M. Rizwan

Simultaneous Reconfiguration, Optimal Placement of DSTATCOM, and Photovoltaic Array in a Distribution System Based on Fuzzy-ACO Approach

In this paper, a combination of a fuzzy multiobjective approach and ant colony optimization (ACO) as a metaheuristic algorithm is used to solve the simultaneous reconfiguration and optimal allocation (size and location) of photovoltaic (PV) arrays as a distributed generation (DG) and distribution static compensator (DSTATCOM) as a distribution flexible ac transmission system (DFACT) device in a distribution system. The purpose of this research includes loss reduction, voltage profile (VP) improvement, and increase in the feeder load balancing (LB). The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The results proved that simultaneous reconfiguration and optimal allocation of PV array and DSTATCOM unit leads to significantly reduced losses, improved VP, and increased LB. Obtained results have been compared with the base value and found that simultaneous placement of PV and DSTATCOM along with reconfiguration is more beneficial than separate single-objective optimization. Also, the proposed fuzzy-ACO approach is more accurate as compared to ACO and other intelligent techniques like fuzzy-genetic algorithm (GA) and fuzzy-particle swarm optimization (PSO).

Generalized Neural Network Approach for Global Solar Energy Estimation in India

In India, large areas of land are barren and sparsely populated, making these areas suitable as locations for large central power stations based on solar energy. India is located in the equatorial sun belt of the earth, thereby receiving abundant radiant energy from the sun. Estimating solar energy accurately is a big task to exploit the solar potential for power generation. A number of conventional and intelligent models are available; how- ever, the results are not satisfactory due to extreme simplicity of their parameterization. In this paper, application of generalized neural network (GNN), a modified approach of artificial neural network (ANN), is proposed to estimate solar energy to overcome the problems of ANN such as a large number of neurons and layers required for complex function approximation, which do not affect the training time only but also the fault tolerant capabilities of the ANN. The mean relative error in the estimation of global solar energy is found around 4% whereas the same using fuzzy logic is 6% approximately. Therefore, it is concluded that the GNN technique is found more accurate for the estimation of global solar energy.

Empirical correlation of estimating global solar radiation using meteorological parameters

An empirical model for the estimation of solar energy on the basis of Angstroms model is proposed in this work. Seven regression equations are developed by using different meteorological parameters such as mean sunshine duration per hour, temperature, relative humidity, wind speed, and rainfall. The performance of the model is determined on the basis of statistical indicators like correlation coefficient(r), coefficient of determination (R2), root mean square error (RMSE), mean percentage error (MPE), and mean bias error (MBE). The results show that the equation with the highest value of r, R2 and the least value of RMSE, MPE, and MBE provides better results.

A predictive current control for solar PV fed VSI in distribution system

A control technique based on predictive current control for a voltage source inverter fed by a solar PV system integrating to the distribution grid with Perturb and Observe (P&O) maximum power point tracking (MPPT) technique is presented in this paper. The proposed control is implemented for a three phase system to support the utility grid by providing the power to grid as well as to the connected loads with improved quality of power. This control approach for solar PV fed voltage source inverter (VSI) provides fast transient response and improved accuracy in control. Proposed control algorithm efficiently and continuously tracks reference current to make grid current sinusoidal in steady state and transient conditions. When the solar irradiance is available, the developed system acts as power quality conditioner and supply the PV generated power to the loads and the grid. During unavailability of sun power the system transfer the power from the grid to the load and capable to provide multidirectional power flow. Simulation for the proposed system has been carried out and results are presented.

Development of Fuzzy Logic based MPPT controller for PV system at varying meteorological parameters

The ever growing demand and socio-economic development, the conventional energy sources have almost become obsolete for nurturing this demand. Thus opening new avenues to search for the non-conventional sources. Photovoltaic is inexhaustible, renewable and non-polluting, so it is worth saying that it is one of the prospective solution for this crisis. But, having these benefits does not solve the problem as the efficiency is dependent on various factors such as irradiance, cell temperature, dust, sunlights spectral density. Photovoltaic is a non-conventional and environment friendly technique which still has various challenges to overcome. This paper proposes an impressive Maximum Power Point Tracking (MPPT) algorithm using Fuzzy Logic in MATLAB-Simulink module alongside an analytical study with the traditional approach. This improvised MPPT technique reduces the tracking time and also solves the various issues associated with traditional MPPT algorithms such as Perturb and Observe, Incremental Conductance. The method involves as Fuzzy Logic Controller which controls the switching of the DC-DC Boost Converter. In this paper the fuzzy logic MPPT controller is tested against variable irradiance, and temperature. Various performance indicators have been listed for the proposed MPPT. Fuzzy logic provide user with complete flexibility to choose the inputs and their relationship (rule base). The simulated results show that the fuzzy logic technique controls the integrated PV module output directly and rapidly. Thus showing the effectiveness and feasibility of the proposed controller.

Passive islanding detection technique for multi-DG power system

The use of distributed generators including solar photovoltaic, wind, small hydro, biomass etc. are playing an important role in the restructured power system. However, there are certain issues related to grid integration of DGs including islanding detection. Hence, it becomes an important area of research nowadays for power engineers. There are various islanding detection techniques including passive, active, hybrid, utility, communication based etc. In this paper, a passive islanding detection technique for multi-DGs including solar photovoltaic is presented. Three DGs including solar photovoltaic have been considered in this study. The performance is evaluated on the basis of four parameters i.e. voltage, frequency, positive sequence voltage and negative sequence voltage at the point of common coupling (PCC). This paper considered two practical scenarios for isolation of grid, one due to occurrence of fault and other is intentional disconnection of grid for schedule maintenance of the system. The test system is simulated in DigSILENT Power factory software for detecting the islanding.

Short-Term PV Power Forecasting Using Generalized Neural Network and Weather-Type Classification

Generation of electricity from solar energy is gaining huge attention because of advancement in the solar photovoltaic technology. Power from solar energy is intermittent in nature and requires a good forecasting method for efficient and reliable operation of smart grid systems. A large number of forecasting approaches are available in the literature. Due to intermittent nature of power obtained from sun, the results obtained from mathematical models for solar PV power prediction are not found satisfactory. An intelligent approach based on generalized neural network (GNN) is proposed and applied for the short-term solar PV power forecasting. Short-term forecasting for an hour to day ahead has applications in energy storage optimization, electricity pricing, etc. Keeping in mind aforesaid, 15 min ahead, short-term solar energy forecasting has been done and presented in this work for smart grid applications. The developed model requires an input of historical data set for PV output power, i.e. solar irradiance, temperature and the relative humidity of the site where solar PV is installed. The performance of the developed PV power forecasting model is evaluated with respect to the accuracy of the developed model for a 1 kWp practical system. Further, the evaluation of proposed method has been performed on the basis of root mean square error (RMSE) and mean absolute error (MAE).

Grid Integration of Solar Photovoltaic Systems

Grid Integration of Solar Photovoltaic Systems , Grid Integration of Solar Photovoltaic Systems , کتابخانه‌های دانشگاه کردستان

A three phase grid connected SPV power generating system using EPLL based control technique

The integration of Solar Photovoltaic (SPV) power generation system with the grid by using Enhanced Phase Locked Loop (EPLL) scheme based indirect control. The developed control scheme for synchronizing the SPV system with utility grid improves the power factor of the source and voltage profile at the point of common coupling (PCC). The proposed controller maintain the voltage profile at PCC through reactive power compensation. The proposed system includes SPV array, a DC/DC boost converter, maximum power point tracking (MPPT) controller, voltage source converter (VSC), filter, various loads and three phase utility grid. The proposed grid synchronization technique provides better active and reactive power control, reduced harmonic and load balancing. Testing for the proposed grid integrated SPV system has been performed for two type of operations i.e. unity power factor (UPF) mode operation of the system and voltage regulation with balancing of loads with linear load and non-linear loads.

Analysis of single phase grid connected PV system to identify efficient system configuration

Deregulated power system nowadays is grooming rapidly. Distributed generators are providing support to existing power system to fulfill the demand of power. Solar Photovoltaic system is one of the distributed generators which are being utilized by the customers globally. Small and medium power consumers have started striving on solar power for economically and environmentally reasons. Most of the customers on distribution side have single phase connection. Presently the single phase grid connected photovoltaic system is getting popularity among all renewable energy sources used for the distribution side. This paper mainly focuses on the component analysis of single phase grid connected photovoltaic system. For better understanding of the system and for attaining maximum efficiency of the PV system, an efficient power electronics converter is being required. The paper reviews the existing topologies of the various components used in a single phase grid-tied PV system which leads to identify the efficient system configuration of the PV system.