Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers

Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers.

Informational aspects at model of power consumption by main drainage facilities of iron-ore mining enterprises

The work investigates into variable informational approaches to modeling power consumption by main drainage facilities of ore mining enterprises with underground mining method. Methodological recommendations for using the models are also designed. The research deals with general methodological approaches to model formation with both power consumption indices for drainage facilities and corresponding costs. Logistics of model formation is substantiated, namely, combination of classic multifactor regression modeling with modern digital modeling methods – automated control systems used for drainage facilities. Principles of building fuzzy logic controllers and algorithms of their functioning under multichannel control are determined in detail. The improved fuzzy logic-based variant is proposed and combined, with correlation analysis, to provide the basis for developing algorithms of the automated control systems of electric power consumption. There is an example of developing a “road map” for implementing a generalized algorithm for automated control systems power flows for two current cases – a selective tariff with limited daily contract-based power consumption and that with a variable tariff. It is established that application of the two-rate hourly tariff with its conditional distribution (Night/Peak) instead of the three-rate tariff (Night/Half-Peak/Peak) on a single-use basis leads to a thirteen percent increase of daily power costs with a single-channel control of the ore flow and a seven percent increase with two-channel control (ore flow and drainage simultaneously). The use of fuzzy logic controllers enables minimizing these losses.

Closed Loop V/f Speed Control of Multilevel Inverter Fed Induction Motors Using PID and Fuzzy Logic Controllers

The Volts per Hertz (V/f) speed control method makes induction motors available to be used in variable speed applications. However, the great harmonic distortion in supply voltage is a major issue of this method. Therefore, this study focuses on decreasing the harmonic distortion in the supply to induction motors. For that, the use of thirty-one-level multilevel inverters (MLIs) is investigated. Another drawback of the V/f speed control method is the use of proportional-integral-derivative (PID) controllers as speed controllers, which are difficult to be tuned precisely. To overcome this, the use of fuzzy logic controllers is investigated. The whole model is developed and simulated in MATLAB/SIMULINK by varying two variables, i.e., reference speed and load torque, one at a time using each controller. The observations for the changes in speed, torque, stator current, and supply voltage to the induction motors are made. The system’s performance using fuzzy logic controllers is found superior.

Energy, economic and environmental analysis of fuzzy logic controllers used in smart buildings

This article is divided into three parts: the first presents a simulation study of the effect of occupancy level on energy usage pattern of Engineering building of Applied Science Private university, Amman, Jordan. The simulation was created on simulation mechanism by means of EnergyPlus software and improved by using the building’s data such as building’s as built plan, occupant’s density level based on data about who utilize the building throughout operational hours, energy usage level, Heating Ventilating and air conditioning (HVAC) system, lighting and its control systems and etc. Data regarding occupancy density level estimation is used to provide the proposed controller with random number of users grounded on report were arranged by the university’s facilities operational team. The other division of this paper shows the estimated saved energy by the means of suggested advanced add-on, FUZZY-PID controlling system. The energy savings were divided into summer savings and winter savings. The third division presents economic and environmental analysis of the proposed advanced fuzzy logic controllers of smart buildings in Subtropical Jordan. The economic parameters that were used to evaluate the system economy performance are life-cycle analysis, present worth factor and system payback period. The system economic analysis was done using MATLAB software