Electric Power Quality

Chattopadhyay, Surajit; Mitra, Madhuchhanda; Sengupta, Samarjit

doi:10.1007/978-94-007-0635-4

Cited by 99 publications

(78 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These disturbances are induced by the continuous increment of non-linear loads; furthermore, power distribution systems can be affected by environmental conditions too [1]. Electric power disturbances produce malfunctioning of machinery and appliances with a consequent reduction of their lifetime.…”

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

View full text Add to dashboard Cite

Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The electric power quality (PQ) is repeatedly used to specify the quality of voltage, the quality of current, the reliability of service, and the quality of power supply [22]. In this paper, we also present the actual voltage fluctuation to show the PQ of DN in the study locations.…”

Section: Reliability Of Electric Powermentioning

confidence: 99%

Perceived and Reported Reliability of the Electricity Supply at Three Urban Locations in Indonesia

Kunaifi

Reinders

2018

Energies

View full text Add to dashboard Cite

This paper focuses on the reliability of electricity supply at three different locations in Indonesia, namely in Sumatra, Timor, and Papua, through a comparison of reported indices of power reliability (SAIFI and SAIDI) and experimental results from user surveys and power measurements. The reason for this study is the lack of information about the actual, quantified reliability of power supplied by the main grid in Indonesia, while narratives of end-users indicate the reliability might be unsatisfactory. The study was executed using data from 114 randomly-selected respondents in the city of Pekanbaru in Sumatra, 65 in the city of Kupang in Timor and 26 in the city of Jayapura in Papua, totaling 205 respondents. These users experienced a higher unavailability of power delivered by the grid than expressed by the utility-reported SAIDI and SAIFI. Therefore, for this study, new indices are introduced, namely the Perceived (P) SAIDI and SAIFI, which are based on the frequency and duration of blackouts experienced by the users. It is concluded that the reported reliability indices do not always demonstrate the experience of the grid users. P-SAIFIs were 1.3 to 4.6 times higher in Pekanbaru and Kupang, respectively than the utility-reported SAIFIs for the same provinces. Also, P-SAIDIs were 2.6 to 3.9 times higher in Pekanbaru and Kupang, respectively, than the utilities' SAIDIs. It is therefore not surprising that depending on the location, 14% to 65% of the users own a backup generator and that households are willing to pay $3 to $8 extra per monthly electricity bill or $1c-$3c per kWh for improved reliability.

show abstract

“…Actual allowable voltage deviations depend on the specific application and the specifications of the load components within the system being powered. For reference, voltage fluctuations greater than 110% are considered a voltage swell disturbance, and voltage fluctuations below 90% are a voltage sag disturbance (Chattopadhyay, 2011), which would be equivalent to a deviation of ±24 V with the PP20 gasifier genset.…”

Section: Voltagementioning

confidence: 99%

Performance Analysis of a Biomass Gasifier Genset at Varying Operating Conditions

Palmer¹,

Severy²,

Chamberlin³

et al. 2018

Applied Engineering in Agriculture

View full text Add to dashboard Cite

ABSTRACT. An All Power Labs PP20 gasifier generation set (Berkeley, Calif.) any promising technologies are emerging for the conversion of residual forest waste to useful products. These processes are collectively known as biomass conversion technologies (BCTs) and include processes such as torrefaction, densification, and biochar production. However, the feasibility of BCT projects is highly dependent on the transportation economics of the unprocessed waste biomass (Pan et al., 2008). From a transportation economics standpoint, it is often optimal to place a BCT operation as close to the fuel source as possible. Examples include forest landing sites, at the roadside, or in locations close to forestry operations, such as former sawmills. Although optimal for transportation costs, technological and logistical challenges arise when operating a BCT in remote locations. A key logistical factor for BCT operation is obtaining a reliable source of electricity. Many potential sites do not have access to grid electricity, and a remote power source is therefore required to provide electricity to the BCT. BACKROUNDIn a previous study by Severy et al. (2016), various remote power generation technologies were compared for their potential feasibility for providing power at a remote forest landing site. The technologies evaluated in this study were an organic Rankine cycle (ORC) waste heat recovery device, a thermoelectric generator, a biomass gasifier with an engine generator (All Power Labs Power Pallet, Berkeley, Calif.), a solar PV array with battery storage, and a shaft work power generator. The generation sources were evaluated based on their mobility, footprint, reliability, operational intensity, electrical load following ability, environmental impact, capital cost, operational cost safety, and ease of permitting. The results of the technoeconomic feasibility study concluded that a biomass gasifier was the preferred alternative technology to replace a diesel generator at a BCT site due to its mobility, small footprint, competitive lifecycle cost, and quoted load following abilities (Severy et al., 2016). To validate the results of the feasibility study and extend research on the topic, the goal of this study is to evaluate the suitability of the APL PP20 for powering biomass conversion technologies in the greater Pacific Northwest area. The objectives are to measure the power output and load following capabilities, quantify the emission rates, and provide electricity to a remote BCT that has a fluctuating load. By conducting these tests, the authors can verify whether this gasifier generator is a technically-viable alternative to a diesel generator at offgrid locations. GASIFIER AS AN ELECTRICAL POWER SOURCEGasification technology has been used for decades (Ghosh et al., 2006). However, due to relatively recent technology improvements, there has been an increase in development and interest in the technology. Ahrenfeldt et al. (2013) review the state of the art of biomass gasification combined heat and power (CHP) syst...

show abstract

Electric Power Quality

Cited by 99 publications

References 0 publications

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Perceived and Reported Reliability of the Electricity Supply at Three Urban Locations in Indonesia

Performance Analysis of a Biomass Gasifier Genset at Varying Operating Conditions

Contact Info

Product

Resources

About