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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]

It is important to detect surface defects for controlling product quality and prolonging equipment life. However, detecting surface defects quickly and accurately is still a great challenge due to the complexity of the environment and surface defects. Aiming at the issue, this paper proposes a two-stage attention-based feature fusion network (TAFFNet) to make full use of each level feature for surface defect segmentation. Specifically, the network uses Resnet50 as the backbone network to obtain features, and then extracts multi-scale feature by the atrous convolution feature extraction module. In order to make the features at all levels contain more defect information, the attention-based adjacent feature fusion module is applied to fuse features with adjacent layers; then use the attention-based high-level feature fusion module to merge features with all upper layers, so all level features not only contain multi-scale context but also obtain more defect details. Finally, all features are cascaded together to achieve accurate segmentation of surface defects. In addition, TAFFNet uses a hybrid loss function to overcome blurry boundaries. The experimental results on three surface defects datasets (SD900, MT, and CFD) show that the proposed network outperforms other 13 methods in terms of PR curve, F1, MAE, and mIoU. [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]

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. [ABSTRACT FROM AUTHOR]

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. [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]

Timely and accurate assessment of wind turbine operating condition can save the maintenance cost effectively, because equipment repair and replacement for large wind turbine are expensive. Therefore, an efficient operating condition assessment strategy for wind turbine is proposed based on fuzzy synthesis method. First, the traditional fuzzy assessment process is introduced briefly a foundation for comparison. Second, a wind turbine assessment model is established and assessed in detail, with an improved fuzzy prediction strategy and de-fuzzy strategy. Finally, a comparison between the improved strategy and the traditional strategy is studied based on wind turbine operation data, and the results validate that the strategy proposed in this paper can alarm operation fault more promptly and efficiently, which is better for wind turbine operation safety and maintenance. [ABSTRACT FROM AUTHOR]

Magnesium and nickel were recovered from nickel-rich serpentine through sulfation roasting and water leaching. The factors affecting the extraction percentages of Mg and Ni were discussed. Under the conditions of the ratio of acid to ore of 0.8:1 and roasting temperature of 650 °C for 120 min, 91.6% of Mg and 88.7% of Ni but only 4.8% of Fe were extracted. The roasting kinetics of Mg and Ni were investigated. The results showed that the roasting stage was governed by internal diffusion in the temperature range of 350–650 °C, and the activation energy of nickel and magnesium were different in the time ranges of 0–30 min and 60–120 min, with 17.45 kJ⋅mol−1 (0–30 min) and 14.12 kJ⋅mol−1 (60–120 min) for magnesium and 15.48 kJ⋅mol−1 (0–30 min) and 12.46 kJ⋅mol−1 (60–120 min) for nickel. The kinetic equations were obtained. [ABSTRACT FROM AUTHOR]

A novel chitosan coated poly(butyl cyanoacrylate) (PBCA) nanoparticles loaded doxorubicin (DOX) were synthesized and then conjugated with folic acid to produce a folate-targeted drug carrier for tumor-specific drug delivery. Prepared nanoparticles were surface modified by folate for targeting cancer cells, which is confirmed by FTIR spectroscopy and characterized for shape, size, and zeta potential measurements. The size and zeta potential of prepared DOX-PBCA nanoparticles (DOX-PBCA NPs) were almost 174 ± 8.23 nm and +23.14 ± 4.25 mV, respectively with 46.8 ± 3.32% encapsulation capacity. The transmission electron microscopy study revealed that preparation allowed the formation of spherical nanometric and homogeneous. Fluorescent microscopy imaging and flow cytometry analysis revealed that DOX-PBCA NPs were endocytosed into MCF-7 cells through the interaction with overexpressed folate receptors on the surface of the cancer cells. The results demonstrate that folate-conjugated DOX-PBCA NPs drug delivery system could provide increased therapeutic benefit by delivering the encapsulated drug to the folate receptor positive cancer cells. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to obtain iron-enriched Fe-Ni alloy foil on Ti substrates with good quality from a chloride-sulfate bath used in a normal DC plating mode. The effects of iron content on the hardness, surface morphology and microstructure of the foil were clarified. Design/methodology/approach – Fe-Ni alloy foil was prepared by electrodeposition in a chloride-sulfate based solution. The effects of current density, temperature, stirring rate and sodium propargyl sulfonate concentration on the iron contents of the Fe-Ni alloy foils were studied. The phase composition and surface morphology with various iron contents were characterized by X-ray diffraction (XRD) and atomic force microscopy (AFM), respectively. Cathodic polarization curves were used to evaluate the role of sodium propargyl sulfonate (PS). Findings – Nanocrystalline Fe-Ni alloy foil containing up to 64 wt. percent iron can be obtained from a chloride-sulfate based solution. The foil converts from a face-centered cubic (fcc) Fe3Ni2 phase to a mixture of fcc and body-centered cubic (bcc) Fe7Ni3 with increase in iron content from 55.0 wt. percent to 63.5 wt. percent. AFM studies revealed that the foil had a fine grain structure with a roughness of 30 nm and grain size of 30 nm. With iron increasing to 63.5 wt. percent some islands appeared on the surface. This structure was related to the development of a (200) fiber texture in the BCC phase. Sodium propargyl sulfonate accelerates the discharge of nickel and inhibits the discharge of Fe. Practical implications – The foil has many industrial applications in the area of memory devices for computers, laser components and precise instruments. Originality/value – The paper presents a process to produce a foil with iron up to 64 wt. percent from a chloride-sulfate based solution used in normal DC mode. The dependence of microstructure and surface morphology on iron contents also is presented. Until now, there has been little research or reports on this subject. [ABSTRACT FROM AUTHOR]

Condition monitoring can greatly reduce the maintenance cost for a wind turbine. In this paper, a new condition-monitoring method based on the nonlinear state estimate technique for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of the electrical generator temperature. A new and improved memory matrix construction method is adopted to achieve better coverage of the generator's normal operational space. Generator incipient failure is indicated when the residuals between model estimates and the measured generator temperature become significant. Moving window averaging is used to detect statistically significant changes of the residual mean value and standard deviation in an effective manner; when these parameters exceed predefined thresholds, an incipient failure is flagged. Examples based on data from the Supervisory Control and Data Acquisition system at a wind farm located at Zhangjiakou in northern China have been used to validate the approach and examine its sensitivity to key factors that influence the performance of the approach. It is demonstrated that the technique can identify dangerous generator over temperature before damage has occurred that results in complete shutdown of the turbine. [ABSTRACT FROM PUBLISHER]

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods. [ABSTRACT FROM AUTHOR]

Damaged wind turbine (WT) blades have an imbalanced load and abnormal vibration, which affects their safe and stable operation or even results in blade rupture. To solve this problem, this study proposes a new method to detect damage in WT blades using wavelet packet energy spectrum analysis and operational modal analysis. First, a wavelet packet transform is used to analyze the tip displacement of the blades to obtain the energy spectrum. The damage is detected preliminarily based on the energy change in different frequency bands. Subsequently, an operational modal analysis method is used to obtain the modal parameters of the blade sections and the damage is located based on the modal strain energy change ratio (MSECR). Finally, the professional WT simulation software GH (Garrad Hassan) Bladed is used to simulate the blade damage and the results are verified by developing an online fault diagnosis platform integrated with MATLAB. The results show that the proposed method is able to diagnose and locate the damage accurately and provide a basis for further research of online damage diagnosis for WT blades. [ABSTRACT FROM AUTHOR]

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms. [ABSTRACT FROM AUTHOR]