Spring search algorithm for simultaneous placement of distributed generation and capacitors

Purpose. In this paper, for simultaneous placement of distributed generation (DG) and capacitors, a new approach based on Spring Search Algorithm (SSA), is presented. This method is contained two stages using two sensitive index Sv and Ss. Sv and Ss are calculated according to nominal voltageand network losses. In the first stage, candidate buses are determined for installation DG and capacitors according to Sv and Ss. Then in the second stage, placement and sizing of distributed generation and capacitors are specified using SSA. The spring search algorithm is among the optimization algorithms developed by the idea of laws of nature and the search factors are a set of objects. The proposed algorithm is tested on 33-bus and 69-bus radial distribution networks. The test results indicate good performance of the proposed methodЦель. В статье для одновременного размещения распределенной генерации и конденсаторов представлен новый подход, основанный на "пружинном" алгоритме поиска (Spring Search Algorithm, SSA). Данный метод состоит из двух этапов с использованием двух показателей чувствительности Sv и Ss. Показатели чувствительности Sv и Ss рассчитываются в соответствии с номинальным напряжением и потерями в сети. На первом этапе определяются шины-кандидаты для установки распределенной генерации и конденсаторов согласно Sv и Ss. Затем, на втором этапе размещение и калибровка распределенной генерации и конденсаторов выполняются с использованием алгоритма SSA. "Пружинный" алгоритм поиска входит в число алгоритмов оптимизации, разработанных на основе идей законов природы, а факторы поиска представляют собой набор объектов. Предлагаемый алгоритм тестируется на радиальных распределительных сетях с 33 и 69 шинами. Результаты тестирования показывают хорошую эффективность предложенного метода

Burn Patients Infected With Metallo-Beta-Lactamase-Producing Pseudomonas aeruginosa: Multidrug-Resistant Strains

Background: Metallo-beta-lactamase (MBL) producing Pseudomonas aeruginosa in the burn patients is a leading cause of morbidity and mortality and remains a serious health concern among the clinicians. Objectives: The aim of this study was to detect MBL-producing P. aeruginosa in burn patients and determine multidrug-resistant (MDR) strains, and respective resistance patterns. Patients and Methods: In this cross-sectional study, 270 strains of P. aeruginosa were isolated from the burn patients referred to Ghotbeddin Burn Hospital, Shiraz, Iran. Among them, 55 MBL-producing P. aeruginosa strains were isolated from 55 patients hospitalized in burn unit. Minimum inhibitory concentrations (MICs) and MBLs were determined by the E-test method. Results: Of the 55 burn cases, 29 (53%) were females and 26 (47%) males. Injured burn patients’ ages ranged from 16 to 87 years, with maximum number of cases in the age group of 16 to 36 years (n, 40; 72.7%). Overall, 32 cases were accidental (60%), and 22 were suicidal burns (40%). Of the 55 burn patients, 17 cases were expired (30%). All deaths were due to chemical exposures. In antibiotic susceptibility testing by E-test method, ceftazidime was the most effective one and 35 isolates (63.5%) were resistant to all the 11 tested antibiotics. Conclusions: Routine microbiological surveillance and careful in vitro testing of antibiotics prior to prescription and strict adherence to hospital antibiotic policy may help to prevent, treat, and control MDR and pandrug-resistant (PDR) P. aeruginosa strains in burn units

Energy Commitment for a Power System Supplied by Multiple Energy Carriers System using Following Optimization Algorithm

In today’s world, the development and continuation of life require energy. Supplying this energy demand requires careful and scientific planning of the energy provided by a variety of products, such as oil, gas, coal, electricity, etc. A new study on the operation of energy carriers called Energy Commitment (EC) is proposed. The purpose of the EC is to set a pattern for the use of energy carriers to supply energy demand, considering technical and economic constraints. EC is a constrained optimization problem that can be solved by using optimization methods. This study suggests the Following Optimization Algorithm (FOA) to solve the EC problem to achieve technical and economic benefits. Minimizing energy supply costs for the total study period is considered as an objective function. The FOA simulates social relationships among the community members who try to improve their community by following each other. Simulation is carried out on a 10-unit energy system supplied by various types of energy carriers that includes transportation, agriculture, industrial, residential, commercial, and public sectors. The results show that the optimal energy supply for a grid with 0.15447 Millions of Barrels of Oil Equivalent (MBOE) of energy demand costs 9.0922 millions dollar for a 24-h study period. However, if the energy supply is not optimal, the costs of operating energy carriers will increase and move away from the optimal economic situation. The economic distribution of electrical demand between 10 power plants and the amount of production units per hour of the study period is determined. The EC outputs are presented, which include an appropriate pattern of energy carrier utilization, energy demand supply costs, appropriate combination of units, and power plant production. The behavior and process of achieving the answer in the convergence curve for the implementation of FOA on EC indicates the exploration and exploitation capacity of FOA. Based on the simulated results, EC provides more information than Unit Commitment (UC) and analyzes the network more efficiently and deeply.Peer ReviewedPostprint (published version

Molecular Epidemiology of Pseudomonas aeruginosa Isolated from Burn Patients

Background & aim: Because of emerging multi-drug resistance (MDR) Pseudomonas aeruginosa strains, treatment of burn patients infected by this bacterium is difficult. The aim of this study was to detect antimicrobial profile and molecular epidemiology of metallo-beta-lactamase (MBL) producer strains. Methods: In this cross-sectional investigation 270 Pseudomonas aeruginosa isolates were collected from the burn patients. Carbapenem sresistance strains were detected by phenotypic E-test method. Susceptibility profiles of metallo-β-lactamase (MβL) enzyme producing isolates of this bacterium to 11 antimicrobial drug were determined by disc diffusion method according Clinical and Laboratory Standards Institute (CLSI) guidelines. The genetic correlations between isolates were determined by Pulsed-Field Gel Electrophoresis (PFGE) method. Results: Among 270 P. aeruginosa isolates, 60 (22.2%) strains showed resistant to meropenem (MEM) and imipenem (IMI) and were considered as metallo-β-lactamase positive. All metallo-β-lactamase positive isolates were resistant to five tested antimcrobial while their sensitivities to the three best effective antibiotics including ciprofloxacin, amikacin and ceftazidime were 1.7%, 6.7 % and 23.3%, respectively. Majority of the isolates (71.6%) showed more than 80% similarity based on the drawn dendrogram. Conclusion: Our results showed, the tested antimicrobials are not safe to prescribe for burn patients. According PFGE pulsotypes, a limited number of P.aeruginosa types are common in the hospital burn unit which infect the patients hospitalized in this ward

Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called ``preparation range\u27\u27 to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid