Inspecting Mega Solar Plants through Computer Vision and Drone Technologies

Ahmed, Syed Umaid; Affan, Muhammad; Raza, Muhammad Ilyas; Hashmi, Muhammad Harris

doi:10.1109/fit57066.2022.00014

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…In addition, the current mainstream YOLOv5 deep learning algorithm can detect about 900 photovoltaic panels in one minute [10] , but this is to remove the time of model training, and ignores the impact of hardware, if the model training time is calculated, YOLOv5 can detect about 400 photovoltaic panel images in one minute, the method proposed in this paper does not need to be trained, and the processing time is much more stable, and it can process in one minute 800 PV panel images. The method proposed in this paper is more based on traditional image processing algorithms, combined with their own needs to improve, compared with the current mainstream machine learning methods, the method proposed in this paper is based on the mathematical model and rules of the data, the algorithm can be interpreted, the computational efficiency of the faster and lower consumption of resources, without the need for a large number of samples to be trained for the identification of minor defects of the photovoltaic panels, this paper's proposed method is also more robust.…”

Section: Original Image Resultsmentioning

confidence: 99%

Defect detection of photovoltaic panel based on morphological segmentation

Cheng,

Li,

2024

MIPPR 2023: Automatic Target Recognition and Navigation

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The automatic inspection of photovoltaic panels based on infrared images is one of the important tasks in the daily maintenance of photovoltaic panels in photovoltaic power plants. In this paper, a defect detection method of infrared thermal image photovoltaic panel based on morphological segmentation is proposed. First of all, according to the infrared characteristics of the photovoltaic plant station and the morphological characteristics of the photovoltaic panel, the accurate region of each photovoltaic panel is determined in the inspection image. Then, statistical method is used for feature analysis to determine whether there are defects in photovoltaic panels, and then the defective photovoltaic panels are segmented and different defects are classified according to morphological characteristics, and finally the defect detection and classification of photovoltaic panels are realized. Through the experiment in the actual inspection image of photovoltaic plant station, it is verified that the proposed method has a high accuracy of defect detection and recognition.

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Section: Original Image Resultsmentioning

confidence: 99%

Defect detection of photovoltaic panel based on morphological segmentation

Cheng,

Li,

2024

MIPPR 2023: Automatic Target Recognition and Navigation

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“…Solar is a form of renewable energy that produces energy from solar radiation and temperature via the PV effect [65]. The solar-generated energy depends on the intensity of the sunlight: solar radiation and temperature [66,67].…”

Section: Solar Photovoltaic Modellingmentioning

confidence: 99%

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Alzahrani

Hafeez

Ali³

et al. 2023

Sustainability

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Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

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“…In the near future, smart grids such as wind, solar, biogas, and so on will need to be controlled through artificial intelligence techniques to enhance their efficiency [6,7]. The authors of [8] present a novel approach using the YOLOv5 algorithm and image processing techniques to monitor photovoltaic modules in solar power plants, emphasizing the use of drones and automated detection while suggesting potential enhancements such as thermal infrared integration and robotic cleaning operations. Among renewable energy resources, solar energy has emerged as one of the most important contributors to fulfilling energy demands [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM

et al. 2023

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The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.

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Inspecting Mega Solar Plants through Computer Vision and Drone Technologies

Cited by 10 publications

References 29 publications

Defect detection of photovoltaic panel based on morphological segmentation

Defect detection of photovoltaic panel based on morphological segmentation

Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM

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