Optimal Placement and Sizing of Shunt Capacitor Banks in the Presence of Harmonics

Masoum, Mohammad A. S.; Fuchs, E.F.

doi:10.1016/b978-0-12-800782-2.00010-5

Cited by 15 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Defuzzification is the process of converting a fuzzy output to a crisp value by utilising the fuzzy set and membership degree [60], [65]. The most well-known defuzzification methods are the centre of gravity (COG), mean of maximum (MOM), and centre average [66]. The fuzzy system is a popular method in machine learning since it is easier than other methods and can achieve high accuracy rates simultaneously [67].…”

Section: Methods 21 Fuzzy Systemmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Idris

Ismail

Mohamad

et al. 2022

IJ-AI

View full text Add to dashboard Cite

Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamicbottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.

show abstract

Section: Methods 21 Fuzzy Systemmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Idris

Ismail

Mohamad

et al. 2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The process of defuzzification takes place in defuzzifier ( Saad & Wahyunggoro, 2010 ). There are many methods to execute defuzzification, such as the centre of gravity (COG), mean of maximum (MOM) and centre average methods ( Masoum & Fuchs, 2008 ). The fuzzy system is a well-known classification algorithm in machine learning because of its simplicity but manage to produce high accuracy in classification ( Surya et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Idris¹,

Ismail²

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, ID3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in FID3 algorithm, which is the fuzzy version of the ID3 algorithm. The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of FID3 algorithm with the existing methods is conducted to verify the proposed method’s capability and performance. This study identified that the combination of FID3 algorithm with FUZZYDBD method is reliable, robust and managed to perform well in breast cancer classification.

show abstract

“…The THD in each bus is calculated using Equation (4). 147‐149

italicTHD % goodbreak= \frac{\sqrt{\sum_{i = 2}^{H} {((), V_{i, h})}^{2}}}{V_{i, 1}} goodbreak\times 100

…”

Section: Efficiency and Energy Losses In Hybrid Ac/dc Power Distributionmentioning

confidence: 99%

Efficiency and energy‐loss analysis for hybridAC/DCdistribution systems and microgrids: A review

Charadi

Chaibi

Redouane

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Historically, the development and design of distribution systems (DSs) and microgrids (MGs) were based primarily on alternating current (AC) as a traditional approach due to many advantages such as eliminating the need for synchronization, as well as the ease of integrating distributed energy resources (DERs). Recently, given the democratization of DER through local installations of renewable energy systems and appliances using power electronics, direct current microgrids (DC‐MGs) are gaining more and more momentum. In order to enable durable and economically viable use by integrating DC and AC DERs into microgrids, hybrid AC/DC microgrids (HMGs‐AC/DC) present one of the most promising approaches in eliminating the need for AC‐DC or DC‐AC conversions. The improvement of energy efficiency, protection, management, and control of this kind of systems are relevant research topics. This article provides an overview of theoretical works and industrial applications of hybrid AC/DC microgrids/distribution systems. In addition, an efficiency/energy‐losses study of different literature‐based works is discussed. Accordingly, a critical analysis is provided, and research perspectives related to this subject are outlined. This review article can be considered as a guide for future research on the efficiency and energy losses of hybrid AC/DC distribution systems/microgrids.

show abstract

Optimal Placement and Sizing of Shunt Capacitor Banks in the Presence of Harmonics

Cited by 15 publications

References 48 publications

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Efficiency and energy‐loss analysis for hybridAC/DCdistribution systems and microgrids: A review

Contact Info

Product

Resources

About