Micro photovoltaic charging system based on optimization algorithm

Abstract

The invention discloses a micro-photovoltaic charging system based on an optimization algorithm, and relates to the technical field of photovoltaic power generation and energy storage. Comprising a photovoltaic cell panel which is used for obtaining real-time data of external light radiation intensity, environment temperature, main circuit current and voltage parameters, converting the real-time data into electric energy and inputting the electric energy to an MPPT controller; the MPPT controller is used for extracting power-voltage curve feature points and adjusting the working state of the system; the DC-DC converter is used for adjusting the duty ratio according to the output of the MPPT controller to realize maximum power output; the energy storage module is used for storing the generated electric energy; the charging management module is used for performing constant-current / constant-voltage charging management; and the microcontroller is used for coordinating the communication of each module. The generating capacity is greatly improved, the working state of the system is adjusted, and quick response and timely action of the system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation and energy storage, and in particular to a micro-photovoltaic charging system based on an optimization algorithm. Background Art

[0002] Photovoltaic power generation is a technology that uses the photovoltaic effect of semiconductor interfaces to directly convert light energy into electrical energy. With the popularization of new energy and low-carbon concepts, photovoltaic power generation accounts for an increasingly higher proportion of total electricity consumption. Photovoltaic power generation can reduce energy consumption, promote green urban development, lower the operating costs of some electrical equipment, and avoid air pollution. However, the conversion efficiency of photovoltaic cells is relatively low, and solar energy is an intermittent energy source. Light intensity and external temperature have a significant impact on its output. There are many uncontrollable factors, and the status of photovoltaic power generation systems must be monitored in real time to ensure their normal operation. Existing photovoltaic charging systems are prone to energy waste and pose system stability risks.

[0003] To alleviate the increasingly severe energy crisis and the deteriorating ecological environment caused by traditional energy consumption, the application of new energy sources has become an increasing research and development focus worldwide. Solar energy is a green, safe, and renewable clean energy source. Large-scale adoption of solar power generation can effectively reduce environmental pollution, making solar photovoltaic power generation widely popular. Independent photovoltaic power generation systems typically use batteries for energy storage. However, the stability and charging speed of battery storage are directly affected by the output characteristics of photovoltaic modules. Therefore, selecting a reasonable control strategy and designing a dedicated controller can effectively improve the battery's energy storage performance. Battery life depends on different charging strategies. Choosing a reasonable charging strategy can extend battery life. A three-stage charging control strategy can ensure efficient battery charging and has been widely used in the industrial field. Intelligent maximum power point tracking (MPPT) of photovoltaic modules can achieve efficient energy utilization of photovoltaic power generation. Combining intelligent MPPT with a three-stage battery charging strategy can improve the battery's charge level and extend its service life.

[0004] In order to improve power generation efficiency, the maximum power point tracking algorithm, also known as the MPPT algorithm, is widely used in the power generation field. Among them, the fixed voltage method and the perturbation observation method have the simplest hardware structure. However, the existing algorithms are not suitable for dealing with the micro-photovoltaic charging system and the complex temperature difference changes in the environment, and cannot achieve rapid response to external changes.

[0005] Therefore, it is an urgent problem for those skilled in the art to propose a micro photovoltaic charging system based on an optimization algorithm to solve the difficulties existing in the existing technology. Summary of the Invention

[0006] In view of this, the present invention provides a micro photovoltaic charging system based on an optimization algorithm, which greatly improves the power generation, adjusts the working state of the system, and realizes rapid response and timely action of the system.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A micro photovoltaic charging system based on an optimization algorithm includes a photovoltaic panel, an MPPT control module, a DC-DC converter, an energy storage module, a charging management module and a microcontroller connected in sequence;

[0009] Photovoltaic panels are used to obtain real-time data on external light radiation, ambient temperature, main circuit current, and voltage parameters and convert them into electrical energy to be input into the MPPT controller;

[0010] MPPT controller, used to extract characteristic points of the power-voltage curve and adjust the working state of the system;

[0011] A DC-DC converter is used to adjust the duty cycle according to the output of the MPPT controller to achieve maximum power output;

[0012] Energy storage module, used to store the generated electrical energy;

[0013] Charging management module, used for constant current / constant voltage charging management;

[0014] Microcontroller, used to coordinate communication between modules.

[0015] In the above system, optionally, the photovoltaic panel is composed of multiple flexible photovoltaic power generation units connected in parallel, including a Boost circuit unit, an MSP430 microcontroller circuit unit, a power supply circuit unit, a collection circuit unit, and a drive circuit unit connected in sequence.

[0016] In the above system, the MPPT controller is optionally an FPGA chip that integrates the PSO-P&O algorithm. The PSO-P&O algorithm combines the perturbation observation method with the particle swarm optimization algorithm, uses P&O fast tracking under steady-state conditions, and switches to PSO global search when the illumination suddenly changes.

[0017] In the above system, the PSO-P&O algorithm can optionally calculate the equivalent internal resistance of the photovoltaic panel in real time according to the ambient temperature and light intensity, and dynamically correct the MPPT search step and number of iterations; introduce a fuzzy logic controller to comprehensively consider power gain, response time and energy loss, and dynamically optimize the algorithm weight coefficient.

[0018] In the above system, optionally, the MPPT controller eliminates noise interference through Kalman filtering.

[0019] In the above system, optionally, the energy storage module is a lithium battery pack or a supercapacitor.

[0020] In the above system, optionally, the charging management module switches between constant current and constant voltage modes according to the battery SOC state.

[0021] The above system optionally uses an STM32F407VGT6 microcontroller.

[0022] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a micro photovoltaic charging system based on an optimization algorithm, which has the following beneficial effects:

[0023] (1) The present invention supports modular expansion and is suitable for 10W to 500W micro-photovoltaic systems;

[0024] (2) The present invention effectively reduces hardware costs and is suitable for scenarios such as portable devices, outdoor sensors, and off-grid lighting;

[0025] (3) The system of the present invention operates completely in normal mode, greatly improving power generation, adjusting the working state of the system, and achieving rapid system response and timely action. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0027] Figure 1 This is a structural block diagram of a micro-photovoltaic charging system based on an optimization algorithm provided by the present invention;

[0028] Figure 2 This is a structural diagram of the photovoltaic panel provided by the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Reference Figure 1 As shown, the present invention discloses a micro photovoltaic charging system based on an optimization algorithm, comprising a photovoltaic panel, an MPPT control module, a DC-DC converter, an energy storage module, a charging management module and a microcontroller connected in sequence;

[0031] Photovoltaic panels are used to obtain real-time data on external light radiation, ambient temperature, main circuit current, and voltage parameters and convert them into electrical energy to be input into the MPPT controller;

[0032] MPPT controller, used to extract characteristic points of the power-voltage curve and adjust the working state of the system;

[0033] A DC-DC converter is used to adjust the duty cycle according to the output of the MPPT controller to achieve maximum power output;

[0034] Energy storage module, used to store the generated electrical energy;

[0035] Charging management module, used for constant current / constant voltage charging management;

[0036] Microcontroller, used to coordinate communication between modules.

[0037] Further, such as Figure 2 As shown, the photovoltaic panel consists of multiple flexible photovoltaic power generation units connected in parallel, including a Boost circuit unit, an MSP430 single-chip circuit unit, a power supply circuit unit, a collection circuit unit, and a drive circuit unit connected in sequence.

[0038] Specifically, due to the simple structure and high energy conversion efficiency of the Boost circuit, the Boost circuit is used to achieve the conversion from the solar output voltage to the battery charging voltage;

[0039] Since the signals collected by the current and voltage sensors are relatively small and easily interfered, an acquisition circuit is used to amplify them and then send them to the microcontroller for calculation and processing;

[0040] Since the PWM wave output by the microcontroller is not enough to reach the voltage to drive the IGBT, the TLP250 dedicated chip is used to drive and control the circuit.

[0041] Furthermore, the MPPT controller is an FPGA chip that integrates the PSO-P&O algorithm. The PSO-P&O algorithm combines the perturbation observation method and the particle swarm optimization algorithm. It uses P&O fast tracking under steady-state conditions and switches to PSO global search when the illumination changes suddenly.

[0042] Furthermore, the PSO-P&O algorithm calculates the equivalent internal resistance of photovoltaic panels in real time based on ambient temperature and light intensity, and dynamically corrects the MPPT search step and number of iterations; it introduces a fuzzy logic controller to comprehensively consider power gain, response time, and energy loss, and dynamically optimizes the algorithm weight coefficient.

[0043] Specifically, the core mechanism of the PSO-P&O algorithm is:

[0044] When the working condition is steady (stable light and small temperature change): the perturbation and observation method (P&O) is used to periodically perturb the PV panel voltage (such as ±2% step size), adjust the operating point according to the direction of power change, and quickly track the local maximum power point.

[0045] When the working conditions are dynamic (sudden changes in illumination, shadow occlusion): switch to particle swarm optimization (PSO) to globally search for the maximum power point of the photovoltaic panel to avoid falling into local extremes and adapt to complex environments.

[0046] Switching condition: judged by the power change rate. If the absolute value of the power change rate is less than the threshold (such as 0.5% / s) for three consecutive times, it is judged to be steady state; otherwise, the PSO mode is triggered.

[0047] Combined with environmental parameters: when the light intensity fluctuation is greater than 20% or the temperature changes by more than 5°C, the system is forced to switch to PSO.

[0048] If in PSO mode, a global search is performed and the duty cycle is updated; if in P&O mode, local disturbance tracking is performed.

[0049] Furthermore, the MPPT controller eliminates noise interference through Kalman filtering.

[0050] Furthermore, the energy storage module is a lithium battery pack or a supercapacitor.

[0051] Furthermore, the charging management module switches between constant current and constant voltage modes according to the battery SOC state.

[0052] Furthermore, an STM32F407VGT6 microcontroller is used.

[0053] In a specific embodiment, the following are included:

[0054] Photovoltaic panels obtain real-time data on external light radiation, ambient temperature, main circuit current, and voltage parameters and convert them into electrical energy to be input into the MPPT controller; the MPPT controller eliminates noise interference through Kalman filtering and extracts the characteristic points of the power-voltage curve. The MPPT controller selects the optimal tracking mode according to the current operating conditions and performs adaptive parameter adjustment to regulate the working state of the system; the DC-DC converter adjusts the duty cycle according to the output of the MPPT controller to achieve maximum power output; the energy storage module stores the generated electrical energy; the charging management module switches between constant current and constant voltage modes according to the battery SOC status to prevent overcharging; the microcontroller is used to coordinate communication between each module.

[0055] Determine the working conditions at this time based on environmental parameters:

[0056] When the working condition is steady (stable light and small temperature change): the perturbation and observation method (P&O) is used to periodically perturb the PV panel voltage (such as ±2% step size), adjust the operating point according to the direction of power change, and quickly track the local maximum power point.

[0057] When the working conditions are dynamic (sudden changes in illumination, shadow occlusion): switch to particle swarm optimization (PSO) to globally search for the maximum power point of the photovoltaic panel to avoid falling into local extremes and adapt to complex environments.

[0058] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0059] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A micro photovoltaic charging system based on an optimization algorithm, characterized in that: It includes a photovoltaic panel, an MPPT control module, a DC-DC converter, an energy storage module, a charging management module and a microcontroller connected in sequence; Photovoltaic panels are used to obtain real-time data on external light radiation, ambient temperature, main circuit current, and voltage parameters and convert them into electrical energy to be input into the MPPT controller; MPPT controller, used to extract characteristic points of the power-voltage curve and adjust the working state of the system; A DC-DC converter is used to adjust the duty cycle according to the output of the MPPT controller to achieve maximum power output; Energy storage module, used to store the generated electrical energy; Charging management module, used for constant current / constant voltage charging management; Microcontroller, used to coordinate communication between modules.

2. The micro photovoltaic charging system based on the optimization algorithm according to claim 1, characterized in that: The photovoltaic panel is composed of a plurality of flexible photovoltaic power generation units connected in parallel, including a Boost circuit unit, an MSP430 single-chip microcomputer circuit unit, a power supply circuit unit, a collection circuit unit, and a drive circuit unit connected in sequence.

3. The micro photovoltaic charging system based on the optimization algorithm according to claim 1, characterized in that: The MPPT controller is an FPGA chip that integrates the PSO-P&O algorithm. The PSO-P&O algorithm combines the perturbation-observation method with the particle swarm optimization algorithm. It uses P&O fast tracking under steady-state conditions and switches to PSO global search when the illumination changes suddenly.

4. The micro photovoltaic charging system based on the optimization algorithm according to claim 3, characterized in that: The PSO-P&O algorithm calculates the equivalent internal resistance of photovoltaic panels in real time based on ambient temperature and light intensity, and dynamically corrects the MPPT search step size and number of iterations. It also introduces a fuzzy logic controller to dynamically optimize the algorithm weight coefficient by comprehensively considering power gain, response time, and energy loss.

5. The micro photovoltaic charging system based on the optimization algorithm according to claim 3, characterized in that: The MPPT controller eliminates noise interference through Kalman filtering.

6. The micro photovoltaic charging system based on the optimization algorithm according to claim 1, characterized in that: The energy storage module is a lithium battery pack or a supercapacitor.

7. The micro photovoltaic charging system based on the optimization algorithm according to claim 1, characterized in that: The charging management module switches between constant current and constant voltage modes according to the battery SOC status.

8. The micro photovoltaic charging system based on the optimization algorithm according to claim 1, characterized in that: It uses the STM32F407VGT6 microcontroller.

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