This study examines State of Charge (SoC) balancing control in DC microgrids subject to photovoltaic (PV) fluctuations, aiming to optimize power distribution in energy storage systems influenced by PV disturbances. The proposed approach enhances both the lifespan of storage systems and microgrid stability. To mitigate voltage variation due to PV perturbation, the paper introduces an adjustment in droop control offset. Additionally, it presents a novel discrete Sliding Mode Controller (SMC) characterized by reduced parameter sensitivity, thus enhancing control responsiveness. A SoC balancing control strategy employing sliding mode control is developed to equalize SoC levels across Battery Energy Storage Systems during both charge and discharge cycles. The stability of this strategy is substantiated through the construction of a Lyapunov function. Simulations conducted in a distributed DC microgrid environment using Simulink/SimPower Systems demonstrate the efficacy of the discrete SMC and the SoC balancing algorithm, achieving uniform SoC in energy storage nodes during operation, with improved robustness against PV perturbations.
The current method of smart meter verification relies on manual regular sampling inspection, which is heavy in workload and poor in real-time, and can’t fully monitor all the equipments. Therefore, a remote real-time error monitoring algorithm is indispensable. We propose a smart meter error estimation model based on genetic optimized Levenberg-Marquarelt (LM) algorithm. Firstly, based on the law of conservation of energy, the relationship between smart meter error and electricity consumption is established. Then, LM algorithm is optimized based on genetic algorithm and used to estimate the operating error of electricity meter. Finally, we used the actual data of the pilot cities in a province for the experiment. The results show that the proposed method can effectively improve the accuracy of smart meter error estimation.
In many traditional hierarchical routing protocols proposed for mobile ad hoc networks, each cluster designates a single cluster-head (CH) node to relay inter-cluster traffic. These cluster-head nodes become traffic "hot-spots" and restricting cluster to access through cluster-head nodes can lead to sub-optimal route. To solve these problems, we proposed a novel cluster-based distributed gateways routing protocol, namely DGR. In our proposed scheme, we rely on border mobile terminals (BMTs) as gateways for inter-cluster routing instead of relying on CHs. CHs are not necessarily involved in each route search and data transmission process. In addition, we also proposed a cluster reformation criterion to avoid unnecessary cluster reformation. It is proved that the proposed scheme can reduce the route search time, alleviate the burden of CHs and achieve optimal routes. Moreover, it can make clusters much more stable for nodes mobility under the proposed cluster reformation criteria.
The hierarchical structure, [Formula: see text]-core, is common in various complex networks, and the actual network always has successive layers from 1-core layer (the peripheral layer) to [Formula: see text]-core layer (the core layer). The nodes within the core layer have been proved to be the most influential spreaders, but there is few work about how the depth of [Formula: see text]-core layers (the value of [Formula: see text]) can affect the robustness against cascading failures, rather than the interdependent networks. First, following the preferential attachment, a novel method is proposed to generate the scale-free network with successive [Formula: see text]-core layers (KCBA network), and the KCBA network is validated more realistic than the traditional BA network. Then, with KCBA interdependent networks, the effect of the depth of [Formula: see text]-core layers is investigated. Considering the load-based model, the loss of capacity on nodes is adopted to quantify the robustness instead of the number of functional nodes in the end. We conduct two attacking strategies, i.e. the RO-attack (Randomly remove only one node) and the RF-attack (Randomly remove a fraction of nodes). Results show that the robustness of KCBA networks not only depends on the depth of [Formula: see text]-core layers, but also is slightly influenced by the initial load. With RO-attack, the networks with less [Formula: see text]-core layers are more robust when the initial load is small. With RF-attack, the robustness improves with small [Formula: see text], but the improvement is getting weaker with the increment of the initial load. In a word, the lower the depth is, the more robust the networks will be.
As the edge node in electric Internet of Things, the application of smart metering terminal (SMT) enables the massive electric big data to be widely collected and processed on the edge. This creates a positive condition for short-term electric load forecasting, which is very important for electricity sales company under the back ground of electricity spot market. In this study, the structure of a novel hybrid edge-cloud computing framework for electric load forecasting is proposed, and the forecasting model based on extreme learning machine (ELM) is also developed. In the proposed framework, the distributed SMTs are regarded as the edge nodes and widely collect electric data inner electricity customers. Then, these original data are preprocessed in the regional SMT, and then sent to the cloud server as standard time series data. Finally, the proposed ELM forecasting model runs in the cloud server, and outputs the forecasting load demand for all the customers of electricity sales company. Experimental results show the efficiency of the proposed framework and ELM forecasting model.
In this paper, we investigate the problem of secure communications in multiple-input-multiple-output interference networks from the perspective of physical layer security.Specifically, the legitimate transmitter-receiver pairs are divided into different categories of active and inactive.To enhance the security performances of active pairs, inactive pairs serve as cooperative jammers and broadcast artificial noises to interfere with the eavesdropper.Besides, active pairs improve their own security by using joint transceivers.The encoding of active pairs and inactive pairs are designed by maximizing the difference of mean-squared errors between active pairs and the eavesdropper.In detail, the transmit precoder matrices of active pairs and inactive pairs are solved according to game theory and linear programming respectively.Experimental results show that the proposed algorithm has fast convergence speed, and the security performances in different scenarios are effectively improved.
In this letter, the precoding-aided spatial modulation (PSM) is generalized to a secret PSM (SPSM) scheme, which has the capability to resist malicious eavesdropping from an eavesdropper that is unknown to the transmitter. We design a time-varying precoder for the SPSM so that it can retain all the PSM's advantages at the desired receiver while producing time-varying interference to the eavesdropper. The secrecy capacity of our proposed SPSM is studied, and the optimal power allocation between signal and interference transmission is investigated. Our studies and performance results demonstrate that our SPSM can significantly improve the secrecy performance of the PSM, particularly in a high-signal-to-noise-ratio region. Furthermore, the SPSM is capable of achieving a nonzero secrecy rate even when the eavesdropper has more antennas than the desired receiver.
Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.
Considering the engineering problem of electric energy meter automatic verification and scheduling, this paper proposes a novel scheduling scheme based on an improved Q-learning algorithm. First, by introducing the state variables and behavior variables, the ranking problem of combinatorial optimization is transformed into a sequential decision problem. Then, a novel reward function is proposed to evaluate the pros and cons of the different strategies. In particular, this paper considers adopting the reinforcement learning algorithm to efficiently solve the problem. In addition, this paper also considers the ratio of exploration and utilization in the reinforcement learning process, and then provides reasonable exploration and utilization through an iterative updating scheme. Meanwhile, a decoupling strategy is introduced to address the restriction of over estimation. Finally, real time data from a provincial electric energy meter automatic verification center are used to verify the effectiveness of the proposed algorithm.
A novel interference alignment (IA) scheme based on the errors-in-variables (EIV) mathematic model has been proposed to overcome the channel state information (CSI) estimation error for the MIMO interference channels. By solving an equivalently unconstrained optimization problem, the proposed IA scheme employing a weighted total least squares (WTLS) algorithm can obtain the solution to a constrained optimization problem for transmit precoding (TPC) matrices and minimizes the distortion caused by imperfect CSI according to the EIV model. It is shown that the design of TPC matrices can be realized through an efficient iterative algorithm. The convergence of the proposed scheme is presented as well. Simulation results show that the proposed IA scheme is robust over MIMO interference channels with imperfect CSI, which yields significantly better sum rate performance than the existing IA schemes such as distributed iterative IA, maximum signal-to-interference-plus-noise ratio (Max SINR), and minimum mean square error (MMSE) schemes.
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