Mohd Amran Mohd Radzi

Comparison of ANN and P&O MPPT methods for PV applications under changing solar irradiation

This paper presents an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and is stable under slowly changing of solar irradiation. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB-Simulink, and their performances under rapidly and slowly changing of solar irradiation are compared as well. Simulation results show that ANN method has very fast and more precise response under fast changes of solar irradiation. In addition, this method performs with less power oscillation under constant or slow changes of solar irradiation.

Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

The Effects of Power Quality to the Industries

This paper presents the results of a case study and survey of 10 industrial customers that investigates the effect of power quality to the industries. The survey also observed factory operation, products, equipment and processes used in production. The paper also described the significant of power quality monitoring system and mitigation strategies by industries. The quality of power supply has caused a direct economic impact to many industrial customers. The effects of power quality can be shown up in many aspects of industrial operations.

Evaluation of Fuzzy Logic Subsets Effects on Maximum Power Point Tracking for Photovoltaic System

Photovoltaic system (PV) has nonlinear characteristics which are affected by changing the climate conditions and, in these characteristics, there is an operating point in which the maximum available power of PV is obtained. Fuzzy logic controller (FLC) is the artificial intelligent based maximum power point tracking (MPPT) method for obtaining the maximum power point (MPP). In this method, defining the logical rule and specific range of membership function has the significant effect on achieving the best and desirable results. This paper presents a detailed comparative survey of five general and main fuzzy logic subsets used for FLC technique in DC-DC boost converter. These rules and specific range of membership functions are implemented in the same system and the best fuzzy subset is obtained from the simulation results carried out in MATLAB. The proposed subset is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency (95.7%). This investigation provides valuable results for all users who want to implement the reliable fuzzy logic subset for their works.

Application of smart power grid in developing countries

Smart grid, which is extensively new topic, is the next generation of power grid. Future modern grid will manage all parts of electrical power grid from generation to transmission and distribution by using intelligent methods. Smart grid progress in some developing countries is same to developed ones. In other parts of developing world, research works and pilot projects are newly started. Therefore, in the present work, a study of applied efforts in developing countries by regarding infrastructure of smart grid works was proposed. The analyses show that some countries like China, India and Brazil are pioneer on smart grid in developing world. As a conclusion, a progress pattern based on smart grid development in these three countries is proposed.

Enhanced Instantaneous Power Theory with Average Algorithm for Indirect Current Controlled Three-Level Inverter-Based Shunt Active Power Filter under Dynamic State Conditions

An enhanced harmonics extraction algorithm based on Instantaneous Power (PQ) Theory is proposed for indirect current controlled (ICC) three-level neutral point diode clamped (NPC) inverter-based shunt active power filter (SAPF). SAPF is famous in current harmonics mitigation for its flexibility in dealing with dynamic state conditions. As for its controller, PQ Theory has served the major role in harmonics extraction algorithm due to its simple implementation features. However, it suffers from serious time delay due to its dependency on sluggish numerical filters. Furthermore, the algorithm is mostly designed to suit the operation of direct current controlled (DCC) SAPF which requires the knowledge of actual SAPF current (injection current). This leads to inaccurate mitigation as the injection current does not possess the exact information on actual source current which suffers from switching ripples problems. Therefore, two major modifications are introduced involving the development of mathematical average algorithm to replace numerical filter for fundamental real power computation and the formation of mathematical current relationship to change DCC to ICC based operation. The proposed algorithm is developed and evaluated in MATLAB/Simulink. From the simulation results, significant improvement in terms of Total Harmonic Distortion (THD) and response time is presented in comparison to conventional algorithm.

Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

ABSTRACT This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well.

Software development for energy auditing practice

This paper presents the development of the software to calculate the energy usage. This software can be operated as an expert system. Basic architecture of the expert system contains knowledge base (database), inference engine (decision), graphical user interface (GUI), user and output (energy audit report). Mainly, this software was developed by using Microsoft Visual Basic as a programming language tool that can build the GUI. After testing it with previous and present collected energy data, this software can display the energy audit report. It presents total amount of energy usage in kWh/day, kWh/month and total energy cost in RM.

A Simplified Synchronous Reference Frame for Indirect Current Controlled Three-level Inverter-based Shunt Active Power Filters

This paper presents a new simplified harmonics extraction algorithm based on the synchronous reference frame (SRF) for an indirect current controlled (ICC) three-level neutral point diode clamped (NPC) inverter-based shunt active power filter (SAPF). The shunt APF is widely accepted as one of the most effective current harmonics mitigation tools due to its superior adaptability in dynamic state conditions. In its controller, the SRF algorithm which is derived based on the direct-quadrature (DQ) theory has played a significant role as a harmonics extraction algorithm due to its simple implementation features. However, it suffers from significant delays due to its dependency on a numerical filter and unnecessary computation workloads. Moreover, the algorithm is mostly implemented for the direct current controlled (DCC) based SAPF which operates based on a non-sinusoidal reference current. This degrades the mitigation performances since the DCC based operation does not possess exact information on the actual source current which suffers from switching ripples problems. Therefore, three major improvements are introduced which include the development of a mathematical based fundamental component identifier to replace the numerical filter, the removal of redundant features, and the generation of a sinusoidal reference current. The proposed algorithm is developed and evaluated in MATLAB / Simulink. A laboratory prototype utilizing a TMS320F28335 digital signal processor (DSP) is also implemented to validate effectiveness of the proposed algorithm. Both simulation and experimental results are presented. They show significant improvements in terms of total harmonic distortion (THD) and dynamic response when compared to a conventional SRF algorithm.

Predicting power output of photovoltaic systems with solar radiation model

Predicting of solar resource in general is momentous for preparation of the operations of power plants which transform renewable energies to electricity. In particular, the possibility to predict the solar irradiance (up to one day or even more) can become of significant interest with reference to Grid connected Photovoltaic (PV) Plants, stand alone and hybrid system. In this paper, a MATLAB/SIMULINK simulation based model for predicting hourly solar radiation using modified Hottels radiation model is presented. The proposed modified Hottels model makes it possible to predict solar radiation on hourly basis using current values of day type and geography of the location. While predicting power output, the model requires information of the weather forecast of the day to be predicted, some data from solar module datasheet is use and predicted radiation. The predicted results are compared with outdoor data collected by horizontal mount pyranometer at coordinates (2.945N, 101.728E) on 24th, June 2012. The results indicated the proposed model performed well after comparison with field data, having a correlation coefficient of 97%, also recorded 96% and 76% with Hottels and Ashrae model respectively.