Electrical Engineering

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This paper presents a comparative performance evaluation of SVS auxiliary controllers for transient stability enhancement of long transmission lines. A new controller, namely, the Combined Reactive Power and Frequency (C.R.P.F.) auxiliary controller, has been developed for the SVS located at the middle of the transmission line. The application of auxiliary controllers considerably enhances the system damping, and unstable system modes are stabilized. A digital computer simulation study has been performed to illustrate the comparative effectiveness of the auxiliary controllers under disturbance conditions, and the superiority of the C.R.P.F. auxiliary controller has been established.

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(SoE) School of Engineering

International Journal of Power and Energy Systems

Application of SVS auxiliary controllers for power system control

A model has been developed that can predict the corrosion rate in horizontal slug flows. The effect of the slug frequency and oil type on corrosion rate have been included. The model has been compared to experimental data, and, to the model and field data of Gunaltun (1996). For all conditions, the corrosion rate increased with increase in slug frequency until a maximum in corrosion rate is reached at approximately 35 slugs/minute. At 60 C, the model compares well with that of Gunaltun (1996) if a slug frequency of 10 to 12 is used. For SO C, the Gunaltun model is in good agreement if a frequency of 1 slug/minute is used. His model does include a term that predicts a maximum in the corrosion rate between 60 and 80 C. This has not been noticed in this laboratory for slug flows. For horizontal pipelines, field data suggests that, the slug frequency is usually in the range of 1 to 20 slugs/minute, depending on the liquid velocity. When the pipe is inclined, the slug frequency can increase to values much greater than these and this may lead to higher levels of corrosion. The oil type is accounted for using the suggestion of Efird (1989) based on the product of oil acid number and % nitrogen. When this relation is used, the results compare very well with those of Efird for the oils he studied. © 1997 by NACE International.

NACE - International Corrosion Conference Series

Model for sweet corrosion in horizontal multiphase slug flow

Owing to frequent black-outs world wide, voltage collapse has received much attention in the electric utilities industry. This paper presents an artificial neural network based method for on-line voltage collapse margin estimation. Homotopy Continuation based Newton-Raphson method is used to drive system operating point to knee of nose curve. The distance of operating point from critical point, measured in terms of system loading may be regarded as margin to voltage collapse. This paper utilizes Kohonen classifier to estimate margin so that computational efforts are reduced compared to conventional methods. Also there is ample of saving in training time compared to error back propagation for parameter estimation. Kohonen neural network classifier transforms input patterns into neurons on the 2-dimensional grid. Power system conditions are assigned to neurons on the grid based on self-organized feature mapping. Finally these neurons are allocated voltage collapse margins corresponding to their system conditions representation. The effectiveness of the proposed technique is tested on sample systems. © 1997 Taylor & Francis.

Electric Machines and Power Systems

Kohonen neural network classifier for voltage collapse margin estimation

With the growing stress on today's power system, it is operated much closer to its stability limit. Under such circumstances it is highly desirable that one must be able to assess the security and stability of the electric power system when exposed to disturbances/faults. In the post-fault transient analysis of interconnected systems, the transient energy margin which is a complex function of prefault system conditions, structure of fault (type and location) and network topology at the specified fault clearing time gives a quantitative idea about the stability of the system. High adaptation capabilities of artificial neural networks make them capable of synthesizing the complex mapping that transform the input features in to a single-valued space of energy margin. Appropriate input feature selection has a direct bearing on the consistency and accuracy of mapping. This issue has been addressed in the present paper by comparing the prediction results based on approaches (Sobajic and Pao, 1989), (Sobajic and Pao 1992), (Jeyasurya, 1993) in the time domain, energy domain and its corresponding time domain calibration. Subsequent to the above comparison, the much haunting question of whether to train the network in the energy or time domain has been answered satisfactorily. It has been observed that fault clearing time is a key parameter that anticipates the success of possible calibration of energy margin results into the time domain. Test cases for prediction have been collected from many different operating conditions in power systems. Multilayer perceptron model with ADAPTIVE LEARNING ALGORITHM is used to carry out the present studies. © 1997 Taylor & Francis.

Input-features based comparative study of intelligent transient stability assessment

In the present work, an artificial neural network technique is applied to determine the distance from system operating point to voltage collapse. The distance is measured in terms of the existing loading/generating scenario. The critical/bifurcation point is approached in steps while moving along the nose curve. Generation resheduling is done at each step to ensure economical operation of the system. The homotopy continuation-based Newton Raphson load flow method takes care of numerical instabilities associated with the singularity of Jacobian while approaching critical point. Proximity to critical loading of present operating point is a complex function of operating point attributes and loading/generating pattern followed to approach voltage collapse. The high adaptation capabilities of artificial neural networks make it feasible to synthesize the function that maps system state attributes (bus power injections and tap settings of transformers) to distance from, voltage collapse for uniform dispatch strategy. A three-layer (one hidden layer) feed-forward artificial neural network (ANN) is trained to predict the nearness of current operating point to voltage collapse in terms of existing loading condition. It has been demonstrated here that the predicted values are well in tune with their actual ones. The technique is tested on a Ward-Hale 6-bus system and an IEEE 14-bus system.

Ann-based voltage collapse proximity prediction

This paper describes the development of a nonintrusive flow visualization method for the quantitative study of the dynamic velocity and void profiles across the pipe cross section in slug flow. The method utilizes novel digital image analysis and computer graphics techniques. Slug flow in a 75 mm diameter, 10 m long Plexiglass pipe is recorded on video using super-VHS cameras and an audio-visual mixer. Sixty images are obtained every second and these are digitized and analyzed on a SGI' graphics workstation. Detailed information regarding the local velocity and void distribution is obtained from the analysis and is used to generate velocity and void profiles across the pipe at different distances into the slug. Results show that gas is released into the slug in the form of pulses of bubbles resulting in the existence of large, frothy, highly aerated structures in the mixing zone. The hydrodynamic boundary layer is destroyed at the slug front but begins to redevelop in the mixing zone. It becomes fully developed at the end of the mixing zone and the one-seventh power law profile for velocity profile is applicable. At the end of the mixing zone, the gas moves towards the top of the pipe and the void fraction distribution tends to a steady profile. A process dynamic model is developed to predict the variation of the average liquid holdup within the slug. © 1997 Elsevier Science Ltd.

International Journal of Multiphase Flow

Development of digital image analysis techniques for the study of velocity and void profiles in slug flow

In the present paper the coordinated application of the controlled series compensation (CSC) and the SVS auxiliary controllers has been demonstrated for the dynamic and transient performance enhancement of a series compensated long transmission line. SVS controller, known as the combined reactive power and frequency (CRPF) auxiliary controller, has been developed and incorporated in the SVS control system located at the middle of the transmission line. The application of the auxiliary controllers, considerably, enhances the system damping and the unstable system modes are stabilized. To improve the transient performance, the CSC having bang-bang control characteristics has been incorporated. A digital simulation study, using a nonlinear system model, has been performed to illustrate the comparative effectiveness of the CSC and the SVS auxiliary controllers developed.

Journal of the Institution of Engineers (India): Electrical Engineering Division

Application of CSC and SVS auxiliary controllers for flexible ac transmission systems

Electrochemical Noise(ECN) and Electrochemical Impedance Spectroscope (EIS) measurements were made simultaneously in a 75 mm I.D., 10 m long acrylic pipeline using salt-water/carbon dioxide mixtures. Full pipe flow was studied for liquid velocities of 0.5, 0.75, 1.1, 1.5 m/s and slug flow for Froude numbers 4, 6 and 9. Experiments were carried out at a constant pressure of 136 kPa and temperature of 40°C. ECN data were measured with a fast auto zero resistance ammeter. The ECN technique is able to detect changes in flow regime, showing distinct differences between full pipe flow and siug flow. The choice of sampling rate when using ECN is very important. For slug flows, sampling rates as high as 100 Hz are necessary to include most of the transients in the flow. Distinct differences can be seen in the Fast Fourier Transforms where dominant frequencies exist which correspond to possible bubble action in the slug body. EIS can be used to measure corrosion rate in multiphase flows. It does show an increase in the corrosion rate with liquid flow rates for full pipe flow and Froude numbers for stationary slug flow. A simple statistical analysis of ECN response gives a correlation with corrosion rate. These show ECN could be a very powerful tool for determining corrosion rate and corrosion mechanism in multiphase flow. © 1997 by NACE International.

Comparison of ECN and EIS measurement for corrosion monitoring under multiphase flow conditions

The steady state and dynamic performance of a flexible AC transmission system (FACTS) with midpoint located SVS has been presented with and without series compensation. The maximum capacitor of SVS branch in case of line without series compensation, is split into two locations. Two third of it is at the sending end for the purpose of series compensation and one third remains at SVS branch. This has the advantage of reduced ‘δ’ without upsetting greatly the cost economy of equipment. The degree of series compensation thus obtained is used to evaluate the steady state and dynamic performance of series compensated transmission line. The results are then compared over a wide operating range. The line reactive power auxiliary controller and bus frequency auxiliary controller are employed in the SVS control system to stabilize the unstable system modes and the performance of the two auxiliary controllers is also compared over wide operating conditions. It has been established here that such a series capacitor compensated line can give improved static and dynamic performance at a reduced value of δ. © 1996 Taylor & Francis.

A flexible ac transmission system “steady state and dynamic performance analysis”

This paper presents a robust artificial neural network technique to assess the on-line dynamic stability of power system. Efforts have been made to minimize the error in dynamic stability assessment by optimising the size and introducing more sensitive information in training vector. The problem has been attempted with Kohonen's self-organising feature map (SOFM) to classify the states of power system. The main reason which inspired the authors to apply Kohonen's SOFM technique is to avoid local minima that saturate the learning process in back-propagation algorithm. Conventional QR algrithm in conjunction with S-matrix method is used to allocate dynamic stability indices to output neurons. © 1996 Taylor & Francis.