A Fuzzy Assisted Enhanced Control for Utility Connected Rooftop PV

Pullaguram, Deepak; Bhattacharya, Somesh; Mishra, Sukumar; Senroy, Nilanjan

doi:10.1016/j.ifacol.2017.08.1144

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Moreover, distribution companies are anxious about what is being injected into their grids since local malfunctions in the system cause serious problems in the grid side. Grid connection faults compromise power quality [16] and cause many grid voltage regulation issues [17], these pose several protection-related challenges [18] to avoid islanding, tripping, and interference [19].…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV

Bakdi

Bounoua

Mekhilef

et al. 2019

Energy

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In parallel to sustainable growth in solar fraction, continuous reductions in Photovoltaic (PV) module and installation costs fuelled a profound adoption of residential Rooftop Mounted PV (RMPV) installations already reaching grid parity. RMPVs are promoted for economic, social, and environmental factors where they not only improve energy performance and reduce greenhouse effects but also contribute to bill savings.RMPV modules and energy conversion units are frequently subject to various types of anomalies which compromise power quality and promote fire risk and safety hazards for the personnel for which reliable protection is crucial. This article analyses historical data and presents a novel design that easily integrates with data storage units of RMPV systems to automatically process real-time data streams for reliable supervision. Dominant Transformed Components (TCs) are online extracted through multiblock Principal Component Analysis (PCA), most sensitive components are selected and their time-varying characteristics are recursively estimated in a moving window using smooth Kernel Density Estimation (KDE). Novel monitoring indices are developed as preventive alarms using Kullback-Leibler Divergence (KLD). This work exploits data records during 2015-2017 from thin-film, monocrystalline, and polycrystalline RMPV energy conversion systems. Fourteen test scenarios include array faults (line-to-line, line-to-ground, transient arc faults); DC-side mismatches (shadings, open circuits); grid-side anomalies (voltage sags, frequency variations); in addition to inverter anomalies and sensor faults.

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