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Adaptive neuro-fuzzy inference systems (ANFIS) application to investigate potential use of natural ventilation in new building designs in Turkey
(Pergamon-Elsevier Science Ltd, 2007) Ayata, Tahir; Cam, Ertugrul; Yildiz, Osman
Natural ventilation in living and working places provides both circulation of clear air and a decrease of indoor temperature, especially during hot summer days. In addition to openings, the dimension ratio and position of buildings play a significant role to obtain a uniform indoor air velocity distribution. In this study, the potential use of natural ventilation as a passive cooling system in new building designs in Kayseri, a midsize city in Turkey, was investigated. First, indoor air velocity distributions with respect to changing wind direction and magnitude were simulated by the FLUENT package program, which employs finite element methods. Then, an adaptive neuro-fuzzy inference systems (ANFIS) model was employed to predict indoor average and maximum air velocities using the simulated data by FLUENT. The simulation results suggest that natural ventilation can be used to provide a thermally comfortable indoor environment during the summer season in the study area. Also, the ANFIS model can be proposed for estimation of indoor air velocity values in such studies. (C) 2007 Elsevier Ltd. All rights reserved.
Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller
(Taylor & Francis Inc, 2016) Civelek, Zafer; Luy, Murat; Cam, Ertugrul; Barisci, Necaattin
This article presents a study on set of PID parameters of blade pitch angle controller of wind turbine with fuzzy logic algorithm. Three individual control methods were used to control the wind turbine pitch angle. These control methods are conventional PI, fuzzy and fuzzy PID. With the use of these control methods, the system was protected from possible harms in high wind speed region and maintained changing of nominal output power. It was aimed to the control the wind turbine blade pitch angle in different wind speeds and to hold the output power stable in the set point by simulation of controllers with Matlab/Simulink Software. By evaluating the steady state time of output power received from the simulation results and steady state errors, the performances of the control systems have been measured and compared with one another. As a result of these simulation comparisons, it is clear that fuzzy PID controller performed better than PI and Fuzzy Controller.
Energy Efficiency in Rail Systems with Coasting Control Method Using GA and ABC Optimizations
(Univ Osijek, Tech Fac, 2021) Arikan, Yagmur; Sen, Tolga; Cam, Ertugrul
Today, reducing the energy consumption of rail systems is one of the issues that attract researchers' attention. There are many methods to reduce energy consumption and coasting control method has been used in this study. The driving modelling of the vehicle has been carried out by considering all parameters. A new objective function has been determined and for optimization, genetic algorithm (GA) and artificial bee colony (ABC) algorithm have been preferred. The study has been tested with the data of Ankaray metro line. When the proposed optimized driving has been compared with practical driving of the vehicle, the energy savings rate is 13.79% in GA and 13.45% in ABC for a driving. Despite these significant savings ratios, the increase in travel time has been calculated at 1.7% in GA and 1.55% in ABC. When the obtained savings rates are considered annually, this study may greatly contribute to sustainable life.
Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines
(Elsevier Sci Ltd, 2015) Kaytez, Fazil; Taplamacioglu, M. Cengiz; Cam, Ertugrul; Hardalac, Firat
Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electricity energy consumption of Turkey. In addition to the traditional regression analysis and artificial neural networks (ANNs) are considered. In the models, gross electricity generation, installed capacity, total subscribership and population are used as independent variables using historical data from 1970 to 2009. Forecasting results are compared using diverse performance criteria in this study with each other. Receiver operating characteristic (ROC) analysis is realized for determining the specificity and sensitivity of the empirical results. The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method. (C) 2014 Elsevier Ltd. All rights reserved.
Gün Öncesi Piyasasında Yer Alan Sanal Güç Santrali için Teklif ve İşletme Planlaması
(Kırıkkale Üniversitesi, 2020) Akkaş, Özge Pinar; Cam, Ertugrul
Bu çalışmada, enerji piyasasında yer alan bir Sanal Güç Santralinin (SGS) maksimum kâr elde etmesi amacıyla optimum teklif ve işletme planlamasının belirlenmesi amaçlanmıştır. Bunun için, Dağıtık Üretim Sistemlerine (DÜS) sahip IEEE 6-baralı test sistemi üzerinde Rüzgâr Enerji Santrali (RES), Fotovoltaik Enerji Santrali (FVES) ve Enerji Depolama Sistemi (ESS) içeren bir SGS oluşturulmuştur. Gün Öncesi Piyasasına (GÖP) katılan bu SGS’nin piyasada vereceği tekliflerin ve bileşenlerinin işletim planlaması bir gün için saatlik olarak yapılmıştır. Böylece SGS’nin maksimum kâr elde etmesi amaçlanmıştır. Önerilen problem Karışık Tamsayı Doğrusal Programlama (KTDP) olarak GAMS yazılımında modellenmiş ve CPLEX çözücüsü ile çözülerek optimum sonuç elde edilmeye çalışılmıştır. Elde edilen sonuçlar modelin uygulanabilir olduğunu ve metotun geçerli olduğunu göstermektedir.
Individual pitch control on wind turbines with permanent magnet synchronous generator for reduction of mechanical load and stability of output power
(Pamukkale Univ, 2017) Luy, Murat; Civelek, Zafer; Cam, Ertugrul
In this article, increasing output power quality of wind turbines and decreasing mechanical loads on turbines was studied. By adjusting the blade pitch angle on nominal wind speed, output power of wind turbine kept on nominal value. Beside, by individual pitch angle control, mechanic al loads on wind turbine vas decreased, Wind turbine in which permanent magnet synchronous generator is used, modeled in matlah/simulink. Simulation results show that by individual pitch angle control both output power quality of wind turbine assured and balanced periodical loads on wind turbine reduced,
Initial Results of Testing a Multilayer Laser Scanner in a Collision Avoidance System for Light Rail Vehicles
(Mdpi, 2018) Luy, Murat; Cam, Ertugrul; Ulamis, Faruk; Uzun, Ibrahim; Akin, Salih Ibrahim
This paper presents an application to detect and track obstacles using a multilayer laser scanner. The goal of the detection system is to develop collision avoidance for the Light Rail Vehicle (LRV). The laser scanner, which is mounted in front of the tram, collects information in a four-scan plane. The object recognition and tracking module, which is composed of a three sub-modules segmentation, classification, and Kalman Filter tracking, was carried out on the raw data. Thus, data were provided for collision avoidance module. The proposed system was applied to a tram named "Silkworm" which is manufactured by Durmazlar Machine Inc. (Bursa, Turkey) and initial experimental tests have been conducted at the facilities of Durmazlar Machine Inc. in the city of Bursa, Turkey. This study aims to illustrate parts of the possible tests that can be carried out and to share with the scientific community an important application of multilayer laser scanners, although in the initial implementation phase, in urban rail transportation.
A new fuzzy logic proportional controller approach applied to individual pitch angle for wind turbine load mitigation
(Pergamon-Elsevier Science Ltd, 2017) Civelek, Zafer; Luy, Murat; Cam, Ertugrul; Mamur, Hayati
In the world, efforts to increase the resource diversity in electric generation have accelerated lately. So, the great improvements have been achieved in wind turbines (WTs). The dimensions of WTs have grown for more electric generation and their energy productions have increased. Depending on these developments, it has become more important to reduce the WT load mitigation. Thus, a tendency to pass an individual pitch angle system control rather than a collective pitch angle system control employed to stable the output power of WTs over nominal wind speeds has whetted in recent studies. However, in literature, a controller proposal relating to how to incorporate the blade moments used for providing the individual pitch angle system into the output power control system has not yet been offered. Therefore, in this study, a new fuzzy logic proportional control (FL-P-C) approach has been recommended to mitigate the moment load on blades and tower to a minimum possible value while keeping the output power of WTs at a constant value. The offered FL-P-C has also been verified by MATLAB/Simulink. Through the first application of the FL-P-C on a WT, a significant improvement of 33-83% has been managed for the blade and tower moment loads. Furthermore, the grid fluctuations have been reduced because of the stabilisation of the output power of the WT. Ultimately, by the offered FL-P-C, not only the WT load mitigations and maintenance costs of WTs could be reduced, but also electric costs could be decreased owing to increasing lifetimes of WTs. (C) 2017 Elsevier Ltd. All rights reserved.
A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems
(Springer, 2016) Gucyetmez, Mehmet; Cam, Ertugrul
In this study, a new hybrid genetic teaching learning-based optimization algorithm is proposed for wind-thermal power systems. The proposed algorithm is applied to a 19 bus 7336 MW Turkish-wind-thermal power system under power flow and wind energy generation constraints and three different loading conditions. Also, a conventional genetic algorithm and teaching learning-based (TLBO) algorithms were used to analyse the same power system for the performance comparison. Two performance criteria which are fuel cost and algorithm run time were utilized for comparison. The proposed algorithm combines the specialties of conventional genetic and TLBO algorithms to reach the global and local minimum points effectively. The simulation results show that the proposed algorithm developed in this study performs better than the conventional optimization algorithms with respect to the fuel cost and algorithm run time for wind-thermal power systems.
New Optimization Algorithms for Application to Environmental Economic Load Dispatch in Power Systems
(Istanbul Univ, Fac Engineering, 2018) Akkas, Ozge Pinar; Cam, Ertugrul; Eke, Ibrahim; Arikan, Yagmur
The determination of the most economical generation dispatch in an electrical power system has become a very important issue globally. However, economical dispatch can no longer be considered alone because of environmental problems that are derived from emissions such as nitrogen oxide, carbon dioxide, and sulfur dioxide. In this study, the problem of environmental economic load dispatch (EELD) in a power system of six generators is addressed both by neglecting and including line transmission losses using a modified genetic algorithm and a modified artificial bee colony optimization method. The results of these modified algorithms are compared with those of the unmodified versions. The results demonstrate that the proposed new methods have better economic and environmental distribution performances. Accordingly, it can be concluded that the new methods are more effective and should be adopted.
Optimal operational scheduling of a virtual power plant participating in day-ahead market with consideration of emission and battery degradation cost
(WILEY, 2020) Akkas, Ozge P.; Cam, Ertugrul
In this article, optimal operation of a virtual power plant (VPP) composed of a wind power plant, a photovoltaic power plant, a combined heat and power plant, a heat-only unit, and battery energy storage system is analyzed for day-ahead market. The aim is to adjust the entire generation system for maximum profit and minimum emissions to ensure that the future of the world is clean and investors are not harmed. For these reasons, data obtained from studies in the literature are used in a 24-hour time period. In addition to the four application cases of VPPs, which are mostly discussed in the literature, a fifth case has been proposed in order to be more realistic. All cases and models are modeled and analyzed in the General Algebraic Modeling System (GAMS) software by using the LINDO solver, which has not been used before in previous studies for this problem. Also, battery degradation cost and effects of depth of discharge and temperature are included in the objective function of the VPP model both to provide reality and observe their effects. As a result of this study, it has been shown that the profit of the VPP can be increased while the emission is reduced.
Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm
(Inst Engineering Technology-Iet, 2016) Civelek, Zafer; Cam, Ertugrul; Luy, Murat; Mamur, Hayati
Output powers of wind turbines (WTs) with variable blade pitch over nominal wind speeds are controlled by means of blade pitch adjustment. While tuning the blade pitch, conventional proportional-integral-derivative (PID) controllers and some intelligent genetic algorithms (IGAs) are widely used in hot systems. Since IGAs are community-based optimisation methods, they have an ability to look for multi-point solutions. However, the PID parameter setting optimisation of the IGA controllers is important and quite difficult a step in WTs. To solve this problem, while the optimisation is carried out by regulating mutation rates in some IGA controllers, the optimisation is conducted by altering crossover point numbers in others. In this study, a new IGA algorithm approach has been suggested for the PID parameter setting optimisation of the blade pitch controller. The algorithm rearranging both the mutation rate and the crossover point number together according to the algorithm progress has been firstly used. The new IGA approach has also been tested and validated by using MATLAB/Simulink software. Then, its superiority has been proved by comparing the other genetic algorithm (GAs). Consequently, the new IGA approach has more successfully adjusted the blade pitch of a WT running at higher wind speeds than other GA methods.
Risk-based Optimal Bidding and Operational Scheduling of a Virtual Power Plant Considering Battery Degradation Cost and Emission
(Univ Suceava, Fac Electrical Eng, 2023) Akkas, Ozge Pinar; Cam, Ertugrul
A virtual power plant (VPP) is a system combining various types of distributed energy resources (DERs) to provide a reliable power system operation. It provides the advantage of making changes in generation according to variety, price, and demand conditions with bringing renewable energy sources (RES) in a single portfolio and using their flexibility. In this study, it is tried to find optimal bidding and operational scheduling of a VPP containing Wind Power Plant (WPP), Photovoltaic Power Plant (PVPP), Heat-Only Unit (HOU), Battery Energy Storage System (BESS), Combined Heat and Power Plant (CHPP), and electrical/thermal demands and participating in the day-ahead electricity market in 24-h time interval. It is aimed to maximize profit and minimize emissions with considering the battery cost. A stochastic model is formed by considering the uncertainty arising from RES. In addition, CVaR (Conditional Value at Risk) as a risk measurement technique is applied against the risk arising from low profit scenarios. The proposed optimization problem formulated as a stochastic Mixed Integer Nonlinear Programming (MINLP) model and is solved by solver LINDO in GAMS (General Algebraic Modelling System). The case studies are implemented to show the applicability and effectiveness of the presented model.
Use of the Genetic Algorithm-Based Fuzzy Logic Controller for Load-Frequency Control in a Two Area Interconnected Power System
(Mdpi Ag, 2017) Cam, Ertugrul; Gorel, Goksu; Mamur, Hayati
The use of renewable energy resources has created some problems for power systems. One of the most important of these is load frequency control (LFC). In this study, in order to solve the LFC problem, modern control methods were applied to a two area multi source interconnected power system. A photovoltaic solar power plant (PV-SPP) was also connected, in order to identify the harmful effects on the frequency of the system. A new Genetic-based Fuzzy Logic (GA-FL) controller was designed to control the frequency of the system. For comparison, conventional proportional-integral-derivative (PID), fuzzy logic (FL), and Genetic Algorithm (GA)-PID controllers were also designed. The new control method exhibited a better performance than the conventional and other modern control methods, because of the low overshoot and short settling time. All simulations were realized with the Matlab-Simulink program.

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