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Most of the wind turbines recently used are of PMSG technology due to its high power efficiency, low cost, high power density and its direct power ejection. The technology comes usually in a lower voltage level (less than 1KV) that is not compatible to the low loss medium voltage distribution girds, another way of upgrading the voltage level is through use of step up transformers which adds to cost and loss. This paper suggests a series connection to raise the voltage level for compatibility through modular multilevel converter (MMC) inverter. As with a regular wind energy inverters (WEI), this inverter can be placed between the wind turbine(s) and the grid. The function of the proposed inverter is to convert dc power coming from the dc link (from dc collection point made of series connected wind turbines) to a suitable AC power for the main grid, and to fix the power factor (PF) of the grid at a target PF during normal operation and dc fault contingencies. This is achieved by controlling enough reactive power to the grid. The MMC inverter is for the permanent magnet wind generators connected through a buck converter that not regulates but tracks the turbines fluctuating voltages. It also serves as a bypass control in case of an open circuit from any of the connected wind turbines. The PMSGs have optimized power tracking for each independent variable voltages which depend on individual wind speeds for maximum power extraction. This proposed MMC interface has a d.c linkadjustable voltage level control that has served as part of the MMC inverter. From the results, this control has maintained the inverter ac output voltage and upgraded the power factor despite the voltage variations. The control is achieved by STATCOM constant dc-modulation index control for variable dc voltage from different wind speeds or failure contingencies. complex. Below are the three basic multilevel inverter topologies[2] [3] 1. Diode-Clamped (DC) Topology 2. Flying Capacitor (FC) topology 3. Cascaded H-bridge (CHB) topology 978-1-4799-8598-2/15/$31.00 ©2015 IEEE

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The past few years have witnessed a rapid growth in the number and variety of applications of fuzzy logic (FL). FL techniques have been used in image-understanding applications such as detection of edges, feature extraction, classification, and clustering. Fuzzy logic poses the ability to mimic the human mind to effectively employ modes of reasoning that are approximate rather than exact. In traditional hard computing, decisions or actions are based on precision, certainty, and vigor. Precision and certainty carry a cost. In soft computing, tolerance and impression are explored in decision making. The exploration of the tolerance for imprecision and uncertainty underlies the remarkable human ability to understand distorted speech, decipher sloppy handwriting, comprehend nuances of natural language, summarize text, and recognize and classify images. With FL, we can specify mapping rules in terms of words rather than numbers. Computing with the words explores imprecision and tolerance. Another basic concept in FL is the fuzzy if-then rule. Although rule-based systems have a long history of use in artificial intelligence, what is missing in such systems is machinery for dealing with fuzzy consequents or fuzzy antecedents. In most applications, an FL solution is a translation of a human solution.Thirdly, FL can model nonlinear functions of arbitrary complexity to a desired degree of accuracy. FL is a convenient way to map an input space to an output space. FL is one of the tools used to model a multiinput, multioutput system. Soft computing includes fuzzy logic, neural networks, probabilistic reasoning, and genetic algorithms. Today, techniques or a combination of techniques from all these areas are used to design an intelligence system. Neural networks provide algorithms for learning, classification, and optimization, whereas fuzzy logic deals with issues such as forming impressions and reasoning on a semantic or linguistic level. Probabilistic reasoning deals with uncertainty. Although there are substantial areas of overlap between neural networks, FL, and probabilistic reasoning, 3.fm Page 61 Monday,

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