Radial Power Distribution Systems Research Articles

Chance-Constrained Pre-Contingency Joint Self- Scheduling of Energy and Reserve in VPP

With the increasing penetration of primarily inertia-less distributed energy resources, allowable delay in the fast frequency reserve provision to ensure marginal stability condition can become comparable with the requisite dead-time for fast-switching in a radial power distribution system (PDS) connected to the transmission system. Consequently, the prevailing short-duration load-generation imbalance might propagate as system-wide frequency excursion in the future power system, which traditionally has not been observed. Furthermore, during the fast-switching, some of the fast-acting reserve (FAR) providing local generators remain inaccessible to the bulk power system (BPS). This work has answered the questions of requisite reserve requirements to cater to such events through efficient energy and FAR provision joint-scheduling. Stochasticity of fast-switching, with temporary faults as a use-case, requires modeling of load-generation imbalance and local resource unavailability, vis-á-vis FAR requirement problem, as a chance-constraint. Due to the limited visibility at the BPS level, PDS operators must ensure sufficient FAR availability for a given confidence level of the chance-constraint. Individual chance-constraints are used to ensure mathematical simplicity. Here, the PDS is operated as a virtual power plant (VPP), where the operator can procure resources locally or from the wholesale market with the aim of profit maximization before the contingency occurs. Results show that FAR requirements can influence the local energy schedule and raise energy costs. Furthermore, the confidence level of the chance-constraint can impact overall profitability, and FAR should be scheduled carefully. Comparative analysis with a modified benchmark IEEE 33-node radial test system and a 98-node test system shows the superior performance of the proposed approach. The impact of the limited available FAR is also demonstrated.

Resilient Scheduling of Control Software Updates in Radial Power Distribution Systems

Resilient Scheduling of Control Software Updates in Radial Power Distribution Systems

Evaluation of Transfer Learning Techniques for Fault Classification in Radial Distribution Systems: A Comparative Study

Transfer learning has recently had a detectable impact on the state of the art in a wide variety of applications, and this trend is expected to continue in the near future. Both transfer learning and deep learning algorithms make use of a number of network layers, each of which may be intellectually learned and typically represents the data in a hierarchical fashion with increasing levels of abstraction. Convolutional neural networks have been proven to be exceptionally successful machine learning and deep learning techniques for a number of computer vision problems. These networks were developed by companies such as Alexa, Google, and Squeeze. Fault diagnostic strategies that are based on deep learning techniques are currently a topic of intense investigation due to their higher performance. Using transfer learning technology to carry out fault categorization in a power distribution system in a manner that is both accurate and efficient The work at hand employs a fault classification model for a radial power distribution system that is based on transfer learning and deep learning. Images of time series of three-phase fault currents are acquired via simulation with the assistance of PSCAD software as part of the proposed approach for doing so. In the next step, CNN models that are based on Alex Net, Google Net, and Squeeze Net are utilized to extract fault features from defective photos in order to categorize eleven distinct defects (using the MATLAB platform). For the categorization of defects in a radial distribution system, Alex Net, Google Net, and SqueezNet each offer accuracy of approximately 98.92%, 97.48%, and 99.82%, respectively. In this study, the classification of faults in a distribution system is accomplished with the help of AlexNet, GoogleNet, and SqueezNet. According to the findings of the simulations, the test accuracy for SqueezeNet is the highest it can be, coming in at 99.82%. Because of this, selecting it as the solution to the issue of fault classification in the test distribution system is your best option.

Optimized Fault Location Identification in Power Distribution Systems With Inverter-Interfaced Distributed Generations

This article proposes a fault location identification method for radial power distribution systems with inverter-interfaced distributed generations (IIDGs). The proposed method is not restricted to any specific types of data. It can fully utilize all available data types in power distribution systems to enhance the accuracy of results. The dataset may include synchronized voltage and current measurements collected by micro-phasor measurement units (micro-PMUs) and/or unsynchronized measurements collected by legacy measuring devices, active and reactive power measurements, pseudo measurements of load values, and virtual measurements based on Kirchhoff's laws. The proposed method explicitly considers uncertainties in the dataset and is applicable to different load types. The article proposes a mathematical model for representing IIDGs in fault location identification applications that does not require any synchronized sampled data or during the fault period data collection. Simulation results on IEEE 34-node feeder demonstrate the accuracy of the proposed method.

Radial Power Distribution System Fault Classification Model Based on ANFIS

The classification of problems in power systems plays an extremely important part and has evolved into a necessity that is of the utmost importance to the operation of energy grids. For the purpose of fault classification in IEEE 13 node radial distribution systems, this paper makes use of both an Artificial Neural Network (ANN) and a Neural Fuzzy adaptive Inference System (ANFIS). Simulations of the suggested models are carried out in MATLAB/SIMULINK, and fault currents from all three phases are analyzed in order to extract statistical characteristics. Input data vectors include the standard deviation and correlation factors between the currents of any two phases, while output data vectors include the different sorts of faults. The findings demonstrate that the devised method is appropriate for the classification of all symmetrical and unsymmetrical faults.

Normal operation and reverse action of on-load tap changing transformer with its effect on voltage stability

As electrical grids have expanded significantly, so too has the load on network buses. This, however, causes voltage drops to occur at the load side of the grid. A voltage drop causes a system to become unstable, increases its power loss, and reduces the amount of power that it transfers before finally leading to a collapse. An on-load tap changing (OLTC) transformer can be used to prevent the negative effects of an increased load by restoring the load voltage to its base value when sudden disturbances occur in the source. However, incorrect OLTC placement can cause the system to become unstable and cause collapse. This is referred to as the reverse action phenomenon of an OLTC. Therefore, this present study examined improving the ability of an OLTC to increase system stability and prevent collapse. A simple radial power distribution system was modelled in MATLAB. The results indicate that the proposed model can increase system stability and prevent collapse.

Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Modified Minimum Spanning Tree for Optimised DC Microgrid Cabling Design

The construction of low-cost nanogrids could help speed up the electrification rate in remote communities in sub-Saharan Africa. One of the challenges in nanogrid design for these communities is the uncertainty in future load demand profiles impacting the cabling topology. We propose two new wiring design approaches for radial direct current power distribution systems. The first one is a modified minimum spanning tree (MST) algorithm and the second one is an adaptive shuffled frog leaping algorithm (SFLA). Their objective is to identify the optimal cable path and the lowest cost wiring characteristics to electrify rural areas with poor infrastructural development. A comparative study of computation burdens has shown the applicability limits relative to the SFLA based approach and encouraged the implementation of the MST as it is faster and does not imply any limitation regarding the number of dwellings to electrify, while providing low-cost wiring design option. The latter is applied to a village located in Kenya and demonstrated more than 25% savings on the entire system cabling cost compared to a classical wiring design based on shortest-path calculation.

A novel fault location approach for radial power distribution systems

Power distribution systems (PDS) are increasingly complex and spread over long distances and in different locations. Given their radial configuration, the loads could be inserted at the same distances from the substation. This leads to multiple estimation of fault location (FL) and yields time consuming for iterative FL algorithms. In this article, we provide a novel practical approach to fault localization in order to defeat these limitations. The central idea of the proposed approach is to divide the multilateral distribution system into a possible number of mono-lateral sub systems (MLS) using a proposed communicating sensor. Next, we apply two different fault locator algorithms only to the defective MLS. The first variant of the approach is based on the impedance method, while the second variant is non-parametric used only when there is lack in the line data. To test the proposed technique in practice, we used the IEEE 13 Node test feeder, and a real Algerian PDS. The results obtained clearly show the contribution of the dedicated method for locating faults in the PDS.

Hosting Capacity Distribution System Yogyakarta with Ant Lion Optimization: A Case Multiobjective

Penetration of hosting capacity in radial type power distribution systems aims to increase the voltage profile on the end customer side. The more electricity customers increase, the more electricity user load growth will increase. This results in a voltage drop on the end customer side in the radial distribution system, characterized by a voltage exceeding the minimum standard operating acceptable voltage. This paper aims to provide solutions to cases in radial grid type power systems in solve voltage drops. The addition of hosting capacity for distributed renewable energy generators is one of the goals to increase the capacity of the electricity system. This research uses the Ant Lion Optimization algorithm method to try to find the optimal location and capacity of Distributed Generation in the electric power distribution system. Penetration hosting capacity injects renewable energy generation in the form of solar cells. The simulation results show that the increased voltage profile in the electric power distribution system exceeds the minimum voltage drop standard. So that in reducing power losses in the radial type network.

A hybrid machine learning and meta‐heuristic algorithm based service restoration scheme for radial power distribution system

A hybrid machine learning and meta‐heuristic algorithm based service restoration scheme for radial power distribution system

RETRACTED ARTICLE: Combined phase balancing and reconfiguration for unbalanced distribution system optimization through hybrid fuzzy-MFOA

The phase balance and reconfiguration of the power distribution system is a dynamic analysis to improve and manage the radial power distribution system. Power loss analysis in the distribution system is mainly central, considering the point of view of money. In order to reduce power loss, different technologies are excluded in reconstruction, capacitor layout and phase balance. In addition to these three methods, in unevenness in the distribution system, the phase balance is considered one of three dynamic processes all of those above. Unbalanced feeders are dangerous for incremental power loss and excessive burden conditions; however, they affect quality and cost. A severely uneven circuit can convey an unsafe voltage drop in the intense phase. A positive feeder system is arranged to be load-dependent, recording balanced feed, the desire to improve the load and unbalanced load errors will begin feeder imbalance. For the equilibrium phase feeder, the exchange is a quick and convincing way. This paper proposes a problem of adding phase balance and reconfiguration to try to assist the hybrid fuzzy and Moth flame optimization algorithm (MFOA) to deal with the uneven distribution system. The verification of the estimative program is completed with two standard evaluations, and the work approves the simulation results.

Using nodal specific transportation costs to determine economically beneficial distributed generation source location areas in radial electrical power distribution systems

Using nodal specific transportation costs to determine economically beneficial distributed generation source location areas in radial electrical power distribution systems

Alteration from radial to ring power distribution to improve system reliability: A case study

Outage of power system supplying oil and gas production facilities causes hundreds of barrel of oil equivalent loss per hour. Hence, there is a need to improve the supply continuity by e.g. altering the power distribution configuration from radial to ring. However, switching to ring configuration demands adjustment related to system capability in delivering higher current, taking longer route, or handling higher fault current. This study develops an approach to assess those requirements and benefits. The evaluation includes load flow, short circuit, protection relay coordination, and reliability analysis. This study aims to evaluate the existing system capability in applying ring configuration and recommend any required adjustment. This study also intends to estimate the reduction of outage duration. Overall, the existing radial power distribution system can be switched to ring configuration with several adjustments. The ring application offers 43.07 percent annual outage reduction from 17.322 to 9.861 hours annually.

Distributed Optimization Using Reduced Network Equivalents for Radial Power Distribution Systems

The limitations of centralized optimization methods for power systems operation have led to the distributed computing paradigm, particularly in power distribution systems. The existing techniques reported in recent literature for solving distributed optimization problems are not viable for power distribution systems applications. The essential drawback remains a large number of required communication rounds, i.e., macro-iterations among the computing agents to solve one instance of the optimization problem; the typical number of macro-iterations are in the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ~ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . In this paper, a new and scalable distributed optimization method based on Equivalent Network Approximation (ENApp) is proposed to solve optimal power flow (OPF) for a balanced radial distribution system. Specifically, the distribution system's radial topology is leveraged to reduce the decomposed systems into upstream and downstream network equivalents. The proposed innovations reduce the required number of macro-iterations/communication-rounds for convergence by order of magnitude. The approach is validated using IEEE 123-bus and IEEE 8500-node test systems and is shown to converge to the same optimal solution as obtained using an equivalent centralized OPF (C-OPF) model.

РАСПРЕДЕЛЕНИЕ ЭЛЕКТРОЭНЕРГИИ В ОСНОВНОЙ СЕТИ ЭНЕРГОСИСТЕМЫ НА БОРТУ БОЛЬШИХ КОРАБЛЕЙ

The article describes the schemes of the radial power distribution system of the bulk system on board big ships. Today, when the ship power plants have great capacity, it is found preferable to use a four-wire three-phase system that has a distributed neutral and is not connected to the ground, in which the linear voltage can be easily utilized. The main distribution network of medium voltage can be of a simple radial type with substations or additional distribution switchboards. The advantages of a simple radial scheme on board a large vessel include providing selective protection and using digital automation. The weak points are the impressive amount of protective and switching appliances, high cost, significant power losses, length of wires, and threat to the reliability of the vessel due to the main switchboard failure. A There is presented a multi-part radial chart, in which the number of circuits going from the main switchboard and devices in it is significantly reduced. This chart is considered more suitable for medium-size power plants on large vessels. The calculation was carried out at a steady state of the radial network, the sections and wires of power lines were determined. Power losses in transformers and power supplied to substation bus-lines have been calculated. Before selecting the wires it was recommended to calculate the current flowing through the circuit. The ship power supply with a bridge ring scheme has been considered. In the event of a failure, only a part of the generators and a half of energy will be available. There has been given a conditional division of the power systems with a bridge ring circuit on board into three main parts. A diagram of the high-voltage power supply from the shore is shown. One of the main advantages of this technology is environmentally friendly nature of electricity produced by power plants on the coast, compared to the ship diesel engines running on burner fuel. This technology reduces pollution and improves air quality in port areas.

Coordinated Voltage Regulation by On-Load Tap Changer Operation and Demand Response Based on Voltage Ranking Search Algorithm

The growing penetration of photovoltaic (PV) systems may cause an over-voltage problem in power distribution systems. Meanwhile, charging of massive electric vehicles may cause an under-voltage problem. The over- and under-voltage problems make the voltage regulation become more challenging in future power distribution systems. Due to the development of smart grid and demand response, flexible resources such as PV inverters and controllable loads can be utilized for voltage regulation in distribution systems. However, the voltage regulation needs to calculate the nonlinear power flow; as a result, utilizing flexible resources for voltage regulation is a nonlinear scheduling problem requiring heavy computational resources. This study proposes an intelligent search algorithm called voltage ranking search algorithm (VRSA) to solve the optimization of flexible resource scheduling for voltage regulation. The VRSA is built based on the features of radial power distribution systems. A numerical simulation test is carried out on typical power distribution systems. The VRSA is compared with the genetic algorithm and voltage sensitivity method. The results show that the VRSA has the best optimization effect among the three algorithms. By utilizing flexible resources through demand response, the tap operation times of on-load tap changers can be reduced.

Model-Free Volt-Var Control Based on Measurement Data Analytics

This paper proposes a new data-driven model-free Volt-Var control (VVC) scheme for the operation of radial power distribution systems. Unlike existing methods, the proposed VVC is neither reliant on model-based computer simulations nor limited to intuition/experience-driven rules for the operation of switchable capacitors and voltage regulators inside a feeder. Despite the model-free feature, the proposed scheme can still give optimal performances by using statistical estimations on measurement data such as the one provided by an advanced metering infrastructure system. Effectiveness and feasibility of the proposed idea is demonstrated by simulation studies on an IEEE 123 nodes test feeder. After few days of initial exploration, the proposed VVC is capable to give satisfactory results near to that of a model-based optimization technique. The promising outcome of this study suggests a novel application for emerging measurement and communication technologies in the operation of modern grids.

Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning

In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in radial power distribution systems under uncertain load dynamics. The objective is to find a policy to determine the tap positions that only uses measurements of voltage magnitudes and topology information so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a batch reinforcement learning (RL) algorithm to solve it. By taking advantage of a linearized power flow model, we propose an effective algorithm to estimate the voltage magnitudes under different tap settings, which allows the RL algorithm to explore the state and action spaces freely offline without impacting the system operation. To circumvent the "curse of dimensionality" resulted from the large state and action spaces, we propose a sequential learning algorithm to learn an action-value function for each LTC, based on which the optimal tap positions can be directly determined. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders.

Data-Driven Voltage Regulation in Radial Power Distribution Systems

In this paper, we develop a data-driven voltage regulation framework for distributed energy resources (DERs) in a balanced radial power distribution system. The objective is to determine optimal DER power injections that minimize the voltage deviations from a desirable voltage range without knowing a complete power distribution system model a priori. The nonlinear relationship between the voltage magnitudes and the power injections in the power distribution system is approximated by a linear model, the parameters of which---referred to as the voltage sensitivities---can be computed directly using information on the topology and the line parameters. Assuming the knowledge of feasible topology configurations and distribution line resistance-to-reactance ratios, the true topology configuration and corresponding line parameters can be estimated effectively using a few sets of measurements on voltage magnitudes and power injections. Using the estimated voltage sensitivities, the optimal DER power injections can be readily determined by solving a convex optimization problem. The proposed framework is intrinsically adaptive to changes in system conditions such as unknown topology reconfiguration due to its data-driven nature. The effectiveness and efficiency of the proposed framework is validated via numerical simulations on the IEEE 123-bus distribution test feeder.