Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks

By Martin Valtierra-Rodriguez, René de Jesús Romero-Troncoso, Roque Alfredo Osornio-Rios, Arturo Garcia-Perez. IEEE transactions on industrial electronics (1982. Print), 2014

308 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2a4d0ba09e41ac39cf4ceeb4922d546c3ba65ca2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Zhiwei Gao
2. Michael Z. Q. Chen
3. Xiaoxu Liu
4. Carlo Cecati
5. Steven X. Ding
6. Adnan M. Abu-Mahfouz
7. Khmaies Ouahada
8. Oyeniyi Akeem Alimi

In Electrical and Electronic Engineering

1. Adnan M. Abu-Mahfouz
2. Steven X. Ding
3. Khmaies Ouahada
4. Zhiwei Gao
5. Carlo Cecati
6. Michael Z. Q. Chen
7. Oyeniyi Akeem Alimi

Citations in the corpus

• A Review of Machine Learning Approaches to Power System Security and Stability (2020, background): “and they are capable of creating severe problems which include equipment malfunction, short life span and failures [24], [25].”
• A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches (2015, background): “for combustion engines [34], steam turbine generators [35], nuclear processes [36], induction machines [37], [38], and power network quality [39].”
• Unknown Input Observer-Based Robust Fault Estimation for Systems Corrupted by Partially Decoupled Disturbances (2016, background): “During the last four decades, extensive results were reported on various analytical redundancy based fault diagnosis techniques [1-4] and their applications in aero systems, electro-mechanical systems, energy systems [5-8] and so forth.”