Search

Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development. [ABSTRACT FROM AUTHOR]

This study presents a novel approach that employs a mixture of the tunable-Q wavelet transform (TQWT) and enhanced AdaBoost to address the issue of high impedance fault (HIF) recognition in power distribution networks. Traditional overcurrent protection relays frequently have lower fault current levels than normal current, making it exceedingly difficult to detect this HIF problem with the necessity to use a quick and effective approach to find HIF problems. Since the TQWT performs better with signals that exhibit oscillatory behavior, it has been utilized to extract special features for the training of the improved AdaBoost model. The procedure is accelerated by calculating the Kourtosis (K) value for each level and selecting the ideal level of decomposition to minimize computing work. Faulted zones are categorized using an enhanced AdaBoost approach. Under normal, noisy, and unbalanced conditions, the recommended approach is applied to an imbalanced 123-bus test system and an IEEE 33-bus test system. The efficiency of the recommended method is also being assessed for imbalanced distribution networks incorporating dispersed generation into real-time platforms. This procedure is quick compared to previous methods since it uses an upgraded AdaBoost classifier and optimal decomposition level. [ABSTRACT FROM AUTHOR]

High impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of "Dual Tree Complex Wavelet Transform (DTCWT)," which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation. [ABSTRACT FROM AUTHOR]

Bacterial contamination of water and food is a grave health concern rendering humans quite vulnerable to disease(s), and proving, at times, fatal too. Exploration of the novel diagnostic tools is, accordingly, highly called for to ensure rapid detection of the pathogenic bacteria, particularly Escherichia coli. The current manuscript, accordingly, reports the use of silane-functionalized glass matrices and antibody-conjugated cadmium telluride (CdTe) quantum dots (QDs) for efficient detection of E. coli. Synthesis of QDs (size: 5.4–6.8 nm) using mercaptopropionic acid (MPA) stabilizer yielded stable photoluminescence (∼62%), corroborating superior fluorescent characteristics. A test sample, when added to antibody-conjugated matrices, followed by antibody-conjugated CdTe-MPA QDs, formed a pathogen-antibody QDs complex. The latter, during confocal microscopy, demonstrated rapid detection of the selectively captured pathogenic bacteria (10 microorganism cells/10 μL) with enhanced sensitivity and specificity. The work, overall, encompasses establishment and design of an innovative detection platform in microbial diagnostics for rapid capturing of pathogens in water and food samples. HIGHLIGHTS Antibody-mediated fluorescent biosensor for efficient capturing and detection of E. coli.; Bioconjugation of QDs and E. coli antibodies.; Qualitative, quantitative and selective assessment of E. coli in contaminated water using CLSM.; High sensitivity of glass matrix biosensor, with detection limit of 10 microorganisms/10 mL.; Capability of technology extension for detecting other microbial pathogens.;