Browsing by Author "Dağ, Hasan"

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Ağ Sızma Tespit Sistemleri için Tablosal ve Metin Temelli Özniteliklerden Birlikte Öğrenmeye Dayalı Yeni Bir Mimari
(2023) Düzgün, Berkant; Dağ, Hasan; Dağ, Hasan
Ağ Saldırı Tespit Sistemleri (ASTS) bilgisayar ağlarının güvenliğinin ve bütünlüğünün korunmasında kritik bir rol oynar. Bu sistemler, kötü niyetli veya yetkisiz erişime işaret edebilecek anormal faaliyetleri tespit etmek ve bunlara yanıt vermek üzere tasarlanmıştır. Sürekli gelişen siber tehditlerle karakterize edilen günümüzün dijital ortamında sağlam ASTS çözümlerine duyulan ihtiyaç hiç bu kadar acil olmamıştı. Etkili ASTS'lerin konuşlandırılması, özellikle de sürekli artan sofistike ve tespit edilmesi zor siber tehditlerin ortasında ağ anormalliklerinin doğru bir şekilde tanımlanması zor olabilir. Araştırmamızın motivasyonu, ASTS çalışmaları önemli adımlar atmış olsa da, ağ anormalliklerini tespit etmek için daha etkili ve doğru yöntemlere olan önemli ihtiyacın devam ettiğinin fark edilmesinden kaynaklanmaktadır. STS çalışmalarında yaygın olarak kullanılan özellikler ağ günlüklerini içermektedir ve bazı çalışmalar yük bilgisi gibi metin tabanlı özellikleri araştırmıştır. Ancak geleneksel makine ve derin öğrenme modelleri, tablosal ve metin tabanlı özelliklerden birlikte öğrenme konusunda yetersiz kalabilmektedir. Burada, ASTS'in performansını artırmak için hem tablo hem de metin tabanlı özellikleri entegre eden yeni bir yaklaşım sunuyoruz. Araştırmamız, ASTS'in mevcut sınırlamalarını ele almayı ve ağ anormalliklerini tespit etmek için daha etkili ve doğru yöntemler sunarak daha güvenilir ve verimli ağ güvenliği çözümlerinin geliştirilmesine katkıda bulunmayı amaçlamaktadır. Dahili deneylerimiz, tablosal özelliklerini kullanan derin öğrenme yaklaşımının olumlu sonuçlar verdiğini, metin tabanlı özelliklerini kullanan önceden eğitilmiş dönüştürücü yaklaşımının ise yeterli performans göstermediğini ortaya koymuştur. Bununla birlikte, derin öğrenme ve önceden eğitilmiş dönüştürücü yaklaşımlarını birlikte kullanarak her iki özellik türünü entegre eden önerilen yaklaşımımız üstün performans elde etmektedir. Bu bulgular, derin öğrenme ve önceden eğitilmiş dönüştürücü yaklaşımlarını birlikte kullanarak her iki özellik türünü entegre etmenin ağ aykırılığı tespitinin doğruluğunu önemli ölçüde artırabileceğini göstermektedir. Ayrıca, önerilen yaklaşımımız ISCX-IDS2012, UNSW-NB15 ve CIC-IDS2017 gibi yaygın olarak kullanılan ASTS veri kümelerinde doğruluk, F1-skoru ve duyarlılık açısından son teknoloji yöntemlerden daha iyi performans göstermekte ve sırasıyla %99,80, %92,37 ve %99,69 F1-skorları ile ağ aykırılık tespit etmedeki etkinliğini ortaya koymaktadır.
Citation - WoS: 4
Citation - Scopus: 4
Alternative Credit Scoring and Classification Employing Machine Learning Techniques on a Big Data Platform
(Institute of Electrical and Electronics Engineers Inc., 2019) Dağ, Hasan; Kiyakoğlu, Burhan Yasin; Rezaeinazhad, Arash Mohammadian; Korkmaz, Halil Ergun; Dağ, Hasan
With the bloom of financial technology and innovations aiming to deliver a high standard of financial services, banks and credit service companies, along with other financial institutions, use the most recent technologies available in a variety of ways from addressing the information asymmetry, matching the needs of borrowers and lenders, to facilitating transactions using payment services. In the long list of FinTechs, one of the most attractive platforms is the Peer-to-Peer (P2P) lending which aims to bring the investors and borrowers hand in hand, leaving out the traditional intermediaries like banks. The main purpose of a financial institution as an intermediary is of controlling risk and P2P lending platforms innovate and use new ways of risk assessment. In the era of Big Data, using a diverse source of information from spending behaviors of customers, social media behavior, and geographic information along with traditional methods for credit scoring prove to have new insights for the proper and more accurate credit scoring. In this study, we investigate the machine learning techniques on big data platforms, analyzing the credit scoring methods. It has been concluded that on a HDFS (Hadoop Distributed File System) environment, Logistic Regression performs better than Decision Tree and Random Forest for credit scoring and classification considering performance metrics such as accuracy, precision and recall, and the overall run time of algorithms. Logistic Regression also performs better in time in a single node HDFS configuration compared to a non-HDFS configuration.
Citation - WoS: 5
Citation - Scopus: 5
Anomalyadapters: Parameter-Efficient Multi-Anomaly Task Detection
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Unal, Ugur; Dağ, Hasan; Dag, Hasan
The emergence of technological innovations brings sophisticated threats. Cyberattacks are increasing day by day aligned with these innovations and entails rapid solutions for defense mechanisms. These attacks may hinder enterprise operations or more importantly, interrupt critical infrastructure systems, that are essential to safety, security, and well-being of a society. Anomaly detection, as a protection step, is significant for ensuring a system security. Logs, which are accepted sources universally, are utilized in system health monitoring and intrusion detection systems. Recent developments in Natural Language Processing (NLP) studies show that contextual information decreases false-positives yield in detecting anomalous behaviors. Transformers and their adaptations to various language understanding tasks exemplify the enhanced ability to extract this information. Deep network based anomaly detection solutions use generally feature-based transfer learning methods. This type of learning presents a new set of weights for each log type. It is unfeasible and a redundant way considering various log sources. Also, a vague representation of model decisions prevents learning from threat data and improving model capability. In this paper, we propose AnomalyAdapters (AAs) which is an extensible multi-anomaly task detection model. It uses pretrained transformers' variant to encode a log sequences and utilizes adapters to learn a log structure and anomaly types. Adapter-based approach collects contextual information, eliminates information loss in learning, and learns anomaly detection tasks from different log sources without overuse of parameters. Lastly, our work elucidates the decision making process of the proposed model on different log datasets to emphasize extraction of threat data via explainability experiments.
Citation - WoS: 0
Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction
(Springer-Verlag Berlin, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih
We suggest a simple and an efficient iterative method based on both the Gerschgorin eigenvalue inclusion theorem and the deflation methods to compute a Reduced Order Model (ROM) to lower greatly the order of a given state space system. This method is especially efficient in symmetric state-space systems but it works for the other cases with some modifications.
Applying Machine Learning Algorithms in Sales Prediction
(Kadir Has Üniversitesi, 2019) Sekban, Judi; Dağ, Hasan; Dağ, Hasan
Makine öğrenimi bir çok endüstride üzerinde yoğun çalışmalar yapılan bir konu olmuştur, ve neyse ki şirketler kendi problemlerini çözebilecek çeşitli machine learning yaklaşımları hakkında günden güne daha fazla bilgi sahibi oluyorlar. Fakat, farklı makine öğreniminin modellerinden en iyi şekilde sonuç almak ve verimli sonuçlara ulaşabilmek için, modellerin uygulanış biçimlerini ve verinin doğasını iyi anlamak gerekir. Bu tez, belli bir tahmin görevi için, uygulanan farklı makine öğreniminin algoritmalarını ne kadar iyi sonuç verdiklerini araştırır. Bu amaçla tez, 4 faklı algoritma, bir istifleme topluluğu tekniği ve modeli geliştirmek için belirli bir özelllik seçme yaklaşımı sunar ve uygular. Farklı konfigürasyonlar uygulayarak sonuçlar birbiriyle test edilir. Bütün bu işlemler, gerekli veri önislemeleri ve özellik mühendisliği adımları tamamlandıktan sonra yapılır.
Arama Motorları Mimarisi, Web Sayfalarının İçerik Skoru ve Google Pagerank Formülünün İncelenmesi
(Kadir Has Üniversitesi, 2013) Işık, Muhittin; Dağ, Hasan; Da?, Hasan
Ülkemizde arama motorlarının önemi hızla artmasına rağmen, maalesef hem akademik ortamda hem de güncel teknoloji piyasasında bu alanla ilgili yeterli kaynak oluşturulamamıştır. Özellikle son dönemlerde internet üzerinden alışverişin yaygınlaşmaya başlamasıyla birlikte bu alana duyulan ilgi hızla bir gelişim süreci içine girmiştir. Artık her sektör, web ortamındaki aramalarda kendilerine ait web sayfalarını ilk sıralara koyma yarışına girmişlerdir. Bu yüzden, gerek ülkemizdeki üniversiteler gerekse bilişim alanındaki özel sektörler bu alan ile ilgili yayınlar oluşturmaya ve bireyler yetiştirmeye başlamışlardır. Arama motorları alanında hem derli toplu bir kaynak oluşturmak hem de arama motorlarının derinlemesine çalışma mantığını incelemek için, araştırma bölümler şeklinde sunulmuştur. Birinci bölüm arama motorlarının mimarisi üzerine yoğunlaşırken, ikinci, üçüncü ve dördüncü bölümler web sayfalarını sıralarken arama motorlarının hangi mantık üzerine odaklandığını incelemektedir. İkinci bölümde özellikle web sayfalarını sıralarken arama motorlarının kullandığı içerik skorunun hesaplanması üzerinde durulmuştur. Üçüncü bölümde temelde Google arama motoru olmak üzere arama motorlarının web sayfalarını sıralarken kullandığı popülarite skoruna odaklanılırken, dördüncü bölümde popülarite skorunun hesaplanmasında kullanılan formülün bileşenleri üzerinde durulmuştur. Son olarak, beşinci bölümde ise araştırmanın sonuçları ve arama motorlarının geleceğine dair fikirler ve çıkarsamalar üzerinde durulmuştur.
Audio detection using machine learning & transfer learning models
(Kadir Has Üniversitesi, 2021) Acar, Mesut; Dağ, Hasan; Dağ, Hasan
In this paper, using datasets ESC-50 & ESC-10 of environmental sounds, machine learning algorithms, and feature extraction methods are used to develop recognition performance. K-NN, SVM, Random Forest are used for comparing the recognition results. The different feature extraction methods in the literature are used to get more meaningful attributes from these datasets and obtain a higher accuracy rate. This approach shows that SVM algorithm has a significantly good result with accuracy scores. The best accuracy scores obtained by classic machine learning algorithms are %42,15 for ESC-50 and %77,7 for ESC-10. In addition to this, the experiments have been done with a pre-trained ResNet neural network as a backbone, which achieves successful results despite the machine learning models. In this study, a higher accuracy rate is achieved from baseline machine learning algorithms in literature and using transfer learning with pre-trained Resnet backbones to reach some state of art results. The accuracy scores are %68,95 for ESC-50 and %87,25 for ESC-10.
Bellek Tabanli Verı Platformların Karşılaştırması
(Kadir Has Üniversitesi, 2016) Akbari, Amirmahdi; Dağ, Hasan; Dağ, Hasan
Bellek tabanli verı platformların karşılaştırması
Bobrek Nakli Gecirmis Hastalarda Akilli Yuntem Tabanli Yeni Oznitelik Secme Algoritmasi Gelistirilmesi
(Kadir Has Üniversitesi, 2012) Acar, Saylan Cagil; Dağ, Hasan; Da?, Hasan
Veri madenciligi verilerden kesfedilecek desenler yardimiyla yeni bilgiler elde etme amaciyla cok farkli disiplinlerde kullanilan cesitli metotlardan olusmaktadir. Tip alanindaki verinin buyuklugu ve hayati onem tasimasi Veri madenciliginin bu alanda da uygulanmasini gerekli kilmistir. Bu tezde Veri Madenciliginin Tip alaninda kullanimi incelenmistir. Uygulama calismasi icin Ýstanbul universitesi Cerrahpasa Tip Fakultesi.nde ayakta tedavi goren hastalar arasindan Mart 2006 . Aralik 2007 tarihleri arasinda 21 aylik bir surede tedavisi gormus hastalara ait veriler bir araya getirilerek bir veri kumesi olusturulmustur. Bu veri kumesi uzerinde WEKA yazilimi kullanilarak siniflama kumeleme ve karar agaci algoritmalari calistirilmis elde edilen karar kurallari uzman destegiyle incelenerek koroner arterlerde kalsifikasyon bulunmasinda etkili olan faktorlerin neler oldugu belirlenmis ve oznitelik secme algoritmalariyla ayni faktorlere ulasilip ulasilamadigi belirlenmistir.
Citation - WoS: 2
Citation - Scopus: 6
Branch Outage Simulation Based Contingency Screening by Gravitational Search Algorithm
(Praise Worthy Prize Srl, 2012) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, Hasan; Dağ, Hasan; Özdemir, Serpil
Power systems contingency analysis is an important issue for electric power system operators. This paper performs branch outage simulation based contingency screening using a bounded network approach. Local constrained optimization problem representing the branch outage phenomena is solved by the gravitational search algorithm. The proposed method is applied to IEEE 14 30 57 and 118 Bus Test systems and its performance from the point of capturing violations is evaluated. In addition false alarms and the computational accuracy of the proposed method are also analyzed by using scattering diagrams. Finally the proposed gravitational search based contingency screening is compared with full AC load flow solutions from the point of computational speed. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Citation - Scopus: 15
Branch outage solution using particle swarm optimization
(2008) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan; Dağ, Hasan
For post outage MW line flows and voltage magnitude calculations most of the methods use linear methods because of their simplicity. Especially for reactive power flow calculations one can face high errors. In this paper we use a minimization method that minimizes the errors resulting from the linear system model implementation. We solve the optimization problem using particle swarm optimization. We give some outage examples using IEEE 14 bus IEEE 30 bus and IEEE 57 bus data and compare the results with full ac load flow calculation. © 2008 Australasian Universities Power Engineering Conference (AUPEC'08).
Çelik Sektöründe Enerji Tüketimi Tahmini ile Daha İyi Enerji Verimliliğine Doğru
(2024) Koca, Aslı; Dağ, Hasan
Elektrik tüketiminin en doğru şekilde tahmin edilmesi, maliyet optimizasyonu, operasyonel verimlilik, rekabet gücü, sözleşme müzakereleri ve üretimde sürdürülebilir kalkınmanın küresel hedeflerine ulaşılması için çok önemlidir. Bu çalışma, bütüncül bir yaklaşımla, bir çelik şirketinde elektrik tüketimi için en uygun tahmin algoritmasının ve en etkin uygulama alanlarının belirlenmesine odaklanmaktadır. Rastgele Orman, Gradyan Destekli Ağaçlar, Genelleştirilmiş Doğrusal Modeller, Karar Ağaçları ve Derin Sinir Ağı verilen probleme uygun oldukları ve tahmin amacıyla yaygın olarak kullanılan regresyon algoritmaları oldukları için kullanılmıştır. Tahmin modellerinin performansı, artıkların standart sapmasına (RMSE) ve açıklanan varyans oranına (R-kare) göre değerlendirilir. Bu çalışma, Rastgele Orman modelinin Gradyan Destekli Ağaçlar, Genelleştirilmiş Doğrusal Modeller, Karar Ağaçları ve Derin Sinir Ağı modellerinden daha iyi performans ortaya koyduğunu göstermektedir. Sonuçlar birçok farklı alanda fayda sağlayacaktır. İlk olarak, sözleşme görüşmeleri sırasında, gün öncesi piyasasında elektrik satın almak için rekabet avantajı elde etmemizi sağlayacaktır. İkinci olarak, üretim planlama aşamasında, elektrik tüketimi en yüksek olan bobinlerin, en uygun fiyatlarla, talebin en az olduğu saatlerde üretimlerinin planlanmasına izin verecektir. Ve son olarak, satış siparişleri önceliklendirilirken, mevcut kapasitenin, daha düşük enerji tüketimi olan veya daha yüksek kar marjına sahip satış siparişleri için kullanılması sağlanacaktır.
Citation - WoS: 0
Citation - Scopus: 0
Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations
(Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, Mert İlhan; Çolhak,F.; Ecevit,M.İ.; Dağ, Hasan; Daǧ,H.; Creutzburg,R.
This study highlights the effectiveness of deep neural network (DNN) models, particularly those integrating natural language processing (NLP) and multilayer perceptron (MLP) techniques, in detecting malicious domain registrations compared to traditional machine learning (ML) approaches. The integrated DNN models significantly outperform traditional ML models. Notably, DNN models that incorporate both textual and numeric features demonstrate enhanced detection capabilities. The utilized Canine + MLP model achieves 85.81% accuracy and an 86.46% Fl-score on the MTLP Dataset. While traditional ML models offer advantages such as faster training times and smaller model sizes, their performance generally falls short compared to DNN models. This study underscores the trade-offs between computational efficiency and detection accuracy, suggesting that their superior performance often justifies the added costs despite higher resource requirements, © 2024 IEEE.
Citation - WoS: 0
Citation - Scopus: 0
Comparison of Cost-Free Computational Tools for Teaching Physics
(IEEE, 2010) Er, Neslihan Fatma; Dağ, Hasan; Dağ, Hasan
It is widely accepted that it is quite difficult to engage today's students, from high schools to university, both in educational activities in class and "teaching" them physics due to their prejudices about the complexity of physics. The difficulty in capturing students' attention in class for a long time also plays a role in less effective teaching during learning activities. Research shows that students learn little from traditional lectures. According to constructivist learning theories, visual aids and hands-on activities play a major role in learning physics. In addition to laboratory work there are many computational tools for teaching physics, which help teachers and students in constructing a conceptual framework. With this in mind, this paper compares freeware and open source computational tools for teaching physics.
Citation - Scopus: 17
Comparison of Feature Selection Algorithms for Medical Data
(IEEE, 2012) Dağ, Hasan; Dağ, Hasan; Sayın, Kamran Emre; Yenidoğan Dağ, Işıl; Yenidoğan, Işıl; Albayrak, Songül Varli; Acar, Can
Data mining application areas widen day by day. Among those areas medical area has been receiving quite a big attention. However working with very large data sets with many attributes is hard. Experts in this field use heavily advanced statistical analysis. The use of data mining techniques is fairly new. This paper compares three feature selection algorithms on medical data sets and comments on the importance of discretization of attributes. © 2012 IEEE.
Citation - Scopus: 12
Comparison of Post Outage Bus Voltage Magnitudes Estimated by Harmony Search and Differential Evolution Methods
(2009) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, Hasan; Dağ, Hasan; Özdemir, Serpil
Contingency studies are indispensable tools of both the power system planning and operational studies. Real time implementation of operational problems makes necessary the use of high speed computational methods while requiring reasonable accuracies. On the other hand, accuracy of the results and the speed of calculation depend on branch outage modeling as well as solution algorithm used. This paper presents a comparison of post outage bus voltage magnitudes calculated by two meta-heuristic approaches; namely differential evolution (DE) and harmony search (HS) methods. The methods are tested on IEEE 14, IEEE 30, IEEE 57, and IEEE 118 bus test systems and the results are compared both in terms of accuracy and calculation speed.
Citation - WoS: 0
A Comprehensive Review of Open Source Intelligence in Intelligent Transportation Systems
(Ieee Computer Soc, 2024) Ucar, Bilal Emir; Ecevit, Mert İlhan; Ecevit, Mert Ilhan; Dağ, Hasan; Dag, Hasan; Creutzburg, Reiner
This paper offers an insightful review of Open Source Intelligence (OSINT) within Intelligent Transportation Systems (ITS), emphasizing its heightened importance amidst the digital and connected evolution of the transportation sector. It highlights the integration of technologies like IoT and SCADA systems, which, while beneficial, introduce new cyber vulnerabilities. Focusing on the utilization of OSINT for surveillance, threat detection, and risk assessment, the study evaluates key tools such as Shodan and Aircrack-ng, addressing their roles in enhancing transportation system security. The paper also tackles challenges in OSINT application, from data reliability to ethical and legal considerations, stressing the need for a balance between technological advancement and privacy protection. Through realworld case studies, the paper illustrates OSINT's practical applications in scenarios like maritime security and military surveillance. Conclusively, it underscores the necessity for continuous dialogue among experts to navigate the complexities of OSINT in transportation, particularly as technology evolves and data volumes increase.
Citation - WoS: 59
Citation - Scopus: 84
Deepfake Detection Using Deep Learning Methods: a Systematic and Comprehensive Review
(Wiley Periodicals, inc, 2024) Heidari, Arash; Dağ, Hasan; Navimipour, Nima Jafari; Jafari Navimipour, Nima; Dag, Hasan; Unal, Mehmet
Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. This article is categorized under:Technologies > Machine LearningAlgorithmic Development > MultimediaApplication Areas > Science and Technology
Citation - WoS: 0
Citation - Scopus: 1
Distributed Memory Parallel Transient Stability Analysis on a Pc Cluster With Ethernet
(Praise Worthy Prize Srl, 2010) Soykan, Gürkan; Dağ, Hasan; Flueck, Alexander J.; Dağ, Hasan
On-line transient stability analysis is a necessity for real-time power system control and security. Parallel processing is a natural technology for achieving real-time solution performance. This paper presents a parallel-in-space algorithm based on a multi-level partitioning scheme in a distributed memory cluster environment. The main aim of the research is to decrease the wallclock time of transient stability analysis of large scale power systems by leveraging open source software and commodity off the shelf hardware of a Linux PC cluster. The proposed solution algorithm focuses on speeding up the transient stability simulation by partitioning via METIS the linearized update solution process of the Very Dishonest Newton Method for solving the differential-algebraic equation system. Results are presented for two power systems: I) 3493 buses 844 generators 6689 branches and 2) 7935 buses 2135 generators 13624 branches. The simulations were run on a small Linux-cluster with a 100 Mbit/s ethernet interconnect which is cheaper than any specially constructed parallel computer. By tuning vertex weights the performance of the partition strategy can be improved relative to the no-weight case. The proposed method easily can be adapted by commercial packages and used in various parallel environments including multicore architectures with non-uniform memory access. Copyright (C) 2010 Praise Worthy Prize S.r.l. - All rights reserved.
Citation - Scopus: 5
Double Branch Outage Modeling and Its Solution Using Differential Evolution Method
(2011) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, Hasan
Power system operators need to check the system security by contingency analysis which requires power flow solutions repeatedly. AC power flow is computationally slow even for a moderately sized system. Thus fast and accurate outage models and approximated solutions have been developed. This paper adopts a single branch outage model to a double branch outage one. The final constrained optimization problem resulted from modeling is then solved by using differential evolution method. Simulation results for IEEE 30 and 118 bus test systems are presented and compared to those of full AC load flow in terms of solution accuracy. © 2011 IEEE.