By utilizing Fuzzy based FOPID-controller, this work is designed via the energy reshaping concept for Grid connected Photovoltaic (PV) systems for electric vehicles and this PV module has its own inverter which is synchorised with the utility grid. In grid connected PV system, the mitigation plays an important role where the capacity of PV arrays increases it maintains power quality and it is necessary to comply with some requirements such as harmonic mitigation. Unless a maximum power point tracking (MPPT) algorithm is used, PV systems do not continuously produce their theoretical optimal power. Under various atmospheric conditions, MPPT is obtained by using Perturb and Observe (P&O) techniques. A storage function in terms of DC-link voltage and current, and q-axis current is created, by means of which physical characteristics are studied. Further the energy reshaping is done with the aid of the proposed Fuzzy Fractional Order PID (Fuzzy-FoPID) framework. The FOPID control parameters λ and μ are optimally tuned by Grey Wolf Optimization technique (GWO). The results obtained verify the advantages of using Fuzzy-FoPID control as compared to conventional PID (Proportional Integral Derivative) control and FoPID control. The injected reactive power into the grid and the efficiency of the proposed systems is studied by extensive simulations.
Among various types of renewable energy resources as per the abundance of renewable resources, simple installation of PV technologies, secure operation with low operating costs, PV schemes are getting enormous popularity [
Proportional resonant control have fulfilled steady-state efficiency, but any variation in the frequency of the grid can have adverse effects on its performance [
Shun-Cheung wang et al. (2020) suggest a simple and efficient maximum power tracking method that combines simplified model-based state estimation (SMSE) with the adaptive alpha perturb-and- observe (P&O) method. Swati Singh et al. (2020) represented that review of optimization and modeling methodologies, as well as the interface mechanism of various renewable energy sources with various control designs such as the Fuzzy Controller, Fractional Order FPID (Fuzzy PID) Controller, and FOPID (Fractional Order PID) Controller. Majid Dehghani et al. (2021) presented that to achieve the full PowerPoint; a fuzzy logic controller (FLC) was optimized by a combination of particle swarm optimization (PSO) and genetic algorithm (GA).
Research Gap
As stated above, various control algorithms have been proposed to improve the efficiency and various energy reshaping [
Contribution of the proposed research include
A novel energy reshaping approach using Fuzzy FOPID controller is proposed.
The above strategy has been applied to a grid connected PV inverter.
To obtain maximum solar energy from the PV array under various atmospheric conditions, MPPT technique is utilized.
The efficiency of the proposed approach is studied extensively and compared with the existing approaches available in the literatures.
The proposed system is based on a grid-connected PV inverter with MPPT technology and the following converter descriptions and
With the interconnection of several PV modules a photovoltaic array is a simple serial and/or parallel. In general, the modules are initially connected in a serial [
Mathematical modelling of PV panel: As irradiation is proportional to current, diode is connected in parallel with the current source in the recombination losses are represented. The equivalent circuit of a functional PV cell where the current is produced is shown in
q –electron charge,
I_{PV} - cell’s photo current,
The photo electric current as a function of the short circuit current is expressed as follows.
where
To get the MPP effectively under time-varying atmosphere P & O method is used. Temperature and power of the PV array (voltage and current), accurate monitoring of the MPP [
P & O method: Variation in power is considered the major factor for MPPT in this process. Here, the successful perturbation in the duty cycle is in the same direction to hit the MPP on the rise in power, and would be in the opposite direction, on the decrease in power.
For the PV system in which a storage function associated DC-link current, DC-link voltage and q-axis current are constructed. Apply Kirchhoff’s current law, the dynamics [
The three-phase two-level PV inverter dynamics in d_{q} frame is given in the below equation
e_{d}, e_{q}, i_{d}, i_{q}, v_{d} and v_{q} - dq-axis components of grid voltage, grid current, and PV inverter output voltage
R & L – resistance & inductance,
Assume the power loss in PV inverter switches are ignored, the power balance equation relating DC input and the AC output is given by
The state equation of PV inverter as in
The control input ‘u’ appears explicitly, as differentiate the tracking error
The storage function
The inverter connected to the grid transforms DC into AC suitable for injection into the grid, usually 120 V RMS at 60 Hz or 240 V RMS at 50 Hz. Inverters [
The ideal voltage source [
The relative stability is proved, as the Pm of Z_{g}(s)/Z_{inv}(s) does not affect real Pm of the grid connected system although I_{cr} is stable and the mathematical model of grid-connected inverter control system is shown in
Provided that, according to
In
The coefficients of m_{1}, m_{2} and m_{3} can be seen in
And G_{closed}(s) is
The double loop control mode is adopted by a control system. The outer loop is used to manage the dc link voltage (V dc). The proportional integral (PI) controller is used to regulate the grid current and the dc-link voltage. The transfer function [
Proportional Control, the most important of these, calculates the extent of the difference between set point and process variable (known as error) and then makes sufficient proportional adjustments to the control variable to remove the error. The Control law for PID Controller [
The fractional order controller (PID (λδ)) is the same as the traditional PID controller, but the only difference is the fractional derivative order (δ) and integral order (λ). The FoPID controller transfer function [
The time domain representation of FoPID controller is given in
The Fuzzy controller computes the scaling factor from error inputs to refresh the gain parameters of FOPID controller in an advanced systematic way.
where
Δ
For input and output fuzzy, triangle membership functions are used by applying Mamdani type inferencing system.
In the proposed method, FOPID attempts to designate a closed-loop energy feature which is analogous to the contrast between the system energy and the energy given by the controller. As follows, the energy modification equation is
From
Grey wolf belongs to Canidae family. The pioneers also called as alpha are a male and female. The alpha is responsible for settling on decisions. The pack are directed by the decision taken by the alpha.
In various design problems, GWO can fundamentally increase the global optimum and provide an efficient and viable instrument for controller tuning Encircling behavior is demonstrated by the following equations,
Hunting behaviour is described by following equations
Pseudo code of the GWO algorithm is presented below,
Step 1: Initialize grey wolf population size X^{i} (i = 1, 2,3, …, n)
Step 2: Initialize the parameters a, A, and C
Step 3: Calculate fitness values of each search agent
Xα is best search agent
Xβ is second-best search agent
Xδ is third best search agent
Step 4: Determine α, β and δ_{1}
Step 5:Update Hunting and Scouting
Hunting
Scouting
Step 6: Fitness calculation of all search agents
The fitness value is measured at the k^{th} and (k-1)^{th} iteration, respectively as F_{k} and F_{k−1.}
Step 7: Determine the α, β and δ_{1} based on the Fitness value
Step 8: Update the position of the current search agent by equation
Step 9: Update the parameters a, A, and C
Step 10: Calculate fitness of entire search agent
Update the parameters Xα, Xβ, and
While k = k + 1; end; return Xα.
The Fuzzy based FoPID control parameters in
To improve the efficiency and robustness of PV power generation, proposed approach develops a specific model of grid-connected PV system by MATLAB/Simulink environment that accurately represents the system’s qualities. To figure the performance of proposed GWO based Meta heuristic MPPT algorithm, the performance was compared with PI controller, PID controller, FOPID controller and Fuzzy FoPID controller. The proposed Grid connected PV system Simulink chart is shown in
Controller | PI controller | PID controller | FoPID controller | Fuzzy FoPID controller |
---|---|---|---|---|
Irradiance (W/ |
1000 | 1000 | 1000 | 1000 |
V_{dc}mean (V) | 490 V | 549 V | 510 V | 600 V |
240 kW | 150 kW | 300 kW | 350 kW | |
0.18A | 0.33A | 0.28A | 0.28A |
From the outcomes, we can see that the output voltage of the PV panel settles down at 2 KV while the current worth changes depending on the irradiation. The Id reference esteem 0.03p.u which is given as the input sign of PI controller is shown in
Sun irradiance is rapidly ramped down from 1000 W/m2 to 200 W/m2. Due to the MPPT operation, the control system lessens the V_{DC} reference to 464 V to extricate maximum power from the PV cluster (46 kW). At t = 0.5 sec. At time t = 0.5 sec to t = 1sec, current decreases to 2A and so power drop occurs.
The irradiation esteems utilized in the simulations for example 800 W/m2 and 1000 W/m2 and its shown in
Simulink waveform of input power, irradiance, input dc voltage to PV panel based on FOPID controller is shown in
Tracking of the highest power point and levels of irradiation below 1000W/m2 and 500W/m^{2}. In
.
The Fuzzy-based FOPID control output is evaluated from the suggested work and contrasted with that of PI, PID control, and FOPID control respectively. The comparison using GWO techniques is illustrated in
Controller | q axis current | DC link Voltage | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
K_{P1} | K_{I1} | K_{D1} | λ_{1} | μ_{1} | K_{P2} | K_{I2} | K_{D2} | λ_{2} | μ_{2} | |
PI | 197 | 120 | - | - | - | 172 | 119 | - | - | - |
PID | 201 | 137 | 61 | - | - | 149 | 185 | 60 | - | - |
FOPID | 186 | 145 | 27 | 1.76 | 1.32 | 175 | 138 | 23 | 1.65 | 1.29 |
Fuzzy based FOPID | 191 | 152 | 28 | 1.79 | 1.37 | 187 | 150 | 23 | 1.67 | 1.32 |
The efficiency comparison graph is shown in
A Fuzzy FoPID energy reshaping control is designed and intended for a grid-connected PV inverter in electric vehicles to optimally extract PV energy under different operating conditions.The performance of current control loop in a three-phase grid-connected PV system has been improved. The performance of current control loop and voltage regulation in a three-phase grid-connected PV system has been dissected. The optimization technique Simulink results show that under different atmospheric conditions, Fuzzy FoPID control can achieve superior control efficiency of 98% with the low control costs as compared to the techniques existing in the literatures. The inverter design in the proposed research can be effectively used in electric vehicles.
The efficiency of current control loops in a three-phase grid-connected PV system has been enhanced by using a fractional order PID controller with other optimization techniques.
The authors with a deep sense of gratitude would thank the supervisor for his guidance and constant support rendered during this research.