Power quality analysis of low voltage distributed generators in standalone and grid connected modes

Mosobi, Rinchin W.; Gao, Sarsing

doi:10.1504/ijpec.2021.118029

Cited by 1 publication

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“…Mosobi and Gao established a distributed generator (DG) system for addressing power quality (PQ) issues in low-voltage networks based on renewable energy sources. This DG system integrates components such as a photovoltaic system (PVS), wind energy generating system (WEGS), and micro-hydro generating system (MHGS), and connects them to a common DC bus [17]. Regarding efficiency improvement for other equipment in addition to the boiler, Khaleel et al verified the improvement in efficiency by modeling using an exergy evaluation to ensure that coal-fired electrical power plants could still be operated optimally under new conditions instead of the initial design conditions, as conditions can deteriorate [18].…”

Section: Conventional Studies On Improvements In Power Plant Efficiencymentioning

confidence: 99%

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Lee,

Choi,

Lee

2023

Energies

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The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses.

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Section: Conventional Studies On Improvements In Power Plant Efficiencymentioning

confidence: 99%

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Lee,

Choi,

Lee

2023

Energies

View full text Add to dashboard Cite

show abstract

Power quality analysis of low voltage distributed generators in standalone and grid connected modes

Cited by 1 publication

References 0 publications

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

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