Exploiting Solar Energy during an Aerial Mapping Mission on a Lightweight UAV

Hrovatin, Dejan; Žemva, A.

doi:10.3390/electronics10222876

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Another option is virtual storage, which is also a low-cost solution for realtime applications. For example, Bramor UAVs utilize a high-speed Internet protocol (IP) link for transmitting and storing data to its computing infrastructure, cloud computing, or ground control station (GCS) [59]. To overcome the routing and strong Internet connections, researchers are using wireless networking technologies such as the Fifth Generation (5G) and smart Bluetooth [60].…”

Section: Evolving Techniques That Extend the Capacity Of Uav Componen...mentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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Section: Evolving Techniques That Extend the Capacity Of Uav Componen...mentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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“…Renewable energies play a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation [5]. Solar energy can be harvested through a Photovoltaic (PV) panel [6], and turned into electrical energy. PV panels are an arrangement of doped solar cells that generate an electron flow when the photons of solar light hit the surface [7].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

et al. 2023

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Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained.

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“…In [11], the artificial bee colony optimization algorithm was implemented for the MPPT operation of a hybrid PV/wind power generation plant. In [12], a perturb and observe (P&O) algorithm was used to obtain the MPPT of solar PV. In [13], an advanced multi-sector P&O-based MPPT scheme was developed for a grid-connected wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Global Sliding-Mode Control with Fractional-Order Terms for the Robust Optimal Operation of a Hybrid Renewable Microgrid with Battery Energy Storage

Maaruf

Khalid

2021

Electronics

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The efficiency of hybrid microgrid systems is drastically affected by the number of power electronics converters interfacing with its components. Integrating distributed energy sources with microgrids with the optimal number of converters is crucial to minimizing the switching losses and power conversion stages, thereby improving the efficiency of the systems. This paper considers an efficient and economical configuration for a wind/solar photovoltaic (PV) system integrated with a battery energy storage system (BES). The PV system is connected directly to the DC-link, thus lowering the losses and cost by eliminating the PV boost converter. In the literature, only a few publications have investigated this effective microgrid configuration. In addition, none of the publications have developed a nonlinear control approach for the microgrid configuration. Due to the greater flexibility of fractional calculus in speeding up the system response and improving the robustness, this article proposes a global sliding-mode control method with fractional-order terms (GSMCFO) to enhance the transient, steady-state, and robust operation of the hybrid microgrid. This controller provides the maximum power point tracking (MPPT) of both the solar PV and wind power generators, regulates the DC-link voltage, ensures proper power transfer to the grid, and maintains the power balance. In addition, the GSMCFO guarantees the global stability of the hybrid microgrid. Furthermore, considering the simplicity, robustness, few control variables, and fast convergence rate of the differential evolution (DE) optimization method, it is utilized to optimize the performance of the GSMCFO. The proposed hybrid microgrid configuration under the action of the GSMCFO was simulated in MATLAB/SIMULINK. Various scenarios were investigated to illustrate the feasibility of the proposed scheme. The simulation results show that the GSMCFO can achieve superior dynamic performances than the proportional–integral (PI) controller with zero overshoot, a shorter settling time, and stronger robustness, thus improving the power balance of the hybrid microgrid.

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Exploiting Solar Energy during an Aerial Mapping Mission on a Lightweight UAV

Cited by 5 publications

References 21 publications

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Global Sliding-Mode Control with Fractional-Order Terms for the Robust Optimal Operation of a Hybrid Renewable Microgrid with Battery Energy Storage

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