Improvement of reactive power calculation in electric arc furnaces utilising Kalman filter

Samet, Haidar; Gashtasbi, Sara; Tashakor, Nima; Ghanbari, Teymoor

doi:10.1049/iet-smt.2016.0084

Cited by 8 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, to improve step and time-varying responses, an appropriate combination of the methods based on differential equations and continuous integration has been introduced. In [19], a Kalman filter method has been used to extract the fundamental components of voltage and current, and then they are used as inputs for the differential reactive power calculation method. Although these methods have some advantages and disadvantages from different aspects, some of them such as the filters-based approaches have found more attention in industries.…”

Section: B Literature Reviewmentioning

confidence: 99%

Electric Arc Furnaces Reactive Power Optimized Calculation Used in SVC

Shojaei

Samet

Ghanbari

2022

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Section: B Literature Reviewmentioning

confidence: 99%

Electric Arc Furnaces Reactive Power Optimized Calculation Used in SVC

Shojaei

Samet

Ghanbari

2022

IEEE Trans. Ind. Electron.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Collected data are voltage and current samples with sampling time equal to 128 microseconds and length of 100 seconds for each record. By using a method introduced in the previous studies in details, the reactive power corresponding to the fundamental component of voltage and current is attained from voltage and current signals with data windows equal to a full cycle (0.02 s), updated in every half cycle (0.01 s). Hence, each 100‐second reactive power time series consists of 10 000 (=100/0.01) samples.…”

Section: Recorded Datamentioning

confidence: 99%

Enhancement of SVC performance in electric arc furnace for flicker suppression using a Gray‐ANN based prediction method

Samet

Mojallal

Ghanbari

et al. 2018

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Summary Delays in reactive power measurement and thyristor ignition limit SVC performance in flicker mitigation of electric arc furnaces (EAFs). To overcome this limitation, prediction methods can be employed to forecast the EAF reactive power for half cycle ahead, used as a reference signal of the SVC. The utilized prediction methods in this area can be divided into linear and black‐box approaches. However, the linear approaches cannot extract the nonlinear governed relations, and using a black‐box model is not efficient for linear relations. A Gray‐ANN method is proposed here to take the advantages of the two mentioned approaches. Results from indices based on actual records of Mobarakeh Steel Company confirm superiorities of the proposed method over previously utilized prediction methods in this application. Furthermore, SVC's flicker mitigation ability is evaluated using the actual data. The results confirm the significant reduction of flicker compared with the regular system.

show abstract

“…However, the SVCs are more cost efficient for the same ratings of volt‐ampere (VA) . Furthermore, the effectiveness of SVC can be increased by using a suitable control algorithm …”

Section: Introductionmentioning

confidence: 99%

“…23 Furthermore, the effectiveness of SVC can be increased by using a suitable control algorithm. 24 In terms of control strategies used for voltage balancing, several performance metrics must be considered, including accuracy and complexity of calculations. 25 Additionally, the time required for algorithm computations must be considered, and it is recommended to be more than twice of the system frequency.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the response of thyristor‐controlled reactor using neural network

Ragab

Ghaeb

Al‐Naimi

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In this paper, a neural network controller is proposed to retrieve the voltage balancing conditions in three‐phase power systems. The neural network is suggested to calculate the required set of firing angles for the thyristor‐controlled reactor accurately to balance the three‐load voltages quickly. The proposed controller is fed by different parameters within different feeding techniques, namely, root mean square (RMS) values of the three load voltages, RMS value of the space vector signal calculated from the three load voltages, and the RMS values of both the three load voltages and their associated space vector. The intentions of the proposed techniques are to combine between reducing the number of measured parameters and providing the controller with qualitative data about system status. The influence of the measured parameters on the neural network performance is examined by calculating the regression coefficients through several test cases. Accordingly, only the effective parameters are utilized to reduce the neural network complexity and to enhance the controller response time. Additionally, the calculations of the controller input parameters are made in terms of space vector cycle, which is half of system sinusoidal cycle. Consequently, the calculation time is reduced significantly. The Aqaba‐Qatrana‐South Amman power system is considered and modeled as a real case study. In addition, several test cases have been conducted to test and validate the ability of the proposed neural network controller in retrieving the voltage balance conditions precisely and quickly. The results have revealed the ability of the proposed neural network controller to calculate the firing angles quickly within 10 milliseconds and achieve very low voltage unbalance factor.

show abstract

Improvement of reactive power calculation in electric arc furnaces utilising Kalman filter

Cited by 8 publications

References 22 publications

Electric Arc Furnaces Reactive Power Optimized Calculation Used in SVC

Electric Arc Furnaces Reactive Power Optimized Calculation Used in SVC

Enhancement of SVC performance in electric arc furnace for flicker suppression using a Gray‐ANN based prediction method

Enhancing the response of thyristor‐controlled reactor using neural network

Contact Info

Product

Resources

About