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To address the challenges of complex modeling and control objective conflicts, a game control scheme based on data‐driven and reliability awareness is proposed for floating offshore wind turbines (FOWTs). In this scheme, first, an enhanced model‐free adaptive control method with the integration of iterative learning is proposed, which utilized online data for driving modeling and control command derivation to ensure the functionality of the FOWT and maintain stable power generation. Then, a healthy reliability‐aware model of the pitching system is developed and integrated into the controller of FOWTs. Finally, a trade‐off between the reliability and functionality of FOWTs is intelligently made through bargaining game theory to derive reasonable control commands. Simulation experiments of the proposed control scheme are carried out using FAST under different wind conditions. The results show that, compared with the gain‐scheduled PI controller and model‐free adaptive controller, the output power of the proposed controller is more stable and the pitch motion of the FOWT is significantly reduced; compared with the improved model‐free adaptive controller, the proposed controller improves the reliability of the pitch system. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic power generation can efficiently utilize idle resources and reduce carbon emissions. In order to reduce the impact of grid-connected distributed photovoltaic power fluctuations on grid operation, this paper simultaneously exploits the temporal dependence of power series and the spatial correlation of meteorological data to propose a combined prediction model with temporal characteristics and spatial relationships fused for distributed photovoltaic power plants with spatiotemporal information. First, in the study of time-dependent prediction, we propose a long and short-term memory neural network ensemble prediction model based on genetic algorithm-natural gradient boosting, which efficiently fits multiple sets of temporal characteristics of distributed photovoltaic. In the study of spatial correlation prediction, the meteorological data affecting photovoltaic power generation are selected by κ correlation coefficients, the target power plant and spatial reference power plant meteorological data are reconstructed into a two-dimensional matrix, and a two-dimensional convolutional neural network spatial feature extraction power prediction model is designed. Finally, the advantages of the two prediction models of temporal information and spatial features are fused by multiple error evaluation criteria improved information entropy, and a distributed photovoltaic power plant is constructed and implements highly accurate spatiotemporal information combination prediction model. The effect of the forecasting model in this study is validated using the photovoltaic cluster dataset in Hebei Province, China. Compared with other models, the results of this study show that the five prediction performance evaluation metrics of the proposed combined spatiotemporal information model are better. [ABSTRACT FROM AUTHOR]

To cope with the problems of cumbersome modeling and strongly disturbances in the control of floating offshore wind turbines (FOWTs), an improved model-free adaptive control (MFAC) strategy considering both wind speed disturbance and floating platform motion response to wave disturbance is proposed. In this strategy, a detailed mathematical model of the FOWT is avoided and the feedback controller is designed based on improved MFAC using only the input–output data to dynamically linearize the FOWT. Strongly disturbances caused by random wind and floating platform motion response to wave are compensated by a feedforward controller. The proposed method and the comparison method including the baseline controller are simulated under different wind scenarios using FAST. The results show that the proposed controller regulates the generator speed more smoothly and closer to the rated value, suppresses the motion of the floating platform more effectively, and can significantly improve the performance of FOWTs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The share of renewable energy in the electricity market is increasing year by year. It is necessary to identify damage of solar panels in a timely manner, as solar panels are important components in photovoltaic power generation. In this paper, a lightweight solar panel fault diagnosis system based on image pre-processing and an improved VGG-19 network is proposed to address the problem of blurred solar panel field images, which are not easy for defects detection. First, we use Daubechies 4(DB4) wavelet and morphology-based enhancements to improve the quality of solar panel images. Then, conventional convolutional layers in the VGG-19 are replaced with separable convolutional layers to reduce the number of network parameters and improve training efficiency. Finally, the Convolutional Block Attention Module (CBAM) is introduced to improve the accuracy of solar panel defects' detection. A dataset consisting of 3344 images of solar panels was used to evaluate the performance of the proposed method in defect detection. The experimental results show that the method has an accuracy of 87.8% and a detection speed of 0.047 s per image. The proposed model has higher accuracy and more stable performance than other conventional networks with a lightweight structure, demonstrating the reliability of the improved VGG-19 in detecting solar panel defects in practical applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 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 interest of two markets is played against each other. A wind power probability density prediction model of kernel extreme learning machine (KELM)-particle swarm optimization (PSO)-adaptive diffusion kernel density estimation (AKDE) is established using an improved extreme learning machine (KELM) with good fitting ability and the AKDE method, wind farm bidding is carried out on the premise of wind power probability prediction, which is optimally solved by the multi-objective quantum genetic algorithm, and the optimization results are filtered using entropy-fuzzy C-means 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. [ABSTRACT FROM AUTHOR]

Summary: The combination of a virtual power plant (VPP) and an active distribution network (ADN) can be used to aggregate distributed energy resources of prosumers. The bi‐directional power flow and information flow between prosumers ensure close coupling and interdependence of the power system and the cyber system so that the VPP gradually evolves into a typical cyber‐physical system (CPS). However, even a small cyber attack on the CPS system significantly affects the security and integrity of the VPP. To study robustness of CPS in the VPP, a novel stochastic cascading failure model based on the complex network theory is introduced, considering the impact of various interdependencies and network relationships. First, the centralized and decentralized multi‐agent system (MAS) structures in the VPP are investigated, and the fault propagation mechanism and cascading failure process of the CPS network are established. Secondly, a simple and effective comparison method is proposed to analyze the different network relationships and coupling interdependencies. Thirdly, A new multiple‐to‐multiple inter‐links addition strategy is proposed to enhance the protection of key nodes, and mainly focus on researching the intra‐ and inter‐links addition sensitivity analysis. The numerical simulation shows that optimal network relationship, coupling interdependency, and intra‐links addition strategy are determined by the proposed robustness metrics. [ABSTRACT FROM AUTHOR]

Surface defect detection is very important for the quality control of product and routine maintenance of facilities, but it is still a big challenge due to the diversity and complexity of defects and environmental factors. To improve the accuracy of defect detection, we proposed a pixel-level segmentation network based on deep feature fusion for surface defect detection. The network adopts encoder–decoder structure, and it extracts low-, middle-, and high-level features via ResNet50 first. Second, by fusing adjacent feature maps at all levels and integrating the highest level feature map, multilevel feature aggregation module makes all feature maps contain context information and more details of defects. Then, multibranch decoder adopts attention modules and a multibranch structure to recover the details of defects gradually and improve the accuracy of defects segmentation. Finally, the segmentation result is produced by fusion of all branches outputs. We have evaluated the proposed network on three public data sets: MT, RSDD, and CFD. The results indicate that our proposed method outperforms the other compared methods in terms of F-measure and intersection of union (MT:73.7%, RSDD:85%, CFD:60.1%). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This trial was conducted to investigate the effect of mannose oligosaccharides (MOS) on the growth performance, antioxidation, immunity and disease resistance of Vibro Parahemolyticus in juvenile abalone Haliotis discus hannai Ino. Four formulated diets were produced to contain 0.00 g/kg, 0.40 g/kg, 0.80 g/kg and 1.60 g/kg Actigen®, with functional ingredients of MOS, respectively. Accordingly, the experimental diets were named as A0, A4, A8 and A16. After 120-days feeding trial, the best growth performance was observed in A8 group (P < 0.05) and there was no significant difference in A0, A4 and A16 groups. With the increase of dietary MOS, the activity of the total antioxidant capacity in hepatopancreas is increasingly elevated (P < 0.05) while no significant difference was observed on activity of glutathione S‐transferase (P > 0.05). The activities of superoxide dismutase and glutathione peroxidase were firstly increased and then decreased, with the highest values in A8 group (P < 0.05). Immune-related parameters were significantly affected by dietary MOS inclusion. Specifically, the activities of alkaline phosphatase and acid phosphatase in hepatopancreas and serum of abalone fed diets containing MOS were significantly higher than those of control A0 group (P < 0.05). Moreover, the highest values of both enzymes were observed in hepatopancreas of A8 group but in serum of A16 group, respectively. The lysozyme activities in hepatopancreas and serum of A4 group were significantly higher than those of other groups (P < 0.05) and there was no significant difference in A0, A8 and A16 groups (P > 0.05). The activities of cytophagy and respiratory burst in serum of abalone were not significantly affected by dietary MOS content (P > 0.05). The mRNA levels of focal adhesion kinase and integrin-linked kinase were gradually elevated with the increase of dietary MOS, with the highest value recorded in A16 group (P < 0.05). The gene expression of caspse-3 in A8 group was dramatically higher than those of other groups (P < 0.05) and there was no significant difference in A0, A4 and A16 groups (P > 0.05). The mRNA level of nuclear factor-κB was not significantly affected by dietary MOS (P > 0.05). During 56 h of V. Parahemolyticus challenge period, the accumulated mortality rate of abalone fed diets containing MOS were significantly lower than that of control A0 group in each time point (P < 0.05). Overall, the lowest rate was happened in A8 group (P < 0.05). In conclusion, MOS inclusion in diet has obviously positive effect on growth, immunity and disease resistance capability of abalone, with the optimal level of Actigen® at 0.80 g/kg in diet. • MOS had positive effect on growth, antioxidation and innate immunity in abalone. • MOS increased expressions of genes related to immune signal pathways. • MOS increased the disease resistance capability against Vibro in abalone. • The optimal addition level for MOS is 0.80 g/kg in diet in the form of Actigen®. [ABSTRACT FROM AUTHOR]

In-situ exploitation of oil shale by electric heating consumes large amounts of electricity. Under the existing dispatch system, using wind power output and photovoltaic power output to support the exploitation of oil shale can promote renewable energy use, reduce the consumption of coal and other fossil fuels, and protect the environment from pollution. In this study, the characteristics of the wind power and photovoltaic power output are analyzed, and the correlation between the power outputs is evaluated using the copula function. The load of exploiting oil shale is presented. In order to match the heating load characteristics of oil shale exploitation, a particle swarm optimization algorithm is used to optimize the heating temperature of the heated well to minimize the cost. An economic analysis is conducted of five different power supply combinations, including wind power, photovoltaic power, and the existing power grid. The income ratio of the five modes is calculated using actual data of a project in Jilin province in China, and the feasibility of in-situ electric heating by wind power, photovoltaic power, and the power grid is determined. The results of this study provide useful references for decision makers to plan the power supply scheme for in-situ oil shale exploitation. [ABSTRACT FROM AUTHOR]

Summary: As the installed capacity of wind power continues to increase, the problem of curtailed wind power is becoming serious in China, especially in the northern region during the winter heating season. To solve the problem of wind‐heat conflict during the heating period in the Three North area, an electric boiler with thermal storage (EBTS) is installed at the end of the grid where wind power is difficult to accommodate and using curtailed wind power to supply heat promotes local accommodation. In this paper, a multi‐objective optimization model of wind power accommodation based on the wind power–EBTS system for heating is established. The goals of maximizing wind power accommodation, minimizing the number of times EBTS must be adjusted, and minimizing operating costs are presented, and a bi‐level optimization scheme is designed. An improved multi‐objective particle swarm optimization algorithm is used to solve these functions, and an optimal compromise solution from the generated Pareto solution set is filtered using the fuzzy membership method. Based on actual data from a demonstration project in China's Jilin Province, the simulation results verify that this method can effectively reduce operating costs and improve wind power accommodation. [ABSTRACT FROM AUTHOR]

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power. [ABSTRACT FROM AUTHOR]

No studies have been focused on the inclusion of organic selenium in formulated feed of abalone when organic minerals are accepted by aquaculture feed industry. This experiment evaluated the effect of organic selenium on the growth, antioxidation, immunity and gene expressions of selenoproteins in abalone. Graded levels of organic selenium were included to the basal diet at 0.00, 0.15, 0.30 and 0.60 ppm providing 157.83, 322.51, 437.59 and 596.59 ug/kg of selenium in the diet. Accordingly, the diets were named as Se‐0.00, Se‐0.15, Se‐0.30 and Se‐0.60 respectively. Results had shown that survival rate, weight gain rate and shell length increase rate were lowest for Se‐0.60 group (p < 0.05). The concentration of selenium in the whole body generally increased with increasing level of dietary organic selenium (p < 0.05). The enzyme activities related to antioxidation increased initially with low levels of dietary selenium and decreased with higher levels, with the highest value in Se‐0.30 group (p < 0.05). The enzyme activities of immunity increased with the addition of dietary selenium. With the increase of dietary selenium, Myostain expression decreased initially and increased afterwards, with lowest value for Se‐0.30 group (p < 0.05). However, the expressions of other genes related to growth, antioxidation and selenoproteins firstly increased and then decreased, with the highest value recorded for Se‐0.15 group. In conclusion, the inclusion rate of 0.15 – 0.30 ppm Se in diet could promote growth, antioxidation, immunity and gene expressions of selenoproteins in abalone; however, excess selenium with 0.60 ppm inclusion lever in diet had toxic effects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to deal with the problems of complicated modeling and the conflicts of control objectives in the control of floating offshore wind turbines (FOWTs), a multi-objective predictive control strategy for FOWTs based on a deep learning model and multi-objective optimization is proposed. In this strategy, the dynamic prediction model of the FOWT based on a gated recurrent neural network is established. Based on the dynamic prediction model, a multi-objective predictive control algorithm for individual pitch control of FOWTs is developed. The multi-objective distribution estimation algorithm is used to optimize the pitch angle considering the constraints of the pitch angle setting. The proposed method is simulated in different wind conditions by FAST. The results show that, compared with the collective pitch control and the gain scheduling PI individual pitch control, the output power of the proposed method is closer to the rated power, and the proposed method significantly reduces the mechanical load of the blade root. Moreover, compared with the gain scheduling PI individual pitch control, the proposed method effectively restrains platform pitching motion. • A control method based on deep learning and multi-objective optimization is proposed. • An prediction model of FOWTs based on gated recurrent neural network is established. • Multi-objective optimization solves control variable to balance multiple objectives. [ABSTRACT FROM AUTHOR]