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Due to the random volatility, a large amount of renewable energy will bring challenges to the security and stability of distribution network. Comprehensive consideration of system economics, security and flexibility has become the focus of research on distribution network optimization planning under the new situation. For the flexible resource allocation problem of distribution network, this paper analyzes the supply and demand relationship of the flexibility of the distribution network, and establishes a bi-level operation-planning joint optimization model for flexible resources. In the operational layer, we not only introduce the insufficient flexibility rate as the evaluation index of system flexibility, but also introduce the network loss, the abandoned wind and solar energy in the economic penalty, aiming at optimizing the annual operating cost of the system. In the planning layer, we evaluate the system security by introducing the comprehensive security index of the system, aiming at optimizing the annual comprehensive cost of the system. In addition, this paper also considers the third-party companies' investment in energy storage system in the electricity market, and further analyses the impact of energy storage operation strategies under different investors on flexible resource allocation. In this paper, the particle swarm optimization algorithm is used to solve the bi-level allocation model. Finally, the IEEE 33-bus test system is used for verification and analysis of the case. The results verify the validity and rationality of the proposed bi-level allocation model.

In this work, α-In 2 Se 3 based transistors with channel widths of 20 μm have been irradiated with 60Co γ-rays. The radiation induced photoresponsivity degradation, radiation stability, and time annealing effect on α-In 2 Se 3 based transistor have been investigated. Both the radiation induced domain evolution and the time annealing effect decrease the photoresponsivity of α-In 2 Se 3 based transistor. Phonon modes, domain structure, and surface topography of the α-In 2 Se 3 nanoflakes have been investigated in details for revealing the mechanism of the photoresponsivity degradation. The results show that the surface topography damage of α-In 2 Se 3 may be the main factor affecting the radiation stability in the time annealing. It is very interesting that the in-situ electric stress can heal the photoresponsivity decreased by the time annealing effect, because the captured free carriers can be freed by the voltage sweeping. Considering that the α-In 2 Se 3 based transistors can still be used as photodetectors after an irradiation of 1 Mrad(Si) and a time annealing of one year, α- In 2 Se 3 is promising for photodetector in extreme environmental conditions.

The impact of Ge profile on the TID susceptibility of SiGe HBTs from two manufacturers is investigated based on TCAD simulation. The results indicate that the modulation of potential distribution across the base region by Ge grading has significant impacts on the base current degradation. The Box Ge profile can cause a strong retarding potential near the EB junction, thereby leading to high recombination rates at the trap-enriched EB-spacer/SiGe interface. While Ge profiles with high accelerating field near the EB junction can prevent the injected carriers from diffusing to the EB-spacer/SiGe interface and suppress the increase of excess base current. Besides, this impact of Ge profile is closely related to the base thickness and integrated Ge content.

The power market reform broke the monopoly operation mode of vertical integration of power grid company, and the open market environment gave birth to diversified market participants. In order to balance the interests of various market entities and maximize the total social benefits. This paper proposes a double-layer optimal configuration model of active distribution network with multiple market entities under power reform. The upper model considers the maximum economic benefits of energy and service providers, including distributed generation investors, distribution companies and energy storage operators, and considers the energy storage operation strategies under different investment entities. The lower model considers the user side economic benefits and ensures maximum user satisfaction through demand response. In this paper, the model is simulated by IEEE33 distribution network system. The results show that the model can effectively improve the interests of various market entities and improve the total social benefits.

With the reform of the power market, the distribution network side is gradually released, and the investment on the distribution network side has a huge impact on the distribution network planning. In particular, investment in energy storage has become the focus of investment in distribution networks in recent years. This paper establishes a comprehensive evaluation system for active distribution network planning considering economic, environmental protection, power supply quality and safety assessment. The grid companies and third-party companies are used as different investment entities to analyze the energy storage operation strategy. Based on the comprehensive evaluation system and energy storage operation strategy, a multi-objective optimization model for distribution energy resource in active distribution networks was established. Finally, the example is simulated by using the "2 supply and 1 standby" medium voltage distribution network topology. The simulation results verify the feasibility ADN effectiveness of the model.

With the continuous enrichment of demand-side resources in the active distribution network, a good source-charge interaction mechanism can take advantage of the complementary characteristics of power generation resources and demand side resources in the active distribution network to promote new energy consumption and cost optimization. In this regard, based on the source-charge interaction response model, this paper proposes a distributed power bi-level programming method that considers power-user interaction. The upper model of the method aims at the comprehensive optimization of economic and environmental indicators, and determines the location and capacity of the distributed power supply. The lower model aims to minimize the annual operating cost after implementing the interactive response. Control distributed power output and load demand response power. Finally, the improved particle swarm optimization algorithm is used to solve the model and verify the effectiveness of the method.

The coordination of multiple VSGs connected in parallel to form a microgrid is the main concern nowadays. In this paper, the relation between the two VSGs is formed; one is operating as a source, whereas other as a load. This correlation defines the maximum power that can be delivered to the VSG controlled energy storage system (ESS), the influence of a load (static), and the effect of contingency (dynamic) on both VSGs. Furthermore, a unique control is presented to draw the constant maximum power from renewable energy source (RES) by interlinking it with the stable parallel-VSG; without compromising the ability of transient stability to dampen any disturbance. The results are illustrated through power and angular frequency graphs.

The different variants of droop controls for the multiple Virtual Synchronous Generator (VSG) sources to form an island microgrid is discussed in this paper. The droop control ω-P is basically a decentralized control that defines the frequency variation of a microgrid on the increase in load. The control equation for the equivalent ω-P slope for two VSGs is derived to see the effect of a load on the overall system's frequency. In the parallel VSG system, the power distribution among the multiple VSGs is dependent on the droop control. The correlation of multiple VSGs is presented in this paper that can bring the firm coordination between the different sources in stabilizing the system; by finding the dependency of each VSG on a microgrid. Furthermore, the master-slave control is introduced to acquire the maximum power of one VSG (e.g. solar) by following the instant frequency of a system as a reference, instead of constant reference frequency. Following controls are verified by performing the experiments on an island microgrid system and the results are shown through graphs.

The electricity load of large consumers has the characteristics of fluctuating randomly in a short period and changing periodically over a longer time scale. In recent years, the influence of these kind of consumers on regional load has been increasing year by year, which has become an important factor to improve the forecasting accuracy of the entire load. In this paper, we use parameters-improved Support Vector Machine (SVM) with better learning capability based on small samples to fit the random fluctuations of these loads. Besides, we introduce a concept of similar time period in order to dig up the periodic trend shown in a long period of the loads. The simulation result of a typical large consumer shows a high forecasting accuracy of the load, which shows the effectiveness of the algorithm.

Ultra wide band (UWB) life detector can penetrate walls and ruins to detect whether there is a sign of life on the rear. This device will play an important role in anti-terrorism and disaster relief. But because the life signal is low frequency, quasi-periodic, non-stationary and low signal to noise ratio (SNR) and so on, such features restrict the accuracy of identifying life signals. According to the characteristics of life signals, back propagation (BP) neural network is chosen as the life signal recognition method, and the proper network structure is designed with appropriate network parameters selected. Simulation results show that, using BP neural network method can greatly improve the life signal identification accuracy; it is proved to be an effective method in the practical project.

The key of objectivity of traditional Chinese Medicine (TCM) pulse condition lies on the objective test and correct recognition for all kinds of pulse conditions. In view of the ambiguity, variety and complexity of TCM pulse conditions, and the shortcomings of the traditional recognition methods and back propagation (BP) neural network method, a kind of TCM pulse-condition recognition method based on probabilistic neural networks (PNN) is put forward. In this research three experienced practitioners of Xi'an TCM Hospital gave us their expert advice and determined the percentage of correct recognition of our PNN method. For the twelve kinds of TCM pulse conditions, we attained an average recognition accuracy of about 93% with PNN method, better than the average recognition accuracy of about 75% attained with the traditional fuzzy cluster method and 87.1% with BP neural network method. The contrastive experiments of the BP neural network method and the PNN method are given. The results show that recognition accuracy of PNN method is out and away higher than that of BP neural network method in high noise circumstances.

In this paper, a method to simulate the electromagnetic parameters of materials with quasi-periodic microstructures is presented and the parameters of fly-ash cenosphere particles are simulated. Results prove that the permittivity and permeability by this method coincide with that from the Bruggeman formula. By applying this method, it is possible to propose some strategies to improve the current cheap fly-ash cenosphere particles to be used as EMI-shielding materials and microwave absorbing materials.

Basing on the posteriori error estimation and Superconvergent Patch Recovery (SPR) technology, Elena Zhelezina proposed a fast adaptive mesh refinement strategy which can generate the refined mesh for the required accuracy with only one step of refinement. However, the strategy generates too many elements in PPFDs which is unpractical to simulate the PCBs and IC packages with complicated multiple layers, where the electromagnetic field in PPFDs is calculated, since too many elements lead to the FEM sparse matrices can't be solved on current machines because of inefficient memory. In this paper, a new formula to estimate the global error is introduced, and then, a new strategy to calculate every element's refinement degree is presented. Results show that the improved method generates less than half of elements comparing to Elena Zhelezina's method, but still requires only one step refinement. This mesh refinement strategy was successfully applied to calculate the electromagnetic field in PPFDs for simulating the PCBs and IC packages with high accuracy.

A domain map finite element method was applied to the three dimensional (3-D) numerical mode-matching (NMM) method to solve the 3-D electric logging problem. This method divides the infinite field domain of every layer into inner and outer field domains, in inner domain the matrices are formed by standard FEM and in outer domain, the Kelvin transformation is applied and the matrices are formed by a new infinite element format. Matrices of two different parts are coupled in the domains' common interface to form the final matrices of the original problem. This method generates smaller size of generalized matrices than original 3-D NMM method. In addition, this method changes the distribution of eigenvalues which needs less eigenvalues under the same error. Results show that the new method spends only 1/7 CPU time and uses only 1/3 memory of the original method.

Based on the three popular open sources as Struts, Spring and Hibernate (SSH), an integrated framework for e-human resource (E-HR) is proposed in this paper as an effective web application framework. E-HR is generally regarded as a complicated enterprise information system and the proper application framework is supposed to improve the system expansibility and the enterprise competitiveness as well. The presented framework derives from model-driven approach with high usage of code generation tools and application layout templates. It not only separates the presentation layer, business layer and data persistence layer, but also provides a convenient platform for software reuse. Furthermore, it allows for speeding-up development of Web applications. By comprehensively analyzing the distinct characteristics of the framework such as scalability, maintainability and less coupling complexity, the specific application of the framework in the file management module of the E-HR system is carried out to further shows the advantages of SSH in software development efficiency.

With highway traffic of China developing faster and faster, safety problems are gradually becoming more and more obvious. Such problems are big threat to both peoplepsilas lives and their property. Some safety researches have been done by Predecessors. Based on these researches, this article is to employ a highway safety evaluation system made of running velocity. By using the BP neural network algorithm in MATLAB software, simulations were made to evaluate the linearity of a certain highway. And the results come out at last to prove that the method contains a certain applicable value.

The problem of delay-dependent robust stabili-zation stabili-zation stabili-zation for uncertain system with time-delay is considered. By means of the Lyapunov function and linear matrix inequalities (LMIs) technique, a delay-dependent sufficient condition is derived for the existence of a state feedback controller, which ensures asymptotic stability of the closed system for all admissible uncertainties. Unlike previous methods, the mathematical development avoids system transforming and bounding certain cross terms which often lead to conservatism. In terms of the solutions of LMIs, the robust stabilization controller is presented. Finally, an illustrative example is given to demonstrate the effectiveness of proposed method.

The advanced static synchronous compensator (STATCOM) is a device that can provide reactive support to bus voltage. A novel control scheme based on reinforcement learning algorithm for the STATCOM is presented in this paper. Design of STATCOM controller presented does not depend on structure and parameters of power system. The adaptive heuristic critic algorithm is adopted in this paper and the parameter of the controller are updating by given reinforcement signal of system. Only local information is required. A simulation example of single-machine infinite power system is given, and the result shows the effectiveness on maintaining voltage and preventing voltage collapse.

A novel scheme based on the principle of reinforcement learning algorithm was proposed for the voltage control in power system. The controller adopted the adaptive heuristic critic (AHC) algorithm, which consists of two networks: action selection network and action critic network. The controller parameters are adjusted by given reinforcement signal of system. The numerical simulation example of single-machine infinite power system is given, and the result shows the designed control scheme can maintain desired voltage profile and prevent/stop a developing voltage collapse in system contingencies.

The ABR service of ATM networks uses a rate-based feedback control mechanism to adapt to varying link capacity left by high-priority CBR/VBR transmission. Control theory is proposed to offer the general means for designing congestion control algorithms for ABR service. Under the consideration of the effects of multiple loop delays and high-priority CBR/VBR transmission, this paper proposes a GPC (general predictive control) method to control traffic where sources adjust their transmission rates to avoid congestion. The controller keeps the queue level at the bottleneck node to the desired steady-state value and satisfies fair bandwidth distribution. Simulation study verifies the effectiveness of the algorithm.