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Induction generators (IG) are widely used in renewable energy systems, such as wind power, and micro/mini hydro-turbines. In applications, such as urban wind-energy systems, electric vehicles and more electric aircrafts, induction machines can be used to supply electrical power to auxiliary loads, deriving power from main engines. In this article, an IG system with open-end stator windings is proposed for simultaneous supply of regulated power to frequency insensitive ac-loads and dc-link loads for the first time. One end of the stator winding is connected to auxiliary loads while the other end is connected to a capacitor-fed voltage source inverter (VSI). In the proposed system, VSI is controlled to supply reactive power required by the IG while simultaneously regulating the dc-link voltage and ac load voltage at required level. The proposed method is based on scalar-control of the induction machine, thus making it a simple and robust system to supply active power demanded by ac and dc loads. The proposed control scheme has been simulated in MATLAB-Simulink and experimentally verified for steady state and transients with various ac loads including linear, nonlinear, and unbalanced loads. [ABSTRACT FROM AUTHOR]

In this paper, the structure, coordinate control method, and parameter design of a hybrid system are proposed for active power injection and nonactive power (reactive, harmonic, and unbalance power) compensation. The proposed hybrid system consists of a static var compensator (SVC) in parallel with a capacitive-coupling grid-connected inverter (CGCI) (SVC//CGCI). In SVC//CGCI, the SVC part is used to dynamic compensate the reactive power and unbalance power, while the low rating CGCI part is used to inject active power, provide harmonic power and a fixed amount of capacitive reactive power. Compared with conventional inductive-coupling grid-connected inverter (IGCI), the CGCI can provide active, reactive and harmonic power with low rating of active inverter part. The cost of the SVC part is much lower than that of active inverter part, thus the reduction of inverter rating can lead to a decrease in the total cost of the SVC//CGCI. Therefore, the SVC//CGCI can be a cost effective solution for active power injection and nonactive power compensation. Finally, simulation and experimental results are provided to show the advantages and validity of the proposed SVC//CGCI in compared with the conventional IGCI and SVC//IGCI. [ABSTRACT FROM AUTHOR]

A new thyristor-controlled reactor scheme is presented in this paper, in which the total bank is split into one Δ- and one Y-connected reactor bank with the addition of a low-rating zig-zag autotransformer, instead of the conventional scheme using a Δ-connected reactor bank with a harmonic current filter. This arrangement prevents the triplen harmonics generated by the Y bank from entering into the supply system and at the same time, the combination results in cancellation of some major characteristic harmonics from the source current. Thus, without using any additional filters or phase-shifting two-winding transformer, this scheme facilitates reactive power control over a wide range and also meets the requisite harmonics standards. The scheme is simple and provides a cost-effective solution to the VAr compensation problem. Simulation and experimental results are provided to validate the proposed concept. [ABSTRACT FROM PUBLISHER]

This paper proposes a probabilistic method to model the uncertainty of reactive power compensation by static VAr compensators (SVCs) in electric arc furnaces (EAFs). The time-varying characteristics of EAF accentuate the voltage fluctuations and produce flicker in power lines as well as neighboring loads. In order to solve this issue, quick and accurate response of SVC within a half-cycle ahead is required. This paper proposes a nonparametric approach based on lower upper bound estimation method to construct prediction intervals (PIs) for the reactive power in EAFs. Due to the nonlinear nature of reactive power signals in EAFs, a set of PIs are produced and combined to find an optimal aggregated PI. The proposed prediction method provides a faster-than-real-time monitoring of SVC, which aims at high speed and efficient reactive power compensation. In order to find the most satisfying PIs with high coverage probability and low average width, an optimization algorithm is developed to improve the training process of neural networks. The appropriate performance of the proposed method is examined on the practical data gathered from the Mobarakeh Steel Company, Iran. [ABSTRACT FROM PUBLISHER]