Match!

PV Array Fault Detection using Radial Basis Networks

Published on Jul 1, 2019
· DOI :10.1109/IISA.2019.8900710
Emma Pedersen1
Estimated H-index: 1
(ASU: Arizona State University),
Sunil Rao5
Estimated H-index: 5
(ASU: Arizona State University)
+ 4 AuthorsElias Kyriakides27
Estimated H-index: 27
(UCY: University of Cyprus)
Abstract
An increase in grid-connected photovoltaic arrays creates a need for efficient and reliable fault detection. In this paper, machine learning strategies for fault detection are presented. An Artificial Neural Network was studied with the goal of detecting three photovoltaic module conditions. In addition, an unsupervised approach was successfully implemented using the -means clustering algorithm, successfully detecting arc and ground faults. To distinguish and localize additional faults such as shading and soiling, a supervised approach is adopted using a Radial Basis Function Network. A solar array dataset with voltage, current, temperature, and irradiance was examined. This dataset had labeled data with normal conditions and faults due to soiling and shading. A radial basis network was trained to classify faults, resulting in an error rate below 2% on synthetic data with realistic levels of noise.
  • References (30)
  • Citations (1)
📖 Papers frequently viewed together
2011
4 Authors (Hongbo He, ..., Andrea Mammoli)
11 Citations
12 Citations
2017
78% of Scinapse members use related papers. After signing in, all features are FREE.
References30
Newest
#1Kristen Jaskie (ASU: Arizona State University)H-Index: 1
#2Andreas Spanias (ASU: Arizona State University)H-Index: 29
This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set...
4 CitationsSource
#1Sunil Rao (ASU: Arizona State University)H-Index: 5
#2Andreas Spanias (ASU: Arizona State University)H-Index: 29
Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 3 authors...
In this paper, we describe a Cyber-Physical system approach to fault detection in Photovoltaic (PV) arrays. More specifically, we explore customized neural network algorithms for fault detection from monitoring devices that sense data and actuate at each individual panel. We develop a framework for the use of feedforward neural networks for fault detection and identification. Our approach promises to improve efficiency by detecting and identifying eight different faults and commonly occurring co...
5 CitationsSource
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 2
#2Anna Scaglione (ASU: Arizona State University)H-Index: 46
Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 4 authors...
In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV...
7 CitationsSource
#1Adel Mellit (ICTP: International Centre for Theoretical Physics)H-Index: 41
#2Giuseppe Marco Tina (University of Catania)H-Index: 27
Last. Soteris A. Kalogirou (CUT: Cyprus University of Technology)H-Index: 60
view all 3 authors...
Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable fo...
30 CitationsSource
#1Farib Khondoker (ASU: Arizona State University)H-Index: 1
#2Sunil Rao (ASU: Arizona State University)H-Index: 5
Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 4 authors...
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...
4 CitationsSource
#1Sameeksha Katoch (ASU: Arizona State University)H-Index: 4
#2Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
Last. Devarajan SrinivasanH-Index: 6
view all 8 authors...
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...
10 CitationsSource
#1Mahmoud DhimishH-Index: 13
#2Violeta HolmesH-Index: 15
Last. Mark DalesH-Index: 12
view all 4 authors...
Abstract This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface. The obtained ...
30 CitationsSource
#1Zhicong Chen (FZU: Fuzhou University)H-Index: 13
#2Lijun Wu (FZU: Fuzhou University)H-Index: 12
Last. Wencheng Lin (FZU: Fuzhou University)H-Index: 2
view all 6 authors...
Abstract Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to i...
44 CitationsSource
#1Andreas Spanias (ASU: Arizona State University)H-Index: 29
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...
22 CitationsSource
#1Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 3
#2Andreas Spanias (ASU: Arizona State University)H-Index: 29
Last. Mike Stanley (NXP Semiconductors)H-Index: 1
view all 4 authors...
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...
52 CitationsSource
Cited By1
Newest
#1Kristen Jaskie (ASU: Arizona State University)H-Index: 1
#2Andreas Spanias (ASU: Arizona State University)H-Index: 29
This paper will address the Positive and Unlabeled learning problem (PU learning) and its importance in the growing field of semi-supervised learning. In most real-world classification applications, well labeled data is expensive or impossible to obtain. We can often label a small subset of data as belonging to the class of interest. It is frequently impractical to manually label all data we are not interested in. We are left with a small set of positive labeled items of interest and a large set...
4 CitationsSource