Improving Power Quality by DSTATCOM Based DQ Theory with Soft Computing Techniques

The development of non-linear loads at consumers has significantly impacted power supply systems.Since,the poor power quality has been found in the three-phase distribution system due to unbalanced loads,harmonic current,undesired voltage regulation,and extreme reactive power demand.To overcome this issue,Distributed STATicCOMpensator(DSTATCOM)is implemented.DSTATCOM is a shunt-connected Voltage Source Converter(VSC)that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor.DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation.A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply.The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink.The DQ theory is used to produce the reference signal for the Pulse Width Modulation(PWM)generator.In addition,a traditional Propor-tional Integral Derivative(PID)controller is designed and compared with pro-posed soft computing approaches such as Fuzzy–PID and Artificial Neural Network(ANN-PID)and compared accurate reference current determination for Direct Current(DC)bus through DC link.An Analytical explores the pro-posed control strategies given to establish superior outcomes.Finally,total harmo-nic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.

Research on Relief Models of Mass Tort of Vaccine

Objective To solve the problem of the incomplete relief model for vaccine damage events in China,multiple models for large-scale vaccine damage events were established based on the cooperation of product liability,compulsory insurance and rescue funds.Methods Through case analysis and comparative research,the situation of liability sharing and relief of mass tort of vaccines was analyzed.Results and Conclusion At present,China should construct a multi-relief model of vaccine damage from the aspects of tort liability and social relief.In addition,after the construction of the relief system,it is necessary to distinguish between emergency situations and general situations to apply the priority of tort compensation and social relief systems in the specific implementation process.

Guest Editorial:Special Issue on Power Quality

More and more distributed generations (DGs)such as solar,wind,and energy storage,etc,inject into the power grid mainly through the power electronics equipments.Large-scale power electronic equipments connected to the grid bring new challenges for traditional power quality.Power quality analysis theory and control technology for smart grid will provide the comprehensive and systematic solutions and approaches in the

Online prediction of network-level public transport demand based on principle component analysis

Online demand prediction plays an important role in transport network services from operations,controls to management,and information provision.However,the online prediction models are impacted by streaming data quality issues with noise measurements and missing data.To address these,we develop a robust prediction method for online network-level demand prediction in public transport.It consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a day.The prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed model.The experimental results demonstrate that the proposed Pattern Recognition Prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and transferability.Moreover,the model shows high robustness in accommodating data quality issues.For example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise level.We also discuss the hidden patterns behind the network level demand.The visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity variabilities.Though the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.