Secondary Distribution Network Research Articles

Deployment of an IoT based sensor node for faults detection and classification in electrical secondary distribution networks

Deployment of an IoT based sensor node for faults detection and classification in electrical secondary distribution networks

Coordinated Unbalance Compensation and Harmonic Mitigation in the Secondary Distribution Network Through EVs Participation

Coordinated Unbalance Compensation and Harmonic Mitigation in the Secondary Distribution Network Through EVs Participation

Real-time voltage control of centralized and distributed coordination in active distribution network under multiple coupling

Abstract At present, the “source-load interaction” distribution network operation mode has gradually replaced the “source-load operation” mode. The uncertainty and volatility of the distribution system have been enhanced, the controllability has been weakened, the voltage fluctuations have been frequent, and the power quality has been decreased. Voltage regulation is crucial to fully absorbing new energy generation and improving the operation safety of the active distribution network. To solve the problem of real-time control of distribution networks, this paper proposes a distributed voltage control method of active distribution networks with global sensitivity, establishes a distributed communication mechanism, and builds a distributed forward push-back communication rule to transfer power and voltage information between nodes. The global sensitivity of voltage, active power, and reactive power is used for real-time, independent, and effective control of multiple devices at each node, and the distributed control process is simulated according to actual production conditions. Furthermore, the centralized and decentralized real-time voltage control methods are proposed, and the centralized and distributed methods are implemented in medium voltage primary distribution network (MVPN) and low voltage secondary distribution network (LVSN), respectively, to play the advantages of high computing efficiency and fast control response. Based on model predictive control (MPC), intra-day rolling time scale voltage regulation is constructed, and second-level control is used to adapt the MPC results, finally promoting the optimal scheduling and rapid response of various controllable flexible resources in the multi-level active distribution network.

Optimal Load Shedding During Service Restoration in Electrical Secondary Distribution Network Based on Reinforcement Learning

Optimal Load Shedding During Service Restoration in Electrical Secondary Distribution Network Based on Reinforcement Learning

Evaluation of Distribution Transformer Placement Optimal Based on Load Balance

The distribution system is divided into primary and secondary distribution networks. The primary distribution network is the network from the main substation transformer (GI) to the distribution substation, while the secondary is the channel network from the distribution substation transformer to the consumer or load. The primary distribution network is better known as the medium voltage network (JTM 20kV) while the secondary distribution is the low voltage network (JTR 220/380V). The distribution network is the part of the electric power system that is closest to customers or loads compared to the transmission network. Currently, network electricity conditions throughout Indonesia are experiencing a decline of 5% which is due to various factors such as the medium-scale diameter of the conductors used, so at the farthest SR the value of overvoltage is 10%, the allowable voltage drop and must anticipate an increase in electrical load of 22.7 %, the placement of the transformer is not optimal and the distance the electrical energy travels from the transformer to the load is not paid enough attention so that the capacity of a transformer is not optimal and the voltage drop is also greater. Given these conditions, re-evaluation and planning is needed that takes into account planning criteria such as allowable voltage drops and continuity of electricity services so that optimization of the network used can emerge.

A big data-based ensemble for fault prediction in electrical secondary distribution network

A big data-based ensemble for fault prediction in electrical secondary distribution network

Sensor placement algorithm for faults detection in electrical secondary distribution network using dynamic programming method: focusing on dynamic change and expansion of the network configurations

Modern power grids are developing toward smartness through the use of sensors in gathering data for situation awareness, visibility, and fault detection. In most developing countries, fault detection in the electrical secondary distribution network (SDN) is very challenging due to the lack of automated systems for network monitoring. Systems for monitoring faults require sensor placement on each node, which is not economically feasible. Hence, optimal placement algorithms are required to ensure that the network is observable with few sensors possible. The existing sensor placement methods based on mathematical and heuristic approaches are efficient for transmission and primary distribution networks which are mostly static in size and layout. Such methods may not be efficient in SDN which is dynamic in size and have a relatively large number of nodes. This study proposes an enhanced dynamic programming method for sensor placement to enhance fault detection in SDN. The proposed algorithm employs the depth search concepts and the parent–children relationship between nodes to determine sensor types and locations considering the optimal cost. The proposed algorithm was compared with other methods including particle swarm optimization, genetic algorithm, and chaotic crow search algorithm using different network configurations. The results revealed that the proposed algorithm suggested the minimum number of sensors and shortest convergence time of 1.27 min. The results also revealed that, on network expansion, maintaining the location of the existing sensors is more cost-effective by 20% than reallocating the existing sensors. Furthermore, the results revealed that an average of 30% of nodes, need sensors to observe the entire network, hence cost optimization.

A novel approach for oil-based transformer fault identification in electrical secondary distribution networks

Oil-immersed transformers are mostly installed equipment in electrical power networks. Thus, ensuring the reliability of these equipment is paramount. Fault identification is one of the measures taken to ensure the reliability. Concerning fault identification mechanisms, several methods exist; one of which is the dissolved gas analysis (DGA) tool that has been widely used purposefully for oil-immersed transformers. The tool is used to capture and analyze the gases dissolved in the oil. Studies have proposed various fault identification methods based on this tool such as the Dornenburg, key gas, and International Electrotechnical Commission (IEC) standards methods using DGA data. However, the accuracy of these methods seems to be limited by the context, such as the type and number of parameters used. In this study, the novel hybrid fault identification method based on multilayer neural networks (MLANN) and expert knowledge is proposed to improve the accuracy fault identification process by considering the specific characteristic parameters extracted from the historical DGA dataset. The proposed hybrid method improves accuracy by 14.46% when compared to 12 benchmark methods. This improvement portrays that our method can be integrated into a scheduling platform for maintenance decision support in an electrical power network with accuracy. Furthermore, the promising accuracy in transformer fault identification is expected to increase safety and power reliability.

Automated Correction of GIS Data for Loads and Distributed Energy Resources in Secondary Distribution Networks

Currently, utilities are making significant efforts to correct their secondary models in geographic information systems (GISs) to capture the distribution system performance due to the increased penetration of distributed energy resources (DERs) such as renewable energy resources, distributed energy storage, and electric vehicles (EVs). However, their databases are often not reliable in terms of GIS coordinates of elements such as loads and DERs resulting in an approximate representation of the secondary distribution network. This paper presents an automated tool for accurate secondary network topology GIS coordinates correction. The proposed automated tool provides a more accurate feeder topology for utilities to estimate and operate distribution systems by assigning the load and DER nodes to their corresponding customer location. To simplify the complexity of the proposed new tool, only two commonly available inputs are being used as input data: municipal parcel GIS delimitation data, and utility secondary feeder topology database. A three-stage framework is proposed to develop the automated tool: the first stage reads and processes the raw input data; the second stage works automatically with no human intervention to assign the load and DER nodes to their associated location; the third stage provides the load and DER coordinates and physical address. The proposed procedure is implemented on an actual feeder from an electrical utility with high penetration of DERs.The results show that the proposed method provides a more accurate estimation of the topology of the feeder in terms of coordinates.

Multiple distribution networks hosting capacity assessment using a stochastic approach

The stochastic approach is applied to an entire concession area with 1264-LV distribution networks to estimate the hosting capacity. About 15 000-customers are connected to the individual secondary distribution networks supplied through 48-medium voltage 10 kV radial feeders.The hosting capacity assessment uses the end-customer voltage magnitude rise and transformer thermal overload. The hosting capacity is estimated by applying the “stochastic mixed aleatory-epistemic method” to determine the voltage magnitude rise and load flow with solar PV. The minimum power consumption is compared with the solar PV power infeed through the individual transformers.The hosting capacity estimation is done for three-phase connected solar PV sizes from 3 to 18 kW. At moderate PV penetration (25%–50%), the results showed that overvoltage would limit the hosting capacity more often than overload, but it becomes an issue only for LV networks studied with more than 8-customers. Considering all LV networks, most of the customers could install 6 kWp. Even when installing PV systems of 18 kWp (about twice the average size today and about the maximum area of a typical residential roof), two-thirds of houses would not need an upgrade to withstand SS-EN 50160 voltage limits. The latter customers can connect solar PV units with 18 kWp size without overvoltage or overload issues.

Transformer faults in tanzanian electrical distribution networks: indicators, types, and causes

Transformers are essential and costly components of electrical secondary distribution networks (ESDNs). Distribution transformers provide electricity to low-voltage consumers that need a consistent power supply for their daily tasks. Transformer faults have an impact on ESDN power reliability. Even though several studies have attempted to investigate fault parameters; types, causes, and indicators in transformers, it is still difficult to generalize these criteria based on diversifications. These diversifications are caused by the architecture of the ESDN itself, transformer type, and insulation materials. Therefore, this paper investigates fault types, causes, and indicators specifically on oil-based transformers in Tanzania’s ESDN using the oil analysis technique and the Dissolved Gas Analysis (DGA) tool based on descriptive statistical analysis. Results show that cellulose deterioration accounted for 33.2% of all faults, and the leading causes are overload, aging, and moisture content. Despite cellulose deterioration issues, the arcing fault is 26.2% caused by trippings, short circuits, and flashovers. The outcome of this work may help the utility implement a more advanced monitoring tool and maintenance mechanisms to enhance power reliability and reduce transformer faults in ESDN.

Multi-criteria decision support systems for electric vehicle charging schedules in a smart secondary distribution system

The operation of a smart secondary distribution network (SDN) has gained significance due to the penetration of chargers for electric vehicle (EV) and in-situ generation. The SDN operators (SDNOs) need dedicated decision support systems (DSS) for scheduling the EVs for charging without overloading the distribution transformer. Multi-criteria DSS(MCDSS) are proposed for the first time to balance the conflicting constraints stemming from the perspectives of both EV users and grid operators. This new approach has also incorporated a contribution-based fairness value (CFV) for each prosumer. MCDSS formulations employing analytical hierarchy process (AHP), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), and Preference Ranking Organisation METHod for Enrichment Evaluation (PROMETHEE) have been developed for generating fair charging schedules. Performance metrics have also been proposed to compare the three MCDSS techniques. The developed scheduling approaches have been tested in an Australian 19-bus SDN with Ausgrid datasets, and the schedules obtained are presented to bring out the significance of the proposed MCDSS.

A Cloud Based Model Symbiotic Organism Search Algorithm for Placement of Distributed Energy Resources in the Electrical Secondary Distribution Networks

The increased penetration of distributed energy resources (DERs) technologies to residential users has fostered the need for DERs integration and control methods in the secondary distribution networks (SDN). In order to reap the potential advantages of DERs and achieve their inclusion in the electrical power system while avoiding their negative impacts, the DERs should be optimally placed and sized. Considering the nature of electrical networks and DER operations, the DERs placement is a nondeterministic polynomial hard (NP-hard) optimization problem. Metaheuristic algorithms are efficient for solving DER placement problems. Metaheuristic algorithms for DER placement in SDN involve high computational effort, theoretical convergence assumptions that cannot be satisfied in the real world and dependence on parameter settings. Therefore, this study proposes a DER placement algorithm that employs a cloud-based model symbiotic organism search algorithm (CMSOS). The CMSOS is attributed to simple implementation and computation, good convergence, and parameter independence. The electrical network segment taken for Tanzania’s electrical distribution network was used for testing the algorithms, considering power loss and voltage deviations. Results show that using DERs in the proposed locations reduces power loss by 89.3%. The convergence profile shows that the proposed CMSOS-based algorithm converges faster than the conventional symbiotic organism search algorithm (SOS).
 Keywords: Metaheuristic Algorithms, Symbiotic Organism Search, DER Placements, Radial Distribution Network, Cloud-based model

Maintenance Scheduling Algorithm for Transformers in Tanzania Electrical Secondary Distribution Networks

The drive by the government of Tanzania to electrify every village has resulted into expansion of the electrical secondary distribution networks (ESDNs). Therefore, maintenance management is of the highest priority for the smooth operation of the ESDNs to reduce unscheduled downtime and unexpected mechanical failures. Studies show that condition-based predictive maintenance (CBPdM) method allows the utility company to monitor, analyze and process the information obtained from ESDNs transformers. Thus, this study adopts the CBPdM method to develop a maintenance scheduling algorithm that can predict the transformer state, forecast maintenance time based on transformer load profile and schedule its maintenance using a knowledge-based system (KBS). Applying the challenge driven education approach, the requirements for developing an algorithm were established through an extensive literature survey and engagement of the key stakeholders from the Tanzania utility company. Our study uses the Dissolved Gas Analysis tool to collect the transformer parameters used in algorithm design. The parameter analysis was performed using Statistical Package for Social Sciences software. Results show that the designed KBS algorithm minimizes human-related maintenance errors and lowers labour costs as the system makes all the maintenance decisions. Specifically, the proposed maintenance scheduling algorithm reduces downtime maintenance costs by 1.45 times relative to the classical inspection-based maintenance model while significantly saving the maintenance costs.
 Keywords: Electrical power network, Forecasted load consumption, Knowledge-Based System, Maintenance Scheduling, Predictive Maintenance, Secondary Distribution

Validation of a Holistic System for Operational Analysis and Provision of Ancillary Services in Active Distribution Networks

The advent of distributed renewable energy sources (DRESs) has led to the progressive transformation of traditional distribution networks to active components of the power system. This transformation, however, may jeopardize the reliable grid operation due to the advent of new technical problems, such as network overloading, over-/under-voltage events, abnormal frequency deviation and dynamic instability. In this challenging scenery, the installation of a modern measuring infrastructure has created new sources of data and information that facilitate the provision of ancillary services (ASs) via measurement-based analysis. The ACTIVATE (ancillary services in active distribution networks based on monitoring and control techniques) project aims to design innovative AS solutions for power system operators. These solutions aim to tackle the technical issues emerged by the ever-increasing DRES penetration and their volatile nature. In this context, in ACTIVATE, a holistic system is proposed comprising centralized and decentralized control features to enhance the overall network performance. Additionally, a network monitoring system is designed to support a number of online and offline dynamic analysis applications by exploiting measurements obtained at the transmission, primary and secondary distribution network. This paper presents a validation of the overall system, which is performed by using simulation and power-hardware-in-the-loop results in combined transmission and distribution network models.

Distribution network optimization: predicting computation times to design scenario analysis for network operators

AbstractThe increased use of renewable energies promotes decarbonization and raises the load on power distribution networks, forcing responsible distribution network operators to re-evaluate and re-design their networks. Infrastructure planners employ a rolling-horizon planning procedure with frequent recalculations to face informational uncertainty, which require solving multiple scenarios. Keeping complexity manageable is particularly challenging as distribution network areas may span multiple cities and counties. In this study, we focus on infrastructural decomposition, where the distribution network is decomposed into multiple parts and planning problems, which are then optimized separately. However, infrastructure planners lack the knowledge of how they should design a scenario analysis for a subnetwork to account for informational uncertainties subject to limited planning time and computing resources. Based on empirical requirements from literature and discussions with experts, we present a novel mixed integer linear optimization model that allows to use exact solution approaches for realistic large-scale distribution networks. Our approach considers the primary and secondary distribution network in an integrated way and designs a flexible topology for high reliability power distribution. We perform extensive computational experiments and a sensitivity analysis to determine correlations between the values of model parameters and computation times required to solve the resulting model instances to optimality. The results of the sensitivity analysis indicate that the combination of the number of buses, lines and the considered action scope have a considerable influence on the solving time. In contrast, a higher number of available transformers led to a better solvability of the model. From these computational insights, we derive implications for infrastructure planners who wish to perform scenario analysis for planning their power distribution networks.

A Genetic Algorithm Approach to Optimal Sizing and Placement of Distributed Generation on Nigerian Radial Feeders

Mitigating power loss and voltage profile problems on radial distribution networks has been a major challenge to distribution system operators. While deployment of distributed generation, as compensators, has made a suitable solution option, optimum placement and sizing of the compensators has been a concern and it has thus been receiving great attention. Meta-heuristic algorithms have been found efficacious in this respect, yet the use of the algorithms in addressing problems of radial feeders is still comparatively low in Nigeria where analytical and numerical programming methods are common. Hence; the use of genetic algorithm to site and size distributed generator for real-time power loss reduction and voltage profile improvement on the Nigerian secondary distribution networks is presented. Backward-forward sweep load flow analysis, together with loss sensitivity factor, is deployed to identify the buses suitable for the installation of the distributed generation, while the algorithm is employed in estimating the optimum size. This approach is tested on the standard IEEE 15-bus system and validated using a Nigerian 11 kV feeder. The result obtained on the IEEE test system shows 183 kW loss using the compensator, as compared to 436 kW loss without the compensator; while on the Nigerian network the loss with the compensator was 4.99 kW, in comparison with no-compensation loss of 10.47kW. By the approach of this study, real power loss on the Nigerian feeder decreased by 52.3% together with energy cost reduction from N658,789.12 to N314,227.38. Likewise the minimum bus voltage magnitude and the voltage stability index of the network are improved to acceptable limits. This approach is therefore recommended as capable of strengthening the performance of the Nigerian radial distribution system.

Improving System Reliability of Secondary Distribution Networks Through Smart Monitoring

This paper presents results of studies of the impacts of substation monitoring on reliability indices of distribution systems. It was detected that the fault detecting and repair time was the main cause of the prolonged outages experienced in the system which increases the poor reliability indices of the system. The intelligent monitoring device detect open circuit faults and relay to the base station in 30s compared with 3 to 24 hours it was taking to detect fault. The intelligent substation monitoring device reduced the fault detection time and hence improves the reliability of distribution systems. The system was simulated, and result showed that SAIDI was reduced from 244 hours of outage per annum to just 98 hours of outage per annum while CAIDI was improved from 10 hours of interruption per outage to 5 hours of interruption per outage. This paper demonstrated that the fault detection and reporting time can be greatly reduced by the use of intelligent monitoring system and reliability indices.

Investigating the Effects of Electric Load Imbalance on Increasing Losses in the Electricity Distribution Network and Ways to reduce it

One of the factors that cause energy losses in distribution networks is unbalanced loads. These losses, which weigh billions of afghanis on the country's economic charter, although impossible to completely eliminate, but its study can be the beginning of inventing ways to reduce it in different categories of the system. The phase between the feeder phases and the other is the random and asynchronous behavior of the subscribers is one phase. In load distribution networks, load imbalance has two important features, one variable with the amount and severity of load imbalance and the other its dispersion along the circuit. The purpose of this study is to investigate the losses due to load imbalance in the electricity distribution network. A large part of the nationwide network waste is generated in the secondary distribution network, part of which is due to load imbalance in distribution networks. In this paper, first the various problems that cause load imbalance are described, and then the calculations of energy waste and voltage drop due to unbalanced load are performed, and in the third stage, the status of a transformer with unbalanced load is investigated. Finally, practical solutions have been proposed to reduce waste due to unbalanced electrical load in the distribution network

Machine Learning Approach for Classifying Power Outage in Secondary Electric Distribution Network

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. Therefore, there is a need to develop a Machine Learning (ML) model to accurately classify power line outage. In this study, machine learning models are constructed from ensemble algorithms and tested using outage AMI data from 2012 to 2019 with 2 hours interval records. We propose the following ensemble-based machine learning approach to enhance classification; data sampling, algorithm weighting and finally ensembling. Results show that the Hybrid Stacking Ensemble Classifier (HSEC) model outperforms the others by accuracy of 0.981 G-mean, followed by Extra tree with accuracy of 0.964 G-mean. This model can be used in power line outage classification in any Secondary Electrical Distribution Network (SEDN). This study can be extended to locate power outage to household.