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This paper presents a comprehensive review of various techniques employed to enhance the low voltage ride through (LVRT) capability of the fixed-speed induction generators (FSIGs)-based wind turbines (WTs), which has a non-negligible 20% contribution of the existing wind energy in the world. As the FSIG-based WT system is directly connected to the grid with no power electronic interfaces, terminal voltage or reactive power output may not be precisely controlled. Thus, various LVRT strategies based on installation of the additional supporting technologies have been proposed in the literature. Although the various individual technologies are well documented, a comparative study of existing approaches has not been reported so far. This paper attempts to fill this void by providing a comprehensive analysis of these LVRT methods for FSIG-based WTs in terms of dynamic performance, controller complexity, and economic feasibility. A novel feature of this paper is to categorize LVRT capability enhancement approaches into three main groups depending on the connection configuration: series, shunt, and series-shunt (hybrid) connections and then discuss their advantages and limitations in detail. For verification purposes, several simulations are presented in MATLAB software to demonstrate and compare the reviewed LVRT schemes. Based on the simulated results, series connection dynamic voltage restorer (DVR) and shunt connection static synchronous compensators (STATCOM) are the highly efficient LVRT capability enhancement approaches.

A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics.

DC to AC inverters are the well-known and improved in various kinds photovoltaic (PV) and gird tied systems. However, these inverters are require interfacing transformers to be synchronized with the grid-connected system. Therefore, the system is bulky and not economy. The transformerless inverter (TLI) topologies and its grid interface techniques are increasingly engrossed for the benefit of high efficiency, reliability, and low cost. The main concern in the TL inverters is common mode voltage (CMV), which causes the switching-frequency leakage current, grid interface concerns and exaggerates the EMI problems. The single-phase inverter two-level topologies are well developed with additional switches and components for eliminating the CMV. Multilevel inverters (MLIs) based grid connected transformerless inverter topology is being researched to avail additional benefits from MLI, even through that are trust topologies presented in the literature. With the above aim, this paper has proposed three -phase three-level T type NP-MLI (TNP-MLI) topology with transformerless PV grid connected proficiency. The CM leakage current should handle over mitigating CMV through removing unwanted switching events in the inverter pulse width modulation (PWM). This paper is proposes PV connected T type NP-MLI interface with three-phase grid connected system with the help of improved space vector modulation (SVM) technique to mitigate the CM leakage current to overcome the above said requests on the PV tied TL grid connected system. This proposed the SVM technique to mitigate the CM leakage current by selecting only mediums, and zero vectors with suitable current control method in order to maintain the inverter current and grid interface requirements. The proposed PV tied TNP-MLI offering higher efficiency, lower breakdown voltage on the devices, smaller THD of output voltage, good reliability, and long life span. The paper also investigated the CM leakage currents envisage and behavior for the three-phase MLI through the inverter switching function, which is not discussed before. The proposed SVM on TL-TNP-MLI offers the reliable PV grid interface with very low switching-frequency leakage current (200mA) for all the PV and inverter operation conditions. The feasibility and effectiveness of the TLI and its control strategy is confirmed through the MATLAB/Simulink simulation model directly as compared with 2kW roof top PV plant connected TL-TNP-MLI experimentation, showing good accordance with theoretical investigation. The simulation and experimental results are demonstrated and presented in the good stability of steady state and dynamics performances. The proposed inverter reduces the cost of grid interface transformer, harmonics filter, and CMV suppressions choke.

This paper provides a two-stage stochastic programming approach for joint operating multi-energy systems under uncertainty. Simulation is carried out in a test system to demonstrate the feasibility and efficiency of the proposed approach. The test energy system includes a gas subsystem with a gas source and a gas storage facility, and electricity subsystem that includes a coal-fired power plant, a combined heat and power unit and a power-to-gas facility, and a conventional district heating subsystem.

This paper presents a novel online technique, which simultaneously exploits the global search ability of differential evolution (DE) and rapid convergence of Newton Raphson (NR) methods (named as DE-NR) to solve intricate simultaneous transcendental trigonometric set of harmonic elimination pulse width modulation equations for modular multilevel cascade converters based power systems. Major contribution of this paper is a harmonically efficient online algorithm with rapid convergence. Switching angles for an extensive range of modulation index (M,0.86⩽M≤9.97) are computed online to demonstrate the harmonically efficient working of proposed method. Total harmonic distortion values of the filtered line-to-line output voltages during online operation are restricted to 0.35% of the fundamental component, which depict successful removal of unwanted harmonics and are significantly better than the values allowed by IEEE standard 519-2014. Comparison between DE-NR, differential evolution and Newton Raphson methods demonstrates the rapid convergence behavior of DE-NR as it requires only (on average) 4 iterations compared to 490 and 182 iterations respectively. It has also shown superior harmonic control than the recently devised online Middle-Level Selective Harmonic Elimination Pulse-Amplitude Modulation method. Successful simulation and experimental validations of DE-NR method have been done by developing three-phase modular multilevel cascade converter in MATLAB-Simulink and single-phase eleven-level modular multilevel cascade converter hardware prototype.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

The recommended power quality (PQ) indices in electric power systems are defined based on the discrete Fourier transform (DFT). DFT is mostly evaluated applying fast Fourier transform (FFT) algorithm which requires signals to be periodic in nature. Hence, it evaluates PQ indices accurately for stationary PQ disturbances only. However, in case of nonstationary PQ disturbances signal is aperiodic and FFT results in erroneous assessment due to spectral leakage phenomena. Furthermore, FFT cannot provide time information of the PQ indices as computation is performed in frequency domain only. Which is significant shortcoming of the FFT when dealing with time-varying PQ disturbances to extract dynamic signature of the signal. Thereby, this paper presents a framework of redefining PQ indices for stationary and nonstationary PQ disturbances employing time-frequency distribution (TFD) method. Among all TFDs, reduced interference distribution (RID) represents the most suitable properties for analyzing time-varying PQ disturbances. Hence, in this work, it is employed to reformulate PQ indices typically used in electric power systems. The results of two synthetic examples considering stationary and nonstationary PQ disturbances imply that the RID-based redefined PQ indices are closer to the actual values. Also, they provide more accurate results than the existing transient PQ indices and traditional FFT-based method.