Power and Energy

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.

A computational efficient battery pack model with thermal consideration is essential for simulation before real-time embedded implementation. The proposed temperature-dependent battery model (LiFePO4 battery cell, ANR26650M1-B from A123 Systems) will increase the lifespan of the battery. The simulation outputs are validated by a set of independent experimental data at a different ambient temperature using the dataset collected at 5 °C, 15 °C, 25 °C, 35 °C and 45 °C.

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 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.