The recent advances in power plants and energy resources have extended the applications of buck-boost converters in the context of DC micro-grids (MGs). However, the implementation of such interface systems in the MG applications is seriously threatened with instability issues imposed by the constant power loads (CPLs). The objective is that without the accurate modeling information of a DC MG system, develop a new adaptive control methodology for voltage stabilization of the DC-DC converters feeding CPLs with low ripples. To achieve this goal, in this letter, the deep reinforcement learning (DRL) technique with the Actor-Critic architecture is incorporated into an ultra-local model (ULM) control scheme to address the de-stabilization effect of the CPLs under the reference voltage variations. In the suggested control approach, the feedback controller gains of the ULM controller are considered as the adjustable controller coefficients which will be adaptively designed by the DRL technique through online learning of its neural networks (NNs). It is proved that the suggested scheme will ensure the rigorous stability of the power electronic system, for simultaneous effects of CPL and reference voltage changes, by adaptively adjusting the ULM controller gains. To appraise the merits and usefulness of the suggested adaptive methodology, some dSPACE MicroLabBox outcomes on a real-time testbed of the DC-DC converter feeding a CPL are presented.

The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online adaptive mechanism based on the Reinforcement-Learning (RL) strategy. Classical PI controllers are simple and easy to build, but they need to be more robust against a wide range of disturbances and more adaptable to operational parameters. To address these issues, the RL adaptive strategy is used to optimize the performance of the PI controller. Some of the main advantages of the RL are lower sensitivity to error, more reliable results through the collection of data from the environment, an ideal model behavior within a specific context, and better frequency matching in real-time applications. Random exploration, nevertheless, can result in disastrous outcomes and surprising performance in real-world settings. Therefore, we opt for the Deterministic Policy Gradient (DPG) technique, which employs a deterministic action function as opposed to a stochastic one. DPG combines the benefits of actor-critics, deep Q-networks, and the deterministic policy gradient method. In addition, this method adopts the Snake Optimization (SO) algorithm to optimize the initial condition of gains, yielding more reliable results with faster dynamics. The SO method is known for its disciplined and nature-inspired approach, which results in faster decision-making and greater accuracy compared to other optimization algorithms. A structure using a hardware setup with CONTROLLINO MAXI Automation is built, which offers a more cost-effective and precise measurement method. Finally, the results achieved by simulations and experiments demonstrate the robustness of this approach.

Today power converters, especially DC/DC converters, is of great importance in power electronics applications such as DC micro-grids (MGs). However, they have some limitation such as inability to handle constant power load (CPL) which results in instability problems in MGs. Thus, a controller with specific characters including, robustness and fast response to system dynamic is vital to address the unsteadiness. In this paper, an adaptive model prediction controller (AMPC) based on Deep Reinforcement Learning (DRL) is developed to tackle the de-stabilization problem. In the proposed AMPC controller, the controlling signal coefficient in each variable operation point is regarded as the adjustable controller parameter and adaptively designed by the learning ability of the Deep Q- Network (DQN) strategy, leading to a robust controlling approach. We have shown that our suggested smart controller for DC/DC converters feeding CPLs is robust and fast in dynamic response.

Nowadays the use of power converters, in particular, DC/DC converters in a large number of power electronics applications such as DC micro-grids (MGs) is an interesting subject in power systems. However, the use of such converters in MGs results in instability problems inflicted by the constant power loads (CPLs) because of their negative impedance feature. Thus, a controller with specific characters including, robustness and fast response to system dynamics to address unsteadiness issues and better performance of DC/DC converters feeding CPLs is vital. In this paper, a model prediction control (MPC) approach is introduced to tackle the de-stabilization problem and mitigate the destructive effect of CPLs, leading to a robust control approach as well as enlarging the system stability margin. To demonstrate and verify the usefulness of the proposed scheme in a real-time setup, the performance of the MPC controller applied to the DC/DC buck-boost converter feeding CPL is examined in a Model-In-the Loop (MiL) environment.

Today, regarding the increase in using renewable energy resources, DC/DC boost converters have become more popular than in the past, in particular, the multilevel DC/DC boost converter due to its high voltage gain and simple components to implement. However, the constant power loads (CPLs) are contributed to instability in these converters because of the intense non-linear nature of CPLs. This paper intends to address this issue by applying a new method based on a combination of the Nonlinear Integral-Backstepping technique (NIB) and Model-Free Control (MFC) to a DC/DC multilevel boost converter (MBC) in presence of a non-ideal CPL. To validate the effectiveness of the proposed controller theoretical analysis and simulation results are represented.

Controlling voltage, frequency, and current in an islanded microgrid is a challenging problem because the distributed generation sources, stochastic and intermittent in nature, are not connected to the main electricity network to provide stable and clean energy. Therefore, the design of a robust controller to control the output parameters of the islanded microgrid and suppress load variations and disturbances is essential. In this paper, a hysteresis controller is proposed and designed to control the output voltage of an islanded AC microgrid and an improved sliding mode controller (SMC) based on adaptive control principle is designed to control the current of the microgrid. The current controller consists of two parts: An adaptation part, which aims to eliminate disturbances and system uncertainties, and a second part, which aims to deal with the tracking problem of the system under parameter-varying topologies. The adaptation strategy has the advantage of solving the gain tuning problem and chattering reduction. It also requires limited information about disturbance and uncertainties of the system. To validate the proposed control methodology and show its effectiveness, a case study of a simulated islanded microgrid is presented. The results show that the proposed controllers can effectively control the current and voltage underload changes and increase the stability and resilience of the microgrid. The results also reveal that the performance of the proposed controller in terms of total harmonic distortion (THD) and dynamic response overcome the performance of conventional controller by a 4× reduction in THD and 40–200× reduction in settling time.