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Power Electronics Converters Applications And Design

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

Investigating the impact of data normalization on classification performance

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

This study provides an approach for hybrid AC/DC microgrids (MGs) planning. The proposed strategy objective is to reduce overall planning expenses, including investment and running costs. This is achieved by selecting each bus type and each feeder type to be DC or AC. The provided model takes into account line power loss and converter efficiency. The planning problem is formulated as a single objective optimization problem, which is implemented in MATLAB software using the Marine Predator Algorithm (MPA). The system used to assess the aforementioned planning model consists of 13-bus network, each including various elements of distributed generators (DGs) and loads such as electric vehicles (EVs) charging units, wind turbine generators (WT), and solar photovoltaic systems (PV). The efficiency of the presented planning model is evaluated by contrasting its output with the traditional AC planning and DC planning solutions. The simulation outcomes demonstrate that the hybrid AC/DC microgrid planning model is efficient and achieves cost savings when compared to the conventional AC and DC planning approaches.

A Planning Model for Hybrid AC/DC Microgrids Using Marine Predator Optimization Technique

Artificial Neural Networks (ANNs) are being advantageously applied for power system problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, two ANNs for Subsynchronous Resonance (SSR) analysis are presented. The designed ANNs measure the possibility of SSR occurrence. The effectiveness of this approach is tested by experimenting it on the first bench mark model proposed by IEEE Task Force on SSR.

ANN for Subsynchronous Resonance Detection

Transient stability improvement of a DFIG-based network-connected wind power plant by control of the flux neutralization current during the voltage drop

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles

An enhanced protection scheme for power transformers integrating alpha plane analysis.

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