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Smooth Integration of Gansu Wind Farm into the Grid Using the Stator Flux-Oriented Vector Method and Fuzzy Logic Control

  • Research Article - Electrical Engineering
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Abstract

Gansu province wind energy potential in China is around 237 GW. According to the schedule, 20 GW wind energy is connected to the grid by 2020. However, there is a chance of instability in the presence of big intermittency. To integrate this huge generated wind power, a reliable control strategy is required. The proposed portable power plant (PPP) energy storage system is fully compatible with a smart grid and mitigates the dispatching complexity and provides better designing and implementation of Gansu wind farm in China. In a two-way power flow, when the generation is bigger than the load demand, the additional power is stored in the PPP for future use, and when the demand is higher than the total generation, the stored power is applied to feed the grid. Also the PPP can charge the grid during peak demand periods or when the local network is stressed. An intelligent controller is linked with PPP to monitor the power flow. The stator flux-oriented vector method is used for modeling the system. Then, a fuzzy controller is applied to adjust the modulation index of PWM inverter and also uses energy storage units to stabilize the output of the power plant. Real field data of the Gansu wind farm with 24-h horizon have been applied on the proposed system. The results show the high performance of the fuzzy-based PPP system in the presence of fluctuations and increase the efficiency of the power system by storing energy through PPP. With a large-scale plan for application of smart grid and renewable energy sources in China, this paper introduces an essential step of this vision to provide a feasible framework for future large-scale smart grid projects in China as well as stable operation of Gansu wind farm as the biggest wind farm in mainland when it is completed.

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Abbreviations

x :

Value of the linguistic variable

\(x_{\min } , x_{\max } \) :

Range limits of linguistic variable x

\(f\left( x \right) \) :

Membership function of input and output

COA:

Center of area defuzzifier

a :

Height of peak value of Gaussian curve

b :

Center of the Gaussian curve

c :

Standard deviation of the Gaussian curve

m :

Modulation index of inverter

\(V_\mathrm{out} \) :

Output voltage of the system

\(V_\mathrm{ref} \) :

Nominal voltage of the system

\(u_\mathrm{s} \) :

Stator voltage

\(u_\mathrm{r} \) :

Rotor voltage

\(\psi _\mathrm{s} \) :

Stator flux linkage

\(\psi _\mathrm{r} \) :

Rotor flux linkage

\(i_\mathrm{s} \) :

Stator current

\(i_\mathrm{r} \) :

Rotor current

\(r_\mathrm{s} \) :

Stator resistance

\(r_\mathrm{r} \) :

Rotor resistance

\(L_\mathrm{s} \) :

Stator inductance

\(L_\mathrm{r} \) :

Rotor inductance

\(L_\mathrm{m} \) :

Magnetizing inductance

\(\omega _\mathrm{s} \) :

Grid frequency

\(\omega _\mathrm{r} \) :

Rotor frequency

\(T_\mathrm{m} \) :

Mechanical torque of turbine

\(T_\mathrm{e} \) :

Electrical torque of turbine

\(\theta _\mathrm{s} \) :

Stator flux angle

\(Z_\mathrm{e} \) :

Grid impedance

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Authors and Affiliations

  1. Shaanxi Key Laboratory of Smart Grid, Xi’an Jiaotong University, Xi’an, 710049, China

    Mostafa Kheshti & Xiaoning Kang

  2. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Yasenjiang Jiarula

Authors
  1. Mostafa Kheshti
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  2. Xiaoning Kang
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  3. Yasenjiang Jiarula
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Correspondence to Mostafa Kheshti.

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Kheshti, M., Kang, X. & Jiarula, Y. Smooth Integration of Gansu Wind Farm into the Grid Using the Stator Flux-Oriented Vector Method and Fuzzy Logic Control. Arab J Sci Eng 42, 5059–5069 (2017). https://doi.org/10.1007/s13369-017-2596-x

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