Artificial intelligent control of energy management PV system

Smadi, Takialddin Al; Handam, Ahmed; Gaeid, Khalaf S; Al-Smadi, Adnan; Al-Husban, Yaseen; Khalid, Al smadi

doi:10.1016/j.rico.2023.100343

Cited by 24 publications

(5 citation statements)

References 18 publications

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“…This integration of cybersecurity measures within an intelligent FLC framework could ensure that energy systems remain resilient to both physical and cyber threats, providing a robust defense mechanism in increasingly interconnected energy networks. Other recommendations for future studies include exploring the integration of artificial intelligence and machine learning techniques to further enhance the predictive capabilities and decision-making processes of intelligent FLCs [48,49]. Additionally, investigating the scalability of the proposed controller for larger and more complex energy systems, including smart cities, can provide insights into its broader applicability.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

Aljohani

2024

Energies

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The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

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Section: Discussionmentioning

confidence: 99%

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

Aljohani

2024

Energies

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“…In this article, we review the most common supervised learning algorithms used in diagnosing faults in solar photovoltaic installations. Machine learning algorithms are efficient and precise in solving complex and non-linear problems, unlike other methods [23]. In the literature, several ML techniques are used for fault diagnosis in PV systems [72,74].…”

Section: Semi-supervised Learningmentioning

confidence: 99%

“…For example, Amiri et al proposed a Deep Learning algorithm that combines convolutional and bidirectional recurrent neural networks to detect faults in a PV system [19]. Additionally, several authors have conducted reviews to highlight the effectiveness of Machine Learning and Deep Learning algorithms in diagnosing PV systems, as they accelerate and improve diagnostic solutions for PV systems [20][21][22][23][24][25][26][27]. This article specifically focuses on supervised machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

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Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

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“…Asphalt concrete pavements can withstand substantial loads and environmental conditions for a reasonable time and various forms of admixtures have been used to prolong the serviceability life of pavements by alleviating temperature and environmental effect [40][41][42]. These pavements consist of a bituminous surface underlay, a layer of granular material, and a layer of a suitable mixture of coarse and fine materials [43], as shown in Figures 5 and 6.…”

Section: Pavement Designmentioning

confidence: 99%

Reducing the Highway Networks Energy Bills using Renewable Energy System

Ayyad,

Bani Baker,

Handam

et al. 2023

Civ Eng J

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Jordan has significant renewable energy potential due to its remarkable geographical location and climate conditions. This potential elevates engaging several innovative renewable alternatives in energy development, which may efficiently minimize the excessive import of traditional energy sources. The objective of this research is to study the potential of utilizing clean and affordable solar energy along roadways such as Jordan’s Desert Highway-15 to be in line with the United Nations Sustainable Development Goals (UN-SDG’s) by installing selected solar panels that possess adequate friction and the ability to allow solar radiation to reach the solar cells, in addition to allowing the load to be bypassed around the cells. The shoulder of the highway, with a length of 315 km and a width of 3.0 meters, has been exploited in order to supply the neighboring areas with energy for those roads, particularly those paved roads, which are poorly lit at night. Furthermore, this study provides direction and guidance concerning the structural performance of non-traditional pavement materials, which are a form of subgrade or pavement reinforcement. The performance of a prototype board on a variety of structural bases has also been evaluated. Overall, this paper found that it is possible to design a solar road panel to withstand traffic loading and that the concrete structural base allows for a significant improvement of the analyzed prototype design, especially in countries with limited energy sources and dependent on imports such as Jordan. Doi: 10.28991/CEJ-2023-09-11-019 Full Text: PDF

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Artificial intelligent control of energy management PV system

Cited by 24 publications

References 18 publications

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Reducing the Highway Networks Energy Bills using Renewable Energy System

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