Showing papers by "Surya Santoso published in 2008"

Noniterative Method to Approximate the Effective Load Carrying Capability of a Wind Plant

Abstract: The effective load carrying capability (ELCC) is considered the preferred metric to evaluate the capacity value of added wind generation. However, the classical method of computing this metric requires substantial reliability modeling and an iterative process that is quite computationally intensive. Consequently, a noniterative method of estimating a wind plant's ELCC is proposed in this paper. Inspired by Garver's approximation and derived based on well-known reliability concepts, the proposed method provides an excellent approximation while requiring only minimal reliability modeling and no computationally-intensive iterative process. It computes ELCC estimates from a single function using only the wind plant's multistate probabilistic representation and a graphically determined parameter that characterizes the existing power system. After presenting the complete mathematical derivation of this function, the method is applied to compute the ELCC estimates of various wind plants at different penetration levels. It is shown that the resultant ELCC estimates only slightly overestimate the classically computed values by relative errors of 2.5% or less. Furthermore, the proposed method yields more accurate ELCC estimates than the capacity factor approximation, which is commonly used to approximate the ELCC of a wind plant.

On Two Fundamental Signatures for Determining the Relative Location of Switched Capacitor Banks

Abstract: This paper describes two fundamental signatures of shunt capacitor bank switching transient phenomena from which one can accurately determine the relative location of an energized capacitor bank whether it is upstream or downstream from the monitoring location. Mathematical analysis of a capacitor bank energizing proves that: 1) the energized capacitor bank affects only the upstream reactive power flow and 2) at the energizing instant, the gradients (time derivatives) of voltage and current waveforms measured upstream from the capacitor location will have opposite signs. The reverse is true in that at the energizing instant, gradients of voltage and current waveforms measured downstream from the same capacitor location will have equal signs. Thus, we can precisely determine the relative location of the switched capacitor bank by simply evaluating power factor changes and the signs of voltage and current waveform gradients at the switching instant. The efficacy of our practical direction-finding technique is demonstrated analytically and by way of time-domain simulation models and actual data.

Analysis of a wind farm’s capacity value using a non-iterative method

Abstract: The effective load carrying capability (ELCC) is considered the preferred metric to evaluate the capacity value of a wind farm when performing generation expansion studies. Unfortunately, the classical implementation of the ELCC concept requires substantial reliability modeling and a computationally-intensive iterative process. The non-iterative method developed as part of our previous work addresses these issues. In this work, a detailed application of the non-iterative method is presented to compute the ELCC of a wind farm in two case studies. From these case studies, we establish that the non-iterative method can be applied successfully whether or not wind generation is a part of the existing generation portfolio. Foremost, we demonstrate that the non-iterative method can be utilized effectively to compute ELCC estimates for various evaluation periods such as a particular month or peak hours period. Results from both case studies and all evaluation periods show that the non-iterative method provides excellent ELCC estimates with an average relative error of only 2.2% for the first case study and 1.4% for the second. Furthermore, the ELCC estimates are compared to the capacity factor approximation which is often used to estimate a wind farmpsilas ELCC. In these case studies, the capacity factor approach is shown to offer inaccurate ELCC estimates with average relative errors of 20.2% for the first case study and 14.1% for the second.

Evaluation of harmonic trends using statistical process control methods

Abstract: Power quality data collected from power quality monitoring instruments are generally voluminous. They are typically stored without analysis unless there is a concern about a particular problem. Data analysis on a regular basis is time consuming and costly. Ideally, analysis modules should be made available to analyze data and construct important information about the health condition of the overall power system and its individual components. This paper presents the application of the well-known statistical process control to analyzing harmonic trend variations. Specifically, it describes how the method can be used to determine if the statistical variation in the harmonic trend represents normal variations or due to problems in the system.

Assessment of Feeder Voltage Regulation Using Statistical Process Control Methods

Abstract: Power-quality voltage and current transient waveform data have been explored rather extensively as the primary input data in predictive maintenance, automatic root-cause analysis, and evaluating system performance to indicate potential problems. Unfortunately, very few efforts have been directed toward making use of the voluminous steady-state data collected alongside waveform data. Therefore, this paper proposes to use steady-state data, particularly, rms voltage data to detect abnormal trend behavior that may be indicative of a problem. Specifically, this paper develops a statistical analysis algorithm based on the well-known statistical process control methods for assessing feeder voltage regulation performance. The assessment results can be used to indicate potential regulator problems as well. The efficacy of the method is demonstrated by applications to two sets of actual RMS voltage data.