A Thermal Power Plant Model for Dynamic Simulation of Load Frequency Control

2006 ◽  
Author(s):  
Toshio Inoue ◽  
Hiroyuki Amano
Keyword(s):  
Power Plant ◽  
Dynamic Simulation ◽  
Thermal Power Plant ◽  
Frequency Control ◽  
Thermal Power ◽  
Load Frequency Control ◽  
Plant Model ◽  
Load Frequency

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.

A Novel Approach for Load Frequency Control of Interconnected Thermal Power Stations

2012 ◽  
Vol 1 (2) ◽  
pp. 85-95 ◽  
Author(s):  
Yogendra Arya ◽  
H.D. Mathur ◽  
S.K. Gupta
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Frequency Control ◽  
Thermal Power ◽  
Load Frequency Control ◽  
System Response ◽  
Load Frequency ◽  
Power Stations ◽  
Novel Approach ◽  
Thermal Power Stations

This paper presents a fuzzy logic controller for load frequency control (LFC) of multi-area interconnected power system. The study has been designed for a three area interconnected thermal power stations with generation rate constraint (GRC). Simulation results of the proposed fuzzy controller are presented and it has been shown that proposed controller can generate the good dynamic response following a step load change. Robustness of proposed controller is achieved by analyzing the system response with varying system parameters.

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