Control issues of distribution system automation in smart grids

In recent years, the world has been exposed to many developments in different areas esp. computer technology, resulting in computers with high power of processing to be built. Among these devices, intelligent electronic devices (IEDs) have the capability to process considerable volume of data at high speed. Since, real-time data processing is vital in distribution networks as the largest users during their operation, IEDs would be applicable in such systems. In addition to IEDS, communication systems have improved during recent decades, providing the desired conditions for a concept known as distribution system automation (DSA) which has been discussed in this paper. Furthermore, the application of distributed generation (DG) in the context of DSA is addressed. Then, different control schemes have been investigated for DG sources while power quality (PQ) issues with DSA in microgrids are proposed in this paper. Moreover, the global automation standard has been presented and a combined strategy is suggested for demand-side management (DSM)

Intelligent approach on sensorless control of permanent magnet synchronous generator

In this paper, a standalone permanent magnet synchronous generator (PMSG) system is designed to generate power at maximum power point (MPP). The variable speed operation of wind energy conversion system consists of PMSG, controlled rectifier and voltage source inverter co to the load. Proportional integral (PI), sliding mode (SM), and feed forward neural network (FFNN) control strategies are applied in field oriented control (FOC) at generator side converter. A comparative study on power generated at maximum power point (MPP) is done with these controllers using simulation. Hill climb search (HCS) method is applied to attain MPP. Load side inverter control strategy involves the PI and SM controllers in order to maintain the unity power factor and to control the active and reactive power for nonlinear load. The control strategies are modelled and simulated with MATLAB/Simulink. The effectiveness of proposed control method is demonstrated using simulation results

MTD method for better prediction of sea surface temperature

A class of incremental learning procedures known as the Modified Temporal Difference (MTD) method is introduced in this paper for fixed-step prediction problems which uses the functional features of Multilayer Perceptron. The method is applied for weekly prediction of the Sea Surface Temperature (SST) from oceanographic data. Temporal Difference (TD) methods suggest how each output of a temporal sequence must be changed, whereas a back-propagation algorithm decides which part(s) of a network to change in order to influence its output and reduce the overall error. In other words, TD methods and back-propagation address temporal credit and structural credit assignment issues, respectively. While the two methods address different sides of the same issues they are quite compatible and easily combined. A new scheme is formed by combining the advantage of back-propagation and TD methods catering to fixed-step problems and is named as the MTD method. The back-propagation algorithm is modified to propagate the temporal error. For prediction problems, the exponential recency has not been found suitable due to its large negative slope. In this paper a weighing scheme is introduced in which alterations are made to past predictions according to a newly proposed recency factor. The stochastic method, back-propagation algorithm, TD and MTD methods are applied to predict the SST values in the Arabian Sea, the Bay of Bengal and Central Indian Ocean and a comparative study is made. From the study it is observed that the proposed alternative recency factor in the MTD method leads to better prediction than the exponential recency

A Malaysian Vehicle License Plate Localization and Recognition System

Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment