Distributed model predictive load frequency control of multi-area power system with DFIGs

2017 ◽  
Vol 4 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Yi Zhang ◽  
Xiangjie Liu ◽  
Bin Qu
Keyword(s):  
Power System ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Distributed Model ◽  
Load Frequency

scholarly journals Robust Distributed Model Predictive Load Frequency Control of Interconnected Power System

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangjie Liu ◽  
Huiyun Nong ◽  
Ke Xi ◽  
Xiuming Yao
Keyword(s):  
Power System ◽  
Large Scale ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Distributed Model ◽  
Linear Matrix ◽  
Interconnected Power System ◽  
Load Frequency ◽  
Distributed Model Predictive Control ◽  
System A

Considering the load frequency control (LFC) of large-scale power system, a robust distributed model predictive control (RDMPC) is presented. The system uncertainty according to power system parameter variation alone with the generation rate constraints (GRC) is included in the synthesis procedure. The entire power system is composed of several control areas, and the problem is formulated as convex optimization problem with linear matrix inequalities (LMI) that can be solved efficiently. It minimizes an upper bound on a robust performance objective for each subsystem. Simulation results show good dynamic response and robustness in the presence of power system dynamic uncertainties.

scholarly journals Robust exponential load frequency control for time delay power system considering wind power

2020 ◽  
Vol 53 (2) ◽  
pp. 12536-12541
Author(s):  
Li Jin ◽  
Xingchen Shang-Guan ◽  
Yong He ◽  
Chuan-Ke Zhang ◽  
Lin Jiang ◽  
...  
Keyword(s):  
Time Delay ◽  
Power System ◽  
Wind Power ◽  
Frequency Control ◽  
Load Frequency Control ◽  
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.

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