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Next Generation Adaptive and Intelligent Algorithmsfor the Control of Complex and Dynamic Systems Dr. Sukumar Kamalasadan Department of Engineering and Computer Technology University of West Florida Pensacola, FL-32514
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Presentation Outline Overview Part I: Theoretical Design and Algorithms Part II: Current Research Projects Speed Control of Synchronous Generator. Multi-Machine Power System Control and Angular Stability. Part III: Other Research Projects and Directions Smart-Grid Applications Wide Area Monitoring and Control based on scalable intelligent supervisory loop concept. Distributed Power Generation Control and Grid Interface. Summary03/21/12 Sukumar Kamalasadan Ph.D. 2
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Overview Main focus Modeling and control of dynamic systems Mathematical modeling Using Computational Intelligence Simulation using computer algorithms Designing and developing novel control, optimization and identification techniques Real-time implementation of scalable algorithms Integrating research elements to teaching Dissemination and Outreach This talk is about one particular dynamic system
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Overview:Importance of Modern Power System Control Fast acting MIMO devices such as generators, Distributed Generation (DG) and their integration, tight and congested transmission systems, deregulated power system … Shows multiple behavior such as: discrete changes (transformer taps), deterministic operations (voltage and speed control), stochastic behavior (load forecasting), optimal needs (power transactions with constraints). Existence of multiple controllers that increases the system complexity and controller interactions. Advances in high speed digital processor and computer architecture enhance the feasibility of modern control design techniques: Operates in real-time Provide some elements of learning and adaptation03/21/12 Sukumar Kamalasadan Ph.D. 4
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Overview: Existing control topologies for Generator/Power Systems Linear controllers such as conventional Automatic Voltage Regulators (AVR) (voltage control) and Governor (speed control). Conventional Power System Stabilizers (CPSS) used for damping of generator oscillations, used in industry (P. Kundur, O.P. Malik et. al.). Model based controllers for generators (adaptive controllers) has been proposed and used (adaptable and simple in architecture) (K.S. Narendra, Ghandakly et. al.)—Provide linear adaptation but no learning and memory. Nonlinear controllers and adaptive nonlinear controller (Feedback Linearization, backstepping)– Useful but often cannot cover entire domain. Neural network based designs (Venayagamoorthy, Harley, Lee)—Provide learning and adaptation especially with time delayed system—Not always needed. Proposed Solution: Provide hybrid control architecture that is system- centric in nature.03/21/12 Sukumar Kamalasadan Ph.D. 5
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Overview: Intelligent Power System Control and Analysis Why Hybrid Intelligent Control Architecture? Operates in a decentralized way while exhibiting desirable system-wide characteristics (Complex tasks can be made simpler). Produces effective local decisions that contribute towards a coherent and effective overall system (Emerging behavior). Ability to interact and coordinate with existing design and are adaptable (organizational behavior). Capable of providing efficient and effective signals based on system needs (case based approach). Provide adaptation, learning and model-less control.03/21/12 Sukumar Kamalasadan Ph.D. 6
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Overview: Current Research Efforts: Focus Areas Hybrid intelligent control- Theoretical formulations, design and development such as, Issues related to stability, adaptation and global contributions in changing plant conditions. Reliability, robustness and adaptability. System modeling, algorithmic development, implementation. New and Suitable computational intelligence techniques: Methods in online and offline learning. Issues such as tuning, autonomous action. Power System Control and Stability Generator control. Wide Area Controllers (WAC). Control of other electric machines. Control of energy sources, integration of Distributed Generation (DG) with mega grid.03/21/12 Sukumar Kamalasadan Ph.D. 7
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Part I: Design Concept: Hybrid Architecture for Coordinated Control Three Design structure with System Supervision Systems that shows parametric uncertainty; A conventional adaptive module (such as Model Reference Adaptive Control) to adaptively monitor system output and develop control action. Systems that shows modal changes; Intelligent module to recover these changes and develop a desired reference model trajectory. Important in the presence of multiple modes of operations. Systems that shows functional changes and/or influenced by external disturbances; An intelligent module to approximate the changing nonlinear function such as offline/online trained neural network.03/21/12 Sukumar Kamalasadan Ph.D. 8
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Part I: Design Concepts: Intelligent Adaptive Control : Supervisory Loop Approach Adaptive Controller (Controller 1) Reference Model/ Reference Output Parameter Estimation Error Input Adaptive System Signal Control Law Under Consideration Plant Output03/21/12 Sukumar Kamalasadan Ph.D. 9
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Part I: Design Concept: Hybrid Intelligent Control Architecture03/21/12 Sukumar Kamalasadan Ph.D. 10
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Part I: Design Concept: System-Centric Controllers: Design Scenarios Fuzzy Reference Monitor Fuzzy Reference Monitor Model Generator Model Generator Adaptive Σ Multi-machine Adaptive System Σ Multi-machine Controller Controller System Monitor RBFNN Figure 1: Scenario 1: Proposed Framework Controller Figure 2: Scenario2: Proposed Framework Hypothesis for System-Centric Controllers• Changes in Modes of Operation: Fuzzy Reference Model Fuzzy Reference Model Generator (FRMG) Monitor Generator (FRMG).• Nonlinear Behavior (ability to cope up with system nonlinearity) but the target of operation known: RBFNN Adaptive System under Σ consideration Controller (with supervisory learning). Controller• Nonlinear Behavior and target unknown: Reinforcement learning. Monitor Challenges RBFNN Creative• Controller’s Integrity, Design and Development Issues Controller Controller• Implementation Issues, continuous-discrete interplay Figure 3: Scenario 3: Proposed Framework 03/21/12 Sukumar Kamalasadan Ph.D. 11
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Part I: Design Concept: Controller 1 The Model Reference Adaptive Controller can be formulated as U ad = θ T ω Where theta isAdaptation Regressor [ Θ = k θ 0 θ 1T θ 2] and omega is T T ω= r [ yp ω 1T ω2 T ] T Start • and • k = −γ 3ϑe sgn( K p )r (t ) θ 0 = −γ ϑe sgn( K ) y • 2 p T θ 1 = −γ 1e sgn( K p )ω1T Calculate error from •T • θ 2 = −γ 1e sgn( K p )ω 2 ω 1 = Λω + LU T • outputs 1 ad ω 2 = Λω2 + Ly where, e represents the error, ϑ represents the fuzzy contribution Adaptive Mechanism γ represents the adaptive factor Calculate theta Λ Is a stable matrix of order (n-1) X(n-1) such that sI − Λ = Z m (s ) Calculate Omega L LT = [ 0, 0,... 1] Calculate control value 1) Model based design, 2) Adaptation capability, however no memory, no learning 3) Able to expand to the next level for plant drastic changes Continue 03/21/12 Sukumar Kamalasadan Ph.D. 12
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Part I: Switching Mechanism– Design Concept Fuzzy system can be represented as r ∑r µ i i f ( Ω) = i =1 r = M T Pϑ = Φ * ϑ Start ∑µ i =1 iA reference model in a state space form will be System Auxiliary States Wm H ( s) = ym H (t ) / r (t ) = Km H * Zm H / Rm H Modal transitions can be included as Fuzzy Logic Scheme Wm H ( s ) = f (Ω) * ( Km H * Zm H / Rm H ) Fuzzification In general it can be written as ∧ ∧ y m H (t ) = ν (Φ i ,ϑi ,Wm H ) Rule Base ∂eref ∧ J = min ∂f eref = ymi (t ) − y mH (t ) Defuzzification 1) Multiple Model switching, 2) Stable 3) Able to work coherently with model based adaptive controller 3) Need offline Reference Model design and knowledge base development Further details: Kamalasadan et al. (2004), (2005), (2006), (2007)03/21/12 Sukumar Kamalasadan Ph.D. 13
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Part I: Design Concept: Growing Dynamic RBFNN Controller X1 Bias Existing Node μ1 α11 Movement y1 X2 . New μ2 Node αp1. . . . . . yp . . Xn Train offline- Adaptive Online μn Bias σInput Layer Hidden Layer Output Layer Sample Basis Function μ Static Network Nodal Region μ=Center positions h=hidden neurons Active Nodes Number of nodes σ=Gaussian functionsCenter required for α=Weights a StaticMovement Network ε= Distance y(t) e=yi-f(xi) 03/21/12 Sukumar Kamalasadan Ph.D. 14
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Part I: Controller 2– Design Concept node The neuro-controller can be written as U nn = ∑ ακ (exp− (1 /(σκ ) j =1 2 ) || Xi − µκ || 2 ) Start εi = max{ε max γ i , ε min}, (0 < γ < 1)Growth parameter criterion i Get System States e i rms = sqrt ∑ i − (nw − 1)e e) T j =1 RBFNN Structure Adding hidden units: if || ei ||> e min and (|| Xi − µi ||) > ει and erms > emin Generate Nodes Add new unit with α(h+1)=ei, μ(h+1)=Xi, Calculate Centers and radii σ(h+1)=k||Xi-μ|| Calculate distance and Output Tuning laws are W = W ( i − 1) + Kiei Update Weight and Generate Control Value Ki = Pi − 1Bi[ Ri + Bi T Pi − 1Bi ] −1 Where P is positive definite matrix and B is the gradient No Grow or prune? 1) Function approximation based design, 2) Learn offline, Adaptation online, associative memory 3) nonlinear and Yes supervisory learning 4) Unique algorithm that can grow and Growing and prune and provide sequential learning 5) Able to expand to the Pruning Stage next level for optimal control/reinforcement learning 03/21/12 Sukumar Kamalasadan Ph.D. 15
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Part I: Creative Controller DHP based controller Critic ErrorAction update 03/21/12 Sukumar Kamalasadan Ph.D. 16
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Part I: Under nonlinear Optimal Condition?? Nonlinear dynamic programming for Reinforce Learning (RL) RBFNN based supervisory learning (SL) Coherency Supervised Actor-Critic Reinforcement Learning Evolved from (Rosentein, Barto et al, 2004) Shaping (Prediction) X(t-Δt) Transport lag TDL Supervisory Learning F-1(X,Xd) Scheduler Block (Earlier Designs) (a=kaE+(1-k)aS) Vref X(t) X(t) Exploration Plant + J(t) Action Network + Critic Network 1.0 A(t) X(t) TDL Dynamic Programming, Given U (utility function), solve the Bellman Equation to get J; use J to calculate optimal actions J ( X (t )) = max[U ( X (t ), u (t ))+ < ( J ( X (t + 1)) > /(1 + r )03/21/12 Sukumar Kamalasadan Ph.D. 17
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Part I: Overall algorithmic functional flowchartCurrent Status Start Multi-machine Power Performed theoretical analysis including Disturbances/ System Uncertainties/ stability while switching for MRAC with Constraints Return FRMG block. Is error > Threshold No Developed algorithms for adaptive Adaptive Mechanism Yes controller and design basis for FRMG. Performed theoretical analysis including Model Adaptive Controller RBFNN Controller stability for MRAC-FRMG block with Fuzzy Model Supervisory Learning (SL), RBFNN Generator Controller Output controller. Is Output Desirable? Yes Developed algorithms for a novel RBFNN J function minimization No Yes controller. Limit Reached? No Developed the novel supervisory loop No based algorithms. Input or Output Constraints?Current theoretical Work Yes Creative Control Analyze the strategy for creative controller Return using dynamic programming in presence of optimal conditions 03/21/12 Sukumar Kamalasadan Ph.D. 18
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Part II: Control of Synchronous Generator Single Machine Infinite Bus System (SMIB) Representation of System Dynamics m Governor • x H = f H ( x,θ ) + ∑ z H j ( x,θ )u j Δω j =1 Pref y H (t ) = hH ( x, θ ) T G Z=Re+jXe 0 = g ( x, y ) Vt - + V Generator Model ref Ut Exciter x = [δ ∆ω id if iq] + + d AVR Σ CPSS V = − RI − ωGI − L I Upss dt + Vd Uad MRAC Rd + Re 0 0 Id R= 0 I = I V = − V f Rf 0 f FRMG 0 Iq Vq 0 Rq + Re 0 0 Lq + Le L d + Le kM f 0 1 3 [ Te = ( Ld − Lq ) I d I q + kM f I q I f ] G= 0 0 − ( Ld + Le ) − kM f 0 L = kM f Lf 0 0 0 0 L q + Le Vq = 3V∞ cos δ + Re I q + Le I q + ωLe I d Vd = − 3V∞ sin δ + Re I d + Le I d + ωLq I q 2 Hω Bω − Dω = Tm − Te 03/21/12 Sukumar Kamalasadan Ph.D. 19
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Part II: Design and Implementation:Modeling of Power System Components (SMIB) I d 0 0 I [− L −1 ( R + ωG ) ] 0 d −1 0 I I f f − L V Iq = 0 0 I + Ld I q kM f I q Lq I d D q ω − 6 Hω − 6 Hω B 6 Hω B − 2 Hω B 0 ω Tm δ 0 δ − 1 B 0 0 0 0 Conventional Power System Stabilizer (CPSS) Model Exciter Model 1 + sT 1 1 + sT 1 1 + sTw IEEE Type I exciter Kstab 1 + sT 2 1 + sT 2 1 + sTw +/-0.8 p.u. E fd = 1 τe [ K A (Vt − Vr ) − E fd ] T1=0.2 T2=0.2 Tw=10s Kstab=803/21/12 Sukumar Kamalasadan Ph.D. 20
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Part II: Design and Implementation: Fuzzy Reference Model Generator DesignKnowledge Base Design The membership function of the load torque is defined over a domain interval of [0, 1.2]. The membership function of the electric power is defined over a domain interval of [0, 1.5]. The membership function of ω n is defined over a domain interval of [0, 1.5]. Each membership function is covered by five fuzzy sets. The fuzzy rules are derived by studying and simulating the response of the process. 25 fuzzy rules are used to perform the fuzzy switching to evaluate the value of ω n 03/21/12 Sukumar Kamalasadan Ph.D. 21
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Part II: Design and Implementation: RBFNN Design and off-line learning Training of RBFNN network At first a Pseudo Random power deviation ΔPref and exciter input deviation while CPSS in place (ΔVfield ) is generated using Matlab® environment. The input are saved. These signals are then fed to the generator model. The resulting output speed deviation in δ and the output terminal voltage deviation (ΔVt) are saved. These values are time delayed by one, two and three time periods. These time delayed signals are ΔP ΔVt the inputs to the RBFNN network. RBFNN ΔVf δ Initially 10 hidden neurons are used and 2000 such samples are included. TDL RBFNN then estimate speed deviations and terminal At this point the nodes growth and voltage deviation for the subsequent period pruning is not performed. (projection). These steps are repeated until the The output is then compared with the generator error is minimized to a threshold value output. The difference is the error signal. Once the error reaches the threshold The error signals are used to calculate change in value the network is used for online weight, width and RBFNN centers. post-control phase learning.03/21/12 Sukumar Kamalasadan Ph.D. 22
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Part II: Design and Implementation: Control and Model DevelopmentAlgorithmic Implementation Step 1: Plant output is used to calculate the regression vector. Step 2: The output of the plant being fed to the error block and the error between the plant output and the reference model output is used to update the adaptive mechanism. Adaptive vector theta is calculated. Step 3: The FRMG monitoring changes in Pe and Te and calculating values for omega at each time stamp. Based on the error dynamics and the monitor block this is fed to the reference model to update the model parameter. Step 4: Input is being fed to RBFNN and the network RBFNN output is calculated. Step 5: Gradient is fed back to RBFNN and W is updated. Step 8: Reference model is updated Step 6: Based on this error, centers and width are based on the fuzzy tuning and updated. requirements of the plant investigating Step 7: MRAC control signal is calculated based on the the monitor module. delayed input from adaptive mechanism and applied to Step 9: Error calculations are the plant along with RBFNN signal. performed 03/21/12 Sukumar Kamalasadan Ph.D. 23
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Part II: Design and Implementation Case 1: Simulation Results Case 1 System Operating Conditions Time Disturbance As the power system stress is not known (sec) a multiple disturbance profile is used. It 0.1 Three Phase Fault can cause small signal or transient 10 25% Mechanical Power instability. Increase The purpose is to assess the stability and the deviation of all parameters. Main parameters under observation are angle, speed, voltage and power. •Power =0.83 pu. − 1.2296 Small signal stability can cause local 2.2349 •Power Factor= 0.85 mode oscillations and this test can show- lag. 0.748 case oscillatory or non-oscillatory •Terminal 1 instability. 1.0472 Voltage=1.062 pu. •State Initial Conditions 0 Figures shows that oscillations are greater in the presence of PSS alone and the Adaptive with FRMG could damp these oscillations effectively.03/21/12 Sukumar Kamalasadan Ph.D. 24
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Part II: Design and Implementation Case 1: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 25
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Part II: Design and Implementation Voltage in p.u. Case 1: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 26
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Part II: Design and Implementation Case 1: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 27
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Part II: Design and Implementation Case 1: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 28
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Part II: Design and Implementation Case 2: Simulation Results Case 2 In this experiment two intelligent loops viz, FMRG augments the MRAC and RBFNN based neuro-controller is being used for Multiple Input Multiple Output control of the system. The system is running under the following specifications: Power =0.28 pu. Power Factor= 0.24 lag. Terminal Voltage=1.062 pu. Conclusions: Different operating points behaved differently. In the first case, RBFNN did not provide much control contribution. With a change in operating point, the contribution was noticeable. This confirms the need for such control. In all these case system supervision concept performed better than individual control.03/21/12 Sukumar Kamalasadan Ph.D. 29
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Part II: Design and Implementation Case 2: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 30
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Part II: Design and Implementation Case 2: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 31
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Part II: Design and Implementation Case 2: Simulation Results03/21/12 Sukumar Kamalasadan Ph.D. 32
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Part II: Control of Two Machine Infinite Bus System03/21/12 Sukumar Kamalasadan Ph.D. 33
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Part II: Control of Two Machine Infinite Bus System Case 3 Machine Parameters Both machines P=0.8 and Q=0.4 p.u. 100ms short circuit in bus 2-3 Machine Operating points03/21/12 Sukumar Kamalasadan Ph.D. 34
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Part II: Control of Two Machine Infinite Bus System 100ms short circuit in bus 2-303/21/12 Sukumar Kamalasadan Ph.D. 35
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Part III: Other Research Projects and Directions (Five year plan) Smart Grid Applications Real-time test bed for power system modeling and control. Various projects. Wide Area Monitoring and Control based on scalable intelligent supervisory loop concept. Theory, development and simulation studies. Distributed Power Generation and Grid Interface. Integrating Fuel-Cell and Micro-Turbine Models. Control system development and assessment.03/21/12 Sukumar Kamalasadan Ph.D. 38
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Part III: Wide Area Monitoring and Control Wide Area Controller (WAC) Bus 9 P45 P25 P78 P16 P46 ω2 ω3 ω4 STATCOMBus 1 Bus 4 Infinite Bus Vref Bus 5 Bus 2 Bus 10 Gen 2 Vref Bus 3 Vref Gen 4 Bus 11 Bus 6 Bus 12 Gen 3 Bus 7 Bus 803/21/12 Sukumar Kamalasadan Ph.D. 44
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Part III: Wide Area Monitoring and Control: Scalability: Supervisory Loop Approach Intelligent Area 1 PMU Area 2 Control PMU Intelligent Control Intelligent PMU PMU Intelligent Control Intelligent PMU Control PMU Intelligent Control Control Agent PMU Energy PMU Intelligent Intelligent Control Area 3 Management Area 4 Control ANN Agent Center Wide Area Controller Voltage Stability Assessment Tool 4503/21/12 Sukumar Kamalasadan Ph.D.
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Part III: Distributed Power Generation and Grid Interface: Concept Objectives Intelligent control of distributed Generation Control (measurement) strategies of voltage and speed of the DG system based on intelligent controllers (agents) Integration of renewable energy based power generation to the grid Development of test bed and hardware in the loop experiments based on simulations Practical Implementation and Integration of the proven research activities to power distribution grid and testing General Conceptual Implementation of DG Grid Interface Control station: Supervisory controller for DG system including protection Coordination with nearest substation Database for power flow, generation and load dynamics Intelligent agents interaction03/21/12 Sukumar Kamalasadan Ph.D. 46
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Current Research Support and Future Considerations Current Support National Science Foundation CAREER Grant (2008-2012) Internal Grant from the University of West Florida (UWF) (2008-2009) Under Consideration Office of Naval Research (ONR) NSF Power Control and Adaptive Network (PCAN) NSF Course, Curriculum and Lab Improvement (CCLI)03/21/12 Sukumar Kamalasadan Ph.D. 51
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Research Collaborations Areas People Mathematical Modeling of Graduate students who physical systems such as power systems, energy systems, are interested in these avionics and robotics. Developing computer algorithms area in the form of control, optimization, identification of Research faculty who systems through mathematical are interested in models Developing computational collaborations. intelligence based (neural network, fuzzy systems, biologically inspired computational intelligent techniques) algorithms that can augment traditional controllers. Applying control, optimization and identification algorithms for dynamic systems models. Real-time implementations03/21/12 Sukumar Kamalasadan Ph.D. 52
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Summary Intelligent Adaptive Controllers based on the supervisory loop concept can be expanded to agent based control and monitoring. This approach is found to be scalable and useful for power system control, identification and optimization. Intelligent tool in the form of agents can be developed and feasible for dynamic voltage stability assessment and improvements. These approaches are expandable to modular technologies, DG control and grid interface, distribution system and in reconfigurable and survivable modes. For modern power system, these techniques would have significant impact especially in the areas of power system control, stability, reliability and security.03/21/12 Sukumar Kamalasadan Ph.D. 53
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