https://github.com/powergridmodel/ict-workshop-newton-raphson

Experimental code for Newton Raphson initial value problem
https://github.com/powergridmodel/ict-workshop-newton-raphson

Last synced: 8 months ago
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Experimental code for Newton Raphson initial value problem

• Host: GitHub
• URL: https://github.com/powergridmodel/ict-workshop-newton-raphson
• Owner: PowerGridModel
• License: mpl-2.0
• Created: 2025-01-14T14:18:14.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-18T15:34:15.000Z (about 1 year ago)
• Last Synced: 2025-06-08T14:02:01.052Z (8 months ago)
• Language: Jupyter Notebook
• Size: 46.9 KB
• Stars: 1
• Watchers: 0
• Forks: 5
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ICT with Industry Workshop

Experimental repo for Newton Raphson initial value problem

## Getting Started

### Installation of Python

#### Windows

1. Download the latest version of Python from the [official website](https://www.python.org/downloads/).

2. Run the installer and ensure you check the box "Add Python to PATH".

3. Follow the installation steps.

#### macOS

1. Download the latest version of Python from the [official website](https://www.python.org/downloads/).

2. Open the downloaded package and follow the installation steps.

#### Linux

1. Open a terminal.

2. Install Python using your package manager. For example, on Debian-based systems:

    ```sh

    sudo apt update

    sudo apt install python3

    ```

### Creating a Virtual Environment

1. Open a terminal or command prompt.

2. Navigate to your project directory:

    ```sh

    cd /path/to/your/project

    ```

3. Create a virtual environment using the `venv` module:

    ```sh

    python3 -m venv .venv

    ```

### Activating the Virtual Environment

#### Windows

```sh

.\.venv\Scripts\activate

```

#### macOS and Linux

```sh

source .venv/bin/activate

```

### Installing Packages

1. Ensure your virtual environment is activated.

2. Install the required packages from `requirements.txt`:

    ```sh

    pip install -r requirements.txt

    ```

## Included Notebooks

### pandapower_init_and_internals.ipynb

This notebook provides an introduction to the `pandapower` library, including its initialization and internal workings. It is designed to help you understand the basics of power system modeling and analysis using `pandapower` in terms of accessing internal states and specify initial states. More detailed tutorial can be found via the [official website](https://www.pandapower.org/).

### pipeline_dnn.ipynb

This notebook demonstrates the implementation of a deep neural network (DNN) pipeline. It includes data preprocessing, model training, and evaluation steps. It is intended to guide you through the process of building and deploying a DNN model in the context of `pandapower` for your project.

## Data

This repository includes four base network and functionality to generate training networks based on parameterization of key parameters.

### Data structure

The data generation is handled by the `net_gen.py` script, which uses the definitions provided in `simple_net/transformer_high_pu.py`,  `simple_net/high_generation_injection.py` `complete_net/high_generation_segmented_grid.py` and `complete_net/transformer_high_pu_segmented_grid.py` to create networks with varying parameters.

#### Base Networks

1. **High Generation Injection Network (`HighGenInjectionNet`)**

   - This network simulates a high generation injection scenario.

   - It consists of:

     - A source bus at 10kV (node_0).

     - A line with 1 ohm resistance connecting to another bus (node_1).

     - A load connected to node_1.

   - Parameters:

     - `vm_pu`: Voltage magnitude at the external grid bus.

     - `p_mw`: Active power generation.

     - `q_mvar`: Reactive power generation.

     - `init_vm_pu`: Initial voltage magnitudes for the power flow calculation.

2. **Transformer High PU Network (`TrafoHighPuNet`)**

   - This network simulates a transformer with high per-unit values.

   - It consists of:

     - Two buses at 10kV.

     - A transformer connecting the two buses.

     - An external grid connected to the first bus.

     - A load connected to the second bus.

   - Parameters:

     - `vm_pu`: Voltage magnitude at the external grid bus.

     - `p_mw`: Active power load.

     - `q_mvar`: Reactive power load.

     - `vn_lv_kv`: Low voltage side nominal voltage of the transformer.

     - `init_vm_pu`: Initial voltage magnitudes for the power flow calculation.

3. **Large Networks with High Generation Injection (`HighGenSegmentedNet`)**

   - This network simulates a high generation injection scenario with multiple segments.

   - It consists of:

     - A source bus at 10kV.

     - Multiple segments with lines and distributed generation.

     - An external grid connected to the first bus.

   - Parameters:

     - `vm_pu`: Voltage magnitude at the external grid bus.

     - `total_p_gen`: Total active power generation distributed across segments.

     - `total_q_gen`: Total reactive power generation distributed across segments.

     - `p_mw`: Active power generation at the last bus.

     - `q_mvar`: Reactive power generation at the last bus.

     - `init_vm_pu`: Initial voltage magnitudes for the power flow calculation.

4. **Large Networks with High PU Transformer (`TrafoHighPuSegmentedNet`)**

   - This network simulates a transformer with high per-unit values and multiple segments.

   - It consists of:

     - Two buses at 10kV.

     - A transformer connecting the two buses.

     - Multiple segments with lines, 10kv bus, and distributed generation.

     - An external grid connected to the first bus.

   - Parameters:

     - `vm_pu`: Voltage magnitude at the external grid bus.

     - `vn_lv_kv`: Low voltage side nominal voltage of the transformer.

     - `total_p_gen`: Total active power generation distributed across segments.

     - `total_q_gen`: Total reactive power generation distributed across segments.

     - `init_vm_pu`: Initial voltage magnitudes for the power flow calculation.

### Data Generation

The `net_gen.py` script provides functions to generate multiple instances of these networks with randomized parameters within specified ranges:

- **`sample_net_high_gen_inj_xs(N)`**:

  - Generates `N` instances of `HighGenInjectionNet` with randomized parameters.

  - Parameter ranges:

    - `VM_PU_RANGE = [0.9, 3.0]`

    - `P_MW_RANGE = [0.0, 1000.0]`

    - `Q_MVAR_RANGE = [0.0, 0.5]`

    - `INIT_VM_PU_MIN = 0.9`

    - `INIT_VM_PU_MAX = 3.0`

- **`sample_net_trafo_high_pu_xs(N)`**:

  - Generates `N` instances of `TrafoHighPuNet` with randomized parameters.

  - Parameter ranges:

    - `VM_PU_RANGE = [0.9, 3.0]`

    - `P_MW_RANGE = [0.0, 1000.0]`

    - `Q_MVAR_RANGE = [0.0, 0.5]`

    - `VN_LV_KV_RANGE = [0.9, 3.0]`

    - `INIT_VM_PU_MIN = 0.9`

    - `INIT_VM_PU_MAX = 3.0`

- **`sample_net_high_gen_segmented_xl(N)`**:

  - Generates `N` instances of `HighGenSegmentedNet` with randomized parameters.

  - Parameter ranges:

    - `VM_PU_RANGE = [0.9, 3.0]`

    - `TOTAL_P_GEN_RANGE = [0.0, 1000.0]`

    - `TOTAL_Q_GEN_RANGE = [0.0, 0.5]`

    - `P_MW_RANGE = [0.0, 1000.0]`

    - `Q_MVAR_RANGE = [0.0, 0.5]`

    - `INIT_VM_PU_MIN = 0.9`

    - `INIT_VM_PU_MAX = 3.0`

- **`sample_net_trafo_high_pu_segmented_xl(N)`**:

  - Generates `N` instances of `TrafoHighPuSegmentedNet` with randomized parameters.

  - Parameter ranges:

    - `VM_PU_RANGE = [0.9, 3.0]`

    - `VN_LV_KV_RANGE = [0.9, 3.0]`

    - `TOTAL_P_GEN_RANGE = [0.0, 1000.0]`

    - `TOTAL_Q_GEN_RANGE = [0.0, 0.5]`

    - `INIT_VM_PU_MIN = 0.9`

    - `INIT_VM_PU_MAX = 3.0`

These functions return lists of network instances that can be used for training or analysis purposes.

You can call these functions and classes like the fllowing:

```python

from data import HighGenInjectionNet, sample_net_high_gen_inj_xs

nets = sample_net_high_gen_inj_xs(2)

nets[0].run_power_flow()