Method for measuring frequency deviation

1961 ◽  
Vol 4 (8) ◽  
pp. 654-658
Author(s):  
V. L. Rychka
Keyword(s):  
Frequency Deviation ◽  
Measuring Frequency

PENGARUH FREKUENSI PERDAGANGAN PADA SEKTOR PROPERTY DAN REAL ESTATE TERHADAP HARGA SAHAM

2019 ◽  
Vol 7 (02) ◽  
pp. 51
Author(s):  
Adri Wihananto
Keyword(s):  
Real Estate ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Trading ◽  
Trading Frequency ◽  
Important Thing ◽  
Know How ◽  
Stock Value ◽  
Measuring Frequency ◽  
Case Based

Trading frequency can be said as the implementation from trader of commerce. This case based on positive or negative trader reaction given by trader information.  Stock trading in BEI always fluctuate with price of volume value and frequency particularly. Frequency itself shows the company  involved or not. In trading frequency, if the indicator frequency it self shown the higher point, it means better. In spite of the most important thing is how the fluctuation or value conversion itself. On the frequencies we also could see which stocks is interested by the investor. When trading frequency high, it  may be create sense of interest from investors.The aim of this research, in order to know how far the effect of trading frequency (X) with stock value (Y) using cover stock value. The information used is begin 2008 with sample from twelve property and real estate companies. According to the research can be conclude from twelve companies in Indonesia Stock Exchange in 2008, 75 % of trading frequency samples doesn’t have signification degree between trading frequency and stock value. This case can be explained count on smaller than t tableEvaluation of this research is the trading measuring frequency at property sector and real estate not influence to stock priceKeywords : Trading Frequency, Stock Price 

scholarly journals Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1581
Author(s):  
Deepak Kumar Gupta ◽  
Amitkumar V. Jha ◽  
Bhargav Appasani ◽  
Avireni Srinivasulu ◽  
Nicu Bizon ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Frequency Deviation ◽  
Source Power ◽  
Load Frequency ◽  
Tie Line

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

Sign in / Sign up

Export Citation Format

Share Document