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ANN MPPT for grid Connected PV wind battery system

ANN MPPT for grid Connected PV wind battery system

This video explains the ANN MPPT for PV and wind systems in grid-connected mode with the battery storage system. The simulation results are discussed for change irradiance conditions, change in wind speed conditions, and change in soc level of the battery.



ANN MPPT for Grid Connected PV Wind Battery System

With the ever-growing demand for renewable energy, photovoltaic (PV) and wind power have become popular choices for generating electricity. However, maximizing the power output of these systems is critical for ensuring their efficiency and effectiveness. One way to achieve this is through the use of Maximum Power Point Tracking (MPPT) algorithms. This article explores the use of Artificial Neural Networks (ANN) in MPPT for grid-connected PV wind battery systems.

Introduction

In recent years, grid-connected PV wind battery systems have gained popularity as a sustainable source of electricity. However, the intermittent nature of these sources makes it challenging to maintain a stable power supply. MPPT is a technique used to maximize the power output of a PV or wind system, ensuring the efficiency and stability of the system. In this article, we will delve into the use of ANN in MPPT for grid-connected PV wind battery systems.

What is MPPT?

MPPT is a technique used in PV and wind systems to maximize their power output by tracking the maximum power point (MPP) of the system. The MPP is the point at which the system can deliver the maximum amount of power. MPPT algorithms adjust the voltage or current of the system to ensure that it operates at the MPP. This helps to maximize the efficiency and stability of the system.

The Role of ANN in MPPT

ANN is a type of machine learning algorithm that is modeled after the structure and function of the human brain. In the context of MPPT, ANN can be used to predict the MPP of the system based on various factors such as solar irradiation, temperature, wind speed, and battery voltage. ANN can also adapt to changing weather conditions, making it more accurate and efficient than conventional MPPT algorithms.

Advantages of ANN MPPT

  • Increased efficiency: ANN MPPT algorithms can improve the efficiency of PV and wind systems by up to 30% compared to conventional MPPT algorithms.

  • Accurate tracking: ANN algorithms can accurately track the MPP of the system, even under changing weather conditions.

  • Easy to implement: ANN MPPT algorithms are easy to implement and can be integrated into existing PV and wind systems.

  • Reduced costs: By maximizing the power output of the system, ANN MPPT algorithms can reduce the number of solar panels or wind turbines required, thereby reducing costs.

How ANN MPPT Works

ANN MPPT algorithms consist of three layers: the input layer, the hidden layer, and the output layer. The input layer consists of various factors such as solar irradiation, temperature, wind speed, and battery voltage. These factors are used as input to the hidden layer, which performs complex calculations to predict the MPP of the system. The output layer provides the final output, which is used to adjust the voltage or current of the system to ensure that it operates at the MPP.

Implementation of ANN MPPT

The implementation of ANN MPPT involves the following steps:

  1. Data collection: Collect data on various factors such as solar irradiation, temperature, wind speed, and battery voltage.

  2. Preprocessing: Preprocess the data to remove any outliers or errors.

  3. Training: Train the ANN algorithm using the preprocessed data.

  4. Testing: Test the algorithm using new data to ensure its accuracy and efficiency.

  5. Integration: Integrate the algorithm into the PV or wind system to ensure that it operates at the MPP.



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