John N. Jiang

University of Oklahoma

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A comparative view of risk management in financial sector and in next generation power grid operation

John N. Jiang Chongqing Kang

This article offers a comparative view of applied risk management in market-based operation of the next generation power grid, based on the principles and lessons of risk management developed in the financial sector. It argues that the dual signatures of public and private goods on electricity products and services in wholesale power markets are the roots of many fundamental challenges including risk management, and due to the symbiotic public and private goods properties of electricity products, the general risk management framework should be more appropriate to support the decisions under uncertainty, instead of the classic ones for complete market environments. Further, we overview the subjective and objective aspects of risk management followed by explanation of three managing philosophies concerning ontological attitude. Based on the conceptual discussions, we show the importance of in-depth understanding of characteristics of the next generation power grid to risk management. In addition, the unique features of risk analysis in power system and market operations are discussed, comparing to those in the financial sector. Finally, some lessons learned from practices in financial sectors as well as the challenges to the power industry are summarized.

Electric Power Industry

10.1109/pmaps.2016.7764181

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A study of the voltage distribution for the interconnection of power grids

Varun Perumalla Qiaoyan Bian Di Wu John N. Jiang

A good understanding of the voltage profile of an interconnected power grid is important for supporting the decision on the interconnection of power grids in terms of power quality and power transmission security, especially for developing countries when making interconnection decisions, with a limited amount of expensive voltage compensation devices. In this paper, a statistic method is presented for assessing the change in the system-wide voltage profile before and after the interconnection of large-scale power grids, which provides additional useful information to support the existing interconnection studies. In this method, the skewness and kurtosis are used to characterize the change in the voltage distribution of the non-generation buses before and after the interconnection. Furthermore, a study is presented to show that the skewness and kurtosis may be essential for a statistical analysis of the change in the system-wide voltage profile, as its distribution might be skewed, particularly for the non-generator buses without automatic voltage regulators or other compensation devices. The demonstration of this method is also provided.

Kurtosis

10.1109/pesgm.2014.6938866

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Supplementary Figure 1 from Copy Number Alterations that Predict Metastatic Capability of Human Breast Cancer

Yi Zhang John W.M. Martens Jack X. Yu John N. Jiang Anieta M. Sieuwerts

Supplementary Figure 1 from Copy Number Alterations that Predict Metastatic Capability of Human Breast Cancer

10.1158/0008-5472.22379823

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Data from Copy Number Alterations that Predict Metastatic Capability of Human Breast Cancer

Yi Zhang John W.M. Martens Jack X. Yu John N. Jiang Anieta M. Sieuwerts

<div>Abstract<p>We have analyzed the DNA copy numbers for over 100,000 single-nucleotide polymorphism loci across the human genome in genomic DNA from 313 lymph node–negative primary breast tumors for which genome-wide gene expression data were also available. Combining these two data sets allowed us to identify the genomic loci and their mapped genes, having high correlation with distant metastasis. An estimation of the likely response based on published predictive signatures was performed in the identified prognostic subgroups defined by gene expression and DNA copy number data. In the training set of 200 patients, we constructed an 81-gene prognostic copy number signature (CNS) that identified a subgroup of patients with increased probability of distant metastasis in the independent validation set of 113 patients [hazard ratio (HR), 2.8; 95% confidence interval (95% CI), 1.4–5.6] and in an external data set of 116 patients (HR, 3.7; 95% CI, 1.3–10.6). These high-risk patients constituted a subset of the high-risk patients predicted by our previously established 76-gene gene expression signature (GES). This very poor prognostic group identified by CNS and GES was putatively more resistant to preoperative paclitaxel and 5-fluorouracil-doxorubicin-cyclophosphamide combination chemotherapy (<i>P</i> = 0.0048), particularly against the doxorubicin compound, while potentially benefiting from etoposide. Our study shows the feasibility of using copy number alterations to predict patient prognostic outcome. When combined with gene expression–based signatures for prognosis, the CNS refines risk classification and can help identify those breast cancer patients who have a significantly worse outlook in prognosis and a potential differential response to chemotherapeutic drugs. [Cancer Res 2009;69(9):3795–801]</p></div>

10.1158/0008-5472.c.6499359

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Model Predictive and Adaptive Control of Wind Farm Active Power Output

Advances in industrial control (2016)

John N. Jiang Choon Yik Tang R. Ramakumar

Model Predictive Control

10.1007/978-3-319-39135-9_4

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Introduction

Advances in industrial control (2016)

John N. Jiang Choon Yik Tang R. Ramakumar

Scope (computer science)

10.1007/978-3-319-39135-9_1

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Cluster analysis of wind turbines of large wind farm

IEEE PES Power Systems Conference and Exposition (2009)

Yong Ma John N. Jiang Thordur Runolfsson

Understanding the dynamics of the power output of a wind farm is important to the integration of large scale wind energy into the power system. In a large complex dynamic engineering system, such as a wind farm, clustering is an effective way to reduce the model complexity and improve the understanding of its local dynamics. The paper proposes a novel methodology to cluster wind turbines of a wind farm into different groups based on a particular distance measure. We first build a weighted graph to represent the complex relationships between power output of wind turbines. The graph is used to construct a Markov Chain and estimate the likelihood of any two wind turbines belong to the same cluster. We analyze the spectral properties of the Markov chain to identify the number of clusters. With the proposed method, the elements of each cluster can be identified in the feature space. Theoretical study showed that the proposed methodology simplifies the model of the dynamics of power output of wind farm without compromising the overall dynamic characteristics of the original system asymptotically. This paper also presents the results of clustering of 25 wind turbines located in three distinct locations of a wind farm with the proposed methodology based on the real power outputs for illustration and verification purpose. Then the results of a comprehensive study of all turbines of the wind farm are also included. We show that the method effectively cluster the wind turbines into three groups. The methodology is very useful for simplification of controller design, operation and forecast of wind generation.

Wind Power Forecasting

10.1109/psce.2009.4840148

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A Graph-theoretic Method for Identification of Electric Power Distribution System Topology

Guomin Ji Dhruv Sharma Wanghao Fei Di Wu John N. Jiang

In this paper, we present a graph-theoretic method for identification of the topology of electric power distribution system based on the real-time measurements. Instead of relying on the electric-circuit based approaches, this new approach utilizes the principles of network theory to find the connecting structure or topology of a distribution system. First, we put forth the theoretical foundation for the proposed approach. Second, applying some principles and methods of network theory, we develop and present the process of topology identification. Third, the algorithm for practical implementation of the concepts is offered with an illustrative example. Finally, the potential applications and the insights obtained from this work are discussed.

Identification

10.1109/gpecom.2019.8778504

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Citations (5)

Supplementary Figure 3 from Copy Number Alterations that Predict Metastatic Capability of Human Breast Cancer

Yi Zhang John W.M. Martens Jack X. Yu John N. Jiang Anieta M. Sieuwerts

Supplementary Figure 3 from Copy Number Alterations that Predict Metastatic Capability of Human Breast Cancer

10.1158/0008-5472.22379817.v1

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Characteristics of Short-term LOLP Considering High Penetration of Wind Generation

Journal of Electrical Engineering & Electronic Technology (2012)

Chenxi Lin Thordur Runolfsson John N. Jiang

Characteristics of Short-term LOLP Considering High Penetration of Wind Generation Loss of Load Probability (LOLP) is an important measure of generation adequacy. A good understanding of the new characteristics of LOLP exhibited after integration of variable renewable generation is essential to the power system reliability. This paper presents the results of a study on the impact of wind generation on short-term LOLP, which then becomes a fastchanging stochastic process, driven by the intermittent and variable wind. We firstly introduce a mathematical model for calculating short-term LOLP, and then a novel quantitative measure of its behavior when converging to its steady-state level is derived. In addition, the corresponding empirical formulas are offered which can be used in practice to estimate the convergence time of LOLP under different conditions. Finally, an application of the outcomes of the analytical work in estimation of the dynamic behavior of shortterm LOLP with an actual wind generation profile is presented to show the significance of the developed measures.

10.4172/2325-9833.1000103

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Citations (3)