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Name	Modified	Size
README.md	2022-11-17	14.6 kB
py_open_dsse-main.zip	2022-11-17	607.4 kB
Totals: 2 Items		622.0 kB

py_open_dsse

It is an open-source library developed in Python for estimating distribution networks (DSSE). It communicates with the free software for the simulation of electrical networks (OpenDSS) and collects the results of power flow and distribution system parameters and executes the DSSE, obtaining an estimated state according to the type and location of measurements.

It is developed within the framework of the OpenREiD project (Integral software for simulation and optimization of electrical distribution networks), of the Instituto de Energía Eléctrica (IEE), UNSJ - CONICET, San Juan - Argentina.

Index

Installation
How to use
Measurements
- Definition and creation of meters
- Generate measurements from OpenDSS results
Run the state estimation algorithm
Sample tests
License

Installation

With pip

pip install py-open-dsse

Without pip, clone or download the repository, in the dist folder is the .whl file, copy the location of the file, and in the CMD:

pip install {path-save-files}/py_open_dsse-{version}-py3-none-any.whl’

How to use

First, in the IDE (Integrated Development Environment) of preference, we import the library:

import py_open_dsse

The object class that contains all the functions of the library is activated as follows:

dsse = py_open_dsse.init_DSSE()

The class init_DSSE(), has default values as shown in table 1 and can be modified as appropriate.

Table 1. Description and attributes of function init_DSSE() | Parameters | Description | Default value | |:---:|---|:---:| | Sbas3ph_MVA | Three-phase system base power | 30 | | tolerance | Convergence tolerance of selected algorithm | 1e-3 | | max_iter | Maximum number of iterations of the selected algorithm | 30 | | init_values | Initial values for state estimation. With flat start with 1.0 p.u. / 0° on all buses and with dss start with OpenDSS voltage and angle results| flat |

Once the class is initialized, we can use the functions described below.

Measurements

Definition and creation of meters

The library supports meters and their respective error variance described in Table 2.

Tabla 2. Measurement type of the py_open_dsse library.

Meter	Description
$\left\|V_{i}\right\|$	Node voltage magnitude.
$PQ_{ft}$	Branch power flow
$\left\|I_{ft}\right\|$	Magnitude of branch current.
$PQ_{i}^{SM}$	Injection power or node consumption obtained by a smart meter
$PQ_{i}^{0}$	Passive node or zero injection power.
$PQ_{i}^{PSD}$	Artificial node injection power known as pseudo-measurement
$\left\|V_{i}\right\|\angle \theta$	Voltage phasor measurement
$\left\|I_{ft}\right\|\angle \delta$	Current phasor measurement

The measurement data per phase 𝜌 (1, 2, 3) and measurement error variance Rii of a network modeled in OpenDSS. They must be entered in the MEAS_Bus_i.json, MEAS_Elem_ft.json, MEAS_Bus_i_PMU.json and MEAS_Elem_ft_PMU.json files. The .json measurement files without data are generated with the empty_MEAS_files() function and the parameters from table 3 must be entered.

Table 3. Parameters and description of empty_file_MEAS() function | Parameter | Description |Default value | |:------------------:|-------------------------------------------------------------------|:----------------:| | DSS_path | A path of the .DSS files of the circuit modeled in OpenDSS | None | | MEAS_path_save | Path where the measurement .json files will be saved | None |

The description of the identifiers that can be modified is detailed in tables 4, 5, 6, and 7. The other identifiers in the .json files, are node characteristics or elements extracted from the circuit modeled in OpenDSS, these data should not be modified since they would affect the result of the state estimation algorithm.

Table 4. Description of identifiers of the MEAS_Bus_i.json file.

Identifier	Description
`STS_Vm`	Status (1: Enabled, 0: Disabled)
`Rii_Vm`	Variance of voltage magnitude measurement error.
`Vρm(pu)`	Measurement of voltage magnitude voltage in phase 𝜌.
`STS_PQd(SM)`	Status (1: Enabled, 0: Disabled)
`Rii_SM`	Measurement error variance of injection power or consumption of a smart meter.
`STS_PQd(0)`	Status (1: Enabled, 0: Disabled)
`Rii_0`	Zero or passive injection power measurement error variance.
`STS_PQd(Psd)`	Status (1: Enabled, 0: Disabled)
`Rii_Psd`	Measurement error variance of pseudo power injection measurement
`Pρmd(pu)`	Measurement of active power injection in phase 𝜌.
`Qρmd(pu)`	Measurement of reactive power injection in phase 𝜌.

Table 5. Description of identifiers of the MEAS_Elem_ft.json file.

Identifier	Description
`STS_PQft`	Status (1: Enabled, 0: Disabled)
`Rii_PQft`	Branch power flow measurement error variance.
`Pρmft(pu)`	Measurement of branch active power in phase 𝜌.
`Qρmft(pu)`	Measurement of branch reactive power in phase 𝜌.
`STS_Ift`	Status (1: Enabled, 0: Disabled)
`Rii_Ift`	Branch current magnitude error variance.
`Iρmft(pu)`	Measurement of branch current magnitude in phase 𝜌.

Table 6. Description of identifiers of the MEAS_Elem_ft_PMU.json file. | Identifier | Description | |:---------------------:|---------------------------------------------| | STS_Vm | Status (1: Enabled, 0: Disabled) | | Rii_Vm | Variance of voltage phasor measurement error| | Vρm(pu) | Measurement of voltage magnitude in phase 𝜌 | | Angρm(deg) | Measurement of volage angle in phase 𝜌 |

Table 7. Description of identifiers of the MEAS_Elem_ft_PMU.json file. | Identifier | Description | |:-----------------:|---------------------------------------------| | STS_Ift | Status (1: Enabled, 0: Disabled) | | Rii_Ift | Variance of current phasor measurement error| | Iρmft(pu) | Measurement of current magnitude in phase 𝜌 | | Angρm(deg) | Measurement of current angle in phase 𝜌 |

Generate measurements from OpenDSS results

Initial measurements with uncertainty of measurement error

The empty_init_files_MEAS_Unc() function generates .json files where all the nodes and elements that can participate as measurement in the state estimation algorithm are placed. For this purpose, the parameters of table 8 must be specified.

Table 8. Parameters and description of empty_file_MEAS() function | Parameters | Description | Default value | |:------------------:|----------------------------------------------------------------|:-----------------:| | DSS_path | A path of the .DSS files of the circuit modeled in OpenDSS | None | | MEAS_path_save | Path where the measurement .json files will be saved | None |

In the MEAS_path_save path, it generates the files Init_Bus_i.json, Init_Elem_ft.json, Init_Bus_i_PMU.json and Init_Elem_ft_PMU.json. Depending on the case study, the STS meter status (1: Enabled, 0: Disabled) and the measurement error rate in Unc(%) can be modified.

Adding random errors and generating measurement files

With the .json files generated by the empty_file_MEAS() function and the changes indicated by the user, with the add_error_file_MEAS() function and the Parameters described in Table 9.

Table 9. Description and attributes of function add_error_files_MEAS() | Parameters | Description | Default value| |:--------------:|---------------------------------------------------------------|:----------------:| | DSS_path | A path of the .DSS files of the circuit modeled in OpenDSS| None | | MEAS_path | Path of initial .json files | None | | seed_DS | Random error generation seed | 1 |

Adds random errors from a normal distribution to the OpenDSS power flow results, for use as a measurement. The result of generating random errors is contained in the files MEAS_Bus_i.json, MEAS_Elem_ft.json, MEAS_Bus_i_PMU.json and MEAS_Elem_ft_PMU.json, stored in MEAS_path.

Run the state estimation algorithm

To run the state estimation algorithm, function estimate() is called, it is necessary to enter or change parameters detailed in table 10.

Table 10. Parameters and description of estimate() function

Parameters	Description	Default value
`DSS_path`	A path of the `.DSS` files of the circuit modeled in OpenDSS.	`None`
`MEAS_path`	Path where measurement files are located.	`None`
`path_save`	Path where the results will be saved	`None`
`Typ_cir`	Circuit type, can be `1ph` or `Pos`.	`None`
`ALG`	Type of algorithm state variables. At the moment `NV`	`NV`
`coord`	Type of coordinates to solve. For the moment `polar`.	`Polar`
`method`	Solution method, can be `nonlinear`, `linear_PMU` and `nonlinear_PMU`.	`nonlinear_PMU`
`name_project`	Project name	`Default`
`View_res`	Displays the result of the selected algorithm by console	`False`
`DSS_coll`	Displays next to the estimated status, the actual status according to OpenDSS	`False`
`summary`	Displays a summary of the simulation by console	`False`
`MEAS_Pos`	`True` if it is a `Pos` circuit, take the `.json` files in positive sequence.	`False`

Sample tests

In the path :{Python_library_path}/py_open_dsse/examples, the .DSS and .json files of single-phase (1ph) and positive sequence equivalent (Pos) circuit measurements detailed in Table 11 are located.

Table 11. Sample tests | Circuit | Tpy_circ | Case | |:------------:|:------------:|:--------:| | 4Node | 1ph | 1 | | 15NodeIEEE | 1ph | 2 | | 13NodeIEEE | Pos | 1 | | 37NodeIEEE | Pos | 2 |

The function test_circuit(Typ_cir, case), returns a dictionary with the keys 'DSS_file', 'MEAS_path', 'save_path', 'name_project' and 'Typ_cir' which correspond to the .DSS file path, measurement file path, path where results will be saved and circuit type respectively.

import py_open_dsse

dsse = py_open_dsse.init_DSSE() #Start the class.

if __name__ == '__main__':
    net = dsse.test_circuit(Typ_cir='1ph', case=1)

    #dsse.empty_init_files_MEAS_Unc(DSS_path=net['DSS_file'], MEAS_path=net['MEAS_path'])
    #dsse.add_error_files_MEAS(DSS_path=net['DSS_file'], MEAS_path=net['MEAS_path'])
    #dsse.empty_MEAS_files(DSS_path=net['DSS_file'], MEAS_path=net['MEAS_path'])

    #execute the state estimation algorithm
    Results = dsse.estimate(
        DSS_path=net['DSS_file'],
        MEAS_path=net['MEAS_path'],
        path_save=net['save_path'],
        Typ_cir=net['Typ_cir'],
        name_project=net['name_project'],
        View_res=True,
        summary=True,
        DSS_coll=True,
        #MEAS_Pos=True,
        #method='nonlinear'
        #method='linear_PMU',
    )