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With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

In this article, the temperature of the needle tip and the ground electrode were changed to obtain different temperature gradients. The harmonic superimposed DC voltage was also simultaneously applied to study the electrical tree initiation, growth and breakdown characteristics in cross-linked polyethylene (XLPE) under the temperature gradient. The tree inception voltage, tree structure, tree length, accumulated damage (AD) and breakdown time were used to analyze the electrical tree degradation process. It was found that the greater the temperature gradient, the easier it is for electrical trees to be initialed which is related to the different charge behaviors affected by the trap distribution indicated by surface potential decay (SPD) results at different temperatures. With the temperature gradient increasing, the electrical tree length increases firstly but then decreases slightly when the temperature of the ground electrode is fixed, but the electrical tree length and AD increase linearly with the temperature gradient increasing when the temperature of the needle tip is fixed. The temperature rise of the ground electrode has a more serious impact on the safety of the insulation compared with the temperature rise of the needle tip. Positive DC bias voltage is more likely to cause damage and breakdown than negative DC bias voltage under the temperature gradient. These results indicate that the temperature gradient and harmonic superimposed DC voltage will promote the electrical growth in cable insulation, so special attention should be paid to the safety of electrical equipment under such special conditions.

Polypropylene (PP) has great potential to be used as recyclable high-voltage direct current (HVDC) cable insulation material. Electrical tree is the key factor that leads to cable insulation failure and limits the increase of HVDC transmission voltage level. In this study, three different polycyclic compounds, namely 4,4'-bis(dimethylamino)phenylazo, 2-hydroxy-2-phenylacetophenone and 4-phenylbenzophenone, are added into PP matrix with the addition content of 0.1 wt%. The electrical treeing experiments are conducted under impulse superimposed DC voltage at 30, 70 and 90 °C. Then the polycyclic compound with the best effect on inhibiting electrical tree is selected, and its filling ratio is further changed from 0.1 wt% to 0.3 wt% to get the optimal inhibition effect. It is found that the addition of 2-hydroxy-2-phenylacetophenone can inhibit the electrical tree degradation. However, the addition of 4-phenylbenzophenone and 4,4'-bis(dimethylamino)benzene will increase the electrical tree length and accumulated damage. As the content of 2-hydroxy-2-phenylacetophenone increases, the electrical tree length and accumulated damage decrease firstly, and then increase. The effect of polycyclic compounds on the electrical tree degradation is affected by the relative polarity of impulse superimposed DC voltage and temperature. The experimental results reveal that adding 0.1 wt% 2-hydroxy-2-phenylacetophenone polycyclic compound to PP can effectively inhibit the electrical tree degradation, which has great potential for application in recycle HVDC cables.

Lung cancer is the leading cause of cancer deaths worldwide with its mortality rate higher than that of other leading cancers. Nodules in the lungs can grow quite large without any obvious symptoms until the condition has reached a certain stage. Early detection and diagnosis of growing nodules can lay a good foundation for further treatment and potentially improve lung cancer survival rate. In this paper a unified framework is proposed for visual prediction and diagnosis of follow-up lung nodules. Future nodule growth is predicted by modeling the nodule growth between consecutive Computed Tomography (CT) scans via spatial transformation using convolutional network. Nodule classification is made based on the predicted nodule growth and previous diagnosis. Experiments are conducted on a longitudinal follow-up dataset of 615 LDCT scans of 153 lung nodules in early stages from 125 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method.

Aiming at the demand of high reliability power supply for important loads in the emergency field, this paper proposes a topology and voltage control of energy storage mobile shelter under multi energy access, which can promote the collection and utilization of various in-field energy, and realize the voltage stability control of AC and DC bus. Paralleled AC bus is adopted to prevent the power supply interruption of important loads, and the detailed topology and parameter design is presented. The DC bus adopts the segmented droop control, which cancels the static error of droop control under steady-state operation and mobilizes external energy to prevent over charge or discharge when energy storage battery near the limit. The automatically updated reference of energy storage converter can be to realize the AC bus frequency correction and the priority power supply of external renewable power and internal energy storage battery. SOC balance function is added to avoid circuit reorganization of emergency support system. Diesel generator is only used as hot standby, which can improve the reliability of power supply for important loads and reduce the burden of fuel transportation. Finally, the effectiveness and feasibility of the proposed scheme is verified by the simulation.

Earthquake disasters can cause damage to various types of infrastructure of the electric-gas system, and in severe cases, power outages and gas outages. Taking into account the uneven distribution of earthquakes in space, this paper constructs an earthquake disaster model based on the three-level latent focal region division scheme and an electric-gas system component failure probability model based on earthquake intensity distribution. Then, a planning-oriented resilience assessment framework of electric-gas system under earthquake disaster is proposed. Finally, this paper uses the electric-gas system composed of the IEEE RTS79 grid and the modified 14-node gas grid to verify the proposed resilience assessment framework.

This paper proposes an optimal group charging method for battery integrated modular multilevel power conversion system in EV. During single phase AC charging mode, different amounts of sub-modules could be switched on due to its modular structure. With different number of sub-module inserted, the circulating current is also affected, which indicates the potential to decrease system losses. The mathematical analysis between inserted number and power losses is presented. With the optimal inserted number selected, the efficiency of power conversion system could be improved. Finally, simulation results are also obtained to verify the effectiveness of optimal group charging method.

District Integrated Energy System can realize the coordination and stepped utilization of multiple energy, improve the integrated utilization efficiency of energy, and take advantages of multiple energy into account. On the other hand, the optimal operation of District Integrated Energy System involves many stakeholders, the investment and operation mode of the system is more complex, and the demand side has different energy demands with different forms and characteristics, which will directly affect the operation economy and environmental protection of the system. This paper mainly studies the multi-objective optimal operation of the District Integrated Energy System considering economy and environmental protection. Firstly, the model of system is constructed, which includes multi-source supply and integrated load. Secondly, a multi-objective optimization problem considering economy and environmental protection is put forward. Finally, the linear weighting method combined with entropy weight method is used to solve the multi-objective optimization problem. The case analysis shows that the system operation can give consideration to economy and environmental protection after multi-objective optimization.

Remaining useful life (RUL) prediction for electric vehicle battery (EV battery) is of great significance for the early replacement and regular maintenance of batteries with potential safety hazards in driving conditions. This paper proposes a novel method based on light gradient boosting machine (LightGBM) to predict the RUL of the battery under driving conditions. LightGBM uses the histogram optimization strategy to reduce the number of traversal of the data sample set and improve the robustness of the method; the depth-first splitting (leaf-wise) strategy reduces the risk of overfitting; the gradient-based one-sided sampling strategy (GOSS), reduce the data dimension; use the exclusive feature bundling strategy (EFB) to reduce the feature dimension. However, the LightGBM method has the difficulty of parameter setting. Therefore, this paper uses Hyperopt based on distributed asynchronous algorithm configuration/hyperparameter optimization to optimize its complicated hyperparameters. Subsequently, the method was applied to the prediction of battery RUL under simulated driving conditions. Based on the comparative cases, the results show that this method can guarantee the rapidity, accuracy and robustness of RUL prediction under the condition of low memory usage.

With the operation of neutral point grounding syste m with small resistance, zero sequence current transformer ha s the problems of huge volume and insufficient accuracy in pra ctical engineering. Using flux constrained zero sequence curren t transformer can solve the problem of crossing three-phase ca ble and reduce the volume of equipment. At present, the design of flux constrained zero sequence current transformer needs t o be studied. Therefore, an optimal design method of flux const rained zero sequence current transformer is proposed. Firstly, the working principle of flux constrained zero sequence curren t transformer is introduced. Secondly, the optimization directio n of flux constrained zero sequence current transformer is pro posed, and the winding method and air gap optimization desig n of flux constrained zero sequence current transformer are ca rried out aiming at accuracy. Finally, the finite element model and circuit simulation model are established for simulation cal culation, and a flux constrained zero sequence current transfor mer is developed and tested. Simulation and experimental results show that the proposed method can be used to design high-p recision flux constrained zero sequence current transformer.

As the insulation material of DC superconducting cable, cross-linked polyethylene (XLPE) will encounter the effect of low temperature environment during the power transmission process. Due to the rectification, XLPE will also be affected by harmonic superimposed DC voltage, and these dual effects will greatly affect the insulation performance of XLPE, leading to the electrical tree initiation. Electrical treeing under harmonic superimposed DC voltage in liquid nitrogen (LN2) in XLPE were investigated in this study. Results revealed that the growth rate was the largest under +10 kV DC superimposed harmonic voltage. The growth rate was the smallest under -10 kV DC superimposed harmonic voltage. This occurrence was associated with the effective value of the electric field and the different charge movement processes under different voltage waveforms.

This paper proposes a multi-function modular multilevel conversion system for electric vehicle, which can solely provide the motor driving function, as well AC and DC battery charging functions. To well illustrate the proposed system, the operational principles and control strategies are fully investigated including state of charge (SOC) balancing control strategy in driving mode, DC or AC charging methods in charging mode. Finally, the proposed power conversion system was verified through Matlab simulations and a scaled down experimental prototype.

With the advancement of electric energy substitution project, many electric heat pumps are connected to rural distribution network, which has different degrees of impact on users. Therefore, this paper proposes evaluation indicators to analyze the impacts of heat pumps to rural distribution network as well as the effects of some control strategies. Firstly, the thermodynamic process and operation characteristics of typical electric heat pump are analyzed. Then the evaluation indicators are introduced and a voltage optimization regulation strategy based on self-regulation of heat pump are evaluated. Finally, the results of case show that the proposed evaluation indicators can effectively reflect the actual situation of the electric energy substitution project in the rural distribution network and the effect of the control strategy.

Distributed generation including wind turbine (WT) and photovoltaic panel increased very fast in recent years around the world, challenging the conventional way of probabilistic load flow (PLF) calculation. Reliable and efficient PLF method is required to take into account such changing. This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation, and then develops a maximum entropy based PLF (ME-PLF) calculation algorithm for power system integrated with wind power generation (WPG). Comparing to traditional Gram–Charlier expansion based PLF (GC-PLF) calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions (PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve the limitation of GC-PLF calculation method that mistakenly gaining negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.

Renewable energy based distributed generation (DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tie-line in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources (DSRs) including DGs, electric vehicles and thermostatically-controlled loads at the demand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs, considering the response order and the comfort levels. Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased. Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.

Named entity recognition is a fundamental task in natural language processing and many studies have done about it in recent decades. Previous word representation methods represent words as a single vector of multiple dimensions, which ignore the ambiguity of the character in Chinese. To solve this problem, we apply a BERT-BiLSTM-CRF model to Chinese electronic medical records named entity recognition in this paper. This model enhances the semantic representation of words by using BERT pre-trained language model, then we combine a BiLSTM network with CRF layer, and the word vector is used as the input for training. To evaluate the performance, we compare this model with several baseline models in CCKS 2017 datasets. Experimental results demonstrate that the BERT-BiLSTM-CRF model could achieve a better performance than the other baseline models.

In view of the problem that the existing transmission system reliability assessment indices cannot determine the weak links of the system, the component importance index is put forward based on the idea of using importance measures. Furthermore, this paper proposes a method for quickly calculating the component importance index of each transmission line combined with the impact-increment based state enumeration method. The results of component importance index can be used to rank the transmission lines and put forward the most effective measures to improve the system reliability. Case studies are performed on IEEE RTS-79 test system to demonstrate the accuracy and computational efficiency of the proposed component importance indices. It also verifies the guiding role of the proposed component importance index in improving the system reliability.

Automatically accurate pulmonary nodule detection plays an important role in lung cancer diagnosis and early treatment. We propose a three-dimensional (3D) Convolutional Neural Networks (ConvNets) fusion model for lung nodule detection on clinical CT scans. Two 3D ConvNets models are trained separately without any pre-training weights: One trained on the LUng Nodule Analysis 2016 dataset (LUNA) and additional augmented data to learn the nodules’ representative features in volumetric space, which may cause overfitting problems meanwhile, so we train another network on original data and fuse the results of the two best-performing models to reduce this risk. Both use reshaped objective function to solve the class imbalance problem and differentiate hard samples from easy samples. More importantly, 335 patients’ CT scans from the hospital are further used to evaluate and help optimize the performance of our approach in the real situation, and we develop a system based on this method. Experimental results show a sensitivity of 95.1% at 8 false positives per scan in Free Receiver Operating Characteristics (FROC) curve analysis, and our system has a pleasing generalization ability in clinical data.

This paper presents a reliability assessment approach for Integrated Community Energy System (ICES). The complex energy couplings and interactions in the ICES is simulated by the energy hub model, which connects electricity, gas and heat systems. The quasi-sequential Monte Carlo simulation technique is utilized to select the system state of ICES. A hierarchical decoupling optimization framework is then implemented to calculate the energy loss after outage occurs. Case studies show that the proposed method can accurately evaluate the reliability of ICES.

With the increasing development of the integrated community energy systems (ICES), the interconnection among electricity, natural gas, cooling, heat and fuels systems is extremely highlighted. Based on energy hub model and Monte Carlo method, this paper proposed an optimal approach to evaluate the reliability of the ICES. By implementing the interior point method and the genetic algorithm, the proposed approach can calculate reliability index more effective in terms of calculation time and accuracy. Besides, this paper analyzes the impact of the penetration of renewable energy on the reliability of ICES.

Disruptive events, such as natural disasters, terrorist attacks and improper operations, may result in multiple transmission line outage of power system. In order to evaluate their impacts on power system, the concept of resilience is introduced. On the basis of existing resilience assessment methods, this paper develops a series of quantified resilience assessment indices, consisting of both system-level indices and component-level indices, as well as relevant assessment approach. With the system-level indices, the overall resilience of power system can be estimated. Based on the component-level indices, weak points of the system can be identified. Accordingly, the corresponding improving schemes can be raised to enhance the resilience of the original system. Finally, the proposed approach is tested on the IEEE RTS-79 test system under different disruptive events, and then the optimal improving scheme of this test system is determined based on the obtained resilience indices.

This paper proposes an impact-increment based Monte Carlo simulation (IIMCS) reliability assessment approach, specially designed for transmission systems. First, the impact-increment based formula of reliability indices is deduced by partially transferring the impact of higher order contingencies to the corresponding lower order ones. Then this formula is transformed into a new formation to adapt to the Monte Carlo sampling process, and IIMCS approach is proposed accordingly. Moreover, a reduction technique is proposed to decrease the number of higher order contingencies without decreasing the accuracy. Consequently, the computational performance is effectively improved by the proposed approach. Case study is performed on the IEEE 300-bus test system. Results indicate that the proposed method is more efficient than traditional Monte Carlo simulation in large scale transmission systems. Also, the efficiency can be controlled by modifying the preset parameters to adapt to different scenarios.

An impact increment-based state enumeration (USE) method is designed to assess reliability of composite generation and transmission systems. The reliability index calculation formula of the traditional state enumeration technique is transformed into an impact increment-based formation. Based on the derived equation, the traditional state enumeration method is modified into an impact increment based one. With the proposed method, calculation of state probability are effectively simplified, and reliability indices of high accuracy can be obtained with only low order contingency states. When it is applied to composite systems, load fluctuation can also be considered by replacing impact increments with their expectations under various load levels. Case studies are performed on the RBTS system and the RTS-79 system. Results indicate that annual reliability indices can be efficiently obtained with the USE method. Comparing with traditional state enumeration and Monte Carlo simulation methods, the proposed method is more precise and efficient.

This paper proposes an impact increments-based state enumeration (IISE) reliability assessment approach, specially designed for transmission systems. Firstly, the reliability index calculation formula of the traditional state enumeration technique is transformed into an impact increments-based formation. With the derived formula, the calculation of state probability is simplified and the weight of low order contingency states is increased. Moreover, it is proved in this paper that impact increments of mutually independent high order contingency states, whose outage components are with remote distances, can be eliminated. Due to this fact, the calculation of impacts of most high order contingencies can be reduced without decreasing the precision of reliability indices. When applied to electrical power transmission systems, load variations can also be considered by replacing impact increments by their expectations under various load levels. Thus, annual reliability indices can be obtained by the proposed method. Case studies are performed on the IEEE-118 bus system and a 1354-bus portion of European transmission system from PEGASE project. Results indicate that annual reliability indices can be efficiently obtained with IISE method. Comparing with the traditional state enumeration and Monte Carlo simulation methods, the proposed method is more precise and efficient in various kinds of transmission systems.

With the increasing utilization of combined heat and power plants (CHP), electrical, gas, and thermal systems are becoming tightly integrated in the urban energy system (UES). However, the three systems are usually planned and operated separately, ignoring their interactions and coordination. To address this issue, the coupling point of different systems in the UES is described by the energy hub model. With this model, an integrated load curtailment method is proposed for the UES. Then a Monte Carlo simulation based approach is developed to assess the reliability of coordinated energy supply systems. Based on this approach, a reliability-optimal energy hub planning method is proposed to accommodate higher renewable energy penetration. Numerical studies indicate that the proposed approach is able to quantify the UES reliability with different structures. Also, optimal energy hub planning scheme can be determined to ensure the reliability of the UES with high renewable penetration.

In the power system, there probably exist some certain risks during the dispatching operation when it is determined by a fallacious recognition of system conditions. A risk assessment approach for dispatching operations is proposed. The risk of dispatching operations is defined as the overload or voltage violation induced after operations. A critical equipment search technique used for contingency searching is developed to enhance computational efficiency. The risk indices thus gained can be used to monitor the dispatching operations, and help operators to determine more credible operations or daily operation plan. The correctness and effectiveness of the proposed approach are validated by IEEE-118 bus test system and the Polish power system.