and system uptime are input into a trained linear regression model, which outputs dynamically adjusted threshold ranges for each operating parameter. , , , , and ;

[0035] When the threshold range predicted by the model deviates from the current actual threshold range by more than 15%, an expert verification mechanism is activated, and the new threshold range is applied only after the verification is successful.

[0036] As a further technical solution of the present invention, based on the feasibility assessment results of photovoltaic power supply, the photovoltaic direct drive control unit and the MPPT board are used to achieve seamless switching and power distribution between photovoltaic power and mains power, specifically including:

[0037] When the feasibility assessment result of photovoltaic power supply is that photovoltaic power supply is allowed, the photovoltaic direct drive control unit first realizes the phase synchronization between photovoltaic DC power and mains power through phase-locked loop technology; the MPPT board continuously works in maximum power point tracking mode to maximize the energy efficiency of the photovoltaic panel output; the photovoltaic direct drive control unit prioritizes the allocation of photovoltaic power supply according to the real-time power demand of the air conditioning system, and the insufficient part is automatically compensated by the mains power.

[0038] When the feasibility assessment result for photovoltaic power supply is that the use of photovoltaic power supply is restricted, the photovoltaic direct drive control unit will limit the proportion of photovoltaic power supply to 30% to 50% of the total power demand; the MPPT board will be adjusted to the power limiting output mode to ensure that the photovoltaic power output is stable within the set range; the mains power supply will serve as the main power source to provide basic power support.

[0039] As a further technical solution of the present invention, to achieve seamless switching between photovoltaic power and mains power, the specific steps are as follows:

[0040] When it is necessary to switch from photovoltaic power supply to mains power, the photovoltaic direct drive control unit first gradually reduces the photovoltaic power output through the MPPT board, while increasing the mains power input by the same amount to keep the total output power constant; when the photovoltaic power drops to near zero, the photovoltaic power supply path is completely cut off.

[0041] When switching from mains power to photovoltaic power, the photovoltaic direct drive control unit first verifies that the photovoltaic power quality meets the switching conditions; then, it gradually increases the photovoltaic power output through the MPPT board while correspondingly reducing the mains power; when the photovoltaic power reaches the target value, the switching process is completed.

[0042] During the seamless switching process, power fluctuations are controlled within ±5% of the rated power, and the switching time does not exceed 100 milliseconds, ensuring that the operation of the air conditioning system is not affected.

[0043] As a further technical solution of the present invention, the operating frequency of the air conditioning system compressor is dynamically adjusted according to the power allocation result so that the compressor power matches the real-time available photovoltaic power, specifically including:

[0044] Real-time acquisition of photovoltaic power supply values ​​in power allocation results and mains power supplement value At the same time, obtain the compressor's required power. ;

[0045] Calculate the difference between the compressor's required power and the real-time available photovoltaic power. Based on the difference Determine the direction and magnitude of compressor frequency adjustment;

[0046] During dynamic adjustment, the compressor frequency change rate is limited to ±5Hz / second to ensure stable operation of the air conditioning system. If the calculated rate of change of frequency exceeds this range, then amplitude limiting is performed. ,in: It is the adjusted compressor frequency. This is the current compressor frequency. It is a symbolic function; It is the change in frequency. ; It is the sampling time interval;

[0047] At the same time, a power matching degree evaluation index is established, and the frequency adjustment parameters are automatically optimized when the matching degree is lower than the preset threshold.

[0048] As a further technical solution of the present invention, the difference between the power required by the compressor and the real-time available photovoltaic power is calculated. Based on the difference Determine the direction and magnitude of compressor frequency adjustment, specifically including:

[0049] When the real-time available photovoltaic power is greater than the compressor's required power, the compressor's operating frequency is increased by a first preset ratio so that the system can make full use of the surplus photovoltaic power.

[0050] When the real-time available photovoltaic power is less than the power required by the compressor, the compressor operating frequency is reduced by the second preset ratio to keep the system power demand and power supply capacity in balance.

[0051] When the real-time available photovoltaic power fluctuation exceeds the set threshold, the anti-disturbance adjustment mechanism is activated, and the compressor frequency change rate is smoothly adjusted through the proportional-integral-derivative control principle.

[0052] The beneficial effects of this invention are as follows:

[0053] 1. Achieve high efficiency, energy saving and cost optimization. Through the DC-DC direct conversion architecture, the inverter device is eliminated, reducing system costs by 25% to 30%, reducing conversion losses, improving energy utilization efficiency by 10% to 13%, and achieving a photovoltaic power supply ratio of over 90%.

[0054] 2. Achieve intelligent switching and stable operation. Based on multi-dimensional power quality assessment, realize disturbance-free intelligent switching between photovoltaic power and grid power, shorten the switching time to less than 100ms, control power fluctuation within ±5%, and effectively avoid system jitter and frequent equipment start-up and shutdown.

[0055] 3. Achieve adaptive matching and performance improvement. By dynamically matching the compressor frequency and photovoltaic power, the system energy efficiency ratio is improved to 3.6-4.1, and the compressor start-stop frequency is reduced by 60%-70%. Combined with a self-learning algorithm, the operating parameters are continuously optimized. Attached Figure Description

[0056] Figure 1 This is a flowchart of a photovoltaic direct-drive intelligent conversion air conditioning control method proposed in this invention;

[0057] Figure 2 A block diagram of a sinusoidal wave positioning and detection unit;

[0058] Figure 3 This is a diagram of the architecture of a power quality assessment model.

[0059] Figure 4 This is a comparison diagram of the effects of the present invention and the prior art. Detailed Implementation

[0060] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0061] Please see the appendix Figure 1 - Appendix Figure 3 A photovoltaic direct-drive intelligent conversion air conditioning control method, comprising:

[0062] S1. The waveform characteristics of photovoltaic DC power are acquired in real time through a sinusoidal wave positioning and detection unit, and power quality assessment results are generated based on waveform distortion rate analysis. The sinusoidal wave positioning and detection unit includes a nanochip group for performing high-frequency sampling, waveform analysis, and power quality assessment, and transmits the power quality assessment results to the photovoltaic direct drive control unit in real time. Specifically, it includes:

[0063] S11. By using a high-frequency sampling circuit installed in the photovoltaic DC circuit, at a sampling frequency Real-time acquisition of voltage waveform data .

[0064] S12. For the acquired voltage waveform data Perform Fourier transform to extract the fundamental component. With each harmonic component Calculate the total harmonic distortion (THD): ,in: It is the amplitude of the fundamental component; It is the first The amplitude of the second harmonic component.

[0065] S13. Analyze the voltage waveform per unit time. The fluctuation range within the range, and the statistical voltage change exceeding the set threshold. Number of times : ,in: It is a unit of time Number of sampling points within; It is the set threshold for voltage surge. It is the change in voltage value between two consecutive sampling points; It is an indicator function, when the condition is met. The value is 1 when it is true, and 0 otherwise.

[0066] S14. Calculate the waveform smoothness index using the sliding time window algorithm. Evaluate the stability of the waveform in the time domain; let the sliding time window length be... The waveform smoothness index Defined as: ,in: It is the number of sampling points within the sliding time window; It is the maximum voltage change within the window.

[0067] S15. The power quality assessment model is constructed based on a multi-layer neural network. The input layer of the power quality assessment model corresponds to three waveform feature parameters. , and Specifically, it includes:

[0068] S151. Regarding total harmonic distortion Voltage fluctuation amplitude statistics Waveform smoothness index They were each normalized separately to convert them into a unified evaluation score. , and :

[0069]

[0070]

[0071]

[0072] in: and These are the minimum and maximum values ​​of the total harmonic distortion rate; and These are the minimum and maximum values ​​of the number of voltage fluctuations; and These are the minimum and maximum values ​​of the waveform smoothness index.

[0073] S152. Calculate the weighted average of the three evaluation scores to generate a weighted comprehensive evaluation value. : ,in: ,For example , , When the photovoltaic DC power supply is detected to be in an unstable operating state, the weight allocation is automatically adjusted, increasing the weight of the voltage fluctuation amplitude statistics value to 0.5, and correspondingly reducing the weight of other parameters. ;

[0074] S153. Weighted composite evaluation value The final power quality level assessment result is determined by comparing the result with five preset quality level thresholds.

[0075] when The power quality level is excellent; when The power quality level is good; when The power quality level is medium; when The power quality level is poor; when The power quality level is unqualified.

[0076] like Figure 2 As shown: The sine wave positioning and detection unit specifically includes:

[0077] Hardware components: The high-frequency sampling module is equipped with a voltage / current sensor with a sampling frequency ≥10kHz; the waveform analysis module performs waveform feature extraction and distortion rate calculation; the quality assessment module performs preliminary quality judgment based on a preset algorithm; and the communication module realizes data transmission with the photovoltaic direct drive control unit.

[0078] Core components: a nanochipset integrating all processing functions into a dedicated chip; a cache unit temporarily stores sampled data and analysis results; and a dedicated processor is responsible for waveform feature extraction and real-time analysis.

[0079] like Figure 3 As shown: The architecture of the power quality assessment model is as follows:

[0080] Input layer: Receives three key parameters (total harmonic distortion, voltage fluctuation amplitude statistics, and waveform smoothness index).

[0081] Data processing layer: Normalization processing, converting parameters of different dimensions into a unified scoring system; weight allocation, default weights (distortion rate 0.5, fluctuation range 0.3, smoothness 0.2).

[0082] Evaluation and calculation layer: weighted comprehensive calculation to generate a comprehensive evaluation score; grade threshold comparison to determine the final quality grade based on preset thresholds;

[0083] Output layer: Five quality levels: Excellent (0.9-1.0), Good (0.7-0.9), Average (0.5-0.7), Poor (0.3-0.5), Unqualified (0-0.3);

[0084] Adaptive mechanism: Dynamically optimize weight allocation and level thresholds based on historical data and operational results, and automatically adjust the evaluation strategy under special operating conditions.

[0085] S2. Perform a collaborative analysis of the power quality assessment results and the current operating status parameters of the air conditioning system to generate a feasibility assessment result for photovoltaic power supply; specifically including:

[0086] S21. Obtain the power quality level from the power quality assessment results, and simultaneously collect the operating status parameters of the air conditioning system in real time, including the compressor load rate. (Percentage), Temperature difference between indoor and outdoor heat exchangers (°C), System operating frequency ( );

[0087] S22. Based on the power quality level, determine the corresponding threshold range of air conditioning system operating parameters:

[0088] When the power quality level is excellent, the compressor load rate threshold range is: The temperature difference threshold range for indoor and outdoor heat exchangers is: The system operating frequency threshold range is ;

[0089] When the power quality level is good, the compressor load rate threshold range is: The temperature difference threshold range for indoor and outdoor heat exchangers is: The system operating frequency threshold range is ;

[0090] When the power quality level is medium, the compressor load rate threshold range is: The temperature difference threshold range for indoor and outdoor heat exchangers is: The system operating frequency threshold range is ;

[0091] When the power quality level is poor, the compressor load rate threshold range is: The temperature difference threshold range for indoor and outdoor heat exchangers is: The system operating frequency threshold range is ;

[0092] When the power quality level is unqualified, the system will immediately switch to mains power supply and continuously monitor the power quality. The operation restriction can only be lifted if the power quality level is maintained at medium or above for 5 consecutive minutes.

[0093] S23. Compare and analyze the actual operating parameters of the current air conditioning system with the threshold range. If all operating parameters are within the allowable range: ,in: , , , , , The minimum and maximum values ​​of the compressor load rate, indoor and outdoor heat exchanger temperature difference, and system operating frequency threshold range are used to generate a judgment result that allows the use of photovoltaic power supply; if any operating parameter exceeds the allowable range, a judgment result that restricts the use of photovoltaic power supply is generated.

[0094] S24. When the ambient temperature Temperatures exceeding 35°C and temperature difference between indoor and outdoor heat exchangers Less than 5℃, that is When this happens, a protective judgment strategy is activated, raising the threshold requirements for operating parameters, and only generating a judgment result allowing the use of photovoltaic power supply when the power quality level is excellent or good.

[0095] S25. The collaborative analysis process also employs a dynamic adjustment mechanism to optimize the operating efficiency of the air conditioning system and the reliability of photovoltaic power supply, specifically including:

[0096] S251. Collect historical operational datasets, including the following variables: power quality level. Compressor load rate Temperature difference between indoor and outdoor heat exchangers System operating frequency System energy efficiency ratio Operational stability indicators Ambient temperature System uptime .

[0097] S252. By power quality level Ambient temperature and system uptime As the independent variable, the optimal threshold range for each operating parameter is used. Establish a multiple linear regression model with the variable as the dependent variable;

[0098] For the optimal threshold range of each operating parameter, the following linear regression model is established:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] in: It is the regression coefficient; This is the error term.

[0106] S253. Retrain the linear regression model weekly, updating the regression coefficients to enable the model to adapt to the impact of seasonal changes and equipment aging on system operation; estimate the regression coefficients using the least squares method. ,in: It is a design matrix, where each row corresponds to the feature vector of a data point. ; yes The transpose of the matrix; It is a vector of dependent variables, for example .

[0107] S254. The real-time collected power quality level Ambient temperature and system uptime Input a pre-trained linear regression model, output the dynamically adjusted threshold ranges for each operating parameter. ;

[0108] S255. When the deviation between the threshold range predicted by the model and the current actual threshold range exceeds... When this occurs, an expert verification mechanism will be activated. The specific verification steps are as follows:

[0109] Calculate the percentage of deviation: ,in: The threshold range predicted by the model. This represents the current actual threshold range;

[0110] If Deviation If the expert verification mechanism is activated, a new threshold range will be applied after the expert verification is passed.

[0111] S3. Based on the aforementioned photovoltaic power supply feasibility assessment results, through the coordinated control of the photovoltaic direct-drive control unit and the MPPT board, seamless switching and power distribution between photovoltaic power and mains power are achieved; during seamless switching, power fluctuations are controlled within ±5% of the rated power, and the switching time does not exceed 100 milliseconds, ensuring that the operation of the air conditioning system is not affected; specifically including:

[0112] S31. When the feasibility assessment result for photovoltaic power supply indicates that photovoltaic power supply is permitted:

[0113] The photovoltaic direct-drive control unit achieves phase synchronization between the photovoltaic DC power and the mains power through phase-locked loop (PLL) technology. The PLL detects the phase difference between the photovoltaic power and the mains power and adjusts the phase of the photovoltaic power to ensure synchronization. The formula is as follows: ,in: It is the phase of photovoltaic power. It is the phase of the mains power. It's a phase difference; the PLL adjusts the phase of the photovoltaic power so that... Approaching zero;

[0114] The MPPT panel continuously operates in maximum power point tracking mode, maximizing the energy efficiency of the photovoltaic panel's output. MPPT adjusts the photovoltaic panel's operating point to ensure it is always at its maximum power point (MPP), as shown in the formula: ,in: It is the output voltage of the photovoltaic panel. It is the output current of the photovoltaic panel. This refers to the output power of the photovoltaic panel; MPPT adjusts the operating point of the photovoltaic panel to make... At its maximum value;

[0115] The photovoltaic direct drive control unit adjusts the power requirements of the air conditioning system according to real-time power demand. Priority will be given to allocating photovoltaic power, with any shortfall automatically compensated by the mains power grid. The formula is as follows: , ,in: This refers to the actual photovoltaic power used. This is the actual mains power used.

[0116] S32. When the feasibility assessment result for photovoltaic power supply is that the use of photovoltaic power supply is restricted:

[0117] The photovoltaic direct-drive control unit limits the proportion of photovoltaic power supply to 30% to 50% of the total power demand, as shown in the formula: , ,in: This is the maximum limit value for photovoltaic power. This is the minimum limit for photovoltaic power.

[0118] The MPPT board is adjusted to power-limited output mode to ensure that the photovoltaic power output is stable within the set range. The formula is: ,in: This is the set photovoltaic output power;

[0119] As the primary source of power, mains electricity provides basic power support, as shown in the formula: .

[0120] S33. When it is necessary to switch from photovoltaic power to mains power:

[0121] The photovoltaic direct drive control unit gradually reduces the photovoltaic power output through the MPPT board, while simultaneously increasing the mains power input by the same amount to maintain a constant total output power. The formula is as follows: , ,in: It is the power step size for each adjustment. and These are the photovoltaic power before and after the adjustment. and These are the mains power before and after the adjustment;

[0122] When the photovoltaic power drops to near zero, the photovoltaic power supply path is completely cut off.

[0123] S34. When it is necessary to switch from mains power to photovoltaic power:

[0124] The photovoltaic direct drive control unit first verifies that the photovoltaic power quality meets the switching conditions. The verification conditions include whether parameters such as power quality level, voltage and frequency are within the allowable range.

[0125] The photovoltaic power output is gradually increased by using an MPPT board, while the mains power is reduced accordingly. The formula is as follows: , ;

[0126] The switching process is completed when the photovoltaic power reaches the target value.

[0127] S4. Based on the power allocation results, dynamically adjust the operating frequency of the air conditioning system compressor to match the compressor power with the real-time available photovoltaic power, thereby achieving adaptive optimization of the air conditioning system load and power supply capacity; specifically including:

[0128] S41. Obtain the photovoltaic power supply value in the power allocation results in real time. and mains power supplement value At the same time, obtain the compressor's required power. .

[0129] S42. Calculate the difference between the compressor's required power and the real-time available photovoltaic power. : Based on the power difference Determine the direction and magnitude of compressor frequency adjustment:

[0130] when When (photovoltaic power exceeds demand power). ,in: It is the adjusted compressor frequency. This is the current compressor frequency. It is the first preset ratio (the ratio coefficient for increasing the frequency).

[0131] when When (photovoltaic power is less than the required power). ,in: It is the second preset ratio (the ratio coefficient for reducing frequency).

[0132] When the real-time available photovoltaic power fluctuates beyond a set threshold At this time, the anti-disturbance adjustment mechanism is activated, and the compressor frequency change rate is smoothly adjusted through the proportional-integral-derivative (PID) control principle: ,in: It's a power error. ; It is proportional gain. It is integral gain. It is the differential gain.

[0133] S43. During dynamic adjustment, the compressor frequency change rate is limited to... Within seconds, ensure stable operation of the air conditioning system: If the calculated rate of change of frequency exceeds this range, then amplitude limiting is performed. ,in: It is the change in frequency. ; It is the sampling time interval;

[0134] S44. Establish power matching evaluation indicators : When the matching degree Below the preset threshold At that time, the frequency adjustment parameters are automatically optimized. and For example, improving the matching degree by adjusting the scaling factor:

[0135]

[0136]

[0137] in: and It is an optimization adjustment coefficient.

[0138] Example 1

[0139] This embodiment selects an office on the top floor of a commercial office building as the test environment. The test period is during the high-temperature period of summer (June to August), and the test period is 30 days. The test environment is equipped with a 5kW photovoltaic power generation system and a 3.5kW variable frequency air conditioning system.

[0140] Test conditions: The photovoltaic system is a 5kW monocrystalline silicon photovoltaic panel array equipped with an MPPT controller; the air conditioning system is a DC inverter multi-split unit with a rated cooling capacity of 12,000W; the test environment is an office with an area of ​​40㎡, a ceiling height of 3m, and a west-facing environment; power quality parameters, system operating parameters, and energy efficiency data are recorded every 5 seconds.

[0141] 1. Implementation Steps

[0142] Phase 1: System Initialization (Days 1-3)

[0143] Install a sine wave positioning detection unit and set the sampling frequency to 15kHz; configure the power quality assessment model parameters: total harmonic distortion rate weight is 0.5, voltage fluctuation amplitude weight is 0.3, and waveform smoothness weight is 0.2; establish the initial parameters of the linear regression model and collect basic operating data.

[0144] Phase Two: System Debugging (Days 4-7)

[0145] Calibrate the measurement accuracy of each sensor, test the performance of seamless switching, and optimize the compressor frequency adjustment algorithm parameters.

[0146] Phase 3: Formal Testing (Days 8-30)

[0147] The intelligent control algorithm is continuously run to record system operation data and compare the performance with that of traditional control methods.

[0148] 2. Comparative Analysis of Test Results

[0149] The effects of the present invention and the prior art are compared in Table 1 and below. Figure 4 As shown:

[0150] Table 1: Comparison of Results

[0151]

[0152] 3. Specific Implementation Cases

[0153] Case 1: Sunny day conditions (July 15, light intensity 800-1000W / ㎡)

[0154] 06:00-08:00: When the light intensity increases from 200 Lux to 800 Lux, the system automatically switches to photovoltaic power supply;

[0155] 08:00-14:00: Photovoltaic power supply accounts for more than 95%, and the compressor frequency is adaptively adjusted between 45 and 85 Hz;

[0156] 14:00-18:00: The light intensity gradually decreases, and the mains power supplementation ratio increases from 5% to 40%;

[0157] The daily photovoltaic power utilization rate was 91.2%.

[0158] Case 2: Cloudy and rainy weather (July 20, light intensity 100-300 Lux)

[0159] The system identifies the power quality level as "medium" and automatically adjusts the operating parameter thresholds; the compressor frequency is limited to the range of 40-75Hz; and the proportion of photovoltaic power supply is maintained at 35%-50%.

[0160] Number of seamless handover attempts: 12, all of which were successful.

[0161] 4. Verification of Technological Advantages

[0162] (1) Power quality improvement effect: the waveform distortion rate is reduced from 8% to 12% in the traditional scheme to 3% to 5%, the voltage fluctuation amplitude is reduced by more than 60%, and the electromagnetic noise caused by harmonic interference is significantly reduced.

[0163] (2) Energy efficiency improvement verification: Compared with the traditional inverter solution, the DC-AC-DC conversion link is eliminated, reducing conversion loss by 10% to 15%, and the overall energy efficiency ratio of the system is improved by 25% to 30%.

[0164] (3) Stability verification: During the test, there was no system protection shutdown due to power quality, the temperature control accuracy was improved by 66.7%, the compressor ran smoothly, and the frequency of start-stop was greatly reduced.

[0165] 5. Conclusion

[0166] Through 30 days of continuous testing and verification, the photovoltaic direct-drive intelligent conversion air conditioning control method provided by this invention significantly outperforms existing technologies in several key performance indicators. In particular, it excels in improving power quality, enhancing system energy efficiency, and improving operational stability, effectively solving technical challenges in traditional photovoltaic air conditioning systems such as unstable power quality, inflexible switching, and low energy utilization. This control method has good practical value and promising prospects for widespread application.

[0167] As can be seen from the above description, the embodiments of the present invention achieve the following technical effects:

[0168] Significantly improves energy utilization efficiency and reduces system costs: The DC-DC direct conversion architecture completely eliminates the inverter and its associated rectifier and filter stages required in traditional solutions. This not only reduces initial system investment costs by approximately 25%–30%, but also directly reduces power conversion losses from 10%–15% in traditional solutions to 3%–5%, resulting in an overall energy utilization efficiency improvement of 10%–13%. This design breaks through the cost bottleneck for the widespread adoption of photovoltaic air conditioning systems. Furthermore, through the collaborative work of the MPPT board and the photovoltaic direct drive control unit, it ensures the maximum utilization of photovoltaic power, enabling photovoltaic power to account for over 90% of power supply under suitable conditions.

[0169] To achieve intelligent and seamless switching and ensure stable system operation: A sine wave positioning and detection unit was introduced, and a multi-dimensional power quality assessment system, including waveform distortion rate analysis, harmonic content detection, and waveform smoothness evaluation, was implemented to accurately determine the quality of photovoltaic DC power. Combined with collaborative analysis of air conditioning operating status parameters, a tiered photovoltaic power supply feasibility assessment mechanism was established. Based on this assessment, phase-locking and power gradient technologies were used to achieve seamless switching between photovoltaic and mains power. The switching time was reduced from 300–500 ms in traditional solutions to 80–100 ms, and power fluctuations during switching were controlled within ±5%, completely resolving system jitter and frequent compressor start-stop issues caused by power switching.

[0170] A load-power adaptive matching mechanism was established to optimize overall performance: a dynamic compressor frequency adjustment mechanism based on power allocation results was created. By comparing the compressor's power demand with available photovoltaic power in real time, the compressor's operating frequency was intelligently adjusted to achieve a precise match between the air conditioning system load and power supply capacity. This design improved the system's energy efficiency ratio from 2.8–3.2 in traditional solutions to 3.6–4.1, while reducing the compressor start-stop frequency from 8–12 times / hour to 2–4 times / hour, significantly extending equipment lifespan. Through the adaptive learning capability of the linear regression model, the system can dynamically optimize operating parameter thresholds based on historical operating data and environmental changes, forming a continuously evolving intelligent control system.

[0171] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0172] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this specification. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A photovoltaic direct-drive intelligent conversion air conditioning control method, characterized in that, include: The waveform characteristics of photovoltaic DC power are acquired in real time by a sinusoidal wave positioning and detection unit, and power quality assessment results are generated based on waveform distortion rate analysis. The power quality assessment results are analyzed in conjunction with the current operating status parameters of the air conditioning system to generate a feasibility assessment result for photovoltaic power supply. Based on the feasibility assessment of photovoltaic power supply, the photovoltaic direct drive control unit and the MPPT board work together to achieve seamless switching and power distribution between photovoltaic power and mains power. Based on the power allocation results, the operating frequency of the air conditioning system compressor is dynamically adjusted to match the compressor power with the real-time available photovoltaic power. The process of acquiring the waveform characteristics of photovoltaic DC power in real time through a sinusoidal wave positioning and detection unit, and generating power quality assessment results based on waveform distortion rate analysis, specifically includes: Voltage waveform data is acquired in real time by a high-frequency sampling circuit set in the photovoltaic DC circuit; Fourier transform is performed on the collected voltage waveform data to extract the fundamental component and each harmonic component, and the total harmonic distortion rate is calculated. Analyze the fluctuation amplitude of the voltage waveform per unit time and count the number of times the voltage sudden change exceeds the set threshold; The waveform smoothness index is calculated using a sliding time window algorithm to evaluate the stability of the waveform in the time domain. The total harmonic distortion rate, voltage fluctuation amplitude statistics and waveform smoothness index are input into the preset power quality assessment model, and the power quality level assessment result is output. The power quality assessment results are then analyzed in conjunction with the current operating parameters of the air conditioning system to generate a feasibility assessment result for photovoltaic power supply, specifically including: Obtain the power quality level from the power quality assessment results, and simultaneously collect the operating status parameters of the air conditioning system in real time, including the compressor load rate. Temperature difference between indoor and outdoor heat exchangers System operating frequency ; Based on the power quality level, determine the corresponding threshold range of air conditioning system operating parameters; The actual operating parameters of the current air conditioning system are compared and analyzed with the threshold range. If all operating parameters are within the allowable range: ,in: , , , , , The minimum and maximum values ​​of the compressor load rate, indoor and outdoor heat exchanger temperature difference, and system operating frequency threshold range are used to generate a judgment result that allows the use of photovoltaic power supply; if any operating parameter exceeds the allowable range, a judgment result that restricts the use of photovoltaic power supply is generated. When the ambient temperature Temperatures exceeding 35°C and temperature difference between indoor and outdoor heat exchangers Less than 5℃, that is When this happens, a protective judgment strategy is activated, and the threshold requirements for operating parameters are increased. Only when the power quality level is excellent or good will a judgment result be generated allowing the use of photovoltaic power supply be generated. Based on the aforementioned photovoltaic power supply feasibility assessment results, through the coordinated control of the photovoltaic direct-drive control unit and the MPPT board, seamless switching and power distribution between photovoltaic power and mains power are achieved, specifically including: When the feasibility assessment result of photovoltaic power supply is that photovoltaic power supply is allowed, the photovoltaic direct drive control unit first realizes the phase synchronization between photovoltaic DC power and mains power through phase-locked loop technology; the MPPT board continuously works in maximum power point tracking mode to maximize the energy efficiency of the photovoltaic panel output; the photovoltaic direct drive control unit prioritizes the allocation of photovoltaic power supply according to the real-time power demand of the air conditioning system, and the insufficient part is automatically compensated by the mains power. When the feasibility assessment result for photovoltaic power supply is that the use of photovoltaic power supply is restricted, the photovoltaic direct drive control unit will limit the proportion of photovoltaic power supply to 30% to 50% of the total power demand; the MPPT board will be adjusted to the power limiting output mode to ensure that the photovoltaic power output is stable within the set range; the mains power supply will serve as the main power source to provide basic power support.

2. The photovoltaic direct-drive intelligent conversion air conditioning control method according to claim 1, characterized in that, The calculation of total harmonic distortion rate, the number of times the voltage abrupt change exceeds a set threshold, and the calculation of waveform smoothness index using a sliding time window algorithm specifically include: For the collected voltage waveform data Perform a Fourier transform to extract the amplitude of the fundamental component. The amplitude of each harmonic component Calculate the total harmonic distortion (THD): ,in: It is the amplitude of the fundamental component; It is the first The amplitude of the second harmonic component; Analyze the voltage waveform per unit time The fluctuation range within the range, and the statistical voltage change exceeding the set threshold. Number of times : ,in: It is a unit of time Number of sampling points within; It is the set threshold for voltage surge. It is the change in voltage value between two consecutive sampling points; It is an indicator function, when the condition is met. The value is 1 if the condition is met, and 0 otherwise. Waveform smoothness index calculated using a sliding time window algorithm. Evaluate the stability of the waveform in the time domain; let the sliding time window length be... The waveform smoothness index Defined as: ,in: It is the number of sampling points within the sliding time window; It is the maximum voltage change within the window.

3. The photovoltaic direct-drive intelligent conversion air conditioning control method according to claim 2, characterized in that, The total harmonic distortion rate, voltage fluctuation amplitude statistics, and waveform smoothness index are input into a preset power quality assessment model, and the power quality level assessment results are output, specifically including: Total Harmonic Distortion Voltage fluctuation amplitude statistics Waveform smoothness index They were each normalized separately to convert them into a unified evaluation score. , and ; The three evaluation scores are weighted and calculated to generate a weighted comprehensive evaluation value. : ,in: ; Weighted comprehensive evaluation value The final power quality level assessment result is determined by comparing it with five preset quality level thresholds: when The power quality level is excellent; when The power quality level is good; when The power quality level is medium; when The power quality level is poor; when The power quality level is unqualified.

4. The photovoltaic direct-drive intelligent conversion air conditioning control method according to claim 3, characterized in that, The process of collaboratively analyzing the power quality assessment results with the current operating parameters of the air conditioning system also employs a dynamic adjustment mechanism, specifically including: Collect historical operation datasets, including power quality level, compressor load rate, indoor and outdoor heat exchanger temperature difference, system operating frequency, and corresponding system energy efficiency ratio and operation stability indicators; A multiple linear regression model was established with power quality level, ambient temperature, and system running time as independent variables and the optimal threshold range of each operating parameter as the dependent variable. The linear regression model is retrained weekly, and the regression coefficients are updated so that the model can adapt to the impact of seasonal changes and equipment aging on system operation. The real-time collected power quality level, ambient temperature, and system uptime are input into a trained linear regression model, which outputs dynamically adjusted threshold ranges for each operating parameter. , , , , and .

5. The photovoltaic direct-drive intelligent conversion air conditioning control method according to claim 4, characterized in that, The specific steps for achieving seamless switching between photovoltaic power and mains power are as follows: When it is necessary to switch from photovoltaic power supply to mains power, the photovoltaic direct drive control unit first gradually reduces the photovoltaic power output through the MPPT board, while increasing the mains power input by the same amount to keep the total output power constant; when the photovoltaic power drops to near zero, the photovoltaic power supply path is completely cut off. When switching from mains power to photovoltaic power, the photovoltaic direct drive control unit first verifies that the photovoltaic power quality meets the switching conditions; then, it gradually increases the photovoltaic power output through the MPPT board while correspondingly reducing the mains power; and finally, when the photovoltaic power reaches the target value, the switching process is completed.

6. The photovoltaic direct-drive intelligent conversion air conditioning control method according to claim 1, characterized in that, Based on the power allocation results, the operating frequency of the air conditioning system compressor is dynamically adjusted to match the compressor power with the real-time available photovoltaic power, specifically including: Real-time acquisition of photovoltaic power supply values ​​in power allocation results and mains power supplement value At the same time, obtain the compressor's required power. ; Calculate the difference between the compressor's required power and the real-time available photovoltaic power. Based on the difference Determine the direction and magnitude of compressor frequency adjustment; During dynamic adjustment, the compressor frequency change rate is limited to ±5Hz / second to ensure stable operation of the air conditioning system. If the calculated rate of change of frequency exceeds this range, then amplitude limiting is performed. ,in: It is the adjusted compressor frequency. This is the current compressor frequency. It is a symbolic function; It is the change in frequency. ; It is the sampling time interval; At the same time, a power matching degree evaluation index is established, and the frequency adjustment parameters are automatically optimized when the matching degree is lower than the preset threshold.

7. The photovoltaic direct-drive intelligent conversion air conditioning control method according to claim 6, characterized in that, The difference between the compressor's required power and the real-time available photovoltaic power is calculated. Based on the difference Determine the direction and magnitude of compressor frequency adjustment, specifically including: When the real-time available photovoltaic power is greater than the compressor's required power, the compressor's operating frequency is increased by a first preset ratio so that the system can make full use of the surplus photovoltaic power. When the real-time available photovoltaic power is less than the power required by the compressor, the compressor operating frequency is reduced by the second preset ratio to keep the system power demand and power supply capacity in balance. When the real-time available photovoltaic power fluctuation exceeds the set threshold, the anti-disturbance adjustment mechanism is activated, and the compressor frequency change rate is smoothly adjusted through the proportional-integral-derivative control principle.

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