IEEE 13-node Test Feeder Research Articles

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise.

Reducing multiple estimation for fault location in medium voltage distribution networks

Short circuits are a matter of major concern in the protection of power distribution systems. They can lead to interruptions in energy supply, and consequently affect the quality of power delivered to consumers. In these situations, there is a need to determine a fault location to ensure that a technical crew can reestablish the system as fast as possible. However, in radial distribution networks, fault location is not a trivial task, as the presence of lateral branches will result in multiple estimations. In this context, the present article addresses the problem of multiple estimation, considering the response of an impedance-based fault locator. To validate the proposed analytical approach, the IEEE 34-node test feeder was used. Moreover, the approach was analyzed in terms of: (a) the percentage reduction of the multiple estimation regions (R%); (b) the number of meters allocated in the feeder; and (c) the distance errors estimated by the fault locator. The results demonstrate that the proposed formulation allowed a minor influence of errors deriving from the impedance-based fault locator and a good trade-off between the number of meters (investment) and the R%.

Improved Genetic Algorithm for Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave Optimization Approach

This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function values.

Voltage regulation in unbalanced power distribution systems with residential PV systems

Increased integration of rooftop photovoltaic (PV) power generation in a low-voltage power distribution system poses significant challenges due to the severity of voltage unbalance problems and complexities in regulating the voltage. Conventional regulation methods are insufficient to address these challenges due to their inability to deal with the voltage unbalance. This paper proposes an advanced supervisory control methodology, with the reactive power control and voltage regulation at residential PV inverters, as an effective means of addressing unbalanced voltages in a power distribution system. The uniqueness of the approach is that it simultaneously controls voltage magnitude and voltage unbalance at affected nodes by precise dispatching of reactive power. Several cases were studied by simulating realistic operating conditions on the unbalanced IEEE 37-node test feeder, and results suggest that the proposed approach can manage the voltage magnitude at nodes within voltage unbalance thresholds. Further cases suggest that the application of the proposed methodology could also reduce the operational stress of residential PV inverters significantly, facilitating distribution system with an increased capacity of PV power generation.

A multi-objective algorithm to determine patrol sequences for out-of-service nodes in power distribution feeders

During outages, Distribution Operation Centers establish a restoration plan to isolate the area that is probably faulty and reconnect all functioning components of a feeder. Despite all technological advances made in the last few decades, the final step to locate the faulty point is taken visually by repair field crews based on a patrol sequence. There is a lack of procedures to determine patrol sequences in literature, which is usually performed by the repair crew itself. Therefore, this paper offers an introduction to define patrol sequences in a multi-objective context. Furthermore, the Multi-Objective Discrete Particle Swarm Optimization has been proposed by using the 3-Opt algorithm and initialization heuristics based on an RNN algorithm - MODPSO+RNN+LS(3-Opt)-, which emulates the decisions made by the repair crew in the field. Lastly, the MODPSO+RNN+LS(3-Opt) was evaluated based on the IEEE 37-node and 123-node test feeders. The obtained results cast light upon how the initialization heuristics and local search method improve the final set of patrol sequence. In addition, it is also possible to find how the multi-objective context of the problem increases its complexity and how likely decisions that are typically made by the field repair crew could be improved by an optimization technique.

Cooperative Peak Shaving and Voltage Regulation in Unbalanced Distribution Feeders

This paper considers the co-operation of distributed generators (DGs), battery energy storage systems (BESSs) and voltage regulating devices for integrated peak shaving and voltage regulation in distribution grids through a co-optimization framework, which aims to minimize the operational costs while fulfilling the operational constraints of network and devices. To account for the uncertainties of load demand and generation, we then convert the co-optimization model into a two-stage stochastic program where state-of-charge (SoC) trajectories of BESSs and operation of voltage regulating devices are optimized at the first stage for day-ahead scheduling, while the reactive powers of DGs and BESSs are left at the second stage for potential intra-day scheduling to handle short-term voltage issues. The proposed co-optimization scheme is validated on the IEEE 37-node test feeder and compared with other practices.

Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model

Micro-grid protection presents great technical challenges to utility company engineers. Several protection schemes have been developed towards a reliable operation on these type of networks, however, the methods are strongly dependent on robust communication systems. This paper presents a decentralized adaptive protection scheme and introduces a data-driven and communication-less approach. The presented solution uses an Artificial Neural Network to train Intelligent Electronic Devices as fault classifiers, also, it uses a cuckoo search metaheuristic to its quasi-optimal adjustment. The Artificial Neural Network enables each classifier to detect faults with only local voltage and current measurements. Presented solution does not assume communication between devices, however, each device brings support to their neighboring devices as back-up protection. Further, system dynamics on the electrical network, such as, changes of topologies, micro-grid status or Distributed Energy Resources outage are considered. The time coordination is simple and easily adjustable due to each fault label of the data-driven model has its own time operation. The presented method is validated on the modified IEEE 34-nodes test feeder. The results of the adaptive protection scheme show accuracy values above of 96% and dependability of 99%. Also, the solution reveals a correlation between the location and the combination of features and hyper-parameters for each Intelligent Electronic Device. The method is development to be easy-to-implement, without hard-to-design parameters, and with highlights potential aspects for real-life applications.

Improving the Power Supply Performance in Rural Smart Grids with Photovoltaic DG by Optimizing Fuse Selection

The recent increase in the use of renewable sources in electrical systems has transformed the electrical distribution network with the subsequent implementation of the distributed generation (DG) concept. The high penetration level of photovoltaic units increases their injected fault current that may result in a lack of coordination of fuse reclosers in distribution networks. One of the main protection devices that is generally used in rural distribution networks is the fuse. A correct size selection is key for ensuring good operation and coordination with other protection devices. The DG implementation makes the selection above more difficult, as the current flow both in steady state and in case of short-circuit is subject to alterations. A new protection fuse selection method for distribution networks with implemented DG is proposed in this paper with the aim of ensuring an effective coordination between them, avoiding untimely behaviors. Different case studies have been analyzed (for diverse locations of DG in the network with various penetration levels which represent 25%, 50%, 75%, and 100% of the total installed load), using an IEEE 13-node test feeder. Besides, a new model to analyze fuse performance is proposed in this work. This model has proven to fit the manufacturer’s data well, with a maximum error of 2% within the normal trip current values.

Microgrids physics model-based fault location formulation: Analytic-based distributed energy resources effect compensation

Distributed energy resources consideration by fault location formulations is a technical challenge. Stochastic behavior complicates deterministic solutions, while probabilistic approaches are yet to be fully explored. This paper presents analytical physics-based models towards the solution to this challenge. Presented models consider information availability from distributed energy resources to improve the fault location reliability. Information considered is divided into two categories: synchronized measurements provided by intelligent electronic devices located in the substation and in each terminal of distributed energy resources; and linear analytical-based models of distributed energy resources. Distributed energy resources models are used only when remote measurements are not available. Different distributed energy resources technologies and their operation modes are modeled. The presented fault location solution is validated on the IEEE 34-nodes test feeder modified. Easy-to-implement model, without hard-to-design parameters, built on the classical impedance-based fault location solution, indicates potential for real-life applications.

Linear and Nonlinear Fault Location in Smart Distribution Network Under Line Parameter Uncertainty

The line parameters of the distribution network (DN) may change because of the atmospheric, structural, and operational conditions. The uncertainty of line parameters can compromise the accuracy of the automatic fault location methods. Besides, arc faults (AFs) may happen in the DN. These faults are difficult to locate in the faulty section because of the nonlinear, asymmetric, and random nature of AF current. In this article, we present a new time-domain fault location (TDFL) method to determine the location of the fault in smart power DN under the line parameters uncertainty. In the suggested method, line parameters' accurate values are determined using a mixed gradient descent particle swarm optimization algorithm. The suggested method's performance is investigated with the help of an IEEE 123-node test feeder in MATLAB (R2018b). The effects of parameter uncertainty, distributed generations operation conditions and modes, different types of AF, various fault distances, resistances, and the fault inception angles are studied. For further evaluation of the proposed method's robustness, two practical tests in the laboratory are carried out. The results confirm that the proposed TDFL method has high accuracy.

Predictive Coordinated and Cooperative Voltage Control for Systems With High Penetration of PV

This article proposes a predictive coordinated and cooperative voltage control method in a power distribution system with high penetration of photovoltaic (PV) units. First, an integrated coordinated voltage control of voltage regulators (VRs) tap positions and cooperative distributed control of the reactive power output from PV inverters are used to maintain system voltages within an appropriate bandwidth. Next, solar power forecasting is applied to predict voltage changes, which are used to set the VR tap positions and capacitor switch status to prevent large voltage fluctuations. The fine tuning of voltage adjustment is then achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on a modified IEEE 123-node test feeder with high penetration of PVs using real measurement data and compared with the base case. Simulation results demonstrate the effectiveness of the integrated voltage control, as well as the enhancement from the predictive control through solar power forecasting-enabled voltage change estimates. Comparison to previous work in the literature shows significant improvement in terms of voltage deviation and reduction in excessive tap changes.

Designing Constraint-Based False Data-Injection Attacks Against the Unbalanced Distribution Smart Grids

The advent of smart power grid, which plays a vital role in the upcoming smart city era, is accompanied with the implementation of a monitoring tool, called state estimation (SE). For the case of the unbalanced residential distribution grid, the state-estimating operation, which is conducted at a regional scale, is considered as an application of the edge computing-based Internet of Things (IoT). While the outcome of the SE is important to the subsequent control activities, its accuracy heavily depends on the data integrity of the information collected from the scattered measurement devices. This fact exposes the vulnerability of the SE module under the effect of data-driven attacks. Among these, the false data-injection (FDI) attack is attracting much attention due to its capability to interfere with the normal operation of the network without being detected. This article presents an attack design scheme based on a nonlinear physical-constraint model that is able to produce an FDI attack with theoretically stealthy characteristic. To demonstrate the effectiveness of the proposed design scheme, simulations with the IEEE 13-node test feeder and the WSCC 9-bus system are conducted. The experimental results indicate that not only the false-positive rate of the bad data detection mechanism is 100% but the physical consequence of the attack is severe. These results pose a serious challenge for the operators in maintaining the integrity of measurement data.

Resilient-enhancing critical load restoration using mobile power sources with incomplete information

Mobile power sources (MPSs) have great potentials to enhance the resilience of distribution system due to mobility and flexibility. However, because of incomplete and asynchronous data after a natural disaster, decisions may lead to under-utilization of MPS and weak recovery performance. This paper proposes a robust receding horizon recovery strategy that dispatches MPSs and forms multiple microgrids (MGs) to maximize the restored critical loads (CLs). The receding horizon framework is utilized to update the system information dynamically and re-optimize the problem until accomplishment of the recovery process. A two-stage robust optimization formulation is adopted to take into account the problem uncertainty, i.e. unknown status of branches in damaged areas. Due to long-lasting duration of the recovery process, charging capability for re-chargeable MPSs is added to the proposed approach. Several case studies are carried out on IEEE 33-node distribution test system and IEEE 123-node test feeder to verify the effectiveness of proposed framework. The benefits of the new model are remarkable when there is a significant level of incomplete information, which is a likely case after high impact natural disasters.

Generalized Formulation of Steady-State Equivalent Circuit Models of Grid-Forming Inverters

Abstract: This work proposes positive- and negative-sequence equivalent circuits of grid-forming inverters for steady-state analysis. The proposed models are especially attractive for performing long-duration voltage regulation analysis and short-circuit studies involving grid-forming inverters. Our proposed equivalent circuit models are based on the inverter’s voltage and current control loops in the αβ and dq frames. For this reason, they operate according to prescribed control functions and specified impedances (i.e., filter impedance, current limiter block, virtual admittance block, and PI/PR controller block). The equivalent circuit model accuracy is validated by comparing system steady-state voltage and current responses obtained by detailed time-domain models in PSCAD/EMTDC to those by the equivalent circuit models implemented in steady-state load flow program (e.g., OpenDSS). Two distinct control structures implemented in the αβ and dq frames are used for the validation. Single line-to-ground and line-to-lineto-ground faults are simulated in a small islanded microgrid as well as the IEEE 34-node test feeder. Fault impedances varying from 0 to 5 ohms are simulated. We show that the equivalent models precisely replicate the steady-state response of the detailed time-domain models.

Robust Distribution System Load Restoration With Time-Dependent Cold Load Pickup

Service restoration is one of the critical functions to enable the future self-healing distribution system. To restore the distribution system in a timely and reliable manner, the realistic system operating conditions need to be accurately characterized. In this paper, two main factors that have great impacts on distribution system restoration (DSR) in practice are investigated. First, cold load pickup (CLPU), generally caused by thermostatically controlled loads (TCLs), is a common phenomenon after an outage and shaped by the outage duration. However, the time-dependent behaviors of CLPU are rarely considered in literature. In this paper, the operating state evolution of TCLs after an outage is analyzed to characterize time-dependent CLPU. And the time-dependent CLPU is analytically embedded in DSR to accurately represent the actual behaviors of the restored loads. Second, it is difficult to predict loads that fluctuate during DSR due to the lack of real-time measurement data. Accordingly, a robust DSR based on the information gap decision theory (IGDT) is proposed to address this challenge, fully considering the uncertainty of CLPU. The proposed models are tested in IEEE 13-node and 123-node test feeders. Simulation results demonstrate that the time-dependent CLPU model and the uncertainty modeling of CLPU can accurately capture the actual behaviors of loads with TCLs after an outage, which greatly improves DSR decisions in practice.

Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration

Large penetration of wind energy systems into electric-grids results in many power quality problems. This paper presents a classification of power quality issues, namely harmonics and short-duration voltage variation observed due to the integration of wind power. Additionally, different techniques and technologies to mitigate the effect of such issues are discussed. The paper highlights the current trends and future scopes in the improvement of the interconnection of wind energy conversion systems (WECSs) into the grid. As the voltage variation is the most severe power quality issue, case studies have been presented to investigate this problem using steady-state time-series simulations. The standard IEEE test system namely IEEE 123-node test feeder and IEEE 30-node grid are solved under different operating conditions with wind power penetration. Typical daily load profiles of a substation in Riyadh, Saudi Arabia, and an intermittent wind power generation profile are used in all case studies. Mitigation of voltage variations due to wind intermittency is achieved using reactive power compensation of the interface inverter. The results show the effectiveness of these approaches to avoid voltage variation and excessive tap setting movements of regulators and keep the voltage within the desired operating conditions.

Cooperative Volt-Ampere Reactive Control for a PV-Enabled Distribution Network With Abnormally High Power Loads

This article presents a cooperative volt-ampere reactive (C-VAR) control strategy to regulate voltage while reducing incurred power loss for a photovoltaic (PV) enabled grid experiencing abnormal (unexpected and extremely high) real power loads. The proposed C-VAR method only requires limited communications about power flow and voltage in a subset of nodes locally and is optimization-free. Thus, it is particularly suitable for the real-time voltage control for a distributed large-scale power grid. For demonstration purposes, C-VAR is evaluated using the IEEE 123-node test feeder and compared with benchmark solutions, such as the IEEE Standard 1547 and centralized model predictive control. The results show that C-VAR does not incur too much power loss and quickly drives the voltage back to the desired range under high power loads and PV indeterminacy.

Toward an adaptive protection scheme in active distribution networks: Intelligent approach fault detector

Conventional protection schemes have proven insufficient for the protection of Active Distribution Networks (ADN). Novel protection schemes with an adaptive approach should be developed to guarantee the protection of ADN under all their operating conditions. This paper proposes an ADN adaptive protection methodology, which is based on an intelligent approach fault detector over locally available measurements. This approach uses Machine Learning (ML) based techniques to reduce the strong dependence of the adaptive protection schemes on the availability of communication systems and to determine if, over a fault condition, an Intelligent Electronic Device (IED) should operate considering the changes in operational conditions of an ADN. Additionally, the methodology takes into account different and remarkable recommendations for the use of ML techniques. The proposed methodology is validated on the modified IEEE 34-nodes test feeder. Additionally, it takes into consideration typical features of ADN and micro-grids like the load imbalance, reconfiguration, changes in impedance upstream from the micro-grid, and off-grid/on-grid operation modes. The results demonstrate the flexibility and simplicity of the methodology to determine the best accuracy performance among several ML models. Besides, they show the methodology’s versatility to find the suitable ML model for IEDs located on different zones of an ADN. The ease of design’s implementation, formulation of parameters, and promising test results indicate the potential for real-life applications.

New phase‐changing soft open point and impacts on optimising unbalanced power distribution networks

Three-phase unbalanced conditions in distribution networks are conventionally caused by load imbalance, asymmetrical fault conditions of transformers and impedances of three phases. The uneven integration of single-phase distributed generation (DG) worsens the imbalance situation. These unbalanced conditions result in financial losses, inefficient utilisation of assets and security risks to the network infrastructure. In this study, a phase-changing soft open point (PC-SOP) is proposed as a new way of connecting soft open points (SOPs) to balance the power flows among three phases by controlling active power and reactive power. Then an operational strategy based on PC-SOPs is presented for three-phase four-wire unbalanced systems. By optimising the regulation of SOPs, optimal energy storage systems dispatch and DG curtailment, the proposed strategy can reduce power losses and three-phase imbalance. Second-order cone programming (SOCP) relaxation is utilised to convert the original non-convex and non-linear model into an SOCP model which can be solved efficiently by commercial solvers. Case studies are conducted on a modified IEEE 34-node three-phase four-wire system and the IEEE 123-node test feeder to verify the effectiveness, efficiency and scalability of the proposed PC-SOP concept and its operational strategy.

Load-Margin Assessments in MicroGrids and the Influence of Power Electronic Converter Operation Mode

This paper deals with static voltage stability assessments in the context of microgrids. It is well known that distributed systems have been changing their operation mode from passive to active due to a high level of distributed resource penetration. Hence, operating of the system in islanded mode could be considered on a routine or emergency basis, and the voltage stability analysis become vital in any planning or operation study in the distribution level of electrical power systems. In this way, this paper develops a novel formulation for voltage stability analysis in islanded systems, showing the differences with respect to traditional methodologies, mainly when they neglect the absence of a swing bus and the frequency of the system as a variable. Further, the present work compares the load margins of a distribution system when it works in both modes, in grid-connected mode and islanded mode, considering the operation strategies of the most usual Power Electronic Converters. The results are carried out in the IEEE-37 Node Test Feeder.