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In this article, we explore the possibility of using spread spectrum time domain reflectometry (SSTDR) for detecting disconnections in a large-scale photovoltaic (PV) array. We discuss the importance, role, and trade-offs of SSTDR resolution, frequency, and attenuation in detecting disconnects in the system. Our results show that if the proper system parameters are chosen, disconnections can be detected in a 1-kV system consisting of twenty-six 60-cell PV panels and located within 1.52 m for the first 22 modules.

Spread spectrum time domain reflectometry (SSTDR) is a non-intrusive method for electrical fault detection and localization that enables continuous monitoring of live electrical systems. Electrical faults create changes in impedance that create subsequent changes in the SSTDR reflection response. These changes in reflection response can be detected only if the changes are outside the window of uncertainty of the SSTDR measurement. In this paper, we establish a method of determining this window of uncertainty and the associated minimum-detectable change in impedance for SSTDR measurements. We demonstrate this for a photovoltaic (PV) systems, although the methods could be similarly applied to other applications. We assess the variability in SSTDR measurements caused by changes in the PV system that are representative of normal maintenance actions such as disconnecting/reconnecting a connector and completely breaking-down/setting-up the entire system. We evaluate how this variability translates to a minimum-detectable change in impedance and how that relates to common faults in PV systems (arc and ground faults, shading, damaged cells, and aging). We also describe methods of increasing SSTDR fidelity to accurately extract minor changes in impedance and therefore, detect small-magnitude electrical faults.

This article explains the use of supervised and unsupervised dictionary learning approaches on spread spectrum time domain (SSTDR) data to detect and locate disconnections in a PV array consisting of five panels. The aim is to decompose an SSTDR reflection signature into different components where each component has a physical interpretation, such as noise, environmental effects, and faults. In the unsupervised dictionary learning approach, the decomposed components are inspected to detect and localize faults. The maximum difference between actual and predicted location of the fault is 0.44 m on a system with five panels connected to an SSTDR box with a leader cable of 59.13 m and total length of 67.36 m including the effective length of the panels. In the supervised dictionary learning approach, the dictionary components are used to classify the SSTDR data to their respective fault types. Our results show a 97% accuracy using the supervised learning approach.

Heterogeneous multicore systems can help adherence to design goals by providing a diverse set of hardware components to meet application requirements. Each core may also have tunable hardware that can reconfigured for different applications. However, scheduling becomes difficult in the presence of tunable hardware due to the additional constraint that an application must be scheduled to a core that offers the best configuration, based on the application’s requirements, to maximize the benefit of the tunable hardware. The scheduling decision can be made by exploring and evaluating each hardware configuration in the design space exhaustively or through the use of a heuristic to evaluate a subset of the design space. However, exhaustive and heuristic approaches do not scale well with the number of available hardware configurations. To improve upon exhaustive and heuristic approaches, in this paper, we present a dynamic scheduling methodology that uses machine learning to schedule applications to the application’s respective best core for reduced energy consumption for a system with configurable caches. We use an artificial neural network (ANN) to train our predictive model, using hardware counters, to predict the best core and a tuning heuristic to determine best configuration on non-best cores. If the best core is busy, our scheduler considers alternative idle cores, or the application is stalled depending on which decision is energy advantageous. Our experiments show that our proposed system can achieve a total energy savings of approximately 33% and 2.5% compared to a fixed-cache, base system when using a configurable instruction and data cache, respectively, while remaining competitive and exhibiting better scaling properties compared to systems adopting a heuristic approach.

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm.

Sequence time-domain reflectometry (STDR) and spread spectrum time-domain reflectometry (SSTDR) detect, locate, and diagnose faults in live (energized) electrical systems. In this paper, we survey the present SSTDR literature for discussions on theory, algorithms used in its analysis, and its more prominent implementations and applications. Our review includes both scientific litera-ture and selected patents. We also discuss future applications of SSTDR.

Heterogeneous multicore systems help meet design goals by using disparate hardware components that are suitable for different application requirements/design goals. The individual cores may also have different tunable hardware parameters for additional specialization. However, this complicates scheduling since to reap the benefits of specialization, applications should be scheduled to the core that offers the best configuration based on the application’s requirements and design goals. This scheduling decision could be made by exploring the design space to evaluate different configurations to determine the best configuration, or by executing the application in a base configuration to gather execution statistics to predict the best configuration. However, given increasingly complex systems, these methods may be infeasible given extremely large design spaces or difficulty in choosing a representative base configuration. In this paper, we present a dynamic scheduling methodology that uses predictive methods to schedule applications to best configurations for reduced energy consumption for a system with configurable caches. We use an artificial neural network (ANN) to train our predictive model using hardware counters. The trained ANN can then be used to predict the best core and a tuning heuristic explores the design space to determine the best configuration on non-best cores. If the best core is busy, our scheduler considers alternative idle cores or the application is stalled depending on which decision is energy advantageous. Our experiments show that system energy can be reduced by 28% on average as compared to a fixed-core system where all cores offer the same configuration.