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The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control.

A wide-area damping controller (WADC) is effective in damping inter-area low-frequency oscillation (LFO), if the time delay in a wide-area control loop can be properly handled. In order to simplify the WADC design and enlarge the delay adaptation range, the classic power system stabilizer (PSS) is adopted, and a new unified residue (UR) method is proposed for compact WADC design. The strategy of control loop selection is also improved by modifying the relative residue index based on a few dominant oscillation modes. The designed PSS-based compact WADC is as simple as classic PSS with no more than two lead-lag phase compensation units. Case studies are carried out on an IEEE 16-machine 68-bus power system. Simulation results demonstrate that the control loop selection before the WADC design is necessary and that the proposed selection strategy can easily pick out the suitable candidate control loops. In addition, it is feasible for the UR method to design WADCs with different time delays in the selected control loops. All the designed WADCs are effective in damping inter-area LFO and robust to time delay variations under operation conditions. Comparisons among five design methods for PSS-based WADC show that the proposed UR method is superior in delay adaptation, the conciseness of WADC structure and computation speed of parameters.

This paper proposes a bi-layer game-theoretic framework for the interactive energy management (IEM) of multi-microgrids (MMGs) under the presence of uncertainties imposed by both supply and demand sides. The higher-layer of the framework manages the energy trading and the consumption behavior for each microgrid (MG) within a slow timescale, where its operation cost model is designed in terms of economic factors and users’ willingness. The lower-layer runs at higher frequency that relies on a model predictive control approach, and adjusts the operations of MGs to minimize the difference between the planned strategies generated by the higher-layer and the real ones. In doing so, the supply and demand uncertainties as well as the outage events can be handled properly. As the internal prices are adjusted with system net load, the power balance levels in MMG would be enhanced by minimizing operation cost. On this basis, the energy trading among MGs can be achieved directly without an intermediary. We design a decentralized interactive algorithm to implement the bilayer IEM framework by using the Nash equilibrium concept. Furthermore, the emergency situation is covered to enhance the resilience of the system. Numerical simulations on a practical MMG verify the effectiveness of the proposed models.

Different from most existing studies that focus on offline demand side management (DSM) in microgrids (MGs) while neglecting forecasting errors of uncertain renewable generations, this paper studies online DSM. A two-stage real-time DSM method for an MG including different time scales, integrated with schedulable ability (SA) and uncertainties, is proposed. In the first stage, a novel internal pricing model is developed. On this basis, a model predictive control-based dynamic optimization is applied to minimize the operation cost and maintain the power balance considering the uncertainties imposed by both supply and demand sides in the MG system. In the second stage, we define the concept of SA for response executors (REs) and also establish an SA evaluation system taking the real-time and history information of the REs into account. In doing so, a faster-time scale online power allocation among REs is carried out in the framework of dynamic optimization to further compensate for the uncertainties in real-time, based on the evaluated SA values of the REs and the required compensation power. Numerical simulations on a residential MG show the reasonableness and effectiveness of the proposed method.

For the power quality problems that exist in the active distribution network with distributed generation, this paper predicts the power quality data in active distribution combining with various external factors affecting the power quality. Considering that the traditional BP neural network prediction is easy to fall into the local optimum solution, a new prediction approach based on K-means clustering and BP neural network for the steady-state indexes of power quality in an active distribution network is proposed. By adding the clustering step before prediction, the power quality influencing factors such as input time, light intensity, temperature and loads are clustered. After clustering, each type of error-minimum BP neural network is trained separately, and then the influencing factors of the prediction are identified by class markers. At last, the best BP neural network model of the corresponding category is called to predict the power quality of the active distribution network. The analysis of the example shows that compared with the traditional BP neural network prediction method to predict the steady-state indexes of the power quality, the use of clustering and neural network for active distribution network quality prediction can improve the prediction accuracy and has good feasibility.

The brain experiences alterations including cerebral ischemia and tissue damage after focal ischemic stroke. A thorough understanding of the spatiotemporal dynamics of blood perfusion and tissue damage is of great importance in stroke research. In chronic rat photothrombotic (PT) stroke model, a parallel study of both vascular and cellular responses to regional ischemia was performed with optical coherence tomography (OCT) in a label-free and depth-resolved manner. OCT revealed that vessels of different types and depth presented various spatial and temporal dynamics. In the ischemic core area, the distal middle cerebral arteries (dMCAs) were blocked gradually with laser irradiation and a spontaneous recanalization was observed at Day 5. In the chronic recovery period, the blocked small pial microvessels presented an apparent neovascularization progressing from the peripheral into the core area, with the final blood flow volume exceeding the baseline before PT. While the cortical capillary perfusion of the core area totally disappeared at Day 3 after PT and never recovered in the core area till the end of observation. The results demonstrated that blood reperfusion mainly occurred in the dMCAs vessels and pial microvessels of the superficial layer, but not in capillaries located deep in the cortex. The response of the cellular scattering and tissue damage showed a high spatial and temporal correlation with the capillary perfusion. On the whole, ischemic area and lesion area from attenuation coefficient are not exactly the same but complentary, with great help in understanding stroke mechanism comprehensively.

In this paper, a new fault-tolerant approach based on improved particle swarm optimization (IPSO) is proposed to automatically locate the transient voltage disturbance source (TVDS) for smart grid with distributed generation (DG) integration. We first analyze the influence of the DG integration on the TVDS direction-judgments. Two new credibility indexes, the monitoring-credibility and the partial-credibility, are defined to measure the reliability of direction-judgment result at each power quality monitor (PQM) with consideration of multiple factors, including DG integration, disturbance intensity and fluctuation characteristic. By using these credibility indexes and a newly defined search space, a heuristic searching approach, called IPSO, is then proposed to obtain the optimal solution of the TVDS location. Simulation study is carried out on the IEEE 34 node test feeder, and the results demonstrate that the proposed approach has significantly improved fault-tolerant capability with satisfatory convergence speed.

The optical attenuation coefficient (OAC) estimated using optical coherence tomography (OAC-OCT) offers a label-free 3D mapping of tissue infarction, but the physiological origin of the OAC contrast remains unclear. For effectively suppressing OAC fluctuations, we propose a hybrid (wavelength/angle) division multiplexing (HDM) method, which improved the OAC contrast by 70.7% in tissue phantoms. To test the feasibility of OAC-based infarction detection, triphenyltetrazolium chloride (TTC) staining was performed on fresh ex vivo brain slices, and the TTC-defined infarction was used as the ground truth. Sharp OAC contrast was observed between the TTC-defined infarction (1.09 mm(−1)) and normal tissue (0.79 mm(−1)). The OAC infarction spatially matched well with the TTC-defined infarction. To further explore the physiological origin of OAC contrast in ischemic stroke at the cellular level, the dynamic changes in OAC were measured in the rat cortex in vivo over 3 weeks after photothrombosis (PT) occlusion and found significantly correlated with the changes in astrocytes and neurons acquired with ex vivo hematoxylin and eosin (HE), glial fibrillary acidic protein (GFAP), and NeuN staining. These results suggest that OAC imaging enables non-invasive infarction detection and its contrast might originate from the changes in astrocytes and neurons in the chronic PT stroke model. The cellular responses revealed by in vivo OAC imaging would be essential for evaluating treatments and even developing novel therapies.

A neutral point voltage feedback control strategy based on double modulation waves carrier-based PWM for three level converters is proposed in this paper. Using this strategy, neutral point voltage can be adjusted at will, output AC current can be guaranteed no distortion caused by imbalanced neutral point voltage, and the neutral point fluctuation can be eliminated completely. The strategy is suitable to enhance conventional neutral point balancing control or applications where need to control up and down DC link voltage separately. The control limits of neutral point current when using proposed strategy is given. Simulations and experiments are made to verify the correctness of the theory.

Aiming at minimizing the customer's charging costs, a charging and discharging dispatching strategy for electric vehicle (EV) is proposed based on the customer's benefit, while the charging and discharging price, battery characteristics and charging requirements are taken into account. Customer decides whether to respond to the charging and discharging plans or not freely, which are determined by dynamic programming algorithm, hence the proposed dispatching strategy for EVs can be realized. Taking the 38-bus system as an example, firstly, the charging performance of customer is obtained by Monte Carlo simulation. Then the owner's costs and the impacts on the distribution system under uncoordinated and coordinated control modes are compared. At the end of this paper, based on the different probabilities of customers' response to the dispatching strategy, the owner's costs and the load fluctuation rate of the distribution system have been simulated and discussed. Simulation results indicate that, through the previously mentioned dispatching strategy, reducing the customer's costs can be achieved as well as peak load shaving and valley load filling on the basis of customer's satisfaction in charging demand. Another conclusion is, the more often the customers respond to the proposed strategy, the better it is for the win-win relationship keeping between the customers and the distribution system.

Partial shading or mismatching of series-connected photovoltaic (PV) arrays makes conventional maximum power point tracking (MPPT) techniques ineffective. Although improved MPPT algorithms are adopted, the power mismatching problem cannot be solved fundamentally. Because of this, significant solar energy is wasted, which raises the cost of solar power generation. In this paper, series-connected photovoltaic modules are divided into several groups, which become the multiple dc units of multi-level inverter. The benefit is that, the voltage of each DC unit in multi-level inverter can be adjusted independently, with no output AC voltage distortion caused by unbalanced dc unit voltage. Based on this principal, each PV group's MPPT control can be done independently, and the overall generated electric energy is increased. The control strategy of a three level PV inverter of this kind is detailed analyzed. Simulation and prototype are made to verify the goodness of proposed strategy.

A power loss estimation method for IGCT NPC-3L converter is discussed in this paper. The undetermined coefficients in switching loss calculation are obtained through double-pulse test. Traditional loss calculation method based on formula is restricted by one particular modulation strategy and the estimation is inaccuracy due to idealized working conditions. To solve this problem, a universal NPC-3L power loss simulation estimator suitable for any modulation strategies is presented. Output current ripple and DC link voltage fluctuation are also considered in the estimator. Through power electronic building block (PEBB) half bridge power test, the accuracy of the estimator is verified to meet the engineering requirements.

This paper proposed a fault-tolerant approach based on information fusion (IF) to automatically locate the transient voltage disturbance source (TVDS) in smart distribution grids. We first defined three credibility factors that will influence the reliability of the direction-judgments at each power quality monitor (PQM). Then we proposed two rules of influence and a verification factor for the distributed generation (DG) integration. Based on the two sets of direction-judgment criteria, a novel decision-making method with fault tolerance based on the IF theory is proposed for automatic location of the TVDS. Three critical schemes, including credibility fusion, conflict weakening, and correction for DG integration, have been integrated in the proposed fusion method, followed by a reliability evaluation of the location results. The proposed approach was validated on the IEEE 13-node test feeder, and the TVDS location results demonstrated the effectiveness and fault tolerance of the IF based approach.

Rapid development of web 2.0 and social networks brings convenience for users' information sharing. The thriving of information sharing inevitably leaves users' private information vulnerable to leakage. Weighted social networks can provide more personal information than unweighted social networks, such as those weight based information. Weight based information (weight distribution, shortest paths etc.) can be not only the objects needing preserving but also clues grasped by adversaries to initiate identity re-identification. Most of current work however overlooks this kind of privacy leakage incurred by adversaries' grasping of some weight distribution information. In this paper, we propose a Hellinger distance based privacy model (k, λ)-similarity to surmount the problem of sensitive identity re-identification leveraging background knowledge of weight distribution. Particularly, a sliding window and binary approximation based data perturbation algorithm SWBADP is devised to realize (k, λ)-similarity privacy constraint. Further, concerning the potential privacy leakage originated from merely k-degree anonymous, an optimization criterion, called depth clustering, is discussed to address the problem. The empirical studies demonstrate our implementation delivers both well defense capability to sensitive identity re-identification and better weight based data utility.

Let G be a simple graph and Gc be the complement of G. Let ω(G) denote the number of components of G. As in Catlin et al. (1992), for a nontrivial graph G, the strength of G is where the minimum is over all subsets S ⊆ E(G) such that ω(G − S) > ω(G). The fractional arboricity of a nontrivial graph G is where the maximum runs over all subgraphs H with | V(H) | > ω(H). In this note, we shall present Nordhaus-Gaddum types of inequalities on the strength and the fractional arboricity of a graph G. In particular, we show that if G is a simple graph on n ⩾ 4 vertices, then each of the following holds: where d ϵ [γo], [γo] + 1 in (e) and (f), and. Moreover, all the bounds are best possible.

A model of comprehensive evaluation of regional power quality based on multilevel extension method is first put forward in this paper. A new index system is established, with the characteristic indexes include voltage level, load capacity, number of users, importance of the user, user sensitivity and quality performance of the subregions. Then, the subjective weighting algorithm based on improved analytical hierarchy process and the objective weighting algorithm based on extension index of power quality are developed respectively. Case study validates the effectiveness of the comprehensive evaluation method of regional power quality.

A new method based on Generalized S-transform (GST) time-frequency analysis and decision-making tree support vector machines (DMT SVMs) classifier for identification of power quality disturbances (PQDs) is presented. Firstly, GST is introduced to analyze the typical PQDs, including the inter-harmonics, where a set of useful characteristics are extracted. Then 50 disturbance training samples are employed to construct the characteristics sets which are applied to train a multi-lay SVMs classifier. Finally, 500 testing PQDs samples are identified using the SVMs classifier, in which N kinds of PQDs are classified by N−1 turns. Results show that the proposed method could detect and classify the PQDs effectively. The classifier has an excellent performance on training speed and correct ratio.

A new method based on generalized S-transform (GST) is presented for detecting and identifying power quality disturbances (PQDs). The GST formula of discrete time series is deduced firstly, and then nine kinds of typical PQDs are formed for GST analysis. As a result of GST analysis, three voltage-based characteristics, one frequency-based characteristic as well as one phase-based characteristic are extracted. According to the comparison of identification validity between GST and S-transform( ST) made in this paper, GST shows its advantage that it can detect not only voltage instantaneous varies, but also PQDs phase variation. Furthermore, the approach is proved to be quite viable for inter-harmonics detection.

This paper gives a brief description of current situation of distributed generation system, and points out that microgrid can run in two kinds of operation modes. The key problems which need to be cautiously considered exist in each operation mode are summarized, and advanced artificial intelligence techniques are adopted to solve those problems as effective tools. The application situation and research status of multi-agent system, artificial neural network, genetic algorithm, fuzzy logic in distributed generation system home and abroad are summarized detailedly. The existing problems and solving thoughts of each artificial intelligence technique are analyzed, and developing directions of artificial intelligence applications in distributed generation system in the future are also prospected.

Multipoint power-quality (PQ) information can be integrated in networked PQ monitoring system, which is the precondition to locate PQ event source in power grid. This paper summarizes several existing methods, then proposes an improved arithmetic to locate PQ event source with networked PQ monitoring scheme. After definition of virtual PQ monitors in systempsilas rooted tree, the coverage matrix and direction matrix can be extended respectively. Based on a simple operation of these two matrices, the location of the PQ event source can be identified accurately and automatically. A radial distribution network with 11 lines is provided to validate the effectiveness of the improved algorithm. Finally, the application process of the algorithm in PQ diagnoses system is discussed.

Sample wave's amplitude is regarded as criterion of vehicle classification and whole sample's data is regarded as character parameter of pattern matching in the vehicle's classification recognition system based on inductive loop detectors. Because of different velocity through inductive loop detector, sample wave has flexibility. When doing automobile type pattern matching, DTW algorithm is adopted to keep similar character consistent. Using MATLAB software, the arithmetic is validated, its feasibility and anticipated effect is obtained.

There are several density functions for graphs which have found use in various applications. In this paper, we examine two of them, the first being given by b(G)=|E(G)|/|V(G)|, and the other being given by g(G)=|E(G)|/(|V(G)|−ω(G)), where ω(G) denotes the number of components of G. Graphs for which b(H)≤b(G) for all subgraphs H of G are called balanced graphs, and graphs for which g(H)≤g(G) for all subgraphs H of G are called 1-balanced graphs (also sometimes called strongly balanced or uniformly dense in the literature). Although the functions b and g are very similar, they distinguish classes of graphs sufficiently differently that b(G) is useful in studying random graphs, g(G) has been useful in designing networks with reduced vulnerability to attack and in studying the World Wide Web, and a similar function is useful in the study of rigidity. First we give a new characterization of balanced graphs. Then we introduce a graph construction which generalizes the Cartesian product of graphs to produce what we call a generalized Cartesian product. We show that generalized Cartesian product derived from a tree and 1-balanced graphs are 1-balanced, and we use this to prove that the generalized Cartesian products derived from 1-balanced graphs are 1-balanced.