A Parallel Processing Approach to Dynamic Simulations of Combined Transmission and Distribution Systems

Simulating a power system with both transmission and distribution networks modeled in detail is a huge computational challenge. In this paper, a Schur-complement-based domain decomposition algorithm is proposed to provide accurate, detailed dynamic simulations of the combined system. The simulation procedure is accelerated with the use of parallel programming techniques, taking advantage of the parallelization opportunities inherent to domain decomposition algorithms. The proposed algorithm is general, portable and scalable on inexpensive, shared-memory, multi-core machines. A large-scale test system is used for its performance evaluation

Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Rapid advancements in smart grid technologies have brought about the proliferation of intelligent and actuating power system components such as distributed generation, storage, and smart appliance units. Capitalizing fully on the potential benefits of these systems for sustainable and economical power generation, management, and delivery is currently a significant challenge due to issues of scalability, intermittency, and heterogeneity of the associated networks. In particular, vertically integrated and centralized power system management is no longer tractable for optimally coordinating these diverse devices at large scale while also accounting for the underlying complex physical grid constraints. To address these challenges, we propose a hierarchical signal processing framework for optimal power flow management whereby the cyber-physical network relationships of the modern grid are leveraged to enable intelligent decision-making by individual devices based on local constraints and external information. Decentralized and distributed techniques based on convex optimization and game theoretic constructs are employed for information exchanges and decision-making at each tier of the proposed framework. It is shown via theoretical and simulation studies that our technique allows for the seamless integration of power components into the grid with low computational and communication overhead while maintaining optimal, sustainable, and feasible grid operations

Voltage Stability Analysis of Grid-Connected Wind Farms with FACTS: Static and Dynamic Analysis

Recently, analysis of some major blackouts and failures of power system shows that voltage instability problem has been one of the main reasons of these disturbances and networks collapse. In this paper, a systematic approach to voltage stability analysis using various techniques for the IEEE 14-bus case study, is presented. Static analysis is used to analyze the voltage stability of the system under study, whilst the dynamic analysis is used to evaluate the performance of compensators. The static techniques used are Power Flow, V–P curve analysis, and Q–V modal analysis. In this study, Flexible Alternating Current Transmission system (FACTS) devices- namely, Static Synchronous Compensators (STATCOMs) and Static Var Compensators (SVCs) - are used as reactive power compensators, taking into account maintaining the violated voltage magnitudes of the weak buses within the acceptable limits defined in ANSI C84.1. Simulation results validate that both the STATCOMs and the SVCs can be effectively used to enhance the static voltage stability and increasing network loadability margin. Additionally, based on the dynamic analysis results, it has been shown that STATCOMs have superior performance, in dynamic voltage stability enhancement, compared to SVCs

Deep Convolutional Neural Network Architecture for Plant Seedling Classification

Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolution neural networks have been used for our classification framework, namely Efficient Net B2 and Efficient Net B4. The plant seedlings dataset is utilized to investigate the proposed work. The evaluation metrics average accuracy, precision, recall, and F1-score were used. The findings demonstrate that the proposed approach is capable of differentiating between 12 species of a plant seedling dataset which contains 3 crops and 9 weeds. The average classification accuracy and F1 score are 99.00% for our Efficient Net B4 model and 97.00% for the Efficient Net B2. In addition, the proposed Efficient Net-B4 model performance is compared to the one of existing models on the plant seedlings dataset and the results showed that the proposed model Efficient Net B4 has superior performance. We intend to detect diseases in the identified plant species in our future research

Energy Storage Optimization for Grid Reliability

Large scale renewable energy integration is being planned for multiple power grids around the world. To achieve secure and stable grid operations, additional resources/reserves are needed to mitigate the inherent intermittency of renewable energy sources (RES). In this paper, we present formulations to understand the effect of fast storage reserves in improving grid reliability under different cost functions. Our formulations and solution schemes not only aim to minimize imbalance but also maintain state-of-charge (SoC) of storage. In particular, we show that accounting for system response due to inertia and local governor response enables a more realistic quantification of storage requirements for damping net load fluctuations. The storage requirement is significantly lower than values determined when such traditional response are not accounted for. We demonstrate the performance of our designed policies through studies using real data from the Elia TSO in Belgium and BPA agency in the USA. The numerical results enable us to benchmark the marginal effect on reliability due to increasing storage size under different system responses and associated cost functions

Minimal set of generators of controllability space for singular linear dynamical systems

Due to the significant role played by singular systems in the form E ¿ x ( t ) = Ax ( t ) , on mathematical modeling of science and engineering problems; in the last years recent years its interest in the descriptive analysis of its structural and dynamic properties. However, much less effort has been devoted to studying the exact con- trollability by measuring the minimum set of controls needed to direct the entire system E ¿ x ( t ) = Ax ( t ) to any desired state. In this work, we focus the study on obtaining the set of all matrices B with a minimal number of columns, by making the singular system E ¿ x ( t ) = Ax ( t ) + Bu ( t ) controllable.Postprint (author's final draft

Role of Network Topology in the Synchronization of Power Systems

We study synchronization dynamics in networks of coupled oscillators with bimodal distribution of natural frequencies. This setup can be interpreted as a simple model of frequency synchronization dynamics among generators and loads working in a power network. We derive the minimum coupling strength required to ensure global frequency synchronization. This threshold value can be efficiently found by solving a binary optimization problem, even for large networks. In order to validate our procedure, we compare its results with numerical simulations on a realistic network describing the European interconnected high-voltage electricity system, finding a very good agreement. Our synchronization threshold can be used to test the stability of frequency synchronization to link removals. As the threshold value changes only in very few cases when aplied to the European realistic network, we conclude that network is resilient in this regard. Since the threshold calculation depends on the local connectivity, it can also be used to identify critical network partitions acting as synchronization bottlenecks. In our stability experiments we observe that when a link removal triggers a change in the critical partition, its limits tend to converge to national borders. This phenomenon, which can have important consequences to synchronization dynamics in case of cascading failure, signals the influence of the uncomplete topological integration of national power grids at the European scale.Comment: The final publication is available at http://www.epj.org (see http://www.springerlink.com/content/l22k574x25u6q61m/

Genetic variability on leaf morpho-anatomical traits in relation to sterility mosaic disease (SMD) resistance in pigeonpea

Abstract Sterility mosaic disease (SMD) is a major biotic constraint in almost all pigeonpea growing areas caused by eriophyid mite transmitted pigeonpea sterility mosaic virus (PPSMV). Direct selection for resistance to SMD is expensive and laborious as it requires dependent of sick plots. Identification of easily assayable and simply inherited morphological traits such as leaf anatomical traits would enable increased efficiency of breeding pigeonpea for SMD resistance. A set of 70 pigeonpea accessions were evaluated for 12 leaf structural features such as leaf thickness (LT), upper epidermal thickness (UEPT), lower epidermal thickness (LEPT), upper cuticle cell wall complex (UCWC), lower cuticle cell wall complex (LCWC), trichome number on upper surface of leaf (TNUS), trichome number on lower surface of leaf (TNLS), trichome length on upper surface of leaf (TLUS) and on lower surface of leaf (TLLS) at experimental plots of Zonal Agricultural Research Station (ZARS), UAS, Bengaluru. The accessions differed significantly for most of the traits except for specific leaf area (SLA) and specific leaf weight (SLW). The accessions were grouped into four clusters, with significant differences in cluster means and variances. Principal component analysis (PCA) showed first three PCs explaining 69.70 % of the total variation and morpho-anatomical traits such as leaf thickness (LT), trichome length on upper (TLUS) and lower (TLLS) surface of leaf were the most important characters for disease incidence. Furthermore, correlation of all the leaf traits in relation to percent incidence (PDI) indicated only TLLS having significant negative correlation (-0.456*) with SMD incidence. While, trichome length also showed higher phenotypic (PCV) and genotypic (GCV) coefficient of variation 34.33 and 34.02, respectively and broad senesce heritability (98.2%) coupled with high genetic advance (69.45). Therefore, breeding for trichome length is very important to impart vector resistance. This may provide broad based resistance to all the isolates of SMD in pigeonpea