•Deep convolutional neural network is used to extract the features in PV power data.•A deterministic model for PV power forecasting based on WT and DCNN is proposed.•Quantile regression based ...probabilistic PV power forecasting model is formulated.•The proposed forecasting models have been verified on PV power dataset from Belgium.
The penetration of photovoltaic (PV) energy into modern electric power and energy systems has been gradually increased in recent years due to its benefits of being abundant, inexhaustible and clean. In order to reduce the negative impacts of PV energy on electric power and energy systems, advanced forecasting approach with high-accuracy is a pressing need. Aimed at this, a novel hybrid method for deterministic PV power forecasting based on wavelet transform (WT) and deep convolutional neural network (DCNN) is firstly proposed in this paper. WT is used to decompose the original signal into several frequency series. Each frequency has better outlines and behaviors. DCNN is employed to extract the nonlinear features and invariant structures exhibited in each frequency. Then, a probabilistic PV power forecasting model that combines the proposed deterministic method and spine quantile regression (QR) is originally developed to statistically evaluate the probabilistic information in PV power data. The proposed deterministic and probabilistic forecasting methods are applied to real PV data series collected from PV farms in Belgium. Numerical results presented in the case studies demonstrate that the proposed methods exhibit the ability of improving forecasting accuracies in terms of seasons and various prediction horizons, when compared to conventional forecasting models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A novel gold nanorods (GNRs) biosensor based on the localized surface plasmon resonance (LSPR) behavior was designed to detect the hepatitis B surface antigen (HBsAg) which indicates active viral ...replication of hepatitis B virus (HBV). The surface of GNRs was modified with monoclonal hepatitis B surface antibody (HBsAb) through physical adsorption. Raman spectrum, dynamic light scattering (DLS) and zeta potential measurement were conducted to access the nature of the GNRs after antibody modification. The binding of analyte to the molecular probe was monitored by the longitudinal wavelength shift of LSPR peak in the UV–Vis extinction spectrum resulting from the changes of local refractive index induced by the immunological reaction. The biosensor could be utilized in quantitative analysis in Tris buffers, which has dose-dependence response ranging from 0.01
IU/mL to 1
IU/mL. Further, the biosensor was suited for qualitative analysis of HBsAg in the actual media of blood serum and plasma. The ease of operation, high sensitivity, and its generality offer specific advantages over other GNRs-based immunoassay methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Many countries have larger land areas and scattered communities. Therefore, to electrify them, small standalone power systems are the more preferred and cost-efficient solution as compared to utility ...grid extensions. The main objective of a standalone power system is to supply cleaner, cheaper, and uninterrupted electricity. However, for standalone power systems, demand-side management always remains a challenging task. In this paper, a load scheduling algorithm driven by K-mean clustering and linear integer programming to schedule consumers’ appliances for the upcoming day is proposed. In addition, the basic power to run the necessary appliances is kept available in the system all the time. Furthermore, to assist the consumer in every situation, the battery storage system and the overall system size reduction are also taken into consideration. Consumer input is also used in scheduling the appliances. The proposed method is evaluated on the publicly available real-world dataset; the simulation results demonstrate that the proposed approach performs better, due to which the reliability and continuity of the system are increased.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Photovoltaic (PV)/battery hybrid power units have attracted vast research interests in recent years. For the conventional distributed power generation systems with PV/battery hybrid power units, two ...independent power converters, including a unidirectional dc-dc converter and a bidirectional converter, are normally required. This paper proposes an energy management and control strategy for the PV/battery hybrid distributed power generation systems with only one integrated three-port power converter. As the integrated bidirectional converter shares power switches with the full-bridge dc-dc converter, the power density and the reliability of the system is enhanced. The corresponding energy management and control strategy are proposed to realize the power balance among three ports in different operating scenarios, which comprehensively takes both the maximum power point tracking (MPPT) benefit and the battery charging/discharging management into consideration. The simulations are conducted using the Matlab/Simulink software to verify the operation performance of the proposed PV/battery hybrid distributed power generation system with the corresponding control algorithms, where the MPPT control loop, the battery charging/discharging management loop are enabled accordingly in different operating scenarios.
Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model ...under perfect operating condition with nominal parameters, or they may adopt a complicated control structure of high order. These LFC schemes may have poor control performance or even loss of stability in real-time implementation due to the grid uncertainties and the change of system operation scenarios. Consequently, a hybrid control method with two control loops considering various practical scenarios is originally proposed in this paper. In the inner loop, variable universe fuzzy logic control is applied to mitigate the impact of load disturbances on control performance. In the outer loop, an incremental genetic algorithm is employed to online optimize the control parameters. The performance of the proposed control method is comprehensively tested on a MATLAB/Simulink-based LFC model and a real-time digital simulator-based real-life 49-bus power system. The extensive results show that the proposed hybrid method exhibits comparatively better control performance than an adaptive fuzzy logic controller and an improved proportion integration controller.
Radiation encephalopathy (RE) refers to radiation-induced brain necrosis and is a life-threatening complication in patients with nasopharyngeal carcinoma (NPC) after radiotherapy (RT), and ...radiation-induced pre-symptomatic glymphatic alterations have not yet been investigated. We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) index to examine the pre-symptomatic glymphatic alterations in NPC patients following RT. A total of 109 patients with NPC consisted of Pre-RT (
= 35) and Post-RT (
= 74) cohorts were included. The post-RT NPC patients, with normal-appearing brain structure at the time of MRI, were further divided into Post-RT-RE- (
= 58) and Post-RT-RE+ (
= 16) subgroups based on the detection of RE in follow-up. We observed lower DTI-ALPS
index, DTI-ALPS
index and DTI-ALPS
index in post-RT patients than that in pre-RT patients (
< 0.05). We further found that post-RT-RE+ patients demonstrated significantly lower DTI-ALPS
(
= 0.013), DTI-ALPS
(
= 0.011) and marginally lower DTI-ALPS
(
= 0.07) than Post-RT
patients. Significant negative correlations were observed between the maximum dosage of radiation-treatment (MDRT) and DTI-ALPS
index (
= 0.003) as well as DTI-ALPS
index (
= 0.004). Receiver operating characteristic (ROC) curve analysis showed that DTI-ALPS
index exhibited good performance (AUC = 0.706) in identifying patients more likely developing RE. We concluded that glympathic function was impaired in NPC patients following RT and DTI-ALPS index may serve as a novel imaging biomarker for diagnosis of RE.
This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement ...learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy process-based coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids.
Multi-terminal High Voltage Direct Current (MTDC) with modular multilevel converter (MMC) is a promising technology, because it integrates diverse energy resources. However, there is still a risk for ...transient stability of a system with MMC in the case of communication delay. In order to improve the transient stability of hybrid AC/DC networks, the point of common coupling (PCC) voltage compensation scheme is proposed for MMC-MTDC to maintain stable operation when communication delay occurs. Based on the established closed-loop network control system (NCS) which the MMC-MTDC participates in, the proposed control strategy utilizes the multi-layer control according to the dynamic characteristic of MMC-MTDC, and compensates the PCC voltage in the controller input signal to reduce inaccuracy case by time delay, thus offset the negative effect of communication delay on power system transient stability. The proposed control method is fully investigated in a MMC-MTDC model based on 4-generator 10-node system model and the simulation results clearly indicate the proposed strategy can effectively enhance transient stability of system.
This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system ...(CAD-RADS) scores.
This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors.
A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0-2 group and 208 (47.1%) were CAD-RADS score 3-5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (
< 0.001), and high-density lipoprotein (HDL-C) lower (
< 0.001) in CAD-RADS score 3-5 group compared to the CAD-RADS score 0-2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores.
ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.
The energy management and energy efficiency optimization are of particularly significant for promoting the sustainable development of industrial processes. However, industrial raw data with ...uncertain, relevant and inaccuracy characteristics influence the reliability and accuracy of energy efficiency analysis and optimization modeling. Therefore, an energy efficiency analysis and optimization method based on a novel data reconciliation (DR) integrating Gaussian mixture model (GMM) and mutual information (MI) is put forward. First, the material flow information with multiple data characteristics corresponding operation modes is divided through the GMM. Moreover, the novel data reconciliation model integrated with critical variable and mutual information is established considering time-scale redundancy information in different modes, then the comprehensive data reconciliation result is evaluated by the hypothesis testing. Furthermore, the reconciled data is applied to analyze the exergy balance and built the energy efficiency optimization model with multi-objective for a case study of industrial evaporation process. Finally, simulation case and industrial application case are used to analyze and discuss, and the results show that the validity and applicability of the proposed approach are illustrated in energy saving potential which is about 14.81%.
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