Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature

Aplicación de análisis de componente principal en sistemas eléctricos de potencia

El análisis de componente principal (ACP) es una técnica estadística de análisis multivariable ampliamente utilizada para encontrar patrones de datos de alta dimensión. La ventaja fundamental de ACP es la reducción del número de dimensiones de los datos, sin que exista mucha pérdida de información. En este artículo se hace una descripción de esta transformación matemática, y se presentan dos aplicaciones en el área de los sistemas eléctricos de potencia. ABSTRACT A common method from statistics for analyzing data is principal component analysis (PCA). The purpose of PCA is to identify the dependence structure behind a multivariable stochastic observation in order to obtain a compact description of it. The paper describes the mathematical fundamentals of PCA and two applications in power system area

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field

Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work presents a data analytic tool for clustering analysis based on Dimensionality Reduction (DR) of power system measurements. The proposed method is applied to frequency measurements of the ENTSO-E dynamic model of continental Europe and the results are compared with other conventional DR approaches. After considerable reduction of the raw measurements, a phasor metric for identification of coherency groups of generators is proposed. The recommended measure stands for its simple implementation, interpretation and fast computation. To illustrate the effectiveness of the clustering approach and the coherency of the metrics, a particular study case following the outage of a representative generation unit in France is presented

Perturbations of weakly resonant power system electromechanical modes

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder

Scaling of Normal Form Analysis Coefficients under Coordinate Change

Power system normal form analysis has developed coefficients and indices in modal coordinates to quantify nonlinear modal interactions. We study the changes in the coefficients and indices when the power system equations are expressed in different coordinates or units and show that they can be normalized to be invariant to coordinate changes and thus intrinsic to the power system. The results are illustrated on a 4 generator system. An example shows that the coefficients and indices not only detect nonlinear interactions but also can become very large near a strong resonance in the system linearization

Scaling of normal form analysis coefficients under coordinate change

Abstract—Power system normal form analysis has developed coefficients and indices in modal coordinates to quantify nonlinear modal interactions. We study the changes in the coefficients and indices when the power system equations are expressed in different coordinates or units and show that they can be normalized to be invariant to coordinate changes and thus intrinsic to the power system. The results are illustrated on a 4–generator system. An example shows that the coefficients and indices not only detect nonlinear interactions but also can become very large near a strong resonance in the system linearization. Index Terms—Nonlinear modal behavior, normal form method, power system dynamics, strong resonance. I