Department of Electrical & Electronics Engineering
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Browsing Department of Electrical & Electronics Engineering by Author "AHMAD, Muhammad Saeed"
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Item COMPARATIVE ANALYSIS OF MPPT TECHNIQUES FOR SOLAR AND WIND SYSTEMS UNDER DIFFERENT OPERATING CONDITIONS(2022-12-26) AHMAD, Muhammad Saeed; SÜNTER, SedatRenewable energy technologies have gained a lot of traction in the last few decades as a means of reducing reliance on fossil fuels and mitigating the impact of climate change. Renewable sources such as sunlight, wind, and water are clean and sustainable. These technologies have gained significant attention in recent years. While renewable energy technologies have many advantages, one of the main challenges is their relatively low efficiency compared to fossil fuels. As a result, renewable energy systems typically require more land and resources to produce the same amount of energy as fossil fuel-based systems. Additionally, the efficiency of renewable energy systems can vary depending on the weather and other environmental conditions. For example, solar panels are less effective on cloudy days and wind turbines are less effective in calm weather. This can make it difficult to predict and control the amount of energy that renewable systems will produce, which can create challenges for integrating them into the grid. The problem with efficiency can be dealt with the use of maximum power point tracking (MPPT) techniques. These techniques are used to optimize the performance of renewable energy systems by ensuring that they operate at the maximum power point, or the point at which they can generate the most power. There are several types of maximum power point tracking (MPPT) techniques, but they can be broadly classified into three categories: simple, artificial intelligence (AI), and hybrid. Simple MPPT techniques such as PO and IC are the most basic and widely used type of MPPT. These techniques use relatively simple algorithms to continuously adjust the operating conditions of the system to maintain the maximum power point. AI-based MPPT techniques like PSO and ANN use advanced algorithms and machine learning techniques to optimize the performance of renewable energy systems. These techniques can adapt to changing environmental conditions and can continuously adjust the operating conditions of the system in real-time. Hybrid MPPT techniques like ANFIS and PSO&PO are a combination of simple and AI based techniques. These techniques use simple algorithms to quickly track the maximum power point, and then use AI-based techniques to fine-tune the operating conditions of the system in real-time. A comparative analysis of simple, AI, ML, and hybrid MPPT techniques for hybrid energy (Solar and Wind) systems is discussed in this thesis. The MPPT algorithms were ranked based on different metrics such as efficiency, settling time, oscillations at MPPT and algorithm complexity. For PV system, AI based techniques performed best as compared to Hybrid and conventional techniques. For Wind system, hybrid techniques yield the best results as they combine the benefits of conventional and AI techniques.