Abstract

Recent regulations have demanded that electronics manufacturing companies control emissions from their products and the susceptibility of their products to emissions from other products.  In addition, unexpected product failure and the ever-present demands of technology are also forcing the electronics industry to face the need to maintain electrical integrity.

Our investigations into high-speed design techniques have shown three major causes of failure: emissions from interconnecting conductors; poor PCB layout and lack of technical knowledge in electromagnetic compatibility (EMC).  Catching these kinds of electrical integrity problems early in the design phase allows designers to take timely action without jeopardising project time scales.  The work reported here presents design for manufacturing guidelines and rules to maintain electrical integrity in printed circuit boards (PCBs).  Currently, a common method for handling EMC is through compliance testing of the final product.  Similarly, noise budget is measured on finished prototypes.  Since product life cycles are reducing, dealing with EMC late in the design cycle is undesirable.  The cost of fixing may also be higher at a final stage because only a few options are available to correct the problem.  A ‘find and fix’ approach is no longer acceptable anymore.

More and more companies are facing or will soon be facing EMC and electrical integrity issues.  The majority of analysis tools available today are targeted toward simulation engineers.  Such tools are not easy to use and are dependent on the availability and accuracy of complex simulation models.  Moreover, they also tend to be ineffective on how to correct potential EMC problems.

Abstract

Now- a — days loss reduction and voltage regulation has become a major issue in Radial Distribution systems. Many loss reduction techniques have come into existence. Many distribution companies all over the world employ fixed capacitors. With increase of load that may not be sufficient for required voltage regulation. In such conditions further improvement in voltage and loss reduction can be achieved using distributed generators without removing the capacitors in the system. DG s are very much beneficial in reducing the losses effectively compared to other methods of loss reduction .In this paper optimal capacitors and DG unit placement using fuzzy logic is discussed. The optimal size of the capacitors and DG unit is calculated analytically Using approximate reasoning suitable nodes are determined for capacitor and DG unit placement.  Voltage and power loss reduction indices of distribution system nodes are modeled by fuzzy membership functions. Fuzzy inference system containing a set of rules is used to determine the capacitor and DG unit placement .First capacitor units are placed with the highest suitability index ,over all system performance is observed. Again same procedure is repeated to find the highest suitability index to place the DG unit. Simulation results show  the improvement of  voltage profile and loss reduction with optimal capacitor  unit placement and optimal DG unit placement .

Abstract

This paper investigates the deviations in the effects of different unbalanced phase voltages with same unbalance factor (1%) on the performance of induction motor in terms of Total Harmonic Distortion (THD) and efficiency. Under all voltage conditions, the performance of a 5 HP three-phase squirrel-cage induction motor was measured through a real load test. According to the test results and analysis, particular unbalanced voltage offers slightly lower negative effects on the motor’s performance. The financial losses caused by unbalanced voltage of the same motor are determined. Theoretical results in terms of temperature rise, losses and torque ripple are also presented in wide range of unbalance voltages by using MATLAB SIMULINK for supporting practical studies.

Abstract

Under open access, market driven transactions have become the new independent decision variables defining the behavior of the power system. The possibility of transmission lines getting over-loaded is relatively more under deregulated operation because different parts of the system are owned by separate companies and with different parts operated under varying service charges. This paper discusses a two-tier algorithm for correcting the line over-loads in conjunction with the conventional power-flow methods. The method uses line-flow sensitivities, which are computed through the Fast Decoupled Power-flow algorithm and can be adapted for on-line implementation.

Abstract

This paper present an artificial neural network (ANN) approach for forecasting the electric energy output from a 25-kWp grid connected solar photovoltaic power plant (SPVPP) installed at Vibhuti Khand, Lucknow, Utter Pardesh, India. The main aim is to develop a model of the system using artificial neural network (ANN) that can accurately forecast electrical energy output generated from the grid connected solar PV system. The ANN interpolates among the solar PV generation output and relevant parameters such as average solar insolation, average module temperature and average humidity . In this study, an ANN model is implemented and validated with reasonable accuracy on real electric energy generation output data. The physical layout and salient features of the power plant is also reported. The proposed ANN method can be extended to any solar photovoltaic power plant (SPVPP) for forecasting energy generation.

Abstract

Multi layered neural network (NN) architecture is proposed for compression of high-resolution image, the architecture is implemented on FPGA as it supports reconfigurability. The architecture considered has N-M-N (64-4-64) multi-layered NN structure, which achieves compression ratio of (CR) 93.75. Compression ratio is reconfigurable with change in M. The architecture is generalized and can achieve compression ratios from 2 to 99, which is reconfigurable. Performance of any neural network architecture for compression depends on training; this architecture considers general back propagation training. The training is performed offline, with known set of image samples consisting most of the properties of any standard image. The Mean Square Error (MSE) computed during every iteration of training is scaled and fed back into the network to update the weight matrix at specific points, this reduces the training time. As the weight matrix occupies more space for storage, the redundancies in the weight matrix are exploited and a storage space is created with minimum memory requirement on FPGA. Compression Ratios obtained demonstrate performance superiority of the network as compared with JPEG compression standard. Inserting noise on the compressed sets of data, tests the network performance. The hardware complexity, area requirement, speed is compared and discussed; there is a saving of 22% of space on FPGA, with 40% increase in speed, and reducing the power by 12%. The major advantage of this architecture is its reconfigurability on the architecture size that achieves different compression ratios.

Abstract

In induction machine, there may be different type of faults (LG, LL, LLG, or LLL), which may occur during induction motor operation. Therefore fault analysis of a machine is essential for superior operation of the machine. The effect of line fault (LG and LLG fault) on the speed estimator’s (MRAS) performance is illustrated in this paper. This is the first time in the literature that the author has investigated the performance of the speed estimator (MRAS) during the fault conditions. No such study has been carried out as far as information to the author is available. The line to ground (LG) fault may occur in any of the phase of induction motor. Also the fault may occur during any of the operating region of the motor - transient, steady state, or loaded condition. The speed estimators are investigated when one and/or two of the incoming phases come in direct contact with the ground. Three cases are studied for a speed estimator namely when LG fault occurs during (a) Transient (starting) period (b) Steady-state no-load period (c) Steady-state loading period. A comparative study is made in terms of tracking capability and production of ripples. From the simulation results, it can be concluded that when the LG fault occurs during any of the operating region, the estimators (MRAS) speed response almost follows the actual speed response of the motor showing a good tracking capability. Large ripples are found only in case of open loop scheme and very small ripples are found in case of close loop schemes. For testing a five-phase induction motor has been taken.

Abstract

This paper presents a feeder reconfiguration algorithm for the line loss reduction and feeder load balancing with minimum consumption of time. The proposed algorithm efficiently utilizes a heuristic based fuzzy strategy and constrained fuzzy operation along with back propagation neural network. This approach reduces the computation cost making it suitable for online application. A new network configuration is obtained through the proposed algorithm, which line achieves loss reduction and feeder load balance at the same time. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to a distribution system. The desired switching operations can be fulfilled in a very efficient manner as indicated from the results. The complete algorithm has been framed with strict top-down dependency is proposed to decrease software couplings using C++ object oriented programming language and provides the support for software reusability.

Abstract

This paper deals with a new method of quenching transients of load frequency of a single area power system. The load frequency power system dynamics are represented by selecting deviation in frequency and its derivatives as variables. The validity of this model was compared in terms of its uncontrolled response obtained in the earlier work [1]. This new model representation is used for further studies in this paper. For a practical single area power system the behavior of uncontrolled system with range of values of regulation constant (R) and load disturbance (?PD) are obtained. The responses of controlled system with different switching times (tc) for range of values of the control parameter (?PC), R and ?PD are obtained using fuzzy control methods. The total time of state transfer from zero (before disturbance) to the final steady state i.e. zero after the disturbance are evaluated. The studies reveal that the static errors are increasing if the demand is more for an uncontrolled system and for higher values of regulation constant. It is also observed that when fuzzy control is applied at a predetermined time, the time of state transfer is more with increase of regulation constant. The time of state transfer in general is increasing with increase of load change. While fuzzy control is applied the frequency transients are quenched and the final state is reached at much faster rates without any oscillations.

Abstract

This paper presents artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) based rotor position estimation techniques for switched reluctance motor (SRM) drive system. The techniques estimate rotor position by measuring the three-phase voltages and currents and using magnetic characteristics of the SRM, with the aid of an ANN and ANFIS. The rotor position estimating techniques are used in a high-performance sensor less variable speed SRM drive. The results are compared with the measured values, and the error analyses are given to determine the performance of the developed method. The error analyses have shown great accuracy and successful rotor position estimation techniques for a 6/4 poleswitched reluctance motor using AI techniques.