A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Yan, Shu-Rong; Dai, Ying; Shakibjoo, Ali Dokht; Zhu, Lixing; Taghizadeh, Sima; Ghaderpour, Ebrahim; Mohammadzadeh, Ardashir

doi:10.1016/j.egyr.2024.06.018

Energy Reports

2024

DOI: 10.1016/j.egyr.2024.06.018

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A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Shu-Rong Yan,

Ying Dai,

Ali Dokht Shakibjoo

et al.

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2024

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Cited by 3 publications

References 33 publications

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Fault Detection and Isolation in Wind Turbines: Type-3 Fuzzy Logic Systems and Adaptive Random Search Learning

Zhou,

Zhu,

Ghaderpour

et al. 2024

IEEE Access

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Fault Detection and Isolation in Wind Turbines: Type-3 Fuzzy Logic Systems and Adaptive Random Search Learning

Zhou,

Zhu,

Ghaderpour

et al. 2024

IEEE Access

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Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement

Bento

2024

Electricity

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The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to inform an adequate system load margin. This paper proposes an optimization model to determine the parameters of a Physics-Informed Neural Network (PINN) that will be responsible for predicting the load margin of power systems. The proposed optimization model will also determine an optimal location of Phasor Measurement Units (PMUs) at system buses whose measurements will be inputs to the PINN. Physical knowledge of the power system is inserted in the PINN training stage to improve its generalization capacity. The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction.

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Direct Fractional-Order Adaptive Control Design for Cascaded Doubly-Fed Induction Generator (CDFIG) in a Wind Energy System

Djebbri,

Ladaci

2024

Wseas Transactions on Power Systems

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This paper is devoted to a fractional-order model reference adaptive control (FO-MRAC) synthesis for the independent control of the active and reactive power flows in the cascaded doubly fed induction generator (CDFIG) in wind energy systems. The proposed adaptive control law combines a second-order-like fractional reference model and a direct MIT adaptation law using a fractional order integrator. This generator configuration can be an interesting alternative to standard double-output wound rotor induction generators. It is made up of two identical wound rotor induction motors such that their rotors are mechanically and electrically coupled. Using two cascaded induction machines permits the elimination of the brushes and copper rings in the traditional doubly-fed induction generator DFIG, which makes the system more resistant and reduces maintenance costs. In the first step, we propose a classical PI controller synthesis to regulate the active and reactive power produced by CDFIG. Then, the FO-MRAC design is realized and a comparative study based on numerical simulations is performed between the classical regulators PI, MRAC, and FO- MRAC, to demonstrate the superiority of the proposed fractional-order adaptive controller relative to conventional integer order PI and MRAC controllers. These results illustrate the reliability and efficiency of the proposed adaptive control scheme.

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A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Cited by 3 publications

References 33 publications

Fault Detection and Isolation in Wind Turbines: Type-3 Fuzzy Logic Systems and Adaptive Random Search Learning

Fault Detection and Isolation in Wind Turbines: Type-3 Fuzzy Logic Systems and Adaptive Random Search Learning

Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement

Direct Fractional-Order Adaptive Control Design for Cascaded Doubly-Fed Induction Generator (CDFIG) in a Wind Energy System

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