Randomness and fluctuations in wind power output may cause changes in important parameters (e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors (such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

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Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.

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To address the significant lifecycle degradation and inadequate state of charge (SOC) balance of electric vehicles(EVs) when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending (SDT) algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm (IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.

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When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.

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The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation.Of the currently available technologies,solar energy has promising application prospects owing to its merits of being clean,safe,and sustainable.Solar energy is converted into electricity through photovoltaic (PV) cells; however,the overall conversion efficiency of PV modules is relatively low,and most of the captured solar energy is dissipated in the form of heat.This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells.To overcome the shortcomings,an efficient approach involves combining a PV cell with a thermoelectric generator (TEG) to form hybrid PV-TEG systems,which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs.Based on a thorough examination of the literature,this study comprehensively reviews 14 maximum power point tracking(MPPT) algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion,namely conventional,mathematics-based,metaheuristic,artificial intelligence,and other algorithms.This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

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Complex microgrid structures and time-varying conditions,among other factors,cause problems in the mechanical modeling of microgrids,making model-based controller optimization difficult.Therefore,this study proposed a secondary frequency adaptive control strategy based on parameter identification,which uses an online parameter identification method to identify the parameters in the microgrid in real-time.The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance.The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters.Finally,a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy.

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The reliability of geographic information system (GIS) partial discharge fault diagnosis is crucial for the safe and stable operation of power grids.This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects.First,the non-subsampled contourlet transform (NSCT) algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge (PRPD) spectrum with richer information.Subsequently,the VAE structure was introduced into the traditional GAN,and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN.Then,the self-attention mechanism was integrated into the VAE-GAN,and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state.Finally,the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples.The eigenvectors of the sets were substituted into the long short-term memory (LSTM) network for partial discharge fault diagnosis.The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method.The structure similarity index measure(SSIM) index is increased by 4.5% and 21.7%,respectively,and the average accuracy of fault diagnosis is increased by 22.9%,9%,5.7%,and 6.5%,respectively.The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.

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The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing.Owing to its excellent performance in computer vision,deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection.Despite their excellent performance,electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope,the need for constant retraining and optimization,and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning.In this study,an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems.This system design is based on the three critical design principles of generalizability,extensibility,and automation.Pre-trained models,fine-tuning (downstream task adaptation),and automated machine learning,which are closely related to these design principles,are reviewed.In addition,an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented.A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition.The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition,respectively.This demonstrates that the system design concept is reasonable and the system architecture feasible.

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Smart cities depend highly on an intelligent electrical networks to provide a reliable,safe,and clean power supplies.A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery,which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts.A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future.However,most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid.To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids,this article proposes a model of smart city development needs from the perspective of residents’ needs based on Maslow’s Hierarchy of Needs theory,which serves the primary purpose of building a smart city.By classifying and reintegrating the needs,an evaluation index system of smart grids supporting smart cities was further constructed.A case analysis concluded that smart grids,as an essential foundation and objective requirement for smart cities,are important in promoting scientific urban management,intelligent infrastructure,refined public services,efficient energy utilization,and industrial development and modernization.Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions,such as electric vehicle charging facilities and wireless coverage.

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Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues.The conventional optimization of long-term energy system models focuses on a single economic goal.However,the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives,such as environmental,social,and energy security.Therefore,a multiobjective optimization of long-term energy system models has been developed.Herein,studies pertaining to the multiobjective optimization of long-term energy system models are summarized; the optimization objectives of long-term energy system models are classified into economic,environmental,social,and energy security aspects; and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers.Finally,the key development direction of the multi-objective optimization of energy system models is discussed.

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