Kumar Venayagamoorthy - Academia.edu (original) (raw)

Papers by Kumar Venayagamoorthy

Energies

Voltage control in modern electric power distribution systems has become challenging due to the i... more Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prior...

2015 Clemson University Power Systems Conference (PSC), 2015

Modern interconnected electric power systems are made up of a large number of buses to meet the d... more Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.

2015 Clemson University Power Systems Conference (PSC), 2015

Modern interconnected electric power systems are made up of a large number of buses to meet the d... more Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.

IEEE Transactions on Industry Applications, 2019

A demand response management (DRM) system is proposed here, in which a service provider determine... more A demand response management (DRM) system is proposed here, in which a service provider determines a mutual optimal solution for the utility and the customers in a microgrid setting. Such a system may find use with a service provider interacting with the respective customers and utilities under the existence of some DRM agreements. The service provider is an entity which acts at different levels of the electrical grid and carry out the optimization. The lowest level controls one “neighborhood” while higher levels of service providers control other lower level service providers. A microgrid consisting of a smart neighborhood of 12 customers was used as experimental case study and an advanced metering infrastructure (AMI) was implemented. Based on the formulation of an optimization problem which exploits price-responsive demand flexibility and the AMI infrastructure, a win-win-win strategy is presented. The interior-point method was used to solve the objective function and the application of particle swarm optimization and artificial immune systems for demand response were explored. Results for a range of typical scenarios were presented to demonstrate the effectiveness of the proposed demand–response management framework.

2009 15th International Conference on Intelligent System Applications to Power Systems

An Echo State Network (ESN) can make multi-step predictions since it can process temporal informa... more An Echo State Network (ESN) can make multi-step predictions since it can process temporal information without the training difficulties encountered by conventional recurrent neural networks. An ESN is applied in this paper to make multistep predictions of solar irradiance, 30 minutes to 270 minutes into the future. The ESN is trained and tested using two performance metrics (correlation coefficient and

IEEE Transactions on Emerging Topics in Computational Intelligence

Interconnected power systems with large-scale penetration of photovoltaic (PV) power introduce fr... more Interconnected power systems with large-scale penetration of photovoltaic (PV) power introduce frequency and tieline power flow fluctuations. This is due to the variability and uncertainty characteristics of PV power. This makes automatic generation control (AGC) to be more challenging. In other words, maintaining system frequencies and tie-line power flows at the desired values, also known as "tie-line bias control" is difficult. In this paper, an enhanced tie-line bias control method is proposed by predicting PV power generation and bus frequencies. A cyber-physical two-area power system with a large PV plant consisting of phasor measurement units (PMUs) is studied. The use of synchrophasor networks consisting of PMUs can enable smooth power system operations overcoming the challenges of PV power variability and uncertainty. However, the use of PMUs in power system control creates vulnerabilities for cyber-attacks that could jeopardize the power system operations. It is shown that the frequency prediction using a virtual synchrophasor network (VSN) can mitigate the impact(s) of denial of service (DoS) attacks on physical PMUs. Enhanced AGC performance is investigated under different weather and load conditions including a weather profile during the "Great American Eclipse" of August 21 st , 2017. Typical results indicate that the enhanced AGC structure provides a resilient and sustainable tie-line bias control in uncertain environments.

2005 Annual Conference Proceedings

ABSTRACT

SAIEE Africa Research Journal

Power system state estimation is a fundamental computational process that requires both speed and... more Power system state estimation is a fundamental computational process that requires both speed and reliability. To meet the needs, some variants of the constant Jacobian methods have been used in the industry over the last several decades. The variants work very well under normal operating conditions with nominal values of the states. However, the convergence of the methods are not analysed mathematically and it may contain pitfalls. In this study, the convergence of the constant Jacobian methods are analysed and it is shown that the methods fail under high variations of the states. To increase the reliability of the processes, a multi-Jacobian method is proposed. Through simulation, a special case is shown for IEEE 68, and IEEE 118-bus systems where the Jacobian calculated with the nominal value fails, and the proposed multi-Jacobian method succeeds.

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control

ABSTRACT

Engineering Applications of Artificial Intelligence

7th International Conference on Information and Automation for Sustainability, 2014

Proceedings of the International Joint Conference on Neural Networks, 2003., 2000

Absfrucf-The design of nonlinear optimal neurocontrollers programming (DP) in classical optimal c... more Absfrucf-The design of nonlinear optimal neurocontrollers programming (DP) in classical optimal control theory [7], the based on the Adaptive Critic Designs (ACDs) family of ACD technique provides an effective method to construct an algorithms has recently attracted interest. This paper presents optimal and robust feedback controller by exploiting a summary of these algorithms, and compares their backpropagation for the calculation of all the derivatives of performance when implemented on two different types of user-defined target quantities [l] in order to minimize the artificial neural networks, namely the multilayer perceptron heuristic cost-to-go approximation. neural network (MLPNN) and the radial basis function neural There are thee representative optimization control network ("). As an example for the application of the techniques among the ACDs family. 'One is the heuristic ACDs, the control of synchronous generator on an electric dynamic programming (HDP), which approximates

2007 IEEE 22nd International Symposium on Intelligent Control, 2007

Abstract—The function of natural immune system is to protect the living organisms against invader... more Abstract—The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved ...

The 2006 Ieee International Joint Conference on Neural Network Proceedings, 2006

Abstract—This paper presents a novel fuzzy logic based controller for a Static Compensator (STATC... more Abstract—This paper presents a novel fuzzy logic based controller for a Static Compensator (STATCOM) connected to a power system. Type-II fuzzy systems are selected that enable the controller to deal with design uncertainties and the noise associated with the ...

The 2006 Ieee International Joint Conference on Neural Network Proceedings, 2006

... load. The interaction between loads connected to a point of common coupling (PCC) is a highly... more ... load. The interaction between loads connected to a point of common coupling (PCC) is a highly dynamic process. ... network. Identification of harmonic sources in a power system has been a challenging task for many years. Harmonic ...

2007 International Joint Conference on Neural Networks, Aug 1, 2007

2009 International Joint Conference on Neural Networks, Jun 14, 2009

A shunt active filter is a power electronic device used in a power system to decrease ldquoharmon... more A shunt active filter is a power electronic device used in a power system to decrease ldquoharmonic current pollutionrdquo caused by nonlinear loads. The Echo State Network (ESN) has been widely used as an effective system identifier with much faster training speed than the other Recurrent Neural Networks (RNNs). However, only a few attempts have been made to use an

The increasing complexity of a modern power grid highlightsthe need for advanced system identific... more The increasing complexity of a modern power grid highlightsthe need for advanced system identification techniques foreffective control of power systems. This paper provides a newmethod for nonlinear identification of turbogenerators in a 3machine6-bus power system using online trained feedforwardneural networks. Each turbogenerator in the power system isequipped with a neuroidentifier, which is able to identt2 itsparticular turbogenerator and the rest

Swarm Intelligence, 2000

This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal a... more This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal allocation of a STATCOM in a 45 bus system which is part of the Brazilian power network. The criterion used in finding the optimal location is based on the voltage profile of the system, i.e. the voltage deviation at each bus, with respect to its optimum

Energies

Voltage control in modern electric power distribution systems has become challenging due to the i... more Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prior...

2015 Clemson University Power Systems Conference (PSC), 2015

Modern interconnected electric power systems are made up of a large number of buses to meet the d... more Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.

2015 Clemson University Power Systems Conference (PSC), 2015

Modern interconnected electric power systems are made up of a large number of buses to meet the d... more Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.

IEEE Transactions on Industry Applications, 2019

A demand response management (DRM) system is proposed here, in which a service provider determine... more A demand response management (DRM) system is proposed here, in which a service provider determines a mutual optimal solution for the utility and the customers in a microgrid setting. Such a system may find use with a service provider interacting with the respective customers and utilities under the existence of some DRM agreements. The service provider is an entity which acts at different levels of the electrical grid and carry out the optimization. The lowest level controls one “neighborhood” while higher levels of service providers control other lower level service providers. A microgrid consisting of a smart neighborhood of 12 customers was used as experimental case study and an advanced metering infrastructure (AMI) was implemented. Based on the formulation of an optimization problem which exploits price-responsive demand flexibility and the AMI infrastructure, a win-win-win strategy is presented. The interior-point method was used to solve the objective function and the application of particle swarm optimization and artificial immune systems for demand response were explored. Results for a range of typical scenarios were presented to demonstrate the effectiveness of the proposed demand–response management framework.

2009 15th International Conference on Intelligent System Applications to Power Systems

An Echo State Network (ESN) can make multi-step predictions since it can process temporal informa... more An Echo State Network (ESN) can make multi-step predictions since it can process temporal information without the training difficulties encountered by conventional recurrent neural networks. An ESN is applied in this paper to make multistep predictions of solar irradiance, 30 minutes to 270 minutes into the future. The ESN is trained and tested using two performance metrics (correlation coefficient and

IEEE Transactions on Emerging Topics in Computational Intelligence

Interconnected power systems with large-scale penetration of photovoltaic (PV) power introduce fr... more Interconnected power systems with large-scale penetration of photovoltaic (PV) power introduce frequency and tieline power flow fluctuations. This is due to the variability and uncertainty characteristics of PV power. This makes automatic generation control (AGC) to be more challenging. In other words, maintaining system frequencies and tie-line power flows at the desired values, also known as "tie-line bias control" is difficult. In this paper, an enhanced tie-line bias control method is proposed by predicting PV power generation and bus frequencies. A cyber-physical two-area power system with a large PV plant consisting of phasor measurement units (PMUs) is studied. The use of synchrophasor networks consisting of PMUs can enable smooth power system operations overcoming the challenges of PV power variability and uncertainty. However, the use of PMUs in power system control creates vulnerabilities for cyber-attacks that could jeopardize the power system operations. It is shown that the frequency prediction using a virtual synchrophasor network (VSN) can mitigate the impact(s) of denial of service (DoS) attacks on physical PMUs. Enhanced AGC performance is investigated under different weather and load conditions including a weather profile during the "Great American Eclipse" of August 21 st , 2017. Typical results indicate that the enhanced AGC structure provides a resilient and sustainable tie-line bias control in uncertain environments.

2005 Annual Conference Proceedings

ABSTRACT

SAIEE Africa Research Journal

Power system state estimation is a fundamental computational process that requires both speed and... more Power system state estimation is a fundamental computational process that requires both speed and reliability. To meet the needs, some variants of the constant Jacobian methods have been used in the industry over the last several decades. The variants work very well under normal operating conditions with nominal values of the states. However, the convergence of the methods are not analysed mathematically and it may contain pitfalls. In this study, the convergence of the constant Jacobian methods are analysed and it is shown that the methods fail under high variations of the states. To increase the reliability of the processes, a multi-Jacobian method is proposed. Through simulation, a special case is shown for IEEE 68, and IEEE 118-bus systems where the Jacobian calculated with the nominal value fails, and the proposed multi-Jacobian method succeeds.

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control

ABSTRACT

Engineering Applications of Artificial Intelligence

7th International Conference on Information and Automation for Sustainability, 2014

Proceedings of the International Joint Conference on Neural Networks, 2003., 2000

Absfrucf-The design of nonlinear optimal neurocontrollers programming (DP) in classical optimal c... more Absfrucf-The design of nonlinear optimal neurocontrollers programming (DP) in classical optimal control theory [7], the based on the Adaptive Critic Designs (ACDs) family of ACD technique provides an effective method to construct an algorithms has recently attracted interest. This paper presents optimal and robust feedback controller by exploiting a summary of these algorithms, and compares their backpropagation for the calculation of all the derivatives of performance when implemented on two different types of user-defined target quantities [l] in order to minimize the artificial neural networks, namely the multilayer perceptron heuristic cost-to-go approximation. neural network (MLPNN) and the radial basis function neural There are thee representative optimization control network ("). As an example for the application of the techniques among the ACDs family. 'One is the heuristic ACDs, the control of synchronous generator on an electric dynamic programming (HDP), which approximates

2007 IEEE 22nd International Symposium on Intelligent Control, 2007

Abstract—The function of natural immune system is to protect the living organisms against invader... more Abstract—The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved ...

The 2006 Ieee International Joint Conference on Neural Network Proceedings, 2006

Abstract—This paper presents a novel fuzzy logic based controller for a Static Compensator (STATC... more Abstract—This paper presents a novel fuzzy logic based controller for a Static Compensator (STATCOM) connected to a power system. Type-II fuzzy systems are selected that enable the controller to deal with design uncertainties and the noise associated with the ...

The 2006 Ieee International Joint Conference on Neural Network Proceedings, 2006

... load. The interaction between loads connected to a point of common coupling (PCC) is a highly... more ... load. The interaction between loads connected to a point of common coupling (PCC) is a highly dynamic process. ... network. Identification of harmonic sources in a power system has been a challenging task for many years. Harmonic ...

2007 International Joint Conference on Neural Networks, Aug 1, 2007

2009 International Joint Conference on Neural Networks, Jun 14, 2009

A shunt active filter is a power electronic device used in a power system to decrease ldquoharmon... more A shunt active filter is a power electronic device used in a power system to decrease ldquoharmonic current pollutionrdquo caused by nonlinear loads. The Echo State Network (ESN) has been widely used as an effective system identifier with much faster training speed than the other Recurrent Neural Networks (RNNs). However, only a few attempts have been made to use an

The increasing complexity of a modern power grid highlightsthe need for advanced system identific... more The increasing complexity of a modern power grid highlightsthe need for advanced system identification techniques foreffective control of power systems. This paper provides a newmethod for nonlinear identification of turbogenerators in a 3machine6-bus power system using online trained feedforwardneural networks. Each turbogenerator in the power system isequipped with a neuroidentifier, which is able to identt2 itsparticular turbogenerator and the rest

Swarm Intelligence, 2000

This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal a... more This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal allocation of a STATCOM in a 45 bus system which is part of the Brazilian power network. The criterion used in finding the optimal location is based on the voltage profile of the system, i.e. the voltage deviation at each bus, with respect to its optimum