The 14th International Conference on Intelligent System Applications to Power Systems, ISAP 2007
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Intelligent Agents Applied to Reconfiguration of Mesh Structured Power Systems Kai Huang, Student Member, IEEE, Sanjeev K. Srivastava, Member, IEEE, David. A. Cartes, Senior Member, IEEE, Mike Sloderbeck, Member, IEEE In recent years, agent technology is increasingly applied to power system reconfiguration [6-9]. The multiagent system (MAS) is a system that composed of agents. The agents in an MAS can work autonomously and can make independent decisions in order to achieve global objectives. The agents can interact with each other, not only for exchanging data, but also for social activities such as cooperation, coordination, and negotiation. However, the agent based reconfiguration methods proposed in [6-9] are not completely decentralized. In [6], an agent based restoration method for power system was proposed. A facilitator agent was required for the restoration of power system. Having a facilitator agent has access to all bus agents in the system and has global information of the system. The facilitator with global information makes the restoration partially centralized. Similarly, in [7], a coordinating agent with global information was used for reconfiguration of the shipboard power system. In [8], the authors improved the restoration methodology proposed in [6]. However, facilitator agents were still required for the coordination of the agents. Compared to [8], the authors of [9] employed power generation agents, bus agents, and circuit breaker agents to distribute the reconfiguration functionalities. However, the agent system in [9] may not work if the facilitator agents fail. In one of the their earlier works, the authors of [10] proposed a decentralized MAS based reconfiguration method for radial shipboard power systems. Compared to the centralized reconfiguration methods, the decentralized reconfiguration avoids single point of failure, and reduces communication burden. The decentralized reconfiguration is also more flexible and robust. However, the method put forward in [10] was only applicable to a radial system reconfiguration. In a power system with a ring structure, the method proposed in [10] may lead to redundant information accumulation (RIA) [11]. RIA is like the positive feedback in a closed loop the system and makes the system’s information flow unstable. In this paper, the authors extend the algorithm proposed in [12], and propose an MAS for a completely decentralized reconfiguration applied to mesh structured power systems. Each agent in the MAS interacts with one major component in the power system. If two components in the power system have physical connection with each other, the two corresponding agents are defined as neighboring agents. Each agent communicates only with its neighboring agents. There is no central controller with preset global information in the
Abstract—In this paper, the authors propose a multiagent system based reconfiguration methodology for mesh structured power systems. One of the important features of this multiagent architecture is that the intelligent agents in the multiagent system work in a completely decentralized manner. There is no central controller in the system. In this multiagent system, an agent can send/receive signals to/from a major electric component in the power system. Each agent only communicates with its immediate neighboring agents. A simulation platform is also introduced for validating the proposed reconfiguration methodology. In the simulation platform, the intelligent agents are implemented using Java agent development framework. The power system is implemented in a real time digital simulator. The simulation results show the proposed reconfiguration methodology is effective and promising. Index Terms—multiagent, mesh structure, reconfiguration, load centric, JADE, RTDS
I. INTRODUCTION
T
he capability to reconfigure is critical for modern power systems. During the operation of the power system, some “events” may happen, such as faults, topology change, generator output change, etc. When these events happen, the power system needs to reconfigure effectively for its survivability, reliability, and efficiency. In [1], the authors proposed a reconfiguration method for a radial power system. The objective of the reconfiguration was to optimize the power flow in the power distribution system. A reconfiguration method was put forward in [2] for shipboard power system reconfiguration. The objective of reconfiguration was to maintain as many vital loads in the system as possible. In [3], the authors proposed a power system reconfiguration method for black-start. A particle swarm optimization was employed in reconstructing a power system network skeleton. In [4], a reliability based reconfiguration power system network reconfiguration was proposed. In [5], a multi objective reconfiguration methodology was put forward for a power distribution system. The methods proposed in [1-5] were centralized. The biggest drawback of a centralized system is that it may lead to single point of failure if the system lacks redundancy.
This work was supported in part by the Office of Naval Research, USA under Grant N00014-02-1-0623. K. Huang, S. K. Srivastava, D. A. Cartes, M. Sloderbeck are with Center for Advanced Power Systems, Florida State University, Tallahassee, FL 32310 USA (email:
[email protected],
[email protected],
[email protected],
[email protected])
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then forwards the updated information to the neighboring agents. A generator agent has following set of rules for reconfiguration. Rule 1: If the fault is detected on the corresponding generator, the generator agent sends information to the neighboring breaker agent to inform that the generator does not work properly. The information is transferred regardless of the action(s) of the protection devices in the power system. Rule 2: If the corresponding generator has no fault and is currently disconnected from the system, and present power supply is less than the power demand in the system, the generator agent sends a request to the neighboring breaker agent to close the circuit breaker, so that the generator can be connected to provide power to the power system.
system. One of the main features of the reconfiguration algorithm proposed in this paper is that it is a load centric approach rather than a generation centric approach used in traditional reconfiguration schemes. The main contributions of this paper are: 1. A decentralized load centric reconfiguration algorithm for a mesh structured power system; and, 2. A real time simulation platform for implementing MAS based reconfiguration in power system. In the next section, an MAS structure for power system reconfiguration is introduced. Then, the algorithm for the decentralized reconfiguration of the power system is discussed. Thereafter, the simulation platform for testing the reconfiguration algorithm is introduced and some simulation results are shown. Finally, some concluding remarks are given.
Agent
Agent
II. MULTIAGENT SYSTEM STRUCTURE
Multiagent System Agent Layer
In this section, an MAS based architecture is proposed for power system reconfiguration. As shown in Fig. 1, the entire system consists of two layers. One layer is the power system layer with a network of electrical components, and the other layer is the MAS layer with agents. Each agent in the MAS can exchange information with one major electric component in the power system layer, such as a generator, load, breaker, bus, etc. If the two electric components in the power system layer have connection with each other, two corresponding agents in the agent layer are defined as neighboring agents of each other. Each agent interacts with its neighboring agents only. There is no central controller in the MAS. The agents in the MAS work autonomously based on the information they receive from the corresponding electric components and the neighboring agents. Each agent has limited view of the system, because it is limited to interact with its neighboring agent. No agent in the system has preset global information. This open architecture provides a lack of dependency on global information and increases the scalability and flexibility of the MAS. Each agent makes control decisions based on the information received from its neighboring agents and data from the electrical component in the power system. After the agent makes a control decision, it can send the execution command to the electrical component. Each agent sends its present status to its neighboring agents. The agent system works in a decentralized manner and no global information of the system is required. More details can be found in [12]. The agents in the MAS can be divided in four categories based on to the electric components with which they interact:
Agent Agent
Power System Layer Electric Component Fig 1. Multiagent System for Power System Reconfiguration
B. Load Agent Load agent: The goal of a load agent is to make sure that the corresponding load can be supplied in the system. There are vital loads and nonvital loads in the system. The load agent follows a set of rules for reconfiguration. Rule 1: If a load agent detects that the power supply is less than the power demand in the system, and the corresponding load is a nonvital load, the load agent sends information to the neighboring breaker agent to show the corresponding load can be shed from the system. Rule 2: If the load has a fault on it, the corresponding load agent sends information to the neighboring breaker agent to inform that the load does not work properly. The information is transferred regardless of the action(s) of the protection devices in the power system. Rule 3: If the load has no fault and is currently disconnected from the system. If the corresponding load agent detects sufficient power supply that can supply the load, the load agent sends a request to the neighboring agent in order to connect the load into the power system
A. Generator Agent Generator agent: A generator agent can receive present information of the corresponding generator in the power system, This information consists of generation capacity, real/reactive power output, generation cost, fault alarm, etc. A generator agent can also exchange information with its neighboring agents. Each generator agent receives the information from its neighboring agents, updates the received information by interacting with the corresponding agent, and
C. Breaker Agent Breaker agent: A breaker agent interacts with the corresponding breaker in the power system layer. The breaker 303
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net
agent can receive the status of the corresponding breaker, and it can also send control signals to the corresponding breaker. Based on the information received from neighboring agents, the breaker agent can reconfigure the power system by controlling the corresponding breaker, such as connect/disconnect generator, load shedding, and load recovery. The reconfiguration functions for breaker agents are: 1) If a breaker agent receives information from the neighboring agents, it updates the received information based on the present status of the corresponding breaker, and sends the updated information to the neighboring agents. 2) If a breaker agent makes some control decisions based on the information received from the neighboring agents, the breaker agent sends control signal to the corresponding circuit breaker
∑ i
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power of the system is defined as: the maximum power supply S i max − D j where S i max is
∑ j
of the component that is represented by the ith agent; D j is the power demand of the component that is represented by the jth agent [9]. The net power is one of the variables for reconfiguration. All agents have the information regarding the net power. Any change in the net power detected by agents will lead to initiate an appropriate reconfiguration action. If net power becomes negative, it means some loads have to be shed in order to keep the balance between the power supply and power demand. C. Reconfiguration A load centric reconfiguration approach is proposed in this paper. The objective of reconfiguration is to maximize the maintained vital loads in the system. (1) Max Li x i
D. Bus Agent Bus agent: A bus agent monitors the present information of the corresponding bus in the power system, such as voltage, current injection, etc. The reconfiguration functions for bus agent are: 1) A bus agent receives voltage and current injection information of the corresponding bus in the power system. If the voltage or the current injection of the bus exceeds the limit, the bus agent sends alarm signal to the neighboring agents. 2) If a bus agent receives the updated information from a neighboring agent, it forwards the received information to its other neighboring agents.
∑ i
In (1) Li is the ith vital load’s power capacity, and x i is the connectivity status of the ith vital load. If the ith vital load is connected, x i = 1 ; otherwise x i = 0 . The objective function of (1) is subject to constraints as follows: 1) Power source constraint: the power generation cannot exceed the power limit of the power supplier. (2) S i min ≤ S i ≤ S i max where S i , S i min , and S i max represent the current, minimum, and maximum power supply of the ith power supplier, respectively. 2) Voltage limit constraint: the bus voltage must be within the voltage limits. (3) Vi min ≤ Vi ≤ Vi max
III. ALGORITHM FOR RECONFIGURATION The algorithm for decentralized reconfiguration proposed in this paper is a general algorithm that can be applied to power system with any topology. However, in a mesh structured power system, the RIA problem may happen and lead to incorrect information flow in the agent system. In the algorithm, it is necessary to detect and break the mesh structure in the agent system. The reconfiguration algorithm includes three steps as follows:
3) Current limit constraint: The current on transmission line must be within the current limit. I ij min ≤ I ij ≤ I ij max
each
(4)
where I ij represents the current that flows from component i
A. Information Exchange In this step, each agent in the MAS exchanges information with its neighboring agents. In the MAS, each agent has a unique id number [12]. The information for exchanging includes agent id number, agent type, component specified information, etc. The agents also exchange the present status of the electric components that the agents represent.
to component j. Due to an event in the power system, e.g. faults, the status of the components in power system may change. The corresponding agents will then receive the updated status of the components and send this updated information to their neighbor agents. If events happen in the system, agents update the net power of the system by communicating with the neighboring agent and interacting with the corresponding components. The reconfiguration can be achieved by following the rules proposed in section II.
B. Tree Structure Generation Tree structure generation: In order to avoid the RIA problem in a system with mesh structure, a tree structure has to be generated in the MAS by disconnecting the communications between certain agents. An algorithm was proposed in [11] to generate the tree structure in the MAS. After the tree structure is generated, the agent with the smallest ID number in the system becomes the root agent of the tree. The root agent also computes the net power of the system, which is used for reconfiguration of the system. The
IV. SIMULATION PLATFORM IMPLEMENTATION The MAS proposed in this paper is implemented using Java Agent DEvelopment framework (JADE) [13]. JADE is a software framework fully implemented in Java language. It simplifies the implementation of MAS through a middle-ware 304
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is developed by Insigna [16] to run Java on Windows CE. Jeode is the fully certified implementation of Sun’s Personal Java specifications, which is based on J2SE 1.1.8. Since JADE is fully implemented in Java, it can run under Jeode runtime environment on iPAQs. The power system is setup on a real time digital simulator (RTDS) [17]. The RTDS is a high speed, real time test system that can be used for control system testing and general power system simulation. The RTDS is capable of high fidelity simulation of transient and dynamic behavior of complex power systems in real time at time steps down to one microsecond. The RTDS simulator uses the parallel processing hardware technology to achieve its performance. The RTDS simulator employs an advanced and easy to use graphical user interface – the RSCAD software [17]. With RSCAD, it is very easy to setup a power system model with the graphical module in the libraries of RSCAD.
that claims to comply with the FIPA specifications [14] and through a set of tools that supports the debugging and deployment phase. The communication architecture of JADE supports agent communication language (ACL) [13], which is used to setup the communication among the agents in the MAS. The latest JADE version supports self-defined ontology for ACL, which gives more flexibility to the communication among agents. In order to test the proposed reconfiguration algorithm in this paper, a simulation platform is setup as shown in Fig. 2. As shown in Fig. 2, the agents are implemented on iPAQs. The iPAQ is a Pocket PC (PPC) developed by Hewlett Packard [15]. The iPAQ has integrated WiFi, which provides wireless connections for iPAQ. The communication protocol for WiFi is IEEE 802.11b, which supports wireless communication up to 11Mbit/s. The operating system of iPAQs is Windows CE, which supports Jeode runtime environment [16]. Jeode runtime environment
Agent
Wireless router Agent
Agent Agent
FPGA Interface
Breaker3 Breaker1
Generator1
Breaker6
Breaker5
Breaker8
Load1
Load2
Breaker4
Breaker2
Generator2
Breaker7
RTDS Fig. 2 Simulation platform for reconfiguration
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The agents on the iPAQs must send and receive information with the simulated power system and control components in the RTDS. Each iPAQ has a serial port for communication; the RTDS has 16-bit digital I/O ports that can be used for real-time input and output data. Hence, the RTDS and iPAQs cannot be directly connected. An FPGA (Field Programmable Gate Array) interface between the RTDS and the iPAQs was developed using an Altera ACEX-based development board from Rapid Technology. A custom Verilog program was developed to inter-connect three 16-bit I/O channels from the RTDS with 15 TTL-level duplex serial channels, with intermediate multiplexing and data buffering in both directions, and parallel/serial conversion. Maxim MAX3233 transceivers were used to achieve RS 232 line levels. The FPGA module receives values from components of the simulated power system via time division multiplexed data transfers, using clocking from the RTDS. These received values are stored in the FPGA memory, since a particular iPAQ serial channel might be unavailable due to data transmission already in progress. When the serial channel to an iPAQ becomes available, the values to that agent are transmitted. Transmission to multiple iPAQs can occur in parallel. The agents on the iPAQs thus receive values from the simulation at a fast rate that is actually limited by the iPAQs processing capability. Concurrently, the agents on the iPAQs can send control commands as needed to the RTDS simulated components, via the FPGA module. Using the same multiplexing clock rate used for receiving values from the RTDS, the FPGA module can transmit control variables to simulated components in the RTDS.
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opens Breaker8 to disconnect Load2 from the system. The vital load, Load1, can still be supplied by the power system. Fig. 4 shows the simulation result of load shedding in RTDS. Fig. 4 (a) and Fig. 4(b) show the change of current that flows into Load2 and power of Load2 respectively due to the reconfiguration. Fig. 5 shows the simulation result for agents. Fig. 5(a) shows Agent10, which is the agent for Load2. Agent10 detects the negative net power in the system and shows that Load2 has to be shed. Fig. 5 (b) shows Agent 11, which is the agent for Breaker8. The Agent11 shows that it opens the Breaker8 and disconnected Load2 from the system. Case2: Continuing the previous case, it is assumed that the fault on Gen 2 is now cleared and can be reconnected to the system. Since Breaker2 is still open, Gen2 is not connected to the system. If Load2 needs to reconnect to the system, Agent10 sends a request to Agent11. The present net power in the system is 5kw, which is smaller than the power demand of Load2 (6.5kw). So Load2 cannot be connected to the power system. The agents in the MAS forward the request to their neighbors until Agent16, the corresponding agent is Gen2, receives the request. After Agent16 receives the request, it sends a request to Agent15 to connect Gen2 into the system. When Agent15 receives the request from Agent16, it closes the Breaker2 and reconnects Gen2 into the system. The net power of the system becomes 15kw now, which is bigger than the power demand of the Load2. So Load2 can be connected to the system now. Agent10, the agent for Load2, sends a request to agent11. When Agent11 receives the request from Agent10, it closes Breaker8 and reconnects the Load2 into the system. Fig. 6 shows the simulation result in RTDS. Fig. 6 (a) and Fig. 6 (b) show the change of the current that flows into Load2 and the power of Load2 respectively. Fig. 8 shows the simulation result on MAS. Fig. 7 (a) shows Agent11, which is the agent for Breaker8. Agent11 shows that it closes Breaker11 and connects Load2 into the system. Fig. 7 (b) shows Agent15, which is the agent for Breaker2. Agent15 shows that it closes the Breaker2 and connects Gen2 into the system. Several simulation experiments were carried out in RTDS that validate the decentralized reconfiguration methodology presented in this paper. The authors do realize that the test system is quite small, but it is complex enough for algorithm validation. For a comprehensive testing of the algorithm, in real time on a large scale, a larger power system and hence a larger MAS is required. Through experiments on that larger power system, the practical applicability of agents for the real world application can be verified. One of the important aspects of the proposed approach is its scalability. Any additions of extra major power components mean that corresponding agents should be added to the MAS. Since the reconfiguration approach presented in this paper involves agent communication with their neighbors only, the communication link set up for newly added agents is minimal. In fact, the addition of an agent, with the generic component specific code, is similar to a plug and play operation. The only reason why it is currently not a complete plug and play operation is because in the present setup the information regarding its neighbors has to be provided to an agent before it is introduced in the MAS. The authors’ next goal is to have
V. SIMULATION RESULTS Fig. 2 shows the example power system for validating the reconfiguration algorithm in RTDS. In this example power system, there are two generators and two loads. Although the testing system is quite small, it is complex enough (with several mesh structures) to validate the algorithm. Gen1 and Gen2 are two generators with power capacity of 20kw and 10kw respectively. Load1 and Load2 have power demand of 15kw and 6.5kw respectively. Assume that Load1 is a vital load and Load2 is a nonvital load. Fig. 3 shows the corresponding MAS. Each agent in Fig. 3 represents one electric component in Fig. 2. The numbers in the brackets are the id numbers of the agents. There are two generator agents, two load agents, and eight breaker agents in the MAS. Two cases will be studied for validating the reconfiguration algorithm. One case is load shedding test, while the other case is load recovery test. Case1: The power system is normal. All loads in the system are supplied. In this case, it is assumed that a fault happens at the Gen2. Agent16, the corresponding agent for Gen2, detects the fault and sends this information to Agent15. When Agent15 detects the fault, it will open the Breaker2 to disconnect Gen2. When Gen2 is disconnected, the net power of the system becomes –1.5kw. The negative net power of the system means some loads have to be shed in order to balance the power supply and power consumption in the system. Since Load1 is a vital load and Load2 is a nonvital load, Agent11 306
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authors’ further work, the generator agents in the MAS will coordinate with each other in order to make the generation in the power system more efficient.
agents that can automatically detect their neighbors and initialize themselves accordingly. Bus1 [6] Bkr3 [3] Gen1 Bkr1 [1] [2] 20kW
15kW Load1 Bkr5 [9] [8] Bkr4 [5]
Bkr6 [12] Bus4 [14] Bus3 [4]
Bkr8 Load2 [11] [10]
Bkr2 [15]
6.5kW Bkr7 [13]
Gen2 [16]
10kW
Bus2 [7]
(b)
(a)
Fig. 3 Multiagent system for reconfiguration
Fig. 6 Load recovery simulation
Agent 11
Agent 15
(b)
(a) Fig. 4 Load shedding simulation
Agent 10
Agent 11
(b)
(a)
Fig. 7 Load recovery simulation on iPAQs
VI. CONCLUSIONS
(a)
In this paper, the authors proposed a multiagent system based reconfiguration methodology for a mesh structured power system. Each agent in the multiagent system interacts with one major component in the power system. The agents are restricted to communicate with neighboring agent only. Each agent has a limited view of the system and works autonomously. The reconfiguration methodology proposed in this paper is completely decentralized. Compared to centralized reconfiguration system, the decentralized system can avoid single point of failure, reduce the communication burden in the system, and make the system more flexible and robust. One of the most important objectives of power systems is to supply power to loads. The reconfiguration methodology proposed in this paper is load centric. The objective of the reconfiguration is to maximize loads (with priority to vital
(b)
Fig. 5 Load shedding simulation on iPAQs
In this paper, the coordination of the generators is not discussed. For example, in a power system with two reserved generators, when the running generators cannot supply enough power to the loads, the generator agents of the reserved generators will detect the power shortage. Then the two generators will be connected into the power system. It may lead to surplus power supply in the power system when only one reserved generator is required to maintain the loads. In the 307
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loads) in the power system. The reconfiguration actions are driven by the requests from the loads. A hardware simulation platform was design to test the proposed reconfiguration methodology. The simulation platform is based on real time digital simulator. The agents are implemented on iPAQs using Java agent development framework. The power system is implemented in RTDS. An FPGA based interface was developed to facilitate the communication between iPAQs and RTDS. The experiment results show that the reconfiguration method proposed in this paper is effective and promising
S. K. Srivastava (S’2001, M’ 2004) is an Assistant Scholar Scientist in Center of Advanced Power System at Florida State University. He received Bachelor of Engineering in Electrical Engineering degree in 1997 from M. M. M. Engineering College, Gorakhpur; and Master of Technology degree in Power Systems in 1999 from I. I. T. Delhi, New Delhi. He was a Project Engineer at Secure Meters Limited, New Delhi, India, from 1999 to 2000. He received his Ph.D. degree in Electrical Engineering from Texas A&M University in 2003. His research interests include expert system application to power systems, reconfiguration of navy shipboard power system, and agent technology application to power systems. Dr. Srivastava is a member of IEEE. He was also the Vice-President of IEEE-PES-IAS-PELS, joint student chapter at Texas A&M University, from Fall 2002 to Spring 2003. D. A. Cartes (M’1996) is an Assistant Professor of Mechanical Engineering at Florida State University. He joined the Department of Mechanical Engineering at FAMU-FSU College of Engineering in January 2001, after receiving his Ph.D. in Engineering Science from Dartmouth College. Dr. Cartes heads the Power Controls Lab at the Center for Advanced Power Systems. He teaches courses in control and dynamic systems. His researchinterests include Distributed Control and Reconfigurable Systems, Real-Time System Identification, and Adaptive Control. In 1994, Dr. Cartes completed a 20-year U.S. Navy career with experience in operation, conversion, overhaul, and repair of complex marine propulsion systems. Dr. Cartes is a senior member of IEEE.
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A. Merlin, H. Back, “Search for a minimal-loss operating spanning tree configuration in urban power distribution systems”, in Proc. 5th Power System Computation Conference, Cambridge, England, Sep., 1975 K. L. Bulter, N. D. R. Sarma, “A new method of network reconfiguration for service restoration in shipboard power systems”, in Proc. 1999 IEEE Power System Society Transmission and Distribution Conf., pp. 11-16 Y. Liu, X. Gu, “Reconfiguration of network skeleton based on discrete particle-swarm optimization for black-start restoration”, IEEE Power Engineering Society General Meeting, Jun. 2006 F. Li, “Distributed processing of reliability index assessment and reliability-based network reconfiguration in power distribution systems”, IEEE Trans. Power Systems, vol. 20, no. 1, pp. 230-238 Feb. 2005 I. Roytelman, V. Melnik, S. S. H. Lee, R. L. Lugtu, “Multi-objective feeder reconfiguration by distribution management system”, IEEE Transaction on Power Systems, vol. 11, no. 2, May 1996 T. Nagata, H. Sasaki, “A Multi-agent approach to power system restoration”, IEEE Trans. Power System, vol. 17, no. 2, May 2002, pp. 457-462 S. K. Srivastava, H. Xiao, and K. L Butler-Purry, “Multi-agent system for automated service restoration of shipboard power systems”, 15th International Conference on Computer Applications in Industry and Engineering, San Diego, CA, 7-9 November 2002 T. Nagata, H. Fujita, H. Sasaki, “Decentralized approach to normal operations for power system network”, in Proc. the 13th International Conference on Intelligent Systems Application to Power Systems, Nov. 2005, pp. 407-412 J. Momoh, O. S. Diouf, “Optimal reconfiguration of the navy ship power system using agents”, PES TD 2005/2006, May, 2006, pp. 562-567 L. Sun and D. A. Cartes, “Reconfiguration of shipboard radial power system using intelligent agents”, ASNE Electric Machine Technique Symposium, Jan. 2004 K. Huang, S. K. Srivastava, D. A. Cartes, “Solving the information accumulation problem in mesh structured agent system”, IEEE Trans. Power Systems, vol. 22, no. 1, pp. 493-495, Feb. 2007 K. Huang, D. Cartes, S. Srivastava, “A Multiagent based algorithm for ring-structured shipboard power system reconfiguration”, in Proc of IEEE Annual Conference on System, Man, and Cybernetics, vol. 1, pp. 530-535, Nov. 2005 F. Bellifemine, G. Caire, T. Trucco, G. Rimassa, “Jade Programmer’s Guide”, http://jade.tilab.com/ Foundation of Intelligent Physical Agent http://www.fipa.org/ Hewlett-Packard Development Company, L.P. http://www.hp.com/country/us/en/prodserv/handheld.html Insignia Solutions, Inc http://www.insignia.com/ RTDS Technologies Inc http://www.rtds.com
M. Sloderbeck (M'88) received his M.S in computer science from the Florida State University in 1990. He is currently employed at the Center for Advanced Power Systems at Florida State University, where he works on hardware-in-the-loop simulations for electric-ship systems. Mr. Sloderbeck is a member of IEEE.
BIOGRAPHIES K. Huang (S’2005) was born in China. He receives his B.S. from Wuhan University, Wuhan, China, and M.S.from Shanghai Jiao Tong University., Shanghai, China, in 2000 and 2003 respectively. He is a Ph.D student in Center for Advanced Power System, Florida State University, Tallahassee, FL 32310, U.S.A. His research interest is multi-agent system and its application in shipboard power system. Mr. Huang is a member of IEEE.
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