Enhancement of transient stability limit and voltage regulation with dynamic loads using robust excitation control

In stressed power systems with large induction machine component, there exist undamped electromechanical modes and unstable montonic voltage modes. This article proposes a sequential design of an excitation controller and a power system stabiliser (PSS) to stabilise the system. The operating region, with induction machines in stressed power systems, is often not captured using a linearisation around an operating point, and to alleviate this situation a robust controller is designed which guaruntees stable operation in a large region of operation. A minimax linear quadratic Gaussian design is used for the design of the supplementary control to automatic voltage regulators, and a classical PSS structure is used to damp electromechanical oscillations. The novelty of this work is in proposing a method to capture the unmodelled nonlinear dynamics as uncertainty in the design of the robust controller. Tight bounds on the uncertainty are obtained using this method which enables high-performance controllers. An IEEE benchmark test system has been used to demonstrate the performance of the designed controller

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyse microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods cannot be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes) to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.

Size and power properties of structural break unit root tests

In this article, we compare the small sample size and power properties of a newly developed endogenous structural break unit root test of Narayan and Popp (NP, 2010) with the existing two break unit root tests, namely the Lumsdaine and Papell (LP, 1997) and the Lee and Strazicich (LS, 2003) tests. In contrast to the widely used LP and LS tests, the NP test chooses the break date by maximizing the significance of the break dummy coefficient. Using Monte Carlo simulations, we show that the NP test has better size and high power, and identifies the structural breaks accurately. Power and size comparisons of the NP test with the LP and LS tests reveal that the NP test is significantly superior.

Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation

This paper presents a comparative study of three algorithms for learning artificial neural network. As neural estimator, back-propagation (BP) algorithm, uncorrelated real time recurrent learning (URTRL) algorithm and correlated real time recurrent learning (CRTRL) algorithm are used in the present work to learn the artificial neural network (ANN). The approach proposed here is based on the flux estimation of high performance induction motor drives. Simulation of the drive system was carried out to study the performance of the motor drive. It is observed that the proposed CRTRL algorithm based methodology provides better performance than the BP and URTRL algorithm based technique. The proposed method can be used for accurate measurement of the rotor flux.

A novel distributed algorithm for power loss minimizing in Smart Grid

Because power generation of renewable resources are unstable and demands of the customers are time-varying, the supply power and demands of the customers are always unequal. To meet the demands of the customers, power is transmitted from primary power generation to secondary power generation. It will cause high power loss. To solve this problem, a distributed algorithm is proposed in this paper. By using the algorithm, the micro-grids are able to exchange power with their neighbors so as to minimize the total power losses of the smart grid. Moreover, communication overhead (bandwidth) is reduced, comparing with centralized algorithm. Through computer simulations, we demonstrate that the proposed algorithm can lead to near-optimal result for alleviating the average power loss per micro-grid and reduce the communication overhead significantly in contrast with the centralized approach.

Synthesis of a porous sheet-like V₂O₅-CNT nanocomposite using an ice-templating 'bricks-and-mortar' assembly approach as a high-capacity, long cyclelife cathode material for lithium-ion batteries

Tailoring the nanostructures of electrode materials is an effective way to enhance their electrochemical performance for energy storage. Herein, an ice-templating "bricks-and-mortar" assembly approach is reported to make ribbon-like V2O5 nanoparticles and CNTs integrated into a two-dimensional (2D) porous sheet-like V2O5-CNT nanocomposite. The obtained sheet-like V2O5-CNT nanocomposite possesses unique structural characteristics, including a hierarchical porous structure, 2D morphology, large specific surface area and internal conducting networks, which lead to superior electrochemical performances in terms of long-term cyclability and significantly enhanced rate capability when used as a cathode material for LIBs. The sheet-like V2O5-CNT nanocomposite can charge/discharge at high rates of 5C, 10C and 20C, with discharge capacities of approximately 240 mA h g-1, 180 mA h g-1, and 160 mA h g-1, respectively. It also retains 71% of the initial discharge capacity after 300 cycles at a high rate of 5C, with only 0.097% capacity loss per cycle. The rate capability and cycling performance of the sheet-like V2O5-CNT nanocomposite are significantly better than those of commercial V2O5 and most of the reported V2O5 nanocomposite.

Power conversion technology in the smart grid and EV

The chapter presents a dynamical model and power conversion technology of electric vehicles (EVs) used in smart grids. The efficient power conversion of EVs in smart grids depends on the operation of bi-directional converters as these EVs need to be either charged or discharged. In this chapter, the mathematical model of a bi-directional converter used in EVs is developed and a nonlinear controller is designed to facilitate the power conversion in the smart grid environments. Since the power conversion of EVs in smart grids requires the communication, a nonlinear partial feedback linearising distributed controller based on the communication with different EVs is proposed to ensure high power quality and system stability.

Minimizing energy consumption of yarn winding in ring spinning

Ring spinning has been and will continue to be an important system for making staple yarns from different fibers in the textile industry. But high power consumption and low productivity remain the two outstanding problems with ring spinning. Based on an analysis of power distribution during yarn winding in ring spinning, models for the ratio of energy consumption to yarn production over a full yarn package are established. Spindle speed, yarn count, and package diameter are the three key parameters affecting this ratio. The effects on energy consumption of these parameters are discussed through a case study. The energy-to-production ratio increases with increased package diameter but decreases with increased spindle speed and/or yarn count (tex). The results will help guide spinners in minimizing energy consumption in ring spinning.

Internal auditor perceptions on audit committee interactions : a qualitative study in Malaysian public corporations

Audit committees (AC) and the internal audit function (IAF) are two key corporate governance mechanisms. The primary objective of this study is to provide insights into internal auditors' perceptions of their interactions with AC members in Malaysia. The paper thus contributes to the extant literature by providing additional evidence from a South-east Asian country which is recognised as being characterised with a high power distance culture and a developing capital market. The findings are based on in-depth interviews of the heads of the internal audit function (HIAFs) from 11 publicly listed companies. The results indicate infrequent informal communications and limited private meetings between the HIAFs and ACs, and a need for clear reporting lines. Further, ACs are seen to be held in high esteem for their authority and are expected to take on greater leadership in the inquiry of management's decision-making. These findings highlight the importance of the leadership role of ACs in supporting the IAF.

Who will be an entrepreneur? How cultural mechanisms and social network structure together influence entrepreneurial participation

Using data collected from 35 countries over five years, this study provides an investigation of the combined influence of cultural factors and social network structure on whether or not an individual, anywhere in the world, becomes an entrepreneur. Results show that knowing someone who has started a business recently, across the world, has a significant impact on entrepreneurship participation. Regarding the potential cultural influences, it seems that importance attached to personally knowing entrepreneurs differs significantly between individuals operating in different cultures. In cultures with high power distance, personally knowing a person who recently started a business is relatively less important as a driver of entrepreneurship participation compared to cultures with low power distance. On the other hand, in cultures where the Hofstede’s ‘masculinity’construct predominates, it is more important than in cultures characterised by ‘femininity’.

Seventy-year-old habitual volleyball players have larger tibial cross-sectional area and may be differentiated from their age-matched peers by the osteogenic index in dynamic performance

The osteogenicity of a given exercise may be estimated by calculating an osteogenic index (OI) consisting of magnitude and rate of strain. Volleyball involves repetitive jumping and requires high power output and thus may be expected to be beneficial to bone and performance. The purpose of the present study was to examine if habitual volleyball playing is reflected in OI. Ten elderly habitual volleyball players [age 69.9 (SD 4.4) years] and ten matched controls volunteered [age 69.7 (4.2) years] as subjects. Distal tibia (d), tibial mid-shaft (50) and femoral neck (F) bone characteristics were measured using pQCT and DXA. To estimate skeletal rigidity, cross-sectional area (ToA50), and compressive (BSId) and bending strength indices (SSImax50) were calculated. Maximal performance was assessed with eccentric ankle plantar flexion, isometric leg press and countermovement jump (CMJ). A fast Fourier transform (FFT) was calculated from the acceleration of the center of mass during the CMJ. Maximal acceleration (MAG) and mean magnitude frequency (MMF) were selected to represent the constituents of OI. OI was calculated as the sum of the products of magnitudes and corresponding frequencies. Volleyball players had 7% larger ToA50 and 37% higher power in CMJ, 15% higher MAG and 36% higher OI (P B 0.047) than the matched controls. No difference was observed in leg press, plantar flexion or the MMF (P C 0.646). In conclusion, habitual volleyball players may be differentiated from their matched peers by their dynamic jumping performance, and the differences are reflected in the magnitude but not rate of loading.