{"title":"Experimental Investigation Based Power Quality Assessment Of The Single- And Three-Pulse Diode Rectifiers","authors":"A. N. Arvindan, M. Krishna, K. Nikhil","doi":"10.1109/ICPEDC47771.2019.9036601","DOIUrl":"https://doi.org/10.1109/ICPEDC47771.2019.9036601","url":null,"abstract":"The single- and three-pulse diode rectifiers constitute the most fundamental of the single- and three-phase topologies respectively, pertaining to uncontrolled ac to dc conversion. Their nomenclature is based on the pulse number (n) they provide at the dc output which is essentially the number of dc voltage notches corresponding to one time period of the input ac source. From the topological perspective, they are referred to as single-phase half-wave rectifier and three-phase half-wave rectifier respectively and are the building blocks of rectifier topologies with higher pulse number. Rectifiers with pulse number greater than or equal to three (n$geq$3) are classified as multipulse rectifiers that comprise series or parallel cascade of these topologies. Usually, three-phase rectifier topologies alone are classified as unidirectional multipulse ac-dc converters among improved power quality ac-dc converters, however, it is noteworthy that the three-phase half-wave rectifier is a parallel cascade of three single-phase half-wave rectifiers. Comprehension and estimation of the power quality indices of these two basic topologies including harmonoic content of the line currents, least order harmonic, power factor, distortion factor, displacement factor, and THD on the ac side and ripple factor, crest factor, and form factor on the dc side provide useful insights into the multipulse rectifiers. In this paper, the power quality assessment of the two basic topologies using MATLAB software and experimental investigation based data are presented.","PeriodicalId":426923,"journal":{"name":"2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129888968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Syama, Gandhi Sweta, P.I.K. Kavyasree, Koti Reddy
{"title":"Classification of ECG Signal Using Machine Learning Techniques","authors":"S. Syama, Gandhi Sweta, P.I.K. Kavyasree, Koti Reddy","doi":"10.1109/ICPEDC47771.2019.9036613","DOIUrl":"https://doi.org/10.1109/ICPEDC47771.2019.9036613","url":null,"abstract":"Electrocardiogram (ECG) signals are the impulses generated by the heart which are used to analyze the proper functioning of heart. Our work deals with the efficient analysis of Electrocardiogram (ECG) signals imported from MIT-BIH database into MATLAB platform, generation of the imported ECG signal, pre-processing the generated signal to remove the noises mainly the baseline wandering and power line interference from which features are extracted. For adequate study of the ECG signal Daubechies and Haar wavelet techniques are compared. Proper decomposition of the signal is achieved using Db4 and Db5 Daubechies wavelets as their scaling functions are analogous to ECG signal. PAN TOMPKINSONS algorithm is considered in our study as it serves best for precise identification of most prominent features namely QRS complexes, RR interval’s as they constitute the major data required for clinical analysis and research. After feature extraction ECG signals are trained using machine learning techniques for detecting the presence of Arrhythmia using different classifiers adopting Weka software.","PeriodicalId":426923,"journal":{"name":"2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125497951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cyber Risks Assessment For Intelligent And Non-Intelligent Attacks In Power System","authors":"A. Sheela, S. Revathi, A. Iqbal","doi":"10.1109/ICPEDC47771.2019.9036522","DOIUrl":"https://doi.org/10.1109/ICPEDC47771.2019.9036522","url":null,"abstract":"Smart power grid is a perfect model of Cyber Physical System (CPS) which is an important component for a comfortable life. The major concern of the electrical network is safety and reliable operation. A cyber attacker in the operation of power system would create a major damage to the entire power system structure and affect the continuity of the power supply by adversely changing its parameters. A risk assessment method is presented for evaluating the cyber security assessment of power systems taking into consideration the need for protection systems. The paper considers the impact of bus and transmission line protection systems located in substations on the cyber physical performance of power systems. The proposed method is to simulate the response of power systems to sudden attacks on various power system preset value and parameters. This paper focuses on the cyber attacks which occur in a co-ordinated way so that many power system components will be in risk. The risk can be modelled as the combined probability of power system impact due to attacks and of successful interruption into the system. Stochastic Petri Nets is employed for assessing the risks. The effectiveness of the proposed cyber security risk assessment method is simulated for a IEEE39 bus system.","PeriodicalId":426923,"journal":{"name":"2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125919363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of Unconstrained Controllers for a Linearized Benchmark Boiler","authors":"Avula Vaishnavi, S. A. Sekhar, R. Jeyanthi","doi":"10.1109/ICPEDC47771.2019.9036496","DOIUrl":"https://doi.org/10.1109/ICPEDC47771.2019.9036496","url":null,"abstract":"Boilers are still used in power plants and other industries for various purposes. Controlling and monitoring of crtical parameters of a boiler are very important. The safety of the process is monitored by proper control strategy. Control of a drum boiler primarily involves pressure inside the boiler, level of drum water and flow of steam outlet at required levels. Interactions among the variables are the issues of a controller. In this paper, model predictive controller (MPC) and linear quadratic regulatory (LQR) controller are applied to a linearized benchmark boiler. Comparison between Dynamic Matrix Controller (DMC) and Linear Quadratic Regulatory Controller are studied and results are presented.","PeriodicalId":426923,"journal":{"name":"2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116799189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}