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... 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...
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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... 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.
Research Interests: Computer Science and IEEE
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... 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.
Research Interests: Computer Science and IEEE
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... 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.
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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... 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
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AbstractThe 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... more
AbstractThe 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 ...
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AbstractThis 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... more
AbstractThis 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 ...
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... 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 ...
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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... 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
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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... 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
Research Interests: Power Systems, System Identification, Nonlinear dynamics, Power System, Neural Networks, and 15 moreNeural Network, Control Systems, Artificial Neural Networks, Identification, Real Time Systems, Feedforward Neural Network, Power Grid, Experimental Study, Real Time, Steady state, Transmission Line, Electric Power, Nonlinear identification, Artificial Neural Network, and Feedforward Neural Networks
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... 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
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ABSTRACT Power system defensive islanding is an efficient way to avoid catastrophic wide area blackouts, such as the 2003 North American Blackout. Finding defensive islands of large-scale power systems is a combinatorial explosion... more
ABSTRACT Power system defensive islanding is an efficient way to avoid catastrophic wide area blackouts, such as the 2003 North American Blackout. Finding defensive islands of large-scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution, if it exists, within reasonable time using analytical methods. This paper presents to utilize the computational efficiency property of binary particle swarm optimization to find some efficient splitting solutions for large-scale power systems. The solutions are optimized based on a fitness function considering the real power balance between generations and loads in islands, the relative importance of customers, and the desired number of islands. Besides providing information about the opening of transmission lines, the algorithm can also provide necessary load shedding information. Furthermore, the algorithm can provide a number of candidate solutions in order to select one satisfying the transmission system capacity constraint. Simulations with power systems of different scales demonstrate the accuracy and effectiveness of the proposed algorithm.
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This paper presents the design of a neurocontroller for a turbogenerator that augments/replaces the conventional Automatic Voltage Regulator (AVR) and the turbine governor. The neurocontroller uses a novel technique based on the Adaptive... more
This paper presents the design of a neurocontroller for a turbogenerator that augments/replaces the conventional Automatic Voltage Regulator (AVR) and the turbine governor. The neurocontroller uses a novel technique based on the Adaptive Critic Designs (ACDs) with emphasis on Heuristic Dynamic Programming (HDP) and Dual Heuristic Programming (DHP). Results are presented to show that the DHP based neurocontroller is robust
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... power quality, and improved dynamic performance during power system disturbances such as network voltage sags and short circuits. ... III. MODELING AND CONTROL OF DFIG The basic configuration of a DFIG driven by a WT is shown in Fig.... more
... power quality, and improved dynamic performance during power system disturbances such as network voltage sags and short circuits. ... III. MODELING AND CONTROL OF DFIG The basic configuration of a DFIG driven by a WT is shown in Fig. ...
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Research Interests: Bioinformatics, Genetics, Artificial Intelligence, Machine Learning, Time Series, and 10 moreSwarm Intelligence, Distributed System, Network Management, Neural Network, Gene expression, Genetic Engineering, Recurrent Neural Network, Gene Network, Evolutionary Algorithm, and Synthetic Data Generation
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Research Interests: Pattern Recognition, Power System, Neural Networks, Neural Network, Recurrent Neural Network, and 11 moreSignal and Image Processing, Multilayer Perceptron, OPTIMIZATION TECHNIQUE, Sliding Window, Function approximation, Hardware Implementation of Algorithms, Nonlinear system identification, Input Output, Particle Swarm Optimizer, Static VAR Compensator (SVC), and Time varying
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Defensive islanding is an efficient way to avoid catastrophic failures and wide area blackouts. Power system splitting especially for large scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an... more
Defensive islanding is an efficient way to avoid catastrophic failures and wide area blackouts. Power system splitting especially for large scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution (if one exists) for large scale power system in real time. This paper proposes to utilize the computational efficiency property of Binary Particle Swarm Optimization (BPSO) to find some efficient splitting solutions in limited timeframe. The solutions are optimized based on a cost function considering the balance between real power generation and consumption, the relative importance of customers, the capacities of distribution and transmission systems, and possibility of region to be impacted, etc. The solutions not only provide the lines to cut but also the corresponding load shedding information in each island. Simulations with large scale power system demonstrate the effectiveness of the proposed algorithm.
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This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS... more
This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.
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Research Interests: Bioinformatics, Artificial Intelligence, Evolutionary Computation, Computational Biology, Biomimetics, and 15 moreDifferential Evolution, Gene expression, Genetic Engineering, Computer Simulation, Animals, Gene Network, Gene Regulatory Networks, Genes, Chimera, Gene Function, Gradient Descent, Gradient Method, Gene Expression Regulation, Gene expression profiling, and Gene Expression Data
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Robust tuning of lead-lag type controller used for regulating FACTS control signal to improve dynamic stability has been an area of interest in research. This paper presents a new approach based on the foraging behavior of E.coli Bacteria... more
Robust tuning of lead-lag type controller used for regulating FACTS control signal to improve dynamic stability has been an area of interest in research. This paper presents a new approach based on the foraging behavior of E.coli Bacteria in the human intestine, to optimize simultaneously three constants each of four lead-lag type UPFC controller present in a Single Machine Infinite Bus (SMIB) power system. For the tuning purpose, a multi-objective cost function is formulated that accounts for damping factors and ratios of various system modes for a wide range of operating conditions. Robustness of the proposed tuning method is shown by transient stability analysis of the system time domain simulations when subjected to disturbances at different operating conditions.
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... 1942–1948. [2] SM Guru, SK Halgamuge, S.Fernando, “Particle Swarm Optimizers for Cluster formation in Wireless Sensor Networks,” Proc. Int. ... 274 – 279 [6] C. Mendis, SM Guru, S. Halgamuge, S. Fernando, “Optimized sink node path... more
... 1942–1948. [2] SM Guru, SK Halgamuge, S.Fernando, “Particle Swarm Optimizers for Cluster formation in Wireless Sensor Networks,” Proc. Int. ... 274 – 279 [6] C. Mendis, SM Guru, S. Halgamuge, S. Fernando, “Optimized sink node path using particle swarm optimization,” Proc. ...
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