Estimator

Estimator (base class)

class onwards.estimators.Estimator(wt: Turbine, meas: list, states: list, req_state: list, avail_states: list)

Bases: object

Inits a Estimator object

Prototype class for user defined Estimator objects. Estimator converts th Turbine’s measurements, m_wt, to the Turbine’s state, s_wt.

\[\mathbf{m}_{wt} \rightarrow \mathbf{\hat{s}}_{wt}\]

The ith state, s_wt^i, is computed from the sensors measurements possibly along with previously computed states, s_wt^J, with J : j < i

\[\mathbf{\hat{s}}_{wt}^i = \mathbf{\hat{s}}_{wt}^i(\mathbf{m}_{wt}, \mathbf{\hat{s}}_{wt}^J)\]
Parameters

  • wt (Turbine) – Parent Turbine object.

  • meas (list) – List of the measurements meas required by the Estimator.

  • states (list) – List of the Turbine’s states computed by the Estimator.

  • req_state (list) – List of the Turbine’s states required by the Estimator.

  • avail_states (list) – List of the Turbine’s states available.

Raises

  • Exception – If the requested Turbine’s state is not (yet) available.

  • Exception – If the requested Turbine’s measurement is not available.

reset()

Resets the Estimator to its original state.

update()

Computes the current value of the Estimator’s state(s).

The corresponding state(s) should be updated in the parent Turbine object: self.wt.states['myState'] = myValue

Est_ct_fromthrust

class onwards.estimators.ct_fromthrust.Est_ct_fromthrust(wt: Turbine, avail_states: list, est_args: dict)

Bases: onwards.estimators.estimator.Estimator

Computes the thrust coefficient, ct, from the measured thrust, T, and from the estimated incident velocity, u_inc, and yaw angle, yaw.

Input(s) states

  • Rotor incident velocity (u_inc) [ms-1]

  • Yaw (yaw) [rad]

Input(s) measurements

  • Thrust (T) [N]

State(s) computed

  • Thrust coefficient (ct) [-]

Est_fld_fromdata

class onwards.estimators.fld_fromdata.Est_fld_fromdata(wt: Turbine, avail_states: list, est_args: dict)

Bases: onwards.estimators.estimator.Estimator

Extract the turbine state directly from the Sensor data.

Input(s) states

  • None

Input(s) measurements

  • User defined (cfr Est_fld_fromdata.update())

State(s) computed

  • User defined (cfr Est_fld_fromdata.update())

Est_ufswfs_waked

class onwards.estimators.ufswfs_waked.Est_ufswfs_waked(wt: Turbine, avail_states: list, est_args: dict)

Bases: onwards.estimators.estimator.Estimator

Corrects the ambient streamwise velocity if the wind turbine is waked.

Input(s) states

  • Incident streamwise velocity component (u_inc) [ms-1]

  • Incident spanwise velocity component (w_inc) [ms-1]

Input(s) measurements

  • None

State(s) computed

  • Estimated ambient streamwise velocity component (u_fs) [ms-1]

  • Estimated ambient spanwise velocity component (w_fs) [ms-1]

Est_uincti_kfbem

StateExport

class onwards.estimators.stateExport.StateExportBuffer(wt: Turbine, export_args: dict)

Bases: object

Saves the wind turbine estimated states fo future simulations.

Along with the SensorsPreprocessed class, it allows direct feed through of the sensors measurements to the Farm.lag_solver.

This allows for fast computations when evaluating the performances of the Lagrangian (ie: skips the turbine states estimation step).

Parameters

  • wt (Turbine) – Parent Turbine object.

  • export_args (dict) –

    Dictionary containing the Estimators’s export parameters.

    Available options:

    name

    (str) - Name of the subdirectory (this subdirectory is created inside Farm.data_dir).

    overwrite

    (bool, optional) - Overwrite export data if set to True, by default False.

    user_field

    (List[str], optional) - List of the measurements, m_wt, not part of the turbine state, s_wt, that should be appended to the exported state vector. By default, empty list.

Raises

  • ValueError – If no subdirectory name is provided.

  • OSError – If the export directory already exist.

  • ValueError – If a conflict is detected between user_field and Turbine.states.

Example

Once an Estimator has been exported exported:
>>> est_args =  {
>>>     'export' = { 'name' : 'my_dir',
>>>                  'overwrite' : True,
>>>                  'export_user_field': ['myField']
>>>     ...
>>>     }
One may load it using:
>>> snrs_args = {
>>>     'type': 'SensorsPreprocessed',
>>>     'name': 'my_dir'
>>>     }
save()

Exports the StateExportBuffer object.

update()

Updates the StateExportBuffer object.