Due to the enormous utilization of solar energy, the photovoltaic (PV) system is used. The PV system is functioned based on a maximum power point (MPP). Due to the climatic change, the Partial shading conditions have occurred under non-uniform irradiance conditions. In the PV system, the global maximum power point (GMPP) is complex to track in the

Solar energy is the most essential source to provide a clean environment and a better gain in economic. Nowadays photovoltaic (PV) systems are acted as a main solar source for electricity generation. But in the PV system, the conversion of insolation into electricity is more difficult and has minimum efficiency [

The PV array-based P-V curve has a single maximum power point (MPP) under a uniform irradiance. Also in the non-uniform irradiance, a few PV modules and also in PV cells have partial shading. The partial shading occurred due to environmental variation of clouds, trees, dust covering and buildings etc. This partial shading condition (PSC) made P–V characteristics more complex and provided multiple peaks whereas one Global peak Maximum Power Point (GMPP) and others are Local peaks Maximum Power Point (LMPP). To control these PSC, the Maximum Power Point Tracking (MPPT) techniques are developed [

Several MPPT techniques are developed for power stability such as perturb and observe (P&O) [

To improve the optimised solution for tracking a GMPP and PSC controllability, the Meta-heuristics methods are applied. The metaheuristic algorithms are based on the sources of inspiration that is categorised into different types namely Swarm Intelligence algorithms (SI), Evolutionary Algorithms (EA) and Natural Phenomenon (NP) algorithms [

For past decades, various swarm intelligence methods are implemented for tracking GMPP. Some of the popular SI methods in MPPT are particle swarm optimization (PSO) [

To overcome these issues, a SI based Honey Badger optimization (HBO) algorithm is presented for optimal GMPP tracking and fast convergence time. The HBO model is motivated by a honey badger’s excellent foraging behaviour. This HBO method is presented mathematically to solve optimization issues using a searching strategy. It carries two strategies of a honey badger with digging and honey searching strategy to provide an effective solution. This HBO provided an ample population diversity process to achieve the best solution in a larger landscape area. The HBO methodology is also validated and compared with existing techniques namely P&O, PSO, and GWO methods through simulation. The result showed that the HBO model provided a higher tracking efficiency than all prior methods.

The remaining part of the work is contributed as: Section 2 provides a related literature work based on MPPT. The preliminary of this work is presented in Section 3. It described PV model characteristics and PSC effects in it. Section 4 explored a proposed methodology of HBO techniques and its algorithm. The results and discussions are discussed in Section 5 and the work is concluded in Section 6.

In this section, the literary work based on MPPT is developed and implemented by various researchers are presented. Many works are relevant to optimization and Artificial Intelligence (AI) techniques. These methods are used to obtain a higher tracking accuracy which is explained in the following.

Lak et al. [

Bharadwaj et al. [

Karmakar et al. [

Lyden et al. [

In this section, the preliminaries for a proposed work are presented. It described a basic idea of PV model characteristics and PSC effects to obtain a tracking accuracy under uniform and non-uniform irradiance conditions.

The PV source module is given in _{ph}), a series resistance (R_{se}) and a shunt resistance (R_{sh}) respectively. Based on these elements, the output current is expressed in the following equations.

The output current (I_{PV}) is expressed as

_{t} denotes a thermal voltage at temperature T and _{rs}represents diode reverse saturation current. Also, T_{n} represents a temperature current, G_{n} indicates irradiance current.

The thermal voltage is evaluated based on series-connected cells (N_{se}) using the below

The diode reverse saturation current is evaluated using _{oc,n} represents an open-circuit voltage at STC(standard test condtion) temperature, _{sc,n} denotes a short-circuit current at STC; K_{I} indicates short-circuit current coefficient and K_{V} are coefficients of an open circuit voltage temperature.

To achieve a higher voltage and current, the series-parallel combinations are connected in PV modules. From _{PV} is expressed in the following _{ser} represents the number of series and _{par} represents the number of parallel modules.

Based on the above characteristics of photovoltaic modules and their connection, the PSC is acted. If all the PV cells received a uniform irradiance under normal conditions, then the P-V curve has a single MPP shown in

In this work, the Honey Badger optimization (HBO) algorithm is implemented to track the GMPP for the photo-voltaic system under every environmental condition. This work proposed an HBO based MPPT for PV systems to provide higher accuracy and fast convergence. The connections of the PV system connected with the boost converter is shown in

In this work, a Honey Badger optimization (HBO) algorithm is presented which is based on Swarm based algorithm. The HBO is motivated by a honey badger’s excellent foraging behaviour. This HBO method is presented mathematically to solve an optimization issue using a searching strategy. It carries two strategies of a honey badger with digging and honey searching strategy to provide an effective solution. This HBO provided an ample population diversity process to achieve the best solution in a larger landscape area. The Honey Badger Optimization is based on the foraging behaviour of Honey Badger.

Therefore, the general biology and inspiration of Honey Badger and also a mathematical model for the HBO Algorithm is presented in the following.

Honeybadger is a black coloured white fluffy mammal often lived in areas like dry regions and forests. It is also known as a fearless mammal that seems like a dog in physics. The foraging behaviour of honey badger is fearless and very intelligent. There are more than sixty preys that are foraged by honey badger including harmful snakes. It smells and forages its prey by a slow walk. It also loves honey to eat.

The HBO model is inspired by the foraging behaviour of honey badgers. The foraging behaviour is based on strategies like smells, digs and follows the prey to their location. It has two stages for foraging namely the digging process and honey process. In the digging process, the honey badger has a high smelling behaviour that followed the smell location and catches the prey by digging the earth. In the honey process, it determined the beehive with the help of a honeyguide bird.

Based on the above foraging behaviour, the HBO model is derived mathematically. The HBO model is derived using two phases namely the digging phase and honey phase. Based on these phases, exploration and exploitation are performed to achieve the best optimal solution. Therefore the mathematical model is discussed in the following.

The candidate solutions population of HBO is expressed in

Honey badger l^{th} position,

Step 1: Initialize the N number of population and its positions using

_{l} → ^{th} position of Honeybadger with respect to N,

_{1} →

_{l} → Search domain upper bounds,

_{l} → Search domain lower bounds.

Step 2: Defining intensity (I) is defined as the prey concentration strength and distance among prey and ^{th} honey badger. It is based on the smell intensity of prey that is derived using Inverse Square Law expressed in

_{l} − _{l+1}) ^{2}, that indicated the concentration strength

_{l} = _{prey} − _{l}, that indicated a distance among prey and ^{th} honey badger.

Step 3: density factor (α) is to be updated which is used to control time-varying randomization. This control is processed for an exploration to exploitation stage to smoothen the solution. It is minimized with an iteration to reduce randomization with time-based on

_{max} → Maximum number of iterations

Step 4: This step is used for Escaping from the local optimum. It has a flag F that is used to alter a search direction. This direction is used to scan the search space severely by availing high opportunities for agents.

Step 5: The agent position is to be updated using the digging phase and honey phase.

In the digging phase, a Cardioid shape motion is performed by a honey badger that is expressed in

_{prey} → Position of prey

There is a flag (F) work is used to modify the direction of search that is expressed in below

In the Honey phase, the beehive is obtained by using a honeyguide bird by honey badger which is given in below

From _{prey}) based on _{l}. The searching behaviour is used for a search in α times and also the disturbance F may be found. Thus the pseudo-code of the HBO method is given in Algorithm 1.

The proposed MPPT algorithm is evaluated using the MATLAB 2019a tool. For a fair comparison, the proposed algorithm compared with P&O, GWO, WOA and FSSO algorithms. The design properties used for the PV system and boost converters are given in

MODEL-sun power SPR-315E-WHT-D | |
---|---|

Maximum power(W) | 300 |

No of Pv module | 4 |

Open circuit voltage(Voc) | 15 |

Short circuit current (Isc) | 6.4 |

The voltage at the maximum power point | 12 |

Current at the maximum power point | 5.62 |

Temperature coefficient of Voc | −0.2727 |

Temperature coefficient of Isc | 0.06174 |

Parameter | Values |
---|---|

L1 | 1000 mH |

C1 | 1000 μF |

R | 75 ohms |

The proposed tracking algorithm is compared in terms of tracking time and efficiency. The tracking efficiency of the proposed MPPT algorithm is calculated by the proportion of average power from the solar system at the steady-state and maximum obtainable power from the solar system partial shading.

The simulated waveform of the proposed MPPT algorithm in CONF1 is shown in

Configuration types | Methods | Average power (W) | Voltage at MPP (V) | Current at MPP (A) | Tracking time (s) | Tracking efficiency (%) |
---|---|---|---|---|---|---|

CONF-1 | P & O | 220.8 | 92 | 2.4 | 0.667 | 66.37 |

WOA | 278.46 | 142.8 | 1.95 | 3.9 | 97.98 | |

FSSO | 293.78 | 146.89 | 2 | 3.1 | 98.89 | |

Proposed | 296 | 148 | 2 | 0.92 | 99.96 | |

CONF-2 | P & O | 249.4 | 43 | 5.8 | 0.82 | 78.62 |

WOA | 263.2 | 56 | 4.7 | 3.18 | 98.4 | |

FSSO | 275.8 | 57.46 | 4.8 | 2.85 | 98.25 | |

Proposed | 300 | 60 | 5 | 0.86 | 99.91 |

The simulated waveform of the proposed MPPT algorithm in CONF2 is shown in

In this study, the HBO based MPP tracking is proposed for a global peak in the solar system. The HBO model is based on the foraging behaviour of Honeybadger that has digging and honey strategies. These phases are used to determine an optimal solution for GMPP tracking. The performance of the HBO strategy is validated in terms of power tracking efficiency and speed of convergence. The simulation result of the HBO method is compared with a conventional method such as P&O, WOA and FSSO through MATLAB software. As a result, it is attained that the HBO model provided a higher tracking efficiency than all prior methods. Also, the HBO based MPPT method has merits of higher tracking efficiency, average power and faster convergence than the prior methods respectively.