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To solve the problem that the fractional-order system is difficult to implement on FPGA, the chaotic behavior of a single fractional-order Chen system is realized on the FPGA platform in this article by using Laplace transform and Bode-domain approximation to transform the fractional-order operator. Then the System Generator model of two different fractional-order systems is studied, and FTS/PTS (fixed/predefined time synchronization) algorithms are designed to realize the FTS/PTS of the FOTD (fractional-order time-delay) chaotic systems on FPGA. By changing the initial conditions and parameters of the FOTD chaotic systems, several groups of experiments are carried out on FPGA. The experimental results show that the FTS/PTS controllers of two different FOTD chaotic systems are feasible on FPGA.

With the rapid development of industrialization, the proportion of electric arc furnaces (EAF) in distribution networks is getting higher and higher. Aiming at the problem that grid voltage is distorted by a large number of arc furnace nonlinear loads accessing to distribution networks, it is crucial to make dynamic analysis of distribution network voltage and adopt a control strategy of grid-connected converter based on the new phase-locked loop (PLL) technology. As a result, the harmonic distortion rate is reduced and the quality of grid-connected current and voltage is improved. In this paper, photovoltaic (PV) system model and the typical dynamic model of the EAF are established. By analyzing the influence of the EAF model on the PV grid-connected converter with the traditional phase-locked loop while connected to distribution network, a control strategy of the PV grid-connected converter with the self-adjusting double SOGI (MAF-SASOGI) phase-locked loop with the ideal low-pass filter is proposed. Through the simulation analysis, it is verified that precise phase locked can be realized and harmonic content of the system can be reduced by the PLL strategy proposed in this paper. Therefore, the quality of PV grid-connected voltage and current is improved.

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

Synchrophasor-based electromechanical modes extraction offers essential information for monitoring the small-signal stability of power systems without models. This article proposes an optimal mode decomposition (OMD) method for extracting electromechanical modes from noise-contaminated measurement data. In the proposed method, an iterative optimal process based on the Grassman manifold is employed to enhance robustness to measurement noise, such that the fixed low-dimensional subspace using proper orthogonal decomposition (POD) is not required in the proposed method. The proposed method gives a reliable extraction of frequency and damping ratio as well as the mode shape using multichannel wide-area measurement data. The performance of the proposed method is tested using simulated data from the IEEE 4-machine 2-area test system, 16-generator 5-area test system and measured data from a real power system.

The battery energy storage system (BESS) integrated with a wind farm is an efficient way to smooth wind power fluctuations and improve wind farm dispatchability. The presented study proposed a model predictive control (MPC)-based power dispatch strategy for a wind farm incorporated with dual-battery energy storage system (DBESS). The state space model of the active power balance of the wind farm incorporated with DBESS was established to analyze the operation mode of the DBESS. The MPC was then applied to acquire the control inputs of the two batteries, thereby enabling both batteries to respectively be in a state of charging and discharging to track the alternating dispatch order. Actual wind power data was applied in the simulation, wherein the results indicate that the proposed MPC-based dispatching strategy of the DBESS-incorporated wind farm effectively tracks the dispatching order using the output of the wind/battery storage hybrid system, thereby improve the dispatchability of the wind farm while simultaneously reducing battery switching times and extending the lifetime of battery.

A novel approach for estimating the inertia of an interconnected power system is presented using the identification of interarea oscillation modes (frequency, damping and mode shape) extracted from ambient data. The proposed method concentrates on estimating the values of the effective inertia of each area rather than the equivalent inertia of the entire system. Based on an equivalent two-machine system (ETmS) obtained by combining small signal stability analysis (SSSA) with the structure of the power grid, we derive a mathematical relationship between the effective inertia of each area and the interarea oscillation modes. Furthermore, the interarea oscillation modes can be extracted from ambient data, and the developed scheme enables an online estimation of the inertia only by using the outputs measured by PMUs. The performance of the proposed methodology is tested via numerical simulation cases and real data.

The increase in the scale of distribution networks significantly reduces the efficiency of intelligent planning for distributed generation (DG). To improve the efficiency of intelligent DG planning and avoid the impact of uncertainty concerning renewable energy on it, this paper proposes a sensitivity index for the bus-embedded multi-objective estimation of distribution algorithm (MEDA) based on the semi-invariant probabilistic power flow approach to achieve an optimal solution. The sensitivity indices of the buses are comprehensively enabled to obtain a new index and determine their sensitivity sequences based on the area grey incidence decision-making method. Subsequently, according to the uncertainty of wind turbine generators and photovoltaics, a probability model is established, and the semi-invariant method is used to solve for the probabilistic power flow according to a correlation model. Finally, the sensitivity of the proposed bus-embedded MEDA to enhancing the efficiency of the solution is examined. The optimal DG allocation scheme is obtained with the goal of achieving the lowest total cost in the planning year. Finally, the feasibility and effectiveness of the proposed model and method are verified using simulations of the IEEE 33-bus, IEEE 69-bus, and IEEE 118-bus test systems.

It is important to find an effective method for power quality (PQ) disturbance recognition under the challenges of increasing power system pollution. This paper proposes a PQ disturbance signal recognition method based on Multiresolution S transform (MST) and decision tree (DT). For improving recognition accuracy, adjustment factors are introduced to obtain a controllable time-frequency resolution. On this basis, five feature statistics are obtained to quantitatively reflect the characteristics of the analyzed power quality disturbance signals, which is less than the traditional S-transform-based method. As the proposed methodology can effectively identify the PQ disturbances, the efficiency of the DT classifier could be guaranteed. In addition, the noise impacts are also taken into consideration, and 16 types of noisy PQ signals with a signal-to-noise ratio (SNR) scoping from 30 to 50 dB are used as the analyzed dataset. Finally, a comparison between the proposed method and other popular recognition algorithms is conducted. The experimental results demonstrate that the proposed method is effective in terms of detection accuracy, especially for combined PQ disturbances.

Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of the wind power output and effectively improve a system's wind power accommodation. However, the high-dimensional historical wind speed information should be taken into account in the wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Furthermore, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. The experiments show that it reduces the information loss. Thus, the model has a higher forecast accuracy than the traditional model.

With the development of new energy technology, more and more plug-in electric vehicles (PEVs) have appeared in our lives. In vehicle to grid (V2G) information exchange, the method that PEVs use to access the grid for information and vehicular data collection is the key factor that affects the reliability and real-time nature of V2G communication. Vehicular power line communication (VPLC) reuses vehicular power cables as a communication medium, which avoids the need for an additional vehicular communication line and optimizes the in-vehicle space. VPLC is being considered as a more competitive solution to reduce the complexity of design, weight, and cost associated with the rapid growth of electronic devices installed in vehicles, especially for PEVs. Nevertheless, there are factors that impact VPLC in such a way that disrupts reliability and introduces problems. One of these challenges is VPLC channel noise, which can reduce communication reliability. Hence, in this paper, we introduce a V2G information interaction system structure, based on power line communication, to establish a VPLC channel model for the vehicular data collection system. The model is developed in Simulink and leverages various vehicular noise models and binary frequency shift keying (BFSK) modulation; it also analyzes the channel characteristics by examining different types of noise based on BFSK. Our simulation results demonstrate the feasibility of PLC under BFSK modulation and demodulation, often used in PEVs, which could provide guidance to the implementation of VPLC in support of practical engineering.