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In recent times, the virtual power plant (VPP) is gaining more attention in power system engineering due to its tremendous potential in enhancing sustainable urbanism, in which, it supplies clean energy from distributed generators. Electricity is deemed a basic requirement for future automotive and ultra-modern technologies. The deficiency of traditional energy resources and their complex generation process make the production cost of electricity increase dramatically. Moreover, traditional power distribution systems are encountering issues in distributing electrical energy to fulfill customer demands. Therefore, this paper proposes a novel power management system named 'the hybrid genetic local binary knowledge (HGLBK) algorithm' to manage power distribution in the transmission lines and to optimize the total operation cost of the network. The hybrid optimization algorithm effectively controls the load by supplying the surplus power load to the adjacent feeders thereby optimally selecting and placing the distributed energy resource (DER). The proposed concept is implemented at Kayathar, Tamil Nadu in India, and their real-time data are utilized for modeling the VPP. The proposed VPP concept is implemented in the IEEE-9 bus system and the performance of VPP is simulated using the MATLAB software. The performance of the proposed HGLBK algorithm is assessed by comparing its effectiveness with the existing approaches. [ABSTRACT FROM AUTHOR]

Significant interest has been shown in switched capacitor (SC)-based multi-level inverters (MLIs), which decrease the need for a DC supply and enhance power quality. The common issues with SC-MLIs include an uneven distribution of conducting paths, increased voltage drop across capacitors, the sum of all inverter DC link voltages across the highest voltage rated switches, and a higher total standing voltage (TSV). The purpose of this paper is to create a SC-MLI with less components in order to maintain a constant voltage across the capacitors, to obtain higher voltage gain with fewer parts, fewer conducting routes, lower TSV, and to create a more affordable and effective inverter. The structure of the MLI is created by a cascade interconnection between the number of SC cells. A single input multiple output (SIMO) converter boosts the DC-link voltage over the stable DC voltage of the solar panels using a modified perturb and observe (P&O) method. Additionally, fewer switches in the conduction path and 50 % of the switches operating at normal frequency guarantee a decrease in an overall loss of power in the proposed network. The benefits of the recommended MLI are made clear by comparing them with 17-level MLIs in terms of the number of elements, stress, gain, and cost factor. Detailed experimental results are shown under various transient conditions to show that the 17-level prototype is operationally viable. The total harmonic distortion (THD) is found to be identical and is less than 5 %, which meets IEEE standards. [ABSTRACT FROM AUTHOR]

In this paper, a novel energy management method that is based on a Multi-Agent System (MAS) is presented for hybrid Distributed Energy Sources (DES) in a microgrid. These DESs include Photovoltaic (PV), wind energy systems, and Fuel Cell (FC) in the Microgrid (MG). The MG is responsible for supplying both active and reactive powers, allowing it to serve variable linear and non-linear loads. The MAS that has been proposed and is based on a decentralized control structure offers control not only for the energy management of the Distributed Generation (DG) but also for the management of power flow between the MG and the power grid that is connected to the MG. This control is offered by the MAS. The main objective of the control strategy is to manage the amount of energy that is transferred between the power grid and the MG concerning the supply conditions of the required internal energy via DES, which will ultimately result in a reduction in the dependence on the MG on the grid. For current harmonic compensation, a Static Compensator (STATCOM) with a Fuzzy Logic (FL) based Instantaneous Reactive Power control scheme is used. On the other hand, a discrete controller is utilized to manage the energy of the MG. The findings of the simulation and the experiments demonstrated that the implementation of the suggested Energy Management System (EMS) has good performance as a novel energy management solution for a hybrid distributed power generating system and harmonic compensation. [ABSTRACT FROM AUTHOR]

The fault diagnosis in motor is substantial as it results in breakdown of production line and the faults may damage motor and results in economic losses. Bearing failure, rotor eccentricity, shaft misalignment and load related faults are the most frequent failures under mechanical fault category. This paper addresses three such faults that may increase the stator temperature namely air gap eccentricity, shaft misalignment and cooling system failure. The thermography technique has been used widely for fault detection in induction motor. In three phase induction motor the thermal images are analyzed for healthy condition and the above mentioned faults conditions. This paper presents thermal pixels counting algorithm to calculate the diagnosis indicators and adaptive neuro-fuzzy inference system classifier is used to classify the faults based on the diagnosis indicator data base. Laboratory based experimental investigation are carried out to verify the accuracy of the proposed method. This method provides accurate diagnosis indicator that will be used as a bench mark value for preparing the maintenance schedule under non-destructive mode. [ABSTRACT FROM AUTHOR]