Numerical Studies and Optimization of Magnetron with Diffraction Output (MDO) Using Particle-in-Cell Simulations

The first magnetron as a vacuum-tube device, capable of generating microwaves, was invented in 1913. This thesis research focuses on numerical simulation-based analysis of magnetron performance. The particle-in-cell (PIC) based MAGIC software tool has been utilized to study the A6 and the Rising-Sun magnetron structures, and to obtain the optimized geometry for optimizing the device performance. The A6 magnetron is the more traditional structure and has been studied more often. The Rising-Sun geometry, consists of two alternating groups of short and long vanes in angular orientation, and was created to achieve mode stability. The effect of endcaps, changes in lengths of the cathode, the location of cathodes with respect to the anode block, and use of transparent cathodes have been probed to gauge the performance of the A6 magnetron with diffraction output. The simulations have been carried out with different types of endcaps. The results of this thesis research demonstrate peak output power in excess of 1GW, with efficiencies on the order of 66% for magnetic (B)-fields in the range of 0.4T - 0.42T. In addition, particle-in-cell simulations have been performed to provide a numerical evaluation of the efficiency, output power and leakage currents for a 12-cavitiy, Rising-Sun magnetron with diffraction output with transparent cathodes. The results demonstrate peak output power in excess of 2GW, with efficiencies on the order of 68% for B-fields in the 0.42T - 0.46T range. While slightly better performance for longer cathode length has been recorded. The results show the efficiency in excess of 70% and peak output power on the order of 2.1GW for an 18 cm cathode length at 0.45T magnetic field and 400 kV applied voltage. All results of this thesis conform to the definite advantage of having endcaps. Furthermore, the role of secondary electron emission (SEE) on the output performance of the12-cavity, 12-cathodes Rising-Sun magnetron has been probed. The results indicate that the role of secondary emission is not very strong, and leads to a lowering of the device efficiency by only a few percentage points

Distribution market as a ramping aggregator for grid flexibility support

The growing proliferation of microgrids and distributed energy resources in distribution networks has resulted in the development of Distribution Market Operator (DMO). This new entity will facilitate the management of the distributed resources and their interactions with upstream network and the wholesale market. At the same time, DMOs can tap into the flexibility potential of these distributed resources to address many of the challenges that system operators are facing. This paper investigates this opportunity and develops a distribution market scheduling model based on upstream network ramping flexibility requirements. That is, the distribution network will play the role of a flexibility resource in the system, with a relatively large size and potential, to help bulk system operators to address emerging ramping concerns. Numerical simulations demonstrate the effectiveness of the proposed model on when tested on a distribution system with several microgrids.Comment: IEEE PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 16-19 Apr. 201

Capturing Distribution Grid-Integrated Solar Variability and Uncertainty Using Microgrids

The variable nature of the solar generation and the inherent uncertainty in solar generation forecasts are two challenging issues for utility grids, especially as the distribution grid integrated solar generation proliferates. This paper offers to utilize microgrids as local solutions for mitigating these negative drawbacks and helping the utility grid in hosting a higher penetration of solar generation. A microgrid optimal scheduling model based on robust optimization is developed to capture solar generation variability and uncertainty. Numerical simulations on a test feeder indicate the effectiveness of the proposed model.Comment: IEEE Power and Energy Society General Meeting, 201

Machine Learning Applications in Estimating Transformer Loss of Life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201

Leveraging Sensory Data in Estimating Transformer Lifetime

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits.Comment: 2017 North American Power Symposium (NAPS), Morgantown, WV, 17-19 Sep. 201

Application of Microgrids in Supporting the Utility Grid

Distributed renewable energy resources have attracted significant attention in recent years due to the falling cost of the renewable energy technology, extensive federal and state incentives, and the application in improving load-point reliability. This growing proliferation, however, is changing the traditional consumption load curves by adding considerable levels of variability and further challenging the electricity supply-demand balance. In this dissertation, the application of microgrids in effectively capturing the distribution network net load variability, caused primarily by the prosumers, is investigated. Microgrids provide a viable and localized solution to this challenge while removing the need for costly investments by the electric utility on reinforcing the existing electricity infrastructure. A flexibility-oriented microgrid optimal scheduling model is proposed and developed to coordinate the microgrid net load with the aggregated consumers/prosumers net load in the distribution network with a focus on ramping issues and flexibility support of utility grid. The proposed coordination is performed to capture both inter-hour and intra-hour net load variabilities. Furthermore, a microgrid optimal scheduling model is developed to demonstrate microgrid\u27s capability in offering ancillary services to the utility grid. The proposed microgrid optimal scheduling model coordinates the microgrid net load with the aggregated consumers/prosumers net load in its connected distribution feeder to capture both inter-hour and intra-hour net load variations in order to offer different ancillary services to the utility grid. The proposed models are developed through mixed-integer programming. In addition, a robust optimization model is applied to the proposed model in order to consider possible uncertainties in forecasting while supporting the utility grid. The microgrid value of ramping is further determined based on its available reserve using a cost-benefit analysis, which helps the microgrid owners for offering the flexibility support to the utility grid. In addition, a distribution market scheduling model is developed to capture and collect the ramping capability of participating microgrids in the distribution market as to offer it to the upstream network to address emerging ramping issues in the system associated with growing proliferation of variable renewable generation. Moreover, numerical simulations on a test distribution feeder with one microgrid and several consumers and prosumers exhibit the effectiveness of the proposed model

Estimation of water content in a power transformer using moisture dynamic measurement of its oil

Careful monitoring of high voltage equipments and diagnosing the critical conditions before they lead to a disaster are prerequisites of condition-based maintenance. Moisture content in a transformer is regarded as one of the major factors in diagnosing its conditions. It causes many problems for a power transformer including electrical breakdown between either its windings or one winding with neutral, increase in the amount of partial discharge and sundry minor problems. Since paper insulation of a power transformer carries large portion of water content, determining moisture content in this part of the transformer is essential. However, the problem is that the direct measurement of moisture in paper is impossible. Therefore, various methods have been proposed to measure the moisture content in a transformer but each one has its limitations. In this study, an approach is introduced to measure water content in a transformer by analysing the moisture dynamics in oil, tracking its variations and analysis of parameters such as temperature, without necessity of disconnecting the transformer from the power grid

Numerical Assessment of Secondary Electron Emission on the Performance of Rising-Sun Magnetrons With Axial Output

Particle-in-cell simulations are performed to analyze the role of secondary electron emission (SEE) on the efficiency, the output power and the leakage currents of 12-cavity, 12-cathode Rising-Sun magnetrons with diffraction output. The simulation results seem to indicate that the role of SEE would be fairly negligible. Small changes are predicted, linked to deviations in the starting trajectories of secondary electrons following their generation and the lower fraction of electrons in clusters with a synchronized rotational velocity. Overall, a peak power output of about 2.48 GW is predicted at a magnetic field of 0.45 T, with efficiencies as high as 75%. Furthermore, deviations in the output power with SEE are predicted to occur at shorter times, but would not be an issue for pulses greater than 25 ns in duration