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

Abstract

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.

Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

“…where P, P 0 are distribution functions in measure space Ω. Many scholars have studied the DRO based on KL divergence [31][32][33][34], and they put forward some reformulation methods in the research. In these studies, KL divergence has been used to solve problems such as unit commitment, and showed its advantages.…”

“…In general, the data are not fully utilized because the distribution knowledge contains more information than moments. Motivated by the deficiency of the moment-based DRO method, reference [24][25][26][27][28][29][30][31][32][33][34] investigated the distance-based DRO method. Usually, in a distance-based DRO model, the ambiguity set is constructed by probability distribution.…”

“…Thus, the model could be transformed into a tractable deterministic model. In [33,34], the Kullback-Leibler divergence method was further studied in-depth in other scopes.…”

“…Energy and Reserve Dispatch. Collect historical data regarding variable uncertainties; Construct the uncertainty set using KL divergence method;3: Design an empirical distribution using(20) and(21); Reformulate the energy and reserve dispatch problem into(33); Decompose the optimization problem into (34) and(35); Use the SAA method to approximate problem(36); Solve problem(38).…”

Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description

“…Analyzing the impact of process variable selection on PCA monitoring performance, Jiang et al used the genetic algorithm (GA) to select a subset of variables that were most relevant to each type of fault to divide the process data into corresponding sub-blocks, and then established a multi-block monitoring model [25]. Bakdi et al proposed that apply multi-block PCA to extract dominant transformed components (TCs), and most sensitive components are selected for the fault detection [26]. Although the monitoring performance of these multi-block monitoring methods has improved when compared to traditional monitoring methods, the dynamic characteristics of the process are still ignored, resulting in the unsatisfactory monitoring results.…”