Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.

Unified fuzzy logic controller and power management for an isolated residential hybrid PV/diesel/battery energy system

Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems(PMSs)aim to reduce fossil fuels and increase the efficiency of renewable energy sources to minimize greenhouse gas emissions.The influential role of the PMS contributes to improving the efficiency and effectiveness of these systems by ensuring a balance between the different sources and loads in all operating modes.However,the abrupt transitions between the various operational modes selected by the PMS generate power loss and imbalance.To handle this issue,a fuzzy logic controller(FLC)-based PMS controlling a photovoltaic(PV)and diesel hybrid system with a battery storage element connected to a DC bus is proposed in this paper.The proposed PMS is wholly based on FLC to ensure a smooth transition between the different modes of the system.The success of using the suggested PMS lies in how well the FLC parameters are chosen before the system is processed.For this purpose,the particle swarm optimization algorithm is adapted to tune the FLC parameters.The resulting optimal intelligent PMS is tested and compared with a classical one using comprehensive simulations performed in a Simscape ElectricalTM MATLAB®environment.The obtained results show an overshoot attenuation at the DC-bus voltage of 2%when changing the mode and an improvement in the PV generator efficiency by 99.5%.

Renewable energy based microgrid system sizing and energy management for green buildings

The objective of this paper is to model a hybrid power system for buildings,which is technically feasible and economically optimal.With a view to promote renewable energy sources,photovoltaics and wind turbines are integrated with the grid connected building.The system is modeled and the optimal system configuration is estimated with the help of hybrid optimization model for electric renewables(HOMER).The logic is illustrated with a case study based on the practical data of a building located in southern India.This building is associated with 3.4 MWh/day priority load(peak load as 422 kW),as well as 3.3 MWh/day deferrable load(peak load as 500 kW).Sensitivity analysis is performed to deal with uncertainties such as the increase in electricity consumption and grid tariff,environmental changes,etc.From the simulation result,it is observed that the designed system is cost effective and environment friendly,which leads to 6.18%annual cost savings and reduces CO_(2) emissions by 38.3%.Sensitivity results indicate that the system is optimal and adaptable in a certain range of unanticipated variances with respect to best estimated value.Finally,an energy management strategy is developed for the optimal system to ensure reliable power during contingency and disturbances.The green and hybrid power system designed can be adaptable to any critical and large consumers of urban buildings.