Search

This paper addresses the challenge of integrating multiple energy sources into a single-domain microgrid, commonly found in urban buildings, while also providing a platform for energy management. A Lyapunov stability analysis of a simple boost converter was used as a basis for designing the dual control loop of the grid. The versatility of the developed control structure allows for the incorporation of an arbitrary number of sources hence achieving scalability. Next, the energy in the microgrid was separated into exogenous energy and actuator energy. This yielded a description of the system that quantified the condition of stability independent of the decision made by a would-be energy management system. This, in turns, liberates the process of designing an optimized energy management system from stability concerns. The acquired theoretical findings were then translated to a simulation model, where multiple components of the grid were simulated under a typical scenario of operation. Once the simulation phase was concluded, a prototype of the designed grid was constructed to emulate the theoretical results. The prototype exhibited promising performance, matching the simulation predictions to a reasonable degree. [ABSTRACT FROM AUTHOR]

Wind energy is an environmentally friendly renewable resource, widely regarded as one of the most effective means to achieve sustainable development goals. A large number of variable-speed wind turbines (WT) around the world utilize double fed induction generators (DFIG). In the typical configuration of these systems, the stator is connected directly to the grid, while the wound rotor is powered via a bidirectional converter. This DFIG-WT configuration is effective for converting wind power due to several advantages: it operates at various speeds while keeping the stator frequency synchronized with the grid, supports both sub-synchronous and super-synchronous operating modes, and features a power converter that is minimized on the rotor side. Controlling such system presents a challenging engineering problem due to their nonlinear and interconnected dynamic models. The DFIG-WT must operate under severe operating conditions such as stochastic wind variations and parametric changes. Numerous robust controllers have been developed for DFIG-WT, focusing on achieving asymptotic stability in closed-loop control systems to ensure tracking error convergence over infinite time. Ensuring finite-time error convergence is essential in practice. This paper introduces a new adaptive neural network finite-time control approach for a variable-speed DFIG-WT. The main control objective is power extraction maximization while enhancing the wind energy system performance regarding convergence rate, tracking precision, and robustness to uncertainties. The proposed control approach utilizes adaptive neural network systems to handle system uncertainties effectively. The closed-loop control system finite-time stability is thoroughly confirmed and established through rigorous verification using the concept of finite-time Lyapunov stability of nonlinear systems. The effectiveness of the suggested controller is validated in numerical simulation using the Matlab/Simulink software. [ABSTRACT FROM AUTHOR]

This work focuses on the issue of designing a fixed-time fuzzy adaptive controller for variable-speed doubly fed induction generator-based wind turbine. The main goal is to optimize the wind turbine power production, while improving the performance of the overall control system such as, convergence speed, tracking accuracy, and robustness. For the control design, the backstepping technique is adopted, and fuzzy adaptive systems are employed as universal approximators to estimate continuous functional uncertainties. Using fixed–time stability concept, a detailed mathematical stability proof of the closed-loop control system is performed. Simulation results are presented to testify the efficiency of the designed control approach. [ABSTRACT FROM AUTHOR]

In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO 2 , noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%. [ABSTRACT FROM AUTHOR]