Test Feeder Research Articles

Optimal partitioning of unbalanced active distribution systems for supply‐sufficient micro‐ grids considering uncertainty

Partitioning large-scale smart distribution systems into multiple microgrids (MG) are considered an effective solution to maintain system reliability and to improve the power system restoration under emergency conditions. The partitioning planning process faces many challenges especially if the uncertain nature of both renewable energy resources (RERs) and loads, and the unbalanced operation of the distribution systems are considered. In this paper, the active distribution system partitioning is handled through a proposed three stages method. The proposed method presents a parallel planning for both energy sources integration process and partitioning process, taking into consideration all the circumstances that the system is exposed to, with determining the optimal specification of the devices that will be added to the system to reach the required efficiency of the system during system partitioning and system unification. The proposed three-stage method considers the uncertainty of PV modules and wind turbines. A new criterion for modeling RERs uncertainty is developed. Four phases for uncertainty reduction are applied based on the main sources of uncertainty involving solar irradiation, temperature, wind speed for the loading ratio. Moreover, the load uncertainty and system unbalance have been considered. The proposed reduction strategy leads to 92% reduction of the uncertainty cases for 1 year is sufficient for effective modeling with an acceptable level of accuracy. The proposed method is tested on the IEEE 123-node as a large-scale unbalanced test feeder. The obtained results verify the effectiveness of the proposed method in partitioning the tested distribution system into five micro-grids with self-dependence exceeded 85%.

Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

The problem of the optimal reactive power flow in transmission systems is addressed in this research from the point of view of combinatorial optimization. A discrete-continuous version of the Chu & Beasley genetic algorithm (CBGA) is proposed to model continuous variables such as voltage outputs in generators and reactive power injection in capacitor banks, as well as binary variables such as tap positions in transformers. The minimization of the total power losses is considered as the objective performance indicator. The main contribution in this research corresponds to the implementation of the CBGA in the DigSILENT Programming Language (DPL), which exploits the advantages of the power flow tool at a low computational effort. The solution of the optimal reactive power flow problem in power systems is a key task since the efficiency and secure operation of the whole electrical system depend on the adequate distribution of the reactive power in generators, transformers, shunt compensators, and transmission lines. To provide an efficient optimization tool for academics and power system operators, this paper selects the DigSILENT software, since this is widely used for power systems for industries and researchers. Numerical results in three IEEE test feeders composed of 6, 14, and 39 buses demonstrate the efficiency of the proposed CBGA in the DPL environment from DigSILENT to reduce the total grid power losses (between 21.17% to 37.62% of the benchmark case) considering four simulation scenarios regarding voltage regulation bounds and slack voltage outputs. In addition, the total processing times for the IEEE 6-, 14-, and 39-bus systems were 32.33 s, 49.45 s, and 138.88 s, which confirms the low computational effort of the optimization methods directly implemented in the DPL environment.

Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a very limited number of available measurements. In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. Two reformulations of the original MINLP problems are investigated. Firstly, in order to remove the nonlinearity in the MINLP formulation, a mixed integer linear programming (MILP) problem that employs auxiliary variables is derived. Furthermore, to achieve a faster solution, convex relaxation of the original formulation is derived. Finally, using a Markovian model for topology changes, prior information about system topology is used to improve topology identification particularly when a limited amount of measurements is available. Simulation results on IEEE 37-bus test feeder and IEEE 123-bus test feeder illustrate the efficiency and scalability of the proposed approaches from both state estimation and topology identification point of view (even with 30% of available data).

Low voltage abnormal user identification based on improved fish swarm algorithm

The power consumption readings of sub meter and total meter of distribution transformer of low-voltage users follow the law of conservation of energy. The meter power loss rate of abnormal low-voltage users must also be abnormal. This paper studies the solution of the meter power loss rate under the four abnormal power consumption scenarios of single (multi) user and full (partial) period. The traditional linear solution method has accurate identification effect for the abnormal power consumption scenario of full period, but it cannot identify the abnormal power consumption scenario of partial period. In this paper, an improved artificial fish swarm algorithm is proposed. By adjusting the fixed step to the adaptive step, the power loss rate of each sub meter is obtained, and the abnormal power users are pinpointed. The research results are verified by simulation examples on IEEE European Low Voltage Test Feeder. The results show that the improved artificial fish swarm algorithm in this paper can identify abnormal power users for the above four abnormal electric field scenarios. The algorithm provides a new alternative for the identification of abnormal low voltage users.

A Comparative Study on Power Flow Methods Applied to AC Distribution Networks with Single-Phase Representation

This paper presents a comparative analysis of six different iterative power flow methods applied to AC distribution networks, which have been recently reported in the scientific literature. These power flow methods are (i) successive approximations, (ii) matricial backward/forward method, (iii) triangular-based approach, (iv) product linearization method, (v) hyperbolic linearization method, and (vi) diagonal approximation method. The first three methods and the last one are formulated without recurring derivatives, and they can be directly formulated in the complex domain; the fourth and fifth methods are based on the linear approximation of the power balance equations that are also formulated in the complex domain. The numerical comparison involves three main aspects: the convergence rate, processing time, and the number of iterations calculated using the classical Newton–Raphson method as the reference case. Numerical results from two test feeders composed of 34 and 85 nodes demonstrate that the derivative-free methods have linear convergence, and the methods that use derivatives in their formulation have quadratic convergence.

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.

Power Electronics Intelligence at the Network Edge (PINE)—An Approach to Interface PV and Battery Energy Storage Systems at the Grid Edge

In this article, a self-organizing power electronic converter with control intelligence at the edge of the distribution network is proposed. The proposed converter is called power electronics intelligence at the network edge (PINE), and it has the potential to add intelligence at the network edge to the electricity delivery system of the present and in the future. The proposed approach consists of a power electronic converter (rectifier/dc-link/inverter) termed PINE to supply residential loads. The rooftop solar and battery energy storage system is connected to the dc link. With the bidirectional characteristic of the PINE, the load voltage is regulated via feedback, whereas input distribution voltage can be allowed to vary in a range. This type of configuration allows for control of input power factor to be unity and reactive power to be injected at the grid edge to regulate the voltage and also enable energy budgeting, i.e., limit the amount of power to the residential load under disaster situations. With the PINE converter at the distribution edge that can communicate with other PINEs as well as to the distribution system operator, intelligence at the distribution edge can be established. Such a system has many advantages that are discussed in this article. Furthermore, the impacts of increased penetration levels of PINE are shown using a test feeder based on the IEEE-37 test node feeder. Simulation and experimental results on a laboratory prototype system validate the PINE converter concept.

Decentralized Control Strategy to Improve Fairness in Active Power Curtailment of PV Inverters in Low-Voltage Distribution Networks

The conventional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">volt-watt</i> control method used in PV inverters to overcome the over-voltage 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 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">volt-var-watt</i> control method. The performance of this method is evaluated using various simulation studies by making reference to the IEEE European low-voltage test feeder and considering different levels of PV system integration. It is shown that the proposed control strategy can significantly improve the fairness of the active power curtailment among PV inverters. Finally, the result of a comparative analysis between the proposed strategy and two state-of-the-art communication-based methods is reported.

Fuzzy based relaying scheme for high impedance faults in DG integrated distribution system

Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions.

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

This paper deals with the problem of the optimal selection and location of batteries in DC distribution grids by proposing a new mixed-integer convex model. The exact mixed-integer nonlinear model is transformed into a mixed-integer quadratic convex model (MIQC) by approximating the product among voltages in the power balance equations as a hyperplane. The most important characteristic of our proposal is that the MIQC formulations ensure the global optimum reaching via branch &amp; bound methods and quadratic programming since each combination of the binary variables generates a node with a convex optimization subproblem. The formulation of the objective function is associated with the minimization of the energy losses for a daily operation scenario considering high renewable energy penetration. Numerical simulations show the effectiveness of the proposed MIQC model to reach the global optimum of the optimization model when compared with the exact optimization model in a 21-node test feeder. All the validations are carried out in the GAMS optimization software.

Limiting current and voltage unbalances in distribution systems: A metaheuristic-based decision support system

Voltage and current unbalances are highly harmful to electrical power systems and the equipment connected to them. Thus, the development of a method that enables the quantification and limitation of unbalances is of great interest, especially in the analysis of distribution systems that are intrinsically unbalanced. In this work, a method for limiting unbalances through the insertion of capacitors based on the iterated local search (ILS) metaheuristic is presented. This method can identify how many capacitors are required, which bus they should be connected to, and the power of each capacitor. A power flow based on the backward forward sweep (BFS) method and three-phase modeling of the system equipment is used, and the symmetrical component method is used to calculate the unbalance index. The following IEEE test feeders are used in the experiments: 4-bus, 13-bus, 34-bus, and 123-bus. The results demonstrate the robustness of the method even when applied to large systems, enabling the limitation of unbalances with a computational time that is compatible with studies on the planning, operation, and expansion of electrical systems.

Distribution System Resilience Under Asynchronous Information Using Deep Reinforcement Learning

Resilience of a distribution system can be enhanced by efficient restoration of critical load following a major outage. Existing models include optimization approaches that consider available information without incorporating the inherent asynchrony of data arrival during execution of the restoration plan. Failure to consider the asynchronous nature of information arrival can lead to underutilization of critical resources. Moreover, analytical models become computationally inefficient for large scale systems. On the other hand, artificial intelligence (AI)-based tools have demonstrated efficient results for power system applications. In this paper, it is proposed a Reinforcement Learning (RL) model that learns how to efficiently restore a distribution system after a major outage. The proposed approach is based on a Monte Carlo Tree Search to expedite the training process. The proposed model strategy provides a robust decision-making tool for asynchronous and partial information scenarios. The results, validated with the IEEE 13-bus test feeder and IEEE 8500-node distribution test feeder, demonstrate the effectiveness and scalability of the proposed method.

Approximated Mixed-Integer Convex Model for Phase Balancing in Three-Phase Electric Networks

With this study, we address the optimal phase balancing problem in three-phase networks with asymmetric loads in reference to a mixed-integer quadratic convex (MIQC) model. The objective function considers the minimization of the sum of the square currents through the distribution lines multiplied by the average resistance value of the line. As constraints are considered for the active and reactive power redistribution in all the nodes considering a 3×3 binary decision variable having six possible combinations, the branch and nodal current relations are related to an extended upper-triangular matrix. The solution offered by the proposed MIQC model is evaluated using the triangular-based three-phase power flow method in order to determine the final steady state of the network with respect to the number of power loss upon the application of the phase balancing approach. The numerical results in three radial test feeders composed of 8, 15, and 25 nodes demonstrated the effectiveness of the proposed MIQC model as compared to metaheuristic optimizers such as the genetic algorithm, black hole optimizer, sine–cosine algorithm, and vortex search algorithm. All simulations were carried out in MATLAB 2020a using the CVX tool and the Gurobi solver.

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.

Heuristic Methodology for Planning AC Rural Medium-Voltage Distribution Grids

The optimal expansion of AC medium-voltage distribution grids for rural applications is addressed in this study from a heuristic perspective. The optimal routes of a distribution feeder are selected by applying the concept of a minimum spanning tree by limiting the number of branches that are connected to a substation (mixed-integer linear programming formulation). In order to choose the caliber of the conductors for the selected feeder routes, the maximum expected current that is absorbed by the loads is calculated, thereby defining the minimum thermal bound of the conductor caliber. With the topology and the initial selection of the conductors, a tabu search algorithm (TSA) is implemented to refine the solution with the help of a three-phase power flow simulation in MATLAB for three different load conditions, i.e., maximum, medium, and minimum consumption with values of 100%, 60%, and 30%, respectively. This helps in calculating the annual costs of the energy losses that will be summed with the investment cost in conductors for determining the final costs of the planning project. Numerical simulations in two test feeders comprising 9 and 25 nodes with one substation show the effectiveness of the proposed methodology regarding the final grid planning cost; in addition, the heuristic selection of the calibers using the minimum expected current absorbed by the loads provides at least 70% of the calibers that are contained in the final solution of the problem. This demonstrates the importance of using adequate starting points to potentiate metaheuristic optimizers such as the TSA.

Investigation & performance of Volt/Var controllers in the electrical network system

Maintaining the voltage limits at consumer level within the tolerable ranges for all loading conditions is the function of Volt/Var control strategy. By coordinating with reactive power control devices and voltage regulating devices it maintains the suitable range of voltage. Due to modernization of grid new challenges are arise and to overcome those challenges we, must go with the new methodologies. In this article, the main point is evaluating the effectiveness of Volt/Var control (VVC) strategies in distribution systems has been presented. The primary purpose of this section is to have a comprehensive set of available performance measures for Volt-Var Control. Furthermore, the new power flow-based methodology is developed for estimating the metrics under consideration. A research model on such a given VVC strategy to high PV penetration on a test feeder is however performed to illustrate the methodology.

A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources.

The increasing integration of Renewable Energy Resources (RERs) in distribution networks forms the Networked Renewable Energy Resources (NRERs). The cooperative Peer-to-Peer (P2P) control architecture is able to fully exploit the resilience and flexibility of NRERs. This study proposes a multi-agent system to achieve P2P control of NRERs based Internet of Things (IoT). The control system is fully distributed and contains two control layers operated in the agent of each RER. For primary control, a droop control is adopted by each RER-agent for localized power sharing. For secondary control, a distributed diffusion algorithm is proposed for arbitrary power sharing among RERs. The proposed levels communication system is implemented to explain the data exchange between the distribution network system and the cloud server. The local communication level utilizes the Internet Protocol (IP)/Transmission Control Protocol (TCP), and Message Queuing Telemetry Transport (MQTT) is used as the protocol for the global communication level. The effectiveness of the proposed system is validated by numerical simulation with the modified IEEE 9 node test feeder. The controller proposed in this paper achieved savings of 20.65% for the system, 25.99% for photovoltaic, 35.52 for diesel generator, 24.59 for batteries, and 52.34% for power loss.