An investigation into the utilization of swarm intellingence for the control of the doubly fed induction generator under the influence of symmetrical and assymmetrical voltage dips.

Doctoral Degree. University of KwaZulu-Natal, Durban.The rapid depletion of fossil, fuels, increase in population, and birth of various industries has put a severe strain on conventional electrical power generation systems. It is because of this, that Wind Energy Conversion Systems has recently come under intense investigation. Among all topologies, the Doubly Fed Induction Generator is the preferred choice, owing to its direct grid connection, and variable speed nature. However, this connection has disadvantages. Wind turbines are generally placed in areas where the national grid is weak. In the case of asymmetrical voltage dips, which is a common occurrence near wind farms, the operation of the DFIG is negatively affected. Further, in the case of symmetrical voltage dips, as in the case of a three-phase short circuit, this direct grid connection poses a severe threat to the health and subsequent operation of the machine. Owing to these risks, there has been various approaches which are utilized to mitigate the effect of such occurrences. Considering asymmetrical voltage dips, symmetrical component theory allows for decomposition and subsequent elimination of negative sequence components. The proportional resonant controller, which introduces an infinite gain at synchronous frequency, is another viable option. When approached with the case of symmetrical voltage dips, the crowbar is an established method to expedite the rate of decay of the rotor current and dc link voltage. However, this requires the DFIG to be disconnected from the grid, which is against the rules of recently grid codes. To overcome such, the Linear Quadratic Regulator may be utilized. As evident, there has been various approaches to these issues. However, they all require obtaining of optimized gain values. Whilst these controllers work well, poor optimization of gain quantities may result in sub-optimal performance of the controllers. This work provides an investigation into the utilization of metaheuristic optimization techniques for these purposes. This research focuses on swarm-intelligence, which have proven to provide good results. Various swarm techniques from across the timeline spectrum, beginning from the well-known Particle Swarm Optimization, to the recently proposed African Vultures Optimization Algorithm, have been applied and analysed

Data Science and AI-Based Optimization in Scientific Programming

International audienceis special issue gives the opportunity to know recent advances in the application of intelligent techniques to data-based optimization problems in scientific programming. Artificial intelligence is today supported for different powerful data science and optimization techniques. For instance, data science commonly relies on AI algorithms to efficiently solve classification, regression, and clustering problems. is fact is particularly interesting nowadays, when big data area gathers strength supplying huge amounts of data from many heterogeneous sources. On the other hand, complex optimization problems that cannot be tackled via traditional mathematical programming techniques are commonly solved with AI-based optimization approaches such as the metaheuristics. ese approaches provide optimal solutions avoiding consumption of many computational resources. Data science and AI-based optimization have also largely been used to solve problems related to scientific programming. Various examples are reported by the literature on task assignment in distributed/parallel systems, knowledge discovery, large-scale data mining, high-performance computing, big data, distributed/parallel search, text analysis/process/classification, and optimization for manufacturing, scheduling, and civil and financial engineering , among others. In this sense, this area provides a wide set of research lines and applications that deserves to be explored. is special issue presents nine original, high-quality articles, clearly focused on theoretical and practical aspects of the interaction between artificial intelligence and data science in scientific programming, including cutting-edge topics about optimization, machine learning, recommender systems, metaheuristics, classification, recognition, and real-world application cases. e first article in this special issue is entitled "Opti-mizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers' Perspective" by Z. Li et al. is article shows a good example of how artificial intelligence algorithms can optimize some parameters involved in problems characterized by data flows. e work elaborates on the advantages of using a three-layer BP neural network algorithm to predict the borrowing limit and interest rate when individuals take advantage of P2P online service to borrow money. is approach provides a novel focus from borrowers to predict and optimize the borrowing limit and interest rate given the limited information. In addition, both parameters are optimized by means of an algorithmic proposal where the neural network and a genetic algorithm work together to solve both single-target and double-target programming optimization problems. e proposal is tested on real-world data to check its goodness as a high-accuracy prediction method. e second article is entitled "Leveragin