Data-driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks

Integration of distributed energy storage(DES)is beneficial for mitigating voltage fluctuations in highly distributed generator(DG)-penetrated active distribution networks(ADNs).Based on an accurate physical model of ADN,conventional model-based methods can realize optimal control of DES.However,absence of network parameters and complex operational states of ADN poses challenges to model-based methods.This paper proposes a data-driven predictive voltage control method for DES.First,considering time-series constraints,a data-driven predictive control model is formulated for DES by using measurement data.Then,a data-driven coordination method is proposed for DES and DGs in each area.Through boundary information interaction,voltage mitigation effects can be improved by interarea coordination control.Finally,control performance is tested on a modified IEEE 33-node test case.Case studies demonstrate that by fully utilizing multi-source data,the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations.

A QoT prediction technique based on machine learning and NLSE for QoS and new lightpaths in optical communication networks

In this paper,we proposed a quality of transmission(QoT)prediction technique for the quality of service(QoS)link setup based on machine learning classifiers,with synthetic data generated using the transmission equations instead of the Gaussian noise(GN)model.The proposed technique uses some link and signal characteristics as input features.The bit error rate(BER)of the signals was compared with the forward error correction threshold BER,and the comparison results were employed as labels.The transmission equations approach is a better alternative to the GN model(or other similar margin-based models)in the absence of real data(i.e.,at the deployment stage of a network)or the case that real data are scarce(i.e.,for enriching the dataset/reducing probing lightpaths);furthermore,the three classifiers trained using the data of the transmission equations are more reliable and practical than those trained using the data of the GN model.Meanwhile,we noted that the priority of the three classifiers should be support vector machine(SVM)>K nearest neighbor(KNN)>logistic regression(LR)as shown in the results obtained by the transmission equations,instead of SVM>LR>KNN as in the results of the GN model.