Pulsed plasma treatment for NOx reduction from filtered/unfiltered stationary diesel engine exhaust

A detailed study on the removal of oxides of nitrogen (NOx) from the filtered/unfiltered exhaust of a stationary diesel engine was carried out using non-thermal plasma (pulsed electrical discharge plasma) process and cascaded processes namely plasma- adsorbent and plasma-catalyst processes. The superior performance of discharge plasma with regard to NOx removal, energy consumption and formation of by-products in unfiltered exhaust environment is identified. In the cascaded plasma-adsorbent process, the plasma was cascaded with adsorbents (MS13X/Activated alumina/Activated charcoal). The cascaded process treating unfiltered exhaust exhibits a very high NOx removal compared to the individual processes and further, the cascaded process gives almost the same NOx removal efficiency irrespective of type of adsorbent used. In the cascaded plasma- catalyst process, the plasma was cascaded with activated alumina catalyst at high temperature. The synergy effect and improved performance of the cascaded process are explained. Further, experiments were conducted at room temperature as well as at higher temperatures.

Corona treatment for NOx reduction from stationary diesel engine exhaust: Impact of nature of energization and exhaust composition

A detailed study on the removal of oxides of nitrogen (NOx) from the exhaust of a stationary diesel engine was carried out using non-thermal plasma (dielectric barrier discharge) process. The objective of the study was to explore the effect of different voltage energizations and exhaust composition on the NOx removal process. Three types of voltage energizations, namely AC, DC and Pulse were examined. Due to the ease of generation of high voltage AC/DC electrical discharges from automobile/Vehicular battery supply for possible retrofitting in exhaust cleaning circuit, it was found relevant to investigate individual energisation cases in detail for NOx removal. AC and Pulse energisations exhibit a superior NOx removal efficiency compared to DC energisation. However,Pulse energisation is found to be more energy efficient. Experiments were further carried out with filtered/ unfiltered (raw) exhaust under pulse energisations. The results were discussed with regard to NOx removal, energy consumption and formation of by-products.

New Insights into Optimal Discrete Rate Adaptation for Average Power Constrained Single and Multi-Node Systems

The throughput-optimal discrete-rate adaptation policy, when nodes are subject to constraints on the average power and bit error rate, is governed by a power control parameter, for which a closed-form characterization has remained an open problem. The parameter is essential in determining the rate adaptation thresholds and the transmit rate and power at any time, and ensuring adherence to the power constraint. We derive novel insightful bounds and approximations that characterize the power control parameter and the throughput in closed-form. The results are comprehensive as they apply to the general class of Nakagami-m (m >= 1) fading channels, which includes Rayleigh fading, uncoded and coded modulation, and single and multi-node systems with selection. The results are appealing as they are provably tight in the asymptotic large average power regime, and are designed and verified to be accurate even for smaller average powers.

A Laboratory Analysis of Plasma Based Hybrid Techniques for Treating Engine Exhaust

A detailed study on the removal of pollutants (NOx, aldehydes and CO) from the exhaust of a stationary diesel engine is carried out using barrier discharge hybrid plasma techniques. The objective of the study is to make a comparative analysis. For this purpose, the exhaust treatment was carried out in two stages. In the first stage, the exhaust was treated with plasma process and plasma-adsorbent hybrid process. The effectiveness of the two processes with regard to NOx removal and by-product reduction was discussed. In the second stage, the exhaust was treated by plasma and plasma-catalyst hybrid process. The effectiveness of the two processes with regard to pollutants (NOx, CO) removal and by-product reduction was analyzed. Finally, a comprehensive comparison of different techniques has been made and feasible plasma based hybrid techniques for stationary and non-stationary engine exhaust treatments were proposed.

Treatment of NOx from Diesel Engine Exhaust by Dielectric Barrier Discharge Method

This paper reports improved performance of advantages when compared to its counterpart as it is cost discharge plasma in filtered engine exhaust treatment. Our effective, low capital and operation costs, salable by- paper deals about the removal of NOX emissions from the diesel products, and integration with the existing systems. In this exhaust by electric discharge plasma. For the treatment of diesel paper we describe an alternate reactor geometry referred to exhaust a new type of reactor referred to as cross-flow dielectric as cross-flow DBD reactor, where the exhaust gas flow barrier discharge reactor has been used, where the gas flow is perpendicular to the wire-cylinder reaction chamber. This perpendicular to the corona electrode. Experiments were reactor is used to treat the actual exhaust of a 3.75 kW diesel- conducted at different flow rates ranging from 2 l/min to 10 l/ generator set. The main emphasis is laid on the NOX treatment min. The discharge plasma assisted barrier discharge reactor of diesel engine exhaust. Experiments were carried out at has shown promising results in NOX removal at high flow rates.

Improved algorithm for optimum reactive power allocation

An improvised algorithm is presented for optimal VAr allocation in a large power system using a linear programming technique. The proposed method requires less computer memory than those algorithms currently available.

Methodology for alleviation of voltage excursions in large power systems

This paper presents a method for minimizing the sum of the square of voltage deviations by a least-square minimization technique, and thus improving the voltage profile in a given system by adjusting control variables, such as tap position of transformers, reactive power injection of VAR sources and generator excitations. The control variables and dependent variables are related by a matrix J whose elements are computed as the sensitivity matrix. Linear programming is used to calculate voltage increments that minimize transmission losses. The active and reactive power optimization sub-problems are solved separately taking advantage of the loose coupling between the two problems. The proposed algorithm is applied to IEEE 14-and 30-bus systems and numerical results are presented. The method is computationally fast and promises to be suitable for implementation in real-time dispatch centres.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

A robust three phase power flow algorithm for radial distribution systems

An efficient load flow solution technique is required as a part of the distribution automation (DA) system for taking various control and operations decisions. This paper presents an efficient and robust three phase power flow algorithm for application to radial distribution networks. This method exploits the radial nature of the network and uses forward and backward propagation to calculate branch currents and node voltages. The proposed method has been tested to analyse several practical distribution networks of various voltage levels and also having high R/X ratio. The results for a practical distribution feeder are presented for illustration purposes. The application of the proposed method is also extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS