Abstract
Determination of accurate operating states of the power system is one of the most challenging tasks due to integration of large number of solar PVs into the power system. Since these sources of energy are inertialess generations, hence may cause instability issues if highly penetrated. Hence, precise monitoring and control of such systems with higher PV penetration is a critical issue to address both in terms of the total number of PV sources in the system and the percentage of penetration. Phasor Measurement Units (PMUs), with their time synchronizing abilities, have made this task a bit easier. However, traditional centralized data handling architecture at control centers are becoming redundant due to various limitations such as data handling, computational constraints etc. To overcome this, a distributed PMU-PDC architecture approach is utilized in this paper. Since local PDC (Phasor Data Concentrator) in an n-area power system can run separate optimization algorithms, by combining the results of these optimization algorithms of n-area PDCs, we can get a much more accurate global consensus about the current operating state of the system. The presented work is divided into two parts. First, the analysis of power system stability is performed in terms of the total number of Photovoltaic Sources (PV) in the system and the percentage of PV penetration. Then, the accuracy of the method is tested both in terms of the number of PMUs in each area along with their channel capacities. Low-frequency oscillations have been simulated on IEEE-68 standard bus system using MATLAB, and the modes of oscillation are estimated using the Alternating Direction Method of Multipliers (ADMM) algorithm.
Highlights
Following the power outage of July 2012 in the northern and north-eastern portions of India, Phasor Measurement Units (PMUs) with their ability to provide time-synchronized measurements, are gaining popularity and momentum
These techniques are Fast Fourier Transform (FFT)[5], Matrix Pencil (MP)[6],[7],Prony Analysis (PA)[8],[9], Principal Component Analysis (PCA)[10],Empirical Mode Decomposition (EMD) [11],[12],Multivariate Empirical Mode Decomposition (MEMD)[13], Hilbert Transform (HT)[14], wavelet transform(WT)[15],robust recursive least square (RRLS),regularized robust recursive least square (R3LS) algorithm[16], Eigensystem Realization Algorithm (ERA)[17], Dynamic Mode Decomposition (DMD) Algorithm [18] etc. All these methods suffer computational challenges due to data handling at a centralized stage. To overcome such challenges, distributed PMU-Phasor Data Concentrator (PDC) architecture is used in [19]–[27] to analyze instability caused by low-frequency oscillations due to various faults occurring in the system
The power system stability is tested under different test conditions that include variations of Photovoltaic Source (PV) penetration and the number of PV generation sources in the system
Summary
Following the power outage of July 2012 in the northern and north-eastern portions of India, PMUs with their ability to provide time-synchronized measurements, are gaining popularity and momentum. The government of India has initiated the various projects for the deployment of PMUs across the grid on a larger scale These synchronized measurements from different locations are sent to the PDC installed at load centres through proper communication for the aim of analysis and usage[1],[2]. Instead of concentrated data storage and analysis, distributed physical architecture needs to be developed under such conditions This distributed architecture contains dedicated local PMUs and PDC that determines the operating states of the power system locally[3]. All these methods suffer computational challenges due to data handling at a centralized stage To overcome such challenges, distributed PMU-PDC architecture is used in [19]–[27] to analyze instability caused by low-frequency oscillations due to various faults occurring in the system. Stability analysis of the power system is performed with varying PV penetration using distributed PMU-PDC architecture.
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