This paper presents a novel method to optimally place capacitors in power systems that incorporate renewable energy resources and sensitive non-linear loads, considering system uncertainties. A voltage severity index is introduced to assist system operators in the planning and programming of capacitors. The effectiveness of the proposed method is evaluated on a real large copper mine network in Iran, equipped with sensitive loads to voltage sags and doubly fed induction generators (DFIGs). The results indicate that the control mode of DFIGs and their output power influence the optimal location and size of capacitors. The proposed capacitor placement method enhances voltage profiles, reduces total harmonic distortion, minimizes grid losses, and lowers the costs associated with capacitors. Furthermore, the results underscore the necessity of selecting the appropriate control method based on the system’s operational priorities and conditions. The proposed capacitor placement method successfully reduced financial losses due to voltage sags up to 62%, leading to more secure operation of sensitive loads.
Unmanned Aerial Vehicles (UAVs) have gained significant growth and demand across various applications in recent years. Battery-powered UAVs, however, face challenges due to limited endurance. An innovative alternative solution is a fuel cell hybrid power system, but it requires an efficient energy management system (EMS) to coordinate power distribution to meet load among hybrid sources. The drone application’s capabilities are restricted by its limited computational resources; thus, the development of lightweight computation EMS methods is required. This paper proposes a computationally efficient random forest (RF)-based EMS that is trained on data extracted offline from another optimization-based EMS in order to optimize the utilization of hybrid sources and to coordinate the power distribution at high computational efficiency. Simulation demonstrated the strategy’s efficacy; it reduces the battery state of charge (SOC) deviation, which extends the battery durability, and it also maintains a stable DC bus voltage stability close to its reference value. The strategy successfully reduces hydrogen consumption, showing economical and technical benefits at low computational demand.
Accurate real-time control of three-phase inverters in AC microgrids is challenged by the need to effectively manage multi-variable control while meeting tight sampling instant time limit. Model predictive control (MPC) offers efficient constraint handling, but it often suffers from computational complexity. To address this, this paper implements a random forest (RF)-based controller that is trained offline with extensive historical data extracted from MPC. Thereby reducing computational demands and improving adaptability to varying loading conditions. The proposed RF-based controller is assessed under various operating scenarios of the AC microgrid, including resistive, inductive, and capacitive loading conditions, demonstrating its ability to maintain high-quality sinusoidal output voltage with low total harmonic distortion (THD). The findings prove that this approach not only preserves the advantages of MPC but also significantly enhances computational efficiency, offering a promising solution for modern power electronics applications.
To improve the flight reliability of distributed electric propulsion unmanned aerial vehicle (DEP-UAV), a novel fault-tolerant control strategy for T-type three-level inverters is proposed in this paper, and the weighting factor tuning of finite control set model predictive control (FCS-MPC) is implemented based on long short-term memory network (LSTM). Compared with a conventional fault-tolerant control strategy, the proposed method could effectively solve simultaneous failures of multiple switching devices and quickly adjust the weighting factors under multiple constraints changes to ensure stable system operation. The method’s feasibility has been verified by simulating the operation of DEP-UAV in a full numerical simulation platform.
In many applications of wireless power transfer (WPT), it would be desirable to activate the primary or transmitter side power stage only when the receiver or secondary side is present. This feature is usually achieved by employing some form of active communication or receiver-side modulation to inform the transmitter side controller of the presence of receiver side, which increases the complexity of the WPT system and raises concerns over reliability in noisy environments. In this paper, a communication-less receiver detection scheme based on S-LCC topology is proposed. The proposed receiver detection method for WPT systems is not affected by load condition, and provides a near constant output voltage at receiver side; except for output short-circuit condition, in which case the transmitter side would not be activated in order to protect the system. The proposed system and method are verified experimentally with a hardware prototype that successfully demonstrates receiver detection and short-circuit protection functionality.
