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2019 

1.  Kuraganti Chetan Kumar; Gurunath Gurrala; Rajesh, Sundaresan A Sequential Testing Framework for Identifying a Transmission Line Outage in a Power System Conference eEnergy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems, June 25  28, 2019, Phoenix, AZ, USA, 2019. Abstract  BibTeX  Tags: Smart Grid  Links: @conference{@Chetan2019, title = {A Sequential Testing Framework for Identifying a Transmission Line Outage in a Power System}, author = {Kuraganti Chetan Kumar; Gurunath Gurrala; Rajesh,Sundaresan}, url = {http://10.0.54.4/wpcontent/uploads/2019/10/ASequentialTestingFrameworkforIdentifyingaTransmissionLineOutageinaPowerSystem.pdf}, doi = {10.1145/3307772.3328297}, year = {2019}, date = {20190615}, booktitle = {eEnergy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems, June 25  28, 2019, Phoenix, AZ, USA}, pages = {331342}, abstract = {The topology of a power system changes when a line outage is encountered. Identifying which line has failed in the shortest possible time is of importance due to the cascading nature of such failures. In this work, we propose a state estimation based sequential hypothesis testing procedure to locate the failed line. We focus on single line outages as these are the most frequently occurring failures. Earlier work on state estimation based sequential testing procedure used a DC approximation model assuming that the sensors provided angle and voltage information. This is known to be a coarse model but results in a simpler linear estimation problem. In this work, we look at a finer nonlinear model of power measurements and treat phase angles and voltages as hidden states. After a local linearization, we propose a Kalman filter based state estimation followed by a generalized likelihood ratio testing procedure to determine which of the lines has failed. We consider both centralized and decentralized approaches. In the centralized case, measurements from every installed meter is made available to the system operator. In the decentralized case, only limited aggregated information is made available because of, for example, communication capacity constraints. We test our algorithms on the IEEE 14 and 118 bus systems and show that all high risk link failures are quickly identified.}, keywords = {Smart Grid}, pubstate = {published}, tppubtype = {conference} } The topology of a power system changes when a line outage is encountered. Identifying which line has failed in the shortest possible time is of importance due to the cascading nature of such failures. In this work, we propose a state estimation based sequential hypothesis testing procedure to locate the failed line. We focus on single line outages as these are the most frequently occurring failures. Earlier work on state estimation based sequential testing procedure used a DC approximation model assuming that the sensors provided angle and voltage information. This is known to be a coarse model but results in a simpler linear estimation problem. In this work, we look at a finer nonlinear model of power measurements and treat phase angles and voltages as hidden states. After a local linearization, we propose a Kalman filter based state estimation followed by a generalized likelihood ratio testing procedure to determine which of the lines has failed. We consider both centralized and decentralized approaches. In the centralized case, measurements from every installed meter is made available to the system operator. In the decentralized case, only limited aggregated information is made available because of, for example, communication capacity constraints. We test our algorithms on the IEEE 14 and 118 bus systems and show that all high risk link failures are quickly identified. 
2018 

2.  Bansal, Sahil; Ghosh, Anindita; Seelamantula, Chandra Sekhar; Gurrala, Gurunath; Ghosh, Prasanta Kumar Adaptive frequency estimation using iterative DESA with RDFTbased filter Conference Proceedings of the 2017 IEEE PES AsiaPacific Power and Energy Engineering Conference (APPEEC), 08.10.11.17, Bangalore, 2018. Abstract  BibTeX  Tags: Smart Grid  Links: @conference{Bansal2018, title = {Adaptive frequency estimation using iterative DESA with RDFTbased filter}, author = {Sahil Bansal and Anindita Ghosh and Chandra Sekhar Seelamantula and Gurunath Gurrala and Prasanta Kumar Ghosh}, url = {http://www.rbccps.org/wpcontent/uploads/2018/12/08308990.pdf}, doi = {10.1109/APPEEC.2017.8308990}, year = {2018}, date = {20180308}, booktitle = {Proceedings of the 2017 IEEE PES AsiaPacific Power and Energy Engineering Conference (APPEEC), 08.10.11.17, Bangalore}, abstract = {This paper proposes a new approach for estimating fundamental frequency of grid signals. The approach is based on a discretetime energy separation algorithm (DESA) combined with an adaptive bandpass filter (BPF). The BPF is built using a discrete Fourier transform (DFT) and inverse DFT both used recursively. The technique is computationally efficient and robust to the harmonics and noise in the signal. The method's performance is validated by comparing the results with some existing algorithms.}, keywords = {Smart Grid}, pubstate = {published}, tppubtype = {conference} } This paper proposes a new approach for estimating fundamental frequency of grid signals. The approach is based on a discretetime energy separation algorithm (DESA) combined with an adaptive bandpass filter (BPF). The BPF is built using a discrete Fourier transform (DFT) and inverse DFT both used recursively. The technique is computationally efficient and robust to the harmonics and noise in the signal. The method's performance is validated by comparing the results with some existing algorithms. 
2017 

3.  Diddigi, Raghuram Bharadwaj; Reddy, Sai Koti D; Narayanam, Krishnasuri; Bhatnagar, Shalabh A unified decision making framework for supply and demand management in microgrid networks Journal Article arXiv: Computer Science, 2017. Abstract  BibTeX  Tags: Smart Grid  Links: @article{Diddigi2017b, title = {A unified decision making framework for supply and demand management in microgrid networks}, author = {Raghuram Bharadwaj Diddigi and D. Sai Koti Reddy and Krishnasuri Narayanam and Shalabh Bhatnagar}, url = {http://www.rbccps.org/wpcontent/uploads/2018/12/1711.05078.pdf}, year = {2017}, date = {20171114}, journal = {arXiv: Computer Science}, abstract = {This paper considers two important problems  on the supplyside and demandside respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while meeting customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the timeadjustable demand  i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide for the first time in the literature, a unified Markov decision process (MDP) framework for these problems. We then apply the Qlearning algorithm, a popular modelfree reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that our model outperforms the traditional power sharing models. }, keywords = {Smart Grid}, pubstate = {published}, tppubtype = {article} } This paper considers two important problems  on the supplyside and demandside respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while meeting customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the timeadjustable demand  i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide for the first time in the literature, a unified Markov decision process (MDP) framework for these problems. We then apply the Qlearning algorithm, a popular modelfree reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that our model outperforms the traditional power sharing models. 
4.  Gupta, Ankita; Gurrala, Gurunath; Sastry, Pidaparthy S Instability prediction in power systems using recurrent neural networks Conference Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 19.25.08.17, Melbourne (Australia), 2017. Abstract  BibTeX  Tags: Smart Grid  Links: @conference{Gupta2017, title = {Instability prediction in power systems using recurrent neural networks}, author = {Ankita Gupta and Gurunath Gurrala and Pidaparthy S. Sastry}, url = {http://www.rbccps.org/wpcontent/uploads/2018/12/0249.pdf}, year = {2017}, date = {20170825}, booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 19.25.08.17, Melbourne (Australia)}, pages = {17951801}, abstract = {Recurrent Neural Networks (RNNs) can model temporal dependencies in time series well. In this paper we present an interesting application of stacked Gated Recurrent Unit (GRU) based RNN for early prediction of imminent instability in a power system based on normal measurements of power system variables over time. In a power system, disturbances like a fault can result in transient instability which may lead to blackouts. Early prediction of any such contingency can aid the operator to take timely preventive control actions. In recent times some machine learning techniques such as SVMs have been proposed to predict such instability. However, these approaches assume availability of accurate fault information like its occurrence and clearance instants which is impractical. In this paper we propose an Online Monitoring System (OMS), which is a GRU based RNN, that continuously keeps predicting the current status based on past measurements. Through extensive simulations using a standard 118bus system, the effectiveness of the proposed system is demonstrated. We also show how we can use PCA and predictions from the RNN to identify the most critical generator that leads to transient instability.}, keywords = {Smart Grid}, pubstate = {published}, tppubtype = {conference} } Recurrent Neural Networks (RNNs) can model temporal dependencies in time series well. In this paper we present an interesting application of stacked Gated Recurrent Unit (GRU) based RNN for early prediction of imminent instability in a power system based on normal measurements of power system variables over time. In a power system, disturbances like a fault can result in transient instability which may lead to blackouts. Early prediction of any such contingency can aid the operator to take timely preventive control actions. In recent times some machine learning techniques such as SVMs have been proposed to predict such instability. However, these approaches assume availability of accurate fault information like its occurrence and clearance instants which is impractical. In this paper we propose an Online Monitoring System (OMS), which is a GRU based RNN, that continuously keeps predicting the current status based on past measurements. Through extensive simulations using a standard 118bus system, the effectiveness of the proposed system is demonstrated. We also show how we can use PCA and predictions from the RNN to identify the most critical generator that leads to transient instability. 
5.  Diddigi, Raghuram Bharadwaj; Reddy, Sai Koti D; Bhatnagar, Shalabh Multiagent Qlearning for minimizing demandsupply power deficit in microgrids Journal Article arXiv: Computer Science, 2017. Abstract  BibTeX  Tags: Smart Grid  Links: @article{Diddigi2017, title = {Multiagent Qlearning for minimizing demandsupply power deficit in microgrids}, author = {Raghuram Bharadwaj Diddigi and D. Sai Koti Reddy and Shalabh Bhatnagar}, url = {http://www.rbccps.org/wpcontent/uploads/2018/12/1708.07732.pdf}, year = {2017}, date = {20170825}, journal = {arXiv: Computer Science}, abstract = {We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable power and also the electrical lines from the main grid. During each time period, these microgrids need to take decision on the amount of renewable power to be used from the batteries as well as the amount of power needed from the main grid. We formulate this problem in the framework of Markov Decision Process (MDP), similar to the one discussed in [1]. The power allotment to the village from the main grid is fixed and bounded, whereas the renewable energy generation is uncertain in nature. Therefore we adapt a distributed version of the popular Reinforcement learning technique, MultiAgent QLearning to the problem. Finally, we also consider a variant of this problem where the cost of power production at the main site is taken into consideration. In this scenario the microgrids need to minimize the demandsupply deficit, while maintaining the desired average cost of the power production. }, keywords = {Smart Grid}, pubstate = {published}, tppubtype = {article} } We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable power and also the electrical lines from the main grid. During each time period, these microgrids need to take decision on the amount of renewable power to be used from the batteries as well as the amount of power needed from the main grid. We formulate this problem in the framework of Markov Decision Process (MDP), similar to the one discussed in [1]. The power allotment to the village from the main grid is fixed and bounded, whereas the renewable energy generation is uncertain in nature. Therefore we adapt a distributed version of the popular Reinforcement learning technique, MultiAgent QLearning to the problem. Finally, we also consider a variant of this problem where the cost of power production at the main site is taken into consideration. In this scenario the microgrids need to minimize the demandsupply deficit, while maintaining the desired average cost of the power production. 