Solar power system efficiency improvement using ANN and hybrid boost converter based MPPT algrathium
The load pressure on electrical power system is increased during last decade. The installation of new power generators (PGS) takes huge time and cost. Therefore, to manage current power demands, the solar plants are considered a fruitful solution. But conventional MPPT techniques are incapable of tr
2025-06-28 16:29:36 - Adil Khan
Solar power system efficiency improvement using ANN and hybrid boost converter based MPPT algrathium
Project Area of Specialization Artificial IntelligenceProject SummaryThe load pressure on electrical power system is increased during last decade. The installation of new power generators (PGS) takes huge time and cost. Therefore, to manage current power demands, the solar plants are considered a fruitful solution. But conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). So, we are going to improve the output power of solar system by using ANN (AI technique). There are two steps, the offline step and the online step. Where the offline status is used for training various terms of ANN in terms of structure and algorithm while in the online step, the online procedure is applied with optimum ANN for maximum power point tracking (MPPT) using traditional converter and hybrid converter in solar plants.
Project Objectives1)FIRST we Introduce AI in power system.
NEED FOR AI IN POWER SYSTEMS
Power system analysis by conventional techniques becomes more difficult because of:
(i) Complex, versatile and large amount of information which is used in calculation, diagnosis and learning.
(ii) Increase in the computational time period and accuracy due to extensive and vast system data handling.
The ANN technique is a supervised machine learning technique that is commonly used in PV systems for MPPT purpose due to its adaptiveness in solving non-linear tasks, fast-tracking speed, improved performance, and its adaptiveness with microcontrollers.
They are classified by their architecture: number of layers and topology: connectivity pattern, feedforward or recurrent
2)Second objective of our project is to improve power efficiency of solar system during partial shading condition(PSC).
ANN or connectionist system is inspired by the biological neural networks from animal brains. It is utilized to train and test for the non-linearity relationship between 1-V and PV. From input current, input voltage, irradiance, temperature to metrological data, ANN fetches these inputs and continuously learns to fit the behavior of the solar power system for the maximum power. The design of FLC can be modelled by using ANN with higher accuracy and simpler implementation of converters.
From the collection of the simulation or hardware setup, the dataset is acquired by inputting solar irradiances, temperatures, solar power system voltage or current to ANN in finding the corresponding Pmax or Vmax output. These data are converted to the training data and to pass into the designed ANN to teach it how to perform. After the training, the test datasets are used to evaluate the performance of the designed ANN, and the errors are feed backed to ANN for further adjustment. It is deployable to assist for the prediction of MPP alongside the state estimation by the sequential Monte Carlo (SMC) filtering. A state space model for the sequential estimation of MPP is able to fit alongside the framework of IC MPPT technique, and the ANN model observes the voltage and current or irradiance data in predicting GMPP to refine the estimation by SMC.
Figure 1 ANN based MPPT
Figure 1 explains the HBC and ANN based MPPT presented model for minimizing the solar power system critical caring and balancing output power issues. The output of PV is attained by ANN based MPPT in order to improve the performance of solar power system. The neural network technique consists of major number of interconnected processors called neurons. Each neuron includes a huge number of weighted links for transforming signals. Thus, it has potential to manage the difficult task of data processing and interpretation. In this proposed model, the feed forward back propagation ANN is installed which contains logsig purelin and purelin activation functions based hidden layers, as depicted in figure. In case of offline step, the ANN training is performed in terms of activation function structure and training.
Benefits of the ProjectBasically,our project is to improve the solar system.This project aims to utilize Artificial Intelligence (AI) technique ANN’s diagnostic ability to increase performance in solar power systems. The ANN technique is a supervised machine learning technique that is commonly used in PV systems for MPPT purpose due to its adaptiveness in solving non-linear tasks, fast-tracking speed, improved performance, and its adaptiveness with microcontrollers.
The hybrid MPPT is favorable in terms of the balance between perfor? mance and complexity, and it combines the advantages of con? ventional and AI-based MPPT techniques.
Why Solar System?
- Power generators take huge time and cost therefore we chose solar system because it requires less time for installation.
- Eco friendly.
- Solar Power is a free source of energy.
- Fossil fuels are depleting and is also harmful for environment.
- Has no moving parts unlike wind turbines.
Final deliverable of our project wil be hardware implementation of Solar Power System Using ANN and Hybrid Boost Converter Based MPPT Algorithm and MATLAB based simulation with its results.
Final Deliverable of the Project Hardware SystemCore Industry Energy Other IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Affordable and Clean EnergyRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 49500 | |||
| Mono Crystalline Solar Panel 60 Watt | Equipment | 1 | 4000 | 4000 |
| Sinko 70A MPPT Plus Hybrid Solar charge controller 12/24V | Equipment | 1 | 13500 | 13500 |
| EHBC (include,inductor capacitor Switch diode etc) 1500 | Equipment | 1 | 1500 | 1500 |
| Altera CycloneII EP2C5T144 FPGA Mini Development Board | Equipment | 1 | 3500 | 3500 |
| Tubular battery | Equipment | 1 | 5000 | 5000 |
| Inverter | Equipment | 1 | 10000 | 10000 |
| Expert Help | Equipment | 1 | 10000 | 10000 |
| Other Equipments | Miscellaneous | 1 | 2000 | 2000 |