2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)最新文献

Current Control Structures for Grid-Connected Photovoltaic System 并网光伏系统的电流控制结构

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051629

Suchismita Roy, P. Sahu, S. Jena, RUDRANARAYAN SENAPATI

{"title":"Current Control Structures for Grid-Connected Photovoltaic System","authors":"Suchismita Roy, P. Sahu, S. Jena, RUDRANARAYAN SENAPATI","doi":"10.1109/iSSSC56467.2022.10051629","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051629","url":null,"abstract":"In the present scenario, renewable energy source like solar power is the more convenient source of energy. Sustaining a steady DC link voltage and regulation of grid-current are challenging task in grid-tied photovoltaic (PV) system. These issues are overcome by designing a dual-loop control schemes; outer dc-link voltage loop which generates the reference signal for the inner current loop. The grid-tried inverter is used in photovoltaic (PV) system to inject the current with low total harmonic distortion (THD) into the utility grid by designing the efficient current controller. Generally, proportional-integral (PI) controller, proportional-resonant (PR) controller, dead-beat controller and hysteresis current controller are used in PV system to regulate the grid-current. These controllers are implemented in any one of the reference frames like; synchronous (d-q), stationary (α-β) or natural (a-b-c) reference frame. In this paper, the above-mentioned grid-current controllers are employed in all three reference frames and evaluate the steady-state performance of the controllers in terms of THD. The advantages and disadvantages of all grid-current controllers in each reference frames are briefly discussed and compared. This paper provides the best possible grid-current controller in three different reference frames. Finally, the simulation results from a two-stage 5.5 kW, 440 V (L-L), three-phase grid-tied PV system are provided to confirm the theoretical analysis and effectiveness of the control schemes.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116684909","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}

引用次数: 1

Enhanced Heuristic technique for short term hydrothermal together with wind-solar generating power units 结合风力-太阳能发电机组的短期热液改进启发式技术

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051573

S. Kar, D. Dash, M. K. Nath, Renu Sharma

引用次数: 0

Optimal design of Fuzzy control based IPFC and SMES in AGC of multi source Power System 基于IPFC和SMES的多源电力系统AGC模糊控制优化设计

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051278

Guru Prasad Pattnaik, P. Sahu, R. Prusty, S. Panda

{"title":"Optimal design of Fuzzy control based IPFC and SMES in AGC of multi source Power System","authors":"Guru Prasad Pattnaik, P. Sahu, R. Prusty, S. Panda","doi":"10.1109/iSSSC56467.2022.10051278","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051278","url":null,"abstract":"The flexible AC transmission system (FACTS) devices play major role for controlling various parameters of the interconnected power system. Owing to large dynamic controlling nature of the FACTS, this research paper addresses a research work which employs interline power flow controller (IPFC) along with an advanced superconducting magnetic energy source to govern the generation schedule.. The power generations are monitored as per the load demand and the concept is referred as automatic generation control (AGC), Further, the work proposes a fuzzy based PID controller to make required control loop for AGC of the multi source power system. The work also has applied a novel Advanced-Whale optimization algorithm (A-WOA) to furnishing the parameters of the fuzzy PID controller. The work also has been progressed with various comparative studies in both controller and technique level to justify superiority of the proposed Fuzzy PID controller and novel A-WOA technique. Finally, it has been observed that proposed A-WOA designed Fuzzy PID controller is found to be most improved method to obtain AGC in the multi source power system under different operatings.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127217109","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}

引用次数: 5

An AI Based Support System For The Diagnosis Of Breast Cancer 基于人工智能的乳腺癌诊断支持系统

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051470

Debabrata Swain, Utsav Mehta, Ayush Bhatt, Ashwini Ramanuj

{"title":"An AI Based Support System For The Diagnosis Of Breast Cancer","authors":"Debabrata Swain, Utsav Mehta, Ayush Bhatt, Ashwini Ramanuj","doi":"10.1109/iSSSC56467.2022.10051470","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051470","url":null,"abstract":"At present, the health-care system is facing a drastic increase in the number of cancer patients. The huge number of Breast cancer cases among women and its constant increase is a matter of concern for the clinical support systems. Early diagnosis plays a crucial role in the treatment of such a fatal ailment. The late diagnosis is the main cause of the death of many people around the globe. Also, Image processing techniques do exist for the prediction of breast cancer however the scope of misdiagnosis still exists in this technique. For this reason, timely and accurate screening of breast cancer is a major challenge for the clinical support system. In such cases, machine learning can be used as an effective tool to reduce uncertainty in clinical decision-making. Machine learning is the technique for making the machine capable through a large amount of data to perform learning and produce useful outputs. In this work, a machine learning based classifier is developed using the Support vector machine for the diagnosis of breast cancer illness. The clinical data used for the creation and validation of the model is obtained from the UCI repository. SMOTE based oversampling has been performed to balance the classes of malignant and benign tumors in the dataset. A set of seven important features were selected based on their f-value to reduce the time as well as the cost of medical examination. The proposed classifier has reported a testing accuracy of 98.32%.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116450744","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}

引用次数: 0

A Simple and Novel Tuning Technique for Load Frequency Control in a Multi-Area Microgrid System 一种用于多区域微电网系统负荷频率控制的简单新颖调谐技术

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051581

Bibhudatta Mishra, R. Pradhan, M. Pattnaik

{"title":"A Simple and Novel Tuning Technique for Load Frequency Control in a Multi-Area Microgrid System","authors":"Bibhudatta Mishra, R. Pradhan, M. Pattnaik","doi":"10.1109/iSSSC56467.2022.10051581","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051581","url":null,"abstract":"A simple and novel analytical tuning technique for load frequency control (LFC) problem is presented in this work. The controllers used in work are conventional fixed parameter PID controllers. Their parameters are evaluated using the most reliable and famous Ziegler-Nichols (ZN) method. The frequencies of each area in a multi-area network as well as the tie-line power get fluctuated due to a slight unexpected load shift in any of the control areas. Further, frequency variations have direct negative influence on the operation, stability, reliability, and efficiency of a power system. Frequency needs to be essentially constant for a power system to operate effectively. Using LFC, an interrelated system can return the frequency of each area and the tie line powers to desired values even with an unexpected load variation. This can be accomplished by usage of a properly tuned controllers like PID controllers. A system model has been constructed in MATLAB/SIMULINK domain. The results from the studied system with ZN-tuned PID controller are compared with that of without PID controller. From the result analysis, the transient performances of studied system with ZN-based PID controller are found to be better than that of without controller case.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130645693","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}

引用次数: 0

Workflow Sensitive Access Management in Serverless Computing 无服务器计算中的工作流敏感访问管理

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051255

Anisha Kumari, Md. Akram Khan, B. Sahoo

{"title":"Workflow Sensitive Access Management in Serverless Computing","authors":"Anisha Kumari, Md. Akram Khan, B. Sahoo","doi":"10.1109/iSSSC56467.2022.10051255","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051255","url":null,"abstract":"In recent years, serverless computing has been emerging as a most profitable cloud framework, which drastically improves the development and deployment policy of online services, but as a result, it is highly exposed to tempting targets for attackers. These attackers are proposing innovative strategies to get beyond the transitory nature of serverless activities by taking advantage of container reuse for the execution of stateless functions. The external request for function invocation must be extensively verified to protect the valuable resources from attackers. Traditional access management policy usually checks the individual inbound request for function invocation by ignoring other dependencies associated with the complete workflow. In this paper, we have proposed a two-phase workflow sensitive access management (WAM) policy that provides authentication tokens and checks whether the incoming request possesses all the necessary permission or not. WAM is based on a state-dependency graph which is a representation of allowable permission required to make transitions among the functions in the workflow. AWS Lambda is considered as the base framework where WAM policy is integrated. The effectiveness of WAM is verified using four real-world serverless applications and the performance is extensively compared with other standard serverless frameworks like Openwhisk, Openfaas, and Microsoft Azure.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":" 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113952323","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}

引用次数: 0

Frequency regulation of multi area interconnected system by using artificial intelligence based controller 基于人工智能控制器的多区域互联系统频率调节

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051434

R. Priyadarsini, Archana M. Nayak, Ajit Kumar Barisal

{"title":"Frequency regulation of multi area interconnected system by using artificial intelligence based controller","authors":"R. Priyadarsini, Archana M. Nayak, Ajit Kumar Barisal","doi":"10.1109/iSSSC56467.2022.10051434","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051434","url":null,"abstract":"A slight impulsive load change in any zone of an interconnected power system will cause fluctuations in frequency and power in all zones. The main intention of load frequency control (LFC) is to stabilize the actual frequency and desired output power (MW) in the interlinked power system, by managing the variations of tie line power in controlled areas. Inherently, the LFC scheme constitutes a suitable control system for connected power systems. The control system has the ability to restore the local frequency and can connect the power to the initial setting point or very close to it after the load is moved by the help of standard controller. In my study, an AI-based controller has been used to analyze the load frequency control in a three-zone interconnected thermal hydro power generation system dynamically. The proposed idea makes use of advanced controlling methods using PI, PID and Fuzzy logic controllers for an interconnected hydrothermal heating power generation system in three areas system. The controller parameters which are made up here is based on the particle swarm optimization (PSO) technique, that uses an objective function called the integral time absolute error (ITAE) to control the deviation in frequency The performance simulation of the controller is done by using MATLAB2016b and by comparing the proposed fuzzy logic- based solution with PI and PID under the same conditions. By proper comparison among these methods, it is clearly noticeable that fuzzy logic controller performs better than other two approaches. The results of simulation are summarized and comparison analysis of the performance is done in terms of peak overshoot and settling time.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"54 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125623957","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}

引用次数: 0

Gene Selection by Hybrid Feature Selection Approaches and Classification Techniques in Microarray Dataset for Cancer Prediction 基于混合特征选择方法和微阵列数据集分类技术的基因选择用于癌症预测

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051247

W. Jaisingh, Subash Chandra Bose Jaganathan, Akanksha Verma

{"title":"Gene Selection by Hybrid Feature Selection Approaches and Classification Techniques in Microarray Dataset for Cancer Prediction","authors":"W. Jaisingh, Subash Chandra Bose Jaganathan, Akanksha Verma","doi":"10.1109/iSSSC56467.2022.10051247","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051247","url":null,"abstract":"The two most intriguing machine learning issues are feature (gene) selection and the categorization of microarray data. In this paper, three current feature selection and extraction methods, namely Information Gain (IG), Gain Ratio (GR), Relief, Information Gain with Relief, and Gain Ratio with Relief, are combined to develop a new selection and extraction method. The primary objective of this study is to use hybrid feature extraction to select the independent components of DNA microarray data. This is done with the intention of enhancing the performance of support vector machine (SVM) and neural network (NN) classifiers while simultaneously reducing the amount of computational resources required to complete the analysis In order to provide evidence that the methodology is reliable, it is applied to reduce the total number of genes present in four different DNA microarray datasets: Breast GSE22820, Breast GSE38959, Breast GSE42568, and Breast. SVM and NN classifiers are being used to classify these datasets. The results of the experiments performed on these four microarray gene expression data prove that the genes found by using the proposed methodology efficiently make improvements to the classification accuracy of SVM and NN classifiers. We compare our proposed method to standard current extraction algorithms and find that by employing SVM and NN classifiers with a reduced number of identified genes, the new method achieves superior classification accuracy. The receiver operating characteristic (ROC) curve shows the best subset of genes for the classifier and the suggested method for each unique dataset. Keywords— Information Gain, Gain Ratio, Relief, support vector machine, Neural Networks, Feature selection, Microarray data.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125636005","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}

引用次数: 0

iSSSC 2022 Cover Page iSSSC 2022封面

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/isssc56467.2022.10051476

引用次数: 0

Deep Learning based Pest Classification in Soybean crop using Residual Network-50 基于深度学习的残差网络大豆病虫害分类[j]

2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Pub Date : 2022-12-15 DOI: 10.1109/iSSSC56467.2022.10051424

Dhyey Shah, R. Gupta, Krishna Patel, Devam Jariwala, Jeet Kanani

{"title":"Deep Learning based Pest Classification in Soybean crop using Residual Network-50","authors":"Dhyey Shah, R. Gupta, Krishna Patel, Devam Jariwala, Jeet Kanani","doi":"10.1109/iSSSC56467.2022.10051424","DOIUrl":"https://doi.org/10.1109/iSSSC56467.2022.10051424","url":null,"abstract":"Agriculture is the fountainhead of human sustenance, farmers have an imminent risk of the crops getting attacked by the pest. Times before the advancement in science and technology, farmers incorporated traditional techniques in dealing with pests, but the major issue faced by them was the detection and classification of the various species of pests. With the advancement of technology, researchers implemented a Deep Learning method to classify various species of pests by analysing pictures captured in real-life situations. In this paper, deep convolutional neural networks (DCNN) are used to classify four different categories of bugs/pests found on the soya bean crops. As Deep Learning outperforms when working with a large data set, various augmentation techniques were applied to the raw images to make a larger dataset and improve accuracy. The results say that the deep learning architecture when fine-tuned can give higher classification accuracy against other traditional classification methods, reaching accuracies up to 96.25%. The results show that the architectures help to understand pest control management in soya bean crop fields.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134104191","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}

引用次数: 2