Abstract: This investigation develops a revisable method for estimating the estimate value of equivalent 10 Hz voltage flicker (DV10) of a DC Electric Arc Furnace (EAF). This study also discusses three 161kV DC EAFs by field measurement, with those results indicating that the estimated DV10 value is significantly smaller than the survey value. The key point is that the conventional means of estimating DV10 is inappropriate. There is a main cause as the assumed Qmax is too small.
Although DC EAF is regularly operated in a constant MVA mode, the reactive power variation in the Main Transformer (MT) is more significant than that in the Furnace Transformer (FT). A substantial difference exists between estimated maximum reactive power fluctuation (DQmax) and the survey value from actual DC EAF operations. However, this study proposes a revisable method that can obtain a more accurate DV10 estimate than the conventional method.
Abstract: Voltage flicker problems have long existed in several
of the distribution areas served by the Taiwan Power Company. In
the past, those research results indicating that the estimated ΔV10
value based on the conventional method is significantly smaller than
the survey value. This paper is used to study the relationship between
the voltage flicker problems and harmonic power variation for the
power system with electric arc furnaces. This investigation discussed
thought the effect of harmonic power fluctuation with flicker
estimate value. The method of field measurement, statistics and
simulation is used. The survey results demonstrate that 10 ΔV
estimate must account for the effect of harmonic power variation.
Abstract: Time varying network induced delays in networked
control systems (NCS) are known for degrading control system-s
quality of performance (QoP) and causing stability problems. In
literature, a control method employing modeling of communication
delays as probability distribution, proves to be a better method. This
paper focuses on modeling of network induced delays as probability
distribution.
CAN and MIL-STD-1553B are extensively used to carry periodic
control and monitoring data in networked control systems.
In literature, methods to estimate only the worst-case delays for
these networks are available. In this paper probabilistic network
delay model for CAN and MIL-STD-1553B networks are given.
A systematic method to estimate values to model parameters from
network parameters is given. A method to predict network delay in
next cycle based on the present network delay is presented. Effect of
active network redundancy and redundancy at node level on network
delay and system response-time is also analyzed.