Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique
Abstract
:1. Introduction
Novelty and Contribution
- This work presented an optimum salp swarm algorithm tuned fractional order PID controller for MPPT to modify input and output during transient operating conditions to attain an ideal duty ratio.
- Compared to other traditional MPPT algorithms utilized in the literature, it offers high-power tracking capability, quick convergence speed, fewer controlling parameters, and ease of implementation.
- The PV-based fuel cell grid connected technology offers a guarantee for steady and practical operation under varying load situations.
2. System Modeling
2.1. Fuel Cell System Modelling
2.2. PV System Modelling
3. Proposed Robust Controller
3.1. Salp Swarm Algorithm
3.2. Fractional Order PID Controller
4. Results and Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sr. # | Reference # | Algorithm/ Approach | Converter Type | Nature/Remarks/Notes |
---|---|---|---|---|
1 | [5] | IC | Boost | Multi-sensors are required |
2 | [6] | PSO | Boost | Easily trapped in local optimum |
3 | [7,8] | ANFIS | Boost | ANN requires excessive data |
4 | [9] | P&O | High step ratio | Oscillations/fluctuations near MPP with large tracking time |
5 | [10] | MPC | Boost | Requires plant model and specific knowledge |
6 | [11,12] | P&O/InC | Buck | Oscillations/fluctuations near MPP multi-sensors needed |
7 | [13] | SMC | Boost | The design of the filter circuit is cumbersome |
8 | [14] | WCA | Boost | It may become stuck in local optima |
9 | [15] | INR | Boost | Multiple sensors are needed |
10 | [16] | Smart drive algorithm | Boost | Low accuracy |
11 | [17] | PSO | Boost | Easily stuck in local optima |
12 | [18] | Extremum seeking control | - | Slow convergence rate |
13 | [19] | Firefly algorithm | Cuke | Easily trapped in local optimum |
14 | [20] | Backstepping | Boost | Complex/Excessive effort in implementation |
15 | [21] | Fuzzy logic | Boost | Lacks precision |
16 | [22,23] | ANN | Boost | ANN requires excessive data |
17 | [24] | Jaya | Cuke | Requires Excessive computational time |
18 | [25] | GWO | Boost | Sluggish convergence and stuck in local optimum |
19 | [26] | SSA | Boost | Excessive computational time required |
20 | [27] | AW-PID | Buck-Boost | Inefficient and sensitive toward large load changings and not suitable for Nonlinear systems |
21 | [28] | FPID | Four switch Buck-Boost | Complex to implement |
22 | [29] | TA | Boost | Complex and accurate results not guaranteed |
23 | [30] | PCL | Boost | High complexity and low accuracy |
24 | [31] | QC | Boost | Cannot be used for 1st order systems, complex and less accurate |
25 | [32] | MPC | Two-level inverter | Requires plant model and specific knowledge |
26 | [33] | MPC | Boost | Requires plant model and specific knowledge |
27 | [34] | SSA-PID | Boost | Can become stuck in the local maximum |
28 | [35] | IFTSMC | Boost | Knowledge of system boundary uncertainty is required, also convergence issues when states are not near equilibrium. |
29 | [36] | GSS | Boost | Implementation cost high and knowledge of plant specification of the fuel cell required |
30 | [37] | FBI-PID | Boost | Multiple sensors required, hence costly |
31 | [38] | EO-FLC | Boost | Fuzzy logic may lack in accuracy |
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Gulzar, M.M. Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique. Sustainability 2023, 15, 3980. https://doi.org/10.3390/su15053980
Gulzar MM. Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique. Sustainability. 2023; 15(5):3980. https://doi.org/10.3390/su15053980
Chicago/Turabian StyleGulzar, Muhammad Majid. 2023. "Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique" Sustainability 15, no. 5: 3980. https://doi.org/10.3390/su15053980