The output power of wind and photovoltaic (PV) systems contain fluctuations due to the intermittent nature of solar irradiance and wind speed. It is required to smooth the fluctuated power before ...supplying it to the grid. Here, a fuzzy-based discrete Kalman filter approach is proposed for smoothing output power fluctuations of the wind and PV generation systems using a battery energy storage system. The proposed approach incorporates the state of health of the battery as a feedback to not only obtain smooth output power but also improve the battery health by adaptively regulating the battery power. The effectiveness of the proposed approach is verified by evaluating the power fluctuation rates and performance indices.
Remote sensing (RS) images play an indispensable role in many key fields such as environmental monitoring, precision agriculture, and urban resource management. Traditional deep convolutional neural ...networks have the problem of limited receptive fields. To address this problem, this paper introduces a hybrid network model that combines the advantages of CNN and Transformer, called MBT-UNet. First, a multi-branch encoder design based on the pyramid vision transformer (PVT) is proposed to effectively capture multi-scale feature information; second, an efficient feature fusion module (FFM) is proposed to optimize the collaboration and integration of features at different scales; finally, in the decoder stage, a multi-scale upsampling module (MSUM) is proposed to further refine the segmentation results and enhance segmentation accuracy. We conduct experiments on the ISPRS Vaihingen dataset, the Potsdam dataset, the LoveDA dataset, and the UAVid dataset. Experimental results show that MBT-UNet surpasses state-of-the-art algorithms in key performance indicators, confirming its superior performance in high-precision remote sensing image segmentation tasks.
Cabled intelligent systems bring with them the complexities of structures, the complications of data measurements and transmission, and a limited scale of application. A wireless sensor network is ...used to eliminate these disadvantages, however reliability of data transmission and energy saving in a wireless sensor network are two challenges that still need to be addressed. The design information on three types of nodes in a wireless sensor network is described in detail. Tree topology for WSN is adopted to decrease the packet loss rate and improve reliability of data transmission. Allowing sensor nodes to sleep and reorganising the data frames are the two approaches used to achieve energy-saving. The experimental results demonstrate the usefulness of these approaches in solving the challenges.
•This system eliminates the disadvantages of existing cable systems.•A favourable topology results in a more reliable system with less packet loss.•Energy-saving strategies significantly contribute to the survival of WSN nodes.
In the papers ‘Adaptive dual model predictive control for linear systems with parametric uncertainties’ by Lin et al. and ‘Generalized predictive controller (GPC) design on single-phase full-bridge ...inverter with a novel identification method’ by Ghamari et al., regressive models including the unknown parameters are used, and the dual adaptive model predictive control (MPC) and the generalized MPC are respectively proposed by incorporating least squares identification algorithms. Liu et al., in their paper ‘Event-triggered fuzzy output feedback fault-tolerant control for interval type-2 Takagi-Sugeno large-scale systems with time delays’, propose an observer-based event-triggered fault-tolerant control law for a class of T-S fuzzy systems. Bi, in his paper ‘Neural networks adaptive control for fractional-order nonlinear with unmodelled dynamics and actuator faults’, investigate the problem of fault-tolerant control for fractional-order nonlinear systems. SUMMARY/CONCLUSION All of the papers selected for this Special Issue show that the parameter-based approach plays an importantly instrumental role in the analysis and design of control systems.
In this study, a discrete Kalman filter-based approach is presented for minimising the output power fluctuations of wind and photovoltaic systems. The control strategy is based on the change in power ...fluctuation which is determined by the weighted average of the highest and lowest values of the power fluctuation for each interval of time. A genetic algorithm optimisation approach is utilised to determine the optimal value of weighted average such that the power fluctuation rate is minimum. This study also gives the optimum battery power and its state of charge to achieve smoothing determined by the optimal weighted average. On the basis of this optimum battery power, the specification and configuration of the battery energy storage system are also determined.
High penetration level of single-phase solar PVs in low voltage distribution systems has raised concern about the Voltage Unbalance (VU) level. Real-time phase reconfiguration using Dynamic Switching ...Devices (DSD) has been proposed as a cost-effective solution to mitigate the excessive level of Voltage Unbalance. DSDs are fast power electronics devices that could change the phase connection of a solar PV with minimum disturbances. Most of the proposed methodologies for controlling DSDs are based on a central controller and the assumption of availability of Advanced Metering Infrastructure (AMI) and a communication system. In this paper, we propose a distributed control methodology to control DSDs based on the measurement from the local node only and using the sensitivity of nodal voltage to power injection. Each DSD decides on phase swapping if it rectifies the unbalance index of the local node. We show that decreasing the Voltage Unbalance Factor (VUF) of individual nodes will improve the unbalance situation of the whole feeder. The proposed scheme does not require a communication system nor a central controller. The result of simulations confirms the efficacy of the proposed methodology.
The slow state variables feedback stabilization problem for semi-Markov jump discrete-time systems with slow sampling singular perturbations is discussed in this work. A new fairly comprehensive ...system model, semi-Markov jump system with singular perturbations, which is more general than Markov jump model, is employed to describe the phenomena of random abrupt changes in structure and parameters of the systems. Based on a slow state variables feedback control scheme, a novel technique to design the desired controller is presented and the allowed maximum of singular perturbation parameter can be calculated. With the help of the discrete-time semi-Markov kernel approach, some sojourn-time-dependent and less-conservative sufficient conditions are established via a novel matrix decoupling technique to ensure the solvability of the problem to be addressed. Finally, an illustrative example is given to show the superiority and usefulness of the proposed method.
The development of a smart grid electricity distribution network with advanced technology in smart metering will produce a massive amount of data. However, the limitation in communication network ...bandwidth makes it hard to transmit these data to the control center. Data compression is one of the best solutions to overcome this limitation. This study presents the use of multiresolution matrix factorisation (MMF) as a data compression technique for a smart distribution system. MMF will compress a data matrix into a core matrix with lower dimension via a series of orthogonal transformations. Experimental results gained from this study show that MMF is applicable in compressing large size data into lower dimension matrix with low error rates and high in speed. The MMF compression technique is able to reduce the volume of data to be transmitted through the communication network and thus save the bandwidth. Besides, MMF compression performs faster than the singular value decomposition method, especially with large size matrix. Findings from this study prove that MMF can serve as an alternative data compression technique for the smart distribution system, with a potential for an online application due to the high speed and high accuracy of the algorithm.
In this paper, we present frequency‐weighted optimal Hankel‐norm model reduction algorithms for linear time‐invariant continuous‐time systems by representing an original higher‐order system into new ...fictitious systems. The new system representations are derived through factorization of the resulting sub‐matrices that are obtained after transformations. As the proposed approaches are factorization dependent, additional results with both approaches are included using another factorization of the fictitious input–output and weight matrices. The proposed algorithms generate stable reduced models with double‐sided weights and provide a substantial improvement in the weighted error. A numerical example is given to compare the efficacy of the proposed algorithms with the well‐known frequency‐weighted techniques.
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