A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid
Abstract
:1. Introduction
- In this paper, the author introduces a novel multi-strategy method to improve the TSO algorithm for MPPT tracking.
- A simulated battery model was established, and the effectiveness of the proposed method was verified using three different shadow conditions. To verify the effectiveness of the algorithm under changing shadow conditions, the photovoltaic array irradiance was set to suddenly change from pattern2, pattern3 to pattern4.
- During the sudden change of radiation conditions, the robustness of the algorithm in tracking GMPP was evaluated.
- By comparing the new algorithm with the basic TSO algorithm and CS algorithm, the superiority of the new algorithm in fitness convergence performance was verified.
- The simulation experiment of MPPT was carried out using a photovoltaic cell model. Compared with the basic TSO, CS, and INC, the results show that the new algorithm has more advantages in MPPT’s fast response and anti-interference.
2. Distributed PV System and Proposed MPPT Algorithm
2.1. Distributed PV System
2.1.1. PV Module
2.1.2. Description of DC–DC Boost Converter
2.1.3. Partial Shading and Its Effects
2.2. Multi-Strategy Improved Tuna Swarm Optimization (ITSO)
2.2.1. Initializing
2.2.2. Spiral Foraging
2.2.3. Parabolic Foraging
2.2.4. Step Length and Restart
2.2.5. Algorithm Test
3. Simulation Results
3.1. Result of Pattern 1
3.2. Result of Pattern 2, Pattern 3 and Pattern 4
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
PV | Photovoltaic |
PSCs | Partial Shadow Conditions |
MPPT | Maximum Power Point Tracking |
P&O | Perturb & Observe |
INC | Incremental Conductance |
LMPP | Local Maximum Power Point |
TSO | Tuna Swarm Optimization |
ITSO-INC | Multi-strategy Improved Tuna Swarm Optimization hybrid INC |
GMPP | Global Maximum Power Point |
MPP | Maximum Power Point |
CS | Cuckoo Search |
FPA | Flower Pollination Algorithm |
BA | Bat Algorithm |
HS | Harmony Search |
SSA | Squirrel Search Algorithm |
DRL | Deep Reinforcement Learning |
MCA | Musical Chairs Algorithm |
PSO | Particle Swarm Optimization |
ABC | Artificial Bee Colony |
TSA | Tunicate Swarm Algorithm |
TSA-PSO | Artificial Bee Colony Artificial Hybrid Particle Swarm Optimization |
CS-GWO | Cuckoo Search Hybrid Gray Wolf Optimization |
ABC-P&O | Artificial Bee Colony Hybrid Perturb & Observe |
MPSO-MP&O | Modified Particle Swarm Optimization and Modified Perturb & Observe |
IABC-SHTS | Improved artificial bee colony and simultaneous heat transfer search algorithm |
MLPE | Module-level power electronics |
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Authors | Years | Algorithms | Control Parameter | Remark |
---|---|---|---|---|
[12] | 2019 | Improve FPA | Duty cycle | The proposed technique merged fractional chaos maps with the Flower Pollination Algorithm (FPA) and compared the accuracy between different chaotic variants. |
[13] | 2021 | CS | Duty cycle | The proposed algorithm avoids spreading the initial particles among the whole curve to predict shading patterns. |
[14] | 2017 | FPA | Duty cycle | FPA was added to the MPPT application and the efficiency of FPA was compared with P&O and INC. |
[15] | 2021 | Improve GWO | Duty cycle | Levy flight strategy was added to GWO to improve MPPT tracking accuracy. |
[16] | 2021 | TSA | Voltage | The TSA algorithm was used to solve the partial shadow problem in MPPT, and a search and skip scheme (SAS) was added to minimize the search range. |
[17] | 2021 | MCA | Duty cycle | Compared to most basic metaheuristic algorithms, the MCA algorithm has significant advantages in tracking speed and reducing vibration amplitude. |
[18] | 2021 | ICS | Duty cycle | By improving the CS algorithm, the problems of high oscillation, high failure rate, and long convergence time in the basic CS algorithm were solved. |
[19] | 2022 | Hybrid TSA-PSO | Duty cycle | The effectiveness of TSA-PSO was proved by simulation experiments, and the tracking accuracy reached 97.64%. Two nonparametric tests were used to prove the reliability of the algorithm. |
[20] | 2022 | Hybrid CS-GWO | Duty cycle | By combining GWO and CS in MPPT, the efficiency of the hybrid GWO-CS algorithm was higher than that of the separate GWO and CS. |
[21] | 2021 | Hybrid ABC-P&O | Duty cycle | The ABC-P&O algorithm has a reasonable computational cost. The experiment proved that ABC-P&O has a good performance under different PSCs. |
[22] | 2022 | MPSO-MP&O | Duty cycle | This paper combined the improved PSO algorithm with an improved P&O method by analyzing two sliding mode control structures. The stability of MPP was achieved under changes in physical parameters of different photovoltaic systems. |
[23] | 2022 | Hybrid IABC-SHTS | Duty cycle | The combination of an improved Artificial Bee Colony algorithm and multiple heat transfer search algorithms effectively reduced the subjectivity of manually setting parameters in the algorithm, ensuring the universality and accuracy of the search process. |
Item | Value |
---|---|
Frequency of switching | 50 Hz |
C | 202.55 × 10−6 F |
L | 8.578 × 10−3 H |
R | 20 Ohm |
Parameters | PV Modules |
---|---|
Maximum Power | 213.15 W |
Peak power voltage (Vmpp) | 29 V |
Peak power current (Impp) | 7.35 A |
Open circuit voltage (Voc) | 36.3 V |
Short circuit current (Isc) | 7.84 A |
Current temperature coefficient (ki) | −0.36099 %/°C |
Voltage temperature coefficient (kv) | 0.102 %/°C |
No. of cells | 48 |
Type of Pattern | Irradiance | Maximum Peak Power Point (MPP)/W | ||
---|---|---|---|---|
W/m2 | ||||
Pattern 1 | G11 = 600 | G12 = 600 | G13 = 600 | GMPP = 6166.58 |
G21 = 600 | G22 = 600 | G23 = 600 | ||
G31 = 600 | G32 = 600 | G33 = 600 | ||
G41 = 600 | G42 = 600 | G43 = 600 | ||
Pattern 2 | G11 = 1000 | G12 = 1000 | G13 = 1000 | GMPP = 6470.72 |
G21 = 1000 | G22 = 800 | G23 = 1000 | LMPP1 = 6159.16 | |
G31 = 800 | G32 = 800 | G33 = 800 | LMPP2 = 4750.62 | |
G41 = 600 | G42 = 600 | G43 = 400 | LMPP3 = 2641.03 | |
Pattern 3 | G11 = 1000 | G11 = 1000 | G13 = 1000 | GMPP = 4725.83 |
G21 = 1000 | G22 = 800 | G23 = 1000 | LMPP1 = 4578.65 | |
G31 = 400 | G32 = 400 | G33 = 400 | LMPP2 = 4577.86 | |
G41 = 600 | G42 = 600 | G43 = 400 | LMPP3 = 2641.03 | |
Pattern 4 | G11 = 1100 | G11 = 1300 | G13 = 1200 | GMPP = 5188.57 |
G21 = 1100 | G22 = 400 | G23 = 1200 | LMPP1 = 4867.55 | |
G31 = 400 | G32 = 800 | G33 = 400 | LMPP2 = 4609.46 | |
G41 = 700 | G42 = 600 | G43 = 400 | LMPP3 = 3115.19 |
Name | Function | Dim | Range | Fmin |
---|---|---|---|---|
Step | 30 | [−100, 100] | 0 | |
Schwefel 2.26 | 30 | [−500, 500] | −418.98 × D | |
Branin | 30 | [−5, 10] | 0.398 | |
Goldstein price | 30 | [−5, 5] | 3 | |
Rosenbrock | 30 | [−30, 30] | 0 | |
Ackley | 30 | [−32, 32] | 0 |
Function | Performance | ITSO | TSO | CS |
---|---|---|---|---|
F1 | Mean | 2.13 × 10−10 | 1.21 × 10−9 | 5.45 × 10−7 |
Std | 2.29 × 10−10 | 8.74 × 10−6 | 6.25 × 10−8 | |
F2 | Mean | −1.195 × 104 | −9.925 × 103 | −6.128 × 103 |
Std | 7.583 × 102 | 3.153 × 103 | 8.997 × 102 | |
F3 | Mean | 3.98 × 10−1 | 3.99 × 10−1 | 3.96 × 10−1 |
Std | 0.00 × 100 | 6.82 × 10−6 | 4.78 × 10−7 | |
F4 | Mean | 3.01 × 100 | 3.00 × 100 | 4.20 × 100 |
Std | 2.81 × 10−15 | 7.75 × 10−16 | 2.09 × 101 | |
F5 | Mean | 2.759 × 101 | 2.896 × 101 | 2.226 × 105 |
Std | 1.058 × 100 | 1.163 × 100 | 9.868 × 103 | |
F6 | Mean | 9.203 × 10−7 | 4.392 × 10−6 | 1.895 × 101 |
Std | 5.295 × 10−6 | 5.961 × 10−5 | 3.459 × 100 |
Technique | Parameters |
---|---|
ITSO-INC | z = 0.5, β = 1.5 |
CS | β = 1.5, α = 0.8, pa = 0.25 |
TSO | z = 0.05, a = 0.8 |
Parameter/Method | TSO | ITSO-INC | CS | INC |
---|---|---|---|---|
Extracted power (w) | 6165.51 | 6166.72 | 6153.07 | 6145.29 |
Tracking efficiency (%) | 99.97 | 99.99 | 99.77 | 99.62 |
Tracking time (s) | 0.340 | 0.048 | 0.275 | 0.41 |
Parameter/Method | TSO | ITSO-INC | CS | INC | |
---|---|---|---|---|---|
Pattern 3 | Extracted power (w) | 6468.86 | 6470.66 | 6265.27 | 6044.33 |
Tracking efficiency (%) | 99.97 | 99.99 | 99.94 | 96.82 | |
Tracking time (s) | 0.272 | 0.095 | 0.188 | 0.022 | |
Pattern 4 | Extracted power (w) | 4352.33 | 4701.45 | 4702.98 | 4489.89 |
Tracking efficiency (%) | 92.09 | 99.48 | 99.51 | 95.00 | |
Tracking time (s) | 0.141 | 0.054 | 0.269 | 0.024 | |
Pattern 5 | Extracted power (w) Tracking efficiency (%) Tracking time (s) | 5110.89 98.50 0.145 | 5114.33 98.56 0.106 | 5106.86 98.41 0.267 | 4672.36 91.08 0.006 |
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Li, Y.; Li, L.; Jiang, Y.; Gan, Y.; Zhang, J.; Yuan, S. A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid. Appl. Sci. 2023, 13, 4998. https://doi.org/10.3390/app13084998
Li Y, Li L, Jiang Y, Gan Y, Zhang J, Yuan S. A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid. Applied Sciences. 2023; 13(8):4998. https://doi.org/10.3390/app13084998
Chicago/Turabian StyleLi, Yanbo, Linyi Li, Yechao Jiang, Yinghao Gan, Jianfeng Zhang, and Shibo Yuan. 2023. "A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid" Applied Sciences 13, no. 8: 4998. https://doi.org/10.3390/app13084998
APA StyleLi, Y., Li, L., Jiang, Y., Gan, Y., Zhang, J., & Yuan, S. (2023). A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid. Applied Sciences, 13(8), 4998. https://doi.org/10.3390/app13084998