Quantum Neural Network Parameter Estimation for Photovoltaic Fault Detection

Uehara, Glen; Rao, Sunil; Dobson, Mathew; Tepedelenlioğlu, Cihan; Spanias, Andreas

doi:10.1109/iisa52424.2021.9555558

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…[ 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 ]. According to the articles [ 140 , 141 , 142 ], the last one generates more power in case of PS as compared to other structures. The GMMPT of an rPV array in case of PS represents a GO task.…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…The long-term contribution, including increased capacity of solar energy, depends on solving the remaining tasks of grids integration, high costs, and low efficiency, mainly through the research and development of a smart solar plant system based on integration of cutting-edge technologies, including DNN [ 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 ]. To attain the smart optimization and high efficiency of solar energy, the cloud, big data, ML, EC, IoT, quantum, and sensor technologies need to be adaptively combined and implemented as smart grid, home, and city applications.…”

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

“…This method can be implemented by an integrating framework (Pennylane) for quantum computer simulators [ 140 ]. A quantum-based solar plant failure detection model was developed in [ 141 ].…”

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

“…In [ 139 ], researchers developed based on EC a lightweight ML real-time solar plant failure detection model. Recent cloud-based monitoring solutions were developed in [ 141 , 142 ]. Forthcoming ML technologies for solar energy will integrate cloud-based solutions in which these technologies take full benefits of ML parallelism, data parallelism, practically limitless big data and ML knowledge storage, and almost boundless parallel computational resources.…”

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

See 3 more Smart Citations

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

Section: Future Technologies For Smart Solar Energymentioning

confidence: 99%

See 2 more Smart Citations

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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“…Ref. [51] demonstrated a fault detection method for PV systems management based on 2 and 4 qubits hybrid quantum neural network (QNN). In hybrid systems, quantum circuits are used only for the hidden layers representing the neurons of the neural network, which exploits QC to encompass the nonlinear mapping capability.…”

Section: Fault Managementmentioning

confidence: 99%

Quantum computing for smart grid applications

Ullah

Eskandarpour

Zheng

et al. 2022

IET Generation Trans & Dist

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Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis.

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Methods of photovoltaic fault detection and classification: A review

Hong

Pula

2022

Energy Reports

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Quantum Neural Network Parameter Estimation for Photovoltaic Fault Detection

Cited by 12 publications

References 22 publications

A Review on Machine Learning Applications for Solar Plants

A Review on Machine Learning Applications for Solar Plants

Quantum computing for smart grid applications

Methods of photovoltaic fault detection and classification: A review

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