Harmonic Level Minimization Using Neuro–Fuzzy-Based SVPWM

ABSTRACT

Recently, the renewable energy systems play the major role to meet the electricity demand. The Distributed energy source provision of balance between the generation and distribution is an attractive research area in the (DES) for providing better power quality during the Distributed Energy Sources deployment. Hence, this paper focuses on the design of an efficient controlling structure to reduce the noise level in the output voltage. Due to the combination of two renewable resources, there is a need for special controlling architecture for switching devices under dynamic load conditions. The generation of the control signals through the Neuro Fuzzy with variations such as the rate of carrier frequency, duty cycle and the pulse position reduces the impulse response that leads to regulated power to the load. Based on the irradiance of the PV system and the torque variations in the induction motor system, the output from the Neuro Fuzzy- with three varied rate parameters as Rate of Carrier Frequency level (RCF), Ratio of Duty Cycle (RDC), and the Rate of Pulse Position (RPP) unit passes to the Space-Vector Pulse Width modulation (SVPWM) block for switching pulse generation. The difference between the frequency of reference and carrier signals has direct impact on the changes in the pulse width that leads to additional noise in the traditional controller techniques. Alternatively, the proposed work provides pulses to the switching devices based on rate analysis that leads to high switching speed and regulated output voltage. The comparative analysis between the proposed NFRRR–SVPWM with the existing methods states the effectiveness of the rate-based controlling technique in the development of renewable energy resources.

KEYWORDS:

• Distributed energy sources (DES)
• Doubly fed induction generators
• Neuro–fuzzy modelling
• Pulse width modulation
• Space-vector pulse width modulation
• Wind energy conversion systems

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Bhanu Ponnapalli

Bhanu Ponnapalli is a full time research scholar at Department of Instrumentation Engineering, MIT campus, Anna University, India. She received her Master's degree from JNTU Hyderabad in 2010 and Bachelor degree from Madras University in 2004. Her research interests include hybrid renewable energy control, power quality and FACTS.

E-mail: [email protected]

Pappa Natarajan

Pappa Natarajan is associate professor at Department of Instrumentation Engineering, MIT campus, Anna University, India since 1998. She received her PhD degree from Anna University, India in 2004, Master's degree from CUSAT University in 1992 and Undergraduate degree from Annamalai University in 1990. Her research interest includes process control & instrumentation, hardware-in-loop, signal and image processing. She is having a teaching experience of more than 20 years and has published technical papers in reputed journals and conferences.

E-mail: [email protected]