Ensuring the uninterrupted operation of Grid-Connected Photovoltaic (GCPV) systems is crucial, as these systems are highly susceptible to faults and downtime caused by various factors, which can lead to significant sy...
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Ensuring the uninterrupted operation of Grid-Connected Photovoltaic (GCPV) systems is crucial, as these systems are highly susceptible to faults and downtime caused by various factors, which can lead to significant system damage. To address these challenges, fault detection and diagnosis (FDD) methods are essential to maintain the reliability and safety of GCPV systems. This paper presents a transient regime based on the FDD approach of uncertain GCPV systems, employing deep learning techniques to detect and classify faults effectively. Furthermore, the proposed method takes advantage of the transition phase between healthy and faulty states in renewable energy systems to enable early fault detection by identifying anomalies in performance signals. By combining transient regime analysis with deep learning techniques, the approach facilitates rapid and accurate fault detection, thereby enhancing the reliability and extending the lifespan of photovoltaic systems. To handle uncertainties in the measured data, an interval-valued data representation is utilized, ensuring robust fault analysis under varying conditions. However, the hyperparameters of the proposed techniques are optimized using the Genetic Algorithm, improving their adaptability to diverse operating scenarios. The robustness of the methodology is further validated by introducing varying levels of noise into the data, simulating real-world perturbations and dynamic variations. The processed outputs are used to train deep learning classifiers to distinguish between various operating modes in GCPV systems. Experimental validation with real-world data demonstrates the efficacy and robustness of the proposed approach, enabling immediate decision-making and preventing fault propagation. The results highlight the strategy’s high accuracy and computational efficiency, contributing to improved reliability and safety of GCPV systems.
The European eSafety initiative aims to improve the safety and efficiency of road transport. The main element of eSafety is the pan-European eCall project—an in-vehicle system which idea is to inform about road colli...
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The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking...
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Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magne...
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This paper investigates adaptive event-triggered distributed state estimation problems for jointly detectable linear time-invariant (LTI) systems with unknown inputs over directed communication networks. In order to r...
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With the rapid development of depth sensors, RGB-D data has become much more accessible. Scene flow is one of the fundamental ways to understand the dynamic content in RGB-D image sequences. Traditional approaches est...
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For the stability of 2-dimensional (2D) filters it is required that the denominator polynomial of the transfer function (or the characteristic polynomial of the state-space model) have no zeros in the closed unit bidi...
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ISBN:
(数字)9781728133201
ISBN:
(纸本)9781728133218
For the stability of 2-dimensional (2D) filters it is required that the denominator polynomial of the transfer function (or the characteristic polynomial of the state-space model) have no zeros in the closed unit bidisc. A new algebraic test is presented to test this stability condition using 3 simple 1D conditions and a generalized eigenvalue problem. The approach is based on using the conjugate of the original polynomial together with Tschebyscheff polynomials to transform the problem of stability analysis of a polynomial with coefficients depending on a complex variable to a problem with a polynomial with coefficients depending on a real variable. It is shown that the latter condition can be tested using a generalized eigenvalue problem. The method is illustrated with an example.
The main goal of the project is to assure a collusion-free passage through the intersection in the shortest possible traveling time. Firstly, the order of the incoming vehicle is proposed. Using the mentioned order, a...
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This paper presents SmartPV-AIoT, a cost-effective and edge-computing-based framework for real-time fault detection and environmental monitoring in photovoltaic (PV) systems. Addressing the challenges faced in remote ...
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This paper presents SmartPV-AIoT, a cost-effective and edge-computing-based framework for real-time fault detection and environmental monitoring in photovoltaic (PV) systems. Addressing the challenges faced in remote or intermittently connected environments, the proposed system integrates electrical and environmental sensor data using a Raspberry Pi 4 and NodeMCU microcontroller. A lightweight Res-BiLSTM model, combining residual learning and bidirectional long short-term memory (BiLSTM) layers, is deployed at the edge to perform local fault classification by analyzing seven key parameters derived from I to V curve measurements and ambient conditions. Additionally, the framework employs multi-sensor data fusion by integrating voltage, current, irradiance, and temperature measurements to support comprehensive fault detection and predictive maintenance. The framework was validated on a 4.56 kW PV installation using a dataset of 2400 labeled samples representing normal operation, open-circuit, short-circuit, and partial shading conditions. The classifier achieved a fault detection accuracy of 96.67 %, with a maximum inference latency of 290 ms and low power overhead, making it suitable for real-time, on-site diagnostics. The system supports offline operation with automatic data synchronization upon connectivity restoration and includes mobile-based monitoring for remote accessibility. The proposed framework is designed for modular deployment and scales linearly with array size. Overall, SmartPV-AIoT offers a scalable, resilient, and practical solution for intelligent PV monitoring, particularly in resource-constrained or off-grid environments.
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