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An 18 kW solar array research facility for fault detection experiments

Published on Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
· DOI :10.1109/MELCON.2016.7495369
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
David Ramirez Dominguez3
Estimated H-index: 3
(ASU: Arizona State University)
+ 8 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
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Abstract
Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. Research planned at this stage includes developing machine learning methods for fault detection. Preliminary simulation results on fault detection using machine learning are given in this paper.
  • References (21)
  • Citations (2)
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References21
Newest
Published on Jun 1, 2016in Sustainable Energy, Grids and Networks
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Santoshi T. Buddha3
Estimated H-index: 3
(ASU: Arizona State University)
+ 4 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
(ASU: Arizona State University)
As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve monitoring and management of PV arrays. A procedure is presented here for optimizing the electrical configuration of a PV array under a variety of operating conditions. Computer simulations and analysis with synthetic and real data are presented in this paper. The performance of the optimization system is evaluated for a variety of partial shading conditions using a SPICE circuit simulator. In general,...
Published on Feb 1, 2016in IEEE Transactions on Signal Processing 5.23
Visar Berisha11
Estimated H-index: 11
(ASU: Arizona State University),
Alan Wisler4
Estimated H-index: 4
(ASU: Arizona State University)
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Information divergence functions play a critical role in statistics and information theory. In this paper we show that a nonparametric f-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distribution and for the case where there exists some mismatch between training and test distributions. We confirm these theoretical results by designing feature selection alg...
Published on Aug 1, 2015in IEEE Transactions on Industrial Electronics 7.50
Lenos Hadjidemetriou9
Estimated H-index: 9
(UCY: University of Cyprus),
Elias Kyriakides24
Estimated H-index: 24
(UCY: University of Cyprus),
Frede Blaabjerg106
Estimated H-index: 106
(AAU: Aalborg University)
The increasing penetration of renewable energy sources (RESs) in the power grid requires high-quality power injection under various grid conditions. The synchronization method, usually a phase-locked loop (PLL) algorithm, is directly affecting the response of the grid-side converter of the RES. This paper proposes a new PLL algorithm that uses an advanced decoupling network implemented in the stationary reference frame with limited requirements for processing time to enable a fast and accurate s...
Published on May 1, 2015in IEEE Transactions on Power Electronics 7.22
Ye Zhao19
Estimated H-index: 19
(NU: Northeastern University),
Roy Ball5
Estimated H-index: 5
+ 2 AuthorsBrad Lehman29
Estimated H-index: 29
(NU: Northeastern University)
Fault detection in solar photovoltaic (PV) arrays is an essential task for increasing reliability and safety in PV systems. Because of PV's nonlinear characteristics, a variety of faults may be difficult to detect by conventional protection devices, leading to safety issues and fire hazards in PV fields. To fill this protection gap, machine learning techniques have been proposed for fault detection based on measurements, such as PV array voltage, current, irradiance, and temperature. However, ex...
Published on Jan 1, 2015
Shwetang Peshin1
Estimated H-index: 1
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
+ 5 AuthorsDevarajan Srinivansan1
Estimated H-index: 1
Published on Jul 1, 2014in IEEE Transactions on Image Processing 6.79
Jayaraman J. Thiagarajan10
Estimated H-index: 10
(ASU: Arizona State University),
Karthikeyan Natesan Ramamurthy11
Estimated H-index: 11
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learn...
Published on Mar 1, 2012 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Santoshi T. Buddha3
Estimated H-index: 3
(ASU: Arizona State University)
+ 4 AuthorsToru Takehara3
Estimated H-index: 3
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the “smart grid,” an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the r...
Published on Jan 1, 2012
Santoshi T. Buddha3
Estimated H-index: 3
(ASU: Arizona State University),
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University)
+ 4 AuthorsToru Takehara3
Estimated H-index: 3
The need for the usage of signal processing and pattern recognition techniques to monitor photovoltaic (PV) arrays and to detect and respond to faults with minimal human involvement is increasing. The data obtained from the array can be used to dynamically modify the array topology and improve array power output. This is beneficial especially when module mismatches such as shading, soiling and aging occur in the PV array. A robust statistics-based fault detection algorithm to find faulty modules...
Published on Jul 1, 2011in IEEE Transactions on Power Delivery 4.42
Jianjun Ni7
Estimated H-index: 7
(Hohai University),
Chuanbiao Zhang2
Estimated H-index: 2
(Hohai University),
Simon X. Yang35
Estimated H-index: 35
(U of G: University of Guelph)
High-voltage circuit breakers (HVCBs) play an important role in power systems, which can control and ensure the power grids are working properly. Real-time fault diagnosis of HVCBs is an essential issue for power systems. In this paper, a novel approach based on an adaptive kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed for real-time fault diagnosis of HVCBs. In the proposed approach, a sample reduction algorithm based on a similarity degree function is p...
Cited By2
Newest
Published on Sep 1, 2018in IEEE Transactions on Energy Conversion 4.61
Albert Alexander Stonier1
Estimated H-index: 1
(Kongu Engineering College),
Brad Lehman29
Estimated H-index: 29
(NU: Northeastern University)
This paper presents an intelligent-based fault-tolerant system for a solar photovoltaic (PV) inverter. Artificial neural network based controller is used to monitor, detect, and diagnose the faults in solar PV panels, battery, semiconductor switches, and inverters. The cascaded multilevel inverter is connected across the combination of solar PV panel and battery for dc–ac conversion. The major advantage of the proposed topology is that it can deliver power from source to the load even under faul...
Published on Jul 1, 2018
Farib Khondoker (ASU: Arizona State University), Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
When collecting solar energy via photovoltaic (PV) panel arrays, one common issue is the potential occurrence of faults. Faults arise from panel short-circuit, soiling, shading, ground leakage and other sources. Machine learning algorithms have enabled data-based classification of faults. In this paper, we present an Internet-based PV array fault monitoring simulation using the Java-Dsp(j-Dsp)simulation environment. We first develop a solar array simulation in J-DSP and then form appropriate gra...
Published on May 1, 2018
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated f...
Published on Dec 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University)
+ 4 AuthorsRafaela Villalpando-Hernandez1
Estimated H-index: 1
A distributed spectral clustering algorithm to group sensors based on their location in a wireless sensor network (WSN) is proposed. For machine learning and data mining applications in WSN's, gathering data at a fusion center is vulnerable to attacks and creates data congestion. To avoid this, we propose a robust distributed clustering method without a fusion center. The algorithm combines distributed eigenvector computation and distributed K-means clustering. A distributed power iteration meth...
Published on Aug 1, 2017
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Photovoltaic (PV) array analytics and control have become necessary for remote solar farms and for intelligent fault detection and power optimization. The management of a PV array requires auxiliary electronics that are attached to each solar panel. A collaborative industry-university-government project was established to create a smart monitoring device (SMD) and establish associated algorithms and software for fault detection and solar array management. First generation smart monitoring device...
Published on Aug 1, 2017
Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
+ 1 AuthorsMike Stanley1
Estimated H-index: 1
(NXP Semiconductors)
This paper provides a brief survey of the basic concepts and algorithms used for Machine Learning and its applications. We begin with a broader definition of machine learning and then introduce various learning modalities including supervised and unsupervised methods and deep learning paradigms. In the rest of the paper, we discuss applications of machine learning algorithms in various fields including pattern recognition, sensor networks, anomaly detection, Internet of Things (IoT) and health m...
Published on Aug 1, 2017
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 8 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled senso...
Published on Dec 1, 2016 in ISSPIT (International Symposium on Signal Processing and Information Technology)
Jongmin Lee3
Estimated H-index: 3
(ASU: Arizona State University),
Michael Stanley1
Estimated H-index: 1
(NXP Semiconductors)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Interpreting sensor data in Internet-of-Things applications is a challenging problem particularly in embedded systems. We consider sensor data analytics where machine learning algorithms can be fully implemented on an embedded processor/sensor board. We develop an efficient real-time realization of a Gaussian mixture model (GMM) for execution on the NXP FRDM-K64F embedded sensor board. We demonstrate the design of a customized program and data structure that generates real-time sensor features, ...