Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730686
Anusha Prudhivi, Sai Sahithya Bonumaddi, Niharika Kota, Chandra Sekhar Vinnamala, V. S. G. Thadikemalla
Overweight and Capsizing of passenger boats are the main reasons that cause catastrophe. To avoid such disasters we are proposing a robust system for disaster detection and prevention. This system aims to alert the control room and to facilitate the communication between control room and disaster management department in such havoc to avoid maximum loss. It comprises of triple axis accelerometer along with triple axis gyroscope to detect unusual orientation, a weighing load sensor to detect overload and GSM module for communication. All these components are securely positioned in boat with anti tamper alert mechanism, that serves a great purpose of avoiding the passenger boat disasters or tragedies
{"title":"A Robust System to Detect and Prevent Boat Accidents","authors":"Anusha Prudhivi, Sai Sahithya Bonumaddi, Niharika Kota, Chandra Sekhar Vinnamala, V. S. G. Thadikemalla","doi":"10.1109/CAPS52117.2021.9730686","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730686","url":null,"abstract":"Overweight and Capsizing of passenger boats are the main reasons that cause catastrophe. To avoid such disasters we are proposing a robust system for disaster detection and prevention. This system aims to alert the control room and to facilitate the communication between control room and disaster management department in such havoc to avoid maximum loss. It comprises of triple axis accelerometer along with triple axis gyroscope to detect unusual orientation, a weighing load sensor to detect overload and GSM module for communication. All these components are securely positioned in boat with anti tamper alert mechanism, that serves a great purpose of avoiding the passenger boat disasters or tragedies","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"41 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114194738","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730716
R. Kumari, Sweta Prasad, Amrita Kumari, Ajit Kumar
The aim of this work is to execute the dynamics using high performance computing (HPC) method. To this end, a parareal algorithm is implemented on a single machine infinite bus (SMIB) power system. OpenMP is used along with parareal algorithm to execute the system in multi-core environment. Thus, entire code is written in ‘C’ language to harness the OpenMP library. It is shown that as the number of threads are increased, execution time of system simulation is profoundly improved.
{"title":"Parallel in Time Simulation of Automatic Generation Control System for Near Real-Time Transient Stability Analysis","authors":"R. Kumari, Sweta Prasad, Amrita Kumari, Ajit Kumar","doi":"10.1109/CAPS52117.2021.9730716","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730716","url":null,"abstract":"The aim of this work is to execute the dynamics using high performance computing (HPC) method. To this end, a parareal algorithm is implemented on a single machine infinite bus (SMIB) power system. OpenMP is used along with parareal algorithm to execute the system in multi-core environment. Thus, entire code is written in ‘C’ language to harness the OpenMP library. It is shown that as the number of threads are increased, execution time of system simulation is profoundly improved.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130795863","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730712
P. R. Sarkar, A. Yadav, A. Minai, R. Pachauri
This article proposes an SPV system, based on maximum power extraction through interleaved boost (IB) converter. For this purpose the converter is connected between solar panel and load. The advantageous features of using interleaved boost converter are to provide highly increased voltage gain and faster transient response. It also reduces the ripple across output voltage, switching losses and electromagnetic interference. Interleaved Boost (IB) Converter with MPPT technology plays major role in terms of economic operation for power generation. In this paper the implementation of SPV system with IB Converter is analyzed with its operational characteristics using MPPT algorithm. The proposed system performance analysis is done by using P&O optimization method to achieve maximum power from SPV system. This proposed topology with IBC improves overall efficiency and minimizes the switching losses. The system performance is validated by simulation results using MATLAB software.
{"title":"MPPT Based SPV System Design and Simulation Using Interleaved Boost Converter","authors":"P. R. Sarkar, A. Yadav, A. Minai, R. Pachauri","doi":"10.1109/CAPS52117.2021.9730712","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730712","url":null,"abstract":"This article proposes an SPV system, based on maximum power extraction through interleaved boost (IB) converter. For this purpose the converter is connected between solar panel and load. The advantageous features of using interleaved boost converter are to provide highly increased voltage gain and faster transient response. It also reduces the ripple across output voltage, switching losses and electromagnetic interference. Interleaved Boost (IB) Converter with MPPT technology plays major role in terms of economic operation for power generation. In this paper the implementation of SPV system with IB Converter is analyzed with its operational characteristics using MPPT algorithm. The proposed system performance analysis is done by using P&O optimization method to achieve maximum power from SPV system. This proposed topology with IBC improves overall efficiency and minimizes the switching losses. The system performance is validated by simulation results using MATLAB software.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129398086","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730647
C. Mahamuni, Zuber Mohammed Jalauddin
Terrestrial Wireless Sensor Networks (WSNs) are used in military environments for region surveillance, healthcare systems for soldiers, and, smart transport, and logistics, etc. In surveillance applications, the sensor nodes are deployed randomly in the field to observe the events of interest, movement of humans, or vehicles. In these sensor networks, the image or video is captured by the camera module. Many times it becomes difficult to correctly detect the intrusion or anomalous activity in the field because the image being captured maybe not clear enough due to prevailing weather conditions, the amount of light, and other reasons. In this paper, in addition to a WSN Surveillance System for military applications, we have used Convolutional Neural Network (CNN) for analyzing and understanding the content of the captured images and videos. CNN is a deep learning neural network that detects and tracks automatically the important features without any human supervision. The distinctive layers of each class are learned by themselves and have the highest accuracy of prediction. The results of the implementation for four test images captured in different conditions show an accuracy of 92%. The results of the video tracking yield the Object Tracking Efficiency of 80.35%.
{"title":"Intrusion Monitoring in Military Surveillance Applications using Wireless Sensor Networks (WSNs) with Deep Learning for Multiple Object Detection and Tracking","authors":"C. Mahamuni, Zuber Mohammed Jalauddin","doi":"10.1109/CAPS52117.2021.9730647","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730647","url":null,"abstract":"Terrestrial Wireless Sensor Networks (WSNs) are used in military environments for region surveillance, healthcare systems for soldiers, and, smart transport, and logistics, etc. In surveillance applications, the sensor nodes are deployed randomly in the field to observe the events of interest, movement of humans, or vehicles. In these sensor networks, the image or video is captured by the camera module. Many times it becomes difficult to correctly detect the intrusion or anomalous activity in the field because the image being captured maybe not clear enough due to prevailing weather conditions, the amount of light, and other reasons. In this paper, in addition to a WSN Surveillance System for military applications, we have used Convolutional Neural Network (CNN) for analyzing and understanding the content of the captured images and videos. CNN is a deep learning neural network that detects and tracks automatically the important features without any human supervision. The distinctive layers of each class are learned by themselves and have the highest accuracy of prediction. The results of the implementation for four test images captured in different conditions show an accuracy of 92%. The results of the video tracking yield the Object Tracking Efficiency of 80.35%.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313812","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730728
Harshwardhan Bhangale, R. Bansal, Shrijeet Jain, J. Sarvaiya
Liver tumor is one of the significant causes of death among men and women, but it is confirmed that early detection of the disease ensures the long survival of the patient. In our research, a hybrid of Multi-feature pyramid based U-Net, short skip connections and a Feature similarity module are proposed for early tumor detection. The proposed algorithm focuses on improving the tumor segmentation performance with fewer training parameters. The robustness of the proposed algorithm is claimed on the basis of the dice score coefficient of tumor segmentation. We have achieved a dice score of 0.753 and 0.950 on tumor and liver, respectively on the Liver Tumor Segmentation (LiTS) dataset. In comparison with earlier models, our model has achieved a higher dice coefficient with less training time with nearly 6 million learnable parameters.
{"title":"Multi-feature Similarity Based Deep Learning Framework for Semantic Segmentation","authors":"Harshwardhan Bhangale, R. Bansal, Shrijeet Jain, J. Sarvaiya","doi":"10.1109/CAPS52117.2021.9730728","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730728","url":null,"abstract":"Liver tumor is one of the significant causes of death among men and women, but it is confirmed that early detection of the disease ensures the long survival of the patient. In our research, a hybrid of Multi-feature pyramid based U-Net, short skip connections and a Feature similarity module are proposed for early tumor detection. The proposed algorithm focuses on improving the tumor segmentation performance with fewer training parameters. The robustness of the proposed algorithm is claimed on the basis of the dice score coefficient of tumor segmentation. We have achieved a dice score of 0.753 and 0.950 on tumor and liver, respectively on the Liver Tumor Segmentation (LiTS) dataset. In comparison with earlier models, our model has achieved a higher dice coefficient with less training time with nearly 6 million learnable parameters.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129067004","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730605
Pushpendra Kumar, Muzammil Khan, Shreya Gupta
Due to the wide range of applications, video surveillance is known as one of the challenging tasks of computer vision which requires detecting and tracking the moving objects in a sequence of images (video). As we are aware that several environmental conditions such as fog, darkness, snow-fall, illumination, rain degrade the quality of vision system. This motivates us to develop a robust infrared (IR) surveillance system to fulfill the open-ended goals of the vision problem. The active motion region is detected by using optical flow. In this paper, an energy functional has been presented for optical flow estimation by incorporating the fractional order total variational (TV) and quadratic terms. In particular, the proposed model is convex and more robust against outliers and provides a dense flow. However, the total variation regularization term is of non-differentiable nature which makes the minimization scheme apparently difficult. The fractional derivative discretization of non-differentiable terms is performed by using Grunwald-Letnikov (GL) derivative. The Primal-dual algorithm is applied in solving the resulting minimization scheme. Finally, the resulting variational formulation is solved by using an appropriate method. The validity, efficiency, and robustness of the proposed system are tested on a variety of datasets under various conditions.
{"title":"Development of an IR Video Surveillance System Based on Fractional Order TV-Model","authors":"Pushpendra Kumar, Muzammil Khan, Shreya Gupta","doi":"10.1109/CAPS52117.2021.9730605","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730605","url":null,"abstract":"Due to the wide range of applications, video surveillance is known as one of the challenging tasks of computer vision which requires detecting and tracking the moving objects in a sequence of images (video). As we are aware that several environmental conditions such as fog, darkness, snow-fall, illumination, rain degrade the quality of vision system. This motivates us to develop a robust infrared (IR) surveillance system to fulfill the open-ended goals of the vision problem. The active motion region is detected by using optical flow. In this paper, an energy functional has been presented for optical flow estimation by incorporating the fractional order total variational (TV) and quadratic terms. In particular, the proposed model is convex and more robust against outliers and provides a dense flow. However, the total variation regularization term is of non-differentiable nature which makes the minimization scheme apparently difficult. The fractional derivative discretization of non-differentiable terms is performed by using Grunwald-Letnikov (GL) derivative. The Primal-dual algorithm is applied in solving the resulting minimization scheme. Finally, the resulting variational formulation is solved by using an appropriate method. The validity, efficiency, and robustness of the proposed system are tested on a variety of datasets under various conditions.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129167226","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730680
Arun Kumar, Adarsh Kumar, A. Vishwakarma
Crop images have different color intensities of a pixel as well as complex backgrounds. Hence, multilevel thresholding of crop images is very significant in the field of computer vision. Entropy-based multilevel thresholding is considered a successful enhancement over the bi-level thresholding technique for image segmentation. It is a time-consuming approach for practical uses. In this paper, minimum cross entropy (MCE) has been combined with the bacterial foraging optimization (BFO) algorithm has to enhance the accuracy of the segmented image. The BFO algorithm is a newly constituted evolutionary algorithm, which offers better search capabilities. The accuracy of the proposed method is tested over 10 different crop images with complex backgrounds and compared with an efficient algorithm such as an artificial bee colony (ABC). The experimental result demonstrates that the proposed technique segments the cropped image more accurately and searches multiple thresholds value very efficiently, which are close to the optimal value. The outcome of the proposed techniques shows a high quality of segmented images.
{"title":"Multilevel Crop Image Segmentation using Bacterial Foraging Optimization Based on Minimum Cross Entropy","authors":"Arun Kumar, Adarsh Kumar, A. Vishwakarma","doi":"10.1109/CAPS52117.2021.9730680","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730680","url":null,"abstract":"Crop images have different color intensities of a pixel as well as complex backgrounds. Hence, multilevel thresholding of crop images is very significant in the field of computer vision. Entropy-based multilevel thresholding is considered a successful enhancement over the bi-level thresholding technique for image segmentation. It is a time-consuming approach for practical uses. In this paper, minimum cross entropy (MCE) has been combined with the bacterial foraging optimization (BFO) algorithm has to enhance the accuracy of the segmented image. The BFO algorithm is a newly constituted evolutionary algorithm, which offers better search capabilities. The accuracy of the proposed method is tested over 10 different crop images with complex backgrounds and compared with an efficient algorithm such as an artificial bee colony (ABC). The experimental result demonstrates that the proposed technique segments the cropped image more accurately and searches multiple thresholds value very efficiently, which are close to the optimal value. The outcome of the proposed techniques shows a high quality of segmented images.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123072180","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730492
S. Panigrahi, U. Raju
Pedestrian detection plays a pivotal role in applications such as robotics, automated driving, assistive living, and surveillance. The problem of pedestrian detection, although approached by many computer vision researchers is far from solved. The scale, pose, occlusion, illumination, and many such factors affect the performance of the methods. In this work, a modification of the most commonly used deep convolutional neural network model ResNet18 is proposed. The modified CNN structure forms the base of the Faster RCNN model utilized to predict the locations of pedestrians in the image. The proposed method has been improved in terms of the feature map extraction of the image. To evaluate the proposed method, two benchmark datasets INRIA Pedestrian and PASCAL VOC 2012 are considered. The performance metrics used for evaluation are Detection Error Trade-off and Precision-Recall Curve. A statistical analysis is also conducted. The proposed method is compared against state-of-the-art detection methods.
{"title":"An improved Faster RCNN for Pedestrian Detection","authors":"S. Panigrahi, U. Raju","doi":"10.1109/CAPS52117.2021.9730492","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730492","url":null,"abstract":"Pedestrian detection plays a pivotal role in applications such as robotics, automated driving, assistive living, and surveillance. The problem of pedestrian detection, although approached by many computer vision researchers is far from solved. The scale, pose, occlusion, illumination, and many such factors affect the performance of the methods. In this work, a modification of the most commonly used deep convolutional neural network model ResNet18 is proposed. The modified CNN structure forms the base of the Faster RCNN model utilized to predict the locations of pedestrians in the image. The proposed method has been improved in terms of the feature map extraction of the image. To evaluate the proposed method, two benchmark datasets INRIA Pedestrian and PASCAL VOC 2012 are considered. The performance metrics used for evaluation are Detection Error Trade-off and Precision-Recall Curve. A statistical analysis is also conducted. The proposed method is compared against state-of-the-art detection methods.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114177105","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730530
A. Karnan, G. Nagamani
This work examines the issue of synchronization control of proportional delayed memristor-based cellular neural networks (MCNNs). Due to the memristor's state transition char-acteristics, parameters mismatching will occur during the syn-chronization process. To overcome such parameters mismatching issue, a discontinuous robust control method is employed. A novel Lyapunov-Krasovskii functional (LKF) including delay parameter is considered for the proposed problem with relaxation on the positive definite constraint in the LKF. A delay-dependent stability criterion is provided and expressed as linear matrix inequalities (LMIs) using Lyapunov stability theory and robust analysis approach. Finally, the obtained theoretical result is verified through an illustrative example.
{"title":"Synchronization Control of Proportional Delayed Memristive Cellular Neural Networks: Robust Analysis Approach","authors":"A. Karnan, G. Nagamani","doi":"10.1109/CAPS52117.2021.9730530","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730530","url":null,"abstract":"This work examines the issue of synchronization control of proportional delayed memristor-based cellular neural networks (MCNNs). Due to the memristor's state transition char-acteristics, parameters mismatching will occur during the syn-chronization process. To overcome such parameters mismatching issue, a discontinuous robust control method is employed. A novel Lyapunov-Krasovskii functional (LKF) including delay parameter is considered for the proposed problem with relaxation on the positive definite constraint in the LKF. A delay-dependent stability criterion is provided and expressed as linear matrix inequalities (LMIs) using Lyapunov stability theory and robust analysis approach. Finally, the obtained theoretical result is verified through an illustrative example.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122463462","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}
Pub Date : 2021-12-10DOI: 10.1109/CAPS52117.2021.9730615
Ajit Kumar, Supriya Kumari, Ashiwani Kumar
This paper proposes a normal form driven controller for damping the oscillatory dynamics in power systems. A normal form based excitation controller is investigated. We are adopting a novel nonlinear control design for the auxiliary controller rather than a traditional linear control technique, and this nonlinear controller is expected to replicate optimal PSS features. We are using eigenvalue analysis to evaluate the performance of this novel controller for the IEEE 1.1 (4th order) model, and the results reveal that the suggested technique has optimal PSS features.
{"title":"Extended Ideal PSS: A Theoretical Study","authors":"Ajit Kumar, Supriya Kumari, Ashiwani Kumar","doi":"10.1109/CAPS52117.2021.9730615","DOIUrl":"https://doi.org/10.1109/CAPS52117.2021.9730615","url":null,"abstract":"This paper proposes a normal form driven controller for damping the oscillatory dynamics in power systems. A normal form based excitation controller is investigated. We are adopting a novel nonlinear control design for the auxiliary controller rather than a traditional linear control technique, and this nonlinear controller is expected to replicate optimal PSS features. We are using eigenvalue analysis to evaluate the performance of this novel controller for the IEEE 1.1 (4th order) model, and the results reveal that the suggested technique has optimal PSS features.","PeriodicalId":445427,"journal":{"name":"2021 International Conference on Control, Automation, Power and Signal Processing (CAPS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116514942","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}
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