I am currently working on a project where I would like to statistically differentiate a temperature sensors readings, from setpoint temperature readings within the traffic of a building automation system.
To elaborate more on which type of problem I am trying to solve here, let us say that I am observing the historical data from sensors recording different data for a common experiment.
If I do not have the title of each chart, how could I, after recognizing a chart as a temperature related one, say for sure if it is a reading or a dynamic setpoint? To add, let us consider a temperature sensor that has precision reaching only integers (no decimal number included).
Thank you for your time.
Respectfully,
Maher.
Related
I need to monitor an AC Voltage waveform and record the RMS value when the breakdown happens. I roughly know how to acquire data from videos I have watched, however, it is difficult for me to produce a solution that reads the Breakdown Voltage Value. Ideally, I would also take a screenshot along with the breakdown voltage value,
In case you are not familiar with this topic, When a breakdown happens the voltage will drop immediately to zero. So what I need is to measure the voltage just before it falls to zero, and if possible take a screenshot. This is an image of a normal waveform (black) with a breakdown one (red).
Naive solution*:
Take the data and get the Y values (this would depend on the datatype you have, which would depend on how you acquire the data).
Find the breakdown point by iterating over the values and maintaining a couple of flags (I would probably say track "got higher than X" and once that's true, track "got lower than Y").
From that, I would just say take the last N points (Get Array Subset) and get the array max. Or just track the maximum value as you run.
Assuming you have the graph in a control, you can just right click and select Create>>Invoke Node>>Export Image.
I would suggest trying playing with that with a VI with static data which you can repeatedly run to check how your code behaves.
*I don't know the problem domain and an not overly familiar with the various analysis VIs that ship with LV, so there are quite possibly more efficient ways of doing this.
I have seen how easy pvlib-python can obtain weather forecasts, as it is presented in this link: https://pvlib-python.readthedocs.io/en/latest/forecasts.html
In this link, the example is just for illustration, the retrieved weather data seem to be limited in length (not more than a month from the past). So, I wonder whether the archived weather forecasts retrieved by pvlib for a practical implementation can be longer.
Can pvlib-python retrieve archived GFS weather forecasts for a year?
For example, I am looking for the temperature and solar irradiance (GHI) for the entire 2018. Can pvlib-python do that, and if so how?
This is not possible with pvlib-python. I think it's out-of-scope and I don't anticipate adding this feature in the future.
However, I wrote a python script to download some archived point forecast data from the NOAA NOMADS server: https://github.com/wholmgren/get_nomads/ It's efficient in that in only downloads the data that you need, but it's still fairly slow and error prone.
I wrote a small client for the CAMS radiation service: https://github.com/GiorgioBalestrieri/cams_radiation_python.
It contains a notebook showing how to combine this with pvlib.
From the website:
Copernicus Atmosphere Monitoring Service (CAMS) radiation service provides time series of Global, Direct, and Diffuse Irradiations on horizontal surface, and Direct Irradiation on normal plane (DNI) for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East (-66° to 66° in both latitudes and longitudes). Time coverage is 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 1 min to 1 month. The number of automatic or manual requests is limited to 40 per day.
See the repo readme file for more information.
I have two identical gps modules running the same program with antennas side by side. I get different results. I don't understand why
One example is 4211.41545 from the first unit and 4211.41481 from the second the timestamp is the same.
You don't say what the values you quote are so I will assume that the value you are showing is the Lat or Long value from the NMEA output data. If this is the case then the difference between the values is 0.00064 minutes of arc. The maximum physical distance that this will represent is around 1.1 metres (along a great circle). At 60 degrees north this would correspond to around 55cm in an E/W direction.
You do not say how far apart the antennae are or whether there are any obstructions above the level of their horizons which will introduce varying multipath signals that will be different for the two antennae. The two receivers will not necessarily be sampling the satellite signals at the same instants and so can have marginally different signal timings resulting in a different position result.
A typical consumer grade GPS will have a CEP figure of 2.5 metres meaning that 50% of the values for position in an unobstructed sky view position will lie within a 2.5 metre radius circle of the true position.
Taking this into account you should not expect any two adjacent GPS devices to give identical results.
I am trying to find a way to determine when a value is stabilized in excel. In this case, temperature. I have a set of time-series data, TIMESTAMP and TEMPERATURE. at T=0, the temperature begins to rise at a rapid rate. When the setpoint temperature is approached, the rate of rise decreases, until finally, it stabilizes around the setpoint. The temperature tends to overshoot and undershoot the setpoint by several degrees before stabilization.
How can I have excel figure out when the temperature is "Stable" around the setpoint? For example, Delta T (SP-PV) <= 2. (Delta T is difference between Set Point SP and Process Variable PV)
I was thinking possibly using a time variable to determine a data set size (i.e. 5 minutes) and see if the average Delta T within that time is <= 2.
I do not know how to get excel to search through the whole series effectively though.
If you only look at the difference between the setpoint and the value at some time 0, you may mistake a crossing as a "stabilized" point. You need to examine both the estimated rate of change (Tn - Tn-1)/dt and the current value.
Stability is going to be a combination of "how fast is my signal still changing" and "how close am I". It isn't uncommon in real systems for there to be a fixed offset (steady state error) between a setpoint and a measurement with no change happening in the signal over time. This is typically corrected by an integrative term in the control system.
In the MS-assisted case, it is the GPS receiver which sends the measurements for the SLP to calculate and revert. I understand the measurements include the Ephemeris, Iono, DGPS etc + Doppler shift that are sent. Please let me know if my understanding is right.
Does the SET send the code (the entire data transmitted by satellites as is) that it receives as is or splits it into the above components and send?
All the assistance information in SUPL is encapsulated using RRLP protocol (Radio resource location services (LCS) protocol for GSM), RRC (Radio Resource Control for UMTS) or TIA 801 (for CDMA 2000) or LPP (LTE Positioning Protocol for LTE). I'm just looking at RRLP standard ETSI TS 101 527. The following part sounds interesting:
A.3.2.5 GPS Measurement Information Element
The purpose of the GPS Measurement Information element is to provide
GPS measurement information from the MS to the SMLC. This information
includes the measurements of code phase and Doppler, which enables the
network-based GPS method where position is computed in the SMLC. The
proposed contents are shown in table A.5 below, and the individual
fields are described subsequently.
In subsequent section it is defined as:
reference frame - optional, 16 bits - the frame number of the last measured burst from the reference BTS modulo 42432
GPS TOW (time of week) - mandatory, 24 bits, unit of 1ms
number of satellites - mandatory, 4 bits
Then for each satellite the following set of data is transmitted:
satellite ID - 6 bits
C/No - 6 bits
Doppler shift - 16 bits, 0.2Hz unit
Whole Chips - 10 bits
Fractional Chips - 10 bits
Multipath Indicator - 2 bits
Pseudorange Multipath Error - 3+3 bits (mantissa/exponent)
I'm not familiar that much with GPS operation to understand all the parameters, but as far as I understand:
C/No is simply a signal(carrier) to noise ratio
Doppler shift - gives the frequency shift for a given satellite, obviously
Whole/Fractional Chips together give the phase (and thus satellite distance)
My understanding is that things like almanac, ephemeris, Iono, DGPS are all known on the network side. As far as I know those things are transferred from network to MS in MS-based mode.
Hope that helps.
Measurements collected from MS-assisted location requests include:
Satellite ID
code phase - whole chips
code phase - fractional chips
Doppler
Signal strength
Multipath indicator
pseudorange RMS indicator
In addition, the GPS time of measurements is also provided as one value (in milliseconds) for the time which all measurements are valid.
In practice, the required fields that need to be accurate and correct are:
Satellite ID
code phase - whole chips
code phase - fractional chips
Doppler
The code phase values for each satellite are almost always used for the most accurate location calculation. Doppler values can be used to estimate a rough location but aren't usually accurate enough to really contribute to the final solution.
The other values for signal strength, multipath indication, and RMS indicator usually vary in meaning so much between vendors that they don't really provide much benefit for the position calculation. They would normally be used for things like weighting other values so that good satellites count more in the final position.
The network already knows (or should know) the ephemeris and ionospheric model. They are not measurements collected by the handset.