Load Frequency Control of a Multi-Area Power System: An Adaptive Fuzzy Logic Approach

By H. Yousef, Khalfan AL-Kharusi, Mohammed H. Albadi, N. Hosseinzadeh. IEEE Transactions on Power Systems, 2014

301 citations recorded by the source corpus.

In this paper, a new load frequency control (LFC) for multi-area power systems is developed based on the direct-indirect adaptive fuzzy control technique. LFCs for each area are designed based on availability of frequency deviation of each area and tie-line power deviation between areas. The fuzzy logic system approximation capabilities are exploited to develop suitable adaptive control law and parameter update algorithms for unknown interconnected LFC areas. An H∞ tracking performance criterion is introduced to minimize the approximation errors and the external disturbance effects. The proposed controller guarantees stability of the overall closed-loop system. Simulation results for a real three-area power system prove the effectiveness of the proposed LFC and show its superiority over a classical PID controller and a type-2 fuzzy controller.

Subfields: Condensed Matter Physics, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/960a16a18cf79fc3501f30664ffe1cdc48dd232b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Condensed Matter Physics

1. Yan Xu
2. Ziming Yan

In Control and Systems Engineering

1. Yan Xu
2. Yan Xu
3. Ziming Yan

In Electrical and Electronic Engineering

1. Yan Xu
2. Yan Xu
3. Ziming Yan
4. Chi-Wang Shu

Citations in the corpus

• A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of a Multi-Area Power System (2020, background): “Besides, robust control [1], [4], [5], fuzzy control [6], [7], variable structure control [8], [9], disturbance rejection con-trol [10] and model-predictive control [11] are reported to be effective in dealing with different control objectives.”
• Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search (2019, methodology): “Conventional LFC methodologies are modelbased, such as robust control [1], fuzzy control [2] and optimal control algorithms [3].”