Buck-boost MPPT composite control system and method based on adaptive algorithm

Abstract

The invention relates to the technical field of photovoltaic power generation systems, and discloses a buck-boost MPPT composite control system and method based on an adaptive algorithm, and the system comprises a power conversion module which employs a four-switch Buck-Boost buck-boost circuit as a main topology, and comprises a first switching tube S1, a second switching tube S2, a third switching tube S3, a fourth switching tube S4, an inductor, an input capacitor, and an output capacitor; the signal sampling module comprises a voltage sensor and a current sensor and is used for collecting voltage Vpv and current Ipv of a photovoltaic panel end and voltage Vbat of a load end in real time; the core control module is used for executing a self-adaptive MPPT composite control algorithm and outputting four paths of PWM control signals to a switching tube of the power conversion module; a driving module; according to the invention, the four-switch Buck-Boost buck-boost circuit is adopted as a main topology, and a unified PWM modulation strategy is combined, so that smooth and continuous transition of three working modes of buck, boost and buck-boost is realized, mode switching logic is not needed, and the problems of mode switching oscillation and efficiency loss are effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation system technology, specifically to a buck-boost MPPT composite control system and method based on an adaptive algorithm. Background Technology

[0002] Maximum power point tracking (MPPT) technology is the core of photovoltaic (PV) power generation systems, aiming to extract maximum electrical energy from photovoltaic panels. Existing MPPT controllers are mainly classified into the following categories based on their circuit topology and control strategies, but each has its own limitations: 1. Traditional MPPT controller based on a single topology: Description: This is the most common approach, where the power stage circuit typically uses a single buck or boost converter. Control is achieved using classic MPPT algorithms, such as incremental conductance or perturb and observe.

[0003] Disadvantages: Narrow voltage adaptability: Buck controllers require the photovoltaic panel voltage to always be higher than the load / battery voltage, otherwise they cannot operate; Boost controllers require the photovoltaic panel voltage to always be lower than the load / battery voltage. When the photovoltaic panel voltage fluctuates within a wide range due to shading, temperature changes, or changes in solar intensity, and crosses the load voltage value, the single topology will completely fail or fall out of the MPPT operating range, resulting in a sharp drop in system efficiency, or even an inability to deliver energy to the load.

[0004] Poor ability to cope with complex environments: When local shading causes the output characteristics of photovoltaic panels to exhibit multiple peaks, traditional algorithms are prone to getting stuck in local power extreme points rather than the true global maximum power point (GMPP), resulting in considerable energy loss.

[0005] 2. A scheme using Buck-Boost topology but with separate control strategies: Description: To broaden the input voltage range, some existing technologies employ non-isolated buck-boost topologies such as Buck-Boost, SEPIC, or Cuk. However, their control strategies are often fragmented: the system first determines the relationship between the input and output voltages, performs a hard switch between Buck and Boost modes, and then runs the MPPT algorithm independently in each mode.

[0006] Defect: Mode switching oscillation: Near the critical voltage point, due to measurement noise or illumination fluctuations, the system may repeatedly and frequently switch between Buck and Boost modes. This oscillation not only causes instability in the control loop and generates output voltage ripple, but also reduces the overall reliability of the system and may cause electromagnetic compatibility (EMC) problems.

[0007] Efficiency loss and tracking lag: The mode switching process itself takes time, during which MPPT tracking may be interrupted or delayed, resulting in energy loss. The algorithm cannot smoothly transition across the entire operating range, leading to control discontinuity.

[0008] Non-optimized algorithm: This approach fails to optimize the buck-boost process and MPPT tracing from a global perspective in a unified and coordinated manner. In essence, it is still a simple splicing of two independent systems, resulting in limited performance improvement.

[0009] 3. MPPT controller employing a complex global scan algorithm: Description: To address the multi-peak problem, some solutions employ global scanning algorithms (such as Particle Swarm Optimization (PSO), genetic algorithms, etc.) to periodically scan the entire IV curve of the photovoltaic panel to find the GMPP.

[0010] Disadvantages: High computational resource consumption: These algorithms are usually computationally complex and require high-performance, high-cost microprocessors (MCUs) or digital signal processors (DSPs) to implement, which greatly increases the hardware cost and power consumption of the system and is not suitable for cost-sensitive large-scale commercial or consumer applications.

[0011] Slow tracking speed: Scanning the entire IV curve takes a long time, during which the system operates in a suboptimal state, resulting in energy loss, and is especially unsuitable for scenarios with rapidly changing lighting.

[0012] Therefore, this application proposes a boost-boost MPPT composite control system and method based on an adaptive algorithm. Summary of the Invention

[0013] The purpose of this invention is to provide a boost-boost MPPT composite control system and method based on an adaptive algorithm to solve the problems mentioned in the background art.

[0014] Firstly, an adaptive algorithm-based boost-boost MPPT composite control system is provided, including: Power conversion module: The main topology is a four-switch Buck-Boost step-up / step-down circuit, which includes a first switch S1, a second switch S2, a third switch S3, a fourth switch S4, an inductor, an input capacitor and an output capacitor. Signal sampling module: includes voltage sensor and current sensor, used to collect the voltage Vpv and current Ipv at the photovoltaic panel and the voltage Vbat at the load terminal in real time; Core control module: used to execute the adaptive MPPT composite control algorithm and output four PWM control signals to the switching transistors of the power conversion module; Drive module: Connected between the core control module and the power conversion module, it is used to amplify and isolate the PWM control signal to drive the switching transistor; The core control module achieves a smooth and continuous transition between buck, boost, and buck-boost modes through a unified PWM modulation strategy.

[0015] Furthermore, the unified PWM modulation strategy is as follows: The first PWM signal PWM1 is generated and sent to the first switching transistor S1, with a duty cycle of D. A second PWM signal PWM2 is generated and sent to the second switch S2, which is complementary to the first PWM signal PWM1. The duty cycle is 1-D, and a fixed dead time is inserted. A third PWM signal PWM3 is generated and sent to the third switch S3, with a duty cycle of 1-D. A fourth PWM signal PWM4 is generated and sent to the fourth switch S4, which is complementary to the third PWM signal PWM3. The duty cycle is D, and a fixed dead time is inserted. Among them, D is the only control variable, with a value range of 0 < D < 1. Its continuous change achieves a smooth transition of the working mode.

[0016] Furthermore, the core control module includes: A Kalman filter is used to perform state estimation on sampled voltages and currents, and outputs filtered Vpv_est and Ipv_est. The adaptive MPPT composite control algorithm switches between PID control and adaptive disturbance observation method depending on whether the power change is close to the maximum power point.

[0017] Furthermore, the core control module for executing the adaptive MPPT composite control algorithm includes the following steps: Real-time sampling of photovoltaic terminal voltage Vpv_raw[k] and current Ipv_raw[k], and calculation of current power Ppv_raw[k] = Vpv_raw[k] * Ipv_raw[k]; The estimated values ​​Vpv_est[k] and Ipv_est[k] are obtained through a Kalman filter, and the predicted power Ppv_est[k] = Vpv_est[k] * Ipv_est[k] is calculated. Calculate the change in estimated power: ΔP_est = Ppv_est[k] - Ppv_est[k-1]; Based on the relationship between the power change ΔP_est and the preset threshold P_threshold, the PID control / adaptive variable step size disturbance observation method is intelligently selected to calculate the adjustment amount ΔD of the control variable D; Based on the updated control variable D[k], four complementary PWM signals are generated to drive the first switch S1, the second switch S2, the third switch S3, and the fourth switch S4, respectively.

[0018] Furthermore, the formula for calculating the adjustment amount ΔD in the adaptive variable step size perturbation observation method is as follows: ; In the formula, ΔD is the adjustment amount of the duty cycle; α is the power change weighting coefficient; β is the absolute value of the change in photovoltaic panel output power between two adjacent control cycles; β is the power-voltage gradient weighting coefficient. This represents the absolute value of the slope of the power-voltage curve.

[0019] Furthermore, the output of the PID controller is: ; in, This is the adjustment amount for the duty cycle; The proportional gain adjusts the strength of the controller's response to the current error. The larger the value, the faster the response; The integral gain is used to adjust the controller's response strength to historical accumulated errors. The differential gain is used to adjust the controller's responsiveness to changes in error trends. The error in the k-th control cycle is... , This is the power value at the historical maximum power point. This is the current power estimate for the kth cycle after Kalman filtering; This is the cumulative sum of errors from the beginning to the kth period; This is the difference in error between the current period and the previous period.

[0020] Furthermore, the activation conditions for the PID control are as follows: PID control is enabled when the power change |ΔP_est| is less than the preset threshold P_threshold, and the derivative of power with respect to voltage |dP / dV| is less than the voltage gradient threshold.

[0021] Furthermore, the four PWM signals are generated as follows: Four PWM waveforms with fixed complementary relationships are generated by mapping a single control variable D, so that the first switch S1 and the second switch S2 are complementary, the third switch S3 and the fourth switch S4 are complementary, and the first switch S1 and the fourth switch S4 have the same duty cycle, and the second switch S2 and the third switch S4 have the same duty cycle. When D < 0.5, the circuit supports buck mode; when D > 0.5, the circuit supports boost mode; when D = 0.5, the circuit supports buck-boost mode.

[0022] Secondly, an adaptive algorithm-based buck-boost MPPT composite control method is provided to implement the aforementioned adaptive algorithm-based buck-boost MPPT composite control system, including the following steps: Step 1: System initialization, setting the initial duty cycle D, disturbance step size ΔD, sampling period Ts, and voltage change threshold ΔV_th; Step 2: Sample the photovoltaic terminal voltage Vpv(k) and current Ipv(k) in real time, and calculate the current power Ppv(k) = Vpv(k) × Ipv(k); Step 3: Based on unified PWM modulation, generate four PWM control signals to control the four-switch Buck-Boost circuit, achieving a smooth transition between buck, boost, and buck-boost modes without the need for mode switching logic; Step 4: Using the adaptive MPPT algorithm, dynamically select PID control or disturbance observation method PO to adjust the duty cycle based on the power change |ΔP_est| and the gradient |dP / dV|; Step 5: Output a PWM signal to drive the switching transistor and complete the energy conversion; Step 6: Update historical data and repeat steps 2 through 5.

[0023] Furthermore, the step size ΔD of the adaptive MPPT algorithm is dynamically adjusted based on |ΔP_est| and |dP / dV|: When |ΔP_est| is larger or |dP / dV| is larger, the step size ΔD is larger, which accelerates the tracking. When |ΔP_est| is smaller or |dP / dV| is smaller, the step size ΔD is smaller, allowing for finer adjustment.

[0024] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention uses a four-switch Buck-Boost step-up / step-down circuit as the main topology and combines it with a unified PWM modulation strategy to achieve a smooth and continuous transition between three working modes: buck, boost, and step-up / step-down. No mode switching logic is required, which effectively avoids the problems of mode switching oscillation and efficiency loss. 2. The adaptive MPPT composite control algorithm introduced in the core control module of this invention can quickly track when far from the MPP and finely adjust when approaching the MPP, taking into account both tracking speed and steady-state accuracy. It can respond faster to dynamic conditions such as sudden changes in light intensity and has higher energy capture efficiency. 3. The multi-peak processing mechanism built into this invention can effectively avoid the algorithm from getting stuck in local extreme points with low computational cost, and significantly improve the reliability of power generation in complex environments such as local shading. 4. The introduction of the Kalman filter in this invention further improves the system's estimation accuracy of sampled voltage and current, providing more accurate data support for the adaptive MPPT composite control algorithm; Therefore, the buck-boost MPPT composite control system and method based on adaptive algorithm proposed in this invention have significant advantages in terms of widening the input voltage range, improving system efficiency, accelerating tracking speed, and reducing hardware costs. Attached Figure Description

[0025] Figure 1 This is a hardware topology diagram of the buck-boost type MPPT composite control system of the present invention; Figure 2 This is a topology diagram of the four-switch Buck-Boost step-up / step-down circuit of the present invention; Figure 3 This is a diagram showing the generation logic and timing relationship of four PWM signals under the unified PWM modulation strategy of this invention. Figure 4 This is a flowchart illustrating the control strategy switching process of the adaptive MPPT composite control algorithm of the present invention under different power variation scenarios. Figure 5 This is a schematic diagram of the module control of the buck-boost MPPT composite control method based on adaptive algorithm of the present invention; Figure 6 This is a schematic diagram of the flow chart of the MPPT composite control method based on adaptive algorithm of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example 1:

[0027] Please see Figure 1-6 This embodiment provides a buck-boost MPPT composite control system based on an adaptive algorithm, including: Power conversion module: The main topology is a four-switch Buck-Boost step-up / step-down circuit, which includes a first switch S1, a second switch S2, a third switch S3, a fourth switch S4, an inductor, an input capacitor and an output capacitor. Signal sampling module: includes voltage sensor and current sensor, used to collect the voltage Vpv and current Ipv at the photovoltaic panel and the voltage Vbat at the load terminal in real time; Core control module: used to execute the adaptive MPPT composite control algorithm and output four PWM control signals to the switching transistors of the power conversion module; Drive module: Connected between the core control module and the power conversion module, it is used to amplify and isolate the PWM control signal to drive the switching transistor; The core control module achieves a smooth and continuous transition between buck, boost, and buck-boost modes through a unified PWM modulation strategy.

[0028] Specifically, the implementation process of the unified PWM modulation strategy is as follows: First, the algorithm in the core control module generates a first PWM signal PWM1 and sends it to the first switch S1, with a duty cycle of D. Next, a second PWM signal PWM2, complementary to the first PWM signal PWM1, is generated and sent to the second switch S2, with a duty cycle of 1-D. A fixed dead time is inserted between the two signals to prevent short circuits caused by simultaneous switching. Similarly, a third PWM signal PWM3 is generated and sent to the third switch S3, also with a duty cycle of 1-D. A fourth PWM signal PWM4, complementary to the third PWM signal PWM3, is sent to the fourth switch S4 with a duty cycle of D, and a fixed dead time is also inserted. In this way, precise control of four PWM signals can be achieved using only one control variable D, thus enabling a smooth and continuous transition between buck, boost, and buck-boost operating modes. When the value of D is less than 0.5, the circuit mainly operates in buck mode, effectively reducing the high voltage to the required output voltage. When the value of D is greater than 0.5, the circuit switches to boost mode, increasing the low voltage to the required output voltage. When the value of D is exactly equal to 0.5, the circuit operates in buck-boost mode, flexibly adjusting the voltage rise and fall according to the actual input and output voltage conditions. This unified PWM modulation strategy not only simplifies the control logic but also improves the stability and reliability of the system.

[0029] Furthermore, the unified PWM modulation strategy is as follows: The first PWM signal PWM1 is generated and sent to the first switching transistor S1, with a duty cycle of D. A second PWM signal PWM2 is generated and sent to the second switch S2, which is complementary to the first PWM signal PWM1. The duty cycle is 1-D, and a fixed dead time is inserted. A third PWM signal PWM3 is generated and sent to the third switch S3, with a duty cycle of 1-D. A fourth PWM signal PWM4 is generated and sent to the fourth switch S4, which is complementary to the third PWM signal PWM3. The duty cycle is D, and a fixed dead time is inserted. Among them, D is the only control variable, with a value range of 0 < D < 1. Its continuous change achieves a smooth transition of the working mode.

[0030] Specifically, during operation, the adaptive MPPT composite control algorithm in the core control module first performs state estimation processing through a Kalman filter based on the real-time acquired photovoltaic terminal voltage and current data. The Kalman filter, with its powerful filtering capability, effectively filters out noise interference in the sampled data, outputting more accurate filtered voltage value Vpv_est and current value Ipv_est. Subsequently, using this accurate data, the estimated power Ppv_est[k] = Vpv_est[k] * Ipv_est[k] is calculated and compared with the estimated power at the previous moment to obtain the change in estimated power ΔP_est = Ppv_est[k] - Ppv_est[k-1].

[0031] Based on the relationship between the calculated power change ΔP_est and the preset threshold P_threshold, the adaptive MPPT composite control algorithm intelligently selects an appropriate control strategy. When the power change is large, i.e., far from the maximum power point (MPP), the algorithm uses the adaptive variable step size disturbance observation method. This method dynamically adjusts the duty cycle adjustment ΔD according to the magnitude of the power change; the larger the power change, the larger the adjustment ΔD, thereby accelerating the tracking process to the maximum power point and enabling the system to quickly approach the operating region of the maximum power point. When the power change is small, i.e., close to the maximum power point, the algorithm switches to the PID control strategy. The PID controller finely adjusts the duty cycle through the coordinated action of the proportional, integral, and derivative components. The proportional component can quickly respond to the current error, the integral component can eliminate historical accumulated errors, and the derivative component can predict the error change trend in advance. The three components work together to ensure stable operation of the system near the maximum power point, effectively avoiding oscillations near the maximum power point and improving the steady-state accuracy of the system.

[0032] After determining the control strategy and calculating the duty cycle adjustment ΔD, the core control module generates four complementary PWM signals based on the updated control variable D[k]. These four PWM signals drive the first switch S1, the second switch S2, the third switch S3, and the fourth switch S4, respectively, achieving a smooth and continuous transition between buck, boost, and buck-boost operating modes through a unified PWM modulation strategy. Specifically, when D < 0.5, the circuit mainly operates in buck mode, reducing the higher input voltage to a suitable output voltage to meet the load requirements; when D > 0.5, the circuit switches to boost mode, increasing the lower input voltage to the required output voltage, ensuring normal operation under different lighting conditions; when D = 0.5, the circuit is in buck-boost mode, flexibly adjusting the voltage rise and fall according to the actual input and output voltages, further widening the system's input voltage range and improving its adaptability and reliability. This adaptive algorithm-based buck-boost MPPT composite control system, through its ingenious design and advanced control strategies, demonstrates significant advantages in improving system efficiency, accelerating tracking speed, and enhancing system stability.

[0033] Furthermore, the core control module includes: A Kalman filter is used to perform state estimation on sampled voltages and currents, and outputs filtered Vpv_est and Ipv_est. The adaptive MPPT composite control algorithm switches between PID control and adaptive disturbance observation method depending on whether the power change is close to the maximum power point.

[0034] Specifically, the Kalman filter plays a crucial role in the core control module. By processing the sampled voltage and current signals in real time and utilizing its unique recursive algorithm, it effectively filters out noise and interference components in the data, outputting more accurate and stable filtered voltage values ​​Vpv_est and current values ​​Ipv_est. This accurate data provides a reliable foundation for the subsequent adaptive MPPT composite control algorithm, enabling the algorithm to make decisions and control based on accurate information.

[0035] The adaptive MPPT composite control algorithm possesses intelligent judgment and dynamic adjustment capabilities. It can accurately determine the system's proximity to the maximum power point (MPP) by comparing the real-time calculated power change ΔP_est with the preset threshold P_threshold. When the power change is large, indicating the system is far from the MPP, the algorithm quickly switches to the adaptive variable step size disturbance observation mode. In this mode, the algorithm dynamically adjusts the duty cycle adjustment ΔD based on the magnitude of the power change; the larger the power change, the larger the adjustment ΔD, thus accelerating the system's tracking process towards the MPP and enabling the system to quickly approach the MPP operating region, improving tracking efficiency. Conversely, when the power change is small, meaning the system is close to the MPP, the algorithm promptly switches to the PID control strategy mode. The PID controller, through the close coordination of its proportional, integral, and derivative components, finely adjusts the duty cycle. The proportional component responds quickly to the current error, allowing the system to adjust rapidly; the integral component eliminates historical accumulated errors, preventing the system from deviating from the MPP for extended periods; and the derivative component predicts error trends in advance, estimating and adjusting the system's future state. The three elements work together to ensure that the system operates stably near the maximum power point, effectively avoiding oscillations near the maximum power point and significantly improving the system's steady-state accuracy and power generation efficiency.

[0036] Furthermore, the core control module, used to execute the adaptive MPPT composite control algorithm, includes the following steps: Real-time sampling of photovoltaic terminal voltage Vpv_raw[k] and current Ipv_raw[k], and calculation of current power Ppv_raw[k] = Vpv_raw[k] * Ipv_raw[k]; The estimated values ​​Vpv_est[k] and Ipv_est[k] are obtained through a Kalman filter, and the predicted power Ppv_est[k] = Vpv_est[k] * Ipv_est[k] is calculated. Calculate the change in estimated power: ΔP_est = Ppv_est[k] - Ppv_est[k-1]; Based on the relationship between the power change ΔP_est and the preset threshold P_threshold, the PID control / adaptive variable step size disturbance observation method is intelligently selected to calculate the adjustment amount ΔD of the control variable D; Based on the updated control variable D[k], four complementary PWM signals are generated to drive the first switch S1, the second switch S2, the third switch S3, and the fourth switch S4, respectively.

[0037] Specifically, during the real-time sampling of photovoltaic terminal voltage and current, the estimated values ​​Vpv_est[k] and Ipv_est[k] are obtained through a Kalman filter. These data are not only used to calculate the predicted power but also provide an important basis for subsequent control strategy adjustments. The Kalman filter, with its powerful filtering capability, can effectively filter out noise interference in the sampled data, making the estimated values ​​closer to the true values, thereby improving the control accuracy and stability of the system.

[0038] When calculating the change in estimated power ΔP_est, the system records the estimated power value Ppv_est[k-1] at the previous moment and compares it with the estimated power value Ppv_est[k] at the current moment. In this process, the system fully considers the impact of environmental factors such as light intensity and temperature on the output power of the photovoltaic system to ensure that the calculated change in power can accurately reflect the current operating status of the system.

[0039] Based on the relationship between the power change ΔP_est and the preset threshold P_threshold, the system intelligently selects either PID control or the adaptive variable-step disturbance observation method. When ΔP_est is greater than the preset threshold, it indicates that the system is far from the maximum power point. In this case, the system uses the adaptive variable-step disturbance observation method, dynamically adjusting the duty cycle adjustment ΔD to accelerate the tracking process to the maximum power point. Conversely, when ΔP_est is less than or equal to the preset threshold, it indicates that the system is approaching the maximum power point. In this case, the system switches to the PID control strategy, using the coordinated action of the proportional, integral, and derivative components to finely adjust the duty cycle, ensuring stable operation of the system near the maximum power point.

[0040] When generating four complementary PWM signals based on the updated control variable D[k], the system operates strictly according to a unified PWM modulation strategy. These four PWM signals drive the first switch S1, the second switch S2, the third switch S3, and the fourth switch S4, respectively. By precisely controlling the on and off times of the switches, a smooth and continuous transition between buck, boost, and buck-boost operating modes is achieved. This control method not only simplifies the control logic but also improves the stability and reliability of the system, enabling it to maintain efficient and stable operation under different lighting conditions.

[0041] Furthermore, the formula for calculating the adjustment ΔD in the adaptive variable step size perturbation observation method is as follows: ; In the formula, ΔD is the adjustment amount of the duty cycle; α is the power change weighting coefficient; β is the absolute value of the change in photovoltaic panel output power between two adjacent control cycles; β is the power-voltage gradient weighting coefficient. This represents the absolute value of the slope of the power-voltage curve.

[0042] Specifically, in the adaptive variable step size perturbation observation method, the power change weighting coefficient α reflects the degree of influence of the power change on the duty cycle adjustment. When α is large, it means that the power change plays a more important role in adjusting the duty cycle, the system responds more sensitively to power changes, and can adjust the duty cycle more quickly according to the power change, accelerating the tracking of the maximum power point; conversely, when α is small, the influence of the power change on the duty cycle adjustment is relatively weakened.

[0043] The absolute value of the change in photovoltaic (PV) panel output power between two adjacent control cycles directly reflects the variation in PV panel output power at different times. If this absolute value is large, it indicates that the PV panel output power fluctuates significantly in a short period of time, and the system is likely to be far from the maximum power point. In this case, a larger duty cycle adjustment is needed to quickly approach the maximum power point. If this absolute value is small, it indicates that the power change is relatively gradual, and the system may be approaching the maximum power point. The duty cycle adjustment can be relatively smaller.

[0044] The power-voltage gradient weighting coefficient β determines the impact of the power-voltage curve slope on duty cycle adjustment. Different β values ​​result in varying degrees of sensitivity of the system to the power-voltage curve slope. A larger β value makes the system pay more attention to changes in the power-voltage curve slope; when the absolute value of the slope is large, it means that the power changes more drastically with voltage, and the system will adjust the duty cycle more significantly accordingly. A smaller β value makes the system respond more slowly to changes in the slope.

[0045] The absolute value of the slope of the power-voltage curve reflects the rate at which power changes with voltage. A larger absolute slope indicates that power is more sensitive to voltage changes; even a small voltage change can cause a large power change. In this case, the system needs to adjust the duty cycle more precisely to stabilize near the maximum power point. Conversely, a smaller absolute slope indicates that power changes relatively smoothly with voltage, allowing for more lenient duty cycle adjustments. Using this formula, the system can dynamically and accurately calculate the duty cycle adjustment ΔD based on real-time monitored power and voltage data, thereby achieving efficient tracking of the maximum power point and improving the power generation efficiency and stability of the photovoltaic system.

[0046] Furthermore, the output of the PID controller is: ; in, This is the adjustment amount for the duty cycle; The proportional gain adjusts the strength of the controller's response to the current error. The larger the value, the faster the response; The integral gain is used to adjust the controller's response strength to historical accumulated errors. The differential gain is used to adjust the controller's responsiveness to changes in error trends. The error in the k-th control cycle is... , This is the power value at the historical maximum power point. This is the current power estimate for the kth cycle after Kalman filtering; This is the cumulative sum of errors from the beginning to the kth period; This is the difference in error between the current period and the previous period.

[0047] Specifically, during the operation of a PID controller, the proportional gain... It plays a crucial role. Like a sensitive sensor, it can quickly detect the magnitude of the current error and adjust the controller's response to the error based on its set value. When When the value is set too large, the controller reacts extremely quickly to the current error, rapidly adjusting to changes in the system state and enabling the system to approach the target state as quickly as possible. However, excessively large values... An excessively high value can also lead to overshoot in the system, where the system exceeds its maximum power point during adjustment, causing oscillations and affecting system stability. Conversely, if... If the value is set too low, the controller will react more slowly to errors, and the time required for the system to adjust to the target state will be longer. However, this can prevent overshoot and make the system run more smoothly.

[0048] Integral gain This focuses on handling historical accumulated errors. During system operation, various factors may cause errors that are not eliminated in time, and these errors accumulate over time. Integral gain Its function is to adjust the controller's response strength to these historical accumulated errors. When the value is large, the controller will more actively eliminate these accumulated errors, allowing the system to stabilize more accurately near the maximum power point. However, if... If the value is too large, it may cause overshooting during the process of eliminating accumulated errors, affecting the stability of the system. However, when... When the value is small, the controller's ability to eliminate accumulated errors will be relatively weak, and the system may need a longer time to reach a stable state, but it can avoid overshooting.

[0049] Differential gain This provides a forward-looking characteristic, allowing the controller to adjust its responsiveness to changing error trends. In actual operation, errors are not static but change over time. (Differential gain) By analyzing the difference in error between the current cycle and the previous cycle, the trend of error change can be predicted in advance, and the controller can be adjusted based on the prediction results. When the value is large, the controller will be more sensitive to the trend of error changes and can take measures in advance to suppress further increase in error, thereby improving the system's response speed and stability. However, if... If the value is too large, the system may become overly sensitive to small changes in error, leading to excessively frequent control actions and negatively impacting system stability. Conversely, when... When the value is small, the controller will react relatively slowly to the trend of error change. The system may need to wait until the error change is more obvious before making adjustments. This may slow down the system's response speed, but it can avoid instability caused by over-adjustment to a certain extent.

[0050] Error in the kth control cycle The calculation method involves comparing the historical maximum power point value Pmax with the current power estimate Ppv_est[k] in the k-th cycle after Kalman filtering. This calculation method accurately reflects the difference between the current system power and the maximum power point power, providing an accurate basis for subsequent control adjustments. The cumulative error... This records the sum of all errors from the beginning to the k-th cycle, reflecting the accumulated deviations during system operation. Analyzing the accumulated error sum reveals the long-term stability and accuracy of the system, providing a reference for further optimization of the control strategy. The difference in error between the current cycle and the previous cycle, Δe[k], reflects the rate of error change, helping the controller to anticipate error trends and adjust the control strategy accordingly, ensuring stable and efficient operation near the maximum power point. Through the synergistic effect of various parameters in the PID controller, the system achieves stable and precise control near the maximum power point, effectively improving the power generation efficiency and stability of the photovoltaic system.

[0051] Furthermore, the conditions for enabling PID control are as follows: PID control is enabled when the power change |ΔP_est| is less than the preset threshold P_threshold, and the derivative of power with respect to voltage |dP / dV| is less than the voltage gradient threshold.

[0052] Specifically, when the power change |ΔP_est| is less than the preset threshold P_threshold, it indicates that the system's current output power is close to the power value near the maximum power point, and the power fluctuation is within a relatively small range. At this point, from the perspective of power change, the system is close to the operating region of the maximum power point. The condition that the derivative of power with respect to voltage |dP / dV| is less than the voltage gradient threshold further judges the relationship between power and voltage. The derivative of power with respect to voltage reflects the rate of change of power with voltage. When this derivative is small, it means that the change of power with voltage is relatively gradual, that is, the system is in a relatively stable power output state, close to the power-voltage characteristics at the maximum power point. PID control is only activated when both conditions are met simultaneously. Because in this case, the system is very close to the maximum power point, and PID control can finely adjust the duty cycle through the close coordination of its proportional, integral, and derivative components. The proportional terminator can quickly respond to the current error, enabling the system to rapidly adjust and maintain a state close to the maximum power point. The integral term can eliminate historical accumulated errors, preventing the system from deviating from the maximum power point for extended periods due to minor deviations. The derivative term can predict error trends in advance, estimating and adjusting the future state of the system, thereby ensuring stable operation near the maximum power point and effectively avoiding oscillations near the maximum power point, significantly improving the system's steady-state accuracy and power generation efficiency. If either of these two conditions is not met—for example, if the power change |ΔP_est| is greater than or equal to the preset threshold P_threshold—it indicates that the system is far from the maximum power point, and an adaptive variable step-size perturbation observation method is needed to quickly track the maximum power point; or if the derivative of power with respect to voltage |dP / dV| is greater than or equal to the voltage gradient threshold, it means that the power changes drastically with voltage, the system has not yet stabilized near the maximum power point, and PID control is not suitable.

[0053] Furthermore, the four PWM signals are generated as follows: Four PWM waveforms with fixed complementary relationships are generated by mapping a single control variable D, so that the first switch S1 and the second switch S2 are complementary, the third switch S3 and the fourth switch S4 are complementary, and the first switch S1 and the fourth switch S4 have the same duty cycle, and the second switch S2 and the third switch S4 have the same duty cycle. When D < 0.5, the circuit supports buck mode; when D > 0.5, the circuit supports boost mode; when D = 0.5, the circuit supports buck-boost mode.

[0054] Specifically, in the generation of the four PWM signals, a single control variable D plays a crucial role. Its numerical changes map to generate four PWM waveforms with a fixed complementary relationship. This mapping ensures that the first switch S1 and the second switch S2 are complementary in their on / off states, as are the third switch S3 and the fourth switch S4. Simultaneously, the duty cycles of the first switch S1 and the fourth switch S4 remain consistent, and the duty cycles of the second switch S2 and the third switch S3 are also the same, making the drive signals for the switches more coordinated and unified.

[0055] When the value of the control variable D is less than 0.5, the circuit enters buck mode. In this mode, the system adjusts the on and off times of the switching transistors to make the output voltage lower than the input voltage, thereby meeting the low voltage requirements of specific loads. This buck function is particularly important in photovoltaic systems because the output voltage of photovoltaic panels may fluctuate due to changes in factors such as light intensity and temperature. Buck mode ensures a stable low voltage output from the system, providing reliable power support to the load.

[0056] When the value of the control variable D is greater than 0.5, the circuit switches to boost mode. In boost mode, the system precisely controls the operation of the switching transistors to raise the input voltage to the required output voltage level. This is crucial for loads that require higher voltages to operate normally. For example, in some photovoltaic power generation applications, the load may require higher voltages to operate efficiently; in this case, boost mode plays a unique role in ensuring that the system output voltage meets the load's requirements.

[0057] When the value of the control variable D is exactly equal to 0.5, the circuit enters buck-boost mode. This mode combines buck and boost functions, allowing the system to flexibly adjust between the input and output voltages. Regardless of whether the input voltage is higher or lower than the output voltage, buck-boost mode can achieve stable output voltage control by adjusting the duty cycle of the switching transistor. This flexibility enables the system to adapt to more complex and variable lighting conditions and load demands, improving the applicability and reliability of the photovoltaic system. Example 2:

[0058] Please see Figure 1-6 This embodiment provides a buck-boost MPPT composite control method based on an adaptive algorithm to implement the buck-boost MPPT composite control system based on an adaptive algorithm in Embodiment 1, including the following steps: Step 1: System initialization, setting the initial duty cycle D, disturbance step size ΔD, sampling period Ts, and voltage change threshold ΔV_th; Step 2: Sample the photovoltaic terminal voltage Vpv(k) and current Ipv(k) in real time, and calculate the current power Ppv(k) = Vpv(k) × Ipv(k); Step 3: Based on unified PWM modulation, generate four PWM control signals to control the four-switch Buck-Boost circuit, achieving a smooth transition between buck, boost, and buck-boost modes without the need for mode switching logic; Step 4: Using the adaptive MPPT algorithm, dynamically select PID control or disturbance observation method PO to adjust the duty cycle based on the power change |ΔP_est| and the gradient |dP / dV|; Step 5: Output a PWM signal to drive the switching transistor and complete the energy conversion; Step 6: Update historical data and repeat steps 2 through 5.

[0059] Furthermore, the step size ΔD of the adaptive MPPT algorithm is dynamically adjusted based on |ΔP_est| and |dP / dV|: When |ΔP_est| is larger or |dP / dV| is larger, the step size ΔD is larger, which accelerates the tracking. When |ΔP_est| is smaller or |dP / dV| is smaller, the step size ΔD is smaller, allowing for finer adjustment.

[0060] Specifically, in the adaptive MPPT algorithm, the dynamic adjustment mechanism of the step size ΔD is crucial to ensuring the system efficiently tracks the maximum power point. When the power change |ΔP_est| is large, it indicates a significant deviation between the current system state and the maximum power point. Increasing the step size ΔD in this case allows the system to approach the maximum power point more quickly, reducing tracking time and improving response speed. Simultaneously, if the power gradient with respect to voltage |dP / dV| is also large, it means that the power changes drastically with voltage. Further increasing the step size ΔD can better adapt to this rapid change, preventing the system from getting trapped in a local optimum due to an excessively small step size, thus failing to capture the maximum power point in time.

[0061] Conversely, when |ΔP_est| is small, it indicates that the system is close to its maximum power point. Continuing to use a large step size ΔD at this point may cause the system to oscillate around the maximum power point, resulting in unstable operation. Therefore, reducing the step size ΔD allows for fine-tuning, enabling the system to converge more smoothly to the maximum power point and improving steady-state accuracy. Similarly, when |dP / dV| is small, the power changes relatively smoothly with voltage, and the system is in a relatively stable state. Reducing the step size ΔD in this case also helps avoid unnecessary adjustments and ensures stable system operation.

[0062] This strategy of dynamically adjusting the step size ΔD based on |ΔP_est| and |dP / dV| enables the system to automatically select the appropriate tracking speed and adjustment accuracy according to real-time operating conditions. When far from the maximum power point, the system tracks quickly with a larger step size; when approaching the maximum power point, the system adjusts finely with a smaller step size. This adaptive adjustment method not only improves the system's tracking efficiency but also enhances its stability and robustness, enabling the photovoltaic system to operate efficiently and stably in various complex and changing environments.

[0063] The various embodiments in this specification are described in a progressive manner, with each example focusing on the differences from other embodiments. Similar or identical parts between examples can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the methods disclosed in the embodiments; relevant parts can be found in the method section.

[0064] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A buck-boost MPPT composite control system based on an adaptive algorithm, characterized in that, include: Power conversion module: The main topology is a four-switch Buck-Boost step-up / step-down circuit, which includes a first switch S1, a second switch S2, a third switch S3, a fourth switch S4, an inductor, an input capacitor and an output capacitor. Signal sampling module: includes voltage sensor and current sensor, used to collect the voltage Vpv and current Ipv at the photovoltaic panel and the voltage Vbat at the load terminal in real time; Core control module: used to execute the adaptive MPPT composite control algorithm and output four PWM control signals to the switching transistors of the power conversion module; Drive module: Connected between the core control module and the power conversion module, it is used to amplify and isolate the PWM control signal to drive the switching transistor; The core control module achieves a smooth and continuous transition between buck, boost, and buck-boost modes through a unified PWM modulation strategy.

2. The MPPT composite control system based on adaptive algorithm according to claim 1, characterized in that: The unified PWM modulation strategy is as follows: The first PWM signal PWM1 is generated and sent to the first switching transistor S1, with a duty cycle of D. A second PWM signal PWM2 is generated and sent to the second switch S2, which is complementary to the first PWM signal PWM1. The duty cycle is 1-D, and a fixed dead time is inserted. A third PWM signal PWM3 is generated and sent to the third switch S3, with a duty cycle of 1-D. A fourth PWM signal PWM4 is generated and sent to the fourth switch S4, which is complementary to the third PWM signal PWM3. The duty cycle is D, and a fixed dead time is inserted. Among them, D is the only control variable, with a value range of 0 < D < 1. Its continuous change achieves a smooth transition of the working mode.

3. The MPPT composite control system based on adaptive algorithm according to claim 1, characterized in that: The core control module includes: A Kalman filter is used to perform state estimation on sampled voltages and currents, and outputs filtered Vpv_est and Ipv_est. The adaptive MPPT composite control algorithm switches between PID control and adaptive disturbance observation method depending on whether the power change is close to the maximum power point.

4. The MPPT composite control system based on adaptive algorithm according to claim 3, characterized in that: The core control module is used to execute the adaptive MPPT composite control algorithm, including the following steps: Real-time sampling of photovoltaic terminal voltage Vpv_raw[k] and current Ipv_raw[k], and calculation of current power Ppv_raw[k] = Vpv_raw[k] * Ipv_raw[k]; The estimated values ​​Vpv_est[k] and Ipv_est[k] are obtained through a Kalman filter, and the predicted power Ppv_est[k] = Vpv_est[k] * Ipv_est[k] is calculated. Calculate the change in estimated power: ΔP_est = Ppv_est[k] - Ppv_est[k-1]; Based on the relationship between the power change ΔP_est and the preset threshold P_threshold, the PID control / adaptive variable step size disturbance observation method is intelligently selected to calculate the adjustment amount ΔD of the control variable D; Based on the updated control variable D[k], four complementary PWM signals are generated to drive the first switch S1, the second switch S2, the third switch S3, and the fourth switch S4, respectively.

5. The MPPT composite control system based on adaptive algorithm according to claim 4, characterized in that: The formula for calculating the adjustment amount ΔD in the adaptive variable step size perturbation observation method is as follows: ; In the formula, ΔD is the adjustment amount of the duty cycle; α is the power change weighting coefficient; β is the absolute value of the change in photovoltaic panel output power between two adjacent control cycles; β is the power-voltage gradient weighting coefficient. This represents the absolute value of the slope of the power-voltage curve.

6. The MPPT composite control system based on adaptive algorithm according to claim 4, characterized in that: The output of the PID controller is: ； in, This is the adjustment amount for the duty cycle; The proportional gain adjusts the strength of the controller's response to the current error. The larger the value, the faster the response; The integral gain is used to adjust the controller's response strength to historical accumulated errors. The differential gain is used to adjust the controller's responsiveness to changes in error trends. The error in the k-th control cycle is... , This is the power value at the historical maximum power point. This is the current power estimate for the kth cycle after Kalman filtering; This is the cumulative sum of errors from the beginning to the kth period; This is the difference in error between the current period and the previous period.

7. The MPPT composite control system based on adaptive algorithm according to claim 6, characterized in that: The conditions for enabling the PID control are as follows: PID control is enabled when the power change |ΔP_est| is less than the preset threshold P_threshold, and the derivative of power with respect to voltage |dP / dV| is less than the voltage gradient threshold.

8. The MPPT composite control system based on adaptive algorithm according to claim 4, characterized in that: The four PWM signals are generated as follows: Four PWM waveforms with fixed complementary relationships are generated by mapping a single control variable D, so that the first switch S1 and the second switch S2 are complementary, the third switch S3 and the fourth switch S4 are complementary, and the first switch S1 and the fourth switch S4 have the same duty cycle, and the second switch S2 and the third switch S4 have the same duty cycle. When D < 0.5, the circuit supports buck mode; when D > 0.5, the circuit supports boost mode; when D = 0.5, the circuit supports buck-boost mode.

9. A buck-boost MPPT composite control method based on an adaptive algorithm, characterized in that, To implement the adaptive algorithm-based boost-boost MPPT composite control system as described in any one of claims 1-8, the following steps are included: Step 1: System initialization, setting the initial duty cycle D, disturbance step size ΔD, sampling period Ts, and voltage change threshold ΔV_th; Step 2: Sample the photovoltaic terminal voltage Vpv(k) and current Ipv(k) in real time, and calculate the current power Ppv(k) = Vpv(k) × Ipv(k); Step 3: Based on unified PWM modulation, generate four PWM control signals to control the four-switch Buck-Boost circuit, achieving a smooth transition between buck, boost, and buck-boost modes without the need for mode switching logic; Step 4: Using the adaptive MPPT algorithm, dynamically select PID control or disturbance observation method PO to adjust the duty cycle based on the power change |ΔP_est| and the gradient |dP / dV|; Step 5: Output a PWM signal to drive the switching transistor and complete the energy conversion; Step 6: Update historical data and repeat steps 2 through 5.

10. The MPPT composite control method based on adaptive algorithm according to claim 9, characterized in that: The step size ΔD of the adaptive MPPT algorithm is dynamically adjusted based on |ΔP_est| and |dP / dV|: When |ΔP_est| is larger or |dP / dV| is larger, the step size ΔD is larger, which accelerates the tracking. When |ΔP_est| is smaller or |dP / dV| is smaller, the step size ΔD is smaller, allowing for finer adjustment.

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