Power quality measurement (IiA 20139)
Invest-in-Arup research project to design and make a convenient way to measure and study the power quality and energy performance of single phase small power and lighting devices, that connect via plug-and-socket.
See IiA page for more project details https://invest.arup.com/?layout=projsheet&projid=20139
The software is designed to run on Raspberry Pi and Raspberry Pi Pico microcontroller, communicating with an analog-to-digital converter (ADC: MCP3912) on a custom PCB to source measurements of power parameters: voltage, current (full range), current (low range) and earth leakage current. Following conversion and scaling to floating point measurement values, the physical measurement samples are further processed to provide a variety of derivative measurements, waveform visualisation and logging. A local touch screen provides user interface.
Partly to enable rapid development cycles, but also validation of calculations, the software will also run on desktop computers using a simulator to take the place of the Pico/ADC to generate sample data.
Raspberry Pi
Minimum hardware requirement is Raspberry Pi 3B+. Non-plus models do not have sufficent thermal dissipation for high CPU loads. Touch screen display with DSI interface, 800x480 pixels. Raspberry Pi OS (32 bit) with desktop. Prepare the image with connectivity to local/required wifi, SSH access, and change the default password. If you have one, pair a Bluetooth keyboard for local access.
Raspberry Pi Pico
Use Thonny on your desktop or laptop computer to install MicroPython on the Pico and verify that you have local REPL access to the microcontroller via a USB cable.
Raspberry PI SD card
Prepare the SD card using Raspberry Pi Imager. As a convenience, a USB stick with Raspberry Pi OS can be prepared first, plugged in the Pi and then that image booted to write to an in-situ SD card -- avoids the need to remove ribbon cable. If using this method, note that an non-initialised Pico must be unplugged from the Pi USB port for this to work. Set a project specific password. Enable the option for remote SSH access. Set the wifi country, SSID and password to be used for initial access.
Pi setup
- Start a terminal session via SSH. (As alternative, you could use a bluetooth keyboard and work locally.)
ssh -X [email protected]
- Clone the repository:
git clone https://github.com/arup-group/pqm-hellebores.git
- Change to the working directory. Create a virtual environment, and activate it:
cd pqm-hellebores
python -m venv venv
source venv/bin/activate
- Install the dependencies:
python -m pip install -r requirements.txt
- Set the device identity and hostname. In place of PQM-n, use the identity of your specific power quality monitor, eg PQM-1:
echo PQM-n > configuration/identity
sudo hostnamectl set-hostname PQM-n
sudo cp /etc/hosts /etc/hosts.old
sed -E 's/^(127.0.1.1\s+).+/\1PQM-n/' /etc/hosts
(The last line edits the active /etc/hosts file to tell the system that the PQM-n hostname refers to the loopback IP address 127.0.1.1. Be sure to change the 'n' to the correct id number that matches the other identification commands.)
- Install a bluetooth file transfer client
sudo apt install blueman
- Add shortcuts to the 'Other' desktop menu:
cp ./pi-setup/find-me.desktop /home/pi/.local/share/applications
cp ./pi-setup/hellebores.desktop /home/pi/.local/share/applications
- Add the Power Quality Monitor launcher to the desktop autostart directory.
cp ./pi-setup/pqm.desktop /home/pi/.config/autostart
See notes file for additional system dependencies.
Pico setup
Using Thonny:
Open ./pico/main.py and save it to the root folder of the microcontroller.
Open ./pico/stream.py and save it to the root folder of the microcontroller.
To use the interactive user interface, run "hellebores" from the Raspberry Pi desktop menu.
It's possible to access the running environment on a Pi using SSH. For working on the Pico microcontroller, use ssh -X to connect to the Pi and then start thonny within your SSH session. This will redirect thonny's program window to your local X-server (needs a Linux or WSL2 system, or install an X-server separately).
Ubuntu (incl. WSL) and macOS
On a system with python3 and git installed, proceed as follows:
git clone https://github.com/arup-group/pqm-hellebores.git
cd pqm-hellebores
python3 -m venv venv
source venv/bin/activate
python3 -m pip install -r requirements.txt
echo PQM-0 > configuration/identity
run/go.sh
Windows
In a command window with python and git available:
git clone https://github.com/arup-group/pqm-hellebores.git
cd pqm-hellebores
python -m venv venv-windows
venv-windows\scripts\activate
python -m pip install -r requirements.txt
echo PQM-0 > configuration\identity
run\go
NB Some code paths are different on Windows to allow the project to run, and the files mswin_pipes.py, go.bat and go.py are exclusive to Windows. Note that some features of the go.sh script (eg software update) are not implemented.
For performance and simplicity benefits, the software is designed with independent programs that are connected in a pipeline with data sent from one program to the next as a text stream. Control settings are stored in a settings.json file: the GUI program modifies this file and sends other programs a Posix signal whenever the settings file is updated, and they then re-load the settings.json. The project is intended to yield to experimentation: the behaviour of the programs at each step of the pipeline can be studied on the command line.
The Pi code will run in WSL, Ubuntu, Mac or similar environments that have a modern python and can support the python dependencies, pipelines and signals. hellebores.py and the rain_chooser.py simulator require SDL for displaying the GUI and to accept touchscreen or mouse input. The GUI can run under X-Windows or in native Mac and MS-Windows if the SDL libraries are installed and are reachable from the python runtime environment. The run script will test whether a serial interface to Pico is available and if not will use the rain_chooser.py simulator as a data source.
The four channels of the ADC each have an associated DC offset and gain calibration factor. The calibration factors for each device are stored in the calibrations.json file. The identity of the specific device at hand is stored in the configuration/identity file. Hardware scaling and calibration factors are applied to the data stream by the scaler.py program.
The calibrator.py program runs the ADC for 10 seconds. In conjunction with a calibrated true RMS multimeter, it can be used to determine device-specific offset and gain calibration factors to be entered into the calibrations.json file.