The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.
The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.
The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.
This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.
This dataset is related to the paper “Quantification of feature importance in automatic classification of power quality distortions” (IEEE International Conference on Harmonics and Quality of Power, March 2020). It includes the features extracted from synthetic signals with power quality distortions obtained from a public model (doi: 10.1109/ICHQP.2018.8378902).
Microbial fuel cells (MFC) are one of the best sources of renewable energy but the power density achieved till now is not enough for present as well as future application purpose. Recent research gives us hope that a high amount of power density can be achieved by doing various modifications at different functional levels on the paper based MFC.
Different instruments, satellite data and simulation softwares are available for measuring solar irradiation intensities but efficiency and accuracy of all devices are different. This paper represents detailed comparison of various measuring instruments based on efficiency, cost effectiveness, availability, accuracy, compactness and reliability .Importing solar radiation measurement instruments is very costly from economic and maintenance point of view for developing countries so it is preferred to construct devices from locally available equipments.
This .zip file contains two files: Temperature_outdoor.xlsx and TestData.xlsx. This .zip file provides detailed configuration information of the case system mentioned in artical "Dynamic Optimal Energy Flow in the Heat and Electricity Integrated Energy System".
This paper utilizes modern statistical and machine learning methodology to track oscillation modes in complex power engineering systems. The damping ratio of the electromechanical oscillation mode is formulated as a function of power of the generators and loads as well as bus voltage magnitudes in the entire power system. The celebrated Lasso algorithm is implemented to solve this high-dimension modeling problem. By the nature of the $L_1$ design, the Lasso algorithm can automatically render a sparse solution, and by eliminating redundant features, it provides desirable prediction power.
Geomagnetic field variations produce geoelectric fields that can affect the operation of technological networks at the Earth’s surface, including power systems, pipelines, phone cables and railway circuits. To assess the geomagnetic hazard to this technology, it is necessary to model the geomagnetically induced currents (GIC) produced in these systems during geomagnetic disturbances. This requires use of geomagnetic data with appropriate Earth conductivity models to calculate the geoelectric fields that drive GIC.
