Artificial intelligence, machine learning and deep learning algorithms have been widely used for Maximum Power Point Tracking (MPPT) in solar systems. In the traditional MPPT strategies, following of worldwide Global Maximum Power Point (GMPP) under incomplete concealing conditions stay overwhelming assignment and tracks different nearby greatest power focuses under halfway concealing conditions. The advent of artificial intelligence in MPPT has guaranteed of accurate following of GMPP while expanding the significant performance and efficiency of MPPT under Partial Shading Conditions (PSC). Still the selection of an efficient learning based MPPT is complex because each model has its advantages and drawbacks. Recently, Meta-heuristic algorithm based Learning techniques have provided better tracking efficiency but still exhibit dull performances under PSC. This work represents an excellent optimization based on Spotted Hyena Enabled Reliable BAT (SHERB) learning models, SHERB-MPPT integrated with powerful extreme learning machines to identify the GMPP with fast convergence, low steady-state oscillations, and good tracking efficiency. Extensive testing using MATLAB-SIMULINK, with 50000 data combinations gathered under partial shade and normal settings. As a result of simulations, the proposed approach offers 99.7% tracking efficiency with a slower convergence speed. To demonstrate the predominance of the proposed system, we have compared the performance of the system with other hybrid MPPT learning models. Results proved that the proposed cross breed MPPT model had beaten different techniques in recognizing GMPP viably under fractional concealing conditions.

The utilization of energy is dramatically expanding internationally. Energy utilization is probably going to arrive at its pinnacle, driven by rising per capita power utilization on one hand and expansion in financial advancement on the other. To repay the high use of energy, sustainable power sources are the suitable answers for covering this energy interest and turned into an essential type of energy because of their adaptability and adaptability [

The Solar power framework is considered as one of the most encouraging sustainable energy sources because of its cost-effectiveness, high efficiency, and high abundance compared with other conventional energy source such as oil, biogases and natural gases [

Several MPPT techniques such as Hill-Climbing [

Recently, the integration of Artificial Intelligence in MPPT techniques is aimed to resolve and rectify the problem mentioned above. Artificial Intelligence (AI) based MPPT techniques such as HERBS-MPPT [

Motivated by this drawback in HERBS-MPPT, this paper proposes Spotted Hyena Enabled Reliable BAT (SHERB) learning models, SHERB-MPPT, the new hybrid optimization technique which works on the principle of Spotted Hyena over BAT algorithms with high-speed Extreme Learning Machine (ELM) to detect the GMPP effectively with high speed convergence, high performance, and zero trapping problems. This proposed method can be represented as reliable, which means consistently well in performances. The paper tells the following information, A novel hybrid integration of the spotted hyena with bat algorithm has been proposed with ELM has been proposed to achieve better tracking efficiency and high speed, which is designed using MATLAB based solar test beds have been designed to collect the different data based on the environmental temperature and solar irradiations. These datasets are used to train the proposed model, which is then used for better analysis.

The rest of the paper is organized as follows: Section-2 discusses the works proposed by more than one author. The working mechanism of the proposed framework is presented in Section-3. The experimental setup and findings with comparative analysis are detailed in section-4. Finally, the paper is concluded with future enhancement in Section-5.

The fundamental MPPT procedures for Photo Voltaic (PV) frameworks are investigated and summed up and isolated into three gatherings as indicated by their control hypothetical and streamlining standards [

A counterfeit neural organization-based MPPT regulator for sunlight-based PV framework [

The improved MPPT strategy using the state assessment by the consecutive Monte Carlo (SMC) sifting is helped by the expectation of MPP using an ANN [

A computation for an ANN-based MPPT controller for wind energy structure and mutt PV/wind is discussed in [

A profound learning-based model DPLSTM utilizing LSTM and crossover enhancement is described in [

The proposed framework is implemented through the PV system with SHERB-MPPT algorithm.

It is critical to construct a numerical model for PV cells to have high power tracking.

The following equation provides the current ‘I’ or ‘I_{L}^{’}, neglecting shunt resistance.

As per

The short circuit current,

where, ^{−19} C, A = ideality factor is 1.5 and K = Boltzmann constant is 1.38 × 10^{-23} J/K. I_{SCC} denotes the short circuit current at the reference condition, G denotes the irradiance in W/m^{2}, the reference temperature is denoted by T_{ref} in K, K_{J} denotes the temperature coefficient at _{S} denotes the series resistance in _{S} and R_{P} are important to identify the system losses. Based on

Thus, based on

Maximum power rating (P_{max}) |
200 W |

Voltage at maximum power (V_{mp}) |
24.5 V |

Current at maximum power (I_{mp}) |
7.8 A |

Open circuit voltage (V_{oc}) |
29.5 V |

Short circuit current (I_{sc}) |
8.0 A |

Temperature coefficient of maximum power | −0.45%/^{o}C |

Temperature coefficient of open circuit voltage | −0.37%/^{o}C |

Temperature coefficient of short circuit current | +0.06%/^{o}C |

Assuming N_{S} and N_{P} denote the number of cells connected in series and parallel and then the current equation of the PV cell is as follows:

where, I_{rs1} denotes the cell reverse saturation current which is given as_{g} is energy gap usually taken as 1.107 eV and R_{P} is shunt resistance in

As discussed in [

The SHERB uses the hunting procedure of spotted hyena for the BAT algorithm to find the best optimal values. At the initial stage, the echolocation principle is used with the minimum loudness, frequency, and velocity.

For every iteration in the process of identifying the prey, loudness, position, frequency and velocity of bats are updated by the hyena‘s procedure of hunting using the following mathematical

where

M is a vector with a value distributed from [0.5, 1] manually.

The pseudo code for the proposed algorithm is given in Algorithm-1

The significant advantage of SHERB has overcome the local convergence of the BAT algorithm, which can detect the best optimal value with a low convergence speed.

For the detection of optimal GMPP value_{sc}), are used as the input for the proposed hybrid SHERB, which determines the global best fitness function. The fitness function of the proposed algorithm is given in

The parameters used for the ELM training is given in

Sl.no | ELM Training parameters | Specifications |
---|---|---|

01 | No of epochs | 20 |

02 | No of inputs | 02-Solar irradiance and temperature |

03 | No of outputs | Multi-class outputs |

04 | No of hidden layers | 05 |

05 | Learning rate | 0.0001 |

06 | Dropout | 0.2 |

The working mechanism of the proposed SHERB-MPPT is depicted in Algorithm-2

The performance of the proposed architecture has been evaluated in four different scenarios. The first scenario, three solar arrays are subjected to solar irradiance at 1000W/m^{2,} and non-uniform irradiance is applied to the other three solar cells. In the second scenario, uniform solar irradiance is exposed to two solar cells, with the remaining cells subjected to PSC. All the algorithms run on the same system. The faster the CPU works the more processes it can perform at once. A CPU with a 3 GHz clock speed, for example, may do 3 thousand million cycles per second. The cache of the CPU is the onboard memory that is used to store the information so that the processor can access it rapidly.

To prove and validate the efficiency, the proposed algorithm’s datasets which consist of different variants of temperature and irradiance, are collected using the model developed using MATLAB and Simulink. The proposed architecture uses six solar cells for 200 watts as inputs, and four varieties of partial shading trial patterns were taken into account.

The performance of the proposed algorithm is evaluated by the metrics called tracking efficiency, which is given by the mathematical expression

Trials | Irradiance (W/m^{2}) |
Maximum power (P_{MAX}) (W) |
Power at MPPT (P_{MPPT}) (W) |
Tracking efficiency (η) (%) | |||||
---|---|---|---|---|---|---|---|---|---|

G_{1} |
G_{2} |
G_{3} |
G_{4} |
G_{5} |
G_{6} |
||||

1 | 1000 | 1000 | 1000 | 900 | 872 | 870 | 906 | 905.4 | 99.43 |

2 | 1000 | 1000 | 1000 | 850 | 862 | 790 | 857.5 | 856.3 | 99.56 |

3 | 1000 | 1000 | 1000 | 750 | 700 | 690 | 743 | 742.6 | 99.62 |

4 | 1000 | 1000 | 1000 | 650 | 540 | 500 | 649 | 648.2 | 99.56 |

5 | 1000 | 1000 | 1000 | 300 | 430 | 400 | 416.3 | 414.8 | 99.67 |

Trials | Irradiance (W/m^{2}) |
Maximum power (P_{MAX}) (W) |
Power at MPPT (P_{MPPT}) (W) |
Tracking efficiency (η) (%) | |||||
---|---|---|---|---|---|---|---|---|---|

G1 | G2 | G3 | G4 | G5 | G6 | ||||

1 | 900 | 820 | 840 | 820 | 810 | 790 | 948.9 | 935.6 | 98.6 |

2 | 820 | 750 | 830 | 750 | 745 | 698 | 878.4 | 852.4 | 97.04 |

3 | 690 | 720 | 750 | 780 | 700 | 650 | 735.8 | 724.5 | 98.47 |

4 | 450 | 540 | 650 | 600 | 640 | 550 | 603.2 | 590.9 | 97.96 |

5 | 300 | 320 | 289 | 320 | 300 | 290 | 492.3 | 479.4 | 97.38 |

From

Different algorithm | (η) |
(η) |
(η) |
(η) |
(η) |
---|---|---|---|---|---|

PSO-ANN | 93.6 | 93.6 | 93 | 92.7 | 93.5 |

GA-ANN | 96 | 95.8 | 95.8 | 95.8 | 95.8 |

HERBS | 97.8 | 97.8 | 97.8 | 98 | 97.85 |

BAT-ANN | 97 | 96.3 | 96 | 96.5 | 97.1 |

Proposed algorithm | 99 | 99 | 98.3 | 99.2 | 99.1 |

From the

From

In this paper, an original crossover SHERB-MPPT approach for the most extreme power extraction in PV frameworks. The spotted hyena and BAT calculation has been utilized alongside ELMs to work on the exhibition of the PV cells during the incomplete concealing conditions. The SHERB uses the hunting procedure of spotted hyena for the BAT algorithm to find the best optimal values. At the initial stage, the echolocation principle is used with the minimum loudness, frequency, and velocity. The experimentation is carried out using six solar cells implemented using the MATLAB-SIMULINK under different operating scenarios of partial shading conditions. Simulation results show that the proposed SHERB-MPPT algorithm has outperformed other existing hybrid frameworks such as PSO-ANN, GA-ANN, HERBS-MPPT, and BAT-ANN. Extensive testing with 50000 data combinations collected in partial shade and typical circumstances. Based on simulation results, the suggested technique provides 99.7% tracking efficiency with a slower convergence speed. The tracking efficiency is as high as 99.6% in all partial shading conditions with high convergence speed. However, the proposed algorithm needs the limelight of improvisation for real-time implementation. In future, the meta-heuristic optimization algorithm should be improved to achieve a more tracking efficiency with a low speed of convergence.