Coordination of UFLS and UFGC by Application of D-SMES

In this paper, the authors studied the coordination of under frequency load shedding (UFLS) and under frequency governor control (UFGC) by applying the distributed superconducting magnetic energy storage (D-SMES) devices. The active power of D-SMES device is controlled to eliminate the initial rapid frequency drop and allow time for the full action of UFGC to take over. The reactive power of D-SMES is controlled to stabilise the local bus voltage. The research results show that D-SMES devices can damp the quick dropping of system frequency and hold it waiting for the full activation of system spinning reserve. D-SMES can help the governors output their maximum reserve before UFLS drops more load

Damping Inter-Area Oscillations by UPFCs Based on Selected Global Measurements

This paper introduces a method of using a selected set of the global data for controlling inter-area oscillations of the power network using unified power flow controllers. This novel algorithm utilizes reduced order observers for estimating the missing data the purpose of control when all the data is unavailable through frequency measurements in a wide area control approach. The paper will also address the problem of time-delay in data acquisition through examples

Mining Firm-level Uncertainty in Stock Market: A Text Mining Approach

The traditional finance paradigm seeks to understand uncertainty and their impact on stock market. However, most previous studies try to quantify uncertainty at macro-level such as the EPU index. There are few studies tapping into firm-level uncertainty. In this paper, we address this empirical anomaly by integrating text mining tools to measure the firm-level uncertainty score from news content. We focus on companies listed in S&P 1500. We crawled a total of 2,196,975 news articles from LexisNexis database from April 2007 to July 2017. We extracted uncertainty related information as features by using named entity extraction, LM dictionary, and other linguistic features. We employed nonlinear machine learning models to investigate the impact on stocks future returns by uncertainty-related features. To address the theoretical problem, we use traditional asset pricing techniques to test the relationship among information derived uncertainty and the financial market performance

Timestamp Error Detection and Estimation for PMU Data based on Linear Correlation between Relative Phase Angle and Frequency

Time synchronization is essential to synchro-phasor-based applications. However, Timestamp Error (TE) in synchrophasor data can result in application failures. This paper proposes a method for TE detection based on the linear correlation between frequency and relative phase angle. The TE converts the short-term relative phase angle from noise-like signal to one that linear with the frequency. Pearson Correlation Coefficient (PCC) is applied to measure the linear correlation and then detect the timestamp error. The time error is estimated based on the variation of frequency and relative phase angle. Case studies with actual synchrophasor data demonstrate the effectiveness of TE detection and excellent accuracy of TE estimation

Power System Inertia Estimation Using Synchrophasor Frequency Measurements

A method includes performing by a processor: receiving a first plurality of power system frequency measurements from a plurality of phasor measurement units (PMUs) in the power system over a first time interval, generating a first plurality of multi-dimensional ellipsoids based on the first plurality of power system frequency measurements, extracting a plurality of first graphic parameter values from the first plurality of multi-dimensional ellipsoids, respectively, performing a regression analysis on the plurality of first graphic parameter values to generate a predictive relationship between the plurality of first graphic parameter values and inertia values of the power system, receiving a second plurality of power system frequency measurements from the plurality of PMUs over a second time interval, generating a second plurality of multi-dimensional ellipsoids based on the second plurality of power system frequency measurements, extracting a plurality of second graphic parameter values from the second plurality of multi-dimensional ellipsoids, respectively, and estimating a current inertia value of the power system based on the plurality of second graphic parameter values by using the predictive relationship between the plurality of first graphic parameter values and the inertia values of the power system