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Optimal control of class of non-linear plants using artificial immune systems: Application of the clonal selection algorithm

Optimal control of class of non-linear plants using artifici...

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2007 IEEE 22nd International Symposium on Intelligent Control, ISIC 2007

作者： Panimadai Ramaswamy, S.A. Venayagamoorthy, G.K. Balakrishnan, S.N. IEEE Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409 United States Department of Mechanical Aerospace and Engineering Mechanics University of Missouri Rolla MO 65409 United States

ISBN: (纸本)142440441X

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 in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of non-linear plants which are affine in control. The clonal selection algorithm is adapted for optimal control. A new mutation operator that operates on real values and one that aids in fast convergence is developed in this paper. AIS is used to obtain constant coefficient Kalman gain matrices. The validation and evaluation of the results thus obtained are carried out by comparing with standard and the widely used State Dependent Algebraic Riccati Equation (SDARE) method for the non-linear plants. In case of non-linear systems with hard state constraints, the SDARE formulation requires the use of mathematically involved expressions to incorporate these state constraints. However, the modified clonal selection algorithm developed in this paper has been used with hardly any changes to incorporate the hard state constraints and obtain the Kalman gain matrix. © 2007 IEEE.

关键词： Artificial intelligence

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Dynamic modeling of wind farms with fixed-speed wind turbine generators

Dynamic modeling of wind farms with fixed-speed wind turbine...

引用

2007 IEEE Power engineering Society General Meeting, PES

作者： Wei, Qiao Harley, Ronald G. Venayagamoorthy, Ganesh K. IEEE School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States University of KwaZulu-Natal Durban South Africa Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla Rolla MO 65409-0249 United States

ISBN: (纸本)1424412986

A wind farm typically consists of a large number of individual wind turbine generators (WTGs) connected by an internal electrical network. To study the impact of wind farms on the dynamics of the power system, an important issue is to develop appropriate wind farm models to represent the dynamics of many individual WTGs. This paper presents various dynamic models, including a detailed model and three reduced-order equivalent models, of wind farms with fixed-speed WTGs. These models are developed and compared by simulation studies in the PSCAD/EMTDC environment under different wind velocity and fluctuation conditions as well as gird fault conditions. Concluding remarks are provided on how to choose an appropriate wind farm model for power system dynamic and transient studies. © 2007 IEEE.

关键词： Power plants

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Collaborative Routing Algorithm for Wireless Sensor Network Longevity

Collaborative Routing Algorithm for Wireless Sensor Network ...

引用

Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： Shishir Bashyal Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri MO USA

This study proposes a new parameter for evaluating longevity of wireless sensor networks after showing that the existing parameters do not properly evaluate the performance of algorithms in increasing longevity. This study also proposes an ant inspired Collaborative Routing Algorithm for Wireless Sensor Network Longevity (CRAWL) that has scalability and adaptability features required in most wireless sensor networks. Using the proposed longevity metrics and implementing the algorithm in simulations, it is shown that CRAWL is much more adaptive to non-uniform distribution of available energy in sensor networks. The performance of CRAWL is compared to that of a non-collaborative algorithm. Both algorithms perform equally well when the available energy distribution is uniform but when the distribution is non-uniform, CRAWL is found to have 20.2% longer network life. CRAWL performance degraded by just 10.1% when the available energy was unevenly distributed in the sensor network proving the algorithms adaptability.

关键词： Collaboration Routing Wireless sensor networks Scalability Energy resources Protocols Sensor phenomena and characterization Monitoring Intelligent sensors Energy consumption

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An Estimation of Distribution Improved Particle Swarm Optimization Algorithm

An Estimation of Distribution Improved Particle Swarm Optimi...

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Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： R. V. Kulkarni G. K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri MO USA

PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation of distribution algorithm and two PSO variants. These algorithms are tested on benchmark functions having high dimensionalities. Results indicate that the methods strengthen the global optimization abilities of PSO and therefore, serve as attractive choices to determine solutions to optimization problems in areas including sensor networks.

关键词： Particle swarm optimization Ant colony optimization Electronic design automation and methodology Probability distribution Benchmark testing Optimization methods Equations Genetic mutations Space exploration real time systems

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Using micro-climate sensing to enhance RF localization in assisted living environments

Using micro-climate sensing to enhance RF localization in as...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Anthony Rowe Zane Starr Raj Rajkumar Real-Time and Multimedia Systems Laboratory Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

In this paper, we propose micro-climate sensing as an effective means of enhancing conventional RF-based localization. Our system targets people-tracking applications in dynamic indoor environments, such as nursing homes, hospitals and office spaces that require simple deployment and where conventional RF-based tracking using signal strengths alone is very likely to suffer from time-varying signal attenuation and inevitable changes in the environment over time such as new furniture arrangements, people traffic, changing obstacle patterns etc. To help mitigate these effects, we use time-synchronized windows of sensor samples to dynamically associate a mobile node with its nearest beacon nodes. Comparisons and matches are always relative to the ambient attributes at the time of localization, and hence our technique automatically evolves with environmental changes. We consider this property of localization techniques to be a significant contribution and a necessary requirement for any long- lived localization system. In assisted-living environments, sensor networks likely already have basic sensors to collect contextual information about users and to monitor the environment. We propose using light, humidity, temperature and audio data samples over a short window of time to model the micro-climate of a beacon node. Using microclimate matching in conjunction with RF signal strength decreases the worst-case localization error significantly by a factor of more than 3 (from 25 m to 8 m) while making the system more resilient to environment changes. Microclimate data helps ensure at least room level location tracking even in buildings like hospitals with many rooms in close proximity.

关键词： Radio frequency Sensor phenomena and characterization Hospitals Biomedical monitoring Indoor environments Medical services Target tracking time varying systems Attenuation Humidity

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Identification of Induction Machines Stator Currents with Generalized Neurons

Identification of Induction Machines Stator Currents with Ge...

引用

2007 IEEE Power engineering Society Conference and Exposition in Africa (IEEE PES PowerAfrica 2007), part 2

作者： Jing Huang Ganesh K. Venayagamoorthy Keith Corzine Real-Time Power and Intelligent Systems Laboratory Electrical and Computer Engineering Department University of Missouri Rolla MO USA

A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal injected to the induction machine operating at its rated value was chosen as the test input signal. For validation, the trained GN model is applied on the different operating conditions of the system.

关键词： Induction machines Stators Neurons Multilayer perceptrons Neural networks Multi-layer neural network Feedforward neural networks Neural network hardware Particle swarm optimization Algorithm design and analysis

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Optimal scheduling of generator maintenance using modified discrete particle swarm optimization

Optimal scheduling of generator maintenance using modified d...

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iREP Symposium on Bulk Power System Dynamics and Control

作者： Yusuf Yare Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

This paper presents a modified discrete particle swarm optimization (PSO) based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. While GA and other analytical methods might suffer from premature convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. PSO is known to effectively solve large scale multi-objective optimization problems. Here, a modified discrete PSO approach is proposed for the GMS optimization problem in order to overcome the limitations of the conventional methods and come up with a feasible and an optimal solution.

关键词： Optimal scheduling Particle swarm optimization Power system reliability Power generation Preventive maintenance Power system economics Power generation economics Power system analysis computing Convergence Large-scale systems

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A Novel Impedance Measurement Technique for Power Electronic Systems

A Novel Impedance Measurement Technique for Power Electronic...

引用

Annual IEEE Conference on Power Electronics Specialists (PESC)

作者： Peng Xiao Ganesh Venayagamoorthy Keith Corzine Real-Time Power and Intelligent Systems Laboratory Electrical and Computer Engineering Department University of Missouri Rolla Rolla MO USA

When designing and building power systems that contain power electronic switching sources and loads, system integrators must consider the frequency-dependent impedance characteristics at an interface to ensure system stability. Stability criteria have been developed in terms of source and load impedance for both dc and ac systems and it is often necessary to measure system impedance through experiments. Traditional injection-based impedance measurement techniques require multiple online tests which lead to many disadvantages. The impedance identification method proposed in this paper greatly reduces online test time by modeling the system with recurrent neural networks. The recurrent networks are trained with measured signals from the system with only one injection. The measurement and identification processes for dc and three-phase ac interfaces are developed. Simulation tests demonstrate the effectiveness of this new technique.

关键词： Impedance measurement Power electronics Power system stability Stability analysis Frequency measurement System testing Admittance Power system measurements Recurrent neural networks Power system simulation

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Self-healing control with multifunctional gate drive circuits for power converters

Self-healing control with multifunctional gate drive circuit...

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2007 IEEE Industry Applications Conference 42nd Annual Meeting, IAS

作者： Peng, Xiao Venayagamoorthy, Ganesh K. Corzine, Keith A. Woodley, Robert Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla United States 21st Century Systems Inc. 199 E 4th St. Ft. Leonard Wood MO United States

ISBN: (纸本)1424403642

Many commercial and military transport systems have fault diagnostic functions implemented to help protect the device when a severe fault occurs. However, most present systems do not contain prognostics capability which would allow operators to observe an unhealthy system component in its prefault condition. In industry applications, scheduled downtime can result in considerable cost avoidance. The next technology step is self-healing system components which observe not only potential problems, but can also take steps to continue operation under abnormal conditions - whether due to long-term normal wear-and-tear or sudden combat damage. In this paper, current and voltage information using the double-layer gate drive concept is fed to intelligent networks to identify the type of fault and its location. These intelligent networks are based on unsupervised and supervised learning networks (self-organizing maps and learning vector quantization networks respectively). The proposed concept allows the reconfiguration of the electric machinery system for continued normal operation of the machine. This paper presents an intelligent health monitoring and self-healing control strategy for a multi-phase multilevel motor drive under various types of faults. © 2007 IEEE.

关键词： Power converters

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Swarm Intelligence for Transmission System Control

Swarm Intelligence for Transmission System Control

引用

IEEE General Meeting Power& Energy Society

作者： G. K. Venayagamoorthy R. G. Harley Real Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Many areas related to power system transmission require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. This paper highlights the application of swam intelligence techniques for solving some of the transmission system control problems.

关键词： Particle swarm optimization Control systems Power system analysis computing Power systems Power system stability Birds Marine animals Educational institutions Ant colony optimization Nonlinear control systems

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