MATLAB Simulation of V2G and G2V operation with Three Phase Grid | LMS Solution Inter
In MATLAB, simulating Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations with a Three-Phase Grid involves modeling the interactions between electric vehicles (EVs), the power grid, and charging infrastructure. V2G enables EVs to discharge stored energy back to the grid during peak demand periods, while G2V allows EVs to charge from the grid when electricity demand is low. The simulation typically involves modeling the behavior of EV batteries, charging stations, power converters, and grid infrastructure. MATLAB provides tools such as Simulink for modeling the dynamic behavior of these components and simulating their interactions under different scenarios, including varying grid conditions, EV charging patterns, and grid load profiles. By simulating V2G and G2V operations in MATLAB, researchers and engineers can assess the impact of EV integration on grid stability, energy efficiency, and economic viability, helping to optimize grid management strategies and support the transition to sustainable transportation systems.
Power Harmonic Passive Filters | LMS Solution
Passive harmonic filters are series filters (which means that current goes through the filter) that are used to reduce or mitigate, harmonics to tolerable levels as defined by IEEE-519.Passive filters use the following items to ‘filter’ (or clean) the current wave: Input reactor Output reactor Shunt reactor Capacitor A passive filter consists of a series circuit of reactors and capacitors. Harmonic currents generated by, for example, a frequency converter are shunted by this circuit designed to have low impedance at a given frequency compared with the rest of the network. Among several harmonics solutions, a passive filter is a very common and effective mitigation method. Passive filters are designed to provide a low impedance shunt path for harmonic currents. In this way, the harmonic currents are deflected to the ground. Another function of passive filters is to suppress the flow of harmonic currents between parts of the system by tuning the passive elements to create resonance at a single frequency or a band of frequencies.Passive harmonic filters generally consist of capacitors, inductors, and resistors. An array of these elements is arranged in one or more shunt arms to form different topologies. Among popular topologies of passive filters are single tuned, second-order, third-order, and C-type filters.
PV System With Battery Storage Using Bidirectional DC-DC Converter | LMS Solution
PV (Photovoltaic) systems are one of the most renowned renewable, green and clean sources of energy where power is generated from sunlight converting into electricity by the use of PV solar cells. Unlike fossil fuels, solar energy has great environmental advantages as they have no harmful emissions during power generation. In this work, a PV system with battery storage using a bidirectional DC-DC converter has been designed and simulated on MATLAB Simulink. The simulation outcomes verify the PV system‘s performance under standard testing conditions.
Fault detection classification and location in power system using ANFIS | LMS Solution Inter
Fault detection classification and location in power system using ANFIS This work explains ANFIS-based fault detection, classification, and location finding in the transmission or distribution line of the power system.
Hybrid PO-PSO MPPT for solar PV system | LMS Solution
Conventional maximum power point tracking (MPPT) methods such as the perturb-and-observe (P&O) method can only track the first local maximum point and stop progressing to the next maximum point. MPPT methods based on particle swarm optimization (PSO) have been proposed to track the global maximum point (GMP). However, the problem with the PSO method is that the time required for convergence may be long if the range of the search space is large. This work proposes a hybrid method, which combines P&O and PSO methods. Both the P&O method and PSO method work parallelly to find the GMP. The advantage of using the proposed hybrid method is that the search space for the PSO is reduced, and hence, the time that is required for convergence can be greatly improved. The excellent performance of the proposed hybrid method is verified by comparing it against the Conventional P&O, and PSO methods using MATLAB Simulation.
Demand-side management in Grid-Connected Battery System using Neural Network | LMS Solution
Demand-side management in Grid-Connected Battery System using Neural Network A novel energy management system to improve the efficiency of grid-connected energy storage systems using a deep neural network is developed. The high penetration of renewable energy and decentralization of the grid has led to an increase in the instability of the grid. To reduce this instability, a balance between the consumption demand and production rate needs to be maintained. For this objective, electric vehicle batteries can be integrated with demand-side management techniques using a deep neural network. The controller can be programmed with the timing of the peak and the off-peak hours obtained from the demand curve data and state of charge of the battery. The controller will take two inputs: The time of the day and the State of Charge of the battery. The NN controller will detect the arrival of the peak and will send a message to the EV battery to supply a programmed percentage of power to the household appliances. The direct communication between the grid and the battery can be eliminated to reduce the infrastructure requirements and data processing. The grid can operate successfully during normal working hours and can supply the total power consumption by the loads at any time of the day. The peak to average power ratio can be reduced by operating the EV battery during peak hours for providing that programmed percentage to the appliances for better grid operation. This drained battery will be further fully charging during low loading of the grid and keep ready for the following days’ operation. According to the results of simulation studies, it is demonstrated that our proposed model not only enhances users’ utility but also reduces energy consumption costs.
Photovoltaic battery charger based on the Zeta converter | LMS Solution
In this work, a complete analysis of the Zeta converter applied as a photovoltaic battery charger is carried out. The design methodology of the solar battery charger system, including the power circuit main devices and system controllers, are presented in detail. The solar battery charger includes a Constant Voltage (CV) charging method with an inherent Perturb and Observe (P&O) maximum power point tracking algorithm. This way, the battery pack is properly charged as well as the maximum power is harvest from the solar module. PV Panel: 2000 watts, 123.6 V, 16.18 A (Standard test condition)Battery: Lead-acid battery 48 V, 200AhMPPT: P&OCharging: Constant VoltageEfficiency: 97.1 %MATLAB: 2017b Version
PSO Trained ANFIS MPPT for Solar PV system | LMS Solution
In this Work, an ANFIS technique using experimental data is designed for predicting the maximum power point of a photovoltaic array. An ANFIS model training strategy is challenging due to the variations in the training and the operation conditions of a photovoltaic system. In order to improve ANFIS model accuracy, the Particle Swarm Optimisation (PSO) algorithm is utilized to find the best topology and to calculate the optimum initial weights of the ANFIS model. Hence, the dilemma between computational time and the best-fitting regression of the ANFIS model is addressed, as well as the mean squared error being minimized. To evaluate the proposed method, a MATLAB/Simulink model for an installed photovoltaic system is developed. The results show that the optimized feedforward ANFIS technique based on the PSO algorithm using real data predicts the maximum power point accurately.
PSO Trained Neural Network MPPT for Solar PV system | LMS Solution
In this Work, a feed-forward Artificial Neural Network (ANN) technique using experimental data is designed for predicting the maximum power point of a photovoltaic array. An ANN model training strategy is challenging due to the variations in the training and the operation conditions of a photovoltaic system. In order to improve ANN model accuracy, the Particle Swarm Optimisation (PSO) algorithm is utilized to find the best topology and to calculate the optimum initial weights of the ANN model. Hence, the dilemma between computational time and the best-fitting regression of the ANN model is addressed, as well as the mean squared error being minimized. To evaluate the proposed method, a MATLAB/Simulink model for an installed photovoltaic system is developed. The results show that the optimized feedforward ANN technique based on the PSO algorithm using real data predicts the maximum power point accurately.
MATLAB Implementation of ANFIS Based Fault Classification Location and Detection in Power System
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