Electrical Distribution Networks Research Articles

Kajian Manajerial Efektifitas Pemeliharaan Jaringan dan Transformator Distribusi 20 kV Menggunakan Uji ANOVA

PT PLN (Persero) is an electricity company in Indonesia which supports the development and industrial sectors. Process Business PLN starts from power plant, Transmission and distribution until Retail to Customers. For Supply the needed for customer in electricity power, PLN keep the reliability electricity network for fulfil it. The reliability of electric power distribution network depends on several factors, including the quality of materials, maintenance, operating patterns, protective equipment used and also network configuration. One of the ways to maintain electricity service is to carry out distribution transformer maintenance. After the maintenance is carried out, it is necessary to evaluate the distribution transformer maintenance activities that have been carried out, whether the maintenance activities have an impact or not on management, this is measured by the quality of the voltage and the rupiah per kWh that can be distributed and is denominated in rupiah. The purpose of this study is to find out whether the network and transformer maintenance has had an impact or not. One way that can be done statistically is through the ANOVA test. From the result, Minitab results obtained a value of 0.321 > α = 0.05 and the results of the autocorrelation do not show plots that are outside the lines or margins. This means that the maintenance activities of the 20 kV distribution transformers in data collection unit affect the company’s income. This means that maintenance activities affect the company's income. Therefore, the maintenance factor affects the company's income. Thus, it can be the main focus for improving distribution reliability by doing transformer 20 kV maintenance.

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

Line electrical transmission damage identification tool in distributors electrical networks

In the paper showing that one of the main way for Ukraine electric power industry evolution is electric power networks and systems improvement and construction considering Smart Grid concept. The main idea of Smart grid is the reliable, energy-efficient and high-quality energy supply system. To implement the idea, it is necessary to create a high-performance information and computing infrastructure. The main components of Smart Grid is emergency mode diagnostics and damage monitoring. Providing fault diagnostics and fault monitoring can improve the power supply reliability and quality to consumers. So, the task of quickly and accurately determining the place of damage is important. In the article showing that emergency detection tools in sections of both cable and overhead electrical networks it’s a way to improve efficiency of the networks. The diagram of the placement of damage indicators on the section of the electrical distribution network is presented, which allows determining the direction of the search for the location of the damage. A comparative analysis of measuring current transformers of optical and electromagnetic type was carried out. It is shown that a significant number of advantages of optical measuring current transformers, which can be used in damage indicators, can be provided by measuring current transformers of the electromagnetic type. Creating fault indicator based on the Smart Grid concept lets do to reduce searching time for the cause and location of an emergency situation to a minimum. In this concept application fault indicator in energy grid ensure it connection to operating overhead and cable lines without removing voltage by connecting the output of the secondary winding of the measuring current transformer of the detachable design to the measuring converters directly placed near the current measuring transformers with the help of a mechanical spring fastener. A block diagram of a specialized information-measuring system with a damage indicator was created, taking into account the requirements of the Smart Grid concept, which allows to reduce the time of searching for the cause to a minimum.

Enhancing Service Restoration in Tanzanian Power Grid using Internet of Things Sensors and Renewable Energy Sources

The increased dependence on the electric power grid, coupled with the increasing number of new customers, motivates the need to improve the reliability and resilience of the electric distribution systems. Modern distribution systems are becoming more resilient against power outages due to the increasing integration of Distributed Energy Resources (DERs) and the availability of efficient mechanisms for load shedding. Several studies considered fault detection and Service Restoration (SR) as separate problems without considering the implementation feasibility of integrating both solutions. To ensure the resiliency in the power system, it is important to have proper mechanisms which integrate sensing, detection, and SR as one problem while considering the load shedding and DER for improved capacity. Hence, this study proposed an IoT-based-sensor network framework with an enhanced algorithm coupled with a Binary Bat metaheuristic algorithm for SR integrating sensing, detection, and restoration. The proposed algorithms have been tested in Tanzania's electrical distribution network, considering the inclusion of DERs and load shedding. The results showed that DERs' size and locations significantly impact restoration schemes' performance with a power loss reduction of 74%. Therefore, efficient SR schemes should consider optimal DERs placement and a combination of load shedding and DERs integration for improved performance.

Big Data Aggregation, Visualization and Clustering for Smart Grid in Smart City using Machine Learning

Electrical energy is one of the most important components of life today where different fields depend on it. The field of electrical energy distribution (electricity network), which transmits electrical energy from sources to consumers, is one of the most important areas that need to be developed and improved. In addition to analyzing electrical energy consumption, it needs to forecast consumption and determine consumer behavior in terms of consumption and how to balance supply and demand. The research aims to analyze weather data and find the relation between the weather factors and energy consumption in order to prepare data to use as a suitable data in machine learning model for future use. This model analyzes the building consumption rate for a particular area and takes into account the weather factors that affect electrical energy consumption, where (temperature, dew point, ultraviolet index) are selects based on the correlation confidence and then divided these factors into a set of categories using the K-Means algorithm to show the effect of each factors on the other. Index Terms— Big data, IoT, smart city, smart grid, smart meter, clustering, k-means clustering.

Optimal soft open point placement and open switch position selection simultaneously for power loss reduction on the electric distribution network

Network reconfiguration through the selection of open switch positions on the electric distribution network (EDN) is an effective solution to reduce power loss on the EDN. However, when the switches are opened, there will be no power flow through the corresponding branches, which may not keep the EDN reaching its best operating condition. This paper considers the optimal problem of Soft-Open-Point (SOP) placement and open switch position selection simultaneously on the EDN to reduce the power loss of the system and consider improving the voltage, current profiles, and maintain the radial-topology structure of the EDN. The effect of the number of SOPs on reducing power loss is also considered by increasing the number of SOPs from zero to maximum number of SOPs that still to ensure the radial-topology EDN structure. Growth Optimizer (GO) is first adjusted for finding the optimal solution for the problem of SOP placement and open switch position selection simultaneously. The considered problem and proposed GO method are applied to two test EDNs consisting 33-node and 69-node systems. The GO performance is compared with two well-known genetic algorithm (GA) and cuckoo search algorithm (CSA) and the recently developed northern goshawk optimization algorithm (NGO). The results show that the optimal SOP placement and open switch position simultaneously helps to reduce significantly power loss. The loss reduction compared to the original structure of the two systems in cases of increasing number of SOPs from 0 to 5 is respectively 30.94%, 45.52%, 48.34%, 50.7%, 55.43% and 58.65% for the 33-node system, and 56.16%, 58.43%, 61.99%, 63.78%, 72.80% and 75.05% for 69-node system. The optimal SOP placement and open switch position also helps to improve the voltage quality and capacity of the EDN. In addition, GO reaches the better performance than GA, CSA and NGO for the optimal problem of SOP placement and open switch position selection simultaneously.

Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni

The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users’ participation at different levels and limiting the potential with various constraints.

Battling the extreme: lessons learned from weather-induced disasters on electricity distribution networks and climate change adaptation strategies

Abstract Electricity infrastructures are critical lifeline systems that are designed to serve with a high degree of reliability the power supply of consumers under normal operating conditions and in case of common failures or expected disturbances. However, many recent weather-induced disasters have brought unprecedented challenges to the electricity networks, underlining that power systems remain unprepared to absorb disruptive large-scale and severe events. Worse still, it is expected that such climate hazards will take place at rising frequency and intensity rates due to climate change. The intensification of meteorological extremes will lead to higher losses and changes in transmission capacity, increasing the frequency and importance of material damage to the aging electric infrastructure, thus resulting in significant disruptions, cascading failures, and unpredictable power outages. This review paper presents real-life examples of different types of extreme weather incidents and their impacts on the distribution network in Greece, a country that is highly vulnerable because of its location, geomorphology, and the existing overhead network assets, highlighting lessons learned related to adaptation options and disaster management best practices. Literature review and benchmarking with other grid operators are also employed to explore resilience-enhancing technical capabilities, weatherproof solutions, and operational strategies on which policy-making initiatives should focus.

Analysis of The Electrical Vehicles Charging Stations into Electrical Distribution Network in Residential Areas

Ekonomik ve çevresel etkenler elektrikli taşıt sayısını küresel boyutta önemli bir seviyeye ulaştırmıştır. Bu durum, dağıtım şebekelerinin tasarım ve yönetimi üzerine etkilerinin incelenmesi gerekliliğini zorunlu kılmaktadır. Elektrikli taşıtların dağıtım şebekelerine etkisi üzerine birçok çalışma yapılmasına rağmen, Türkiye dağıtım şebekesine uygulanan çalışmalar çok fazla yapılmamıştır. Çevre dostu olan elektrikli taşıtların gerekli altyapı hazır olmadan yaygınlaşmasıyla birlikte taşıtların şarj işlemlerinde problemlerin ortaya çıkması beklenmektedir. Toplu konut projelerinde kurulu ve talep güçleri arasında ciddi fark bulunmaktadır. Bu farktan dolayı konut bölgeleri içerisinde elektrikli taşıt şarj istasyonu kurulması planlama açısından daha uygun olacaktır. Bu çalışma, konut bölgelerinde elektrikli taşıtların Türkiye alçak gerilim dağıtım şebekelerine entegrasyonu analizi üzerine yoğunlaşmaktadır. Gerçek kullanıcı verileri ile ortaya çıkan durumlara göre elektrikli taşıt şarj istasyonları kullanım karakteristiği entegre edilmiş dağıtım şebekesi modellenip incelenmiştir ve geniş çaplı elektrikli taşıt şarj istasyonu kullanım modeli geliştirilmiştir. Ek olarak, transformatör yüklenmeleri, hat yüklenmeleri ve şebeke kayıplarına dayalı sonuçların elde edilmesi ve sunulması üzerine bir yöntem geliştirilmiştir. Bu yöntem, dağıtım şirketlerinin gerekli yatırımları ve zamanlarını belirlemede yardımcı olacaktır. Dağıtım şirketlerinin planlama mekanizmasını geliştirip dağıtım şirketlerinin daha gerçekçi ve doğru yatırım planlarını düzenleme kuruluna ibraz edebilecektir.

Optimal design and cost of superconducting magnetic energy storage for voltage sag mitigation in a real distribution network

One of the most important aspects of power quality for a distribution network's operation is the voltage sag issue. Simultaneous starting of irrigation motors fed from a distribution network leads to a voltage drop, which degrades the network's power quality. Mitigation of the voltage sag was carried out before by using superconducting magnetic energy storage (SMES) with a pre-defined capacity. The innovation of the present research work is optimal design of SMES including optimal sizing of SMES and its controller parameters with the consideration of its optimal cost for mitigating voltage sag resulting from simultaneous starting of irrigation motors in a real Egyptian distribution network. This is made by minimizing a multi-objective function formulated by a weighted-sum voltage sag and SMES cost. A new optimization technique called Mountain Gazelle Optimizer (MGO) is used to optimize the sizing of fuzzy logic controller (FLC)-SMES as well as the weight factors of multi-objective function. These factors are not free for the decision maker to choose. An actual Egyptian electric distribution network named Karot feeding 16 induction-driven irrigation motors and residential loads, is implemented as a test case study to exhibit the performance of the suggested optimal FLC-SMES strategy in mitigating the voltage sag. For validating the performance of the MGO technique, the obtained results are compared with those determined by particle swarm optimization (PSO). The present results show that the MGO-optimized SMES unit with a capacity of 0.135 MJ and actual cost of 0.2483 M$ successfully mitigated the voltage-sag in the investigated network due to simultaneous starting of the irrigation motors where the voltage never drops below 0.9 p.u. against 0.625 MJ capacity and 0.6934 M$ actual cost for the non-optimized SMES. This corresponds to about 78 % and 64 % reduction in capacity and actual cost of the SMES unit.

Dynamic energy pricing considering agent specific losses in residential energy hubs

The rise of distributed energy generation and storage is creating new opportunities for energy consumers to actively engage with energy markets. Achieving these potential benefits will require the implementation of new business models to address limitations of existing market structures. One promising area of research involves the use of decentralised energy trading markets. These markets can increase renewable energy generation through improved infrastructure utilisation and financial returns. However, facilitating these markets is challenging due to the constraints of physical laws and energy losses. The model presented in this paper addresses these challenges by demonstrating an energy market structure for local energy trading that accounts for physical constraints and losses. It fairly allocates the gains of trade and incentivises agents to minimise distribution losses. This market structure can be implemented with existing metering data and approximate values of electrical distribution network properties.

Congestion management in electricity distribution networks: Smart tariffs, local markets and direct control

Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors.

Dielectric Failure Analysis of Distribution Transformer

The distribution Transformer acts an important function in the electrical distribution network. Failure of the transformer ends in an outage of power supply to end users. Failure of the distribution transformer usually results in the spare time of some weeks transformer accompanied by commercial misfortunes and the necessity to fix the system at the earliest. The prime reason for the outage of distribution transformers is a short circuit, insulation breakdown, and prolonged overloading. This paper investigates failure analysis of distribution transformers during separate source voltage withstand tests, induced over-voltage withstand tests and short circuit withstand strength tests. In this paper failure cases of three distribution transformers are resolved and the results are analyzed.

Algorithmic Study for Power Restoration in Electrical Distribution Networks

We study the automated power restoration in electrical distribution networks, from the algorithmic viewpoint. During power outages, blackout sections without faults may be able to be recovered early using the capacity margins of surrounding supply sources. However, remote supply sources must be utilized in cases where the capacity margins of the neighboring supply sources are insufficient for the scale of the power outage, which is called multi-stage power restoration. In multi-stage power restoration, the distribution network subject to control becomes broader, and in addition, even healthy sections are subjected to control. In this study, we give an efficient algorithm which determines whether multi-stage power restoration is needed for power restoration, and in either case, the algorithm calculates the switching procedure which recovers power with the minimum number of switch operations. Our algorithm employs a novel algorithmic technique of combinatorial reconfiguration, which enables to maintain power supply to the healthy sections.

An Efficient Modular Optimization Scheme for Unbalanced Active Distribution Networks With Uncertain EV and PV Penetrations

Integrated control strategies with Network Reconfiguration (NR), Demand Response (DR), and voltage control can reduce peak demand, energy loss, and system-wide unbalances in modern three-phase active Electric Distribution Networks (EDNs). However, simultaneous handling of these strategies is computationally complex and challenging. This paper presents a stochastic optimization formulation that slices the problem, solves the sub-problems separately, and splices them back to find optimal solutions efficiently. At the outset, all constraint-violating configurations are eliminated using a graph-theory-based edge traversal search developed from the classic Knuth’s Algorithm-S. Subsequently, deploying suitable indices, cyclic NR-DR assignments are performed to find near-optimal topologies and load schedules concerning the minimum loss, peak load, and unbalances. Further, Voltage Regulators (VRs) are set to achieve loss and unbalance reduction with minimal tap operations. The proposed scheme is tested on the modified IEEE 123-node test feeder with extensive EV and PV penetrations. The results show that this modular scheme provides superior solutions with an almost 75% reduction in time than the conventional co-optimization method. Various other case studies illustrate the effectiveness of the proposed scheme.

Power distribution network reliability calculation method based on collective multi-label classifier and energy entropy

Reliable power supply from the grid is vital to life and work, production activities, and national security. At present, the simulation calculation method of distribution network reliability needs a lot of time. In view of this shortcoming, this paper proposes a multi-label classifier model using the importance sampling method, which can shorten the calculation time by reasonably extracting samples. At the same time, the multi-label classifier can evaluate the failure probability of the electric power distribution network at the bus level. Finally, we use the IEEE RTS model to test the proposed method, and the effectiveness of the proposed method is verified.

Optimal truck-mounted mobile generators allocation to improve distribution network resilience using the consolidation index

Electrical energy supply is a vital part of a modern network, so enhancing its resilience to natural hazards is of paramount importance. The present study aims to determine the number and capacity of truck-mounted mobile generators that can be utilized in electric power distribution networks to provide a certain level of resilience, taking into account the costs and revenues associated with their use. To this aim, a "consolidation index" is used, which is based on the "curve analysis index" and considers "cost (either of the type of investment or of the type of repairs imposed by the event)" and "lack of load supply (with critical and non-critical load separation)" simultaneously. Presenting a "decision curve" allows the planner or policymaker to perform the best by applying the amount of capital or budget at his/her disposal to improve a certain percentage of the resilience in the evaluated network. Finally, the effectiveness and efficiency results of the proposed method are shown in the modified IEEE 33-bus network.

Application of Artificial Neural Network Model for Improved Power System Protection in Port Harcourt 33 kV Network


 
 
 Electric power distribution networks are exposed to the environment due to their length, for this reason, they are mostly affected by faults. These faults disrupt the continuous flow of power supply. There is also an associated loss of power that is generated that also determines the state of the economy. In order to reduce system downtime, it is necessary to integrate a system that detects and classifies faults quickly in order to hasten its clearance. This will bring about improvement in the efficiency and integrity of the power network. The artificial neural network as proposed in this study is meant to detect, classify and locate fault on the Rukpokwu 33-kV
 feeder of Port Harcourt Electricity Distribution Company (PHEDC). Fault detector, classifier and locator, with feed-forward back propagation were employed in the research. Matlab Simulink software was used to model and simulate the distribution network. The model was trained using s values of voltages and currents. With the simulation results, the efficiency of the proposed network was demonstrated for fault detection, classification and location. Mean square error (MSE) and confusion matrix were used to evaluate the performance of the proposed model. Results showed that the acceptable MSE of 0.00000027736 and an accuracy of 100% were achieved, which is satisfactory.
 
 

Multivariable-Controlled Shunt Compensator for Damping Subsynchronous Interactions in Electrical Distribution Networks

In recent years, the massive integration of converter-interfaced devices to electrical distribution networks has lead to appearance of poorly-damped subsynchronous interactions in the grid. These oscillation modes amplify electrical disturbances originated from the grid and may cause an interaction between grid elements threatening the system stability. Shunt compensators at the distribution level have already been used to improve the damping of these networks. However, their control system strongly depends on the distribution network dynamics, that is difficult to know at all times. In this paper, a multivariable controller for a shunt compensator is proposed. First, the dynamics of a distribution network with high penetration of electronic interfaces is explored in order to understand the interactions. Then, the controller of the compensator is designed based on an estimated model of the plant so that no previous information of the network is required. This controller is based on a state-feedback controller and a state observer, both designed by using the linear quadratic regulator (LQR) theory. Analytical results and computer simulations are used to verify the effectiveness of the controller. The main findings are validated in a laboratory environment comprising two 15 kW distributed generators, a 15 kW compensator and a 75 kW grid emulator.

Time-synchronized carbon flow metering scheme for electric power transmission, transformation, and distribution networks

Faced with the pressure of energy conservation and emission reduction, the power industry is urgently requires low-carbon transformation. The carbon flow calculation theory redistributes the actual carbon generated by the power plant to the branch and loads customers with the power flow. This paper first introduces the theory of carbon flow calculation and the carbon metrics corresponding to the electricity metrics. Second, a time-synchronous technology is introduced for the carbon flow calculation of transmission, transformation, and distribution networks, and a time-synchronous-based carbon metering system is conceived. The impact of time deviation on carbon metering is elucidated through simulation experiments of IEEE14 standard nodes, and finally, relevant suggestions are made for future research ideas and technical routes.