In this article, a new ensemble learning model called GATE is proposed to improve the accuracy and stability of time-series forecasting, which is a crucial aspect of modern engineering practices. ...Despite the promise of deep learning (DL) models in this area, their performance can be volatile due to the diversity of time series data. To address this, the GATE model combines the strengths of recurrent neural networks (RNN), long short-term memory network (LSTM), and convolution-LSTM (Conv-LSTM) structures and utilizes an unsupervised learning strategy to steer the ensemble output using a guided network. To prevent overfitting in DL models, GATE optimizes the sample loss function and the weight updating function for each individual model within the ensemble structure. A comprehensive evaluation of the proposed GATE method on four real-world datasets is presented. The experimental results unequivocally demonstrate that GATE surpasses state-of-the-art ensemble methods and individual models, exhibiting the best performance in terms of testing errors. Notably, GATE outperforms existing single models in addressing long-term prediction tasks. To validate the effectiveness of GATE, ablation studies is carried out, comparing different ensemble combinations involving two distinct models. Through systematic analysis, these studies provided valuable insights into the performance of various ensemble configurations, further confirming the effectiveness and superiority of the proposed GATE method.
•RNN, LSTM, and Conv-LSTM blend decodes time series features and prevents overfitting.•The guided network identifies optimal models during training.•Error metrics enhance feature extraction from diverse samples.•Outperforms contemporaries with superior accuracy on 4 real-world datasets.
This study aims to review the potential benefits of peak load shaving in a microgrid system. The relevance of peak shaving for a microgrid system is presented in this research review at the outset to ...justify the peak load shaving efficacy. The prospective benefits of peak shaving in microgrid systems, including technological, economic, and environmental advantages, are thoroughly examined. This review study also presents a cost–benefit numerical analysis to illustrate the economic viability of peak load shaving for a microgrid system. Different peak shaving approaches are briefly discussed, as well as the obstacles of putting them into practice. Finally, this review study reveals some potential future trends and possible directions for peak shaving research in microgrid systems. This review paper lays a strong foundation for identifying the potential benefits of peak shaving in microgrid systems and establishing suitable projects for practical effectuation.
Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power ...system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.
Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the ...energy in the wind that can be correlated to the power coefficient ( C p ) of HAWT system. In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of C p for HAWT. The parameters are tip speed ratio, blade radius, lift to drag ratio, solidity ratio, and chord length. The performance of these three algorithms in obtaining the optimal blade design based on the C p are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the C p of wind turbine blades for investigation of algorithm performance based on the coefficient determination (R2) and root mean square error (RMSE). The optimized blade design parameters are validated with experimental results from the National Renewable Energy Laboratory (NREL). It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. The predicted C p using ANFIS-ABC also outperformed the ANFIS-ACO and ANFIS-PSO. The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms.
Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached ...from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from a photovoltaic system is vital for ensuring economic and uninterrupted power flow. This paper presents an optimal energy distribution technique for a small-scale smart household system to ensure uninterrupted and economical operation. A photovoltaic (PV) system is considered as the primary generation system, and a battery energy storage system (BESS) is viewed as a backup power supply source. The actual load and PV generation data are used to validate the proposed technique collected from the test household system. Two different load profiles and photovoltaic power generation scenarios, namely summer and winter scenarios, are considered for case studies in this research. An actual test household system is designed in MATLAB/Simulink software for analyzing the proposed technique. The result reveals the effectiveness of the proposed technique, which can distribute the generated power and utilize the BESS unit to ensure the optimal operation. An economic analysis is conducted for the household system to determine the economic feasibility. The capital investment of the system can be returned within around 5.67 years, and the net profit of the system is 2.53 times more than the total capital investment of the system. The proposed technique can ensure economical operation, reducing the overall operating cost and ensuring an environment-friendly power system. The developed strategy can be implemented in a small-scale detached interconnected smart household system for practical operation to distribute the generated energy optimally and economically.
The production of maximum wind energy requires controlling various parts of medium to large-scale wind turbines (WTs). This paper presents a robust pitch angle control system for the rated wind ...turbine power at a wide range of simulated wind speeds by means of a proportional–integral–derivative (PID) controller. In addition, ant colony optimization (ACO), particle swarm optimization (PSO), and classical Ziegler–Nichols (Z-N) algorithms have been used for tuning the PID controller parameters to obtain within rated stable output power of WTs from fluctuating wind speeds. The proposed system is simulated under fast wind speed variation, and its results are compared with those of the PID-ZN controller and PID-PSO to verify its effeteness. The proposed approach contains several benefits including simple implementation, as well as tolerance of turbine parameters and several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered a significant advantage of this strategy. Theoretical analyses, as well as simulation results, indicate that the proposed controller can perform better in a wide range of wind speed compared with the PID-ZN and PID-PSO controllers. The WT model and hybrid controllers (PID-ACO and PID-PSO) have been developed in MATLAB/Simulink with validated controller models. The hybrid PID-ACO controller was found to be the most suitable in comparison to the PID-PSO and conventional PID. The root mean square (RMS) error calculated between the desired power and the WT’s output power with PID-ACO is found to be 0.00036, which is the smallest result among the studied controllers.
A novel peak load shaving algorithm has been proposed in this study for peak shaving application in hybrid PV-BESS connected Isolated Microgrid (IMG) system. This algorithm will help an IMG system to ...operate its generation systems optimally and economically along with PV generation unit. An IMG model has been developed in MATLAB/Simulink environment with actual load data, conventional Gas Turbine Generator (GTG), Photovoltaic (PV) generation system and Battery Energy Storage System (BESS). The proposed algorithm has been tested with the designed IMG model. To evaluate the effectiveness of the algorithm, simulation case studies have been conducted with actual load data and actual PV generation data. The simulation results demonstrate that the algorithm can minimize the limitations of the existing methods and it can use the PV generation system effectively. Peak shaving service can be found from this algorithm as a simple and forcible way. A comparative analysis has been conducted of the proposed algorithm with the conventional methods. The comparison results can easily reflect that the proposed algorithm can ensure the optimal use of PV generation system and can serve peak shaving service effectively. It can also ensure an economic and environment friendly system where the existing algorithms are limited to serve these. The proposed algorithm can mitigate the available power issues for hybrid PV-BESS connected system and it can also minimize the operating cost by dispatching the generation units optimally.
•A novel peak shaving algorithm is presented for isolated microgrid system•The proposed algorithm is tested in a microgrid under various load conditions and PV generations.•Result shows that the proposed algorithm can successfully provide peak shaving services.•A comparative analysis and economic benefit analysis reveal the improve and economic performance.•The proposed algorithm shows a robust behavior under the realistic circumstance.
This paper presents the design and execution of a solar tracker system devoted to photovoltaic (PV) conversion panels. The proposed single-axis solar tracker is shifted automatically based on the ...sunlight detector or tracking sensor. This system also removes incident sunlight overlapping from sensors that are inside the sunlight tracking system. The Light Dependent Resistor (LDR) is used as a sensor to sense the intensity of light accurately. The sensors are placed at a certain distance from each other in the tracker system to avoid sunlight overlapping for maximum power production. The total system is designed by using a microcontroller (PIC16F877A) as a brain to control the whole system. The solar panel converts sunlight into electricity. The PV panel is fixed with a vertical axis of the tracker. This microcontroller will compare the data and rotate a solar panel via a stepper motor in the right direction to collect maximum photon energy from sunlight. From the experimental results, it can be determined that the automatic (PV solar tracker) sun tracking system is 72.45% more efficient than fixed panels, where the output power of the fixed panel and automatically adjusted panel are 8.289 watts and 14.287 watts, respectively.
An experimental study was conducted to study the effects of flow uniformity on vibration and power generation of a small vertical axis wind turbine (VAWT). Previous studies have confirmed that one of ...the sources of vibration in the turbine is due to aerodynamic forces, which are due to incident wind. Firstly, understanding vibration is essential before proceeding to the measurements. In this experiment, further understand the vibrations of the turbine in operation, the operating deflection shape (ODS) technique was used. A wind tunnel and flow conditioner were fabricated. Experimental modal analysis (EMA) was conducted, and the dynamic characteristics are gathered. The ODS was conducted for operating the turbine at different speeds, with and without the flow conditioner. Results from EMA and ODS are correlated to explain the behavior of structures. In conclusion, the flow conditioner tested did have a big impact on the response of the structure in terms of vibration up to 30% indifference, but not so much in power generated about 2% indifference.
Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management ...and scheduled wind power generation in wind farm. With this aim, a number of forecasting methods have been proposed in different studies till now, among many soft computing-based approaches are the most successful ones as they offer high accuracy as well as application simplicity. Among them, artificial neural networks (ANN) have drawn a major attention and ANNs can make any complex nonlinear input-output relationship by just learning from datasets given to it regardless any discontinuity and without any extra mathematical model.It is found that past studies used Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) for wind speed forecasting. There have two most uses activation function namely tansig and logsig. The essence of this study is that it compares the effect of activation functions (tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model.On the other hand, blade design of the horizontal axis wind turbine (HAWT) is very significant parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the energy in the wind that can be correlated to the power coefficient ( ) of HAWT system. Several researchers have reported different optimization methods for blade parameters such as Blade Element Momentum theory (BEM), Computational Fluid Dynamics (CFD) and Supervisory Control and Data Acquisition (SCADA) system. There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient ( ). In this study, the performance of these three algorithms in obtaining the optimal blade design based on the are investigated and compared. In addition, ANFIS approach is implemented to predict the of wind turbine blades for investigation of algorithms performance based on Coefficient Determination (R2) and Root Mean Square Error (RMSE).Instead, in order to produce maximum wind energy, controlling of various parts are needed for medium to large scale wind turbines (WT). This study presents robust pitch angle control for the output wind power model in wide range wind speed by proportionalintegral-derivative (PID) controller. In addition, ACO algorithm has been used for optimization of PID controller parameters to obtain within rated smooth output power of WT from fluctuating wind speed. The proposed system is simulated under fast wind speed variation and its results are compared with conventional PID controller and Fuzzy-PID to verify its effeteness. The proposed approach contains several benefits including simple implementation, tolerance of turbine parameter or several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered as a big advantage of this strategy.
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