• Energy Research

Electrical grid has lots of changes in its morphology and managing style since first installed nearly two hundred years ago. Today, smart grid structure plays a crucial role when creating a sustainable and reliable operation. In smart grid context, power quality issues are monitored and required measures are obtained from smart meters. Power quality term include voltage quality. When it is about voltage quality, sags take the lead among other disturbances. System operators have to track voltage sags to provide a better service quality. In this study, a fast and simple algorithm called Hilbert Transform is used to detect voltage sags in synthetic dataset. Then, a voltage sag table is built considering related IEEE and IEC standards to identify site indices SARFI-X and SIARFI-X. Purpose of the study is being a first step to voltage sag detection and defining indices with real data. Obtained results denote and feed this aim.

Electrical grid has lots of changes in its morphology and managing style since first installed nearly two hundred years ago. Today, smart grid structure plays a crucial role when creating a sustainable and reliable operation. In smart grid context, power quality issues are monitored and required measures are obtained from smart meters. Power quality term include voltage quality. When it is about voltage quality, sags take the lead among other disturbances. System operators have to track voltage sags to provide a better service quality. In this study, a fast and simple algorithm called Hilbert Transform is used to detect voltage sags in synthetic dataset. Then, a voltage sag table is built considering related IEEE and IEC standards to identify site indices SARFI-X and SIARFI-X. Purpose of the study is being a first step to voltage sag detection and defining indices with real data. Obtained results denote and feed this aim.

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.

Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.

Industrial plants and residential areas need to utilize electrical energy effectively. For this purpose smart grids were performed within power system voltage and current signals are processed and monitored in advanced. Thus controller systems provide such solutions that will keep the grid sustainability both faulty and normal conditions. In this study, single phase voltage data set consists of power quality events is composed in software and classified by an intelligent classifier. Distinctive features are extracted by discrete wavelet transform method. Feature vector size reduction is held via entropy values determining of discrete wavelet details. Extreme learning machine is used as classifier and its advantages in performance are evaluated with conventional artificial neural networks.