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Thesis schematics.pptx
- 1. 𝜔𝑚 PMA MSC 𝑉𝑑𝑐 𝑃 𝑔𝑒𝑛 𝑃𝑚 𝑃𝑠 GSC DC Chopper SMES COIL Filter Grid 𝑃𝑔𝑟𝑖𝑑 Grid fault. 𝑃 𝑔 ,𝑄𝑔 Wind Speed
- 3. 𝜽𝑺 𝜽𝑽 𝜽𝒊 𝑰𝒅𝒔 = 𝟎 𝝎𝒆 𝑰𝒒𝒔 𝝎𝒆 q-axis d-axis q-axis 𝑽𝒅𝒔 = 𝝎𝒆 𝑳𝒒 𝒊𝒔𝒒 𝝎𝒆 𝝀𝒓 𝝎𝒆 𝝎𝒆 𝑽𝒒𝒔 = 𝝀𝒓𝝎𝒆 𝑽𝒔
- 5. 𝜔𝑚 PMSG MSC 𝑉𝑑𝑐 𝑃 𝑔𝑒𝑛 SVPWM Pitch controller ; 𝛽 abc dq dqo 𝜃𝑟 𝐼𝑎𝑟 𝐼𝑑 𝐼𝑏𝑟 abc PI + 𝜔(𝐿𝑑𝑖𝑞 + 𝜙) PI PI - + + 𝐼∗ 𝑞 𝐼𝑞 - 𝜔𝑚 −𝜔𝐿𝑞𝑖𝑞 + - 𝑰∗ 𝒅 = 𝟎 + 𝜔∗ 𝑚 𝑖𝑑 𝑖𝑞 𝐼𝑐𝑟 𝑃𝑚
- 7. GSC Filter 𝑃𝑔𝑟𝑖𝑑 𝑉𝑑𝑐 PWM 𝜶𝜷 dq 𝜽 abc dq abc + - PI 𝑉𝑑𝑐 𝑉𝑑𝑐_𝑟𝑒𝑓 𝑖𝑑 𝑖𝑞 𝑖𝑎 𝑖𝑏 𝑖𝑐 𝑉 𝑎 𝑉 𝑐 𝑉𝑏 𝝎. 𝑳 + + - 𝑉𝑑𝑐 + + 𝝎. 𝑳 PI PI + + - - 𝑖𝑞 𝑖𝑞_𝑟𝑒𝑓 = 0 𝑖𝑑_𝑟𝑒𝑓 𝑖𝑑 𝜶𝜷
- 9. + GSC Lg 𝑉𝑑𝑐 PWM PLL 𝑖𝑎𝑏𝑐𝑔 𝑉𝑎𝑏𝑐𝑔 𝐺𝑟𝑖𝑑 abc abc dq dq 𝑖𝑑𝑔 𝑖𝑞𝑔 𝑉𝑑𝑔 𝑉 𝑞𝑔 𝑉𝑑𝑐 𝑉𝑑𝑐∗ ∆𝑉𝑑 𝑉𝑎∗ 𝑉𝑏∗ 𝑉𝑐∗ +- ++ PI PI PI - + + ∆𝑉 𝑞 𝑖𝑞𝑔∗ − 3 2 / Ɵ 𝑄𝑔∗
- 10. DC-link capacitor IGBT 1 Diode 1 Diode 2 IGBT 2 SMES 𝑰𝑺𝑴𝑬𝑺 SMES Charge Mode SMES Standby Mode SMES Discharge Mode
- 11. Initialize Objective function Apply SMES constraints Evaluate objective function Converged? Select new candidate SMES size Select SMES size YES No
- 12. + + + + Input Output Hidden 1 2 b W W b Output
- 14. Start Preparation of dataset Training dataset (80%) Testing dataset (20%) Development of accurate models Selection of best fitted model Number of neurons Number of hidden layer Number of epoch Activation function Training Algorithm Validation of the selected model End Mean absolute error (MAE) Root mean square error (RMSE) Correlation coefficient (R) Coefficient of determination (𝑅2 ) Implementation of Artificial neural network (ANN) Splitting the dataset No Yes
- 15. Start Define input & output pairs Control the DC/DC inverter using the trained ANN Dataset extraction from simulation Define Number of hidden layers Adjust number of hidden layers Chose training method (Levenberg Marquardt technique) Validation of the selected model End Dataset distribution Training= 60% Testing=20% Validation=20% FALSE TRUE Check MSE & Confusion Matrix FALSE TRUE
- 16. Comparator - 𝑽𝒅𝒄 𝑽𝒅𝒄_𝒓𝒆𝒇 P.I + D + + 𝚫D 𝑺𝟏 PWM 𝑺𝟐 𝑰𝑺𝑴𝑬𝑺 𝑰𝒍𝒊𝒎 + - 𝑴𝒂𝒈𝒏𝒆𝒕 𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝒍𝒊𝒎𝒊𝒕𝒊𝒏𝒈 𝑫𝑺 𝒁𝒊𝒈𝒛𝒂𝒈 𝒘𝒂𝒗e
- 17. Start Define input & output pairs Control the DC/DC inverter using the trained ANN Dataset extraction from simulation Define Number of hidden layers Adjust number of hidden layers Chose training method (Levenberg Marquardt technique) Validation of the selected model End Dataset distribution Training= 60% Testing=20% Validation=20% FALSE TRUE Check MSE & Confusion Matrix FALSE TRUE
- 19. Anode Cathode Separator Electrolyte Solid + Li Li + + V(t) I(t) I(t) 𝑪𝑺 − (𝒓, 𝒕) ----Single particle model---- 𝒓 𝑪𝑺 + (𝒓, 𝒕) 𝑹𝑺 − 𝑹𝑺 + 𝒓 Li
- 20. ANN Controller-1 B 𝟏 𝟏 + 𝒔𝑻𝑯 𝟏 𝑹𝟏 𝑺𝑳𝑷𝑨 𝟐𝝅𝑻𝑨𝑩 𝑺 Ʃ - - + Ʃ 𝑨𝑪𝑬𝑨 𝑨𝑹𝑬𝑨𝑨 Ʃ Ʃ 𝟏 𝟏 + 𝒔𝑻𝑻 𝟏 + 𝒔𝑲𝒓𝑻𝒓 𝟏 + 𝒔𝑻𝒓 𝑲𝑷 𝟏 + 𝒔𝑻𝑷 Reheat steam turbine Hydraulic Amplifier ANN Controller-2 B 𝟏 𝟏 + 𝒔𝑻𝑯 𝟏 𝑹𝟏 𝑺𝑳𝑷𝑩 - Ʃ 𝑨𝑪𝑬𝑩 𝑨𝑹𝑬𝑨𝑩 Ʃ Ʃ 𝟏 𝟏 + 𝒔𝑻𝑻 𝟏 + 𝒔𝑲𝒓𝑻𝒓 𝟏 + 𝒔𝑻𝒓 𝑲𝑷𝑺 𝟏 + 𝒔𝑻𝑷𝑺 𝑪𝑺𝑩 𝑪𝑺𝑨 - + + - Reheat steam turbine governor Control area-2 ∆𝑷𝑻𝒊𝒆 ∆𝑭𝑨 ∆𝑭𝑩 + - - - - + + Control area-1
- 21. Great Britain 1 0.9 0.75 0.45 0.3 0.2 0 0.15 0.625 1 1.5 3 Egypt Denmark Germany China Italy Japan Spain U.S.A Time (s) Voltage (p.u) Area B Area A Area C
- 22. Percentage 0.01 0.1 1 1000 Time period (seconds) 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 10 100 Transmission and HV distribution systems LV distribution systems Percentage Overvoltage Must Stay Connected Disconnection Permitted
- 23. 𝑃 𝑔𝑒𝑛 𝑃𝑔𝑟𝑖𝑑 𝜔𝑚 MSC 𝑉𝑑𝑐 𝑃𝑚 𝑃𝑠 GSC Filter Grid 𝑃 𝑔 ,𝑄𝑔 Wind Speed PMA
- 24. Wind Speed 𝜔𝑚 SCIG MSC 𝑉𝑑𝑐 𝑃 𝑔𝑒𝑛 𝑃𝑚 𝑃𝑠 GSC Filter Grid 𝑃𝑔𝑟𝑖𝑑 𝑃 𝑔 ,𝑄𝑔 Gear Box
- 31. 𝑃 𝑔𝑒𝑛 𝜔𝑚 MSC 𝑉𝑑𝑐 PWM Pitch controller ; 𝛽 abc dq dqo 𝜃𝑟 𝐼𝑎𝑟 𝐼𝑑𝑒 𝐼𝑏𝑟 abc PI + 𝜔𝐿𝑠𝐼𝑑𝑒 + 𝜔𝜆𝑓 PI PI - + + 𝐼∗ 𝑞𝑒 𝐼𝑞𝑒 - 𝜔𝑚 −𝜔𝐿𝑠𝐼𝑑𝑒 + - 𝐼∗ 𝑑𝑒 = 0 + 𝜔∗ 𝑚 𝑖𝑑 𝑖𝑞 𝐼𝑐𝑟 𝑃𝑚 PMA
- 32. Time (s) 1.0 0.9 0.75 0.45 0.3 0.2 0 0.625 1.3 …. 5.0 6.0 Voltage (p.u) 1.1 1.2 1.3 …. 10.0 Normal Operation State No Tripping HVRT LVRT
- 33. Past K K+1 K+2 K+p Future Prediction Horizon Sample Time Reference Trajectory Predicted Output Predicted Output Predicted Control Input Past control Input
- 35. Minimization of the cost function s X(k+1) n x(k) Measurements DC-DC Chopper SMES Predictive Model Dc Dc X(k) X∗ (k)
- 36. 𝑫𝟏 𝑫𝟐 𝑺𝟏 𝑺𝟐 Cost Function g1 𝑽𝒅𝒄 ∗ 𝑽𝒅𝒄 Eq.(30) Eq. (30) Eq. (30) 𝑺𝟐 𝑺𝟏 𝑰𝒅𝒄(k+1) 𝑷𝑺𝑴𝑬𝑺 ∗ (k+1) 𝒊𝒔(k+1) 𝑰𝒔𝒄(k+1) 𝒊𝒍 𝑳𝒔 SMES Coil 𝒊𝒔 𝒊𝒄
- 37. MSC 𝑃 𝑔𝑒𝑛 GSC 𝑃𝑔𝑟𝑖𝑑 Switching Signal 𝑫𝟐 𝑫𝟏 𝑮𝟏 𝑮𝟐 𝒊𝒍 𝑳𝒔 SMES Coil Minimize Cost Function g1 𝑽𝒅𝒄 𝑖𝑙 𝑘 , 𝑖𝑆 𝑘 , 𝑖𝑔 𝑘 , 𝑉𝑑𝑐* 𝒊𝒔 𝒊𝒄 𝑽𝒅𝒄 ∗ Predict capacitor voltage 𝑉 𝑑𝑐 𝑝 (k+1) 𝒊𝒈
- 38. 𝑫𝟏 𝑫𝟐 𝑺𝟏 𝑺𝟐 Cost Function g1 𝑽𝒅𝒄 ∗ 𝑽𝒅𝒄 Eq.(30) Eq. (30) Eq. (30) 𝑺𝟐 𝑺𝟏 𝑰𝒅𝒄(k+1) 𝑷𝑺𝑴𝑬𝑺 ∗ (k+1) 𝑰𝒔𝒄(k+1) 𝒊𝒍 𝑳𝒔 SMES Coil 𝒊𝒔 𝒊𝒄
- 39. 𝑉𝑑𝑐 ∗ 1 1 + 𝑠𝑇𝑐 1 𝑠𝐿 + + ∏ 𝐼𝑆𝐶 Δ𝐼𝑠𝑐 Δ𝐸𝑠𝑐 Δ𝑃𝑆𝑀𝐸𝑆 𝐾𝑀𝑃𝐶 1 + 𝑠𝑇𝑑 + − 𝑉𝑑𝑐_𝑚
- 40. Past K K+1 K+2 K+p Future Prediction Horizon Reference Trajectory Predicted Output Measured Output Predicted Control Input Past control Input Sample Time
- 41. Yes No Measure 𝑉𝑑𝑐(𝑘 + 1) x=0 x=x+1 g=𝑉 𝑠 ∗ − 𝑉 𝑐 𝑝 (k+1) 𝑊𝑎𝑖𝑡 𝑓𝑜𝑟 𝑛𝑒𝑥𝑡 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑖𝑛𝑠𝑡𝑎𝑛𝑡 Start-up x < 𝑁2 Store optimal values Apply optimal Switch state s(k+1) is p k + 1 = G1iL p k + 1 𝑉 𝑐 𝑝 𝑘 + 1 = 𝑇𝑠 2 𝐿1𝑐1 𝑠 𝑘 ∗ 𝑉 𝑠 − 𝑉 𝑐 𝑝 (𝐾) + 2𝑉 𝑐 𝑝 𝐾 − 𝑉 𝑐 𝑘 − 1
- 42. Yes No Measure 𝑖𝑙 𝑘 , 𝑖𝑆 𝑘 , 𝑖𝑔 𝑘 , 𝑉𝑑𝑐(𝑘) x=0 x=x+1 Evaluate 𝑔1 𝑊𝑎𝑖𝑡 𝑓𝑜𝑟 𝑛𝑒𝑥𝑡 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑖𝑛𝑠𝑡𝑎𝑛𝑡 Start-up x < 𝑁2 Store optimal values Compute 𝑖𝑙 𝑝 𝑘 + 1 𝑃𝑟𝑒𝑑𝑖𝑐𝑡 𝑉 𝑑𝑐 𝑝 𝑘 + 1 S_opt
- 46. DC-link capacitor IGBT 1 Diode 1 Diode 2 IGBT 2 SMES 𝑰𝑺𝑴𝑬𝑺 SMES Charge Mode SMES Standby Mode SMES Discharge Mode 𝜔𝑚 PMA MSC 𝑉𝑑𝑐 𝑃 𝑔𝑒𝑛 𝑃 𝑚 𝑃𝑠 GSC DC Chopp er SM ES Filter Grid 𝑃𝑔𝑟𝑖𝑑 Grid fault. 𝑃 𝑔 ,𝑄𝑔 Wind Speed
- 47. 𝑖𝑔𝑑 PWM PI(2) PI(4) FRT Mode Detection PI(3) 𝑖𝑔𝑞𝑟𝑒𝑓 = 𝑘 × 𝐼𝑁 × (𝑉𝑁 − 𝑉) - 𝑉𝑑𝑐𝑟𝑒𝑓 MA X 𝑖𝑔𝑑𝑟𝑒𝑓 ≤ 𝐼𝑚𝑎𝑥 2 − 𝐼𝑔𝑞𝑟𝑒𝑓 2 PI(1) 𝑉𝑑 𝑄𝑟𝑒𝑓 Q 𝑖𝑔𝑞𝑟𝑒𝑓 𝑖𝑔𝑑𝑟𝑒𝑓 𝑖𝑔𝑞 ω𝑔 𝐿𝑔𝐼𝑔𝑞+ 𝑢𝑔𝑑 −ω𝑔 𝐿𝑔𝐼𝑔𝑞+ 𝑢𝑔𝑑 𝑢𝑒𝑑 𝑢𝑒𝑞
- 48. 𝜔𝑚 PMA 𝑃𝑚 𝑖𝑑𝑠 Wind Speed MSC 𝑉𝑑𝑐 𝑃 𝑔𝑒𝑛 PWM 𝜔𝑚(measured) 𝜃𝑟 𝜃𝑟 𝑖𝑏𝑠 𝑖𝑐𝑠 𝑖𝑎𝑠 abc/dq 𝑖𝑞𝑠 𝑉𝑊 𝜃𝑟 Wind speed sensor Optimal tip speed ratio control 𝑉 𝛼𝑠* 𝑉𝛽𝑠* 𝑉 𝑎𝑠* abc/ 𝛼𝛽 dq/ abc PI(1) PI(3) PI(2) 𝜔𝑚* 𝑉 𝑞𝑠* 𝑉𝑑𝑠* 𝑉𝑏𝑠* 𝑉 𝑐𝑠* − 𝑖𝑞𝑠 − 𝑖𝑑𝑠 𝑖𝑞𝑠* 𝑖𝑑𝑠*=0 + - + +
- 49. 𝑺𝟐 Comparator P.I - 𝑽𝒅𝒄 𝑽𝒅𝒄_𝒓𝒆𝒇 + D + + 𝚫D 𝑺𝟏 PWM 𝑰𝑺𝑴𝑬𝑺 𝑰𝒍𝒊𝒎 + - 𝑴𝒂𝒈𝒏𝒆𝒕 𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝒍𝒊𝒎𝒊𝒕𝒊𝒏𝒈 𝑫𝑺 𝒁𝒊𝒈𝒛𝒂𝒈 𝒘𝒂𝒗e
- 50. 𝑺𝟐 Comparator P.I - 𝑽𝒅𝒄 𝑽𝒅𝒄_𝒓𝒆𝒇 + D + + 𝚫D 𝑺𝟏 𝑰𝑺𝑴𝑬𝑺 𝑰𝒍𝒊𝒎 + - 𝑴𝒂𝒈𝒏𝒆𝒕 𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝒍𝒊𝒎𝒊𝒕𝒊𝒏𝒈 𝑫𝑺 𝒁𝒊𝒈𝒛𝒂𝒈 𝒘𝒂𝒗e
- 51. 1 0.9 0.75 0.45 0.3 0.2 0.5 0.4 0.3 0 0.3 0.5 E.ON Q/𝑃𝑛 0.1 0.4 0.1 0.2 0.2 P/𝑃 𝑛 Inductive Capacitive
- 52. 1 0.2 0.41 0 0.48 E.ON Q/𝑃𝑛 (p.u) 0.1 0.1 P/𝑃 𝑛 (p.u) Inductive Capacitive 0
- 53. MSC 𝑃 𝑔𝑒𝑛 GSC 𝑃𝑔𝑟𝑖𝑑 𝑫𝟐 𝑫𝟏 𝑮𝟏 𝑮𝟐 𝒊𝒍 𝑳𝒔 SMES Coil 𝑽𝒅𝒄 Comparator P.I 𝑽𝒅𝒄 ∗ - + D + + 𝚫D 𝑺𝟏 PWM 𝑺𝟐 𝑰𝑺𝑴𝑬𝑺 𝑰𝒍𝒊𝒎 + - 𝑴𝒂𝒈𝒏𝒆𝒕 𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝒍𝒊𝒎𝒊𝒕𝒊𝒏𝒈 𝑫𝑺 + + PWM
- 54. 𝑺𝟐 Comparator P.I - 𝑽𝒅𝒄 𝑽𝒅𝒄_𝒓𝒆𝒇 + D ++ 𝚫D 𝑺𝟏 𝑰𝑺𝑴𝑬𝑺 𝑰𝒍𝒊𝒎 + - 𝑴𝒂𝒈𝒏𝒆𝒕 𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝒍𝒊𝒎𝒊𝒕𝒊𝒏𝒈 𝑫𝑺 𝒁𝒊𝒈𝒛𝒂𝒈 𝒘𝒂𝒗e