Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Dr. Ganesh K. Venayagamoorthy, Dept of Electrical & Computer Engineering Dr. Keith A. Corzine, Dept. of Electrical & Computer Engineering Prajwal K. Gautam, Dept. of Electrical and Computer Engineering Sensing Circuit A CT PT Vabc Iabc abc qd Firing Pulses to 6 IGBT Gates 3 Phase Inverter Multiple Reference Frame Transformation • Inverter control maintains positive sequence voltage at a fixed level in q axis and all other components controlled at zero. • Positive and negative sequence currents are supplied by energy storage system to compensate single phase wind. Battery Bank Vref* 4- PI Controller Vqd Icmd* Triangular Wave Energy Storage Instantaneous Current i (θ ) i (θ ) i (θ ) iw(θda, e ) db, dc Vcmd* qd 4- PI abc Controller ae e al e as e Iqd Comparator V ) i (θ ) i (θ ) i (θ ) i (θ be e Circuit bl e bs e w e Controls ref* Voltage in Volts 140 Vqsen 100 Vqsep 60 Instantaneous Power Instantaneous Current & Power Equations for Energy Storage Energy Storage Instantaneous Current pl(θe ) val(θe ).ial(θe ) vbl(θe ).ibl(θe ) vcl(θe ).icl(θe ) ps(θe ) val(θe ).ias(θe ) vbl(θe ).ibs(θe ) vcl(θe ).ics(θe ) pw(θe ) (val(θe ) vbl(θe )).iw(θe ) pe(θe ) pl(θe ) ps(θe ) pw(θe ) ice(θe ) icl (θe ) ics(θe ) (2.4 kW) is iw (1.6 kW) is MICRO GRID il ie System States Critic Error Signal E(t) Battery Bank Critical Load Energy Storage System Controllable Load Loads Approach Energy storage system has been designed utilizing a multiple reference frame control to compensate for the single-phase generation by injecting unbalanced currents into the micro-grid. Simulation of the micro grid system is carried out Real Time Digital Simulator (RTDS/RSCAD). Action Network (MVO) J(t) Control Signals δJ(t)/δA(t) Critic Network (FFNN with BP) + γ Σ + U(t) - J(t-1) Block diagram for a ACD based energy dispatch controller Control Signals System States Predicted PV Power Predicted Wind Power Predicted SOC Actual SOC Critical Load Non Critical Load Power Dispatch to Critical Load Action Network (MVO) 0 -5 ESS ESS+Solar ESS+Solar+Wind -20 ESS+Solar ESS ESS+Solar+Wind -20 -25 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 -30 5 0 0.5 1 1.5 2 2.5 3 Time in Seconds 3.5 4 4.5 5 Time in Seconds Positive sequence q axix voltage maintained at all times Positive & negative sequence currents supplied by ESS Power flow when ESS, Solar and Wind are active Power flow when ESS and Solar are active 6 6 5.5 5 4 3.5 3 2.5 2 Pload Psolar Pwind Pess Qload Qess 5.5 5 4.5 4 Pload Psolar Pwind Pbatt Qload Qess 3.5 3 2.5 2 1.5 1.5 1 1 0.5 0.5 0 0 Active & reactive power flow with solar, wind, ESS and 5.6 kW load with micro-grid system (pf= 0.8) System States Power Inverter Acknowledgements: Instantaneous Power Dynamic Programming approach, the intelligent Using Adaptive pl(θe ) vdispatch vbl(θe ).ibl(θe )ensures vcl(θe ).icl(θe ) energy al(θe ).ial(θe ) controller power to critical loads all the time ps(θSupply ) v (θ ) . i (θ ) v (θ ) . i (θ ) v (θ ) . i (θ ) e al e as e bl e bs e cl e cs e Sustain SOC of battery at required level based on predicted pw(θe ) (val(θe ) vbl(θe )).iw(θe ) power pe(θIfe )above pl(θe ) conditions ps(θe ) pw(θeare ) satisfied, maximize power supply to non-critical loads Micro Grid System 5 -10 20 Power in Kilo Watts Power Generation 1ø Wind Turbine 3ø Solar Panel 10 0 ibe(θe ) ibl(θe ) ibs(θe ) iw(θe ) Iqsep -15 Control forEnergy Energy Storage Storage System ControlSchematics Method for System iae(θe ) ial(θe ) ias(θe ) iw(θe ) Iqsen 15 80 40 ice(θe ) icl (θe ) ics(θe ) Idsep 20 Vdsep 120 4.5 (1.6 kW) Vdsen Energy Storage • Integration of hybrid three-phase solar and singlephase wind turbine supply power to a three-phase grid independent micro-grid system resulting into an unbalanced set of currents. • A battery inverter system is used as power conditioning system to compensate single-phase generation. • Traditional priority based load management controller is highly inefficient as energy is dispatched to entire critical and non critical loads without assigning priority to loads based on available power. • ACD based energy dispatch controller prioritize between critical loads, state of charge of battery and non critical load such that energy dispatch is maximized. Idsen 25 160 Background 3ø Solar Panel 30 180 Power in Kilo Watts • Extensive modeling of energy storage system (ESS), three-phase photovoltaic system and single-phase wind generation. • Design ESS to compensate single phase wind generation. • Develop adaptive critic design (ACD) based intelligent load management in a micro-grid. Results Micro Grid Current in Amps Project Objectives Power Dispatch to Battery Power Dispatch to Non Critical Load Reduction of Power Generation Conclusion • Energy storage system with negative sequence current control has been presented for regulation of the micro grid voltage and compensation of the various source types. • Simulation results demonstrate effectiveness of control when solar and wind sources are connected to the grid and during steady-state. Future Works • Implement intelligent load management controller in the micro grid. •Tune PI gains of power converters on extensive models using intelligent methods. • Step ahead prediction of solar & wind generation, critical and noncritical load and battery SOC. Intelligent Systems Centre, Missouri University of Science and Technology National Science Foundation under the grant Neuroscience and Neutral Networks for Engineering the Future Intelligent Power Grid, NSF/EFRI COPN #083617 Department of Education under the grant Advanced Computational Techniques and Real-Time Simulation Studies for the Next Generation Energy System, GAANN #P200A070504