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There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.

Agenesis and hypoplasia affecting multiple flexor tendons within the same hand represent an exceedingly uncommon occurrence, with no previous studies addressing this condition. This report details a 4-year-old girl with agenesis of the right third and fourth fingers, who sought consultation due to the inability to flex her seemingly unaffected second and fifth fingers. Ultrasound examination revealed substantial thinning of the flexor tendons in the second to fifth digits, with a notable absence of attachment to the middle phalanx. In addition to flexor tendon hypoplasia, hypoplasia of the third and fourth middle phalanges was observed. Hand deformities featuring both finger agenesis and flexor tendon hypoplasia across multiple fingers were exceptionally rare. In such instances, ultrasound, in conjunction with radiography, emerges as the recommended initial imaging tool for comprehensive evaluation of both the phalangeal bones and flexor tendons.

This study presents a voltage restoration control (VRC) based on battery energy storage system (BESS), which can be used for both supporting power source and voltage compensation. Voltage restoration is an important task for the power control of microgrid during utility disturbances. One of the disturbances is caused by short circuit on power line of the microgrid, which may lead to voltage sag and even blackout of the microgrid system. To tackle this problem, the recurrent wavelet petri fuzzy neural network (RWPFNN) controller is proposed in this study for the VRC of BESS to provide fast control response to mitigate the transient impact. Moreover, to examine the compliance with the requirements of low voltage ride through (LVRT) of the photovoltaic (PV) plant and investigate the performance of the proposed VRC, the microgrid built in Cimei Island in Penghu Archipelago, Taiwan, is investigated. Furthermore, the PV system, the wind turbine generator (WTG) system and the BESS are connected to the same point of common coupling (PCC) with separated step-up transformers in the microgrid. In addition, the diesel generators provide the main power sources and form the isolated microgrid system. Through the hardware in the loop (HIL) mechanism, which is built using OPAL-RT real-time simulator, with two floating-point digital signal processors (DSPs), the effectiveness of proposed intelligent controllers can be verified and demonstrated.

In a low-voltage electric distribution network, arc fault presents a high energy density electricity-discharging phenomenon between conductors, which is often caused by aging of electric facilities, loose contacts and terminals, or insulation failure due to internal and external destructions. A large amount of heat may be created during this discharging, which will further cause the risk of fire hazards to mitigate in the residential environment. Currently, many utility grid operators and electricity users are still devoted to seeking effective detection technology for arc fault protection. This paper proposes a hybrid approach that combines discrete wavelet transform (DWT), empirical mode decomposition (EMD), and dynamic time warping (DTW) methods for low-voltage AC series arc fault detection. In DWT, it uses time–frequency domain characteristics of the arc current signal to extract the occurrence of arc fault. In EMD, it decomposes the complex arc fault current signal into a finite intrinsic mode (IMF) signal; then, instantaneous amplitude of IMF signal is obtained by Hilbert–Huang transform (HHT) as a feature for arc fault identification. Firstly, the results of arc fault detections depend on the results from DWT and EMD. When both two methods detect different results, DTW method will be activated, using the similarity measurements between normal and arc fault current waveforms as an assistant measure to determine the occurrence of arc fault. The performance of the proposed approach is tested and validated using various electric appliances, and the results show that the proposed approach can effectively detect low-voltage AC series arc fault.

The usage of a shunt active power filter (SAPF) is one of the helpful means to mitigate the reactive power and harmonic current of a power grid. The compensation performance of the SAPF is related to the accuracy of the reference voltage extraction from the utility grid, the control stability of the DC-link voltage regulation, and the synchronization between the source voltage and the reference compensation current. To modify the performance of the SAPF for the harmonic compensation, the control strategy of the SAPF reference compensation current based on the recurrent wavelet fuzzy neural network (RWFNN) is proposed in this paper. There are three sections in the proposed control strategy, including the regulated fundamental positive-sequence extraction (section A), DC-link voltage regulation (section B), and calculation of reference compensation current (section C). By regulating the analysis mechanism with the variation of fundamental frequency in the section A, the accurate reference voltage would be obtained. The control stability for the regulation of the DC-link voltage can be accomplished by applying the RWFNN-based controller in the section B. With the synchronized reference voltage in the section A and the estimated control current in the section B, the reference compensation current can be correctly obtained in the section C. From the case studies with the real-time simulator produced by OPAL-RT Technologies Inc., the effectiveness of proposed control strategy for the SAPF reference compensation current can be verified.

To realise the resonant characteristics of motor drive system, a wavelet multiresolution filter (WMF)-based scheme is proposed in this study to perform the detection of the mechanical resonant frequencies for interior permanent magnet synchronous motor servo drives. In a conventional motor drive system, the compliance in the coupling between motor and load may cause resonance. Moreover, the parasitic torque ripples resulting from the harmonics of a pulse-width modulation inverter may excite mechanical vibrations. In the proposed scheme, the localisation analysis of signal can be completed with the WMF at any time and frequency domains for the extraction of resonant frequencies. Then, a band-pass filter is applied to perform the frequency sweeping for the detection of resonant frequencies. In the final stage, the signal smoothing is implemented with the calculation of absolute and average values. To verify the effectiveness of the proposed resonant-frequency detection scheme using the WMF, some experimental results at different rotor speed and load conditions are provided. In addition, a two-channel dynamic signal analyser is also adopted in the experimentation for the comparison of resonant-frequency detection.

Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.

Dynamic voltage restorers (DVRs) are one of the effective solutions to regulate the voltage of power systems and protect sensitive loads against voltage disturbances, such as voltage sags, voltage fluctuations, et cetera. The performance of voltage compensation with DVRs relies on the robustness to the power quality disturbances and rapid detection of voltage disturbances. In this paper, the recurrent wavelet fuzzy neural network (RWFNN)-based controller for the DVR is developed. With positive-sequence voltage analysis, the reference signal for the DVR compensation can be accurately obtained. In order to enhance the response time for the DVR controller, the RWFNN is introduced due to the merits of rapid convergence and superior dynamic modeling behavior. From the experimental results with the OPAL-RT real-time simulator (OP4510, OPAL-RT Technologies Inc., Montreal, Quebec, Canada), the effectiveness of proposed controller can be verified.

To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.

The shunt active power filter (SAPF) is an effective means for the modification of power quality. However, the compensation performance of SAPF would be deteriorated when the unbalanced nonlinear loads are present in the power system. To enhance the compensation performance of SAPF, the adaptive frequency-based reference compensation current control strategy is proposed in this paper. The proposed solution procedure can be divided into three stages including adaptive frequency detection, phase synchronization, and adaptive compensation. With the tracking of power system frequency, the phase synchronization for the SAPF compensation can be effectively modified under the power variation of unbalanced nonlinear loads. Based on the correct synchronization phase angle, the reference compensation current of SAPF can be accurately obtained with the adaptive linear neural network (ALNN) in the stage of adaptive compensation. In addition, the direct current (DC)-link voltage of SAPF can also be effectively regulated to maintain the compensation performance. To verify the effectiveness of the proposed adaptive frequency-based reference compensation current control strategy, the comprehensive case studies implemented with the hardware-in-the-loop (HIL) mechanism are performed to examine the compliance with the specification limits of IEEE Standard 519-2014. The experimental results reveal that the performance of proposed SAPF control strategy is superior to that of the traditional instantaneous reactive power compensation control technique (p-q method) and sliding discrete Fourier transform (DFT).

Objective Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals. Methods Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk. Results The gradient boosting model was selected demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.887 in the internal validation set. Further external validation in hold-out test set and three other medical centers upheld the model's robustness with consistent high AUCs, ranging from 0.852 to 0.879. Conclusion The results endorse the integration of ML algorithms in healthcare to enhance patient risk stratification, potentially optimizing clinical interventions, and diminishing the burden of CCU readmissions.

The prognostic value of easily accessible hematologic biomarkers, such as the neutrophil-to-HDL ratio, as well as the monocyte-to-HDL, lymphocyte-to-HDL, and platelet-to-HDL ratios, remains underexplored in patients with established ischemic heart disease (IHD). Community-dwelling adults aged ≥ 20 with established IHD from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 were eligible. Mortality was tracked through linkage to the National Death Index (NDI) until the end of 2019. Cox regressions examined the associations between these hematologic ratios and all-cause mortality. Receiver operating characteristic (ROC) curve analysis assessed the predictive accuracy of these ratios for mortality. A total of 2265 patients were analyzed, with a median follow-up period of 80 months. After adjusting for demographic factors, lifestyle variables, and comorbidities, patients in the highest quartile of the neutrophil/HDL had a significantly increased all-cause mortality risk (aHR = 1.41, 95% CI: 1.13–1.77) compared to those in the lowest quartile. No significant associations were found between the other ratios and mortality. In conclusion, this study found that among the hematologic ratios analyzed, an elevated neutrophil-to-HDL ratio has the strongest potential for mortality risk stratification in community-dwelling patients with ischemic heart disease in the US, offering important guidance for both patients and clinicians.