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Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization

Author

Listed:
  • Solomon Feleke

    (Department of Electrical and Computer Engineering, Debre Berhan University, Debre Berhan 445, Ethiopia)

  • Raavi Satish

    (Department of Electrical & Electronics Engineering, Anil Neerukonda Institute of Technology and Science, Visakhapatnam 531162, Andhra Pradesh, India)

  • Balamurali Pydi

    (Department of Electrical & Electronics Engineering, Aditya Institute of Technology & Management, Tekkali 532201, Andhra Pradesh, India)

  • Degarege Anteneh

    (Department of Electrical and Computer Engineering, Debre Berhan University, Debre Berhan 445, Ethiopia)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Adel El-Shahat

    (Energy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA)

Abstract

Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.

Suggested Citation

  • Solomon Feleke & Raavi Satish & Balamurali Pydi & Degarege Anteneh & Almoataz Y. Abdelaziz & Adel El-Shahat, 2023. "Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4751-:d:1090327
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    References listed on IDEAS

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    1. Solomon Feleke & Balamurali Pydi & Raavi Satish & Hossam Kotb & Mohammed Alenezi & Mokhtar Shouran, 2023. "Frequency Stability Enhancement Using Differential-Evolution- and Genetic-Algorithm-Optimized Intelligent Controllers in Multiple Virtual Synchronous Machine Systems," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    2. Weichao He & Yuemin Zheng & Jin Tao & Yujuan Zhou & Jiayan Wen & Qinglin Sun, 2023. "A Novel Fractional-Order Active Disturbance Rejection Load Frequency Control Based on An Improved Marine Predator Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-23, June.

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