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Honey badger-tuned ANFIS controller for STATCOM employed in hybrid renewable energy source

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Abstract

Energy production with uncertainties in renewable energy sources (RESs) leads to voltage instability at the point of common coupling (PCC), which affects the normal operation of various RESs interconnected with the system. The static synchronous compensator (STATCOM) employed in PCC reduces the voltage fluctuations at PCC with better dynamic performance. The controller used in STATCOM determines the effectiveness of voltage stability within the system. Thus, in this research work, a honey badger-tuned adaptive neuro-fuzzy inference system (ANFIS) controller is proposed for the STATCOM in hybrid RES. A hybrid RES system is designed with a solar photovoltaic (PV), wind-based doubly-fed induction generator (DFIG) and diesel generator. A diesel generator feeds power to the system when RESs fail to meet the load requirements. A STATCOM-based FACTS device is inserted within the system to preserve voltage stability in the PCC. To enhance the static and dynamic performance of the STATCOM functionality, a hybrid controller consisting of ANFIS and the honey badger provides pulses to the STATCOM. The ANFIS has to extract the numerical models from the numerical data, improving the control performance; moreover, the honey badger has to solve the complex search space and its superiority in terms of convergence speed. Thus, the ANFIS and the honey badger are used in this system by combining both advantages to control the STATCOM functionality, thus leading to the error-free output voltage. The proposed hybrid RES with STATCOM has been implemented using MATLAB/Simulink platform. The performance efficacy of a proposed method is compared with proportional integral-based ant colony optimization (PI-ACO), proportional integral derivative-based genetic algorithm (PID-GA), marine predator algorithm PID acceleration (MPA-PIDA) and improved field-oriented control (IFOC). The overall efficiency of a proposed ANFIS-HB is 99.1%, the computing time is 40 min, the settling time is 0.46 s, the maximum voltage is 1.1 V and the percentage of overshoot (POT) is 11%.

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Abbreviations

\(I_{{{\text{PH}}}}\) :

Photovoltaic current

\(I_{{\text{S}}}\) :

Saturation current

\(q\) :

Coulomb equivalent of one electron equal to 1.6 1019 C

\(A\) :

Diode ideality factor

\({\text{k}}\) :

Boltzmann’s constant

\(T\) :

Temperature of PV panel

\(I\) :

Load current

\(V\) :

Terminal voltage

\(R_{{\text{L}}}\) :

Load resistance

\(R_{{\text{S}}}\) :

Series equivalent circuit resistance

\(R_{{{\text{SH}}}}\) :

Parallel equivalent circuit resistance

\(R_{{\text{s}}}\) :

Stator resistance

\(R_{{\text{r}}}\) :

Rotor resistance

\(M_{{{\text{sr}}}}\) :

Mutual inductance of stator and rotor winding

\(\omega\) :

Speed

\(\omega_{{\text{r}}}\) :

Rotor mechanical angular speed

\(\omega_{{\text{s}}}\) :

Stator electrical angular speed

\(\rho\) :

Number of poles

\(u_{{\text{s}}}\) :

Stator self-inductance

\(u_{{\text{r}}}\) :

Rotor self-inductance

\(i_{{\text{s}}}\) :

Stator current

\(i_{{\text{r}}}\) :

Rotor current

\(\left( {V_{{\text{s}}} } \right)\) :

Stator output voltage

\(\gamma_{{\text{s}}}\) :

Stator flux linkage

\(p_{{{\text{BG}}\;\max }}\) :

Higher power from the generator

\(F_{{{\text{BG}}}}^{{}}\) :

Diesel consumption per hour by the DG

\(\alpha_{0}^{{}}\) :

Fuel intercept coefficient of the modelling

\(p_{{{\text{BGR}}}}\) :

Generator capacity

\(\alpha_{1}\) :

Fuel slope

\(P_{{{\text{BG}}}}^{{}}\) :

Power from generator

\(V_{dst}\) :

\(d\,\,\)-axes voltages

\(V_{qst}\) :

\(\,q\)-axes voltage

\(K_{mst}\) :

Modulation index

\(\alpha_{st}\) :

Phase angle

\(\theta_{{{\text{pcc}}}}\) :

Voltage phase angle

\(V_{{{\text{dc}}}}\) :

DC voltage

\(I_{{{\text{dc}}}}\) and \(R_{m}\) :

Equivalent resistance

\(\nabla V_{{{\text{pcc}}}}\) :

Error in PCC voltage

\(\left( {\nabla V_{{{\text{pcc}}}} } \right)^{^{\prime}}\) :

Variations in voltage

\(K\) :

Number of nonlinear ANFIS parameters

\(L\) :

Location of nonlinear ANFIS parameters

\(r_{1}\) :

Random number

\(lb_{i}\) and \(ub_{i}\) :

Search domain for lower and upper ranges

\(S\) :

Strength

\(d_{i}\) :

Distance

C :

Constant

\(t_{\max }\) :

Maximum number of iterations

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  1. Department of Electrical Engineering, Rajasthan Technical University, Kota, 324010, India

    Senthil Lakshmanan, Seema Agrawal & Ashok Kumar Sharma

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  1. Senthil Lakshmanan
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  2. Seema Agrawal
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  3. Ashok Kumar Sharma
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Correspondence to Senthil Lakshmanan.

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Lakshmanan, S., Agrawal, S. & Sharma, A.K. Honey badger-tuned ANFIS controller for STATCOM employed in hybrid renewable energy source. Electr Eng 105, 3241–3253 (2023). https://doi.org/10.1007/s00202-023-01862-y

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