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The dynamic behaviors of microgrid become more complicated due to the increasing implementation of distributed energy generation, energy storage. It is significant to study the dynamic response for power planning, analysis, and control of microgrid. Hence, an accurate dynamic equivalent model is essential, since it can help to evaluate the performance by simulation to avoid the loss and danger in practical test. However, the dynamic model based on differential equation cannot be established because of the lack of information in most of time. To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper. The proposed neural network can be treated as a black-box differential–algebraic equations. The structure design and model training procedure are presented in detail. Study cases are implemented to evaluate the performance of modeling method. The comparison results show that the proposed dynamic modeling method can precisely estimate the dynamic response of microgrid.

In the power sector, microgrids play a supportive role in bridging the adequacy gap in the conventional electricity supply. Trading of the generated energy has recently been improved by blockchain technology which offers a new cheap, secure, and decentralized transaction approach. Its operation is however associated with an undesired inherent delay during energy transactions initiated by the prosumers, thus, failure to timely attend to incidences of urgent demand could end up in catastrophe at the consumer's side. This article thus proposes a cyber-enhanced transactive microgrid model using blockchain technology with optimized participants' permission protocol to ameliorate this challenge. It is demonstrated that the optimized blockchain participants' permission model leads to improved transaction speed and greater convenience. The transaction speed simulation is thereafter performed and it was also demonstrated that the node population has a greater effect than the transaction block size on the transaction speed improvement.

The virtual synchronous generator (VSG) technology of inverter is widely used to provide the inertia and damping support for power system. However, an additional measurement device PLL (phase-locked loop) is required in the virtual synchronous generator grid connection to track the voltage phase, amplitude, and frequency, which restricts the flexible output of the distributed power generation system. To tackle this challenge, a method for grid-connected control of virtual synchronous generator based on virtual impedance is proposed. It is assumed that there is a virtual power exchange between the synchronous machine and the power grid when the virtual synchronous generator is off-grid, the virtual impedance is developed to calculate the virtual current, and when the virtual current is zero, the output voltage of the VSG can be synchronized with the voltage of the power grid, thereby seamlessly switching between off-grid and grid-connected VSG. A semiphysical simulation platform is built based on RT-LAB; simulation and experimental results show that the proposed grid synchronization control strategy of the VSG can achieve seamless transform between different VSG modes, which is simpler than the conventional synchronization control, while having a good active and reactive power tracing performance.

In this paper, we report a novel demodulation algorithm of particle swarm optimization based simulated annealing (PSO-SA) for multiplexing fiber Bragg grating (FBG) sensor networks. We theoretically prove that multiple FBGs can be demodulated in a 2, 3 and 4-grating-network respectively even though the grating spectra are fully or partially overlapped and the demodulation resolution can reach 10 pm. Wavelength demodulation of a 4-grating-network with grating spectra overlapped is realized experimentally. To the best of our knowledge, this is the first time that four fully overlapped gratings are recognized. This method may find implications in massive multiplexing FBG sensor network.

With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.

In this paper, a frequency control method for off-grid operation of energy storage systems has been proposed with virtual synchronous control. Firstly, the single-machine mathematical model of the power converter system (PCS) controlled by the virtual synchronous machine is established. Secondly, energy storage systems based on virtual synchronous control have been analyzed. Finally, two battery energy storage systems (BESS) were built on the SIMULINK simulation platform to realize island operation of the energy storage system, which verified the effectiveness of the proposed control strategy.

In order to absorb large-scale clean energy for power grid, the use of energy storage technology is paid more and more attention. However, most of the current researches are focused on a single kind of energy storage forms, and few are involved in the joint use of multiple energy storage devices. This paper aims at the insufficient application of a single energy storage device and the conflict between multiple energy storage technologies. A demand-side integrated flexible load regulation optimization method is proposed. For the heat storage electric boiler system and the ice storage air conditioning system, the combined algorithm is used to optimize their operation. It is clear that the combined algorithm based on genetic algorithm combined with cold, hot and electrical system has a more significant effect of absorbing clean energy than the single energy storage system. And, the example result proves the correctness and feasibility of the proposed comprehensive demand side response strategy.

With the power data becoming more and more complex, it is of great significance to reduce the dimension and reconstruct the power data for the subsequent processing and application of power big data. This paper presents an algorithm based on variational mode decomposition (VMD) decomposition and orthogonal matching pursuit (OMP) reconstruction to reduce the dimension of power load data. Firstly, VMD method is used to decompose and filter the power load data; Secondly, K-means clustering algorithm is used to synthesize the decomposed data mode into a cluster center data set. Then, the data set is used as a dictionary; Thirdly, an OMP algorithm is proposed to select the elements to be reconstructed in the dictionary. And the corresponding data weights are given to reconstruct the load data with high-precision. As a result, the dimensionality of massive load data is reduced automatically. Finally, this paper builds a flexible load data dimensionality reduction model based on VMD method to verify the proposed method.

Recently, decentralized optimization (DO) has received widespread attentions. Since the scale of DO is usually very large, research on acceleration has become a hotspot. However, the error caused by the global information deficiency for each agent becomes the key problem cumbering the existing elaborately designed accelerated methods from being applied to DO, directly. On the other hand, recent studies illustrate that a group of accelerated methods can be covered from a viewpoint of momentum. In this paper, we propose to follow this methodology to design accelerated algorithms and adapt them to DO over time-varying directed networks, of which the main benefit is that, because the proposed algorithms are derived from momentum, they not only avoid the design of elaborate iterative structures but also inherit the physical interpretability of momentum. Furthermore, we show that our proposed algorithms can achieve sharper convergence rates than their competitors under the same condition. In the end, experiments on a number of benchmark datasets validate well the competitiveness of our algorithms.

Due to the rapid development of electric vehicles, the randomness of charging and discharging modes brings huge challenges to load forecasting. Considering the large-scale gridding of electric vehicles, a load combination forecasting algorithm is proposed in this paper which considers the flexibility of electric vehicles. Firstly, the user behaviors of electric vehicle are modeled based on Monte Carlo method. Secondly, different from traditional single forecasting models, the combination of deep belief network and long short-term memory net-work is used in order to improve the forecasting accuracy and calculation speed for the complex case. Thirdly, a dynamic weight distribution method is proposed to combine the two forecasting results. Finally, Numerical examples have been performed with the results proving that the proposed method outperforms the state-of-art significantly in load forecasting, and the combined model has a higher forecasting accuracy than traditional methods.

Image retargeting technique requires important information preservation and less edge distortion during increasing/decreasing image retargeting. In order to preserve the important contents, the saliency feature and depth feature are used to differentiate the foreground and background information. The edge information is used to improve the seam carving algorithm on smooth object contour maintenance. Therefore, the multi-modality content-aware retargeting (MCR) algorithm is proposed to improve the retargeting results efficiently. Because of the difficulty on methodologically evaluate image retargeting results, the majority methods do the small sample comparison and visual assessment, which is tedious and unstable on quality. In this paper, the automatic image similarity measurement between images with different sizes is proposed, the variable scale similarity measurement (VSSM). Through doing experiments on RetargetMe, the well-recognized image retargeting database, the feasibilities of MCR and VSSM are proofed with promising results.

The principle of discernibility matrix serves as a tool to discuss and analyze two algorithms of traditional inductive machine learning, AQ11 and ID3. The results are: (1) AQ11 and its family can be completely specified by the principle of discernibility matrix; (2) ID3 can be partly, but not naturally, specified by the principle of discernibility matrix; and (3) The principle of discernibility matrix is employed to analyze Cendrowska sample set, and it shows the weaknesses of knowledge representation style of decision tree in theory.

In this paper, a novel evolutionary algorithm is proposed for solving route planning problems. Firstly, the route planning attributes were analyzed in detail. Secondly, the constraint expressions, evaluation function, and genetic algorithm were presented. Finally, a numerical example described in this paper shows that the genetic algorithm can generate routes very quickly and can efficiently produce high quality results for hard combinatorial real world problems.

In order to overcome the lack of depth information in monocular vision measurement,an improved two-stage iterative algorithm was proposed to optimize the posture and attitude measurement models.The characteristics of the target layout optimization were given.The relationships between the calculating accuracy with the target size,baseline length and characteristics of the target dot size were analyzed theoretically.Experimental results show that optimizing the layout of the target characteristic, the larger the length of the baseline and smaller characteristics of the target dots can improve the measurement target solver accuracy.The actual research has a certain guiding theoretical significance to improve the practical measurement system calculation accuracy.