# Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

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## Abstract

**:**

## 1. Introduction

#### 1.1. Motivation and Incitement

#### 1.2. Research Gap

#### 1.3. Contribution

## 2. Solar PV Modeling and Characteristics

#### 2.1. PV Cell Model

_{PVC}[78] as follows:

_{LC}stands for light generated current (A), I

_{d}for diode current (A), V

_{PVC}for photovoltaic cell voltage (V) and R

_{S}and R

_{SH}are the series and shunt resistances of the PV cell (Ω).

_{LC}, is expressed as follows:

_{cell}and T

_{ref}are the cell and standard temperatures in Kelvin (K), G for irradiance (w/m

^{2}), and µ

_{sc}for the temperature coefficient.

_{d}is

_{t}denotes thermal voltage (V).

#### 2.2. Characteristics of a PV System

#### 2.3. Solar PV System under Partial Shading

## 3. MPPT Techniques

_{PV}and voltage V

_{PV}of the boost converter are measured to calculate the SPVS power P

_{PV}(k). Now, based on initial changes in power, the controller increases the duty cycle. At this stage, new I

_{PV}and V

_{PV}are measured to calculate new power P

_{PV}(k + 1). Based on present and past information on SPVS power, the controller decides to decrease or increase the duty cycle. This process of tracking is continuous until the MPP is reached.

#### 3.1. Off-Line MPPT Techniques

_{PV}, V

_{PV}, and P

_{PV}[100]. After calculating these coefficients, the voltage at which the power achieves maximum is determined using Equation (6).

_{PV}) and voltage (V

_{PV}) are measured to track the MPP. Technical information about the PV panel, such as the output voltage for varying irradiance and temperature levels, is stored for various climatic situations. The lookup method then tracks the MPP by comparing the measured V

_{PV}and I

_{PV}with the stored data. As a result, a significant amount of data is stored in the lookup memory for reliable MPP monitoring and increased productivity [96]. This approach is complicated and error-prone because it requires a large amount of data collection, storage, and several sensors to monitor the precise MPP.

_{OC}is measured using a PC rather than the PV module/array [97]. As shown in Equation (7), the V

_{OC}of the pilot cell is determined by multiplying a constant (K

_{1}). The constant K

_{1}is pre-determined to track the array’s MPP in response to variations in temperature or irradiances.

_{1}< 1.

_{SC}= 0.78–0.92.

_{OC}limits. This approach makes use of the temperature and irradiance-dependent, roughly linear relationship between the open circuit voltage (V

_{OC}) and MPP voltage (V

_{MPP}) [99].

_{OC}= 0.72–0.8.

_{OC}and I

_{SC}of the PV module has an effect on the accuracy of both methods.

- ➢
- They are not suitable for high-efficiency operations.
- ➢
- No real-time adjustment is made.
- ➢
- It is noticeable that with full day operation, the irradiance and temperature vary; hence, intermittent measurement offline parameters (V
_{OC}, I_{SC}) are required. This intermittent/periodic measurement causes a power loss. - ➢
- These approaches never operate at accurate MPP, and hence are not suitable for efficient systems.
- ➢
- Not suitable for environmental changing conditions.

#### 3.2. On-Line MPPT Techniques

Perturbation (∆V) | Change in Power (∆P) | Next Perturbation Direction |
---|---|---|

∆V > 0 (Positive) | ∆P > 0 (Positive) | Positive |

∆V < 0 (Negative) | ∆P > 0 (Positive) | Negative |

∆V > 0 (Positive) | ∆P < 0 (Negative) | Negative |

∆V < 0 (Negative) | ∆P < 0 (Negative) | Positive |

_{MPP}, and vice versa.

#### 3.3. Intelligent MPPT Techniques

#### 3.3.1. Fuzzy Logic Control (FLC)

- E(k) represents the change in slope of the P-V curve;
- ∆e(k) denotes the change in the value of the slope of the P-V curve;
- ∆D denotes the change in the duty cycle.

#### 3.3.2. Artificial Neural Network

#### 3.3.3. Particle Swarm Optimization

_{i});

_{i}).

**is the**particle position at k+1th iteration, ${\mathit{X}}_{\mathit{i}}\left(\mathit{k}\right)$

**is the**particle position at kth iteration, ${\mathit{V}}_{\mathit{i}}\left(\mathit{k}+1\right)$

**is the**particle velocity at k+1th iteration, ${\mathit{V}}_{\mathit{i}}\left(\mathit{k}\right)$

**is the**particle velocity at kth iteration,${\mathit{C}}_{\mathbf{1}}$

**is the**acceleration component associated with Gbest, ${\mathit{C}}_{\mathbf{2}}$

**is the**acceleration component associated with Lbest,

**W**is the inertia weight,

**rand1**and

**rand2**are random numbers from 0 and 1,

**Gbest is the**best position of all particles, and

**Pbest is the**best position of the particle.

#### 3.3.4. Grasshopper Optimization

_{i}represents the position of the ith grasshopper, S

_{i}is the social interaction of the ith grasshopper, G

_{i}is the gravity force of the ith grasshopper, A

_{i}is the wind advection of the ith grasshopper, and r

_{1,}r

_{2}, and r

_{3}are random numbers.

#### 3.3.5. Grey Wolf Optimization

_{i}(k + 1) = D

_{i}(k) − A·D

_{i}

^{k}) > P(d

_{i}

^{k−1})

#### 3.3.6. Jaya Algorithm

_{best}and the population’s worst solution as X

_{worst}. The value of the vth variable for the mth candidate in the ith iteration is given as X

_{v}

_{,m,i}. Then, the updated value of each variable ${X}_{v,m,i}^{\prime}$ in the population can be obtained as follows:

_{1}and r

_{2}are random numbers ranging from 0 to 1.

_{i.}

_{i}) represents the instantaneous power at the duty cycle D

_{i}.

_{i}are generated, and then they are iteratively updated by taking the best and worst solutions into account. The flow chart of Jaya-based MPPT is presented in Figure 16. The mathematical equation for updating particle solutions is as follows:

## 4. Discussions and Comparative Analysis

#### 4.1. Capability of Tracking GMPP

#### 4.2. Convergence Speed

#### 4.3. Complexity

#### 4.4. Sensitivity

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 4.**Solar PV Characteristics for (

**a**) different values of irradiances and (

**b**) different values of temperature.

MPPT Technique | Parameter Dependency | Control Variable | Circuitry | Parameter Tuning | Tracking Accuracy | Efficiency | Complexity | ||
---|---|---|---|---|---|---|---|---|---|

V | I | A | D | ||||||

CF | Yes | ✓ | ✓ | ✓ | Yes | Medium | Medium | Complex | |

Lookup table | Yes | ✓ | ✓ | ✓ | No | Low | Medium | Complex | |

Pilot cell | Yes | ✓ | ✓ | ✓ | No | Low | Low | Simple | |

FSCC | Yes | ✓ | ✓ | ✓ | No | Low | Low | Simple | |

FOCV | Yes | ✓ | ✓ | ✓ | No | Low | Low | Simple |

Mode | Perturbation | MPP Level | Status |
---|---|---|---|

Mode-I | $\frac{dP}{dV}=0\frac{dI}{dV}=-\frac{I}{V}$ | At MPP | Hold V_{PV} = V_{MPP} |

Mode-II | $\frac{dP}{dV}>0\frac{dI}{dV}-\frac{I}{V}$ | Left side of MPP | Increase the voltage until V_{PV} = V_{MPP} |

Mode-III | $\frac{dP}{dV}<0\frac{dI}{dV}-\frac{I}{V}$ | Right side of MPP | Decrease the voltage until V_{PV} = V_{MPP} |

MPPT Technique | PV Array Dependency | Control Variable | Circuitry | Parameter Tuning | Tracking Accuracy | Efficiency | Complexity | ||
---|---|---|---|---|---|---|---|---|---|

V | I | A | D | ||||||

P&O | No | ✓ | ✓ | ✓ | ✓ | No | Moderate | High | Simple |

INC | No | ✓ | ✓ | ✓ | No | High | High | Complex | |

HC | No | ✓ | ✓ | ✓ | ✓ | No | Moderate | High | Simple |

CS | Yes | ✓ | ✓ | ✓ | ✓ | Yes | Medium | Medium | Complex |

ESC | No | ✓ | ✓ | ✓ | ✓ | No | High | High | Medium |

RCC | No | ✓ | ✓ | ✓ | Yes | Moderate | High | Complex | |

SMC | No | ✓ | ✓ | ✓ | No | Medium | High | Complex |

ΔE | ||||||
---|---|---|---|---|---|---|

BN | SN | ZE | SP | BP | ||

E | BN | ZE | ZE | ZE | BN | SP |

SN | ZE | ZE | SN | SN | SN | |

ZE | SN | ZE | ZE | ZE | SP | |

SP | SP | SP | SP | ZE | ZE | |

BP | BP | BP | BP | ZE | ZE |

Type of MPPT Technique | Offline MPPT | Online MPPT | Intelligent MPPT |
---|---|---|---|

Tracking speed | High | High | Medium |

Tracking accuracy | Less | Moderate | High |

Tracking efficiency | Poor | Medium | Very good |

Steady-state Oscillations | Less | High | Less |

GMPPT tracking under PSC | Yes | No | Yes |

Suitability for high-efficiency operations | No | Yes | Yes |

Suitability for environmental changing conditions | No | No | Yes |

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**MDPI and ACS Style**

Bhukya, L.; Kedika, N.R.; Salkuti, S.R.
Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review. *Algorithms* **2022**, *15*, 365.
https://doi.org/10.3390/a15100365

**AMA Style**

Bhukya L, Kedika NR, Salkuti SR.
Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review. *Algorithms*. 2022; 15(10):365.
https://doi.org/10.3390/a15100365

**Chicago/Turabian Style**

Bhukya, Laxman, Narender Reddy Kedika, and Surender Reddy Salkuti.
2022. "Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review" *Algorithms* 15, no. 10: 365.
https://doi.org/10.3390/a15100365