The application and development of a wide-area measurement system (WAMS) has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system, and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels: the underlying communication protocol, source data, and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

more

Conventional multi-stage constant current charging strategies often use higher multiples of current to charge the battery in pursuit of shorter charging times.However, this leads to an increase in battery temperature, while shortening the charging time.This in turn affects the safety of the charging process.Furthermore, the higher charging currents are not ideal for shortening the charging time in the later stages of charging.To solve the aforementioned problems, in this study,a multi-stage constant current charging strategy is presented.This strategy can shorten the battery charging time by using the increase in battery temperature during the charging process as a constraint, using a genetic algorithm to calculate the charging current value, and investigating the phased approach to charging.Finally, the charging strategy is experimentally validated at different ambient temperatures and different initial SOCs.The experimental results show that the charging strategy proposed in this paper not only reduces the amount of calculations, but also reduces the temperature rise by up to 46.4% and charging time by up to 4.2% under different operating conditions.

more

Multi-objective optimal dispatching schemes with intelligent algorithms are recognized as effective measures to promote the economics and environmental friendliness of microgrid applications.However, the low accuracy and poor convergence of these algorithms have been challenging for system operators.The bird swarm algorithm (BSA), a new bioheuristic cluster intelligent algorithm, can potentially address these challenges; however, its computational iterative process may fall into a local optimum and result in premature convergence when optimizing small portions of multi-extremum functions.To analyze the impact of a multi-objective economic–environmental dispatching of a microgrid and overcome the aforementioned problems of the BSA, a self-adaptive levy flight strategy-based BSA (LF–BSA) was proposed.It can solve the dispatching problems of microgrid and enhance its dispatching convergence accuracy, stability, and speed, thereby improving its optimization performance.Six typical test functions were used to compare the LF–BSA with three commonly accepted algorithms to verify its excellence.Finally, a typical summer-time daily microgrid scenario under grid-connected operational conditions was simulated.The results proved the feasibility of the proposed LF–BSA, effectiveness of the multiobjective optimization, and necessity of using renewable energy and energy storage in microgrid dispatching optimization.

more

A “cloud-edge-end” collaborative system architecture is adopted for real-time security management of power system on-site work, and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for onsite risk assessment and alert.Owing to its lightweight and fast speed, YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection; however, its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods, achieving higher accuracy without compromising the speed.Specifically, a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover, three optimized training methods: transfer learning, mosaic data augmentation, and label smoothing are used to improve the training effect of this improved algorithm.Finally, an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results, the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS, and the mean average precision (mAP) is increased from 70.89% to 85.03%.

more

Coal-fired electricity enterprises are caught in the dilemma of relative fixed prices and rising costs under the scenario of decarbonization.Meanwhile, soaring market-oriented coal pricing results in coal enterprises’ increasing defaults on thermal coal medium-and long-term contracts (MLC).To investigate the implementation of MLC at the microlevel, this study formalized the contractual behaviors of coal and coal-fired electricity enterprises based on the asymmetric evolutionary game.We formalized the evolving behaviors of both parties using replicator dynamics equations and proved that there were two evolutionary stabilization strategies (ESSs): compliance and coal enterprises’ unilateral default.A multiagent-based simulation was applied to verify the evolving process of ESSs and determine the critical values of MLC design by sensitive analysis.From the simulation results, coal-fired electricity enterprises do not stop generation under the current carbon quota allocation mechanism, even if carbon emission trading increases electricity generation costs.Coal enterprises choose to “default” when the market price of coal is higher than the contracted price by 18%.However, if the original reparation is increased by 5%, the compliance rate of the coal enterprises improves.Dynamic reparations embedded in the MLC improved enforcement during the contracting period.Moreover, the proposed policy implications have practical significance for enhancing the coordinated operation of coal-electricity energy supply chains.

more

Photocatalytic methane (CH4) production wherein CO2 is reduced to CH4 by utilizing solar radiation energy is gaining research and industrial focus because of its environmental-friendly notion.It offers twofold advantages: reduction in CO2 emission and production of artificial natural gas (methane) at the same time.In this paper, comparative energy,economic and environmental assessment of such photocatalytic methane production has been carried out between Japan and Malaysian conditions.Assumptions on the photocatalytic methane production plant and estimation of energy production,CO2 emission reduction, and economic indicators are made based on previous research and existing technologies.Energy analysis shows that Malaysia has a higher potential for energy production and CO2 emission reduction than Japan.Economic analysis reveals that the feasible reaction efficiencies of the plant in Japan and Malaysia are 8%.The slightly higher conversion efficiency in Malaysia is due to the energy price and CO2 tax.For the implementation of the photocatalytic methane production plant, the high energy price and CO2 tax will work as a driving force.

more

To ensure the stable operation of power systems with large proportions of wind power, China has published a series of national, industry, and enterprise standards for wind power.The increase in the number of standards and the expansion of their application scope have given rise to a situation where multiple standards overlap and conflict with regard to the establishment of models and their applicability, resulting in unclear standard application scenarios.Therefore, it is imperative to analyze the development of wind-turbine and wind-farm modeling, along with the relevant standards.This paper presents the methods for wind-turbine modeling, the equivalent model of wind farms based on the general model of wind turbines, and the technical provisions and application scenarios involved in the relevant domestic and international standards.The adaptability of the relevant standards is examined.The results of this study are helpful for advancing wind power generation in China and ensuring the safe and stable operation of large-scale wind power systems.

more

Numerical Weather Prediction (NWP) is a necessary input for short-term wind power forecasting.Existing NWP models are all based on purely physical models.This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high.There is a need to further improve the timeliness and accuracy of the assimilation process.In order to solve the above problems, NWP method based on artificial intelligence is proposed in this paper.It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position.We considered the actual data of a wind farm in north China as an example to analyze the calculation example.The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model.The prediction accuracy in some months is better than that of the purely physical model, and the calculation efficiency is considerably improved.The validity and advantages of the proposed method are verified from the results, and the traditional NWP method is replaced to a certain extent.

more

Prediction of power output plays a vital role in the installation and operation of photovoltaic modules.In this paper,two photovoltaic module technologies, amorphous silicon and copper indium gallium selenide installed outdoors on the rooftop of the University of Dodoma, located at 6.5738° S and 36.2631° E in Tanzania, were used to record the power output during the winter season.The average data of ambient temperature, module temperature, solar irradiance, relative humidity, and wind speed recorded is used to predict the power output using a non-linear autoregressive artificial neural network.We consider the Levenberg-Marquardt optimization, Bayesian regularization, resilient propagation, and scaled conjugate gradient algorithms to understand their abilities in training, testing and validating the data.A comparison with reference to the performance indices: coefficient of determination, root mean square error, mean absolute percentage error,and mean absolute bias error is drawn for both modules.According to the findings of our investigation, the predicted results are in good agreement with the experimental results.All the algorithms performed better, and the predicted power out of both modules using the Bayesian regularization algorithm is observed to exhibit good processing capabilities compared to the other three algorithms that are evident from the measured performance indices.

more

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator, a gate recurrent unit-convolutional neural network (GRU-CNN) model whose structural parameters were determined by improved particle swarm optimization (IPSO) is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance, which is the fusion of the offset distance of the excitation current and offset distance of the reactive power, was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and faultwarning ratio were set according to the normal total offset distance, and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

more