A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm

To harvest maximum amount of solar energy and to attain higher efficiency, photovoltaic generation (PVG) systems are to be operated at their maximum power  point (MPP) under both variable climatic and partial shaded condition (PSC). From literature most of conventional MPP tracking (MPPT) methods are able to guarantee MPP successfully under uniform shading condition but fails to get global MPP as they may trap at local MPP under PSC, which adversely deteriorates the efficiency of Photovoltaic Generation (PVG) system. In this paper a novel MPPT based on Whale Optimization Algorithm (WOA) is proposed to analyze analytic modeling of PV system considering both series and shunt resistances for MPP tracking under PSC. The proposed algorithm is tested on 6S, 3S2P and 2S3P Photovoltaic array configurations for different shading patterns and results are presented. To compare the performance, GWO and PSO MPPT algorithms are also simulated and results are also presented.  From the results it is noticed that proposed MPPT method is superior to other MPPT methods with reference to accuracy and tracking speed. Article History: Received July 23rd 2016; Received in revised form September 15th 2016; Accepted October 1st 2016; Available online How to Cite This Article: Kumar, C.H.S and Rao, R.S. (2016) A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. Int. Journal of Renewable Energy Development, 5(3), 225-232. http://dx.doi.org/10.14710/ijred.5.3.225-23

Optimal siting and sizing of unified power flow controller using sensitivity constrained differential evolution algorithm

This paper presents Sensitivity constrained placement of unified power flow controller (UPFC) considering active-power flow sensitive index (APFSI) and static voltage stability index (STATIC-VSI) to minimize active-power losses and to improve power transmission capacity. The sensitive factors are derived with respect to voltage, phase angle and current to formulate APFSI. Transmission line impedance parameters along with active and reactivepower flow measurements are considered to formulate static-VSI. Sensitivity constrained differential evolutionary (SCDE) algorithm is proposed for parameter setting through which power control and minimization of losses in system can be achieved. Testing is performed on IEEE-5, 14 and 30-bus networks in MATLAB and results indicate that SCDE is robust optimization technique compared to conventional method and genetic algorithm (GA

Design of robust modified power system stabilizer for dynamic stability improvement using Particle Swarm Optimization technique

This paper presents a novel method for designing robust Power System Stabilizer (PSS) using Particle Swarm Optimization (PSO) technique to improve the dynamic stability of the power system. Modified Heffron-Phillip’s model is developed by considering the generator side transformer voltage as the reference instead of considering the infinite bus voltage as the reference to reduce the complexity and computational time. Using this MHP model, a power system stabilizer called Modified Power System Stabilizer (MPSS) is developed and integrated with P-I-D controller using PSO (PSO-P-I-D-MPSS) on a Single Machine Infinite Bus System. PSO algorithm is used to determine the gain settings of the P-I-D-MPSS. The developed PSO-P-I-D-MPSS is simple to implement and will be a better alternative to the conventional or evolutionary based PSSs. The efficacy of the proposed PSO-P-I-D-MPSS is validated by the application of proposed stabilizer to a benchmark system for several operating conditions under various disturbances. The results clearly show that the proposed stabilizer improves the dynamic stability of the power system under wide range of operating conditions. Keywords: Power system stabilizer, Modified Heffron-Phillips model, Small signal stability, Particle Swarm Optimization, P-I-D controller, Eigenvalue

DWT based bearing fault detection in induction motor using noise cancellation

This paper presents an approach to detect the bearing faults experienced by induction machine using motor current signature analysis (MCSA). At the incipient stage of bearing fault, the current signature analysis has shown poor performance due to domination of pre fault components in the stator current. Therefore, in this paper domination of pre fault components is suppressed using noise cancellation by Wiener filter. The spectral analysis is carried out using discrete wavelet transform (DWT). The fault severity is estimated by calculating fault indexing parameter of wavelet coefficients. It is further proposed that, the fault indexing parameter of power spectral density (PSD) based wavelet coefficients gives better results. The proposed method is examined using simulation and experiment on 2.2 kW test bed

Bearing fault detection in a 3 phase induction motor using stator current frequency spectral subtraction with various wavelet decomposition techniques

Induction motors consumes 90% of total power consumed by industries due to large scale utilisation. Even though these motors are rugged in structure, they often face unexpected failure due to long usage without maintenance. Bearing failure is a major problem among various faults, which cause catastrophic damage to machine when unnoticed at incipient stage. So the bearing faults in induction machines should be continuously monitored. Motor current signature analysis (MCSA) has become popular for detection and localisation of these faults and has attracted concentration of many researchers. In this paper stator current is monitored by means of frequency spectral subtraction using various wavelet transforms to suppress dominant components. The spectral subtraction using discrete wavelet transform (DWT), stationary wavelet transform (SWT) and wavelet packet decomposition (WPD) is performed and a comparative analysis is carried out by means of different fault indexing parameters. The proposed topology is examined using 2.2 kW induction machine test bed. Keywords: Condition monitoring, Motor current signature analysis, Bearing faults, Discrete wavelet transform, Stationary wavelet transform, Wavelet packet decompositio