• Energy Research

This paper presents a novel adaptive neuro-fuzzy controller applies on transverse flux linear motor for controlling its speed. The proposed controller presents fuzzy logic controller with self tuning scaling factors based on artificial neural network structure. It has two input variables and one control output variable. Firstly the fuzzy logic control rules are described then NN architecture is represented to self tune the output scaling factors of the controller. The application of this control technique represents the novelty of work, where this algorithm has so far not been stated before for this type of drives. This methodology solves the problem of nonlinearities and load changes of TFLM drives. The dynamic response of the motor is studied under the rated load condition as well as load disturbances. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. The dynamic response of the motor with the proposed controller is compared with PI and adaptive NN controllers. It is found that the proposed controller gives better and faster response from the viewpoint of overshoot and settling time. Matlab/Simulink tool is used for this dynamic simulation study.

For the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This negatively affects the microgrid dynamics under uncertainties, lowering the microgrid frequency stability, specifically in the islanded mode of operation. Therefore, this work aims to enhance the islanded microgrid frequency resilience using the virtual inertia frequency control concept. Additionally, optimal model predictive control (MPC) is employed in the virtual inertial control model. The optimum design of the MPC is attained using an optimization algorithm, the African Vultures Optimization Algorithm (AVOA). To certify the efficacy of the proposed controller, the AVOA-based MPC is compared with a conventional proportional–integral (PI) controller that is optimally designed using various optimization techniques. The actual data of RES is utilized, and a random load power pattern is applied to achieve practical simulation outcomes. Additionally, the microgrid paradigm contains battery energy storage (BES) units for enhancing the islanded microgrid transient stability. The simulation findings show the effectiveness of AVOA-based MPC in improving the microgrid frequency resilience. Furthermore, the results secure the role of BES in improving transient responses in the time domain simulations. The simulation outcomes are obtained using MATLAB software.

In simulation studies of photovoltaic (PV) systems with power electronic converters, the simulation results are affected by the accuracy of the PV model. The maximum power point tracking, transient and dynamic analysis of the PV systems, and operation of microgrids systems are examples of these simulation studies. The mathematical model of the PV module is a nonlinear $I-V$ characteristic that includes several unknown parameters because of the limited information provided by the PV manufacturers. This paper presents a novel approach using the shuffled frog leaping algorithm (SFLA) to determine the unknown parameters of the single diode PV model. The validity of the proposed PV model is verified by the simulation results which are performed under different environmental conditions. The simulation results are compared with the experimental results of different PV modules such as Kyocera KC200GT and Solarex MSX-60. The effectiveness of the proposed PV model is evaluated by comparing the absolute error of the model with respect to the experimental results with that of other PV models. With the application of the SFLA technology, an accurate PV model can be achieved.

This article utilizes a Fractional Order Proportional-Integral-Derivative (FOPID) controller for voltage regulation of a Proton Exchange Membrane fuel cell (PEMFC) in DC Microgrids. The PEMFC is considered a promising candidate for integration into DC Microgrids. However, maintaining a stable and efficient operation requires precise voltage control, especially under varying load conditions and inherent nonlinearities. The FOPID controller tuned by artificial rabbits optimization algorithm (FOPID-ARO) is recommended to address this challenge, which extends the conventional PID controller by introducing two additional parameters: the fractional orders of the derivative and integral actions. This enhancement allows for a more flexible control strategy that is capable of handling the complex dynamics of PEMFCs more effectively than traditional PID controllers. The suggested controller effectiveness is assessed under different operational scenarios, such as load and solar irradiance variations, and compared with a PID controller tuned by the ARO algorithm (PID-ARO) and an FOPID controller tuned by the jellyfish search algorithm and grey wolf optimizer. Moreover, actual data on solar irradiance are considered. The findings indicate that the FOPID-ARO controller performs better than the PID-ARO controller in terms of dynamic response and minimizes steady-state error more effectively.

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand.