Henry Braun

Signal processing for fault detection in photovoltaic arrays

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 modules voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays.

Signal processing for photovoltaic applications

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 is presented. Further, topology optimization of PV arrays using module level data is considered. Various topologies such as the series-parallel (SP), the total cross-tied (TCT), the bridge link (BL) and their bypassed versions are considered. The performance associated with these topologies for a possible shading pattern is analyzed and a topology reconfiguration algorithm is employed to find an optimal configuration. The results demonstrate the benefit of having an electrically re-configurable array topology. Results were generated in a SPICE simulator using synthetic and real data obtained from the APS experimental PV array facility.

Embedding Android signal processing apps in a high school math class — An RET project

The objective of this project is to develop and design mobile content for introducing engineering technology to high school students. More specifically, we intend to work on a sequence of modules that will establish connections between high school mathematics and physics to modern technologies associated with smart phones, iPods and other high-tech products. The participants of the project will use the previously developed AJDSP (for Android devices) and iJDSP (for iPhones and iPads) apps to facilitate this process. Additionally, modules have been developed that have been embedded in math classes. Anticipated benefits of the project include creating positive attitudes towards STEM areas that will help recruit high school students and minorities in engineering, math and science fields. After an initial pilot study and assessments at CDS High School, these activities will be disseminated to other high schools. In order to obtain feedback from high school students and teachers, we will hold workshops and collect assessment results. These results will also provide us assessments about the effectiveness of the project, and allow us to make modifications to the project as necessary. The project is part of TUES Phase 3 and I/UCRC RET activities.

Optical flow for compressive sensing video reconstruction

Although considerable effort has been devoted to the problem of reconstructing compressively sensed video, no existing algorithm achieves results comparable to commonly available video compression methods such as H.264. One possible avenue for improving compressively sensed video reconstruction is the use of optical flow information. Current efforts reported in the literature have not fully utilized optical flow information, instead focusing on limited cases such as stationary backgrounds with sparse foreground motion. In this paper, a reconstruction method is presented which fully utilizes optical flow information to increase the quality of reconstruction. The special cases of known image motion and constant global image motion are presented, and the performance of the algorithm on existing datasets is evaluated.

An 18 kW solar array research facility for fault detection experiments

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.

Direct classification from compressively sensed images via deep Boltzmann machine

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 approach achieves a 1.21% test data error rate at a sensing rate of 0.4, compared to a 0.99% error rate for non-compressive data.

Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms

System size estimation in distributed wireless sensor networks is important in various applications such as network management and maintenance. One popular method for system size estimation is to use distributed consensus algorithms with randomly generated initial values at nodes. In this paper, the performance of such methods is studied and Fisher information and Cramer-Rao bounds (CRBs) for different consensus algorithms are derived. Errors caused by communication noise and lack of convergence is also considered, and their effect on Fisher information and CRB is given. The results provide a lower bound on the variance of the estimator of system size. This in turn, provides guidelines on how to choose consensus algorithms and initial values at the nodes.

Irradiance estimation for a smart PV array

Electrical mismatch between modules in a PV array due to partial shading causes energy losses beyond the shaded module. This occurs because unshaded modules are forced to operate away from their maximum power point in order to compensate for the shading. Here we present an irradiance estimation algorithm for use in a mismatch mitigation system. Irradiance is estimated using measurements of module voltage, current, and back surface temperature. These estimates may be used to optimize an arrays electrical configuration and reduce the mismatch losses caused by partial shading. Propagation of error in the estimation is examined; we find that accuracy is sufficient for use in the proposed mismatch mitigation application.

Direct tracking from compressive imagers: A proof of concept

The compressive sensing paradigm holds promise for more cost-effective imaging outside of the visible range, particularly in infrared wavelengths. However, the process of reconstructing compressively sensed images remains computationally expensive. The proof-of-concept tracker described here uses a particle filter with a likelihood update based on a “smashed filter” which estimates correlation directly, avoiding the reconstruction step. This approach leads to increased noise in correlation estimates, but by implementing the track-before-detect concept in the particle filter, tracker convergence may still be achieved with reasonable sensing rates. The tracker has been successfully tested on sequences of moving cars in the PETS2000 dataset.

A COMPREHENSIVE MONITORING SYSTEM FOR PHOTOVOLTAIC ARRAYS

With increased adoption of renewable energy, more effective array management strategies are needed. Many arrays are located in remote locations, where faults within the array often go unnoticed and unattended for large periods of time. Technicians sent to rectify the faults must spend a large amount of time determining the location of the fault manually. Automated monitoring systems are needed to obtain the information about the performance of the array and detect faults. Such systems must monitor the DC side of the array in addition to the AC side to identify non catastrophic faults. In this paper, a prototype comprehensive monitoring system is presented for management of utilityscale PV installations. The system continuously monitors and logs the performance and condition of the array and provides a variety of data visualization and reporting functionality to the facility operator.