Signal Processing and Classification Tools for Intelligent Distributed Monitoring and Analysis of the Smart Grid

This paper proposes a novel framework for an intelligent monitoring system that supervises the performance of the future power system. The increased complexity of the power system could endanger the reliability, voltage quality, operational security or resilience of the power system. A distributed structure for such a monitoring system is described and some of the advanced signal processing techniques or tools that could be used in such a monitoring system are given. Several examples for seeking the spatial locations and finding the underlying causes of disturbances are included

A Method to Evaluate Harmonic Model-Based Estimations under Non-White Measured Noise

Automatic extracting information from power-system event recordings requires applications of signal-processing estimation techniques whose performance has been verified under white noise. This paper proposes a method to test these techniques under real power-system noise, which is very different from white noise, to evaluate their application feasibility. The first part of the paper describes the evaluation method used to evaluate the techniques in a statistical sense and a method to extract noise from measured power-system recordings. The second part of the paper focuses on the evaluation of a number of harmonic model-based techniques under non-white noise, including: Kalman filter, MUSIC, ESPRIT, and segmentation algorithms. The paper shows that for the Kalman filter, a very high order with high computational burden is necessary only if high frequency components are of interest. The application of MUSIC, ESPRIT, and the segmentation algorithms under natural power-system noise is shown to be feasible

Voltage dip immunity aspects of power-electronic equipment : recommendations from CIGRE/CIRED/UIE JWG C4.110

This paper presents some of the results from an international working group on voltage-dip immunity. The working group has made a number of recommendations to reduce the adverse impact of voltage dips. Specific recommendations to researchers and manufacturers of powerelectronic equipment are considering all voltage dip characteristics early in the design of equipment; characterize performance of equipment by means of voltage-dip immunity curves; and made equipment with different immunity available

Voltage dip immunity of equipment and installations : messages to stakeholders

This paper presents the messages to the stakeholders on voltage-dip immunity as extracted by UIE WG2 from CIGRE TB412 [1]. The paper summarizes the main recommendations from this technical brochure in the form of messages towards regulators, standard-settingorganizations, network operators, industrial customers, equipment manufacturers, and power quality monitor manufacturers, researchers

A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances

This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data