Evolution of signal processing techniques in power quality

Vega, Tomas Yebra; Fuster-Roig, Vicente; Segundo, Héctor Beltrán San

doi:10.1109/epqu.2007.4424200

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…However, unlike the field of computer engineering, where ML models are traditionally applied, the power system industry relies on tangible sensors such as current and voltage transformers. The data these sensors generate can contain noise and may be imbalanced, both factors that could deter model performance (Vega et al, 2007). Additionally, the necessary infrastructure for effective data collection often falls short.…”

Section: Scarcity Of High-quality Datamentioning

confidence: 99%

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Porawagamage,

Dharmapala,

Chaves

et al. 2024

Front. Smart Grids

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Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.

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Section: Scarcity Of High-quality Datamentioning

confidence: 99%

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Porawagamage,

Dharmapala,

Chaves

et al. 2024

Front. Smart Grids

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“…In accordance with IEEE Standards 1100-1999 power quality is defined as the entirety of conducted electromagnetic disturbances in AC electrical supply networks and aims to ensure appropriate powering and grounding of sensitive electronic equipment [1,2]. In Europe, requirements for the power quality are defined in the standards EN 50160, EN 61000-2-2 and EN 61000-2-4.…”

Section: Power Quality In Distribution Networkmentioning

confidence: 99%

Low Budget Experimental Setup for Harmonic Detection and Correction

Böhm

Paulsburg

Hamann

et al. 2017

AMM

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Adherance of power quality standards is crucial for electricity network operation and bothindustrial and private customers. However, the transition to electricity supply based on renewable, de-centralized plants which feed in using inverters is accompanied by challenges regarding compliancewith power quality standards. Accordingly, detection and automated processing of relevant variablesof power quality is of increased importance. Digital signal processing offers various approaches of sig-nal evaluation each having individual advantages and disadvantages regarding different power qualityvariables. As a result of incresing decentralized feed in of regenerative plats using inverters, the si-nusoidal fundamental of the power system is distorted and harmonics occur. For elimination of thoseunwanted signal components, a variety of methods is available. Supple- menting current research ac-tivites a laboratory model of an active ﬁlter using low budget prototyping hardware is developed andevaluated. Therefore, an experimental circuit containing a band pass ﬁlter for signal adjustment aswell as a PWM with ampliﬁcation circuit for signal correction have been elaborated. Necessary cal-culations are performed by a standard Atmel microprocessor as used by Arduino Uno, including an8-bit analog to digital converter (ADC).

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“…The diagnosis of failures in electrical power systems through Signal Processing (SP) is applied to the Power Quality (PQ) associated with the implementation of Smart Grid (SG) technologies. The increasing complexity of the electric grid requires signal processing in order to characterize, identify, and diagnose the system behavior accurately [1,2].…”

Section: Introductionmentioning

confidence: 99%

Electrical Faults Signals Restoring Based on Compressed Sensing Techniques

Ruiz

Montalvo

2020

Energies

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This research focuses on restoring signals caused by power failures in transmission lines using the basis pursuit, matching pursuit, and orthogonal matching pursuit sensing techniques. The original signal corresponds to the instantaneous current and voltage values of the electrical power system. The heuristic known as brute force is used to find the quasi-optimal number of atoms k in the original signal. Next, we search for the minimum number of samples known as m; this value is necessary to reconstruct the original signal from sparse and random samples. Once the values of k and m have been identified, the signal restoration is performed by sampling sparse and random data at other bus bars of the power electrical system. Basis pursuit allows recovering the original signal from 70% of the random samples of the same signal. The higher the number of samples, the longer the restoration times, approximately 12 s for recovering the entire signal. Matching pursuit allows recovering the same percentage, but with the lowest restoration time. Finally, orthogonal matching pursuit recovers a slightly lower percentage with a higher number of samples with a significant increase in its recovery time. Therefore, for real-time electrical fault signal restoration applications, the best selection will be matching pursuit due to the fact that it presents the lowest machine time, but requires more samples compared with orthogonal matching pursuit. Basis pursuit and orthogonal matching pursuit require fewer sparse and random samples despite the fact that these require a longer processing time for signal recovery. These two techniques can be used to reduce the volume of data that is stored by phasor measurement systems.

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Evolution of signal processing techniques in power quality

Cited by 7 publications

References 6 publications

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Low Budget Experimental Setup for Harmonic Detection and Correction

Electrical Faults Signals Restoring Based on Compressed Sensing Techniques

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