# Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review

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## Abstract

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## 1. Introduction

^{2}solar irradiance at 25 °C is assumed, as per IEC-61215 standard testing conditions. However, the green, red and blue curves concern the array in Figure 2B (A, B and C), where non-uniform solar irradiation conditions are assumed according to the practically installed system, where solar irradiance is not uniform between adjacent locations.

## 2. Soft Computing (SC)-Based MPP Tracking

#### 2.1. Artificial Intelligence (AI)-Based SC-MPPT Methods

#### 2.2. Bio-Inspired (BI)-Based SC-MPPT Methods

#### 2.3. Generalized Processes of SCMPPT

#### 2.3.1. Initialization

#### 2.3.2. Reproduction

#### 2.3.3. Selection

#### 2.3.4. Stopping Criterion

- (a)
- Generation of a predefined number of iterations ($n$). With this criterion, the algorithm is halted after reaching a certain number of iterations, which is defined at the onset of the SC algorithm.
- (b)
- Convergence of the population. This particular condition halts the algorithm when the value difference between the maximum and minimum values of the members of the generation is within a certain acceptable value.
- (c)
- Finest fitness threshold. When the value of (${P}_{PVBest}$), also known as the objective function, becomes lower than the predefined value of (${P}_{PVDefined}$), this condition kicks in and stops the algorithm.
- (d)
- Fitness convergence. When the difference in the objective function (${P}_{PV}$) between the maximum and minimum becomes lower than a predefined recommended tolerance values for all members of the population.

#### 2.4. Bayesian Network Method (BN)

#### 2.5. Non Linear Predictor Method (NLP)

#### 2.6. Ant Colony Optimization Method (ACO)

#### 2.7. Cuckoo Search Method (CS)

#### 2.8. Fibonacci Search Method (FS)

#### 2.9. Particle Swarm Optimization Method (PSO)

#### 2.10. Fuzzy Logic Control Method (FLC)

- (a)
- The fuzzification block; this block converts the elements of the system from numerical values to binary values that are either 1 or 0.
- (b)
- The knowledge base block; the function of this block is to contain the controlling regulations and the data bank of the linguistic explanations set by the developer.
- (c)
- The inference engine block, which takes the fuzzified values from the fuzzification block and applies the regulations from the knowledge block to make decisions on what elements satisfy the regulations, which are passed to the next block.
- (d)
- The defuzzification block, which transfers the values that satisfied the inference engine from binary values into a defuzzified control action.

#### 2.11. Artificial Neural Network Method (ANN)

#### 2.12. Extremum Seeking Method (ES)

#### 2.13. Chaotic Search Method (CS)

#### 2.14. Differential Evolution Method (DE)

_{p}remains the same as the total number of particles in each iteration:

#### 2.15. Genetic Algorithm Method (GA)

- (a)
- Selection, where genotypes are picked from the current population to be passed directly onto the next generation based on their fitness level to the PV equation.
- (b)
- Crossover, where new genotypes are produced by choosing some characteristics from the first and second generation and combining them.
- (c)
- Mutation, where new genes are produced to maintain chromosome diversity in produced generations to reach stochastic variability in the gene pool.

#### 2.16. Simple Moving Voltage Average Method (SMVA)

#### 2.17. Gauss–Newton Method (GN)

#### 2.18. Grasshopper-Optimized Fuzzy Logic Method (GOFL)

#### 2.19. Memetic Salp Swarm Algorithm (MSSA)

**Each chain Local search:**MSSA contains various similar salp chains. Individually and independently, every salp chain will apply a local search bestowing to the searching in each iteration contrivance. In this procedure, the leader is accountable for controlling the group to look for the food source, whereas the supporters follow each other. For the ${m}^{th}$ salp chain, the leader position can be updated as in Equation (25):

**Global virtual population coordination:**MSSA inhabitants can only be considered as hosts of memes, where a meme is a part of cultural growth. Principally, a meme is selected for enlarged contagiousness among the hosts. Furthermore, the physical features of each distinct will not be altered throughout the global coordination.

#### 2.20. Dynamic Leader-Based Collective Intelligence (DLCI)

#### 2.21. Shuffled Frog Leaping and Pattern Search (HSFL–PS)

#### Comparative Analysis

## 3. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 6.**Bayesian network information fusion for MPPT [29].

**Figure 7.**MPP tracking with the non-linear predictor [35].

**Figure 8.**Cuckoo Search under partial shading conditions [45].

**Figure 9.**MPP Search with Fibonacci [52].

**Figure 11.**Fuzzy logic control block diagram [57].

**Figure 12.**ANN feed-forward approximation function [66].

**Figure 13.**Sinusoidal extremum seeking method [67].

**Figure 14.**Sinusoidal extremum seeking schema [67].

**Figure 17.**SMVA PV system configuration [2].

**Figure 18.**(

**a**) Signal with noise, (

**b**) buffer size with 10 sample points and (

**c**) buffer size with 20 sample points [2].

**Figure 19.**GOFL adaptive fuzzy logic controller [79].

**Figure 20.**GOFL with the adaptive fuzzy logic control MPP tracking flowchart [79].

**Figure 21.**Deep-ocean salp swarm shape and structure. (

**A**) single salp, (

**B**) single salp chain and (

**C**) double salp chains. Adapted with permission from Elsevier (2021) [83].

**Figure 22.**MSSA Optimization framework. Adapted with permission from Elsevier (2021) [83].

**Figure 23.**Global coordination of virtual population for regroup operation. Adapted with permission from Elsevier (2021) [83].

**Figure 24.**DLCI optimization framework. Adapted with permission from Elsevier (2021) [84].

**Figure 25.**SFL–PS flowchart. Adapted with permission from Elsevier (2021) [86].

AI- and BI-Based Soft Computing Methods | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

MPTT Technique | Dependency on PV Array | Sensor Type | MPP Tracking Speed | MPP Tracking Accuracy | Efficiency | Circuitry Type | Application | ||||

T | I | V | D | A | Grid Connected | Stand Alone | |||||

AI-Based SC-MPPT Techniques | |||||||||||

Bayesian Network | × | × | √ | √ | M | Me | H | √ | √ | √ | √ |

Nonlinear Predictor | × | × | √ | √ | F | H | H | √ | √ | √ | √ |

Fibonacci Search | × | × | √ | √ | F | M | M | √ | × | × | √ |

Fuzzy Logic Control | √ | × | √ | √ | F | M | H | √ | × | × | √ |

Artificial Neural Network | √ | √ | √ | √ | F | M | H | √ | × | × | √ |

Extremum Seeking | × | × | √ | F | M | M | √ | × | × | √ | |

Differential Evolution | × | × | √ | √ | F | M | H | √ | × | × | √ |

Simple Moving Voltage Average | √ | × | √ | F | H | H | √ | √ | × | √ | |

Gauss–Newton | × | × | √ | √ | F | H | H | √ | × | × | √ |

BI-Based SC-MPPT Techniques | |||||||||||

Ant Colony Optimization | √ | √ | √ | √ | F | M | H | √ | × | × | √ |

Cuckoo Search | × | × | √ | VF | H | H | √ | × | × | √ | |

Chaotic Search | × | × | √ | √ | F | M | M | √ | × | × | √ |

Genetic Algorithm | × | × | √ | √ | F | M | H | √ | × | × | √ |

Practical Swarm Optimization | × | × | √ | √ | F | M | H | √ | × | × | √ |

Grasshopper | × | × | √ | √ | F | H | H | √ | × | × | √ |

Memetic Salp Swarm Algorithm | × | × | √ | √ | VF | H | H | √ | × | × | √ |

Dynamic Leader-based Collective Intelligence | × | × | √ | √ | VF | H | H | √ | × | × | √ |

Shuffled Frog Leaping and Pattern Search | × | × | √ | √ | VF | H | H | √ | × | × | √ |

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## Share and Cite

**MDPI and ACS Style**

Ali, A.; Irshad, K.; Khan, M.F.; Hossain, M.M.; Al-Duais, I.N.A.; Malik, M.Z.
Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review. *Sustainability* **2021**, *13*, 10575.
https://doi.org/10.3390/su131910575

**AMA Style**

Ali A, Irshad K, Khan MF, Hossain MM, Al-Duais INA, Malik MZ.
Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review. *Sustainability*. 2021; 13(19):10575.
https://doi.org/10.3390/su131910575

**Chicago/Turabian Style**

Ali, Amjad, Kashif Irshad, Mohammad Farhan Khan, Md Moinul Hossain, Ibrahim N. A. Al-Duais, and Muhammad Zeeshan Malik.
2021. "Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review" *Sustainability* 13, no. 19: 10575.
https://doi.org/10.3390/su131910575