GridCal.Engine.Simulations.Optimization package¶
Submodules¶
GridCal.Engine.Simulations.Optimization.optimization_driver module¶
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class
GridCal.Engine.Simulations.Optimization.optimization_driver.Optimize(circuit: GridCal.Engine.Core.multi_circuit.MultiCircuit, options: GridCal.Engine.Simulations.PowerFlow.power_flow_driver.PowerFlowOptions, max_iter=1000)¶ Bases:
PySide2.QtCore.QThread-
cancel()¶ Cancel the simulation
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done_signal= <PySide2.QtCore.Signal object>¶
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plot(ax=None)¶ Plot the optimization convergence
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progress_signal= <PySide2.QtCore.Signal object>¶
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progress_text= <PySide2.QtCore.Signal object>¶
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run()¶ Run the optimization @return: Nothing
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staticMetaObject= <PySide2.QtCore.QMetaObject object>¶
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class
GridCal.Engine.Simulations.Optimization.optimization_driver.VoltageOptimizationProblem(circuit: GridCal.Engine.Core.multi_circuit.MultiCircuit, options: GridCal.Engine.Simulations.PowerFlow.power_flow_driver.PowerFlowOptions, max_iter=1000, callback=None)¶ Bases:
pySOT.optimization_problems.OptimizationProblemVariables: - dim – Number of dimensions
- lb – Lower variable bounds
- ub – Upper variable bounds
- int_var – Integer variables
- cont_var – Continuous variables
- min – Global minimum value
- minimum – Global minimizer
- info – String with problem info
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eval(x)¶ Evaluate the Ackley function at x
Parameters: x (numpy.array) – Data point Returns: Value at x Return type: float