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Due to the increase in the number of electric vehicles (EVs) in the world, there is a need to design superior performance and lower operating costs of charging infrastructures to serve these vehicles. Merging extreme fast charging technology with the portable feature can provide a user-friendly and cost-effective structure for charging EVs. This brief proposes a Smart Extreme Fast Portable Charger (SEFPC) for Electric Vehicles which have several input ports (e.g., the power grid or Renewable Energy Sources (RESs)/Energy Storage Systems (ESSs)) and an output port with an optimal charging operation mode based on considering the condition of the available power sources and battery of EVs for saving the energy and time charging. Moreover, a model-free sliding mode controller-based machine learning algorithm is applied to find optimal charging operation mode based on the battery state and power of sources condition to increase battery life and overall system efficiency. Finally, real-time results based on the OPAL-RT are provided to validate the efficacy and feasibility of the proposed SEFPC and model-free sliding mode controller.

—Traditional marine power grids, using diesel motors/generators, pollute the atmosphere with their CO2 emissions and adverse particles. Nowadays, green energy supplies like solar systems and fuel cells play significant roles in the electrification of marine propulsion plants. In general, marine power systems with green energy units are often considered as a kind of mobile DC microgrids under constant power loads (CPLs). However, since the CPLs impose nonlinearity and negative impedance instability on marine power systems, traditional linear controllers no longer stabilize the system under various disturbances. This motivated this study to develop a non-integer model predictive control for the accurate control of DC/DC converters with CPLs to stabilize the main bus voltage and current in the full-electric ferry ship. In practice, the stability of ship systems is highly threatened due to the negative-independence effect of CPLs. To address the adverse challenges of CPLs, the deep deterministic policy gradient (DDPG) with actor and critic neural networks (NNs) is applied to effectively design the coefficients embedded in the non-integer MPC. In this approach, the actor NN provides the regulatory signals to adjust the noninteger MPC controller while the critic NN is trained to evaluate the quality of actions of actor NN. The experimental results with comparative analysis are carried out to validate the accuracy of the non-integer MPC under the change in the CPL’s power. The results demonstrated that the proposed controller offers a higher level of robustness than the state-of-art-schemes.

Due to the negative impedance potential of constant power loads (CPLs), the stability of power electronic converters-based electrical distribution networks is prone to instability. This brief proposes a robust control technique based on deep reinforcement learning to stabilize a DC microgrid (MG) with parallel boost converters when feeding CPLs. For this purpose, a model-free sliding mode controller (MFSMC) has been applied to the DC MG. The MFSMC controller does not need any identification of converters in the DC MG system while ensuring the efficiency and the stability of the control synthesis. The key control coefficients are designed by Proximal Policy Optimization (PPO) Reinforcement Learning (RL). The PPO is made from two deep neural networks (NNs) (actor NN and critic NN) which are trained to adjust the coefficients of the MFSMC controller. The MFSMC controller designed by the PPO with actor-critic architecture is applied to the DC MG system feeding CPL in an OPAL-RT setup to verify the efficiency of the proposed controller through Hardware-in-the-Loop (HiL) simulations.

For the 5G base transceiver stations (BTSs), the effective stabilization of full-bridge (FB) converters is necessary to supply the connected loads without any interruption. The stability challenges of such technologies are more intensified when the 5G BTS supplies constant power loads (CPL) with negative impedance instabilities. To meet this need, this brief presents an adaptive interval type-3 fuzzy logic system (IT3-FLS) employing deep reinforcement learning (DRL) for the efficient voltage stabilization of 5G-telecom power system (5G-TPS) supplying CPL. The Hardware-in-the-Loop (HiL) examinations are accomplished using an OPAL-RT platform to test the usefulness of the adaptive IT3-FLS from a systematic perspective.

Reducing the order of positive real and bounded real systems has received great interest over the years. Several methods have been published regarding these issues which are able to produce positive real or bounded real reduced models. However, none of them can guarantee that the reduced models are both positive real and bounded real at the same time. In this brief, a novel model reduction scheme is presented which focuses on preserving two possible properties of the system (positive realness and bounded realness) simultaneously. This is done by using one positive real Riccati equation and one bounded real Riccati equation in the reduction procedure. Thereafter, the gramians extracted from these Riccati equations are balanced based on the Balanced Truncation (BT) concept. Finally, a numerical example is provided to investigate the efficacy of the proposed method.

This brief proposes an intelligent and fast controller for the secondary voltage control in the islanded DC microgrids (DCMGs) for shipboard power system applications (SPSs). The existence of constant power loads (CPLs) and propulsion loads in the SPS makes the voltage regulation as an important issue in the islanded DCMG. In order to achieve fast response and proper voltage regulation as well as optimal current sharing in the stand-Alone shipboard DCMG, this brief proposes a new nonlinear droop control in the primary control level. The proposed method eliminates the voltage oscillation in dynamic situations and improves the current sharing among paralleled sources. Furthermore, a novel high bandwidth fractional order general type-2 fuzzy logic proportional-integral-derivative (FOGT2FPID) controller is applied in the dynamic consensus algorithm (DCA)-based method in the secondary control level to ensure fast and accurate voltage regulation in the DCMG. The effectiveness and applicability of the new suggested approach are validated with hardware-in-The-loop (HiL) experimental results.

The negative impedance characteristic of the constant power loads (CPLs) causes instability in DC/DC converters in the DC microgrids. To improve the stability of the DC/DC converter feeding CPLs, a robust and fast controller is required. This paper presents a robust pulse-width modulation-based type-II fuzzy controller for a DC/DC boost converter feeding the CPL in a DC microgrid. Theoretical analysis and real-time simulation are presented to demonstrate the effectiveness of the proposed non-integer intelligent controller. Finally, the experimental results demonstrate that the proposed intelligent controller for the DC/DC converter has a faster and more robust response in comparison to the previously suggested control techniques.

This brief proposes a new scheme for the online minimization of harmonic distortion of an islanded microgrid based on a population-based optimization method. In this scheme, a new central controller is presented, which can optimize network voltage harmonics according to particle swarm optimization (PSO) algorithm, while active power is shared between distributed generation units. In the suggested intelligent controller, the optimal amplitude, and angle of harmonic components are generated by the PSO algorithm and added to the voltage reference generated by the droop controller. The objective functions of the problem are minimizing the harmonic distortion of the critical bus and the total harmonic distortion of the microgrid. Simulation and experimental results show the effectiveness of the proposed method for minimizing harmonic distortion in microgrid networks.

This brief proposes a new optimal intelligent controller for the secondary load frequency control (SLFC) in islanded microgrids (MGs). Particularly, this brief is applied a precise model of the tidal power generation system for the SLFC in the stand-alone MG. Moreover, in order to reduce the cost of energy storage (ES) devices in the MG, vehicle-to-grid (V2G) technology has utilized as a new idea to overcome this difficulty. Hence, a fractional type-2 fuzzy P+ID controller is applied to have a robust balance between generation and demand. Furthermore, it is quite clear that optimal tuning of the controller's parameters can cause a significant effect on the performance of the whole of the system. Consequently, a new heuristic optimization algorithm, entitled sine-cosine, is used to find the best value of the proposed controller's parameters. Finally, by using real-data, the performance and robustness of the suggested controller over other controller techniques have examined by the dSPACE real-time simulator.

The dc/dc full-bridge (FB) converters are often utilized as the interface system in telecom power applications. The constant power loads (CPLs) in the telecom power systems demonstrate negative impedance which threatens the stability of the dc/dc converters. To address this issue, in this letter, a nonlinear controller is designed for the stabilization of the dc/dc full-bridge converter feeding CPLs. The soft actor-critic (SAC) algorithm with deep neural networks is adopted based on deep reinforcement learning (DRL) for optimal tuning of the controller parameters in the control law of an established nonlinear controller. According to the control requirements of the FB interface system, a reward signal is defined to train the neural networks of SAC. An efficient solution based on Hardware-in-the-Loop (HIL) is adopted for verifying and validating the feasibility of the proposed scheme using the OPAL-RT 5600.