Use of Micro-Cogeneration in Microgrids to Support Renewables

The use of renewable energy sources has experienced great development so as to meet energy demand. With the intention of increasing the utilization of the renewable energy sources near the demand side and compensate the fluctuation of the output power, the use of micro-cogeneration systems with solar (PV) and wind energy overcomes both technical and economic barriers. Micro-cogeneration-based hybrid PV/wind energy system can get stable power output. This new energy model also improves the power quality and significantly reduces the impact of power instability on the power network. In this study, the grid-connected hybrid PV/wind energy-based micro-cogeneration system is modeled and analyzed in detail. In order to test the performance analysis of the system, seven different scenarios are analyzed during the case studies. The analysis results show that the new energy model presents effective solutions to electrical power balance because of its properties such as safety, incombustible structure, and being eco-friendly. It is aimed at providing a broad perspective on the status of optimum design and analysis for the micro-cogeneration-based hybrid PV/wind energy system to the researchers and the application engineers dealing with these issues

The PV/Wind System for Sustainable Development and Power Generation with Real Dynamic Input Datasets in the Distribution Power Systems

Rapid population growth and industrialization in developing countries cause an increase in demand for energy. In order to meet this energy demand, two types of resources are used: renewable energy and nonrenewable energy. Nonrenewable sources, also called fossil fuels, cause environmental problems in serious and dangerous dimensions. For this reason, it is a necessity to find alternatives. It is a renewable energy source that can be used as an alternative to fossil fuels. This chapter deals with power control of a PV/wind system for power generation with dynamic input dataset. The main contribution of this chapter is that it is the first time to use real data from PV/wind system and observe the system reliability with real-time simulation results. The proposed system consists of doubly fed induction-based wind generator, rotor-side converter (RSC), grid-side converter (GSC), solar arrays, DC-DC converter and grid-side converter, and grid and dynamic loads. The aim of the proposed strategy is to use wind and solar energies with maximum efficiency by simulating the real condition of wind and insolation with input datasets. The modeling and the validation of the operation of the system and its controllers are done by using PSCAD/EMTDC

CVID Associated with Systemic Amyloidosis

Common variable immunodeficiency (CVID) is a frequent primary immune deficiency (PID), which consists of a heterogeneous group of disorders and can present with recurrent infections, chronic diarrhea, autoimmunity, chronic pulmonary and gastrointestinal diseases, and malignancy. Secondary amyloidosis is an uncommon complication of CVID. We report an unusual case of a 27-year-old male patient who presented with recurrent sinopulmonary infections, chronic diarrhea, and hypogammaglobulinemia and was diagnosed with CVID. The patient was treated with intravenous immunoglobulin (IVIg) therapy once every 21 days and daily trimethoprim-sulfamethoxazole for prophylaxis. Two years after initial diagnosis, the patient was found to have progressive decline in IgG levels (as low as 200–300 mg/dL) despite regular Ig infusions. The laboratory tests revealed massive proteinuria and his kidney biopsy showed accumulation of AA type amyloid. We believe that the delay in the diagnosis of CVID and initiation of Ig replacement therapy caused chronic inflammation due to recurrent infections in our patient and this led to an uncommon and life-threatening complication, amyloidosis. Patients with CVID require regular follow-up for the control of infections and assessment of adequacy of Ig replacement therapy. Amyloidosis should be kept in the differential diagnosis when managing patients with CVID

Exercise-Induced Anaphylaxis: A Case Report And Review Of The Literature

Exercise-induced anaphylaxis (EIAn) is a rare, unpredictable, and potentially fatal cause of anaphylaxis which occurs with physical exhaustion. To the best of our knowledge, here we report the first case of EIAn from Turkey with no prior history of allergy. A detailed patient history is the most crucial point in the diagnosis of EIAn. The triggering and co-factors should be questioned in detail to determine treatment recommendations.WoSScopu

Primary Immunodeficiencies Associated With Atopic Dermatitis

Atopic dermatitis is the most common skin disease seen during childhood. Other allergic diseases may accompany atopic dermatitis and increased IgE and peripheral blood eosinophilia are common findings. Patients with atopic dermatitis who do not respond to standard treatment measures should be reassessed for differential diagnosis. Early-onset, treatment resistant severe atopic dermatitis with recurrent infection history apart from the infections occurring due to defective skin integrity are the warning signs for an underlying primary immunodeficiency. Clinicians should always remember that atopic dermatitis may be the first finding of an underlying primer immunodeficiency in patients. The sooner the diagnosis is made, the more likely it will be to avoid complications and morbidity.Wo

Measurement of Uncertainty in Prediction of No-Reflow Phenomenon after Primary Percutaneous Coronary Intervention Using Systemic Immune Inflammation Index: The Gray Zone Approach

Systemic immune-inflammation index (SII), which is a good predictive marker for coronary artery disease, can be calculated by using platelet, neutrophil, and lymphocyte counts. The no-reflow occurrence can also be predicted using the SII. The aim of this study is to reveal the uncertainty of SII for diagnosing ST-elevation myocardial infarction (STEMI) patients who were admitted for primary percutaneous coronary intervention (PCI) for the no-reflow phenomenon. A total of 510 consecutive acute (STEMI) patients with primary PCI were reviewed and included retrospectively. For diagnostic tests which are not a gold standard, there is always an overlap between the results of patients with and without a certain disease. In the literature, for quantitative diagnostic tests where the diagnosis is not certain, two approaches have been proposed, named “grey zone” and “uncertain interval”. The uncertain area of the SII, which is given the general term “gray zone” in this article, was constructed and its results were compared with the “grey zone” and “uncertain interval” approaches. The lower and upper limits of the gray zone were found to be 611.504–1790.827 and 1186.576–1565.088 for the grey zone and uncertain interval approaches, respectively. A higher number of patients inside the gray zone and higher performance outside the gray zone were found for the grey zone approach. One should be aware of the differences between the two approaches when making a decision. The patients who were in this gray zone should be observed carefully for detection of the no-reflow phenomenon

A new method for generating short-term power forecasting based on artificial neural networks and optimization methods for solar photovoltaic power plants

In recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants. © Springer Nature Singapore Pte Ltd. 2019

A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine

The increasing damage caused by fossil fuels has made it a necessity for new and clean energy sources. In recent years, the use of wind energy from renewable energy sources has increased, which is a new and clean energy source. Wind energy is everywhere in nature. The wind speed changes depending on time. Thus, the wind power is unstable. In order to keep this disadvantage at a minimum level, future power estimation studies have been carried out. In these studies, different methods and algorithms are applied to estimate short and medium term in wind power. In this study, artificial neural network, particle swarm optimization and firefly algorithm (FA) as a new method are used for the first time in predicting wind power. As input data, temperature, wind speed and rotor speed the data recorded in the SCADA in wind turbines are used to predict medium-term wind speed and also wind power. Each method is compared in detail and their performances are revealed. © IMechE 2019.18103015, 18103016The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the Scientific Project Unit of Adana Science and Technology University (Project Number: 18103015 and 18103016) for full financial support