484 research outputs found
Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration
The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area
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Detection and classification of power quality disturbances based on Hilbert-Huang transform and feed forward neural networks
This paper presents a hybrid detection method and classification Technique based on Hilbert-Huang Transform (HHT) and Feed Forward Neural Networks (FFNNs) to improve the efficient delivery and ensure accurate detection of quality disturbances in the electrical power grids. First, quantities characteristics of power quality disturbances (PQDs) are introduced according its parametrical conditions. Thereafter, a detection and recognition algorithm is used for single and multiple disturbances. Then, a decomposition process and features extraction using Empirical Mode Decompensation (EMD) is conducted for each of these distorted waveforms into Intrinsic Mode Functions (IMFs). Finally, these features are constructed using signal amplitude and frequency and then after fed to one of the powerful Artificial Intelligence Techniques in this field for training, evaluating and testing using (FFNNs) classifier to verify and confirm the effectiveness of the detection methodology
Fault Management in DC Microgrids:A Review of Challenges, Countermeasures, and Future Research Trends
The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed
Data Consistency for Data-Driven Smart Energy Assessment
In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received.
Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid
and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects
A review of the enabling methodologies for knowledge discovery from smart grids data
The large-scale deployment of pervasive sensors and decentralized computing in modern smart
grids is expected to exponentially increase the volume of data exchanged by power system applications.
In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions
in a data rich, but information limited environment represents a relevant issue to address. To this aim,
this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing,
exploring its various application fields by considering the power system stakeholder available data and
knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors
summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms
for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the
IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors
propose the development of a data-driven forecasting methodology, which is modeled by considering
the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described
methodology is applied to solve the load forecasting problem for a complex user case, in order to
emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich
environments, as feedback for the improvement of the forecasting performances
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