Abstract
This paper presents the design and implementation of a Maximum Power Point Tracking (MPPT)-based tracking control system for grid-connected photovoltaic system. The objective of this study is to address the challenges associated with power fluctuations, voltage instabilities, and current inconsistencies during the grid integration of photovoltaic systems. By integrating a microcontroller module and electric quantity pulse tracking equipment into the grid-connected control circuitry, this study aims to achieve accurate tracking and control of the grid-connected electric energy. Experimental results demonstrate that the proposed system can effectively limit the voltage deviations to less than 60V and the current standard deviations to less than 5A, ensuring stable and reliable grid integration of photovoltaic systems.
1. Introduction
Photovoltaic system have emerged as a promising renewable energy technology due to their environmental friendliness and abundance of solar radiation. However, grid integration of photovoltaic system is often hindered by power fluctuations, voltage instabilities, and current inconsistencies caused by various external factors such as weather conditions and grid interactions. To address these challenges, it is crucial to develop efficient tracking and control mechanisms that can ensure stable and reliable grid integration of photovoltaic system.
This paper proposes an MPPT-based tracking control system for grid-connected photovoltaic system. The system combines a microcontroller module with electric quantity pulse tracking equipment to monitor and control the grid-connected electric energy. The key objectives of this study are to:
- Minimize power fluctuations during grid integration.
- Stabilize the voltage and current output of photovoltaic system.
- Achieve accurate tracking and control of grid-connected electric energy.
The rest of the paper is organized as follows: Section 2 discusses the related work in PV grid integration. Section 3 outlines the hardware design of the proposed tracking control system. Section 4 describes the software design based on the MPPT algorithm. Section 5 presents the experimental setup and results. Finally, Section 6 concludes the paper with a summary and directions for future work.
2. Related Work
Several studies have been conducted to improve the grid integration of photovoltaic system. For instance, some researchers have focused on optimizing the coil structure of photovoltaic system to improve energy distribution and transmission efficiency . Others have designed control systems based on Controller Area Network (CAN) buses to regulate the voltage and current loads according to specific distribution principles . However, these approaches have limitations in addressing the power fluctuations and grid interactions that can significantly impact the stability of PV systems during grid integration.
In contrast, MPPT algorithms have been widely recognized for their effectiveness in maximizing power output from photovoltaic system under varying environmental conditions. By dynamically adjusting the operating point of the photovoltaic system to its maximum power point, MPPT algorithms can help stabilize the power output and improve overall system efficiency [3]. However, traditional MPPT algorithms may not be sufficient to address the complex power oscillations and grid interactions that occur during grid integration of large-scale photovoltaic system.
To address these challenges, this study proposes an MPPT-based tracking control system that integrates a microcontroller module and electric quantity pulse tracking equipment to achieve accurate tracking and control of grid-connected electric energy.
3. Hardware Design
The hardware design of the proposed MPPT-based tracking control system consists of three main components: the grid-connected control circuitry, microcontroller module, and electric quantity pulse tracking equipment.
3.1 Grid-Connected Control Circuitry
The grid-connected control circuitry is the backbone of the system, responsible for converting optical signals from solar panels into electrical signals that can be integrated into the grid. The core chip used in this circuitry is the SN74ALS1245N, which converts optical signals into electrical signals and maintains a stable operation through its +VCC and -VDD ports .
To avoid reverse current flow and protect the system from electrical breakdown, the positive and negative terminals of the solar panels are connected to the corresponding ports of the SN74ALS1245N chip. The MPU-6050 load-handling component is also integrated into the circuitry to manage the electrical signals during transmission.
3.2 Microcontroller Module
The microcontroller module serves as the brain of the tracking control system, responsible for scheduling the electrical signals output by the grid-connected control circuitry and transmitting them to the electric quantity pulse tracking equipment. The module consists of three layers: pin access, device, and application .
The pin access layer provides various types of application pins (e.g., CMOS, PFO, XTAL, RESET) to interface with downstream devices. The device layer includes a W77E58 crystal oscillator and a timer chip to regulate the operation of the electric quantity pulse tracking equipment. Finally, the application layer transmits and tracks the electrical signals using I/O ports.
3.3 Electric Quantity Pulse Tracking Equipment
The electric quantity pulse tracking equipment consists of a reset device, current pulse encoder, and UART signal tracking device. This equipment is responsible for simultaneously transmitting pulse signals, electrical signals, and feedback instructions to ensure high-quality grid-connected electric energy.
The reset device initializes the system, while the current pulse encoder encodes the current signals. The UART signal tracking device communicates with the microcontroller module to receive and transmit the necessary control signals.
4. Software Design
The software design of the MPPT-based tracking control system revolves around the MPPT algorithm and its application in grid-connected photovoltaic system. This section describes the MPPT-based control parameters and the tracking control algorithm.
4.1 MPPT-Based Control Parameters
The MPPT algorithm aims to maximize the power output of photovoltaic system by dynamically adjusting their operating points to the maximum power point. To achieve this, the following MPPT-based control parameters are defined:
- Random Electric Quantity Marker (α): Represents a random electric quantity in the system.
- Current (Iα) and Voltage (Uα): Represent the current and voltage associated with α.
- MPPT Evaluation Coefficient (β): Reflects the quality of the electric energy being evaluated.
- Electrical Signal Output Parameter (χ): Represents the overall electrical signal output of the photovoltaic system.
The MPPT evaluation index can be expressed as:
l=β1(Iα2⋅Uα2)χ1−1(1)
where It and Ut are the rated output current and voltage at time t, respectively. Additionally, the following constraints must be satisfied:
It>Iα>0,Ut>Uα>0(2)
The power oscillation coefficient (β) is calculated based on the stability of the MPPT evaluation index. Lower values of β indicate more stable power output:
beta=j0+fSˉg(4)
where j0 is the initial output power, g represents the transmission oscillation intensity, f is the power marker parameter, and Sˉ is the average output power.
4.2 Tracking Control Algorithm
Based on the MPPT evaluation index and power oscillation coefficient, a tracking control algorithm is designed to regulate the grid-connected electric energy. The task quantity expression for tracking control is defined as:
A=d⋅O1+hlp′ϕ(5)
where d is a scaling factor, h is the grid integration adjustment parameter, γ is the quality discriminant coefficient, p′ is the real-time tracking vector, ϕ is the real-time control vector, and O represents the cumulative electric energy in the control loop.
By combining the hardware and software designs, the proposed MPPT-based tracking control system can effectively track and regulate the grid-connected electric energy, minimizing power fluctuations and ensuring stable voltage and current outputs.
5. Experimental Results
To validate the effectiveness of the proposed MPPT-based tracking control system, comparative experiments were conducted using a test setup comprising a grid-connected photovoltaic system. The experiments compared the performance of the proposed system with two existing control systems: one based on a spherical coil structure and another based on a CAN bus.
5.1 Experimental Setup
The experimental setup consisted of a grid-connected photovoltaic system with the following components:
- solar panels
- Inverters
- Controllers
- DC/DC converters
- Batteries
The system was designed to simulate real-world grid integration scenarios, including power oscillations and varying environmental conditions.
5.2 Results and Analysis
The experimental results are presented, which show the voltage and current outputs of the three systems under different power oscillation conditions. The standard voltage and current values were set at 390V and 40A, respectively.
The voltage output of the proposed MPPT-based system remained relatively stable, with a maximum deviation of 60V from the standard value. In contrast, the spherical coil system exhibited significant voltage fluctuations, deviating up to 220V from the standard value. The CAN bus system also showed substantial deviations from the standard voltage.
Similarly, that the current output of the proposed system remained close to the standard value, with a maximum deviation of 5A. In contrast, the current outputs of the spherical coil and CAN bus systems deviated significantly from the standard value under increasing power oscillation conditions.
Table 1: Comparative Performance Metrics
| System | Maximum Voltage Deviation (V) | Maximum Current Deviation (A) |
|---|---|---|
| Proposed MPPT-based | 60 | 5 |
| Spherical Coil-based | 220 | 15 |
| CAN Bus-based | 180 | 10 |
These results clearly demonstrate the superiority of the proposed MPPT-based tracking control system in regulating voltage and current outputs during grid integration of photovoltaic system.
6. Conclusion
This paper presents the design and implementation of an MPPT-based tracking control system for grid-connected photovoltaic system. By integrating a microcontroller module and electric quantity pulse tracking equipment into the grid-connected control circuitry, the proposed system can effectively minimize power fluctuations, stabilize voltage and current outputs, and achieve accurate tracking and control of grid-connected electric energy. Experimental results show that the proposed system can limit voltage deviations to less than 60V and current standard deviations to less than 5A, outperforming existing control systems. Future work will focus on improving the algorithm efficiency and scalability of the system to accommodate larger PV installations.