Electric Vehicle Charging Load Allocation at Residential Locations Utilizing the Energy Savings Gained by Optimal Network Reconductoring

In this study, a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. During the first stage, the Flower Pollination Algorithm (FPA) was applied to minimize the annual energy losses of the radial distribution system through optimum network reconductoring. A multi-objective function was formulated to minimize investment, peak loss, and annual energy loss costs at different load factors. The results obtained with the flower pollination algorithm were compared with the particle swarm optimization algorithm. In the second stage, a simple heuristic procedure was developed for the sizing and allocation of EV charging load at every node of the distribution system utilizing part of the annual energy savings obtained by optimal network reconductoring. The number of electric cars, electric bikes, and electric scooters that can be charged at every node was computed while maintaining the voltage and branch current constraints. The simulation results were demonstrated on 123 bus and 51 bus radial distribution networks to validate the effectiveness of the proposed methodology

Direct Torque Control of an Induction Motor Using Fractional-Order Sliding Mode Control Technique for Quick Response and Reduced Torque Ripple

The performance of electric drive propulsion systems is often degraded by the high torque and flux ripples of an electric drive. Traditional control methods, such as proportional plus integral (PI) controllers and classical sliding mode controllers (SMCs), have shown good response and reduced torque ripple, but even lower ripple content at low voltage levels is required for its effective use in electric vehicle (EV) applications. In this paper, a new direct torque control (DTC) technique with space vector pulse width modulation (SVPWM) using fractional-order sliding mode control (FOSMC) for a two-level inverter (2LI) at constant switching frequency is proposed. The effectiveness of this proposed controller is compared with a conventional proportional-integral controller and a conventional sliding mode controller (SMC). Simulink models are developed using MATLAB version R2018a to analyze the robustness of the proposed control strategy. Simulation results demonstrate the advantage of the proposed controller in reducing the torque ripples at steady state with less settling time during sudden load change conditions. The proposed control technique also demonstrates better utilization of the stator flux through flux trajectory waveforms

Fault classification of three phase induction motors using Bi-LSTM networks

Abstract The induction motors are back bone of the modern industry and play very important role in manufacturing and transportation sectors. The induction motor faults are mainly classified into internal faults such as inter turn short circuits , broken rotors and external faults such as over load, over voltage faults and asymmetry in supply voltage. The identification of type of fault is very important for safe operation and for preventing risk of machine failures. In this work, a Bidirectional Long Short Term memory networks (Bi-LSTM)-based machine learning methodology is proposed for classification of external faults of Induction Motors. The line voltages of the three phases and the three line currents are considered as the inputs to the Bi-LSTM network for identifying types of fault. Line voltage and line current data sets are considered for six different types of fault conditions. The six different conditions of the three phase induction motor are normal output (NO), overload (OL), over voltage (OV), under voltage (UV), Voltage unbalance (VUB) and single phasing (SP). The BI-LSTM network is trained using Adam optimization algorithm. The classification results are obtained with Bi-LSTM network are compared with LSTM networks to show the advantage of the proposed approach

Pareto optimality based PID controller design for vehicle active suspension system using grasshopper optimization algorithm

Abstract In this paper, a Pareto multiobjective and grasshopper optimization algorithm (GOA) based optimum proportional–integral–derivative (P–I–D) controller design is proposed for improving the vehicle active suspension system dynamics under road disturbance conditions. The Pareto objectives considered are minimization of sprung mass suspension deflection, tyre deflection, sprung mass acceleration minimization and eigenvalue-based objective function. State space model for quarter vehicle active suspension system with P–I–D controller is developed for analyzing the stability and dynamic performance of the system. The sinusoidal-based bump road disturbances are used for testing the robustness of the proposed control technique. Simulation results have been presented to show the advantage of the proposed Pareto multiobjective and GOA-based P–I–D controller over the weighted multiobjective and genetic algorithm-based P–I–D controller in terms of stability and dynamics of the active suspension system