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In this article, two high step-down bridgeless power factor correction rectifiers based on the switched inductor network (SIN) are introduced. The proposed rectifiers employ the SIN to provide high step-down voltage gain with a higher duty cycle than the competitors. They also offer higher efficiency, lower current stress, and total peak switching device powers. A thorough and straightforward design algorithm in the discontinuous conduction mode is provided that ensures a unity power factor and a low total harmonic distortion with a simple control scheme. As a demonstration of the superior performance of the proposed rectifiers, a 300-W high-gain sepic rectifier setup with 48V dc output voltage from a 230V rms /50Hz source is built in the laboratory.

Many accidents always happen in the electricity distribution sector, which requires the use of control strategies in this field. Therefore, this study presented a risk assessment method based on a fuzzy Bayesian network (FBN) and William Fine method in low-voltage power distribution systems to reduce the structural uncertainties of the studies and examined the reliability of these systems to achieve proper performance in emergencies. The first step is to develop a fault tree (FT) and event tree (ET) in bow tie (BT) format. In this study, fuzzy theory and expert judgment were used to qualitatively analyze the root events affecting the system failure and determine the relevant probabilities. Then, deductive and inductive reasoning was done after determining the likelihood of basic events (BEs) and establishing the Bayesian network (BN). Then, the final risk was estimated using William Fine’s method and fuzzy logic. The results of a case study in a low-voltage power distribution system showed that there were 36 BEs and 20 intermediate events (IEs) for system failure, and the reliability of the system was obtained as 0.96654476 and 0.96654476 based on the FT and FBN, respectively. According to the results of BE25 (no installation of the earth) and BE18 (contact of metal objects with the live wire), the most critical events were based on FBN. The results showed that FBN simulation and its combination with static methods such as BT and William Fine provide a suitable method for the accurate identification of distribution systems. Therefore, the approach of the present study can help the decision makers and analysts of the electricity industry to prevent possible failures due to system changes.

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