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A cyber-physical system approach for photovoltaic array monitoring and control

Published on Aug 1, 2017
· DOI :10.1109/iisa.2017.8316458
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 8 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
Cite
Abstract
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 sensory analysis and fusion.
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  • Citations (3)
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Newest
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 Nov 1, 2016 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
Henry Braun4
Estimated H-index: 4
,
Pavan K. Turaga21
Estimated H-index: 21
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
We examine a potential technique of performing a classification task based on compressively sensed (CS) data, skipping a computationally expensive reconstruction step. A deep Boltzmann machine is trained on a compressive representation of MNIST handwritten digit data, using a random orthoprojector sensing matrix. The network is first pre-trained on uncompressed data in order to learn the structure of the dataset. The outer network layers are then optimized using backpropagation. We find this app...
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 May 1, 2016 in CCDC (Chinese Control and Decision Conference)
Guannan Du1
Estimated H-index: 1
,
Xiuxia Zhang1
Estimated H-index: 1
+ 2 AuthorsHaicheng Wei1
Estimated H-index: 1
As panels located in the wilderness and affected by the dust and sandstorm, photovoltaic efficiency was reduced by 30%–40% after a period of time even worse. Based on the Internet of things the self-cleaning solar power system of the household micro-grid was designed in this paper. It included micro-grids and control systems; a self-cleaning solar power system of the household micro-grid was presented. Nano-diamond powder, ethyl cellulose, and solvent were mixed. They accorded to mass ratio of 1...
Published on Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
David Ramirez Dominguez3
Estimated H-index: 3
(ASU: Arizona State University)
+ 8 AuthorsYoshitaka Morimoto1
Estimated H-index: 1
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 exchan...
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 Oct 1, 2015in IEEE Transactions on Sustainable Energy 7.65
Mohd Nafis Akram1
Estimated H-index: 1
(UCF: University of Central Florida),
Saeed Lotfifard13
Estimated H-index: 13
(WSU: Washington State University)
In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short- and open-circuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturers datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The pr...
Published on Jul 1, 2015 in IJCNN (International Joint Conference on Neural Network)
Lian Lian Jiang7
Estimated H-index: 7
(NTU: Nanyang Technological University),
Douglas L. Maskell24
Estimated H-index: 24
(NTU: Nanyang Technological University)
Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical task to ensure the reliability and safety in PV systems. The current state-of-the-art technique...
Cited By3
Newest
Published on May 2, 2019in Synthesis Lectures on Signal Processing
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
Published on Oct 1, 2018 in ICIP (International Conference on Image Processing)
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate pr...
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 Jul 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2