Comparison of Power Quality Distortion Types and Methods Used in Classification

Tür, Mehmet Rıda; Bayındır, Ramazan

doi:10.1109/cispsse49931.2020.9212261

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The infrastructure of power systems utilizes ESSs at various stages. However, ESS technologies vary depending on their applications [57][58][59]. Despite the multidimensional application of ESS, it is essential to identify their applications and scopes in accordance with their technical specifications (such as power density, rating, energy density, lifetime, self-discharge rate, etc.)…”

Section: Power Balance With Storage Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Solution for Integration of Renewable Energy Power Plants into Smart Grids with Active Power Control

Tur,

Ertuğrul,

Tür

2024

JSTER

View full text Add to dashboard Cite

This article addresses the integration of renewable energy power plants into smart grids and active power control. Renewable energy sources contribute to environmentally friendly and sustainable energy production, but the fluctuations inherent in these sources pose a challenge for energy grids. The article examines various technologies that can be used to overcome this challenge and make energy grids more reliable. Smart grids aim to improve energy grids by optimizing energy production, transmission, and distribution using data analytics, automation, and communication technologies. The integration of renewable energy power plants into these smart grids offers significant advantages, including the ability to predict energy production, integrate with energy storage systems, and manage energy demand. The article also emphasizes the importance of active power control. Active power control is used to manage energy production steadily, thereby maintaining grid stability. Balancing energy fluctuations from renewable energy sources and storing excess energy when needed enhances grid stability. In conclusion, this article discusses the crucial role of integrating renewable energy power plants into smart grids and implementing active power control in the energy sector. These integration and control methods are important steps in making energy grids more sustainable, efficient, and reliable.

show abstract

Section: Power Balance With Storage Methodsmentioning

confidence: 99%

“…Leveraging the smart grid's advantages, as outlined above, allows for effective real-time management of the active power generation-consumption mismatch, a capability not present in traditional power systems. [59].…”

Section: B Flow Chart Of the Recommended Algorithm In Scanned Optimiz...mentioning

confidence: 99%

Solution for Integration of Renewable Energy Power Plants into Smart Grids with Active Power Control

Tur,

Ertuğrul,

Tür

2024

JSTER

View full text Add to dashboard Cite

show abstract

“…The term power quality refers to a wide range of electromagnetic phenomena that characterize the voltage and current quality at a given time and location in the power system [6]. Generally, PQ disturbances are defined into two types based on the characteristics: voltage variations [7,8] and voltage events [9,10]. The analysis of PQ can be used to monitor the characteristic disturbances to capture PQ events that potentially detect faults associated with power quality problems in electrical power systems [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Clustering Methods for Power Quality Measurements in Virtual Power Plant

et al. 2021

View full text Add to dashboard Cite

In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.

show abstract