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Abstract—A large-scale power system is required to have a

new control system to operate at a higher level of automation, flexibility, and robustness. In this paper, a Multi-Agent System based Intelligent Identification System (MAS-IIS) is presented for identification and fault-diagnosis methodologies that improve the performance of the plant in a wide-range of operation. With proposed architecture of a single agent and an organization of the multi-agent system, the MAS-IIS realizes on-line adaptive identifiers for control, and off-line identifiers for fault-diagnosis in real-time power plant operation. The proposed MAS-IIS is one of the functions in Multi-Agent System based Intelligent Control Systems (MAS-ICSs) which has several functions that provide efficient ways to control locally and globally, and to accommodate and overcome the complexity of large-scale distributed systems.

Index Terms— Distributed system, large-scale system, multi-agent system, intelligent identification system, fault-diagnosis, power plant control.

I. INTRODUCTION

N the design of control systems for a power plant, an identifier is one of the components for power plant control

that provides information about the plant to controllers. On the other hand, fault-diagnosis is an important function for power plant monitoring to detect incipient faults for accommodation. However, while power plants are getting more complex and expansive to run, the identifier and fault-diagnosis are required to have a new framework to handle high computational complexity, huge amount of distributed data and coupling problems among many subsystems. Recently, there has been a growing interest in multi-agent systems (MASs) in order to deal successfully with the complexity and distributed problems of power systems. Each agent system has special functions to solve the distributed problems. Moreover, in the multi-agent system the agents can work together to solve problems, which are beyond the capabilities or knowledge of an individual agent [1]. There have been many applications of intelligent identifier for power plant models using basic neural network, diagonal recurrent neural network, fuzzy logic and neuro-fuzzy logic [2]-[8]. For power plant monitoring, fault-diagnosis systems

J. S. Heo and K. Y. Lee are with the Department of Electrical Engineering,

The Pennsylvania State University, University Park, PA 16802. (email: juh138@psu.edu, kwanglee@psu.edu).

have been developed by using data mining, neural network and fuzzy logic [9]-[11]. In order to overcome the problem of large-scale distributed systems, many applications of multi-agent systems or agent-oriented systems have been presented in control and monitoring systems [12]-[18]. Especially, the multi-agent system can help in monitoring the condition of system and providing effective asset management by diagnostics and protection against faults [17], [18]. Intelligent identifiers identify a power plant model which is characterized by a limited number of inputs and outputs. The fault-diagnosis system finds the faults by learning and analyzing the presence of faults. Both systems should perform in real-time operation while avoiding high computational complexity. However, a major concern for the networked sensing and actuation of a large-scale system is the complexity, due to the number of components and their interaction patterns and communication delays. The intelligent system and multi-agent system paradigms, as the state-of-the-art artificial intelligence software engineering concepts, may provide a comprehensive and unifying framework for building large-scale intelligent identification and fault-diagnosis systems for efficient power plant control and monitoring.

In this paper, a Multi-Agent System based Intelligent Identification System (MAS-IIS) is presented for identification and fault-diagnosis methodologies that improve the performance of the plant in a wide-range of operation. With the proposed architecture of a single agent and an organization of the multi-agent system, the MAS-IIS realizes on-line adaptive identifiers for control, and off-line identifiers for fault-diagnosis in real-time operation. The proposed MAS-IIS will be one of the functions in Multi-Agent System based Intelligent Control Systems (MAS-ICSs) which has several functions that provide efficient way to control locally and globally, and accommodate and overcome the complexity of large-scale distributed systems.

Following the introduction, the power plant and Multi-Agent System (MAS) are described in Section 2. Section 3 describes Multi-Agent System based Intelligent Identification System (MAS-IIS). Section 4 shows simulation results to demonstrate the feasibility of the proposed approach and the final section draws some conclusions.

A Multi-Agent System-Based Intelligent Identification System for Power Plant Control

and Fault-Diagnosis Jin S. Heo and Kwang Y. Lee, Fellow, IEEE

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1-4244-0493-2/06/$20.00 ©2006 IEEE.

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II. POWER PLANT AND MULTI-AGENT SYSTEM

A. Description of Power System [19], [20]

The power plant is a 600MW oil-filed drum-type boiler-turbine-generator unit. It is a balanced draft, controlled recirculation drum boiler capable of delivering 64.2 10 lb/hr× of steam at a pressure of 2600 psig and at 1005 F° . Six recirculation pumps supply the required recirculation flow to provide sufficient flow for full load operation. Two forced draft fans supply the primary air, and two induced draft fans are controlled to maintain a furnace pressure at a desired pre-set value. Two condensate pumps and a combined booster and main boiler feedpumps handle the feedwater flow.

The turbine is a tandem compound triple pressure steam turbine. It consists of three parts: a high-pressure turbine, an intermediate pressure turbine, and low twin pressure turbines rotating on a common shaft at a rated speed of 3600 rpm and exhausting pressure at a 2 inch Hg absolute. The generator is coupled with the turbine and has a 685,600 kVA, 3 phase, 60 Hz, 22 kV, with a power factor of 0.90.

The developed model represents an extension of some existing models [21]-[23] in two primary areas. First, the condensate and feedwater side dynamics have been modeled and second, the electrical prime movers which run fans and pumps and their dependence upon driving voltage and frequency have been modeled. The overwhelming majority of electric power generation is by conventional, drum-type, steam power plants. In this paper, the model has twenty-three state variables associated with physical processes. The model is reorganized into four main modules; which are boiler system, turbine-generator system, condenser system, and feedwater system [20]. The proposed MAS-IIS is one of the functional systems based on multi-agent system which is

interconnected with the subdivided and distributed subsystems that are components of the four main modules. Fig. 1 shows the large-scale distributed thermal power plant model and MAS. Most blocks are subsystems, represented by model. The proposed scheme will be applicable to other types of plants, including nuclear and fuel cell plants.

B. Multi-Agent System [24]

An agent is a computer software program that is autonomous and situated in some distributed environments in order to meet its design objectives. Since the agents are faced with different environments, they are designed differently and properly for the given environment. Moreover, the agent is intelligent because it is reactive, proactive, social, flexible, and robust. In a large-scale distributed complex system, the agent’s autonomous and intelligent properties can reduce the complexity by reducing the coupling problems between the subsystems. Furthermore, the proactive, reactive, and robust properties can be well suited for applications in a dynamic and unreliable situation [25],[26].

In order to design the control systems, design of architecture for single agent systems and an organization for multi-agent system are required in advance. First, the architecture of single agent systems is shown in Fig. 2. Since the agent is situated in an environment that is the power plant, it needs a perceptor and effecter to act and react. First, the sensed raw data are processed and mapped into a scenario, and then an objective, which is a sub-goal, is initialized under the situation to achieve the main goal that is the optimal operation. The initial objective is sent to other agents through the communicator for eliminating redundancy and conveying the mission of the agent to others. After confirming the objective, the best plan is chosen for the objective (sub-goal) in the decision-making. Depending on the plan, an algorithm module is selected to launch the plan. Finally, the action made

Fuel

Generator

Sea

CondenserPump

HPTurbine

Economizer &Air preheater

Reheater

Spray from

Feedwater

PrimarySuperheater

SecondarySuperheater

FeedwaterTurbine

Waterwall

Cross-overPipe

H.P. Feedwater

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Deaerator

FW.Turbine

IPTurbine

LPTurbine

IP TurbineIP Turbine

SecondarySuperheater

Spray to Boiler

HP.FW.

HeaterFeed Pump

DeaeratorL.P. FW.

Heater

Drum

CondenserFurnace

StackIDFAN

FDFAN

Feedwater valve

Feedpump

Fuel flow

Throttle valve

Intercept valve

Superheat Temp.

Reheat Temp.

Condensate flow

Gas recirculation

Recir.pump

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rhea

ter

Pre

ssur

e

`

`

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......

Air

Boiler System Turbine & Generator

Feedwater System

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P-I

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Fig. 1. The large-scale power plant model and MAS.

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by the algorithm module effects through the effecter into the environment. Most decisions are made in the decision-making process, which is like in a human brain [12],[13].

A Multi-Agent System (MAS) can be defined as a loosely coupled network (organization) of problem solvers (agents), which interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver (agent). In order to perform the cooperative works, it is presented to build multiple hierarchical structures for the multi-agent system organization as shown in Fig. 3. The organization has low level, middle level, and high level, and agent in each level has a specific role in the society so that there is a conceptual idea of supervision for processing the tasks. In this paper, the high level agents are the task delegation and interface agents, the middle level agents are the mediate and monitoring agents, and the low level agents are intelligent agents. The hierarchical structure that has three levels gives advantages for dynamic organization and autonomous systems. Moreover, the idea of multiple hierarchical structures is well suited for the large-scale distributed system [14],[25]. Although there are multiple hierarchical structures, each hierarchical structure has a different formation from others because the structures are constructed to fit for controlling each real physical subsystem in the power plant so that the organization is better optimized for a given power plant system [27],[28].

III. MULTI-AGENT SYSTEM-BASED INTELLIGENT

IDENTIFICATION SYSTEM

With the proposed architecture of a single agent and an organization of the multi-agent system, the MAS-IIS is developed for on-line adaptive identifiers and off-line identifiers of all subsystems for control and fault-diagnosis. The composition of MAS-ICSs for power plant is shown Fig. 4. Although all agents are connected with network, the Intelligent Identification System (IIS) cluster, which is made of on-line modeling agents and off-line modeling agents, perform mainly for the MAS-IIS. However, the IIS cluster will cooperate with the monitoring system and knowledge processing system clusters to realize the identification and fault-diagnosis system. For fault-diagnosis, faults will be informed to the operators through the interface agent by alarming on a terminal.

A. Off-line Identifiers in MAS-IIS

In order to realize the off-line identifiers for all subsystems, first, the knowledge database agent is gathering input/output data from all subsystems. There are 33 distributed subsystems in the four main modules; boiler, turbine-generator, condenser and feedwater systems. For instance, the boiler subsystems are shown in Table I. Every subsystem is connected with several inputs and outputs of other subsystems. Intelligent and monitoring agents send/receive inputs and outputs to/from actuators and sensors. While the knowledge database agent is having the data during the start-up procedure, the off-line modeling agents (#1 - #12) identify each subsystem using Neural Network (NN) as one of the algorithm modules in the

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Communicator Objectives

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Plan#nDecision-Making

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Reactive Situated

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Fig. 2. Single agent architecture.

High Level

Middle Level

Low Level

: Networking

Dynamic Organization

(cluster)

Environment (Sensors, Actuators)

: Interface agents

: Intelligent agents

: Mediate & Monitoring agents

Managing layer

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: Task delegation agents

Fig. 3. Organization of MAS.

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Task delegation agent

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Intelligent Identification

SystemReinforcement

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Fig. 4. Composition of MAS-ICSs for power plant.

TABLE I

Boiler subsystems in 600 MW oil-fired thermal power unit

Subsystem

#1 Forced Draft Fan

Subsystem #7

Drum

Subsystem #2

Air Preheater Subsystem

#8 Primary super gas

Subsystem #3

Induced Draft Fan Subsystem

#9 Primary Superheater

Subsystem #4

Downcomers Subsystem

#10 Spray Heater

Subsystem #5

Furnace Gas Subsystem

#11 Secondary super gas

Subsystem #6

Waterwall Subsystem

#12 Secondary

Superheater

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agents. Once, the off-line modeling agents complete a cycle of learning for identification of all subsystems with data from the knowledge database agent, the on-line performance monitoring agent validates the performances of off-line modeling agents with a new data set of inputs/outputs from the actual subsystems. Using the error criteria defined for the off-line identifiers, the on-line performance monitoring agent asks relearning to the off-line modeling agents which do not meet the criteria. The errors of off-line modeling agents will be recorded to distinguish from the faults in the subsystems. Fig. 5 shows the structure of MAS-IIS for the boiler system. The main purpose of off-line identifiers is to obtain nominal subsystem models for fault-diagnosis. Power plant faults can occur in sensors, actuator or components, and detecting these faults at early stage is a necessity. In this paper, faults are detected using the concept of redundancy [11]. The outputs of actual plant are compared to the outputs of nominal subsystem models, which are modeled by the off-line identifiers, and residuals are generated by the on-line performance monitoring agent. Residuals are the difference between the two sets of outputs, and if there are no faults, residuals are very small. In fault-diagnosis, dead-band on the residuals serves the purpose of avoiding any accumulation of small residuals that are caused by minor drifts or noise. However, if there is a fault in the plant, some or all of the residuals will not be small. Residuals at time t reflect the inputs/outputs of the plant at a particular time, whereas their integrals embed much more information about the time history of the outputs. Integrals of the residuals are fed into interface agent to alarm on a terminal. The terminal describes where the faults have occurred for accommodation.

B. On-line Adaptive Identifiers in MAS-IIS

In this paper, the on-line adaptive identifiers will be used for a modified predictive optimal controller to produce an optimal operation and preserve stability. In order to implement the on-line adaptive identifiers, first, initialization of identifiers is required for the on-line modeling agents. Since the Neural Network (NN) is the best nonlinear approximator, the on-line modeling agents have the NN algorithm in their algorithm modules. Initialization of NN is accomplished by using the structure of NN from the off-line modeling agents. With the initialized NN structures for all subsystems, the on-line modeling agents are ready to update the weights in the NN with inputs/outputs data set of the actual plant. Since there are four main modules, which are boiler, turbine-generator, condenser and feedwater systems in the power unit, computational complexity is very high and it should be managed properly. The proposed multi-agent system can be decentralized and calculate asynchronously to overcome those problems of large-scale distributed systems. For instance, the on-line adaptive identifiers of all boiler subsystems are connected with each other as exactly the same as the connections of actual boiler subsystems. While the actual boiler subsystems are receiving the control inputs from the boiler control agents and other subsystems, the on-line modeling agents also have the same inputs and calculate the outputs. After comparing with the outputs of actual boiler

subsystems, the on-line modeling agents update continuously the weights of NN. Since the on-line adaptive identifiers approximate the actual plant, a modified predictive optimal controller will generate the optimal control actions using the on-line adaptive identifiers. In emergency, the control action will be changed by observing the behavior of on-line adaptive identifiers. Furthermore, the on-line modeling agents check the inputs from the boiler control agent and other subsystems with their historical data. If an abnormal event is detected, the on-line modeling agents inform the event to their higher level agents. This kind of scenario will produce a rule to launch a plan for the agents to prevent a redundant measurement.

IV. SIMULATION RESULTS

In the following simulation, the results by the MAS-IIS will be shown. Simulation deals with two cases. The first case is the fault-diagnosis using off-line identifiers under the presence of errors in the boiler subsystems. The second case is for on-line adaptive identifiers of all boiler subsystems in real-time operation. Both cases are simulated with currently developed boiler control agent that uses a PID control algorithm as its algorithm module.

A. Fault-Diagnosis using Off-Line Identifiers

The simulation is run for the fault-diagnosis in the boiler subsystems. For instance, if the fuel valve actuator of furnace gas subsystem is not operating properly, the proposed fault-diagnosis system will detect and inform the operators.

First, the variable of fuel valve actuator in furnace gas subsystem, subsystem #5, is fixed intentionally from t=150 sec. to t=550. The off-line identifiers compute outputs with the same inputs to all actual subsystems in every second. The fault-diagnosis agent detects the errors between the outputs of

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Fault-diagnosis

agent

Fig. 5. Structure of MAS-IIS for the boiler system.

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actual subsystems and off-line identifiers as shown in Fig. 5. Fig. 6 shows the simulation results of fault-diagnosis using the off-line modeling, fault-diagnosis and interface agents. When the fault occurs in the furnace gas subsystem, the accumulated errors of the subsystem #5 are increased as shown in Fig. 6 (a) by linear scale. Since the subsystems are connected with each other, the errors can occur in other neighboring subsystems. Fig 6 (b) shows in log scale the accumulated errors of subsystems #5, #8 and #11 which are the furnace gas, primary super gas and secondary super gas subsystems. Since the hot gas exits the furnace and enters the primary superheater and the secondary superheater, the furnace gas is directly related to the primary gas and the secondary gas. Thus, the simulation results successfully show the validity of proposed fault-diagnosis system.

B. On-Line Adaptive Identifiers of Boiler Subsystems

The simulation for the on-line adaptive identifiers of boiler subsystems is performed simultaneously when the power plant model is simulated by the boiler control agent. The on-line adaptive identifiers keep minimizing the errors by comparing with the outputs of actual subsystems during the simulation. Fig. 7 shows the major performances of on-line adaptive identifiers for the boiler subsystems. Fig. 7 (a) shows the outputs of air flow rate in the forced draft fan, subsystem #1. Fig. 7 (b) shows the outputs of the water level in the drum, subsystem #7. Fig. 7 (c) shows the outputs of the outlet pressure in the primary superheater, subsystem #9. Fig. 7 (d) shows the outputs of the outlet temperature in the primary superheater, subsystem #9. Fig. 7 (e) shows the outputs of the outlet pressure in the secondary superheater, subsystem #12. Fig. 7 (f) shows the outputs of the outlet temperature in the secondary superheater, subsystem #12.

The on-line adaptive identifiers successfully approximate the actual subsystems by adapting in real-time operation. These on-line adaptive identifiers provide plant information for the modified predictive optimal controller to find optimal control actions while preserving the stability. Since the MAS can reduce the computational complexity by the cooperation of agents, the on-line adaptive identifiers can be implemented in real-time in the MAS-IIS. Moreover, the faults can be accommodated using the proposed on-line adaptive identifiers and fault-diagnosis system to adjust the plant in an emergency situation.

V. CONCLUSION

A new concept of the intelligent identification system based on multi-agent system is presented for a large-scale power plant. In order to deal with the difficulty of handling a large-scale system, architecture of single-agent system and an organization of Multi-Agent System (MAS) are designed as basis for MAS-IIS. The MAS-IIS provides off-line identifiers and on-line adaptive identifiers for fault-diagnosis and the modified predictive optimal controller. With the proposed off-line identifiers representing the nominal plant model, the fault-diagnosis system can successfully detect where and when the incipient faults occur in real-time

Fig. 6. Validation of MAS-IIS for the fault-diagnosis in the boiler subsystems. (a) Accumulated errors in linear scale, (b) Accumulated errors in log scale

Fig. 7. Validation of MAS-IIS for the output responses in the boiler subsystems. (a) Air flow rate in the forced draft fan, (b) water level in the drum, (c) pressure in the primary superheater, (d) outlet temperature in the primary superheater, (e) outlet pressure in the secondary superheater, (f) outlet temperature in the secondary superheater.

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operation. Moreover, the on-line adaptive identifiers perform well by minimizing the error between the outputs of actual subsystems and on-line adaptive identifiers in real-time operation, hence can be utilized in overcoming an unpredictable operation in the power plant. By the cooperation of multiple agents, the MAS-IIS provides efficient identification for control and monitoring systems.

In the future work, a modified predictive optimal controller based on MAS will be developed by using the proposed on-line adaptive identifier.

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practice,” The Knowledge Engineering Review, vol. 10, (2), pp. 115-152, 1995.

[2] C.-L.-M. Harnold, K.Y. Lee, J. H. Lee and Y. M. Park, “Free-model based model reference adaptive inverse controller design for power plants,” IEEE Power Engineering Society Summer Meeting, vol. 2, pp. 1208-1212, 1999.

[3] H. Ghezelayagh and K. Y. Lee, “Intelligent predictive control of a power plant with evolutionary programming optimizer and neuro-fuzzy identifier,” in Proc. 2002 Congress on Evolutionary Computation, vol. 2, pp. 1308-1313.

[4] H. Ghezelayagh and K.Y. Lee. “Training neuro-fuzzy boiler identifier with genetic algorithm and error back-propagation” IEEE Power Engineering Society Summer Meeting, vol. 2, pp. 978-982, 1999.

[5] C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic system control,” IEEE Trans. On Neural Networks, vol. 6, no. 1, pp. 144-156, Jan. 1995.

[6] C. C. Ku, K. Y. Lee and R. M. Edwards, “Improved nuclear reactor temperature control using diagonal recurrent neural networks,” IEEE Trans. on Nuclear Science, vol. 39, no. 6, pp. 2298-2308, Dec. 1992.

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[8] Y. M. Park, U. C. Moon and K. Y. Lee, “A self-organizing power system stabilizer using fuzzy auto-regressive moving average (FARMA) model,” IEEE Trans. on Energy Conversion, vol. 11, no. 2, pp. 442-448, June 1996.

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[11] A. Ben-Abdennour and K.Y. Lee, “An autonomous control system for boiler-turbine units,” IEEE Transactions on Energy Conversion, vol. 11, issue 2, pp. 401 – 406, 1996.

[12] W. Junpu, C. Hao, X. Yang and L. Shuhui, “An architecture of agent-based intelligent control systems,” in Proc. 2000 the 3rd World Congress on Intelligent Control and Automation, pp. 404-407.

[13] T. Wittig, N.R. Jennings and E.H. Mamdani, “ARCHON: framework for intelligent co-operation,” IEEE Trans. Intelligent Systems Engineering, vol. 3, issue 3, pp. 168-179, 1994.

[14] J. Kosakaya, A. Kobayashi and K. Yamaoka, “Cooperative multi-agent-based control technology for supervisory control and data-acquisition systems,” in Proc. 2003 Emerging Technologies and Factory Automation Conf., pp.19-26.

[15] J. Jung and C.-H. Liu, “Multi-agent system technologies and an application for power system vulnerability,” 2004 IEEE Power Engineering society General Meeting, PESGM2004-000975.PDF.

[16] R. P. Gupta and R. K. Varma, “Agent based software integration at distribution control center,” 2004 IEEE Power Engineering society General Meeting, PESGM2004-000930.PDF.

[17] S. D. J. McArthur and E. M. Davidson, “Multi-agent systems for diagnostic and condition monitoring applications,” 2004 IEEE Power Engineering society General Meeting, PESGM2004-000972.PDF.

[18] J. A. Hossack, J. Menal, S. D. J. McArthur and J. R. McDonald, “A multiagent architecture for protection engineering diagnostic assistance,” IEEE Trans. Power Systems, vol. 18, issue 2, pp. 639-647, 2003.

[19] P. B. Usoro, “Modeling and simulation of a drum-turbine power plant under emergency state control”, Master of Science Thesis, Massachusetts Institute of Technology, 1977.

[20] S. J. Al-Nasurs, “Simulation of a fossil-fuel power plant unit in matlab environment,” M.S. Thesis, Dept. Electrical Eng., The Pennsylvania State Univ., University Park, 2001.

[21] J. P. McDonald, H. G. Kwatny and J. H. Spare, “A non-linear model for reheat boiler-turbine generator systems; Part I – General description and evaluation,” in Proc. 12th Joint Automatic Control Conf., pp. 219-226, 1971.

[22] H. G. Kwatny, J. P. McDonald and J. H. Spare, “A non-linear model for reheat boiler-turbine generator systems; Part II – Development,” in Proc. 12th Joint Automation Control Conf., pp. 227-236, 1971.

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[25] L. Padgham and M. Winikoff, “developing intelligent agent systems,” John Wiley & Sons, NY, 2004.

[26] M. Wooldridge, “An introduction to multiagent systems,” John Wiley & Sons, Chichester, UK, 2002.

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BIOGRAPHIES

Jin S. Heo received his B.S. and M.S. degrees in Electronics Engineering from Inje University, Korea, in 1999 and 2001, respectively. As a candidate, he is currently pursuing the Ph.D. degree in Electrical Engineering at the Pennsylvania State University. His interests are multiobjective optimization in control systems, intelligent distributed control, multiagents systems, modeling and control of fuel cell power plants, and real-time embedded system.

Kwang Y. Lee received his B.S. degree in Electrical Engineering from Seoul National University, Korea, in 1964, M.S. degree in Electrical Engineering from North Dakota State University, Fargo, in 1968, and Ph.D. degree in System Science from Michigan State University, East Lansing, in 1971. He has been with Michigan State, Oregon State, Univ. of Houston, and the Pennsylvania State University, where he is a Professor of Electrical Engineering and Director of Power Systems Control Laboratory. His interests

include power system control, operation, planning, and intelligent system applications to power systems. Dr. Lee is a Fellow of IEEE, Associate Editor of IEEE Transactions on Neural Networks, and Editor of IEEE Transactions on Energy Conversion. He is also a registered Professional Engineer.