Impact of Measurement Accuracy on Fault Detection Obtained with Compressive Sensing

Carta, Daniele; Pegoraro, Paolo Attilio; Sulis, Sara

doi:10.1109/amps.2018.8494846

Cited by 4 publications

(6 citation statements)

References 19 publications

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“…Both values comply with the accuracy requirements. Reference [11] states values 0.7 % and 0.7 grad = 0.63 ° for steady states, what is our case. However we have set even stricter limits (0.5 % and 0.6 °) in Section II, which are still met.…”

Section: Thermal Testing Of a Complete Capacitive Dividersupporting

confidence: 49%

“…When developing a combined voltage and current sensor, there was set a target to have a voltage measurement error up to 0.5 % and a current measurement error up to 2 % at rated values (this is influenced by the used current sensor, which we don't deal with in this paper). These values were stated according to [11] and in accordance with experiences of our industrial partner (producer of power network diagnostic devices) also. The permissible error of active power measurement of the first harmonic component was set at 5 % (indicative power measurement).…”

Section: Accuracy Requirements Of the Designed Sensormentioning

confidence: 65%

“…For proper operation of the combined voltage and current sensor and its main purpose (failure location), it is very important to keep maximum voltage error below the stated limit [11] in all operation conditions during sensor lifetime. Because properties of different capacitors are influenced by temperature changes and aging [12]- [14], it was necessary to verify the behaviour of developed capacitive divider.…”

Section: An Experimental Workplace For the Capacitive Divider Tesmentioning

confidence: 99%

See 2 more Smart Citations

A Development of a Capacitive Voltage Divider for High Voltage Measurement as Part of a Combined Current and Voltage Sensor

Hrbáč

Kolář

Bartłomiejczyk

et al. 2020

ELEKTRON ELEKTROTECH

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This article deals with the development of capacitive voltage divider for high voltage measurements and presents a method of analysis and optimization of its parameters. This divider is a part of a combined voltage and current sensor for measurements in high voltage power networks. The sensor allows continuous monitoring of the network distribution status and performs a quick diagnosis and location of possible network failures. Deployment of these devices will support semi-autonomous control of power networks and it can be considered as a step from traditional power grids toward smart grids. This is a worldwide trend connected with increasing number of renewable energy sources and plug-in electric vehicles as described in. In this way, it contributes to the reliability of the distribution network. Together with automated control techniques and fault location methods, it enables its self-healing capability. The following characteristics required for the sensor include: current measurement error up to 2 %, voltage measurement error up to 0.5 %, and power measurement error up to 5 %. At the same time, it is necessary that the sensor is cost-effective - relatively cheap. There were selected capacitors made in series production for the capacitive divider designing. The capacitive voltage divider was tested in terms of time and temperature stability; the results are described in the paper. Then, the method of mathematical correction of a temperature dependence of the capacitive voltage divider was suggested and tested.

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Section: Thermal Testing Of a Complete Capacitive Dividersupporting

confidence: 49%

Section: Accuracy Requirements Of the Designed Sensormentioning

confidence: 65%

Section: An Experimental Workplace For the Capacitive Divider Tesmentioning

confidence: 99%

See 1 more Smart Citation

A Development of a Capacitive Voltage Divider for High Voltage Measurement as Part of a Combined Current and Voltage Sensor

Hrbáč

Kolář

Bartłomiejczyk

et al. 2020

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

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“…This is also an algorithm (Algorithm 1) which develop an sparse approximation in order to obtain the "best matching" projections of multidimensional data onto the span of an over-complete dictionary D. This means that a signal f cab be represent starting from Hilbert space H approximately as the weighted sum of finitely many functions g γ n (called atoms) taken from D [25]. Input: Signal: f (t), dictionary D with normalized columns g i Output: List of coefficients (a n ) N n=1 and indices for corresponding atoms γ ( n) N n=1 Initialization:…”

Section: Matching Pursuitmentioning

confidence: 99%

“…The complexity of the electrical grid will require not only advanced signal processing that can identify specific parameters, but also intelligent methods that identify the behavioral patterns of the system under fault conditions. There are no previous studies or comparative analyses of how compressed sensing can be used to detect faults in electric power systems [15,25].…”

Section: Problem Formulationmentioning

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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