In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or on high frequency reflections from impedance discontinuities. Of these high frequency test methods, we review phasor, travelling wave and reflectometry methods. The reflectometry methods summarized include time domain reflectometry (TDR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR), orthogonal multi-tone reflectometry (OMTDR), noise domain reflectometry (NDR), chaos time domain reflectometry (CTDR), binary time domain reflectometry (BTDR), frequency domain reflectometry (FDR), multicarrier reflectometry (MCR), and time-frequency domain reflectometry (TFDR). All of these reflectometry methods result in complex data sets (reflectometry signatures) that are the result of reflections in the time/frequency/spatial domains. Automated analysis techniques are needed to detect, locate, and diagnose the fault including genetic algorithm (GA), neural networks (NN), particle swarm optimization, teaching–learning-based optimization, backtracking search optimization, inverse scattering, and iterative approaches. We summarize several of these methods including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach. We also discuss the issue of soft faults (small impedance changes) and methods to augment their signatures, and the challenges of branched networks. We also suggest directions for future research and development.

This paper presents a new method based on the electromagnetic time-reversal (EMTR) theory for locating faults in power networks. The applicability of the EMTR technique to locate faults is first discussed. Using the classical transmission-line equations in the frequency domain, analytical expressions are derived to infer the location of the fault. The accuracy of the proposed method is then discussed in relation to the number of observation points adopted to record the fault-originated electromagnetic transients. Then, this paper illustrates the extension of the proposed method to the time domain. The experimental validation of the proposed method is presented by making reference to a reduced-scale coaxial cable system where real faults are hardware-emulated. Finally, the application of the proposed EMTR-based fault-location method to Electromagnetic Transients Program-simulated cases is presented. The simulated test cases are: a mixed overhead/coaxial cable transmission system and the IEEE 34-bus distribution test feeder. Compared to other transient-based fault-location techniques, the proposed method presents a number of advantages, namely, its straightforward applicability to inhomogeneous media (mixed overhead and coaxial power cable lines), the use of a single observation (measurement) point, and robustness against fault type and fault impedance.

Grid-forming inverters (GFMIs) are recognized as one of the key enablers towards highly renewable energy proliferated grids. One of the pivotal characteristicsof GFMIs is the ability to seamlessly switch between grid-connected (GC) and standalone (SA) modes. In this paper, a novel controller is proposed to accurately follow the power reference commands in the GC mode while providing the required amount of virtual inertia in the SA mode to slow down the rate of change of frequency (RoCoF) following a disturbance. The proposed control design, where straightforward equations are given to calculate the controller gains directly, is based on the frequency response of the open-loop system. Furthermore, based on the frequency response of the controller, a condition for the placement of the poles of the controller is derived to guarantee the RoCoF relay limit compliance in the SA mode. The experimental results show that the proposed controller results in lower overshoots and shorter settling times in step responses in the GC mode than the virtual synchronous generator (VSG) controller while providing more virtual inertia than the VSG controller to slow down RoCoF in the SA mode. Furthermore, the experimental results illustrate that the proposed controller can also be designed to support the grid during frequency transients. The performance of the controller is extensively validated experimentally during GC to SA mode transition and vice versa, in a multi-inverter network, and in a wide-area model.

This paper presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.

The conventional volt-watt control method used in PV inverters to overcome the overvoltage problems can result in significant unfairness in the curtailed active power in PV-rich distribution networks. In this paper, a novel decentralized control strategy is presented to improve the fairness in the curtailed active power of inverters. In this strategy, first, each PV inverter calculates the ratio of its node voltage magnitude change to its injected active power. Then, the calculated value by the farthest PV inverter of each phase from the transformer is shared with other PV inverters connected to the same phase. Using this value, PV inverters adjust their control parameters of an enhanced volt-var-watt control method. The performance of this method is evaluated using various simulation studies by making reference to the IEEE European low-voltage (LV) test feeder and considering different levels of PV system integration. It is shown that the proposed control strategy is able to improve the fairness of the active power curtailment among PV inverters significantly alongside solving the overvoltage problem. Finally, the result of a comparative analysis between the proposed strategy and a state-of-the-art communication-based method is reported.

Accurate and fast fault detection in transmission lines is of high importance to maintain the reliability of power systems. Most of the existing methods suffer from false detection of high-impedance faults. In this paper, the transfer function (TF) method is introduced to evaluate the effect of impedance and location of faults by analyzing the voltage and current signals in the frequency domain. Interpretation of the results of the TF method is considered as a weakness of this method. In order to alleviate this problem, a convolutional neural network (CNN) and the hybrid model of deep reinforcement learning (DRL) are utilized to identify and locate single-phase to ground short circuit faults in transmission lines. Single-phase to ground short circuit faults with various fault impedances are applied on an IEEE standard transmission line system. Then, the TF traces are calculated and are collected as input datasets for the proposed models. The fault location results for each network are evaluated via various statistical performance metrics such as correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE). The R-value of the CNN and DRL models in fault identification is presented as 96.12% and 98.04%, respectively. Finally, in the early detection of single-phase to ground short circuit fault location (high impedance), the results revealed the efficiency of the DRL model with R=96.61% compared to CNN with R=95.21%.