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2 Journal of Power Technologies

2 2019

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Technical and economical comparison of different autotransformer based 36 pulse AC-DC Converters

Abdollahi Rohollah

Journal of Power Technologies

2019

Vol. 99, nr 4

281--288

Three-phase multipulse ac–dc converters (MPC) are being developed to improve power quality to reduce harmonics in ac mains and ripples in dc output. This study, based on technical and economic factors, compares different autotransformer based 36 pulse AC-DC Converters. This paper presents a comparison of tapped delta, polygon and T connected autotransformer based 36-pulse AC-DC converters. These converters were implemented and simulations were made using Matlab/Simulink software for similar ratings under different load conditions. A set of power quality indices on input ac mains and on a dc bus for a DTCIMD fed from different 36-pulse ac-dc converters is given to compare their performance. The economic comparison of 36 pulse ac–dc converters is based on the apparent power (kVA) ratings of the different autotransformers for 36 pulse AC-DC converters. Also, a prototype is developed and the experimental measurements obtained are presented to validate the feasibility and operability of the 36-pulse AC-DC converter. The 36-pulse AC-DC converter offers a total harmonic distortion of 4% and can operate at near-unity power factor, in compliance with IEEE and IEC standards.

Long-term prediction of underground gas storage user gas flow nominations

Kurek Teresa, Wojdan Konrad, Świrski Konrad

Journal of Power Technologies

2019

Vol. 99, nr 4

272--280

Many companies operating on the natural gas market use natural gas storage to balance production and transport capacities with major variations in gas demand. This paper presents an approach to predicting users’ gas flow nomination in underground gas storage by different users. A one-year prediction horizon is considered with weekly data resolution. Basic models show that whereas for the great majority of users we can predict nomination based only on weather data and technical parameters, for some users additional macro-economic data significantly improved prediction accuracy. Various modeling techniques such as linear regression, autoregressive exogenous model and Artificial Neural Network were used to develop prediction models. Results show that for most users an Artificial Neural Network provides optimal accuracy, indicating the non-linearity of the relationship between input and output variables. The models developed are intended to be used as support for facility operation decisions and gas storage product portfolio modifications.