Power Burden Research Articles

Homogeneous Ensemble Feature Selection for Mass Spectrometry Data Prediction in Cancer Studies

The identification of important proteins is critical for the medical diagnosis and prognosis of common diseases. Diverse sets of computational tools have been developed for omics data reduction and protein selection. However, standard statistical models with single-feature selection involve the multi-testing burden of low power with limited available samples. Furthermore, high correlations among proteins with high redundancy and moderate effects often lead to unstable selections and cause reproducibility issues. Ensemble feature selection in machine learning (ML) may identify a stable set of disease biomarkers that could improve the prediction performance of subsequent classification models and thereby simplify their interpretability. In this study, we developed a three-stage homogeneous ensemble feature selection (HEFS) approach for both identifying proteins and improving prediction accuracy. This approach was implemented and applied to ovarian cancer proteogenomics datasets comprising (1) binary putative homologous recombination deficiency (HRD)- positive or -negative samples; (2) multiple mRNA classes (differentiated, proliferative, immunoreactive, mesenchymal, and unknown samples). We conducted and compared various ML methods with HEFS including random forest (RF), support vector machine (SVM), and neural network (NN) for predicting both binary and multiple-class outcomes. The results indicated that the prediction accuracies varied for both binary and multiple-class classifications using various ML approaches with the proposed HEFS method. RF and NN provided better prediction accuracies than simple Naive Bayes or logistic models. For binary outcomes, with a sample size of 122 and nine selected prediction proteins using our proposed three-stage HEFS approach, the best ensemble ML (Treebag) achieved 83% accuracy, 85% sensitivity, and 81% specificity. For multiple (five)-class outcomes, the proposed HEFS-selected proteins combined with Principal Component Analysis (PCA) in NN resulted in prediction accuracies for multiple-class classifications ranging from 75% to 96% for each of the five classes. Despite the different prediction accuracies of the various models, HEFS identified consistent sets of proteins linked to the binary and multiple-class outcomes.

3-level inverter based compensator for power quality enhancement using proposed Trianguzoidal PWM technique

In today’s scenario, the integration of a grid-connected load system with a hybrid energy system (HES) is encouraged to improve the reliability of the system. With the stunning rise in nonlinear loads in HES over the last two decades, the power quality (PQ) of the system has emerged as a paramount concern in contemporary times. The power quality problems include the injection of harmonics in the source current, low input power factor, poor voltage regulation, the burden of reactive power, etc. So, to mitigate these power quality problems in a single-phase distribution system, a 3-level Cascade H-bridge (CHB) inverter-based shunt active power filter (SAPF) is employed alongside a proposed Trianguzoidal pulse width modulation (TRZ PWM) strategy. The single-phase distribution system with SAPF is simulated in fixed and dynamic load conditions to check the system’s efficacy. The proposed PWM techniques for SAPF are compared with conventional PWM techniques, i.e., level shift, phase shift, and hybrid PWM techniques. Results indicate satisfactory performance of the proposed PWM techniques, exhibiting low harmonic distortion in source current, well within IEEE 519 limits, and high active filtering efficiency (AFE) compared to conventional PWM methods. Furthermore, this paper provides detailed comparisons of conventional and proposed PWM techniques in the context of active & reactive power supplied or delivered by load, source, and compensator, input power factor, harmonics in source or grid current, AFE, and individual harmonic components concerning fundamental component of source current.

Improved solar photovoltaic performance in standalone low‐voltage direct current microgrids using sensor fault tolerant control

AbstractThe advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid.

Efficient signal-to-noise ratio enhancement model for severely contaminated distributed acoustic sensing seismic data based on heterogeneous knowledge distillation

Distributed acoustic sensing (DAS) is an emerging seismic acquisition technique with great practical potential. However, various types of noise seriously corrupt DAS signals, making it difficult to recover signals, particularly in low signal-to-noise ratio (S/N) regions. Existing deep-learning methods address this challenge by augmenting data sets or strengthening the complex architecture, which can cause overdenoising and a computational power burden. Hence, the heterogeneous knowledge distillation (HKD) method is developed to more efficiently address the signal reconstruction under low S/N. HKD uses ResNet 20 as the teacher and student (T-S) model. It uses residual learning and skip connections to facilitate feature representation at deeper levels. The main contribution is the training of the T-S framework with different noise levels. The teacher model that is trained using slightly noisy data serves as a powerful feature extractor to capture more accurate signal features because high-quality data are easy to recover. By minimizing the difference between the outputs of T-S models, the student model that is trained using severely noisy data can distill the absent signal features from the teacher to improve its own signal recovery, which enables heterogeneous feature distillation. Furthermore, simultaneous positive and negative learning (P&NL) is developed to extract more useful features from the teacher, enabling the T-S framework to learn from the predicted signal and noise during training. Consequently, a new loss function that combines student denoising loss and HKD loss weighted by P&NL is developed to alleviate signal leakage. The experimental results demonstrate that the HKD achieves distinct and consistent signal recovery without increasing computational costs.

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

Web browsing is an important application domain, but it imposes a significant power burden on mobile devices. While the heterogeneous multi-core design offers the potential for energy-efficient computing, existing web browsers fail to exploit the hardware to optimize mobile web browsing. Our work aims to offer a better way to optimize web browsing on heterogeneous mobile devices. We achieve this by developing a machine learning based approach to predict the optimal processor setting for rendering the web content. The prediction is based on the web content, the network status and the optimization goal. We evaluate our approach by applying it to the Chromium browser and testing it on a representative big.LITTLE mobile platform. We apply our approach to the top 1,000 hottest websites across seven typical networking environments. Our approach achieves over 80% of the performance delivered by a perfect predictor. Our approach achieves over 30%, 50%, and 60% improvement respectively for load time, energy consumption and the energy delay product when compared to two state-of-the arts approaches.

Analytical voltage sensitivity-based distributed Volt/Var control for mitigating voltage-violations in low-voltage distribution networks

Integrating solar photovoltaic system in low-voltage distribution networks leads to significant voltage violations. This issue can be alleviated using the cutting-edge control techniques (Volt/Var control, Volt/Watt control, etc.) of smart inverters. Moreover, the smart inverters absorb/inject the necessary amount of reactive power from/into the substation to alleviate the over/under voltages. This increases the reactive power burden on the substation and deteriorates its power factor. Therefore, this paper proposes a novel distributed Volt/Var control technique to reduce the dependency on substations for reactive power support and improve its power factor. The proposed technique is based on analytical voltage sensitivity analysis. By developing suitable relations, the feeder nodes are categorized into two groups, one operating in lagging mode, while the other in leading mode. This ensures proper utilization of reactive power capability of the inverters. An IEEE 13-node low voltage distribution network with the solar photovoltaic system at different load conditions is used to test the proposed and other existing Volt/Var control techniques. Compared to other distributed Volt/Var control techniques, the proposed method requires a minimum reactive power support to regulate the feeder voltage. Additionally, the obtained results by the proposed method are closer to those by the optimal reactive power support approach using GAMS software and are validated on a real-time digital simulator platform.

Construct and criterion validity of muscle ultrasonography for assessment of skeletal muscle in patients recovering from COVID-19.

Background: The purpose was to investigate the content, construct, and criterion validity of muscle ultrasound in a mixed cohort of participants recovering from mild and critical COVID-19. Methods: A secondary analysis of a prospective cross-sectional study was conducted on data obtained from a battery of muscle and physical function assessments including a muscle biopsy and muscle ultrasonography (US). Rectus femoris (RF) muscle thickness (mT), quadricep complex (QC) mT, RF muscle cross-sectional area (CSA) using 2D freeform trace and estimated from Feret's diameter, and RF echo intensity (EI) were assessed with US. Muscle fiber CSA, fiber type, protein content in muscle fibers, extracellular matrix content (ECM; wheat-germ agglutin), and percent area of collagen in ECM (picrosirius red) were examined from vastus lateralis muscle biopsies. Spearman rho correlations (r) were performed to assess validity of ultrasound parameters. Results: Thirty-three individuals participated including 11 patients surviving critical COVID-19, 15 individuals recovering from mild-COVID, and 7 controls. There were several significant correlations between RF mT, QC mT, RF CSA, and RF EI with age, comorbid burden, body-mass index, and measures of muscle strength, muscle power, and physical function (range r = 0.35-0.83). RF Feret's CSA correlated to CSA of type II muscle fibers (r = 0.41, p = 0.022) and the average size of all muscle fibers (r = 0.39, p = 0.031). RF EI was correlated with collagen in muscle ECM (r = 0.53, p = 0.003) and protein content in muscle tissue (r = -0.52, p = 0.012). Conclusion: Muscle size and quality measured using US has moderate content and construct validity, and to lesser extent, fair to moderate criterion validity in a mixed cohort of individuals recovering from COVID. Muscle ultrasound quality (EI) appears to be sensitive at detecting muscle dysfunction as it is associated with strength, power, physical function, and collagen distribution in a mixed group of individuals recovering from COVID-19.

Biogas untuk Menghasilkan Energi Listrik

Biogas is a methane gas produced from the fermentation of organic matter by anaerobic bacteria. Utilization of biogas as an energy source can be done by burning biogas directly, and indirectly by converting it into electrical energy. Biogas-fueled electric energy potential to be developed in the NTB area. The fundamental reason is: the government program "Earth One Million Cows" which is supposed to happen in 2013, rising prices of petroleum fuel and the scarcity of inorganic fertilizers. To realize the above research activities, will do the following steps: 1) Development of fixed-dome digester with a size of 8 m3, 2) the charging of organic materials in the digester to produce biogas, 3) perform gas reservoirs in the media that has been prepared, 4) integrate system are made, 5) test and measurement under load conditions. Test results on Biogas Power Plant in the laboratory EBT of the Engineering Faculty of Mataram Universit, with 8m3 digester produces biogas production at the effective time of 2.152 m3/day. Consumption of biogas to the generator at 200watt load is 0.133 m3 / 15, or the conversion of biogas into electricity coefficient is 2.4 m3/kwh. Production of biogas a day if converted to a 200Watt power burden to last about 4 hours.

Analysis on DC Fault Current Limiting Operation of Twice-Quench Trigger Type SFCL Using Transformer Considering Magnetizing Current and Current Limiting Reactor

As the penetration of distributed energy resources (DER) has increased, research on direct current (DC) power transmission and distribution has been actively performed. The DC system has the advantage of high-power transmission efficiency. However, it has a very large and rapid increase in fault current in the DC system directly after a fault occurs. As one of the countermeasures, studies on the application of the superconducting fault current limiter (SFCL) into the DC system have been conducted to protect major facilities from DC fault current, which is expected to alleviate the power burden on the DC circuit breaker through its quench operation. Among the studied DC SFCLs, the trigger-type DC SFCL using a transformer, which can achieve the peak DC fault current-limiting operation, has been suggested. However, the DC fault current-limiting operation, in the case of the DC SFCL with a current-limiting reactor (CLR), was analyzed to not be effectively executed in the steady state since the transient state directly follows the fault occurrence. In this paper, the DC fault current-limiting operation of a twice-quench trigger type SFCL using a transformer considering magnetizing current and its CLR was analyzed. Through DC fault current-limiting experiments according to the inductance of its current-limiting reactor (CLR), the effective current-limiting design of twice-quench trigger type SFCL using a transformer was described.

Load Shedding Technique for Power System Using Neural Network Improved by Cuckoo Search Algorithm

The present paper introduces a load shedding methodology that leverages an upgraded neural network that relies on the Cuckoo search (CS) optimization algorithm to compare the efficiency and applicability with other methods in terms of speed and feasibility. The proposed method will be tested on the IEEE-37 bus system. The results of the method are compared with other optimization methods. Thereby, this method gives good results and feasibility in application. The criteria of voltage are considered, specifically the sensitivity index dV/dQ is proposed to find weak buses in the system that need to be relieved of the active power burden. Then, the shedding priority bus ranking is created to ensure the most favorable load shedding plan for the system to maintain voltage stability. Besides, the frequency parameter is also considered to calculate the optimal amount of shed load. The model system was tested by using POWERWORLD software. After comparing the results with other methods outlined in the paper, it has been determined that the proposed approach is highly effective for optimizing grid shedding in the system.

Privacy-Preserving Image Acquisition for Neural Vision Systems

Preserving privacy is a growing concern in our society where cameras are ubiquitous. In this work, we propose a trainable image acquisition method that removes the sensitive information in the optical domain before it reaches the image sensor. The method benefits from a trainable optical convolution kernel, which transmits the desired information whilst filtering out the sensitive information, making it irretrievable against different privacy attacks in the digital domain. This is in contrast with the current digital privacy-preserving methods that are all vulnerable to direct access attacks. Also, in contrast with most of the previous optical privacy-preserving methods that cannot be trained, our method is data-driven and optimized for the specific application at hand. Moreover, there is no additional computation or power burden on the acquisition system since it works passively in the optical domain and can be even used in conjunction with other privacy-preserving techniques in the digital domain. We demonstrate our new, generic method in several scenarios such as smile or open-mouth detection as the desired attribute while the gender or wearing make-up is filtered out as the sensitive content. Through several experiments, we show that this method is able to reduce around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${65}\%$</tex-math></inline-formula> of sensitive content while causing a negligible reduction in the desired information. Moreover, we tested our method by deep reconstruction attack and confirmed the ineffectiveness of this attack to reconstruct the original sensitive content. This new method has different use cases such as feedback systems for smart TV content or outdoor advertising.

A Low Power Sub-GHz Wideband LNA Employing Current-Reuse and Device-Reuse Positive Shunt-Feedback Technique

This letter presents a low-power current-reuse and device-reuse low noise amplifier (LNA) for sub-GHz wideband applications. Based on the shunt-feedback common-gate (SFBCG) hybrid topology and the proposed current/device-reuse shunt-feedback (SFB) technique, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g_{m}$ </tex-math></inline-formula> restriction is alleviated. Moreover, the degree of design freedom is added by coupling the output to the gate of the in-phase current source transistor to activate positive feedback without extra power burden, thereby achieving a higher gain and lower noise design. Implemented in 90-nm CMOS technology, this LNA prototype has an active area of 0.075 mm2. The measurement results show a peak gain of 21.3 dB with 3-dB bandwidth of 50–800 MHz, noise figure of 4.5 dB, third-order intercept point (IIP3) of −7.1 dBm, and power dissipation of 1.2 mW.

Reducing temperature swing of space objects with temperature-adaptive solar or radiative coating

Lacking the atmosphere for temperature neutralization, objects in outer space without thermal control undergo large temperature swings. Effective temperature management technologies (TMTs) are essential to avoid undesirable effects caused by extreme thermal conditions. However, existing high-performance TMTs impose additional burden on the limited mass and power budgets of spacecrafts. Very recently, temperature-adaptive solar coatings (TASCs) and temperature-adaptive radiative coatings (TARCs) emerged as novel light-weight, energy-free temperature-regulation approaches for terrestrial objects with excellent thermal performance. Here, we simulate and present the great potential of TASCs and TARCs as future passive TMTs for space objects. A case study of a geosynchronous satellite with body-mounted solar panels covered by TARC exhibits an interior temperature swing as small as 20.3°C–25.6°C in an orbital period even with solar eclipses. These findings provide insight into the superior performance of TASCs and TARCs in space and will promote their application in extraterrestrial missions. • TASCs and TARCs are promising for passive temperature management in outer space • TASCs and TARCs do not bring in extra mass, volume, or power burdens to spacecrafts • The thermal performance of TASCs and TARCs is simulated with satellite thermal models • Simulations show a temperature swing as low as 5.3°C in a 1U-CubeSat covered by TARCs Advanced high-performance passive temperature management technologies without additional mass/volume/power burdens are required for space exploration. Dong et al. propose use of emerging temperature-adaptive solar or radiative coatings (TASCs or TARCs, respectively) on space objects and simulate their temperature-regulation performance with satellite thermal models, demonstrating the potential of TARCs and TARCs in outer space.

Mitigation of Reactive Power and Harmonics in a Case of Industrial Customer

In most industrial plants, power quality is a major issue which hinders productivity and efficiency of the plants due to the use of semiconductor based loads and non-linear loads. As a result, to address the power quality problems in these industries, specialized power devices such as the Unified Power Quality Conditioner (UPQC), can be installed at the customer's location to resolve practically all power quality issues. UPQC consist of a series of active power filters (APF) which mitigate voltage quality issues and shunt active power filters which are used to reduce current quality issues, like harmonics and reactive power burdens. This paper, therefore presents an investigation and assessment of the power quality problems associated with Bahir Dar Textile Share Company. This was achieved by examining the voltage and current harmonic levels of various types of loads using PI and fuzzy logic controllers by measuring the level of total harmonic distortion (THD) with and without the insertion of the UPQC. The novelty of this paper is the implementation of the controller based customized UPQC for power factor and reactive power compensation. It was observed that by designing the system without and with UPQC, the results of the FFT analysis show that the fuzzy logic controller (FLC) reduced the harmonics level of the load voltage and current by 1.10% and 2.14% respectively. Generally, harmonics was alleviated by 90%, reactive power was reduced by 20%, and power factor was improved by 33%. Hence, the proposed UPQC is capable of holding the voltage and current harmonics levels within the acceptable limit, which satisfies the standards imposed by IEEE 519-1995.

Utilizing UPQC-DG to export reactive power to grid with power angle control method

Unified Power Quality Conditioner with Distributed Generation (UPQC-DG) is combined equipment that not only compensates power quality issues but also integrates distributed generation into the grid. This paper proposes to incorporate additional functionality in UPQC-DG to provide reactive power support to the grid. The proposed functionality enables the UPQC-DG to not only compensate for load reactive power but also to export it to the grid in a controllable and regulated manner. The control method of the proposed UPQC-DG system is based on a combination of Synchronous Reference Frame (SRF) theory and Unit Vector Template Generation (UVTG) and utilizes an additional PI controller to control the reactive power flow to the grid. Since the UPQC-DG in the proposed research handles an increased quantity of reactive power, the work incorporates the Power Angle Control (PAC) method to share reactive power burden between series and shunt Active Power Filters (APFs) of UPQC-DG for reducing the overall rating. The proposed UPQC-DG system is validated using exhaustive real-time simulations in Opal-RT for steady-state and dynamic performance.

Impact of Improved Unified Power Quality Conditioner Allocation in Radial Distribution Network

This paper presents the investigative study on the Unified Power Quality Conditioner (UPQC) impact on Radial Distribution System (RDS). The architecture of Power Angle Controlled UPQC named Improved Unified Power Quality Conditioner (I-UPQC) was implemented in RDS. The problem of power loss, under-voltage, and reactive power burden on shunt inverters are the significant issues addressed in this study. The allocation of I-UPQC by placing it at each bus of the RDS one node at each iteration, excluding the swing bus, is studied by considering its impact on each bus of the radial network. The Power Loss Index (PLI) and Degree of Under Voltage Mitigation Node (DUVMN) values of all the buses are calculated analytically using distribution framework expressions of I-UPQC. Hence, the bus having the highest PLI value, and the minimum permissible node voltage is the most favourable. The determination of the candidate bus for I-UPQC was achieved by the load flow algorithm. The results obtained in this study on IEEE 33 and 69 bus system shows 3.9% and 4.2% power loss reduction respectively for both networks. Also, the minimum bus voltage was improved to 0.954 p.u. and 0.955 p.u. in each case for both networks, after the allocation of I-UPQC in RDS, compared to the base case. Consequently, the VA burden on shunt inverter was reduced by reactive power compensation of the series inverter. The results and simulation obtained in MATLAB / SIMULINK environment and discussion to support the concept developed are also presented. The results from the study confirmed that the concept of I-UPQC placement impacted the operation of RDS compared to the other connected UPQC model.

Operation Characteristics of a Superconducting LC Resonant DC Circuit Breaker According to Pole-to-Pole and Pole-to-Ground

DC technology has become an issue as it is applied to DC distribution systems, due to the acceptance increment of the renewable energy. For this reason, research on DC circuit breaker to be applied to Medium Voltage Direct Current(MVDC) or higher is active and various cut-off technologies are being developed. In this paper, we propose a fast and reliable superconducting LC resonant DC circuit breaker in DC system. It reduces the initial fault current through the superconducting element and the fault current reaches zero-point by LC resonance and is interrupted. We designed Pole-to-Pole(PTP) and Pole-to-Ground(PTG) simulation circuits using the PSCAD/EMTDC program. Also, we analyzed and compared the cut-off operation characteristics in the circuit. We confirmed that the superconducting element reduced the initial fault current and the power burden. It is expected that the deterioration of the protective devices and the surrounding equipment can be reduced.

Stability issues and mitigating techniques employed in AC grid

The AC grid in the present scenario comprises of traditional energy sources and renewable energy sources material system. The integration of renewable energy sources through the power electronic material factor results in instability in the grid. The common material quality issues include poor power factor, reactive power burden, harmonic currents, and unbalanced currents which causes increased losses in the distribution system, noise, vibration, over voltage and excessive current, resonance, de-rating of cables, interference to communication lines, and malfunction of relay and circuit breaker due to improper selection of the material for the AC grid system. This paper reviews the various stability issues and mitigating techniques associated to the materials used in AC grid system has been analyzed and studied and thereby to find the research gap which require immediate attention for further research and the stability issue in AC grid is due to improper power fluctuations and instability in the position of power grids in the AC system The major drawback in mitigation in the available technique is small increase in power fluctuation can increase energy of the system to a greater extent and cause the system overheating. In this article the commonly used mitigation techniques have been discussed for clear understanding. Under this proposed mitigation technique the loss in power during the running is minimal in operation.

Pulmonary Nodule Clinical Trial Data Collection and Intelligent Differential Diagnosis for Medical Internet of Things.

In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system components are applied to electroencephalogram (EEG) signal transmission, to which a threshold compression module and a vector reconstruction module are added to address the system power burden associated with the acquisition and transmission of large amounts of real-time EEG data in the medical IoT. In the threshold compression module, the inherent characteristics of EEG signals are analysed, and the generated EEG data are decomposed into multiple symbolic streams and compressed by applying different thresholds to improve the compression ratio while ensuring the quality of service of the application. A deep neural network-based approach is proposed for the detection and diagnosis of lung nodules. Automatic identification and measurement of simulated lung nodules and the corresponding volumes of nodules in images under different conditions are applied. The sensitivity of each AIADS in identifying lung nodules under different convolution kernel conditions, false positives (FP), false negatives (FN), relative volume errors (RVE), the miss detection rate (MDR) for different types of lung nodules, and the performance of each system in predicting the four types of nodules are calculated. In this paper, an interpretable multibranch feature convolutional neural network model is proposed for the diagnosis of benign and malignant lung nodules. It is demonstrated that the proposed model not only yields interpretable lung nodule classification results but also achieves better lung nodule classification performance with an accuracy rate of 97.8%.

Seamless Switching Technology of UPQC Based on Improved Power Angle Control

This paper presents a novel seamless switching technology between different operating conditions of the Unified Power Quality Conditioner (UPQC) with the energy storage system. Frequent start and stop of the series inverter will cause shock and loss under the conventional control method. Moreover, it is a key issue to achieve seamless switching when the UPQC operating condition changes. This paper proposes an improved power angle control (PAC) method to solve these problems. An agile power angle is calculated by this method under stable operating conditions. This feature enables the series inverter to share the load reactive power with the shunt inverter. On the other hand, both the series and shunt inverters are in an easy-to-switch operating state which can help achieve better seamless switching between different operating conditions. Simulation results show the shunt inverter capacity can be reduced up to 47% due to the reactive power sharing by both the inverters which means the reactive power burden of the shunt inverter is great lightened. It is also found that the switching time is about 300μs faster and the maximum voltage fluctuation amplitude is about 90V smaller under improved PAC than the conventional control method.