# A Cyber-Physical Photovoltaic Array Monitoring and Control System

Published on Jul 1, 2017

· DOI :10.4018/ijmstr.2017070103

Abstract

Published on Jul 1, 2017

· DOI :10.4018/ijmstr.2017070103

Abstract

- References (28)
- Citations (5)

References28

Newest

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...

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...

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...

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...

Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory

This paper is concerned with developing a distributed {k}-means algorithm and a distributed fuzzy {c}-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optim...

Distributed node counting in wireless sensor networks can be important in various applications, such as network maintenance and information aggregation. In this paper, a distributed consensus algorithm for estimating the number of nodes in a wireless sensor network in the presence of communication noise is introduced. In networks with a fusion center, counting the number of nodes can easily be done by letting each node to transmit a fixed constant value to the fusion center. In a network without...

A Consensus-Based Distributed Computational Intelligence Technique for Real-Time Optimal Control in Smart Distribution Grids

In real-time large-scale optimization problems, such as in smart grids, centralized algorithms may face difficulties in handling fast-varying system conditions, such as high variability of renewable-based distributed generators (DGs) and controllable loads (CLs). Further, centralized algorithms may encounter computation and communication bottlenecks while handling a large number of variables. To tackle these issues, consensus-based distributed strategies have been proposed recently. However, dis...

A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation to...

In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas of human activity analysis, where there is a need to perform fast search, and recognition in very high dimensional spaces. The problem is made more challenging when representations such as landmarks, contours, and human skeletons etc. are naturally studied in a non-Euclidean setting where even simple operations are muc...

Dynamic phenomena such as human activities, dynamic scenes, and moving crowds are commonly observed through visual sensors, resulting in feature trajectories sampled in time. Such phenomena can be accurately modeled by taking the temporal variations and changes into account. For problems where the trajectories are sufficiently different, elastic metrics can provide distances that are invariant to speed, but for more complex problems such as fine grained activity classification, one needs to expl...

Cited By5

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A distributed algorithm to compute the spectral radius of the graph in the presence of additive channel noise is proposed. The spectral radius of the graph is the eigenvalue with the largest magnitude of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. We devise an algorithm to reach consensus on the spec...

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 s...

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...

A cyber-physical system (CPS) approach for optimizing the output power of photovoltaic (PV) energy systems is proposed. In particular, a novel connection topology reconfiguration strategy for PV arrays to maximize power output under partial shading conditions using neural networks is put forth. Depending upon an irradiance/shading profile of the panels, topologies, namely series parallel (SP), total cross tied (TCT) or bridge link (BL) produce different maximum power points (MPP). The connection...

The analysis of a distributed consensus algorithm for estimating the maximum of the node initial state values in a network is considered in the presence of communication noise. Conventionally, the maximum is estimated by updating the node state value with the largest received measurements in every iteration at each node. However, due to additive channel noise, the estimate of the maximum at each node has a positive drift at each iteration and this results in nodes diverging from the true max val...

Oct 1, 2018 in ICIP (International Conference on Image Processing)

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...

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...