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For data-driven fast economic dispatch in the electricity-gas cyber-physical system (EGCPS), the strong reliance on communication networks makes it vulnerable to intentional cyberattacks. False data injection attack (FDIA) is emerging as a severe threat to secure system operation. To enhance the cybersecurity and computational efficiency in data-driven fast economic dispatch, a data-driven approach is proposed to serve both exact locational detection of FDIA and fast economic dispatch in real time. The data-driven approach concatenates a residual network (ResNet) and attention long short term memory model (ALSTM) based on LSTM and attention mechanism (called "ResNet-ALSTM"), which can achieve temporal correlations and feature extraction of large amounts of measurements. The proposed ResNet-ALSTM can serve as a multi-label classifier to detect the outlier locations of tampered measurements in the power system of the EGCPS. Further, the Fast Dynamic Time Warping algorithm can timely recover the tampered measurements of power system after FDIA. Finally, the proposed ResNet-ALSTM can achieve fast economic dispatch in the EGCPS with the recovered measurements of power system. The performances of the proposed methods are experimentally evaluated on IEEE 24-bus power system and Belgian 20-node gas system. The results demonstrate that the proposed methods can achieve the locational detection of FDIA, tampered data recovery and data-driven fast economic dispatch with high accuracy and computational efficiency. This research is a promising solution for safeguarding secure system operation of EGCPS against FDIA.

An image recognition model based on a deep learning network is proposed for the automatic extraction of image features and the accurate and efficient detection of wind turbine blade damage. The Otsu threshold segmentation method is used to segment the blade image to eliminate the influence of the image background on the detection task. In order to improve the recognition performance of the proposed deep learning model, transfer learning and an ensemble learning classifier are used in a convolutional neural network model. Transfer learning is used to enhance the ability of the proposed model to extract abstract features and accelerate the convergence efficiency, whereas the random forest-based ensemble learning classifier is used to improve the accuracy of detecting the blade defects. The performance of the proposed model is verified by using unmanned aerial vehicle (UAV) images of the wind turbine blades. The proposed model provided better performance than the support vector machine (SVM) method, the basic deep learning model and the deep learning model combined with the ensemble learning approach.

This paper describes a leaching-purifying-precipitation process to recover magnesium from serpentine acid-leaching solution and to synthesize high purity Mg(OH)2 and 4MgCO3·Mg(OH)2·4H2O. Fe, Al, and Cr in the leaching solution were removed using the oxidation precipitation method with active MgO as a precipitant and H2O2 as an oxidant. Ni, Co and Mn were removed by Na2S precipitation to obtain a pure MgSO4 solution. Mg2+ ions were first precipitated with NH3·H2O to synthesize Mg(OH)2, followed by NH4HCO3 precipitation to obtain 4MgCO3·Mg(OH)2·4H2O. A small part of MgSO4 coprecipitates with Mg(OH)2 to form MgSO4·5Mg(OH)2·3H2O. The Mg(OH)2 was aged with a diluted NaOH solution to remove the sulfur; the two-stage precipitation percentage of Mg is 96.3%. Mg(OH)2 has a purity of 98.48% with a sulfur content of 0.28%. This process provides a promising method for the high-efficiency recovery of Mg and the large-scale production of the high purity of Mg(OH)2 from the serpentine.

The intermittence and uncertainty of wind power pose challenges to large-scale wind power grid integration. The study of wind power uncertainty is becoming increasingly important for power system planning and operation. This paper proposes a wind power probabilistic interval prediction model, and a novel reliability assessment approach is presented for electrical power systems. First, the unknown parameters estimation of the autoregressive integrated moving average (ARIMA) prediction model is based on the Markov chain Monte Carlo (MCMC)-based Bayesian estimation method to improve the quality of statistical inference. Then, a quantum genetic algorithm is used to segment the power to determine the best output for each power segment weight and calculate the probabilistic prediction interval of wind power. Finally, reliability assessment by the sequential Monte Carlo simulation is presented combining with the probabilistic prediction interval of wind power on IEEE-RTS79 reliability test system. The simulation results that proposed variation range of reliability assessment indices consider the uncertain scenario of wind power and has guiding significance for power generation scheduling. Compared with genetic algorithm and particle swarm optimization algorithm, it is proved that the proposed prediction interval model has better prediction interval coverage probability index and interval average bandwidth index.

Li1.6Mn1.6−xCrxO4 was synthesized by hydrothermal reaction followed by acid leaching to form lithium ion sieve. The structure, morphology and composition were examined using X-ray diffraction, SEM ...

Precipitation of magnesium hydroxide [Mg(OH)2] from bischofite (MgCl2·6H2O) using lime milk, i.e., the traditional lime milk method, features a low cost. However, insoluble impurities such as CaO in the lime limit the purity of the magnesium hydroxide product. For this reason, a new method has been developed, using saturated lime supernatant instead of lime milk. The insoluble impurities in lime cannot dissolve in saturated lime supernatant, thus improving the purity of the produced magnesium hydroxide. This improved lime method, which achieves high Mg(OH)2 purity of 99% with CaO content of 0.094%, offers better performance in comparison with the traditional lime milk method. Besides, the optimum processing parameters including pH, crystal seed amount, agitation speed, reaction temperature, MgCl2 concentration, reaction time, aging time, and polyacrylamide (PAM) addition are reported. High-purity light magnesia (MgO) is obtained from magnesium hydroxide by carbonation and calcination, with quality fulfilling the HG1-324-77 standard. This improved lime method represents a promising approach for production of high-purity magnesium hydroxide and light magnesia from bischofite.

Wind turbines located in high humidity and high altitude areas are often accompanied by blade icing, which adverses the operating efficiency and even causes safety accidents. Early identification of blade icing will help improve the operating efficiency of the wind turbine. This paper proposes an icing diagnosis method for wind turbine blades based on feature optimization and the one-dimensional convolutional neural network (1D-CNN). First, feature optimization is achieved by feature selection and feature reconstruction. The XGBoost algorithm is used to calculate the importance of each feature and select the features comprehensively that reflect blade icing. Second, the important features related to blade icing are reconstructed by using the deviation principle to extract the deviation information of features accurately when blades ice. Finally, the features screened by XGBoost and the reconstructed features are combined into the final feature set as the input of the 1D-CNN, which takes the temporal and spatial characteristics of data into account, to diagnose the icing state of blades. The method is validated on the data set collected from a real wind farm. The experimental results show that the proposed icing diagnosis method for wind turbine blades is superior to the traditional deep learning methods. It is favorable to improve the efficiency of wind turbine operation and maintenance.

The typical method for the preparation of chromium-magnesia is by energy-consuming grinding, but the pollution of Cr6+ ions is a problem. However, the chromium-magnesia refractory is still irreplaceable due to its outstanding performance. In this regard, low-chromium magnesia was prepared by the alkaline chromium precipitation method from cheap light-burned magnesia. The impurities in the light-burned magnesia was removed in order to avoid the formation of Cr6+. The chromium was precipitated directly on the light-burned magnesia surface without the grinding process. The low-chromium magnesia was obtained after the calcination. The reaction temperature, solid content, reaction time and stirring speed were examined and low-chromium magnesia with Cr3+ 0.4%, MgO 98.5% was obtained. The effect of temperature on the surface tomography of low-chromium magnesia was also discussed. The present novel alkaline method should be a promising way for the preparation of low-chromium magnesia.

A novel process of recovery of Mn as lithium ion-sieve from H2SO4 leaching liquor of spent lithium-ion batteries was developed. After precipitation of Fe3+ and Cu2+ in the leaching liquor, Mn was recovered with D2EHPA extraction followed by two-step stripping with H2SO4 solution. Lithium ion-sieve was synthesized by oxidation of the stripping solution using KMnO4, then roasting at atmosphere followed by acid leaching. The effects of solvent extraction parameters on Mn recovery rate and Co doping content on the performance of lithium ion-sieve were investigated. The result indicates that through secondary countercurrent solvent extraction, Mn is fully separated from Ni and a MnSO4 solution containing a small amount of Co is obtained. Co can incorporate into the lattice of lithium ion-sieve and improve the structure stability and adsorption capacity. The Co doped lithium ion-sieve shows Li adsorption capacity of 33.1 mg g−1 and Mn dissolution rate of 1.39%. The total recovery efficiency of Mn is 84.6%. The process provides a simple method for recovery and utilization of Mn in the spent lithium-ion batteries.

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases.

For wind farms to participate in the combined energy-frequency regulation(E-FR) market, wind farms are considered as a combination of both power generation and frequency regulation capability, and wind farms bid in both the energy market and the frequency regulation(FR) market. In this paper, the impact of different bidding decisions on the distribution of wind farm revenues is analyzed in a process where the interests of two markets are played against each other. A wind power probability density prediction model of KELM-PSO-AKDE is established using a improved extreme learning machine (KELM) with good fitting ability and adaptive diffusion kernel density estimation method(AKDE), and wind farm bidding is carried out on the premise of wind power probability prediction, which is optimally solved by multi-objective quantum genetic algorithm and the optimization results are filtered using entropy-FCM fuzzy clustering. Based on the actual wind farm operation data for simulation analysis, the model analyzes the benefits of wind farm participation in the joint market from different preference perspectives, which is a reference for wind farm participation in bidding decisions.

A virtual power plant is a system that integrates different types of distributed energy resources to achieve the rational and optimized allocation and utilization of resources. As energy consumers and providers, electric vehicles (EVs) play a crucial role in the operation and scheduling of virtual power plants (VPPs). In this review, the integration of EVs and electric vehicles is systematically summarized, focusing specifically on different types of EVs and the combination of various distributed energy sources. The uncertainties and constraints associated with the use of EVs in VPPs are systematically summarized, and the scheduling methods for the optimization of VPPs with EVs are analyzed and compared. Finally, the real cases and the future trend of EVs integrated in VPP are studied and analyzed. This review provides references for research on the integration of EVs into VPPs.

Under the application scenario of smoothing photovoltaic (PV) power fluctuation, a novel typical daily power curve mining method is developed for a battery energy storage system (BESS) that utilizes the power probability distribution and Bloch spherical quantum genetic algorithm. The charging/discharging of BESS is analyzed by applying fuzzy-c means clustering techniques. In the mining approach, at any sample time, those distribution intervals containing concentrated power points are individually located by using probability distribution information and Bloch spherical quantum genetic algorithm. Character power for the specified interval can also be determined using Bloch spherical quantum genetic algorithm. Next, a roulette principal is employed, to determine one value from the character power data as a typical value of the mined power curve at the sample time. By connecting the typical power at each sample time, the typical daily power curve for BESS is achieved. Based on typical power curve, decision-maker can master the important operating parameters of BESS and analyze optimal capacity allocation. By error evaluation indexes between the mined typical daily power curve and power curve under different weather patterns, the simulation results verify that the mined power curve can address the operating power of the BESS under different weather patterns.

The intermittency and fluctuations of wind power cause problems in integration of wind energy systems into the grid. To smooth the fluctuations of wind power, a control scheme based on fuzzy empirical mode decomposition (EMD) is proposed with the use of battery energy storage system (BESS). With this idea, the wind power signal is decomposed via EMD and filtered into two parts: the low-frequency part is taken as the target smooth wind power for grid connection, and the high-frequency part is stored by BESS before connecting to the grid to compensate for the fluctuations in the original wind power signal. A fuzzy EMD filter is developed by considering constraints on both the power fluctuation rate and the state of charge (SOC) of the BESS. The performance of the proposed strategy has been examined through case study simulations, which demonstrates improvement in mitigating wind power fluctuations and also in reducing over charging/discharging operations of the integrated BESS.

Blade icing of a wind turbine will affect the startup performance of the blades, resulting in the loss of power generation of the wind turbine, and even affect the safety of production and operation. In order to reflect the blade icing state of wind turbines as truthfully and objectively as possible, this paper proposes a wind turbine blade icing state recognition model based on the combination of vine-Copula network model and Long Short-Term Memory (LSTM)-Autoencoder algorithm. First, the vine-Copula model is used to analyze the correlation between the various parameters in supervisory control and data acquisition (SCADA) system and the blade icing state, thereby constructing a high-dimensional vine-Copula structure. Then, removing the features that are not directly related to the blade icing state, the final vine-Copula model and related features are obtained. The filtered features are input into the LSTM-Autoencoder algorithm, then the “memory” function and non-linear feature extraction capabilities of the LSTM-Autoencoder algorithm are used to obtain the evaluation results of the blade icing state of wind turbines. The experimental results show that the indicators of the wind turbine blade icing state recognition based on this method are overall better than the indicators of the Recurrent Neural Network-Autoencoder algorithm without feature reduction and the LSTM-Autoencoder algorithm without feature reduction and traditional classification algorithms.

According to the solubility isotherms of the ternary system (LiCl + NH 4 Cl + H 2 O), fractional crystallization including primary crystallization and secondary crystallization, was designed to remove the maximum amount of NH 4 + from Cha'erhan tail brine and raise the concentration of Li + . The particle size and surface topography during fractional crystallization were studied by XRD and SEM techniques. Then, sodium hydroxide was used for the removal of magnesium, and high purity sodium carbonate was obtained by the method of carbonation from Cha'erhan tail brine.