This paper compares a single source boosted bipolar PWM half-bridge inverter with a single source boosted bipolar PWM H-bridge inverter. Using the H-bridge inverter as a benchmark, the paper draws a comparison between the two topologies by evaluating the system performance and providing a comparative analysis. Both inverters are fed by a single DC source and employ a boost converter to maintain constant output voltage level over a variable load range. PSIM simulation software is used to examine the system performance of half-bridge inverter and compares it against its H-bridge counterpart for various performance parameters. Simulations demonstrate that half-bridge inverter is more power efficient and yields lower THD due to its smaller number of switches in the inverter's bridge.
Transmission expansion planning (TEP) is crucial for maintaining the reliable and efficient operation of the power systems, particularly in the face of increasing electricity demand and the integration of renewable energy sources. This paper aims to investigate the application of unconventional high surge impedance loading (HSIL) lines in TEP and presents a comparative analysis of their outcomes against conventional line-based TEP approaches. Starting with a 17-bus 500 kV test system, which can operate well under normal operating condition as well as all single contingency conditions, the objective is to connect a new load located in a new bus, bus #18, to the existing test system via two approaches: using conventional lines and incorporating unconventional HSIL lines. By comparing the number of lines required for the conventional and unconventional approaches, maintaining identical conductor weight per circuit, the effectiveness of unconventional HSIL lines in TEP is evaluated where using only two unconventional HSIL lines is sufficient to connect 1250 MW load demand at bus 18 while three transmission lines are required when using the conventional line. Finally, a thorough economic analysis has been conducted on both TEP scenarios, revealing that implementing unconventional HSIL lines leads to remarkable cost savings and thus can be considered a promising option for TEP studies.
Due to thermal, electrical, mechanical, and chemical stresses, line-post insulators in the power system may degrade over time. The degradation process continuously gets exacerbated by the above-mentioned factors. Therefore, condition monitoring of line insulators must be frequently carried out. Optical cameras are considered the most accurate among existing technologies for detecting such defects. Computer vision techniques aided by optical cameras could automate faulty insulator identification. However, there is a limited size of the training data set obtained from real-world optical camera images. In this paper, we propose a generative approach to creating a massive amount of line-post insulator fault images through Deep Convolutional Generative Adversarial Networks (DCGAN). The additional training data obtained from DCGAN-based approach is shown to improve the accuracy of the insulator fault classification. In the case study, we show that with an increasing number of synthetic images created by DCGAN, the accuracy of the fault classification continuously improves. The ability to classify true faulty insulators has increased from 56% to 94%. The performance of the DCGAN-based approach is also compared with the random oversampling approach. The numerical results suggest that the DCGAN-based approach has the advantage of detection accuracy and a lower false positive rate.
The resilience of microgrids, which is crucial for maintaining a stable and reliable power supply, has become increasingly important in the face of rising energy demands and the growing threat of extreme weather events. Incorporating wave energy systems into the grid infrastructure can diversify energy sources, improve grid stability, and ensure uninterrupted electricity supply even during disruptive events. This paper presents an innovative study on the integration of wave energy into a modified IEEE 33-node distribution network, providing a unique perspective on the impacts and opportunities of harnessing marine renewable energy in distribution microgrids. To understand the impact of wave energy on grid resilience, service restoration is compared under fault conditions with different renewable generation portfolios along the coast of North Carolina. This research underpins the significant potential of wave energy as a reliable and sustainable energy source for future microgrids in coastal or island areas. The case studies revealed that by introducing wave energy, the use of hybrid renewable energy sources, can reduce the value of lost load and contribute to improved network resilience and more effective load-shedding management during faults.
Geomagnetic disturbances (GMDs) threaten the grid through geomagnetically induced currents (GICs), which saturate transformers, causing operational effects such as voltage stability issues, potentially leading to load curtailment and, in extreme cases, grid blackout. GMDs pose a severe threat to system reliability, and it is imperative to model the effects of GMDs in the reliability assessment. Hence, this paper proposes an integrated reliability assessment framework wherein the GMD effects are included by adding a GMD reliability module to the generally accepted reliability assessment framework. The paper addresses the first subprocess in the integrated framework - reliability modeling of GMDs. A way to characterize the GMD storms in the context of reliability analysis is shown by introducing three parameters (TTGMD, GMDT, and GMDC) that model the storms' frequency, duration, and intensity. Over 90 years of historical geomagnetic data was processed, and historical observations for TTGMD, GMDT, and GMDC were obtained. An automatic fitter procedure then fits the historical data to probability distributions culminating in the initial steps for developing a GMD-integrated reliability assessment framework.
