SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool

Abstract

In this paper, we present SoDa, an irradiancebased synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) output power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant's 30-min power output, based on the configuration of the panel. The working hypothesis to generate high-resolution (e.g. 1 second) solar data is that the conditional distribution of the time series of solar power output given the cloud density is the same for different locations. We therefore propose a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, and train our stochastic model parameters for the cloudy states on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU). In the paper we introduce the stochastic model, and the methodology used for the training of its parameters. The numerical results show that our tool creates synthetic solar time series at high resolutions that are statistically representative of the measured solar power and illustrate how to make use of the tool to create synthetic data for arbitrary sites in the footprint covered by the NSRDB.

Transient event classification using pmu data with deep learning techniques and synthetically supported training‐set

“…In the actual system, training set will be supported with synthetic data to increase the performance of the classification system for the case of limited training-set and the system will be used to classify brand-new incoming test data. However, some research work in the literature uses the regenerated data to support both the training-set and the test set, which is not very realistic [20,27]. 2.…”

“…However, for the cases when it is not possible to obtain and transfer all data produced by a PMU, it is necessary to generate synthetic data to train a DL-based classification system which uses PMU data to analyse the power system. Many methods have been proposed in the literature to generate synthetic data for such circumstances [20][21][22][23][24][25][26][27][28][29][30]. In [20], a Generative Adversarial Networks (GAN)-based PMU data generation method is proposed to improve the classification accuracy.…”

“…In another study, a new method to generate realistic PMU signals is proposed by using noise patterns of PMUs with the help of both analytical and Machine Learning (ML) based methods [26]. In [27], another method is suggested to generate PMU data from the synthetic network by considering realistic noise and distortion effects. A solar PMU data generator is developed by using a stochastic model in [28].…”

Log(v) 3LPF: A Linear Power Flow Formulation for Unbalanced Three-Phase Distribution Systems

“…( 34) is in general the maximum power produced by the panel p = p unless curtailment is necessary for the safe operation of the system. The calculation of p is out of the scope of this paper, but can be obtained using methods like in [29]. Also, q is the available reactive power, and ṽ is a lowpass filtered version of the voltage magnitude, introduced to avoid undesired controls caused by noisy measurements.…”

Log(v) 3LPF: A Linear Power Flow Formulation for Unbalanced Three-Phase Distribution Systems

“…(34) is in general the maximum power produced by the panelp = p unless curtailment is necessary for the safe operation of the system. The calculation of p is out of the scope of this paper, but can be obtained using methods like in [29]. Also,q is the available reactive power, andṽ is a lowpass filtered version of the voltage magnitude, introduced to avoid undesired controls caused by noisy measurements.…”