An adaptive supervised wide-area backup protection scheme for transmission lines protection

Maloperation of conventional relays is becoming prevalent due to ever increase in complexity of conventional power grids.They are dominant during power system contingencies like power swing,load encroachment,voltage instability,unbalanced loading,etc.In these situations,adaptive supervised wide-area backup protection(ASWABP)plays a major role in enhancing the power system reliability.A balance between security and dependability of protection is essential to maintain the reliability.This paper proposes multi-phasor measurement units(MPMU)based ASWABP scheme that can function effectively during faults besides power system contingencies.MPMU is an extended version of Phasor Measurement Unit(PMU).It is an intelligent electronic device which estimates the synchronized predominant harmonic phasors(100Hz and 150Hz)along with the fundamental phasors(50Hz)of three phase voltages and currents with high precision.The proposed ASWABP scheme can detect the fault,identify the parent bus,determine the faulty branch and classify the faults using MPMU measurements at System Protection Center(SPC).Based on these MPMU measurements(received at phasor data concentrator(PDC)at SPC)the appropriate relays will be supervised to enhance the overall reliability of the power grid.Numerous case studies are conducted on WSCC-9 bus and IEEE-14 bus systems to illustrate the security and dependability attributes of proposed ASWABP scheme in MATLAB/Simulink environment.Also,comparative studies are performed with the existing conventional distance protection(Mho relays)for corroborating the superiority of the proposed scheme regarding security and dependability.Comparative studies have shown that the proposed scheme can be used as adaptive supervised wide-area backup protection of conventional distance protection.

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as active distribution network(ADN)and microgrid(MG)play an important role in power system operation.The design and implementation of appropriate protection systems for MG and ADN must be addressed,which imposes new technical challenges.This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks(NNs)and multiple sampling points for ADN and MG.The solution is implemented on an edge device.NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure.Multiple sampling points are used to derive a data-driven model,which allows the generalization considering the implementation in physical systems.The adaptive fault detector model is implemented on a Jetson Nano system,which is a single-board computer(SBC)with a small graphic processing unit(GPU)intended to run machine learning loads at the edge.The proposed method is tested in a physical,real-life,low-voltage network located at Universidad del Norte,Colombia.This testing network is based on the IEEE 13-node test feeder scaled down to 220 V.The validation in a simulation environment shows the accuracy and dependability above 99.6%,while the real-time tests show the accuracy and dependability of 95.5%and 100%,respectively.Without hard-to-derive parameters,the easy-to-implement embedded model highlights the potential for real-life applications.