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Machine learning-based state prediction and optimization of orthogonal iterative abort strategy for unbalanced power grids

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21 mar 2025
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Figure 1.

The grid current and work and reactive power of the equilibrium current control
The grid current and work and reactive power of the equilibrium current control

Figure 2.

The grid current and work and reactive power of our method
The grid current and work and reactive power of our method

Figure 3.

The similarity of history and the verification of the nuclear section
The similarity of history and the verification of the nuclear section

Figure 4.

The convergence of state prediction calculation
The convergence of state prediction calculation

Figure 5.

The difference of the initial calculation of redistributed trend and planning trend
The difference of the initial calculation of redistributed trend and planning trend

The results of the prediction of the operation section of the grid

Project Bus qualification (%) Line qualification (%) Voltage qualification (%)
Reforecast 99.31 89.81 98.66

The parameters of the simulation system

Parameter Value Parameter Value
Udc/V 500 C/μF 60
LS/mH 15 S*/(Kv·A) 15
Rs 0.2 f/Hz 60
Lg/mH 3

The comparison of the control of the balance of the grid

Time/s Current inequality Active fluctuation/W Reactive fluctuation/war
Balance current control Ours Balance current control Ours Balance current control Ours
0~0.1 0.02 0.02 50 50 70 70
0.1~0.2 0.08 0.08 838 611 1083 1002

Initial forecast results

Project Bus qualification (%) Line qualification (%) Voltage qualification (%)
Single cross matching 88.98 72.33 87.59
Similar day matching 96.21 85.05 99.38
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro