Figure 1.
Steady state equivalent circuit.
Figure 1.
Steady state equivalent circuit.
Figure 2.
Sensibility analysis for different conditions of train voltage and phase: (a) Active power flow at TPS; (b) Reactive power flow at TPS.
Figure 2.
Sensibility analysis for different conditions of train voltage and phase: (a) Active power flow at TPS; (b) Reactive power flow at TPS.
Figure 3.
Sensibility analysis for different conditions of train power: (a) active power flow at TPS; (b) reactive power flow at TPS.
Figure 3.
Sensibility analysis for different conditions of train power: (a) active power flow at TPS; (b) reactive power flow at TPS.
Figure 4.
Framework of the 1 × 25 kV models: (a) illustration of physical representation; (b) PI model diagram; (c) considered bus-branch model for MatPower (Note: the traction transformer is not considered in this work).
Figure 4.
Framework of the 1 × 25 kV models: (a) illustration of physical representation; (b) PI model diagram; (c) considered bus-branch model for MatPower (Note: the traction transformer is not considered in this work).
Figure 5.
Sensibility analysis for : voltage levels for different train power factor values, spawned across different train active power values and line distances.
Figure 5.
Sensibility analysis for : voltage levels for different train power factor values, spawned across different train active power values and line distances.
Figure 6.
Sensibility analysis for : voltage levels for different ratios.
Figure 6.
Sensibility analysis for : voltage levels for different ratios.
Figure 7.
Illustration of algorithm evolution for reactive power compensation: (a) evolution of train voltage; (b) evolution of SST reactive power. Note, for illustration purposes, that the reactive compensation procedure is only enabled at iteration .
Figure 7.
Illustration of algorithm evolution for reactive power compensation: (a) evolution of train voltage; (b) evolution of SST reactive power. Note, for illustration purposes, that the reactive compensation procedure is only enabled at iteration .
Figure 8.
Illustration of algorithm evolution for reactive power compensation: (a) evolution of train active power; (b) evolution of train reactive power; (c) evolution of train apparent power.
Figure 8.
Illustration of algorithm evolution for reactive power compensation: (a) evolution of train active power; (b) evolution of train reactive power; (c) evolution of train apparent power.
Figure 9.
Illustration of algorithm evolution for reactive power compensation: (a) evolution of active power losses in catenary; (b) evolution of reactive power losses in catenary.
Figure 9.
Illustration of algorithm evolution for reactive power compensation: (a) evolution of active power losses in catenary; (b) evolution of reactive power losses in catenary.
Figure 10.
Sensibility analysis for reactive power compensation: line power loss reduction, in percentage, for different power factors. Note that the values near 0 MW or near 0 km are not relevant for this demonstration.
Figure 10.
Sensibility analysis for reactive power compensation: line power loss reduction, in percentage, for different power factors. Note that the values near 0 MW or near 0 km are not relevant for this demonstration.
Figure 11.
Framework for the increase of railway capacity: (a) illustration of physical representation; (b) considered bus-branch model for MatPower.
Figure 11.
Framework for the increase of railway capacity: (a) illustration of physical representation; (b) considered bus-branch model for MatPower.
Figure 12.
Flowchart to test the increasing of capacity procedure.
Figure 12.
Flowchart to test the increasing of capacity procedure.
Figure 13.
Relation of number of trains with voltage in neutral zone: (a) variation of active power in each train, for fixed ind.; (b) variation of power factor in each train, for fixed active power of MW and 2 MW.
Figure 13.
Relation of number of trains with voltage in neutral zone: (a) variation of active power in each train, for fixed ind.; (b) variation of power factor in each train, for fixed active power of MW and 2 MW.
Figure 14.
Relation of number of trains ( ind.) with lower voltage in line: (a) variation of active power in each train, for fixed NZ limit power (15 MVA maximum power, as example, visible after 30 trains for 1 MW train power); (b) variation of NZ limit power, for fixed train active power of 2 MW.
Figure 14.
Relation of number of trains ( ind.) with lower voltage in line: (a) variation of active power in each train, for fixed NZ limit power (15 MVA maximum power, as example, visible after 30 trains for 1 MW train power); (b) variation of NZ limit power, for fixed train active power of 2 MW.
Figure 15.
Relation of capacity improvement with the increase of the NZ reactive power. The dots display the
Table 2 percentage improvement values; the smaller dot lines present polynomial regression curves from each of those points. The blue dash-dot line (Average improvement) presents a polynomial regression curve from the average of all the improvement values.
Figure 15.
Relation of capacity improvement with the increase of the NZ reactive power. The dots display the
Table 2 percentage improvement values; the smaller dot lines present polynomial regression curves from each of those points. The blue dash-dot line (Average improvement) presents a polynomial regression curve from the average of all the improvement values.
Figure 16.
Relation of changing power factor, from unitary one, with an increase of train apparent power.
Figure 16.
Relation of changing power factor, from unitary one, with an increase of train apparent power.
Figure 17.
Illustration of the adjustment of power factor for mobile reactive power compensation.
Figure 17.
Illustration of the adjustment of power factor for mobile reactive power compensation.
Figure 18.
Relation of number of trains (PF ind.) with lower voltage in line: (a) variation of active power in each train, for fixed train PF limit (0.98 cap.); (b) variation of compensation, for fixed train active power of 2 MW.
Figure 18.
Relation of number of trains (PF ind.) with lower voltage in line: (a) variation of active power in each train, for fixed train PF limit (0.98 cap.); (b) variation of compensation, for fixed train active power of 2 MW.
Figure 19.
Integration of reactive power compensation in a smart railway framework.
Figure 19.
Integration of reactive power compensation in a smart railway framework.
Figure 20.
Droop strategy for on-board adjustment. The is the difference between the real train voltage and the expected train voltage. The is the output of the droop curve, where this value is added to the predicted .
Figure 20.
Droop strategy for on-board adjustment. The is the difference between the real train voltage and the expected train voltage. The is the output of the droop curve, where this value is added to the predicted .
Figure 21.
Illustration of the on-board adaptation, supported by the predicted and from the smart railways framework.
Figure 21.
Illustration of the on-board adaptation, supported by the predicted and from the smart railways framework.
Table 1.
Maximum number of trains, for different train power consumptions and for different train power factors. Note: the percentage reduction from unitary power factor is presented in parentheses. As an example, the baseline for MW and is 20 trains; then, for ind., it is only possible to have 12 trains (8 less than the unitary power factor, corresponding −40% less than baseline).
Table 1.
Maximum number of trains, for different train power consumptions and for different train power factors. Note: the percentage reduction from unitary power factor is presented in parentheses. As an example, the baseline for MW and is 20 trains; then, for ind., it is only possible to have 12 trains (8 less than the unitary power factor, corresponding −40% less than baseline).
| Train Power Factor |
---|
Train Active Power | 0.9 ind. | 0.95 ind. | 0.98 ind. | 1 |
---|
0.5 MW | 50 | 58 | 66 | 82 |
(−39.0%) | (−29.3%) | (−19.5%) | (0%) |
1 MW | 25 | 29 | 33 | 41 |
(−39.0%) | (−29.3%) | (−19.5%) | (0%) |
2 MW | 12 | 14 | 16 | 20 |
(−40.0%) | (−30.0%) | (−20.0%) | (0%) |
3 MW | 8 | 9 | 11 | 13 |
(−38.5%) | (−30.8%) | (−15.4%) | (0%) |
4 MW | 6 | 7 | 8 | 10 |
(−40.0%) | (−30.0%) | (−20.0%) | 0%) |
5 MW | 5 | 5 | 6 | 8 |
(−37.5%) | (−37.5%) | (−25.0%) | (0%) |
Table 2.
Maximum number of trains, for different train power consumptions and for different Neutral Zone power limits. Note: the percentage improvement from baseline is presented in parentheses (where 0 MVAr means no compensation). As an example, the baseline for MW is 16 trains; then, for MVAr, it is possible to have nine more trains (+56% more than baseline).
Table 2.
Maximum number of trains, for different train power consumptions and for different Neutral Zone power limits. Note: the percentage improvement from baseline is presented in parentheses (where 0 MVAr means no compensation). As an example, the baseline for MW is 16 trains; then, for MVAr, it is possible to have nine more trains (+56% more than baseline).
| Neutral Zone Power Limit (for Compensation) |
---|
Train Active Power | 0 MVAr | 5 MVAr | 10 MVAr | 20 MVAr | 30 MVAr |
---|
0.5 MW | 66 | 81 | 90 | 100 | 105 |
(0%) | (+22.73%) | (+36.36%) | (+51.52%) | (+59.09%) |
1 MW | 33 | 40 | 45 | 50 | 52 |
(0%) | (+21.21%) | (+36.36%) | (+51.52%) | (+57.58%) |
2 MW | 16 | 20 | 22 | 25 | 26 |
(0%) | (+25%) | (+37.50%) | (+56.25%) | (+62.50%) |
3 MW | 11 | 13 | 15 | 16 | 17 |
(0%) | (+18.18%) | (+36.36%) | (+45.45%) | (+54.55%) |
4 MW | 8 | 10 | 11 | 12 | 12 |
(0%) | (+25%) | (+37.50%) | (+50%) | (+50%) |
5 MW | 6 | 8 | 8 | 9 | 10 |
(0%) | (+33.33%) | (+33.33%) | (+50%) | (+66.67%) |
Table 3.
Maximum number of trains, for different train power consumptions and for different power factor limit. Note: the percentage improvement from baseline (train PF ind.) is presented in parentheses. As an example, the baseline for MW is 16 trains; then for PF cap., it is possible to have eight more trains (+50% more than baseline).
Table 3.
Maximum number of trains, for different train power consumptions and for different power factor limit. Note: the percentage improvement from baseline (train PF ind.) is presented in parentheses. As an example, the baseline for MW is 16 trains; then for PF cap., it is possible to have eight more trains (+50% more than baseline).
| Train Active Power |
---|
Train Power Factor | 0.5 MW | 1 MW | 2 MW | 3 MW | 4 MW | 5 MW |
---|
0.98 ind. | 66 | 33 | 16 | 11 | 8 | 6 |
(+0%) | (+0%) | (+0%) | (+0%) | (+0%) | (+0%) |
0.985 ind. | 74 | 37 | 18 | 12 | 9 | 7 |
(+12%) | (+12%) | (+13%) | (+9%) | (+13%) | (+17%) |
0.99 ind. | 77 | 38 | 19 | 12 | 9 | 7 |
(+17%) | (+15%) | (+19%) | (+9%) | (+13%) | (+17%) |
0.995 ind. | 80 | 40 | 20 | 13 | 10 | 7 |
(+21%) | (+21%) | (+25%) | (+18%) | (+25%) | (+17%) |
1.00 | 82 | 41 | 20 | 13 | 10 | 8 |
(+24%) | (+24%) | (+25%) | (+18%) | (+25%) | (+33%) |
0.99 cap. | 86 | 43 | 21 | 14 | 10 | 8 |
(+30%) | (+30%) | (+31%) | (+27%) | (+25%) | (+33%) |
0.98 cap. | 88 | 44 | 22 | 14 | 11 | 8 |
(+33%) | (+33%) | (+38%) | (+27%) | (+38%) | (+33%) |
0.97 cap. | 90 | 45 | 22 | 15 | 11 | 9 |
(+36%) | (+36%) | (+38%) | (+36%) | (+38%) | (+50%) |
0.96 cap. | 92 | 46 | 23 | 15 | 11 | 9 |
(+39%) | (+39%) | (+44%) | (+36%) | (+38%) | (+50%) |
0.94 cap. | 95 | 47 | 24 | 16 | 12 | 9 |
(+44%) | (+42%) | (+50%) | (+45%) | (+50%) | (+50%) |
0.92 cap. | 98 | 49 | 24 | 16 | 12 | 10 |
(+48%) | (+48%) | (+50%) | (+45%) | (+50%) | (+67%) |
Table 4.
Comparison of power factor with apparent power increase and average improvement of railway infrastructure capacity.
Table 4.
Comparison of power factor with apparent power increase and average improvement of railway infrastructure capacity.
Train Power Factor | Power Increase | Capacity Improvement |
---|
0.98 ind. | −2.0% | 0.0% |
0.985 ind. | −1.5% | 10.7% |
0.99 ind. | −1.0% | 15.5% |
0.995 ind. | −0.5% | 20.2% |
1.00 | 0.0% | 23.9% |
0.99 cap. | 1.0% | 28.6% |
0.98 cap. | 2.0% | 32.7% |
0.97 cap. | 3.1% | 39.1% |
0.96 cap. | 4.2% | 40.2% |
0.95 cap. | 5.3% | 41.1% |
0.94 cap. | 6.4% | 44.9% |
0.93 cap. | 7.5% | 46.5% |
0.92 cap. | 8.7% | 51.2% |