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A power transformer winding is usually subject to mechanical stress and tension because of improper transportation or operation. Radial deformation (RD) is an example of mechanical stress that can impact power transformer operation through short circuit faults and insulation damages. Frequency response analysis (FRA) is a well-known method to diagnose mechanical defects in transformers. Despite the precision of FRA, the interpretation of the calculated frequency response curves is not straightforward and requires complex calculations. In this paper, a deep learning algorithm called long short-term memory (LSTM) is used as a feature extraction technique to locate RD faults in their early stages. The experimental results verify the effectiveness of the proposed method in the diagnosis and locating of RD defects., Comment: 5 pages, 10 figures, IEEE PES ISGT NA 2021

In the recent years, due to the economic and environmental requirements, the use of distributed generations (DGs) has increased. If DGs have the optimal size and are located at the optimal locations, they are capable of enhancing the voltage profile and reducing the power loss. This paper proposes a new approach to obtain the optimal location and size of DGs. To this end, exchange market algorithm (EMA) is offered to find the optimal size and location of DGs subject to minimizing loss, increasing voltage profile, and improving voltage stability in the distribution systems. The effectiveness of the proposed approach is verified on both 33- and 69-bus IEEE standard systems., Comment: 6 pages, 3 figures, IEEE Green Energy and Smart Systems Conference (IGESSC 2020)

Experience shows that semiconductor switches in power electronics systems are the most vulnerable components. One of the most common ways to solve this reliability challenge is component-level redundant design. There are four possible configurations for the redundant design in component-level. This paper presents a comparative reliability analysis between different component-level redundant designs for solid state fault current limiter (SSFCL). The aim of the proposed analysis is to determine the more reliable component-level redundant configuration. The mean time to failure (MTTF) is used as the reliability parameter. Considering both fault types (open circuit and short circuit), the MTTFs of different configurations are calculated. It is demonstrated that more reliable configuration depends on the junction temperature of the semiconductor switches in the steady state. That junction temperature is a function of i) ambient temperature, ii) power loss of the semiconductor switch and iii) thermal resistance of heat sink. Also, results sensitivity to each parameter is investigated. The results show that in different conditions, various configurations have higher reliability. The experimental results are presented to clarify the theory and feasibility of the proposed approaches. At last, levelized costs of different configurations are analyzed for a fair comparison.

The interactions of the natural gas (NG) network and the electricity system are increased by using gas-fired generation units, which use NG to produce electricity. There are various uncertainty sources such as the forced outage of generating units or market price fluctuations that affect the economic operation of both NG and electricity systems. This study focuses on the steady-state formulation of the integrated NG transmission grid and electricity system by considering the uncertainty of electricity market price based on information gap decision theory. The higher and lower costs than the expected cost originated from the fluctuations of electricity market price are modelled by the robustness and opportunity functions, respectively. The objective is to minimise the cost of zone one while satisfying the constraints of two interdependent systems, which can obtain revenue from selling power to its connected zones in short-term scheduling. The capability of the proposed method is demonstrated by applying it on a 20-node NG network and IEEE RTS 24-bus. The proposed short-term coordination between NG and electricity infrastructures is solved and discussed.

Due to the penetration of renewable energy resources and load deviation, uncertainty handling is one of the main challenges for power system; therefore the need for accurate decision-making in a power system under the penetration of uncertainties is essential. However, decision makers should use suitable methods for uncertainty management. In this chapter, some of the uncertainty modeling methods in power system studies are analyzed. At first, multiple uncertain parameters that the power system deals with are introduced, then some useful uncertainty modeling methods are introduced. To show the uncertainty modeling process and its effect on the decision-making, a microgrid consisting of multiple uncertain parameters is considered, and stochastic scenario-based approach is used for uncertainty modeling. The scheduling of microgrids in the presence of different types of uncertainty is solved from the profit-maximization point of view. The simulation results are presented for a 33-bus microgrid that shows the effectiveness of the proposed method for decision-making under high level of uncertainty.