NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF CAREER proposal are presented

Optimal Control Parameters for a UPFC in a Multimachine Using PSO

The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the conventional techniques. This paper presents an approach using particle swarm optimization (PSO) for the design of optimal conventional controllers for a UPFC in a multimachine power system. Simulation results are presented to show the effectiveness of the proposed PSO based approach for the design of optimal conventional controllers for a UPFC in a multimachine power system

Dynamic Optimization of a Multimachine Power System with a FACTS Device Using Identification and Control ObjectNets

This work presents a novel technique for dynamic optimization of the electric power grid using brain-like stochastic identifiers and controllers called ObjectNets based on neural network architectures with recurrence. ObjectNets are neural network architectures developed to identify/control a particular object with a specific objective in hand. The IEEE 14 bus multimachine power system with a FACTS device is considered in this paper. The paper focuses on the combined minimization of the terminal voltage deviations and speed deviations at the generator terminals and the bus voltage deviation at the point of contact of the FACTS device to the power network. Simulation results are provided for the identifier and controller ObjectNets for the generators and the FACT device

Choice of Utility Functions for Adaptive Critic Designs

This paper first presents a general overview of Adaptive Critic Designs (ACDs) and their existing control applications. It describes the importance of the right choice of utility functions for the development of critic networks and their convergence to the cost-to-go function J. A closer look into the step by step derivation of a utility function for the design of an ACD nonlinear optimal neurocontroller to replace/augment the conventional controllers, the automatic voltage regulator and governor, in a power system consisting of a generator connected to the power grid is described and some results are presented

Adaptive Critics for Dynamic Particle Swarm Optimization

This work introduces a novel technique for dynamic particle swarm optimization (DPSO) using adaptive critic designs. The adaptation between global and local search in an optimization algorithm is critical for optimization problems especially in a dynamically changing environment or process over time. The inertia weight in particle swarm optimization (PSO) is dynamically adjusted in this paper in order to provide a nonlinear search capability for the PSO algorithm. Results on benchmark functions in the literature are provided

Neural Network Based Classification of Road Pavement Structures

Roads have formed the basic infrastructure of commerce since flints and other tools and artifacts were first exchanged along the trade routes of prehistory. Roadways are very large, in volume, in extent, and in value. They also wear out, and their useful life is directly proportional to their initial strength and inversely proportional to the number of heavy goods vehicles using them. Therefore, the increasing complexity of road transportation needs advanced techniques for effective design of pavements. This paper proposes an intelligent technique using neural networks to classify different types of road pavement structures, which is essential in estimating bearing capacities and load equivalency factors of pavements under different loadings

Speaker Identification Using a Combination of Different Parameters as Feature Inputs to an Artificial Neural Network Classifier

This paper presents a technique using artificial neural networks (ANNs) for speaker identification that results in a better success rate compared to other techniques. The technique used in this paper uses both power spectral densities (PSDs) and linear prediction coefficients (LPCs) as feature inputs to a self organizing feature map to achieve a better identification performance. Results for speaker identification with different methods are presented and compared

An Adaptive Control Strategy for DSTATCOM Applications in an Electric Ship Power System

Distribution static compensator (DSTATCOM) is a shunt compensation device that is generally used to solve power quality problems in distribution systems. In an all-electric ship power system, power quality issues arise due to high-energy demand loads such as pulse loads. This paper presents the application of a DSTATCOM to improve the power quality in a ship power system during and after pulse loads. The control strategy of the DSTATCOM plays an important role in maintaining the voltage at the point of common coupling. A novel adaptive control strategy for the DSTATCOM based on artificial immune system (AIS) is presented in this paper. The optimal parameters of the controller are first obtained by using the particle swarm optimization algorithm. This provides a sort of innate immunity (robustness) to common system disturbances. For unknown and random system disturbances, the controller parameters are modified online, thus providing adaptive immunity to the control system. The performance of the DSTATCOM and the AIS-based adaptive control strategy is first investigated in MATLAB-/Simulink-based simulation platform. It is verified through a real-time ship power system implementation on a real-time digital simulator and the control algorithm on a digital signal processor

Artificial Immune System Based DSTATCOM Control for an Electric Ship Power System

Distribution static compensator (DSTATCOM) is a shunt compensation device which is generally used to solve power quality problems in distribution systems. In an all-electric ship power system, these power quality problems mainly arise due to the pulsed loads, which causes the degradation of the entire system performance. This paper presents the application of DSTATCOM to improve the power quality in a ship power system during and after pulsed loads. The control strategy of the DSTATCOM plays an important role in maintaining the voltage at the point of common coupling. A novel adaptive control strategy for the DSTATCOM based on artificial immune system (AIS) is proposed. The optimal parameters of the controller are first found using particle swarm optimization. This provides a sort of innate immunity to common system disturbances. For unusual system disturbances, these optimal parameters are modified online, thus providing adaptive immunity in the control system. To evaluate the performance of the DSTATCOM and the AIS adaptive controller, a ship power system is developed in the MATLAB/SIMULINK environment. The effectiveness of the DSTATCOM and the AIS controller is examined for pulsed loads of different magnitudes and durations

Intelligent Coordinated Control of a Wind Farm and Distributed Smartparks

Energy storage is generally recommended in presence of an intermittent source like wind farm for a better control over the power generation from the wind turbine with the variation of the wind speed. In this paper, the potential of plug-in electric vehicle parking lot (SmartPark) as an energy storage in a power system with a large wind farm has been investigated. Also, a fuzzy logic based coordination controller of the wind farm and the distributed SmartParks has been proposed in this paper. The fuzzy controller uses the total state-of-charge of the SmartParks and the difference between instantaneous demand and the available wind power generation as the inputs and thereby generates the charging or discharging power commands of the SmartParks and the pitch angle reference for the wind turbine. A 12-bus multimachine power system with a 400 MW wind farm is used as a test system. Six SmartParks are also connected to the same bus where the wind farm is connected. The entire model is developed in Real-Time Digital Simulator (RTDS) for power system. The results demonstrate the action of the coordinated controller to reduce the oscillations in the tie-line power flow with the sudden variations of the wind speed