Subrahmanyam S. Venkata

Distribution system reliability assessment: momentary interruptions and storms

The goal of distribution system reliability assessment is to predict the availability of power at each customers service entrance. Existing methods predict the interruption frequency and duration each customer can expect, but omit two major contributing factors: momentary interruptions and storms. This paper presents methods to determine the impact of each phenomena. These methods are then used to assess the reliability of an existing utility distribution system and to explore the reliability impact of distribution automation.

Modeling and analysis of distribution reliability indices

Assessment of customer power supply reliability is an important part of distribution system operation and planning. Monte Carlo simulations can be used to find the statistical distribution of the reliability indices, along with their mean and standard deviation. The standard deviation of the reliability indices provides distribution engineers with information on the expected range of the annual values. However, the Monte Carlo simulation usually is a time-consuming computation. In this paper, an efficient Monte Carlo simulation method for distribution system reliability assessment is presented. Analysis of outage data from a practical distribution system is performed to determine the failure and repair models appropriate for use in the Monte Carlo simulation. The sensitivity of the reliability indices to the choice of model is presented. Finally, the impact of protection devices on the statistical distribution of System Average Interruption Frequency Index (SAIFI) for a practical distribution feeder is presented.

Distribution system reliability assessment due to lightning storms

Lightning is a significant cause of faults and outages in many electric power systems and is one of the major causes of poor system reliability. Predictive assessment of distribution reliability indices can be used to identify areas that have poor reliability so that appropriate changes in system design can be implemented. The assessment of distribution system performance under lightning conditions requires modeling of storm characteristics and system response. In this paper, a Monte Carlo simulation for evaluating distribution system reliability under lightning storm conditions is presented. The results from a practical distribution system show the importance of detailed modeling of storm characteristics and simulation of the system response in assessing distribution system reliability during lightning storms.

Nonlinear deterministic modeling of highly varying loads

Typically, the modeling of highly varying, nonlinear loads such as electric arc furnaces has involved stochastic techniques. This paper presents the use of chaotic dynamics to describe the operation of nonlinear loads. Included is a discussion of the Lyapunov exponents, a measure of chaotic behavior. The alternate approach is applied to electric arc furnaces. A tuning mode is described to develop the parameters of a chaotic model. This model is trained to have time and frequency responses that are tuned to match the current from the arc furnace under study. The simulated data are compared to actual arc furnace data to validate the model. This model is used to assess the impact of various highly varying nonlinear loads that exhibit chaos in power systems.

Predicting vegetation-related failure rates for overhead distribution feeders

Faults on the electric power distribution system are responsible for a large portion of the interruptions that a customer will experience. To maintain a high level of system reliability, vegetation maintenance is often required. Analytical prediction of the effects of vegetation maintenance on distribution system reliability requires a model of the expected failure rate of line sections that includes the effects of vegetation. Vegetation-related failures are more likely to occur as the vegetation near the overhead power lines grows, increasing the line-section failure rate. Due to difficulties in using existing growth models, this paper proposes to use a direct model for failure-rate prediction based on factors that affect vegetation growth. Four models are considered: linear regression, exponential regression, linear multivariable regression, and an artificial neural network (ANN). The models are tested with historical vegetation growth parameter data and feeder failure rates. Results are compared and the features of each model are discussed.

A Reliability Centered Optimal Visual Inspection Model for Distribution Feeders

For an electric power distribution system, highly reliable operation is important for maintaining customer satisfaction. To maintain high levels of system reliability, inspection is used to identify potential problems, allowing necessary maintenance actions to be taken before failure occurs. The question is then how much inspection is required to maintain high reliability levels. This paper describes a Markov model of the inspection process. The model finds the optimal inspection frequency by considering the tradeoff between the cost of inspection and the cost of poor reliability. An objective function is formulated that minimizes the total cost of inspection, repair, and reliability. This model has been specifically created for determining the optimal visual inspection frequency for distribution feeder rights-of-way. The results from applying this model to a practical distribution system are presented, and extensions of this model to other systems are discussed.

Predicting distribution system performance against regulatory reliability standards

In order to ensure that the changing utility environment does not adversely affect the reliability of electric power supplied to customers, several state regulatory agencies have started to prescribe reliability standards-minimum reliability levels-to be maintained by electric power distribution companies. The standards are based on reliability indexes computed from historical outage data. The reliability indexes vary from year to year because of the statistical variation in the number of customer interruptions and the duration of such interruptions. To be effective, the reliability standards adopted must identify feeders that consistently perform poorly, while being insensitive to those that occasionally have poor reliability. This paper employs a duration based Monte Carlo simulation to explore the predicted impact of various reliability standards on a large practical distribution system. The sensitivity of different standards to differences in system size and component failure rate is also explored.

High Impact Papers in Power Engineering (1900-1999)

was reduced to 39 nominations. The list of 39 papers and books identified were circulated to colleagues, members of the Power Globe, and some students. An informal vote was taken to identify the three papers that seemed to have the highest impact on power engineering practice. Based on the informal vote, recommendation by colleagues, areas of interest, and the opinion of the organizers, four papers were identified as having the greatest impact on the growth of electrical power engineering. Though the process used in identifying these papers is not very scientific, the general consensus seems to be that these papers represent milestone achievements that stimulated great advances that lead to the widespread electrification of the world. Additional details of the “vote” appear in [2].

Power education: Growing the crop of power engineering professionals

july/august 2003 P POWER & ENERGY ENGINEERING education is alive and well, with more activity than ever. This is largely due to the dedicated faculty that has created a strong academic community that is flexible enough to adapt to all the changes in regulatory issues and has recognized the new opportunities that they bring. More importantly, the future is brighter for all of us as power engineering custodians. We should learn from our experiences and do whatever is necessary to give renewed and rejuvenated attention to all facets of power engineering needs, including curriculum design. Current industry leaders are taking aggressive and proactive steps to meet the future human resource needs in the wake of the retirement of unprecedented numbers of baby boomers. They realize that they need to take quick and important actions now or else face the vacuum created by a tremendous shortage of engineers. The academic leaders are also equally working hard to make to make the power and energy option more attractive for entering freshman and sophomore students. For example, they are working on revising the curriculum to attract the best and brightest to specialize in power and energy engineering under the new environment created by deregulation and the infusion of new information and materials technologies. Several recent articles listed at the end of this editorial provide convincing arguments for the changes needed in the future. S.S. Venkata

2001 Winter Meeting Pictorial Review

was reduced to 39 nominations. The list of 39 papers and books identified were circulated to colleagues, members of the Power Globe, and some students. An informal vote was taken to identify the three papers that seemed to have the highest impact on power engineering practice. Based on the informal vote, recommendation by colleagues, areas of interest, and the opinion of the organizers, four papers were identified as having the greatest impact on the growth of electrical power engineering. Though the process used in identifying these papers is not very scientific, the general consensus seems to be that these papers represent milestone achievements that stimulated great advances that lead to the widespread electrification of the world. Additional details of the “vote” appear in [2].