Improving Reliability of Synchrophasor Data Gathering Method Using Network Coding Technique

We consider synchrophasor data gathering method over the network of Phasor Measurement Units (PMUs) for Wide Area Measurement System (WAMS) applications. Although this network plays an important role to monitor, protect and control distribution electric grid, the research efforts in efficient data collection method are lacking in the literature. In this paper, we represent a novel data gathering approach using Network Coding technique and develop a mathematic model to predict the PMUs network reliability using our proposed model. It allows the PMU nodes in network to perform linear combination of many packets in order to improve the packet delivering ratio at the collector. We demonstrate our proposed method using a distribution grid test case and evaluate the performance by using Monter Carlo simulation. The numerical results have verified the effectiveness of our proposed method.We consider synchrophasor data gathering method over the network of Phasor Measurement Units (PMUs) for Wide Area Measurement System (WAMS) applications. Although this network plays an important role to monitor, protect and control distribution electric grid, the research efforts in efficient data collection method are lacking in the literature. In this paper, we represent a novel data gathering approach using Network Coding technique and develop a mathematic model to predict the PMUs network reliability using our proposed model. It allows the PMU nodes in network to perform linear combination of many packets in order to improve the packet delivering ratio at the collector. We demonstrate our proposed method using a distribution grid test case and evaluate the performance by using Monter Carlo simulation. The numerical results have verified the effectiveness of our proposed method

Optimization Of Hydrocarbon Ejector Using Computational Fluid Dynamics

Ejector is a powerful emerging thermo-compressor, which is more effective when used with hydrocarbon refrigerants because of its unique thermophysical properties. Therefore, in the present work, a steam ejector model is designed and validated with experimental results to evaluate its accuracy, followed by a detailed comparative study of hydrocarbons and synthetic refrigerants namely pentane, propane, butane, iso-butane, R1234-ze and R1234-yf by computational fluid dynamics and literature Review. The effectiveness of both classes of refrigerants is measured through entrainment ratio, critical backpressure, and thermophysical properties (Literature Review). Pentane was selected as a working fluid since it has comparatively high combination of entrainment ratio and critical back pressure with refrigeration compatible properties. Lastly, the optimized geometry was simulated by varying diameter of constant area zone, nozzle exit position and nozzle expansion angle through Computational Fluid dynamics. The simulation results provide insight into shockwaves, boundary layer separation, vortex formation of ejector flow

Recent Advances and Applications of Fractional-Order Neural Networks

This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed

A Theoretical Approach to Optimize the Pipeline Data Communication in Oil and Gas Remote Locations Using Sky X Technology

Oil, gas, and water distribution networks in remote locations require optimized data transmission from their sources to prevent or detect leakage or improve production flow in their manufacturing units. Remote oil and gas installations frequently encounter substantial obstacles in terms of data connectivity and transfer. Slow data transmission rates, data loss, and decision-making delays can all be caused by a lack of dependable network infrastructure, restricted bandwidth, and severe climatic conditions. The purpose of this research work is to identify critical concerns concerning data communication and data transfer in oil and gas distant areas and to investigate feasible approaches to these challenges. The survey was carried out to gather feedback from oil and gas experts on issues concerning data transmission in remote locations. This study provides a theoretical approach to optimizing data transmission and communication in remote areas using Sky X technology. This study presents a new theoretical method that improves the performance of IP over satellite using the critical aspects of data transmission issues from experts. This technology's contribution can improve the reliability of all users on a satellite network by delivering all features with a successful data transfer rate discreetly. This attempt may also aid oil and gas companies in optimizing data transmission/communication in remote regions

Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision

Received signal strength data of ZigBee technology for on-street environment at 2.4 GHz band and the interruption of vehicle to link quality

ZigBee technique is the common wireless network approaches for many smart indoor management applications such as home energy management or building management system. However, the application of ZigBee for outdoor applications is still not mature since the characteristic of signal strength depends on the outdoor conditions. To solve this issue, the received signal strength index (RSSI) and packet delivery are measured in real outdoor conditions for wireless neighborhood area network (WNAN) planning and optimization. Unfortunately, these important data are usually not publicly available for academic use. In this data article, RSSI data and packet delivery rate of a wireless network based on ZigBee 2.4 GHz are collected at the on-street condition, considering the effect of moving and stopping vehicles. In addition, the transmission ranges at two module location levels are also collected. The data provided in this article will help to estimate the transmission range, avoid the interferences and adjust the power level of ZigBee devices for many outdoor applications