Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review DOI Open Access
S. Mohammad Shojaei, Reihaneh Aghamolaei, Mohammad Reza Ghaani

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9555 - 9555

Published: Nov. 2, 2024

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched density and flexibility. However, as world shifts toward renewable energy, overcoming limitations intermittent power sources requires a bold reimagining storage integration. Power-to-X (PtX) technologies, which convert excess electricity into storable carriers, offer promising solution for long-term sector coupling. Recent advancements in machine learning (ML) revolutionized PtX systems by enhancing efficiency, scalability, sustainability. This review provides detailed analysis how ML techniques, such deep reinforcement learning, data-driven optimization, predictive diagnostics, are driving innovation Power-to-Gas (PtG), Power-to-Liquid (PtL), Power-to-Heat (PtH) systems. example, has improved real-time decision-making PtG reducing operational costs improving grid stability. Additionally, diagnostics powered increased system reliability identifying early failures critical components proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges data quality, processing, scalability remain, presenting future research opportunities. These to decarbonizing hard-to-electrify sectors, heavy industry, transportation, aviation, aligning with global sustainability goals.

Language: Английский

Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management DOI Creative Commons
Ashkan Safari, Mohammadreza Daneshvar, Amjad Anvari‐Moghaddam

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11112 - 11112

Published: Nov. 28, 2024

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of power system by improving reliability resilience. The rapid advancement AI ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, electric vehicles (EVs). Consequently, to form a complete resource for cognitive techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews 155 research studies) addressing utilization EMSs its influence on sector. additionally investigates essential features smart grids, big data, their integration with EMS, emphasizing capacity improve efficiency reliability. Despite these advances, there are still additional challenges that remain, concerns regarding privacy integrating different systems, issues related scalability. finishes analyzing problems providing future perspectives ongoing use EMS.

Language: Английский

Citations

8
Optimal active unsupervised fault detection in cascaded h-bridge inverters based on machine learning DOI Creative Commons
Ashkan Safari,

Mohammad Hosein Tehranidoost,

Mehran Sabahi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 3, 2025

Multi-Level Inverters (MLIs) are commonly used in high-voltage, high-power industrial applications. In this regard, their reliability, and health optimal performance the first priority. However, as number of switches a multilevel inverter increases, it comes so common to occur faults within system. Ensuring reliability MLI is an important concern power industries, making effective fault detection methods essential. Developing precise physics-based, model-based, hardware-based models for challenging, largely due unknown parameters incomplete understanding physical processes At end, proposed paper presents highly efficient hyper-tuned machine learning (ML) model known Isolation Forest (IF). This algorithm unsupervised method anomaly detection, which isolates outliers by recursively partitioning data points, way identifying or rare events large datasets with minimal computational complexity To test algorithm, 17-level Cascaded H-Bridge (CHB) simulated several faults, IF tested. next phase, compared others, based on indicators F1-Score, Precision, Recall, Accuracy, highest results retained have accurate model, that smoothens fully automated, self-healing application

Language: Английский

Citations

0
Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review DOI Open Access
S. Mohammad Shojaei, Reihaneh Aghamolaei, Mohammad Reza Ghaani

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9555 - 9555

Published: Nov. 2, 2024

For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched density and flexibility. However, as world shifts toward renewable energy, overcoming limitations intermittent power sources requires a bold reimagining storage integration. Power-to-X (PtX) technologies, which convert excess electricity into storable carriers, offer promising solution for long-term sector coupling. Recent advancements in machine learning (ML) revolutionized PtX systems by enhancing efficiency, scalability, sustainability. This review provides detailed analysis how ML techniques, such deep reinforcement learning, data-driven optimization, predictive diagnostics, are driving innovation Power-to-Gas (PtG), Power-to-Liquid (PtL), Power-to-Heat (PtH) systems. example, has improved real-time decision-making PtG reducing operational costs improving grid stability. Additionally, diagnostics powered increased system reliability identifying early failures critical components proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges data quality, processing, scalability remain, presenting future research opportunities. These to decarbonizing hard-to-electrify sectors, heavy industry, transportation, aviation, aligning with global sustainability goals.

Language: Английский

Citations

0