The root cause of the instability problem of the least‐squares (LS) solution of the resistivity inverse problem is the ill‐conditioning of the sensitivity matrix. To circumvent this problem a new LS approach has been investigated in this paper. At each iteration, the sensitivity matrix is weighted in multiple ways generating a set of systems of linear equations. By solving each system, several candidate models are obtained. As a consequence, the space of models is explored in a more extensive and effective way resulting in a more robust and stable LS approach to solving the resistivity inverse problem. This new approach is called the multiple reweighted LS method (MRLS). The problems encountered when using the L1‐ or L2‐norm are discussed and the advantages of working with the MRLS method are highlighted. A five‐layer earth model which generates an ill‐conditioned matrix due to equivalence is used to generate a synthetic data set for the Schlumberger configuration. The data are randomly corrupted by noise and then inverted by using L2, L1 and the MRLS algorithm. The stabilized solutions, even though blurred, could only be obtained by using a heavy ridge regression parameter in L2‐ and L1‐norms. On the other hand, the MRLS solution is stable without regression factors and is superior and clearer. For a better appraisal the same initial model was used in all cases. The MRLS algorithm is also demonstrated for a field data set: a stable solution is obtained.

The objective of this work was to analyze an overvoltage case in a rural distribution feeder belonging to an electrical distribution company in the southeast of the Buenos Aires Province in Argentina. The network was modeled in the Electromagnetic Transients Program, based on the electrical parameters that make up the circuit, in order to evaluate its behavior under various switching and load states. The simulation analysis showed that during certain operation and load situations, the conditions for the overvoltage phenomenon occurred, causing a voltage increase in the single-phase transformer feeding. The guidelines for prevention and control of the phenomenon were provided taking into account the results obtained in the study.

No abstract

Nontechnical losses are a major problem in Brazil as in many developing countries. This paper proposes a methodology that quantifies sources of nontechnical losses and their regional distribution using Bayesian Network. This is a probabilistic graphical model in which a problem is structured as a set of variables linked through probabilistic relationships. Five groups were inspected through random sampling, and the diagnosis with likelihoods for each variable was calculated. Then, situations of conformity and nontechnical losses were estimated. The model has been applied in a Brazilian distribution concessionary, showing its utility in planning efficient actions to reduce nontechnical losses.

No abstract