MVDC & MVAC Baseline Architecture Models in RTDS and ESRDC’s VV&A Process Michael “Mischa” Steurer Grant No. N Discussion at RedTeam Meeting August 20, 2010

Baseline Notional Shipboard Integrated Power System PM = Propulsion Motor

04/28/2010 VVA_ESRDC_20aug2010.ppt 3 MVDC Baseline Architecture - Implemented on RTDS

04/28/2010 VVA_ESRDC_20aug2010.ppt 4 RTDS Notional MVDC IPS Model – Zonal Load Center Definition LOAD 800 VDC Bus 450 VAC Bus LOAD Pumps Multiple Lumped Load Catagories: Resistive, pump motor, electronic equip VDC Starboard Bus 5000 VDC Port Bus DC/DC Converter DC/AC Converter

Variant of MVAC Baseline Architecture Implemented on RTDS AC Circuit Breaker 4160V MVAC Ring Bus AC Circuit Breaker Auxiliary AC Generator 1 ATG 1 Auxiliary AC Generator 2 ATG 2 Stern Cross-hull Disconnect Bow Cross-hull Disconnect Port PM Capacitor Bank Energy Storage AC/DC/AC Converter = ≈ Pulse Charging Circuit AC/DC Converter Pulsed Load ≈ = Zone 5 Deck house 1000V MVDC Port Bus GT Main AC Generator 2 MTG2 GT AC Circuit Breaker 4160V MVAC Ring Bus GT 1000V MVDC Starboard Bus Zone 4 Load Center Zone 3 Load Center Zone 2 Load Center Zone 1 Load Center VSD Starboard PM VSD Port Mid-ship AC CB Stbd Mid-ship AC CB AC/DC Converter Radar = ≈ = ≈ Main AC Generator 1 AC Circuit Breaker MTG1 GT = ≈ Port Rectifier DC Sectionalizer = ≈ Stbd Rectifier

04/28/2010 VVA_ESRDC_20aug2010.ppt 6 VV&A Process Accreditation –Independent corroborator(s) approve model Accuracy Range of validity (limitations) Applications (sim environment) Validation –Model developer with 2 nd party test model against Analytical solution Existing and accredited models Experiment(s) Verification – Model developer reviews model Double check coding Rationalizing output w.r.t. solver settings, model assumptions, etc. Must be driven by requirements from A Documented and rigorously followed Time Requirements

04/28/2010 VVA_ESRDC_20aug2010.ppt 7 Concept of “Model Readiness Level” Model – tied to application Mathematical model documented, with assumptions and limits of validity Identification of verification activities that should be performed for implementations (scenarios, parameters of interest, response variables, determination of reasonable behavior) Provision of experimental data to be used for validation of implementations Definition of model interface Multiple validated implementations Validated implementation Implementation Analysis of model behavior, including sensitivity and uncertainty analyses Verification against implementations of model in other software packages Validation against data from physical experiments Documentation including deviations from associated model documentation Independent (3 rd party) analysis of model implementation Validation against data from multiple physical experiments over a range of parameter values

04/28/2010 VVA_ESRDC_20aug2010.ppt 8 Example Verification Scenario for Synchronous Machine Model: Three-phase fault Scenario: At constant speed, f 0, with a fixed resistive load, R load, at the terminals of the generator, bring the system to steady state with 1.0 pu voltage. Holding the excitation voltage and rotational speed constant, apply a three phase fault at the terminals of the machine at the zero crossing of the phase A line-to-neutral voltage. Current (kA)

04/28/2010 VVA_ESRDC_20aug2010.ppt 9 Example Verification Scenario: Three- phase fault Response variables: – I a-max – The peak phase-a current during the fault – I f-max – The peak field winding current during the fault Current (kA) Current I a-max I f-max

04/28/2010 VVA_ESRDC_20aug2010.ppt 10 Example Verification Test Set Carry out fault scenario for specified parameter values: f o (Hz)

04/28/2010 VVA_ESRDC_20aug2010.ppt 11 Example Comparison of Verification Test Set Results R 2 = Maximum Error Magnitude = 1.15% Mean Error Magnitude = 0.37% R 2 = Maximum Error Magnitude = 8.64% Mean Error Magnitude = 2.6% Good Agreement Significant Discrepancies Compare results from tests in terms of response variables. Current (kA) (RTDS) Current (RTDS)

04/28/2010 VVA_ESRDC_20aug2010.ppt 12 Identification of Response Variables for Analysis of Model Behavior Example: Uncertainty Analysis Consider variation in α For this case, considering variation at each point in time provides useful information. Sample function evaluations. Here, mean and 95% confidence bounds work well

04/28/2010 VVA_ESRDC_20aug2010.ppt 13 Identification of Response Variables for Analysis of Model Behavior Consider variation in α and f In this case, variation at each point in time provides less useful information. More information may be gained from distributions of descriptive response variables such as peak value, frequency, settling time, decay time constant, etc. Sample function evaluations. Here, mean and 95% confidence bounds on time domain waveform do NOT work at all