IEEE 13-node Test Feeder Research Articles

Restoration Strategy for Active Distribution Systems Considering Endogenous Uncertainty in Cold Load Pickup

Cold load pickup (CLPU) phenomenon is identified as the persistent power inrush upon a sudden load pickup after an outage. Under the active distribution system (ADS) paradigm, where distributed energy resources (DERs) are extensively installed, the decreased outage duration can induce a strong interdependence between CLPU pattern and load pickup decisions. In this paper, we propose a novel modelling technique to tractably capture the decision-dependent uncertainty (DDU) inherent in the CLPU process. Subsequently, a two-stage stochastic decision-dependent service restoration (SDDSR) model is constructed, where first stage searches for the optimal switching sequences to decide step-wise network topology, and the second stage optimizes the detailed generation schedule of DERs as well as the energization of switchable loads. Moreover, to tackle the computational burdens introduced by mixed-integer recourse, the progressive hedging algorithm (PHA) is utilized to decompose the original model into scenario-wise subproblems that can be solved in parallel. The numerical test on modified IEEE 123-node test feeders has verified the efficiency of our proposed SDDSR model and provided fresh insights into the monetary and secure values of DDU quantification.

Optimal Number and Locations of Smart RMUs for Self-Healing Distribution Networks

Smart grids with self-healing (SH) capability provide an important intelligent feature to help in fast correction actions in case of network faults. SH architecture consists of modern communication systems, smart equipment, and intelligent sensors. With the high cost of SH components (especially smart ring main unit (SRMU)), optimization is required to achieve optimum performance with minimum cost. This study presents a proposed methodology to determine the optimum number and locations of SRMUs in electricity distribution networks considering various cost issues. The disconnection cost of on-grid photovoltaic (PV) plants is taken into consideration as an important factor in determining the locations of the SRMUs. The nonlinear programming (NLP) optimization technique is used to determine the required number of SRMUs, considering the cost/benefit analysis (cost of upgrading MRMUs to SRMUs/benefit due to interruption time reduction), which is the most important factor from DISCOs’ perspective. The mixed integer linear programming (MILP) optimization technique is employed for selecting the optimal locations of the SRMUs considering the cost of losses, energy not supplied (ENS), and PV disconnection, which improves network operation cost. The methodology takes into consideration the cable failure rate and the interest rate. Moreover, the study introduces the Egyptian electrical distribution network and a pilot project for control centre development using SRMUs. The methodology is applied to a modified IEEE 37-node test feeder and a part of a specific district network in South Cairo consisting of 158 nodes; both systems include a number of PV distributed generation plants. Simulation results are presented to show the effectiveness of the proposed method.

Load Flow and Short-Circuit Methods for Grids Dominated by Inverter-Based Distributed Generation

The use of power-electronics-based devices in distribution generation seeks to improve energy quality and reduce costs. The inverter-based distributed generator, that works in different operation modes, has emerged as a promising technology. In a high distributed generation penetration scenario it is important to know the voltage profile and fault information due to the uncertainty in the generator operation and the impact that have on the network. This study aims to use two proposed methods of analysis: for power flow, based on backward/forward sweep method, and short-circuit, based on hybrid impedance matrix, that considers the inverter operation modes and represents each generator as a voltage-controlled current source. The chosen network is the IEEE 34-Node Test Feeder with a generator on each load per phase. The voltage profiles obtained will be validated with a Simulink/Matlab phasorial model. The results show an average error of 2.39% and a gain in voltage profile processing time of 2185.24%, making its use consistent for larger systems.

Voltage Sags Characterization Using Fault Analysis and Deep Convolutional Neural Networks

The power systems are transitioning to renewables. The power system infrastructure digitalization is the next stage in this evolution. A digitalized future distribution network (DN) has an opportunity to evolve into real-time analysis based on a huge volume of data. Such real-time analysis will be feasible through the integration of the theoretical background of fault analysis and machine learning techniques. Fault location is one of the major issues to improve reliability indices. The voltage sags characterization is used to locate faults in the DN. This article presents a methodology to characterize voltage sags using fault analysis and deep convolutional neural networks. The voltage divider model allows the characterization and the discrete wavelet transform is used in signal processing. The machine learning and deep learning models used allow estimating the sag magnitude, fault location, phases involved, duration, and impact of distributed generation (DG) in each event. The IEEE 13-node test feeder including DG was used to validate the effectiveness of the proposed methodology. This paper provides a way to handle a Big Data stream in the DN control center and to efficiently locate faults in several operational scenarios.

Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion

This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile in electrical power distribution networks. The algorithm is accordingly termed the Accelerated-PPF (APPF), since it can accelerate “any” sampling-based PPF solver. As the APPF runs, it concurrently generates a low-dimensional subspace of orthonormalized solution vectors. This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles. When constructing and updating the subspace, the power flow problem must still be solved on the full nonlinear system. In order to accelerate the computation of these solutions, a Neumann expansion of a modified power flow Jacobian is implemented. Applicable when load bus injections are small, this Neumann expansion allows for a considerable speed up of Jacobian system solves during the standard Newton iterations. APPF test results, from experiments run on the full IEEE 8500-node test feeder, are finally presented.

Anonymization of distribution feeder data using statistical distribution and parameter estimation approach

Distribution networks are undergoing a transition due to the rapid increase in distributed energy resources (DERs). Concerns over revealing critical infrastructure information and breaching consumer privacy have significantly hindered their development. Many applications that require data sharing are still impractical due to the lack of sufficient datasets, privacy concerns, and data access problems. To address these issues, a statistical distribution approach for parameter estimation is proposed. The statistical patterns of real distribution feeders are examined by accessing the confidential database of a local distribution network service provider (DNSP). An algorithm based on the Maximum Likelihood Estimate (MLE) is applied to estimate the statistical distribution parameters that represent the actual data. Then, these statistical distribution parameters are used to generate anonymized datasets that are realistic. A Kolmogorov–Smirnov (K-S) test is conducted to confirm the effectiveness of anonymized datasets, and results are compared with the actual feeder datasets. Validation is carried out with existing methods and comparisons are shown on the different portions of the datasets (25 percent, 50 percent, 75 percent, and 100 percent). The comparison results indicate superior performance over traditional methods, with a performance improvement ranging from 1 to 13 percent. The practical application of the method is demonstrated on the IEEE 123-node test feeder. The method achieves consistent results on voltage profiles, with a maximum difference of 0.420 percent between actual and anonymized datasets.

Optimal coordination of unbalanced power distribution systems with integrated photovoltaic systems and semi‐fast electric vehicles charging stations

Increased integration of photovoltaics (PVs) systems and charging stations for electric vehicles (EVs) has led to a substantial increase in the level of voltage unbalance beyond the acceptable limit. Ordinary voltage regulation devices such as on-load tap changers (OLTCs) and distribution static synchronous compensator (DSTATCOM) are sometimes incapable of adequately addressing this issue without proper coordination with PVs and EVs. This paper presents a novel real-time optimal coordination scheme to determine the tap position of OLTC, the amount of reactive power to be exchanged by DSTATCOM and a PV inverter, and the phase connection of EVs. The proposed scheme aims to maintain the voltage magnitude and voltage unbalance within the statutory limit while minimising the power losses in an active unbalanced power distribution system. Advanced and hybrid particle swarm optimisation (AHPSO) algorithm is also developed to solve the optimisation problem, and its robustness in comparison with other techniques is verified. The impact of uncoordinated voltage control and proposed control on voltage unbalance and power losses are investigated. Time-series simulations confirm the significance and scalability of the developed coordination control scheme on IEEE 37-node and IEEE 123-node test feeders with real data and different PV penetration levels.

An online resilience assessment method for islanded distribution systems considering uncertainty of intermittent RESs and loads

The frequent natural disasters caused by global warming have been threatening the safety of power systems for decades. A resilient power system has the ability to recover quickly following a disaster and has been continuously studied these years. However, with the progress of energy transition, a large quantity of renewable energy sources (RESs) has been integrated into the power distribution systems and the uncertainty has complicated the decision and operation of resilient distribution systems. This paper proposes an online resilience assessment method for islanded distribution systems considering the time-varying uncertainty of RESs and loads, based on only the measured data without any forecasting tools. An offline probabilistic modeling and online conditional probability distribution derivation method is proposed to generate short-term power scenarios of RESs and loads dynamically, and an online resilience assessment framework is established with an optimization-based system response model in different outage scenarios of distribution systems. The case study to a modified IEEE 37-node test feeder validates the advantages of the proposed method in generating accurate short-term scenarios of uncertainties and providing reliable resilience assessment results. The proposed framework can be used as a basic tool for the online operation of resilient distribution systems.

Enhancing Active Distribution Systems Resilience by Fully Distributed Self-Healing Strategy

Distributed restoration can exploit smart grid technologies to enhance the resilience of active distribution networks toward a self-healing smart grid. However, the large number of decision variables, especially the binary ones for reconfiguration, bring challenges to developing scalable distributed distribution service restoration (DDSR) strategies. This paper proposes a fully distributed solution procedure based on the alternating direction method of multipliers (ADMM) for mixed-integer programming problems and applies to develop the DDSR framework. The method consists of relax-drive-polish phases, 1) relaxing binary variables, and applying the convex ADMM as a warm start; 2) driving the solutions toward Boolean values through a proximal operator; 3) fixing the obtained binding binary variables and solving the rest of the problem to polish results and achieve a high-quality suboptimal solution. Then, an autonomous clustering strategy and consensus ADMM are integrated with the proposed method to realize the fully distributed cluster-based framework of DDSR. This framework can first determine DER scheduling and switch status for reconfiguration to energize the out-of-service areas from local faults, and then provide the load restoration solution in a distributed manner for total blackouts in large-scale distribution networks. The effectiveness and scalability of the proposed DDSR framework are demonstrated through testing on the IEEE 123-node, IEEE 8500-node, and synthetic 100k-node test feeders.

Convolutional autoencoder anomaly detection and classification based on distribution PMU measurements

The huge volume of data that is streamed from distribution phasor measurement units (DPMU) toward distribution management system (DMS) can rarely be used in the raw format. Data-driven analysis to extract information out of massive raw data has revealed promising opportunities to overcome this challenge. This paper utilizes convolutional autoencoders (Conv-AE) for the sake of anomaly detection based on the DPMU measurements in distribution systems. The Conv-AE is unsupervised and independent of the event type. It dispenses preprocessing and feature extraction along with obtaining the essential information directly from the measured data. The anomaly detection is followed by a supervised classifier which is developed to identify the type of anomaly. This classifier is a convolutional neural network that is designed and fine-tuned for the problem at hand. It uses an ensemble learning method to augment the fully-labelled dataset. Performance of the proposed methodology is evaluated on modified IEEE 34-node and IEEE 123-node test feeders in various operating conditions, presence of noise, and different scenarios of DPMU outage. Moreover, a real-world DPMU dataset is utilized to evaluate performance of the Conv-AE model in practical conditions. Results confirmed effectiveness of the proposed technique to be used in future DMS platforms.

Deep Reinforcement Learning-Based Robust Protection in DER-Rich Distribution Grids

This paper introduces a new framework of deep reinforcement learning based protective relay design in power distribution systems with many distributed energy resources (DERs). With increasing penetration of power electronically-interfaced resources, conventional overcurrent relays’ performance is rendered less effective due to the two-way uncertainties in power flow patterns. In this paper, a machine learning-based protective relay that is designed for adaptively deciding the threshold for relay action is proposed. The particular algorithm used is an Long Short-Term Memory (LSTM) enhanced deep neural network that is highly accurate, communication-free and easy to implement. The proposed relay design is tested in OpenDSS simulation on the IEEE 34-node test feeder and a collection of large synthetic feeders in Austin, Texas area. By designing adaptability upfront, the proposed relay is shown to substantially improve the performance of relay in terms of failure rate, robustness, and response speed, in particular in scenarios with high level of distributed energy resources.

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.

Accurate fault location method for distribution network in presence of DG using distributed time‐domain line model

In this paper a novel technique is presented for fault location in distribution networks in the presence of distributed generators (DGs). The proposed approach uses recorded voltage and current at the beginning of feeder and DG terminals. This method is based on transient state data, and due to presence of high frequencies in this state, to achieve high accuracy it utilizes the distributed time-domain (DTD) line model. The fault location problem is formulated as an optimization problem and is solved using Genetic Algorithm (GA). Through modal transformation, in addition to locate the symmetrical three-phase to ground (3LG) faults, the location of asymmetrical faults including single line to ground (SLG) and double line (LL) faults can also be determined. The effects of fault distance, fault resistance and fault inception angle on the accuracy of the proposed algorithm have also been investigated. A solution is also proposed to overcome the multi-response problem. The simulations are conducted and the results are obtained via PSCAD and MATLAB softwares, respectively. The effectiveness of the proposed approach has been verified using the modified IEEE 34-node test feeder under different fault conditions. The simulation results demonstrate the high efficiency and accuracy of the proposed method.

High Impedance Fault detection based on harmonic energy variation via S-transform

Successfully detecting High Impedance Faults (HIFs) is a challenge for distribution systems’ protection specialists due to the random behavior and low magnitude of the fault current. To contribute to this problem’s solution, this paper proposes a new approach for HIF detection based on the energy variation of the low harmonic orders obtained by the Stockwell transform and an adaptive threshold used to ensure high sensitivity and secure detection. Tests with the IEEE 34-node test feeder were performed using actual HIF currents recorded experimentally at a test field, considering different soil types. The proposed method also considered the HIF distinction from other events, such as capacitor banks and lateral branches switching, transformer energizing, motor starting, and linear and non-linear loads (arc furnace and a rectifier) connection. A thorough analysis regarding false operation has shown that the proposed approach is effective for distinguishing HIFs from other events. Furthermore, test results indicated that the method is a secure and promising technique for HIF detection, even with noise in the current measurements.

An efficient fault diagnostic approach for active distribution networks considering adaptive detection thresholds

As support to fault location algorithms, this paper presents a new method for detecting and classifying faults in active distribution networks using the energy moving averages of the current signals measured at the feeder head. A proposal for updating the energy thresholds for fault detection is introduced, aiming to achieve higher sensitivity in fault detection, especially for scenarios with high penetration levels of distributed generation (DG). Since the power injected by DG can rapidly change, this proposal is essential for maintaining the fault detection method’s reliability. Also, the fault classification is based on an analytical approach, making it possible to be easily applied to different operating scenarios of a distribution system. Thus, its potential for real-world implementation is higher when compared to classifiers based on supervised learning algorithms. Performance tests on the IEEE 34-node test feeder showed that this method is reliable for detecting and classifying different fault types, with diverse generation-load scenarios and noisy signals.

Optimal Placement of Fault Indicators to Identify Fault Zones in Distribution Systems

The optimal placement of fault indicatorss (FIs) is one of the most viable alternatives to improve the reliability of customer service of the distribution utilities. This letter approaches the concept of fault zone as being the portion of the system characterized by the actuation of FI devices. The proposed method consists of FI placement in locations of the system that provide smaller fault zones. Moreover, these devices must be close to customers with high power demand to decrease the service interruption costs. The proposed method is tested on the IEEE 34-node test feeder, in which the results demonstrated that it is efficient to reach the stated objectives.

Load Currents Behavior in Distribution Feeders Dominated by Photovoltaic Distributed Generation

The distributed power generation (DG), like photovoltaic (PV) systems, promptly pleased consumers worldwide due to the availability of the source and the ease of installation. Despite the environmental advantages, it is known that the new generating sources connected to the power grid can provide modifications in the power flow, changing, for example, the load current values. Several papers were focused on present overcurrent protection solutions based on short-circuit current analysis. However, the power system protection depends not only on the short-circuit current, but also on the load current values. This work evaluates distinct scenarios, considering different combinations of topological locations and quantities of DG through the power system. The chosen feeder, IEEE-13 Node Test Feeder, was modelled in Simulink, in steady-state, considering a high penetration of small-scale Photovoltaic Distributed Generation Systems (PVDG). The analysis of current variability in specific buses of the feeder showed a reduction of more than 80% of the load current value for an extreme case. The results indicate that changes in the overcurrent protection devices (OCP) adjusts towards a massive insertion of DG can be fundamental to guarantee the sensitivity of the devices and a correct performance of it for overload.

Capacity allocations of SOPs considering distribution network resilience through elastic models

Disturbances such as natural disasters or man-made attacks exert serious influences on power system, resulting in an increasing awareness of resilience enhancement strategies. As the integration of microgrids, distributed generators (DGs) and power electronic devices adds to the vulnerability of power distribution network to disruptions, it is highly required to enhance the resilience against failure. Soft open points (SOPs) are flexible power electronic devices, which can balance power distribution under normal operation, and supply restoration under abnormal conditions. Thus, the application of SOPs can boost resilience both in pre-failure prevention and post-failure recovery. In this paper, to maximize the effect of SOPs on the boost of resilience of distribution network, a mixed integer non-linear optimization problem is proposed to schedule the siting and sizing of SOPs based on multi-stage elastic mechanical model. A bi-level algorithm is used to tackle the SOP planning problem. The selection of location and capacity is solved by genetic algorithm, after which the control strategies of SOP are optimized by particle swarm algorithm with given planning scheme to obtain the maximum resilience. Finally, case studies on the IEEE 33-bus system and IEEE 123-node test feeder are used to verify the effectiveness and efficiency of the proposed method.

A comprehensive three-phase step voltage regulator model with efficient implementation in the Z-bus power flow

This paper presents a comprehensive three-bus equivalent circuit model of three-phase step voltage regulators. The proposed model can be efficiently integrated in the Z-bus power flow method and can accurately simulate any configuration of step voltage regulators. In contrast to the conventional step voltage regulator models that include the tap variables inside the YBUS matrix of the network, the proposed model simulates them in the form of current sources, outside the YBUS matrix. As a result, the re-factorization of the YBUS matrix is avoided after every tap change reducing significantly the computational burden of the power flow. Furthermore, possible convergence issues caused by the low impedance of step voltage regulators are addressed by introducing fictitious impedances, without, however, affecting the accuracy of the model. The results of the proposed step voltage regulator model are compared against well-known commercial softwares such as Simulink and OpenDSS using the IEEE 4-Bus and an 8-Bus network. According to the simulations, the proposed model outputs almost identical results with Simulink and OpenDSS confirming its high accuracy. Furthermore, the proposed 3-bus equivalent model is compared against a recently published conventional step voltage regulator model in the IEEE 8500-Node test feeder. Simulation results indicate that the proposed step voltage regulator model produces as accurate results as the conventional one, while its computation time is significantly lower. More specifically, in the large IEEE 8500-node network consisting of four SVRs, the proposed model can reduce the computation time of power flow around one minute for every tap variation. Therefore, the proposed step voltage regulator model can constitute an efficient simulation tool in applications where subsequent tap variations are required.

Stochastic Simultaneous Planning of Interruptible Loads, Renewable Generations and Capacitors in Distribution Network

Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal location and capacity of ILs for a given incentive rate is of great interest to distribution companies. To do so, in this paper simultaneous allocation and sizing of ILs, wind turbines (WT), photovoltaic (PV) and capacitors have been done in the radial distribution network for different demand levels and subsequently the optimal value of compensation price for the ILs has been determined. Given the probabilistic nature of load, wind and solar generation as well as the price of energy at the pool, we have also proposed a stochastic model based on fuzzy decision making for modelling the technical constraints of the problem under uncertainty. The objective functions are technical constraint dissatisfaction, the total operating costs of the Distribution Company and CO2 emissions which are minimized by NSGA2. To model the uncertainties, a scenario-based method is used and then by using a scenario reduction method the number of scenarios is reduced to a certain number. The performance of the proposed method is assessed on the IEEE 33-node test feeder to verify the applicability and effectiveness of the method.