In this paper, an efficient methodology is proposed to deal with segmented-time reconfiguration problem of distribution networks coupled with segmented-time reactive power control of distributed ...generators. The target is to find the optimal dispatching schedule of all controllable switches and distributed generators' reactive powers in order to minimize comprehensive cost. Corresponding constraints, including voltage profile, maximum allowable daily switching operation numbers (MADSON), reactive power limits, and so on, are considered. The strategy of grouping branches is used to simplify the formulated mathematical problem and a hybrid particle swarm optimization (HPSO) method is presented to search the optimal solution. Fuzzy adaptive inference is integrated into basic HPSO method in order to avoid being trapped in local optima. The proposed fuzzy adaptive inference-based hybrid PSO algorithm (FAHPSO) is implemented in VC++ 6.0 program language. A modified version of the typical 70-node distribution network and several real distribution networks are used to test the performance of the proposed method. Numerical results show that the proposed methodology is an efficient method for comprehensive cost minimization in distribution systems with distributed generators.
This paper presents results of an artificial stock market and tries to make it more consistent with the statistical features of real stock data. Based on the SFI-ASM, a novel model is proposed to ...make agents more close to the real world. Agents are divided into four kinds in terms of different learning speeds, strategy-sizes, utility functions, and level of intelligence; and a crucial parameter has been found to ensure system stability. So, some parameters are appended to make the model which contains zero-intelligent and less-intelligent agents run steadily. Moreover, considering real stock markets change violently due to the financial crisis; the real stock markets are divided into two segments, before the financial crisis and after it. The optimal modified model before the financial crisis fails to replicate the statistical features of the real market after the financial crisis. Then, the optimal model after the financial crisis is shown. The experiments indicate that the optimal model after the financial crisis is able to replicate several of real market phenomena, including the first-order autocorrelation, kurtosis, standard deviation of yield series and first-order autocorrelation of yield square. We point out that there is a structural change in stock markets after the financial crisis, which can benefit people forecast the financial crisis.
The artificial intelligence (AI) techniques have been widely used in the transient stability analysis of a power system. They are recognized as the most promising approaches for predicting the ...post-fault transient stability status with the use of phasor measurement units data. However, the popular AI methods used for power systems are often "black boxes," which result in the poor interpretation of the model. In this paper, a transient stability prediction method based on extreme gradient boosting is proposed. In this model, a decision graph and feature importance scores are provided to discover the relationship between the features of the power system and transient stability. Meanwhile, the key features are selected according to the feature importance scores to remove redundant variables. The simulation results on the New England 39-bus system have demonstrated the superiority of the proposed model over the prior methods in the computation speed and prediction accuracy. Finally, an algorithm is proposed to interpret the prediction results for a specific fault of the power system, which further improves the interpretability of the model and makes it attractive for real-time transient stability prediction.
Fault location is one of the most essential techniques to maintain the stable operation of power systems. A fast and accurate fault location allows operators to restore power grids faster and avoid ...economic losses. Conventional methods rely on expert knowledge to extract the necessary features (e.g. DWT, DFT). For large systems, more coupling effects of transmission lines require more complex feature engineering, and incomplete features can easily introduce large errors. To overcome this, a deep learning approach without manual feature extraction is introduced to the fault location model under big data application. Towards this end, in the proposed method, the attention mechanism, the Bi-GRU and a dual structure network are applied to analyze the current data from different perspectives. Complete information for the fault features is extracted for the fault location. Based on a time series model and benefit from the ability to internally acquire the information architecture of faulty line, the established model is adaptive to the power grids with very complex topologies. Simulation results indicate that the proposed double-structure model reduces the maximum error and is less affected by noise. In comparison with different structures and different models, the proposed method shows better performance in IEEE 39-bus system.
Rational planning plays a key role in developing and improving operation security and stability of distribution networks (DNs). This paper proposes a novel method to solve the expansion of ...transmission lines (TLs) and the allocation of distributed generators (DGs) considering the uncertainty of load demand and renewable energy. The spanning-tree is a theoretical method specifically tailored to guarantee the radiality operation of distribution networks. Also, to guarantee the stability and reliability of the network, the probability function is used to describe the stochastic nature of load behavior and renewable sources fluctuation. Thus, the most proper installation location and time for TL and DG assets can be identified by modeling the joint expansion planning. The objective of the proposed model is minimizing the present value of the total social costs, including the cost of investment, purchasing energy, and environmental benefits. This mixed-integer nonlinear problem (MINLP) optimization model is solved based on GAMS environment. The effectiveness and scalability of this model is validated and illustrated based on the IEEE RTS-24 system.
The absorption and output characteristics of reactive power of the doubly-fed induction generator (DFIG) greatly influence the voltage stability of PCC (Point of Common Coupling) where the wind farms ...are integrated into the bulk power grid. This study proposes a reactive power compensation strategy for coordinated voltage control (CVC) of PCC with large-scale wind farms to achieve the expected voltage quality of the power grid through a minimum amount of control actions in emergencies. To this end, the mechanism of reactive power and voltage control inside DFIG is first analyzed. Then, the concept of reactive power reserve (RPR) sensitivity concerning control actions is introduced and an index of voltage stability margin is proposed to evaluate and analyze the distance between the current operating point and the voltage collapse point by analyzing the relationship between reactive power reserve and voltage stability margin. In the event of an emergency, critical reactive power reserves are obtained to reduce the dimension and complexity of the control problem. The sensitivity of reactive power reserve and the control are formulated into a convex quadratic programming problem to optimize the control strategies for voltage stability. The proposed technology has been validated on the IEEE 39-bus system.
Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and ...sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.
β-1,4-N-acetylgalactosamine transferase 2 (
) is a vital candidate gene that affects the growth traits in sheep. However, whether it has the same function in goats remains to be investigated further. ...This study selected 348 Nanjiang Yellow goats, screened all exons, and conserved non-coding regions of the
gene for single-nucleotide polymorphisms (SNPs). Our results revealed the presence of a synonymous mutation, rs672215506, within the exon of the
gene in the Nanjiang Yellow goat population. The mutation resulted in a decrease in the mRNA stability of the
gene. The results of SNP detection of the conserved non-coding region of the
gene showed five potential regulatory SNPs in the Nanjiang Yellow goat population. Except for rs66095343, the ~500 bp fragments of the other four SNPs (rs649127714, rs649573228, rs652899012, and rs639183528) significantly increased the luciferase activity both in goat skeletal muscle satellite cells (MuSCs) and 293T cells. The genetic diversity indexes indicated low or intermediate levels for all six SNPs analyzed, and the genotype frequencies were in Hardy-Weinberg equilibrium. Association analysis showed that rs660965343, rs649127714, and rs649573228 significantly correlate with growth traits in the later stage of growth and development of Nanjiang Yellow goats. The haplotype combinations of H2H3 and H2H2 had higher body weight and greater body size. Moreover, H2H2 haplotype combinations significantly correlated with the litter size of the Nanjiang Yellow goats. The results of our study demonstrate the potential role of the
gene as a functional genetic marker in the breeding programs of Nanjiang Yellow goats.
Plant chloroplasts have a highly compartmentalized interior, essential for executing photocatalytic functions. However, the construction of a photocatalytic reaction compartment similar to ...chloroplasts in inorganic–biological hybrid systems (IBS) has not been reported. Drawing inspiration from the compartmentalized chloroplast and the phenomenon of liquid–liquid phase separation, herein, a new strategy is first developed for constructing a photocatalytic subcellular hybrid system through liquid–liquid phase separation technology in living cells. Photosensitizers and in vivo expressed hydrogenases are designed to coassemble within the cell to create subcellular compartments for synergetic photocatalysis. This compartmentalization facilitates efficient electron transfer and light energy utilization, resulting in highly effective H2 production. The subcellular compartments hybrid system (HM/IBSCS) exhibits a nearly 87‐fold increase in H2 production compared to the bare bacteria/hybrid system. Furthermore, the intracellular compartments of the photocatalytic reactor enhance the system's stability obviously, with the bacteria maintaining approximately 81% of their H2 production activity even after undergoing five cycles of photocatalytic hydrogen production. The research brings forward visionary prospects for the field of semi‐artificial photosynthesis, offering new possibilities for advancements in areas such as renewable energy, biomanufacturing, and genetic engineering.
Constructing membraneless organelles in living bacteria for semiartificial photosynthesis is a pioneering approach. This innovative strategy involves precise assembly of specific proteins, promoting quantum dots and hydrogenases, providing a promising approach to improving the efficiency and stability of semiartificial photosynthetic systems.
The increasing penetration of wind power together with its high volatility could significantly impact the transient stability of the power grid. To quickly evaluate this impact, current engineering ...practice is primarily relying on time-domain simulation, which is computationally expensive despite that the results are more accurate. To solve this computational complexity issue, the amplitude–phase motion method is proposed to establish the electromechanical transient simulation model of the double-fed induction generator (DFIG) for wind energy. However, the traditional amplitude–phase motion equation (APME) suffers from the instability control from the abrupt change of terminal voltage induced by the system changes or flickers. To improve the transient stability of DFIG, this study firstly incorporates the q-axis current together with the amplitude change of terminal voltage into the phase error of the phase-locked loop (PLL). Then, the output phase of the terminal voltage of DFIG is highly combined with the q-axis current and the amplitude of terminal voltage to improve the internal control effect of the typical APME. The simulation results in the four-machine two-area power system with one wind farm demonstrate that the proposed method is able to maintain a stable operation of the wind farm and the power grid when experiencing a sharp disturbance of wind speed.
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