Reliability cost/worth assessment of distribution network formed by underground cables and overhead lines. Case study of Latvia

The increasing number of severe weather occurrences that influence the number of large scale outages, especially in rural distribution networks, makes the question on the need of increasing reliability level of power supply of the distribution network even more actual. Distribution system operators and national regulators shall not only find a reliable but also a cost-effective solution for further distribution network development: the optimal reliability level of power supply. One of the reliability improvement solutions that allows effectively dealing with the reduction of the number of outages in rural distribution networks is the cablification of network. Construction costs of the aforementioned solution are quite high in comparison to other network line types, and due to that, the implementation of the solution always raises discussions on cost-effectiveness. The paper presents approaches for the cost/worth assessment of power line lifetime costs in the distribution network and for the assessment of customer costs of reliability that takes into account time-varying loads and outage costs (previously developed by authors of this paper [6, 7]) as well as for the assessment of traders’ losses due to electrical energy not supplied. The case study included in the paper is performed for Latvian conditions and takes into account information on the real customer costs of reliability of Latvian customers (information from the study of the Institute of Physical Energetics), fault statistics of Latvian underground cables and overhead lines, information with a high level of details on the capital costs, operational and fault elimination costs of distribution lines in Latvia.

Automatic extraction of faults and fractal analysis from remote sensing data

Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS).