Application of computational intelligence techniques for load shedding in power systems: A review

•The power system blackout history of last two decades is presented.•Conventional load shedding techniques, their types and limitations are presented.•Applications of intelligent techniques in load shedding are presented.•Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and...

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Veröffentlicht in:Energy conversion and management Jg. 75; S. 130 - 140
Hauptverfasser: Laghari, J.A., Mokhlis, H., Bakar, A.H.A., Mohamad, Hasmaini
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.11.2013
Elsevier
Schlagworte:
Adaptive neuro-fuzzy inference system
algorithms
Applied sciences
Artificial neural network
Artificial neural networks
Collapse
Computation
Dynamical systems
Energy
Exact sciences and technology
Fuzzy logic
Fuzzy logic control
Genetic algorithm
Inference
Intelligence
Load shedding
neural networks
Particle swarm optimization
Fuzzy logic control
Artificial neural network
Adaptive neuro-fuzzy inference system
Genetic algorithm
Particle swarm optimization
Load shedding
Fuzzy logic
Fuzzy control
Fuzzy system
Logic control
Neural network
Optimization
ISSN:0196-8904, 1879-2227
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Abstract •The power system blackout history of last two decades is presented.•Conventional load shedding techniques, their types and limitations are presented.•Applications of intelligent techniques in load shedding are presented.•Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.•The discussion and comparison between these techniques are provided. Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.
AbstractList •The power system blackout history of last two decades is presented.•Conventional load shedding techniques, their types and limitations are presented.•Applications of intelligent techniques in load shedding are presented.•Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.•The discussion and comparison between these techniques are provided. Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.
Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.
Author Bakar, A.H.A.
Mohamad, Hasmaini
Laghari, J.A.
Mokhlis, H.
Author_xml – sequence: 1
  givenname: J.A.
  surname: Laghari
  fullname: Laghari, J.A.
  email: javedahmedleghari@gmail.com
  organization: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
– sequence: 2
  givenname: H.
  surname: Mokhlis
  fullname: Mokhlis, H.
  organization: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
– sequence: 3
  givenname: A.H.A.
  surname: Bakar
  fullname: Bakar, A.H.A.
  organization: University of Malaya Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D UM, Jalan Pantai Baharu, University of Malaya, 59990 Kuala Lumpur, Malaysia
– sequence: 4
  givenname: Hasmaini
  surname: Mohamad
  fullname: Mohamad, Hasmaini
  organization: Faculty of Electrical Engineering, University of Technology MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
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Keywords Fuzzy logic control
Artificial neural network
Adaptive neuro-fuzzy inference system
Genetic algorithm
Particle swarm optimization
Load shedding
Fuzzy logic
Fuzzy control
Fuzzy system
Logic control
Neural network
Optimization
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Snippet •The power system blackout history of last two decades is presented.•Conventional load shedding techniques, their types and limitations are...
Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address...
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SubjectTerms Adaptive neuro-fuzzy inference system
algorithms
Applied sciences
Artificial neural network
Artificial neural networks
Collapse
Computation
Dynamical systems
Energy
Exact sciences and technology
Fuzzy logic
Fuzzy logic control
Genetic algorithm
Inference
Intelligence
Load shedding
neural networks
Particle swarm optimization
Title Application of computational intelligence techniques for load shedding in power systems: A review
URI https://dx.doi.org/10.1016/j.enconman.2013.06.010
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https://www.proquest.com/docview/1710210986
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