Novel Control Approach with FRT capability for Grid connected HYBRID distributed generation system using ANN controller based DVR

For ever increasing power demand and depletion of conventional energy resources, Renewable Energy Systems (RES) became an alternative source of electricity to reduce the load stress on the Power Grid. Although several control & design modifications are presented in past literature to improve reliability & performance of through Distribution Generation (DG) technologies, they always fall short in some aspects of voltage stability and Fault Ride Through (FRT) capabilities. The main aim of the project is Protecting Critical load from Grid side altercations which occur due to harmonics generated by DG’s and Short circuit faults near to load center. This project proposes the application of a Dynamic Voltage Restorer (DVR) to enhance the power quality and improve the Fault Ride Through (FRT) capability of a three-phase medium-voltage network connected to a hybrid distribution generation (DG) system. In this hybrid farm, the Photo Voltaic (PV) plant via single-stage energy conversion (DC-AC inverter) & DFIG (Doubly-Fed Induction Generator) based Wind power plant are connected to the same Point of Common Coupling (PCC). For MPPT of wind power plant, we use Pitch Angle Control (PAC) technique. This topology allows Perturb and observe (P&O) based MPPT algorithm for PV plant through connection of the DG (Distribution generation) system to the public grid through a step-up transformer. In addition, the DVR based on Artificial Neural Network (ANN) controller is connected to the same PCC. Different fault condition scenarios are tested for improving the efficiency and the quality of the power supply and compliance with the requirements of the sensitive Load. The efficiency of this control technique is that it enhances restoration and harmonics suppression capabilities of DVR which are far superior than that of PI controller used in existing model. Keywords: RES, DG, LVRT, FRT, PV, DFIG, PCC, MPPT, P&O, DVR, PI, ANN, THD, Voltage stability.

Scaling trends for balance-of-system costs at land-based wind power plants: Opportunities for innovations in foundation and erection

Wind power plant sizes, hub heights, and turbine ratings have increased since 2008 to optimize the cost and performance of wind power; however, the limits of these economies of scale remain unclear. Here, we explore how the costs incurred to install turbines at a wind power plant—the balance-of-system (BOS) costs—scale with turbine rating, hub height, and plant size. We also investigate how these changes in BOS costs influence the levelized cost of energy (LCOE). We show that increasing the plant size from 150 to 400 MW could reduce the BOS costs by 21%. We also show that if the foundation costs decreased by 50%, building a wind power plant with 5-MW turbines (having rotor diameters of 166 m and hub heights of 120 m) could decrease the LCOE by 5%. These results could help inform future BOS cost-reduction opportunities and thereby reduce future capital costs for land-based wind power.

Comparative analysis of the impact of objects of traditional and alternative energy on the environment

Introduction. Active withdrawal of energy raw materials from the subsoil, as well as technogenic impact from energy sources based on traditional fuel, lead to irreversible environmental consequences. To minimize this impact, it is necessary to start from two main conditions: the search for alternative energy sources and the improvement of the existing ones. Problem Statement. The objective of this study is a comparative analysis of energy facilities in order to identify the plant that has the greatest negative impact on the environment. Theoretical part. The comparative analysis of various energy production systems reflects the ecological and economic components of each. For example, a thermal power plant (TPP), a nuclear power plant (NPP) and a wind power plant (WPP) are considered. The negative impact on the environment is mainly exerted on the atmospheric air, in connection with which the data on the amount of pollutants are considered. Also, a modified Leopold matrix was constructed for an expert assessment of the mentioned stations. Conclusions. The results of the analysis show that among the considered power plants, the wind power plant is the most environmentally friendly and favorable for the health of the population.

Analysis and Research on Fracture Cause of Fixed Shaft of Torsion Arm of Wind Turbine Gearbox

In a wind power plant of a wind power plant limited liability company, the fixed shaft of the torsion arm of the gear box broke during the operation of a wind power generator set. In order to find out the cause of fracture, the fracture fixed shaft of torsion arm was comprehensively detected and analyzed by means of appearance morphology analysis, chemical composition analysis, mechanical properties testing, microstructure testing and fracture micro-area analysis. The results show that the main reasons for the fracture of the fixed shaft of the torsion arm of the fan gear box are as follows: improper heat treatment process of the fixed shaft of the torsion arm of the gear box causes a large amount of massive ferrite in the material structure, resulting in insufficient strength of the material; the inclusion in the material is serious, resulting in unqualified impact toughness; Shaft surface surfacing Cr - Mn stainless steel material causes the fusion zone C migration form brittle layer, at the same time the vast difference between the state of welding layer and substrate organization fusion zone caused by larger remnants stress, the embrittlement in bond layer to form the intergranular crack crack source, and in the process of the equipment operation under the action of cyclic torsional and impact load, Cracks propagate in a fatiguing manner and lead to eventual fracture.

Forecasting model of power generated by wind power plants

The power generated by wind power plants is unstable so forecasting is needed to maintain the power balance in an interconnected system. The purpose of this research is to predict the power generated at the Sidrap and Jeneponto wind power plants. The method used is an optimally pruned extreme learning machine (OPELM). The extreme learning machine (ELM) method is used as a comparison method. The mean absolute percentage error (MAPE) method is used to assess the level of forecasting accuracy. Forecasting power generation with Sidrap wind power plant data using the OPELM method is 0.8970% more accurate than the ELM which is 1.0853%. In general, the OPELM method is more accurate. Forecasting power generation with data from the Jeneponto wind power plant using the OPELM method is 2.4887% more accurate than the ELM method is 2.9984%. These results indicate that linear, sigmoid, and Gaussian activation in the OPELM method can increase accuracy. The OPELM method can be tested in forecasting the power generation at the Sidrap and Jeneponto wind power plants to maintain a power balance in the Sulselbar power grid system.