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Change in customers' consumption patterns due to the introduction of new electricity consuming technologies into human consumption practices has impacted the existing optimal loading profile of the grid facilities. Although some MV/LV distribution transformers (DTs) are operating under a light load profile, this does not guarantee that they are in a healthy condition, since necessary maintenance procedures are required. On the other hand, many unplanned overloading conditions occur on the other DTs and causes accelerated failures which contributes to very high operational costs. This paper presents the outcome of a study on how the planned DTs relocation can save asset managers against their huge DTs replacement costs developed due to the highly overloaded DTs failures in a utility, and how it will improve the overall reliability of the DTs. A mixed-integer linear programming (MILP) solution is proposed to formulate the introduced economic relocation-based asset management (AM) framework while minimizing total cost (TC) of the network. The results show that incorporating relocation techniques into the AM formulation ends in reduction of the distribution network TC and a decrease in asset failure rates if compared with application of traditional techniques. [ABSTRACT FROM AUTHOR]

The short‐term expansion planning of the private utilities, as well as the emerging technologies such as photovoltaic panels (PVs), plug‐in hybrid electric vehicles (PHEVs), cryptocurrency mining, and storage elements spread, make the long‐term load estimation of distribution transformers (DTs) noticeably imprecise. In response, the number of overload and underload transformers is growing in recent years. The utilities normally analyse the loading of their DTs annually to determine the DTs, which should be replaced. It is a common practice for utilities to relocate these DTs to reduce the investment needed to purchase new transformers. Therefore, the utility needs a systematic algorithm to determine the optimal schedule to relocate/replace the DTs with minimum required time and cost. This paper introduces a two‐stage procedure to obtain the optimal schedule to replace DTs. In the proposed algorithm, the maximum permitted number of work‐hours per day for field workers is considered, which converts the problem to a daily‐task based optimisation problem. Also, an useful life investigation of the in‐service DTs is considered in the relocation problem. A real case study in Qom province, Iran, has been studied to prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to the presence of excessive noise in the recorded partial discharge (PD) current signals, de-noising of these signals is a crucial task for performing any investigation on the subject. Meanwhile, to accelerate this de-noising process a single PD pulse can be extracted from the train of those recorded pulses, followed by its de-noising. In this paper a single PD pulse is extracted from the train of recorded PD pulses, using noisy recorded data cumulative energy. A de-noising technique based on adaptive neuro-fuzzy inference systems is proposed. To verify the validity of the proposed method, four different sources of PD signals are physically simulated. The proposed method is applied on laboratory recorded signals and is compared with a conventional method which is based on discrete wavelet transform (DWT) and linear adaptive filtering method. Furthermore, the proposed algorithm is used for extraction and de-noising of single PD activities of an on-site recorded data. This method introduces an efficient pulse distortion mitigation tool which requires no adjustment of any pre-set parameter-neither for different noise levels, nor for different kind of PD pulses. This is an important feature of the proposed technique. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]