To realize the maximum power output of a grid-connected inverter, the MPPT (maximum power point tracking) control method is needed. The perturbation and observation (P&O) method can cause the inverter operating point to oscillate near the maximum power. In this paper, the fuzzy control P&O method is proposed, and the fuzzy control algorithm is applied to the disturbance observation method. The simulation results of the P&O method with fuzzy control and the traditional P&O method prove that not only can the new method reduce the power loss caused by inverter oscillation during maximum power point tracking, but also it has the advantage of speed. Inductive loads in the post-grid-connected stage cause grid-connected current distortion. A fuzzy control algorithm is added to the traditional deadbeat grid-connected control method to improve the quality of the system’s grid-connected operation. The fuzzy deadbeat control method is verified by experiments, and the harmonic current of the grid-connected current is less than 3%.
In view of the traditional photovoltaic grid-connected inverter system, light intensity can affect the output power of a photovoltaic solar array to a large extent. Therefore, maximum power point tracking (MPPT) is performed to improve the utilization efficiency of the photovoltaic array and ensure that it maintains maximum power output.
In the first stage of a grid-connected inverter, an MPPT control algorithm mainly includes the constant voltage method, the perturbation and observation (P&O) method, and the conductance increment method. The advantages of simplicity, easy implementation, and rapid MPPT have helped the P&O method to be widely used in an MPPT algorithm. However, the P&O method can easily produce continuous oscillation around the maximum power point; therefore, a nonlinear control method, named fuzzy control, is added based on the traditional P&O method. Fuzzy control can simplify the system design and is particularly useful for a nonlinear, hysteretic, time-varying, and model-incomplete system owing to its excellent robust performance [
The control methods in the post-grid-connected stage of full bridge inversion include current instantaneous value control (PI control) algorithm, repetitive control algorithm, deadbeat algorithm, and proportion resonance algorithm. The PI control algorithm is widely used owing to its simplicity and easy implementation. However, these control algorithms can only address parts of the problem. For example, the deadbeat algorithm is widely used owing to its high-speed system response time. The system works steadily when its inverter output is combined with resistive loads, yet when the inductive or capacitive loads are connected to the inverter output of system, as well as when the system is suffering from outside interference, the current and voltage at the load end fail to maintain synchronization and the system lacks stability. Besides, the harmonic rates of current and voltage at both ends of the load increase simultaneously.
Based on analysis of the MPPT fuzzy control of the P&O method in the first stage of a photovoltaic grid-connected inverter, this paper proposes a fuzzy control-based deadbeat control strategy that can be used in the poststage of the photovoltaic grid-connected inverter, which can not only adapt to the nonlinear load but also reduce the harmonic waves of the inverter output.
The MPPT of a photovoltaic array needs to be conducted to make the best use of the photovoltaic solar array. The
Output curve of photovoltaic solar array.
Photovoltaic
Photovoltaic
The first-stage MPPT control is conducted using the first-stage interleaving Boost circuit. The output voltage of the photovoltaic solar array used in this study is 200–350 V, and a bus voltage of approximately 400 V can be obtained using the booster circuit. The P&O method is used as the MPPT control method in this study. The disturbance voltages are continuously provided to the output end of the photovoltaic solar array, to calculate the output powers of the two photovoltaic arrays. The output powers are input to PI regulation to produce a pulse width modulation (PWM) control pulse. After passing through the drive circuit, the PWM signal can directly drive the switching element in the boost circuit in order to realize MPPT.
As the name suggests, the P&O method is used to continuously provide the disturbance voltage and calculate the output powers of the two photovoltaic arrays until they are operating around the maximum power point. The operating method is described as follows: “
P&O curve of solar array.
The software flow diagram of the P&O method described in this paper is shown in Figure
Control flow diagram of P&O method.
The step size of the traditional P&O method remains unchanged during the process of MPPT, while the fuzzy-controlled P&O method actually improves the traditional P&O method with a fixed step. The control method can adjust the perturbation step according to the real-time output power of the photovoltaic solar cell to ensure that the operating point can be closer to the maximum power point. According to the principle of the P&O method, the output power of the solar cell is used as the objective function, while the duty ratio is used as the control variable. The current step size is adjusted and confirmed based on the variation in the power value and the duty ratio at the last moment. The input of the fuzzy controller at moment
Fuzzy controller.
The Mamdani controller is selected in the Matlab fuzzy box and the centroid method is used to solve fuzzification. The fuzzy linguistic variables
Rules of MPPT fuzzy control.
| NB | NS | ZE | PS | PB |
---|---|---|---|---|---|
N | PB | PM | NS | PM | NB |
Z | NM | NS | ZE | PS | PM |
P | NB | NM | PS | PM | PB |
Two control models of the poststage full-bridge inversion include controlling of output voltage and controlling of output current. The control strategy of the voltage control mode is to consider the entire system as a controlled voltage source and make the inverter output voltage a system control quantity; the control strategy of the current control mode is to consider the entire system as a controlled current source and make the inverter output current a system control quantity.
The control mode of the output voltage is equivalent to a controlled voltage source; therefore, it is easily affected by the power grid voltage. The quality of the inverter output voltage is significantly impacted if the power grid voltage suffers from any abnormality. However, for the control mode of the output current, the controlled output quantity is the inverter output current and the current source is highly resistive to the voltage source; thus, the quality of the output current cannot suffer any impact from the power grid voltage. In short, the control mode of the output current should be used in the grid-connected operation mode, which can improve the quality of the output power as well.
The grid-connected operation mode generally adopts the double closed-loop control algorithm with an outer loop of bus voltage and inner loop of current output. In this paper, a fuzzy control algorithm is added to the PI modulation of the bus-voltage outer ring and the parameters of bus voltage loop are adjusted constantly to make the closed-loop control more precise. Besides, the output quantity of the outer voltage loop is one-unit current. The inner loop of the double closed loop is a current loop, which adopts the deadbeat control algorithm to ensure the synchronization between the output current and power grid voltage.
The poststage inverter output of the grid-connected operation mode adopts the output current control. The deadbeat control method based on the output current control is used in the grid-connected inverter system described in this paper. The control system is realized using a digital signal processor, which exhibits highly precise AD sampling and rapid internal operation, which is suitable for the deadbeat control.
When the inverter is operating in the grid-connected mode, the poststage inverter circuit is equivalent to the circuit diagram shown in Figure
Schematic of poststage grid-connected inverter circuit.
The following equation can be obtained according to the output inductance characteristics:
The above equation can be transformed into the following equation within one control cycle
The average value of the power grid voltage
From (
From (
The poststage inverter output voltage of the grid-connected inverter is directly proportional to the first-stage DC bus voltage; thus, the duty ratio of the high-frequency tube during the control cycle is
The system works steadily when its inverter output is combined with resistive loads, yet when the inductive or capacitive loads are connected to the inverter output of the system, as well as when the system is suffering from outside interference, the current and voltage at the load end fail to maintain synchronization and the system lacks stability. Besides, the harmonic rate of current and voltage at both ends of the load increases simultaneously. Similar problems exist in the mutual switchover of grid-off and grid-connected operating modes. Owing to the characteristics of fuzzy control, the fuzzy control method can be added to the original unipolar deadbeat control method to improve the stability of the inverter system when the nonlinear load is connected to the output end of the load. Fuzzy control is mainly introduced into the photovoltaic control system to properly modify the PI control parameters,
The double inputs designed in the paper are the current error
Fuzzy controller.
The following seven fuzzy variables are added to the fuzzy set: positive large, positive relatively large, positive relatively small, zero, negative relatively small, negative relatively large, and negative large, which are represented as PB, PE, PS, ZO, NS, NE, and NB, respectively.
The program preparation is conducted based on the rule table of fuzzy control, as shown in Tables
Rule table of
| NB | NE | NS | ZO | PS | PE | PB |
---|---|---|---|---|---|---|---|
NB | PB | PB | PB | PB | PE | PS | ZO |
NE | PB | PB | PB | PE | PS | ZO | NS |
NS | PB | PB | PE | PS | ZO | NS | NE |
ZO | PB | PE | PS | ZO | NS | NE | NB |
PS | PE | PS | ZO | NS | NE | NB | NB |
PE | PS | ZO | NS | NE | NB | NB | NB |
PB | ZO | NS | NE | NB | NB | NB | NB |
Rule table of
| NB | NE | NS | ZO | PS | PE | PB |
---|---|---|---|---|---|---|---|
NB | NB | NB | NB | NB | NE | NS | ZO |
NE | NB | NB | NB | NE | NS | ZO | PS |
NS | NB | NB | NE | NS | ZO | PS | PE |
ZO | NB | NE | NS | ZO | PS | PE | PB |
PS | NE | NS | ZO | PS | PE | PB | PB |
PE | NS | ZO | PS | PE | PB | PB | PB |
PB | ZO | PS | PE | PB | PB | PB | PB |
The control strategy chart for the poststage full-bridge inversion of the photovoltaic grid-connected inverter is shown in Figure
Control strategy chart for poststage inversion of grid-connected operating mode.
The light intensity of the photovoltaic array is
Model of fuzzy MPPT control.
Output power of photovoltaic array.
Output voltage of photovoltaic array.
When the output end is composed of loads with different characteristics, the load characteristics can be allocated via an electronic load device and the loads can be resistive, capacitive, inductive, or mixed. The introduction of a fuzzy control algorithm can significantly improve the quality of the grid-connected output current, especially if the output load is not a pure resistant one; the output waveform of the grid-connected output current appears much smoother than that of the current in the original unipolar algorithm, and there is lesser clutter. Meanwhile, the current at the load end is much smoother. The waveforms of the output current and voltage under the grid-connected operating mode are shown in Figure
Grid-connected output current and load current with different load characteristics.
Waveform of conventional deadbeat control with 500 W resistive load + 300 W capacitive load
Waveform of fuzzy control method with 500 W resistive load + 300 W capacitive load
Waveform of conventional deadbeat control with 100 W capacitive load + 150 W inductive load + 500 W resistive load
Waveform of fuzzy control method with 100 W capacitive load + 150 W inductive load + 500 W resistive load
The loads of (a) and (b) and those of (c) and (d) in Figure
In this study, modeling analysis is conducted for the MPPT of fuzzy control-based P&O method in the first stage of photovoltaic grid connection in Matlab/Simulink. The MPPT of fuzzy control-based P&O method and that of traditional P&O method are analyzed thoroughly by the simulation comparison. According to the simulation results, the MPPT of fuzzy control-based P&O method exhibits rapid response and small steady-state oscillation, which can effectively make up for the shortcomings of the traditional P&O method, improve system efficiency, and reduce power losses to an extreme. In addition, the fuzzy control algorithm is added to the full bridge inversion of the post-grid-connected stage, and the experiment is performed in the formulated experimental platform. Loads with different characteristics are added to the output end of the post-grid-connected stage in order to compare the conventional deadbeat control method and the fuzzy control-based deadbeat control method. The results show that the fuzzy control-based deadbeat control method can enhance the robustness and reduce the harmonic wave when the system relates to nonlinear load.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors acknowledge financial support for this research from: Third Batch Innovative Research Team Introduction Program of Dongguan City in 2015 (2017360004004), Special Funding of Collaborative Innovation and Platform Circumstance Construction of Guangdong Province (2016B090918067), and Industry-University-Research of Dongcheng District, Dongguan City, in 2015; Natural Science Foundation of Guangdong Province (2015A030313675).