A method for controlling operation of building photovoltaic power generation system

Abstract

The present invention relates to the technical field of AC power distribution network, and specifically to a method for controlling the operation of a building photovoltaic power generation system, which collects real-time data information of each cell in the photovoltaic power generation system; analyzes the real-time data information of each cell, calculates the corresponding working point selection contribution, compares based on all cells, and predicts the corresponding selection contribution at future moments according to the change trend of the selection contribution at each sampling moment; obtains the selection confidence of the working point according to the change characteristics of the real-time data information of different cells, and analyzes the selection confidence and predicted selection contribution of different cells to obtain the optimal working point at the next moment, and provides energy supply for the building based on the optimal working point of MPPT at different moments. The present invention shortens the response time of the MPPT controller and improves the transfer speed of the MPPT optimal working point position.

Description

Technical Field

[0001] The present invention relates to the technical field of AC power distribution network, and in particular to an operation control method for a building photovoltaic power generation system. Background Art

[0002] Photovoltaic power generation system (PV system) is an efficient device that converts sunlight into electrical energy through solar cells. In the process of optimizing the output power of photovoltaic cells, maximum power point tracking (MPPT) technology is widely used. This technology monitors the voltage, current and other key parameters of photovoltaic cells in real time, and dynamically adjusts the working point of the battery according to the current lighting conditions to ensure that it is always at or close to the optimal working point, so as to maximize the system's power output and improve the overall efficiency of the photovoltaic system.

[0003] However, when using MPPT technology to determine the optimal operating point at different times, the existing algorithm may show a certain lag when dealing with rapid changes in solar radiation, especially in cloudy weather conditions. When the light intensity fluctuates frequently and rapidly, the optimal operating point of the photovoltaic power generation system will also change rapidly. This causes the adjustment speed of the MPPT controller to be unable to keep up with the actual change speed of the maximum power point in time, and thus cannot ensure that the photovoltaic panel can always output the maximum possible power. Summary of the invention

[0004] In order to solve the technical problems that the existing algorithm has a long response time for the MPPT controller, the transfer speed is lagging, and it cannot meet the energy demand of the building, the purpose of the present invention is to provide a building photovoltaic power generation system operation control method, and the technical solution adopted is as follows:

[0005] Collect real-time data information of each cell in the photovoltaic power generation system and perform pre-processing;

[0006] Analyze the real-time data information of each battery cell, calculate the corresponding working point selection contribution, compare based on all battery cells, and predict the corresponding selection contribution at future moments according to the change trend of the selection contribution at each sampling moment;

[0007] The confidence level of the selected working point is obtained based on the changing characteristics of the real-time data information of different solar cells, and the confidence level of the selected working point and the predicted contribution level of the selected working point are analyzed to obtain the optimal working point at the next moment, and the MPPT controller is used to make adjustments in advance based on the optimal working point.

[0008] Provide energy supply for buildings based on the optimal working point of MPPT at different times.

[0009] Furthermore, the real-time data information includes output current, output voltage, light intensity and temperature data.

[0010] Furthermore, real-time data information of each cell in the photovoltaic power generation system is collected and pre-processed, including the following steps:

[0011] Obtain light sensors and temperature sensors to collect the real-time output current and output voltage of each cell in the photovoltaic power generation system during any working cycle, and monitor the light intensity and temperature data of the cell in real time through light sensors and temperature sensors. After denoising preprocessing based on the collected real-time data information, transmit it through wired and / or wireless networks.

[0012] Furthermore, the sampling interval for collecting real-time data information is 1 second to 2 seconds.

[0013] Furthermore, the real-time data information of each battery cell is analyzed to calculate the corresponding working point selection contribution. The corresponding calculation formula is:

[0014]

[0015] Among them, G i Indicates the selection contribution of a certain battery cell at sampling time i; U i Indicates the output voltage value of the battery cell at sampling time i, I i represents the output current value of the battery cell at sampling time i, T i It represents the temperature value of the cell at sampling time i, and max(T) is the maximum temperature value of all cells in the photovoltaic power generation system at sampling time i.

[0016] Furthermore, based on the comparison of all the cells, the selection contribution corresponding to the future moment is predicted according to the change trend of the selection contribution at each sampling moment, including:

[0017] Define the current sampling time as i, and the predicted sampling time of the selected contribution as the future time i+1. The corresponding calculation formula is:

[0018]

[0019] Among them, G i+1 G represents the selection contribution of the battery cell at the future time i+1; i G represents the selection contribution of the battery cell at the current sampling time i; k With G k-1 They represent the selected contribution of the battery cell at the kth moment and the k-1th moment respectively; S i Indicates the sampling sequence number corresponding to the current sampling time i; It is the sum of the sampling ordinal numbers corresponding to the current sampling time i and all previous sampling times.

[0020] Furthermore, the confidence level of the selected working point is obtained according to the change characteristics of the real-time data information of different battery cells, including:

[0021] The corresponding selection confidence is calculated according to the light intensity of different cells and the relative distance at the sampling time. The corresponding calculation formula is:

[0022]

[0023] in, Indicates the selection confidence of battery cell r at the future time i+1; Represents the light intensity of cell r at time k; represents the average light intensity of cell r at all times up to the current sampling time; D(r,r i ) represents the working point r of the battery cell r and the current time i i The Euclidean distance of represents the predicted light intensity of cell r at the future time i+1; min(L i+1 ) represents the minimum value of the predicted light intensity of all cells at the future time i+1.

[0024] Furthermore, the results of the selected contribution and the selected confidence are normalized to the range of [0,1].

[0025] Furthermore, the selection confidence and predicted selection contribution of different cells are analyzed, and the corresponding calculation formula is:

[0026]

[0027] Among them, Y r Indicates the degree of confidence that any cell r is the optimal MPPT operating point at the future time i+1; It represents the predicted selection contribution of cell r at the future time i+1; Represents the selection confidence of battery cell r at the future time i+1.

[0028] Furthermore, obtaining the optimal working point at the next moment includes: sorting the cells from large to small based on the trust levels to obtain the cell with the highest trust level, and the cell is the optimal working point at the next moment.

[0029] The present invention has the following beneficial effects: in the technical solution of the present application, the response lag problem caused by the sharp fluctuation of light intensity when the traditional MPPT algorithm responds to the rapid change of solar radiation is optimized; by deeply analyzing the dynamic change characteristics of the real-time electrical parameters of the battery cells in the photovoltaic power generation system, the output power of the photovoltaic power generation system at a certain moment in the future can be accurately predicted, that is, the selection contribution corresponding to the predicted future moment is obtained; at the same time, combined with the differences in temperature changes and working point position changes, the corresponding selection confidence is calculated, and the selection confidence and the predicted selection contribution are combined for analysis to accurately determine the optimal working point of the MPPT at the next moment, and make corresponding adjustments in advance. This setting shortens the response time of the MPPT controller, improves the transfer speed of the MPPT optimal working point position, and then optimizes the energy collection efficiency of the photovoltaic system to ensure that the energy needs of the building can be met. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flowchart of an implementation method of a building photovoltaic power generation system operation control method provided by an embodiment of the present invention;

[0032] Figure 2 A flowchart of the steps of a building photovoltaic power generation system operation control method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the operation control method of a building photovoltaic power generation system proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0034] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0035] The specific scheme of the operation control method of a building photovoltaic power generation system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0036] When the light intensity changes rapidly, that is, the optimal operating point on the photovoltaic power generation system changes frequently, the MPPT algorithm has a lag in responding to the rapid changes in solar radiation, which means that when the light intensity changes, the MPPT algorithm cannot immediately respond and adjust the working point of the photovoltaic cell to a new optimal position, and the lag may cause the power output of the photovoltaic cell to decrease, thereby reducing the efficiency of the entire photovoltaic system; and the first embodiment of the present invention provides a building photovoltaic power generation system operation control method, which optimizes the MPPT algorithm's response lag caused by the rapid change in light intensity when responding to the rapid change in solar radiation, predicts the optimal operating point at a future moment by analyzing the change characteristics of the real-time data information of different battery cells, and adjusts in advance through the MPPT controller, thereby reducing the response time of the MPPT controller and improving the accuracy of the MPPT optimal operating point position. The specific steps in the method are now introduced in detail.

[0037] Please combine Figure 1 and Figure 2 , which respectively show an implementation flow chart and a step flow chart of a building photovoltaic power generation system operation control method provided by an embodiment of the present invention, the method comprising:

[0038] Step S1: collecting real-time data information of each cell in the photovoltaic power generation system and performing preprocessing;

[0039] Step S2: Analyze the real-time data information of each battery cell, calculate the corresponding working point selection contribution, compare based on all battery cells, and predict the corresponding selection contribution at the future moment according to the change trend of the selection contribution at each sampling moment;

[0040] Step S3: obtaining the selection confidence of the working point according to the change characteristics of the real-time data information of different battery cells, analyzing the selection confidence and predicted selection contribution of different battery cells, obtaining the optimal working point at the next moment, and adjusting in advance through the MPPT controller based on the optimal working point;

[0041] Step S4: Providing energy supply to the building based on the optimal working point of MPPT at different times.

[0042] It is explained that the photovoltaic power generation system is a technical system that directly converts light energy into electrical energy. It converts solar energy into direct current through photovoltaic cells, and then converts direct current into alternating current through inverters to achieve power supply, utilize solar energy resources, reduce dependence on traditional fossil energy, and help reduce greenhouse gas emissions; specifically, the system usually includes photovoltaic cells (PV panels, Photovoltaic), inverters, brackets and mounting structures, and monitoring systems, wherein photovoltaic cells generate current when exposed to sunlight and convert solar energy into electrical energy through the photoelectric effect; inverters are used to convert the direct current generated by photovoltaic cells into alternating current to ensure that the electrical energy generated by the photovoltaic power generation system can be directly supplied; photovoltaic cells are usually installed on brackets to receive sunlight to the greatest extent, and the mounting structure can ensure that the system is stable and safe and can operate normally under various environmental conditions; the monitoring system tracks the performance of the photovoltaic system, so that users can understand the operation of the system in real time and take necessary measures in time; optionally, the photovoltaic power generation system is equipped with a battery energy storage system to store the electricity generated during the day for use at night or in bad weather, thereby improving energy self-sufficiency and reliability.

[0043] Photovoltaic power generation systems are used in buildings, usually installed on the roof or facade of the building to generate electricity using solar energy. They are usually divided into rooftop photovoltaic systems, facade photovoltaic systems, integrated photovoltaic systems and overhead photovoltaic systems according to the installation location, building structure and usage requirements.

[0044] MPPT (Maximum Power Point Tracking) is an important control technology in photovoltaic power generation systems, used to optimize the power output of photovoltaic cells; MPPT controller, that is, maximum power point tracking solar controller, its main function is to ensure that photovoltaic cells work at the best working point to achieve maximum power conversion efficiency; It can be explained that the output power of photovoltaic cells depends on factors such as light intensity and temperature, and the combination of its voltage and current will produce different power outputs at different working points. With the help of MPPT technology, the maximum power point (MPP) of the current working point of the photovoltaic cell can be tracked, and it can be kept near this point even if the light conditions change; The tracking process is mainly through monitoring and analyzing the voltage and current output of the photovoltaic cell, calculating the current maximum power point according to the current light conditions and temperature, and then adjusting the output voltage and current so that the photovoltaic cell works near the maximum power point to maximize the energy conversion efficiency; There are three main control methods, one is Perturb and Observe (P&O), which finds the maximum power point by periodically fine-tuning the set value of voltage or current, observing the direction of power change, and judging whether to increase or decrease; the second is Incremental Conductance uses the conductivity slope of the photovoltaic cell to adjust the operating point to find the maximum power point; the third is model predictive control, which uses mathematical models and prediction algorithms to predict the maximum power point of photovoltaic cells to adjust the operating point more accurately.

[0045] By using an MPPT controller, the photovoltaic power generation system can maintain high efficiency under different lighting or temperature conditions to generate more electricity. The MPPT controller can quickly respond to changes in light to ensure stable operation of the system and maximize energy collection. It has a wide range of applications and can be used in various types of photovoltaic systems.

[0046] Preferably, the real-time data information includes output current, output voltage, light intensity and temperature data.

[0047] To explain, in a photovoltaic power generation system, the output current (Output Current) refers to the amount of current currently output from the photovoltaic cell to the circuit, which depends on the light intensity, the working point of the photovoltaic cell (voltage and current output under MPPT control), and the load in the circuit, and the unit is usually ampere (A); the output voltage (Output Voltage) refers to the current voltage output from the photovoltaic cell, and the output voltage depends on the configuration and working state of the photovoltaic cell, such as series and parallel combinations, or the result of adjustment by the MPPT controller, and the unit is usually volt (V); the illuminance (Illuminance) refers to the intensity of light irradiating on the photovoltaic cell, which directly affects the power generation capacity of the photovoltaic cell, and determines the number of photons of the photoelectric effect, which is usually measured in illuminance (Lux) or solar radiation (W / m2); the temperature data (Temperature) refers to the surface temperature or ambient temperature of the photovoltaic cell, and the change in temperature will affect the electrical characteristics of the photovoltaic cell, usually the higher the temperature, the lower the voltage and the higher the current, and the unit is usually Celsius (℃) or Fahrenheit (℉).

[0048] Preferably, collecting real-time data information of each cell in the photovoltaic power generation system and performing preprocessing includes the following steps:

[0049] Obtain light sensors and temperature sensors to collect the real-time output current and output voltage of each cell in the photovoltaic power generation system during any working cycle, and monitor the light intensity and temperature data of the cell in real time through light sensors and temperature sensors. After denoising preprocessing based on the collected real-time data information, transmit it through wired and / or wireless networks.

[0050] It is explained that light sensors are used to measure light intensity and are usually used to monitor changes in ambient light. There are many types of light sensors, and the principles of different light sensors are also different. Among them, the resistance value of the photoresistor (LDR) changes with the change of light intensity, and the light intensity is indirectly determined by measuring the value of the resistance. When the ambient light becomes brighter, the resistance value decreases, and when the light becomes darker, the resistance value increases; photodiodes and photoelectric converters use the energy of light to directly convert it into current or voltage signals, and their output signals are proportional to the intensity of light irradiated on them, and can directly measure the light intensity; a photocathode and a collecting electrode are placed inside the phototube. When light is irradiated on the photocathode, electrons are released to form a current, and the magnitude of the current is proportional to the light intensity; specifically, light sensors are used in photovoltaic power generation systems to monitor the intensity of sunlight and assist in optimizing the layout and angle of photovoltaic cells.

[0051] Temperature sensors are used to measure the temperature of the environment or objects. They can convert temperature data into electrical signals or digital signals for output. They include different types. The resistance value of thermistor changes with temperature. Negative temperature coefficient (NTC) thermistor is usually used. Its resistance value decreases with increasing temperature. The temperature of the environment or object is indirectly calculated by the change of resistance. Resistance temperature detector (RTD, Resistance Temperature Detector) uses a resistance element made of metal (such as platinum). Its resistance value changes with temperature according to a certain rule, usually linearly. Thermocouple is a sensor welded by two different metals. It uses the electromotive force caused by temperature change to measure temperature. It has the advantages of wide temperature measurement range and fast response. Infrared temperature sensor calculates the surface temperature of the target by measuring the infrared radiation emitted by the target surface. Temperature sensors can provide accurate and reliable temperature measurement data through different technical principles to support automation and control of various applications.

[0052] Denoising preprocessing refers to the step of removing noise from the signal during signal processing to avoid noise interference or reduce the effect and accuracy of subsequent processing steps. In practical applications, denoising is usually achieved through filtering, wavelet transform, machine learning methods, sparse representation, and dictionary learning. The appropriate denoising method is selected according to the actual situation to significantly improve the effect of subsequent processing.

[0053] Obtain light sensors and temperature sensors to collect the real-time output current and output voltage of each solar panel in the photovoltaic power generation system during any working cycle, and use light sensors and temperature sensors to monitor the light intensity and temperature data of the solar panels in real time. After denoising preprocessing based on the collected real-time data information, transmit it through wired and / or wireless networks.

[0054] It can be explained that necessary sensors and measuring equipment are installed in the photovoltaic power generation system to collect the real-time operation data of each battery cell. The collected data may contain noise, so it is pre-processed to ensure the accuracy and stability of the real-time data information and the quality of the data; then a suitable transmission method is selected, wired or wireless, to send the pre-processed real-time data to the data center, monitoring system or cloud server; if the network coverage is good and security is guaranteed, wireless transmission is used to avoid data loss or tampering; among them, wired transmission refers to the transmission of pre-processed data to the central control system or data storage device through Ethernet or dedicated communication protocol; wireless transmission uses wireless technology (such as Wi-Fi, LoRaWAN, NB-IoT, etc.) to transmit data to the remote monitoring system or cloud server to realize remote monitoring and management; this setting can effectively realize the acquisition, processing and transmission of real-time data information of each battery cell in the photovoltaic power generation system, thereby helping to improve the system's operating efficiency and maintenance management level.

[0055] Specifically, in the daily operation of the photovoltaic power generation system, light sensors and temperature sensors are introduced to capture the detailed operating data of each battery cell during the working cycle in real time; after the data collection is completed, the real-time data information is pre-processed for denoising. Due to various interference factors in the environment, such as electromagnetic interference, temperature fluctuations, etc., the data is contaminated to a certain extent during the collection process. Preprocessing can effectively eliminate these interference factors and ensure the accuracy and reliability of the data; the processed data will choose the appropriate transmission method according to the specific situation to ensure that the data can be transmitted in real time and accurately.

[0056] Preferably, the sampling interval for collecting real-time data information is 1 second to 2 seconds.

[0057] It can be explained that, in actual situations, the sampling interval for sampling real-time data information is 1.5 seconds. This sampling interval can reduce the pressure of data processing and storage while ensuring data accuracy, and at the same time adapt to the rapidly changing real-time environment, providing sufficient real-time performance and responsiveness to rapid changes; and the use of this sampling interval can reduce the amount of data, reduce the cost and complexity of processing and transmission, and improve the efficiency of data processing and transmission; in addition, this setting can ensure the stable operation of the acquisition system while ensuring the reliability of the data, including handling possible abnormal situations and data loss problems.

[0058] Preferably, the real-time data information of each battery cell is analyzed to calculate the corresponding working point selection contribution, and the corresponding calculation formula is:

[0059]

[0060] Among them, G iIndicates the selection contribution of a certain battery cell at sampling time i; U i Indicates the output voltage value of the battery cell at sampling time i, I i represents the output current value of the battery cell at sampling time i, T i represents the temperature value of the cell at sampling time i, max(T) is the maximum temperature value of all cells in the photovoltaic power generation system at sampling time i; norm represents the normalization function.

[0061] To clarify, the selected contribution refers to the contribution degree or importance of each battery cell data feature to the overall data analysis results at a specific sampling moment. This indicator is used to quantitatively describe the role and influence of each battery cell in the overall system. It is used to evaluate the performance of each battery cell, or to understand the layout and optimization of the battery cells, so as to improve the power generation efficiency and stability of the entire system.

[0062] It can be explained that It indicates the relative temperature value of the cell at sampling time i, reflecting the relative performance level of the cell at sampling time i. The temperature of the cell will affect its conductivity. According to the principles of physics, when the temperature rises, the movement of atoms or molecules inside the material intensifies, resulting in an increase in resistivity and a reduction in the conductivity of the material. For solar cells, when the temperature rises, their ability to convert solar energy into electrical energy weakens, affecting their working efficiency. i The larger the value of The smaller the value of is, the poorer the performance of the battery cell is, and the smaller the contribution is; on the contrary, when T i The bigger, The larger the value, the better the performance of the battery cell and the higher its working potential.

[0063] To better illustrate, in the performance evaluation experiment of solar cells, when the temperature of the cell exceeds 60°C, its conversion efficiency drops significantly, which may cause safety problems such as thermal runaway. Therefore, in practical applications, always pay attention to the temperature changes of the cell and take corresponding measures to reduce its temperature to ensure its stable and efficient operation.

[0064] Then combined with the output power U of the battery at sampling time i i ×I i , since the position of the optimal working point is mainly determined by the output power, that is, according to the principle of energy conversion efficiency, when the battery cell works at a certain voltage and current, its output power reaches the maximum, and the photoelectric conversion efficiency is the highest at this time, so U i ×I i The larger the value of , the greater the contribution selected.

[0065] It can be understood that the maximum power point tracking MPPT technology indicates the approximate position of the maximum output power that a photovoltaic cell can generate under current conditions, that is, if a certain cell can generate a greater output power at a certain moment, then the possibility of this point being the optimal operating point is relatively large, and the corresponding selection contribution increases accordingly. This is because a photovoltaic power generation system is composed of multiple cells connected in series or in parallel, and the performance of each cell affects the performance of the entire photovoltaic power generation system. Therefore, cells that can maintain a higher output power under various conditions have a greater contribution to determining the MPPT optimal operating point; in addition, the conductivity of photovoltaic materials will be reduced at high temperatures, that is, the movement of electrons inside the material is disturbed by thermal motion, resulting in an increase in the resistance encountered by electrons when transmitting in the material. At the same time, the internal resistance of the cell will also increase with the increase in temperature, further reducing the output power of the cell. Therefore, at higher temperatures, the overall performance of the photovoltaic power generation system is affected, and the corresponding selection contribution of the cell will be relatively reduced.

[0066] Preferably, based on the comparison of all the cells, the selection contribution corresponding to the future moment is predicted according to the change trend of the selection contribution at each sampling moment, including:

[0067] Define the current sampling time as i, and the predicted sampling time of the selected contribution as the future time i+1. The corresponding calculation formula is:

[0068]

[0069] Among them, G i+1 G represents the selection contribution of the battery cell at the future time i+1; i G represents the selection contribution of the battery cell at the current sampling time i; k With G k-1 They represent the selected contribution of the battery cell at the kth moment and the k-1th moment respectively; S i Indicates the sampling sequence number corresponding to the current sampling time i; is the sum of the sampling ordinal numbers corresponding to the current sampling time i and all previous sampling times; S j Represents the sampling sequence number corresponding to sampling time j.

[0070] It is explained that by deeply analyzing the selection contribution of each sampling moment, the selection contribution corresponding to the future moment is predicted. First, the current sampling moment is defined as i, and the target value is the selection contribution predicted at the future moment i+1; the real-time data information collected as mentioned above is analyzed to find the changing trend of the selection contribution; optionally, a linear regression model is used to fit the historical data, and the selection contribution of the future moment is predicted based on the fitting results; or a machine learning algorithm, such as a neural network, support vector machine, etc., is used to build a prediction model to more accurately capture the nonlinear relationships and trends in the data and improve the accuracy of the prediction. In the process of data prediction, it is necessary to ensure the accuracy and completeness of the data and avoid outliers.

[0071] It can be explained that S i Indicates the sampling sequence number corresponding to the current sampling time i, that is, the sampling sequence number for the third sampling is three; It represents the average difference in the selection contribution between any two adjacent historical sampling moments, which reveals the dynamic changes in the selection contribution of the battery cell. If it is a positive value, it indicates that the selection contribution of this battery cell is increasing, indicating that its performance may be improving; conversely, if it is a negative value, the selection contribution of this battery cell is decreasing and the battery performance is declining.

[0072] As an optional implementation, since the historical moments closer to the current moment have a higher reference value for the predicted selection contribution of future moments, it is necessary to weight the above difference values, that is, the closer the selection contribution difference is to the current moment, the greater the corresponding weight is, and the greater the reference value for the predicted selection contribution of future moments is, so the sampling sequence number corresponding to the sampling moment i is used as the weight, that is, If the difference in contribution between two adjacent historical sampling moments G k -G k-1 The closer to the current time i, the corresponding sampling sequence number S i The larger the value, The larger the difference, the higher the accuracy of the predicted value. In simple terms, if the user is analyzing the data at the 10th sampling moment, the difference in selected contributions at the 9th and 10th sampling moments will obtain a higher weight than the difference between the 1st and 2nd sampling moments. This is because the data at the 9th and 10th sampling moments are closer to the current moment and can better reflect the recent change trend of the battery cell performance.

[0073] After weighted processing, a weighted average difference value is obtained, which combines the change trend of historical data and fully integrates the time value of data. The weighted average difference value is added to the selection contribution G at the current moment. i Combine to get the selected contribution G at the future time i+1 i+1 , the predicted value is more accurate and reliable.

[0074] For better explanation, the above method is used to predict the selection contribution of all cells at the current moment in the future. If the selection contribution of a cell in the future is large, the possibility of the cell being the optimal working point of the MPPT at that moment is high. On the contrary, if its selection contribution is small, the possibility of being the optimal working point is low. However, in actual applications, the area of ​​a single cell in a photovoltaic power generation system is relatively small, while the light source is relatively widely distributed, which means that at a specific moment, there may be multiple cells with similar or equal selection contributions, and it is impossible to determine a unique cell as the optimal working point. Therefore, in this case, a comprehensive analysis is required to more accurately evaluate the performance status and future trends of each cell, thereby introducing the parameters of the selection confidence.

[0075] Preferably, the confidence level of the selected working point is obtained according to the change characteristics of the real-time data information of different battery cells, including:

[0076] The corresponding selection confidence is calculated according to the light intensity of different cells and the relative distance at the sampling time. The corresponding calculation formula is:

[0077]

[0078] in, Indicates the selection confidence of battery cell r at the future time i+1; Represents the light intensity of cell r at time k; represents the average light intensity of cell r at all times up to the current sampling time; D(r,r i ) represents the working point r of the battery cell r and the current time i i The Euclidean distance of represents the predicted light intensity of cell r at the future time i+1; min(L i+1 ) represents the minimum value of the predicted light intensity of all cells at the future time i+1.

[0079] It is explained that the selection confidence refers to a measure of the credibility of the battery cell performance evaluation results after comprehensive consideration of multiple factors. It is a measure of the reliability of the selection of a specific operating point to provide the best performance output for the battery cell under current conditions.

[0080] It can be explained that the light intensity directly determines the current and voltage that the battery cell can generate. The relative distance of the sampling time refers to the time interval between two adjacent samplings. If the sampling interval is short, data redundancy will occur; if the sampling interval is too long, changes in battery cell performance may not be captured in time.

[0081] In actual operation, if the working point selection at the future moment can be optimized, a balance point needs to be found, that is, the output power corresponding to the optimal working point at the future moment is more stable, and the cost of adjusting the working point in advance is minimized. Specifically, the stability of the output power means that the photovoltaic power generation system can continuously and stably convert solar energy into electrical energy without being significantly affected by external environmental factors such as light intensity and temperature. If the working point is not selected properly, the photovoltaic power generation system cannot make full use of the light resources, or excessive power fluctuations will occur when the light intensity changes, which will not only reduce energy utilization efficiency, but also have adverse effects on the power grid. The more stable the output power, the more stable the light intensity corresponding to this working point, and the minimum adjustment cost indicates that the optimal working point at the future moment is closer to the working point at this moment.

[0082] D(r,r i ) represents the working point r of the battery cell r and the current time i i The Euclidean distance of the working point indicates the advance adjustment cost of the working point. If D(r,r i ) is larger, indicating that the optimal working point at the future moment is far away from the working point at this moment, and it will cost more to adjust in advance, so the selected confidence will also decrease accordingly; It represents the discrete degree of change of the solar cell r in terms of historical and predicted light intensity. The smaller the value, the more stable the light intensity at the location of the cell r, that is, the smaller the fluctuation of the light intensity, which is conducive to the stable operation of the cell, and the greater the corresponding selection confidence. The stable light intensity indicates that the cell outputs electric energy more stably, reducing the performance fluctuation caused by light changes. In addition, combined with the predicted light intensity at the future time i+1 (the prediction method for obtaining the predicted light intensity is the same as the prediction method for selecting the contribution degree) the relative level If the light intensity of a certain cell at a future moment is higher than that of other cells, then it is most likely to become the optimal operating point. This is because high light intensity represents higher power generation potential and efficiency. When the predicted light intensity is relatively high, the confidence in selecting the cell will also increase accordingly.

[0083] Preferably, the results of selecting contribution and selecting confidence are normalized to the range of [0, 1].

[0084] It is explained that in order to compare parameter indicators more intuitively and ensure their statistical consistency and comparability, the results are usually normalized to a unified range, that is, the range is [0,1]; it can be explained that no matter how large the original value is, it can be regarded as a percentage, which makes it easier to compare and analyze, and helps users understand the differences between different data more clearly; and normalization does not change the intrinsic characteristics or relationships of the original data.

[0085] Preferably, the selection confidence and predicted selection contribution of different battery cells are analyzed, and the corresponding calculation formula is:

[0086]

[0087] Among them, Y r Indicates the degree of confidence that any cell r is the optimal MPPT operating point at the future time i+1; It represents the predicted selection contribution of cell r at the future time i+1; Represents the selection confidence of battery cell r at the future time i+1.

[0088] It is explained that the degree of trust refers to the confidence that the battery cell r can accurately track the MPPT optimal working point at the future time i+1 under a given system state and external environment; in the power system, the degree of trust indicates the accuracy of the prediction of the working state of a certain battery cell at a future time.

[0089] Preferably, obtaining the optimal working point at the next moment includes:

[0090] The cells with the highest degree of trust are sorted from highest to lowest based on the degree of trust, and the cell is the optimal working point at the next moment.

[0091] It is explained that the trust level is sorted from small to large, so that users can quickly locate the battery cell with the highest trust level. The higher the trust level of the battery cell, the more accurate the prediction of its future working state, and therefore the more likely it is to be the optimal working point at the next moment. Through this optimal working point, it can be ensured that the photovoltaic power generation system can operate in the most efficient and stable state at the next moment.

[0092] It is explained that the MPPT controller is used for early adjustment based on the optimal working point; energy supply is provided to the building according to the optimal working point of the MPPT at different times.

[0093] It is explained that through real-time adjustment, the photovoltaic system can always operate at the maximum power point, which can maximize the energy conversion efficiency. This not only helps the building to achieve self-sufficient energy supply, but also provides additional power support to the power grid during peak energy demand periods; through the aforementioned algorithm, the MPPT controller can predict and adjust to the next optimal operating point in advance, ensuring that the photovoltaic power generation system always operates in the best state, so that the building energy management system can dynamically optimize the energy supply according to the lighting conditions and energy demand at different times, and provide the building with a stable and sustainable energy supply.

[0094] To better illustrate, when there is sufficient sunshine during the day, the MPPT controller can ensure that the photovoltaic power generation system outputs at maximum power and provides sufficient electricity for the building; at night or on rainy days, the MPPT controller can intelligently adjust the energy supply strategy according to the status of the energy storage system to ensure that the building's electricity needs are met; that is, according to the different needs of the building and the scale of the photovoltaic power generation system, its working parameters and strategies can be flexibly adjusted, so that the MPPT controller is widely used in various building energy management systems and achieves significant energy-saving effects.

[0095] Specifically, first, the real-time data information such as the real-time output current, output voltage, light intensity and temperature data of each battery cell in the photovoltaic power generation system is collected and denoised preprocessed; then, the real-time data information is analyzed and calculated to determine the working point selection contribution of each battery cell at the sampling moment; then, by comparing the selection contribution change trend of each battery cell at historical moments, the corresponding selection contribution at future moments is predicted; at the same time, combined with the characteristics of different batteries in terms of light intensity and adjustment distance changes, the selection confidence of the working point is evaluated and determined; finally, the degree of trust is obtained according to the predicted selection contribution and selection confidence, and sorted from large to small to determine the optimal working point at the next moment, and pre-adjusted through the MPPT controller, based on the optimal working point of the MPPT at different moments, to ensure a stable and sufficient energy supply for the building.

[0096] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for controlling the operation of a building photovoltaic power generation system, characterized in that: The method comprises: Collect real-time data information of each cell in the photovoltaic power generation system and perform pre-processing; Analyze the real-time data information of each battery cell, calculate the corresponding working point selection contribution, compare based on all battery cells, and predict the corresponding selection contribution at future moments according to the change trend of the selection contribution at each sampling moment; The confidence level of the selected working point is obtained based on the changing characteristics of the real-time data information of different solar cells, and the confidence level of the selected working point and the predicted contribution level of the selected working point are analyzed to obtain the optimal working point at the next moment, and the MPPT controller is used to make adjustments in advance based on the optimal working point. Provide energy supply for buildings based on the optimal working point of MPPT at different times; The real-time data information includes output current, output voltage, light intensity and temperature data; Analyze the real-time data information of each battery cell and calculate the corresponding working point selection contribution. The corresponding calculation formula is: Among them, G i Indicates the selection contribution of a certain battery cell at sampling time i; U i Indicates the output voltage value of the battery cell at sampling time i, I i represents the output current value of the battery cell at sampling time i, T i represents the temperature value of the cell at sampling time i, and max(T) is the maximum temperature value of all cells in the photovoltaic power generation system at sampling time i; Based on the comparison of all cells, the selection contribution at the future moment is predicted according to the change trend of the selection contribution at each sampling moment, including: Define the current sampling time as i, and the predicted sampling time of the selected contribution as the future time i+1. The corresponding calculation formula is: Among them, G i+1 G represents the selection contribution of the battery cell at the future time i+1; i represents the selection contribution of the cell at the current sampling time i; G k With G k-1 They represent the selected contribution of the battery cell at the kth moment and the k-1th moment respectively; S i Indicates the sampling sequence number corresponding to the current sampling time i; is the sum of the sampling ordinal numbers corresponding to the current sampling time i and all previous sampling times; The confidence level of the selected working point is obtained based on the changing characteristics of the real-time data information of different cells, including: The corresponding selection confidence is calculated according to the light intensity of different cells and the relative distance at the sampling time. The corresponding calculation formula is: in, Indicates the confidence level of selecting cell r at the future time i+1; Represents the light intensity of cell r at time k; represents the average light intensity of cell r at all times up to the current sampling time; D(r,r i ) represents the working point r of the battery cell r and the current time i i The Euclidean distance of represents the predicted light intensity of cell r at the future time i+1; min(L i+1 ) represents the minimum value of the predicted light intensity of all cells at the future time i+1; The selection confidence and predicted selection contribution of different cells are analyzed, and the corresponding calculation formula is: Among them, Y r Indicates the degree of confidence that any cell r is the optimal MPPT operating point at the future time i+1; It represents the predicted selection contribution of cell r at the future time i+1; Represents the selection confidence of battery cell r at the future time i+1.

2. The building photovoltaic power generation system operation control method according to claim 1, characterized in that: Collecting real-time data information of each cell in the photovoltaic power generation system and preprocessing it includes the following steps: Obtain light sensors and temperature sensors to collect the real-time output current and output voltage of each cell in the photovoltaic power generation system during any working cycle, and monitor the light intensity and temperature data of the cell in real time through light sensors and temperature sensors. After denoising preprocessing based on the collected real-time data information, transmit it through wired and / or wireless networks.

3. The building photovoltaic power generation system operation control method according to claim 2, characterized in that: The sampling interval for collecting real-time data information is 1 second to 2 seconds.

4. The building photovoltaic power generation system operation control method according to claim 1, characterized in that: The results of the selected contribution and selected confidence are normalized to the range of [0,1].

5. The building photovoltaic power generation system operation control method according to claim 1, characterized in that: Obtaining the optimal working point at the next moment includes: sorting the cells from large to small based on the trust levels to obtain the cell with the highest trust level, the cell being the optimal working point at the next moment.

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