Nonlinear Model Predictive Controller for Compensations of Single Line-to-ground Fault in Resonant Grounded Power Distribution Networks

An nonlinear model predictive controller(NMPC)is proposed in this paper for compensations of single line-to-ground(SLG)faults in resonant grounded power distribution networks(RGPDNs),which reduces the likelihood of power line bushfire due to electric faults.Residual current compensation(RCC)inverters with arc suppression coils(ASCs)in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG faults.The proposed NMPC is incorporated with the estimation of ASC inductance,where the estimation is carried out based on voltage and current measurements from the neutral point of the power distribution network.The compensation scheme is developed in the discrete time using the equivalent circuit of RGPDNs.The proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults,which is verified through both simulations and control hardware-in-the-loop(CHIL)validations.Comparative results are also presented against an integral sliding mode controller(ISMC)by demon-strating the capability of power line bushfire mitigation.

A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction

The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.

FedDiSC:A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination

With the growing concern about the security and privacy of smart grid systems,cyberattacks on critical power grid components,such as state estimation,have proven to be one of the top-priority cyber-related issues and have received significant attention in recent years.However,cyberattack detection in smart grids now faces new challenges,including privacy preservation and decentralized power zones with strategic data owners.To address these technical bottlenecks,this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework,known as FedDiSC,that enables Discrimination between power System disturbances and Cyberattacks.Specifically,we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related data.Secondly,we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies.Lastly,to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs,we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency.Extensive simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related information.Furthermore,we find that the gradient quantization scheme utilized improves communication efficiency by 40%when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.