Reliability Of Power Distribution Network Research Articles

ID-YOLOv7: an efficient method for insulator defect detection in power distribution network.

Insulators play a pivotal role in the reliability of power distribution networks, necessitating precise defect detection. However, compared with aerial insulator images of transmission network, insulator images of power distribution network contain more complex backgrounds and subtle insulator defects, it leads to high false detection rates and omission rates in current mainstream detection algorithms. In response, this study presents ID-YOLOv7, a tailored convolutional neural network. First, we design a novel Edge Detailed Shape Data Augmentation (EDSDA) method to enhance the model's sensitivity to insulator's edge shapes. Meanwhile, a Cross-Channel and Spatial Multi-Scale Attention (CCSMA) module is proposed, which can interactively model across different channels and spatial domains, to augment the network's attention to high-level insulator defect features. Second, we design a Re-BiC module to fuse multi-scale contextual features and reconstruct the Neck component, alleviating the issue of critical feature loss during inter-feature layer interaction in traditional FPN structures. Finally, we utilize the MPDIoU function to calculate the model's localization loss, effectively reducing redundant computational costs. We perform comprehensive experiments using the Su22kV_broken and PASCAL VOC 2007 datasets to validate our algorithm's effectiveness. On the Su22kV_broken dataset, our approach attains an 85.7% mAP on a single NVIDIA RTX 2080ti graphics card, marking a 7.2% increase over the original YOLOv7. On the PASCAL VOC 2007 dataset, we achieve an impressive 90.3% mAP at a processing speed of 53 FPS, showing a 2.9% improvement compared to the original YOLOv7.

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.

Providing an optimal demand response program through placement of automatic switches and energy storage systems to improve the reliability of power distribution networks

AbstractCreating highly reliable distribution networks is becoming increasingly vital in today's society. In a similar vein, utilities have issues in properly planning and developing distribution networks, both to reduce their imposed costs and to meet the demands of their consumers. In the presence of Demand Response Program (DRP), this research provides a coordinated architecture that considers automated switches and Energy Storage Units (ESUs) placement with the uncertainty of repair time to enhance the reliability of power distribution network. The suggested objective for optimal placement of automatic switches, ESUs and DRPs is to minimize a combination of reliability index (SAIDI) and Total Cost of system (TC). Customer interruption cost, energy not supplied cost, automated switch investment, ESU investment, ESU participation cost, and DRP cost are all considered as parts of the total cost. The proposed method is used in four different cases on a common reliability test system, followed by numerous sensitivity studies, in order to illustrate applicability and efficiency of suggested method.

Optimal placement of manual and remote-controlled switches based on the Pareto front

Optimal switch placement is of paramount importance for improving the reliability of power distribution networks. In this context, this work proposes a model based on mixed-integer linear programming (MILP) for determining the optimal number of manual switches (MSs) and remote-controlled switches (RCSs) to be placed on the system nodes. The analysis relies on obtaining the Pareto front between cost and the expected energy not supplied (EENS) index. The method allows the simultaneous placement of both types of switches, thus being an important tool that can be incorporated into the decision-making process while taking into account two conflicting objectives, that is, cost and reliability. The proposed approach is applied to a benchmark test system, whereas the Pareto front is defined in terms of a set of non-dominated solutions. The results evidence that using MSs and RCSs simultaneously can significantly reduce the EENS by up to 80%.

Integrated Operation of Electricity and Natural Gas Distribution Networks: A Reliability Analysis

Currently the world faces a great challenge, to achieve a sustainable production of energy, which allows the adequate development of humanity but at the same time does not irreversibly affect the environment. For this, it is absolutely necessary to make optimal and effective use of the available energy resources, in order to aim for energy transition objectives that result in the rational and efficient use of energy, the penetration of renewable resources, and social development. This requires that at a technical level, methodologies be proposed that allow for a holistic analysis of the different interactions and synergies present in the energy system. Therefore, it is essential to delve into the knowledge associated with the interaction between the electricity and natural gas networks, since natural gas is expected to be the energy source that supports the increase in generation from intermittent renewable energy sources. It is for the above that this research work analyzes the reliability of the electric power distribution network based on the impact associated with a contingency in the natural gas distribution network, when both networks are coupled through natural gas-based distributed power generators. A novel non-supplied energy index and a single contingency criterion are used for estimation purposes, considering failure rates and repair times of the natural gas network to obtain a more accurate in the estimation. Numerical results show that significant penetration of natural gas-based distributed generation can compromise the reliability of the power distribution network if the natural gas network is of low reliability.

A budget allocation and programming-based RCM approach to improve the reliability of power distribution networks

Reliability-centered maintenance (RCM) has recently emerged as a powerful tool to reasonably cut the operation and maintenance cost of power destruction systems, whilst meeting both power quality and reliability requirements. This study introduces a new RMC approach that allocates maintenance budgets based on component criticality and importance. A new diagnostic importance factor, implemented based on the criticality and importance of each component, is introduced as a new index for prioritizing components for maintenance activities and budget allocation. The process of budget allocation is carried out without using any optimization method and just by multiplying the maintenance budget by this newly designed factor. A multi-attribute decision-making technique (MAXIMAX) is considered to find the appropriate weighting coefficient of failure causes to reduce the effect of human dependency in the modeling failure rate of each component. Based on the priority and amount of budget allocated, a reliability-centered maintenance program for the sample distribution system is designed and implemented. The proposed RMC framework was implemented to a part of a distribution network in Iran to highlight its effectiveness and practicality. The obtained results showed an improvement in reliability indexes, which in turn confirms that prioritizing component criticality in budget allocation and maintenance programming enables reaching the reliability goals. Improvement in reliability indexes leads to maintenance cost reduction in future time intervals and efficient management of limited resources. A further comparison analysis with other approaches have shown that the proposed framework outperforms these methods in terms of speed, accuracy and budget allocation.

Contribution of active management technologies to the reliability of power distribution networks

Active Network Management (ANM) technologies represent an effective tool for increasing the penetration of Distributed Generators (DGs) in electrical distribution systems. Under normal operating conditions, these technologies allow a massive integration of renewable generation without exceeding rated values of electrical equipment. In addition to that, these technologies can be used to restore the supply when there is a network outage, yet this additional use has been commonly neglected in reliability studies. In this paper a novel methodology to assess the contribution of ANM technologies to reliability of distribution networks is proposed. Technologies like renewable DGs with controllable output power, on-load tap-changers (OLTC) transformers, demand control solutions and soft open points (SOPs) are all included in the reliability study. Coordinated operation of these technologies during outages and performance of multi-terminal SOPs are also modelled and evaluated. The proposed methodology was tested on two distribution networks. The contribution of different ANM technologies to reliability was assessed and their benefits confirmed.

A Contactless Method for Unbalanced Loading Detection in Power Distribution Lines by Magnetic Measurements

Reliability of power distribution network is highly desired by end customers. The power utility companies make best efforts to ensure balanced power transfer for single-phase customers. Phase unbalancing is common in congested power distribution systems where a large number of single-phase consumers are served by three-phase transformers. It deteriorates transformer efficiency and results in increased power system losses. As a standard practice, utility providers use portable CTs to periodically measure phase currents to reduce the unbalanced loading of transformers. The approach requires manual intervention and regular inspections. This article proposes a novel noncontact method for the measurement of unbalanced single-phase loading using a single magnetic sensor and processing with a trained neural network (NN). This article employs variation in phase and magnitude of the magnetic field produced by three-phase line conductors to detect and identify the unbalanced lines. The optimal sensing location is determined by using outcomes of coherence between the training and test data. Then, a trained NN uses a pattern recognition algorithm to estimate the unbalanced and the balanced loading. The method is comprehensively tested by numerical simulations and experimentation, which involves various potential configurations of distribution lines (flat, vertical, and delta). The method of estimation of unbalanced loading by magnetic field sensing and pattern recognition method has been experimentally implemented and verified in the laboratory setup. The results provide accurate unbalance phase detection with an error of less than 1%.

Optimum management of manual sectionalizers in electric power distribution networks integrating distributed generations using binary exchange market algorithm

Purpose The use of power sectionalizers in electric power distribution networks as disconnecting devices for optimum network configuration is indispensable. Major reasons to use sectionalizers, here manual sectionalizers, is their lower installation and operating prices compared to other types of disconnecting devices and that most of conventional realistic electric power distribution systems are still using manual sectionalizers due to their ease of procurement. However, in case of failure for these switches, power supply interruptions are unavoidable unless optimum solutions are used for configuration (and possibly reconfiguration) of sectionalizers. Thus, in this research, binary exchange market algorithm (BEMA) as a novel evolutionary metaheuristic is used to meet the maximized customer satisfaction by optimized configuration of sectionalizers within electric power distribution networks in the presence of distributed generations (DGs). To solve the problem, BEMA is used on sectionalizing switch placement problem, which has only two open and close (0/1) states. A novel multi-objective optimization problem has been formulated as a function of two aspects, namely, improved reliability index (for customer benefit) and minimized sectionalizing switch costs (for utility benefits). Simulations are carried out in three different case studies to validate the effectiveness of the BEMA both in theory and practice: Standard IEEE 33-bus test system, practical feeder-8 of MeshkinShahr Town’s electric power distribution network in northwest of Iran; and Roy Billinton test system Bus 4 (RBTS-Bus 4). The obtained results are compared with those of the previously validated ant colony optimization (ACO) technique in RBTS-Bus 4. Design/methodology/approach The optimum configuration of sectionalizers in the presence of DGs has been formulated as a multi-objective function consisting of two conflicting objectives. First objective is to improve the power distribution network reliability indices. Second objective is to fulfill the first objective with a minimized sectionalizing switch cost. The latter is probably obtained by reducing the number of installed sectionalizers. The obtained results by BEMA have been compared with those of ACO technique. Findings In this paper, optimal configuration of sectionalizers has been performed based on a multiobjective function by binary exchange market algorithm. By simulations carried out on two standards and one practical test systems, the proposed algorithm effectiveness was confirmed and the obtained results were compared to ACO algorithm. Changing weighting factors shows that better satisfaction can be obtained when difference between the weighting factors is relatively greater. In other words, the reliability membership function is more than switch cost membership, and thus, if the values of two weighting factors are close enough, the satisfaction level reduces. The number of installed sectionalizing switches by BEMA and ACO techniques in different scenarios were performed. Originality/value Proposal of a novel multi-objective function for finding optimal location of sectionalizers in the presence of DGs with binary exchange market algorithm whose merit over the other heuristics is to consider all the problem specifications only in one multi-objective function. Despite previously reported works that have used various high-priced protective devices for achieving the enhanced reliability this research only utilizes inexpensive manual sectionalizers with the least possible cost in the presence of DGs. Two standard test cases IEEE 33-bus test system and RBTS-Bus 4 and one realistic test case feeder-8 of MeshkinShahr Town power distribution network in northwest of Iran are used to demonstrate the effectiveness of the proposed technique in theory and real-world applications. Thus, utilities may take the advantage of the proposed method for configuration of sectionalizers in their own local power distribution systems throughout the country.

Reliability Analysis of Power Distribution Network Based on PSO-DBN

The main problem dealt with in this paper is to find a method to improve the performance of the reliability analysis of power distribution networks. With the help of deep learning, which has the characteristics of large-scale parallel processing and self-learning, a deep belief network (DBN) simulation model for power distribution network reliability analysis is established. After training RBM layer by layer and extracting feature information from complex data, DBN model parameters are adaptively adjusted by particle swarm optimization (PSO) algorithm. The results of power distribution network reliability analysis based on PSO-DBN model is compared with those of Monte Carlo model. In order to evaluate the performance of the proposed model, the coefficient R2, the mean absolute error and the root mean square error are used to evaluate the model. The results show that the reliability analysis model based on PSO-DBN is more accurate, and the reliability analysis efficiency of the trained PSO-DBN model is higher, which to some extent proves the superiority of applying deep neural network to the reliability analysis of distribution network.

Realistic Multi-Scale Modeling of Household Electricity Behaviors

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of the information from census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a bottom-up approach based on Monte Carlo Non-Homogeneous Semi-Markov, we provide household end-user behaviors and realistic households load profiles on a daily as well as on a weekly basis, for weekdays and weekends. The proposed approach overcomes the limitations of the state-of-the-art solutions that consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited to a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained by simulating realistic populations in a period covering a whole calendar year and analyze our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at the household, national, and European levels, respectively.

Study on Reliability Evaluation Method Based on Improved Monte Carlo Method

The advancement in science and technology comes with continuously expanding power system scale, increasingly complex system operation condition and higher requirements for accuracy and speed of power system reliability evaluation, but actual calculation methods cannot meet the needs. Therefore, there is need to improve the reliability of conventional power distribution network so that requirements of calculation speed and calculation accuracy can be met. In this paper, reliability of the power distribution network will be evaluated using improved Monte Carlo method with uniform sampling. The average value is obtained through calculation of state of multiple sub-intervals and test functions, which effectively improves calculation accuracy, and further increases the utilization of random numbers. By improving the uniform sampling method, the Monte Carlo simulation variance is reduced, and evaluation and calculation efficiency is improved. At the same time, unqualified power grid is selected for analysis. Based on the simulation results, qualified power distribution networks are compared to point out where the requirements are not met. Also, comparative analysis is made on the effect of power distribution network grid structure etc. on the user’s power supply. Finally, suggestions for improving power distribution network reliability are given from equipment reliability, grid structure.

Electrical Power Distribution Network Reliability: A Case Study in Wates Substation, Yogyakarta, Indonesia

This paper presents electrical power distribution network reliability. Reliability is one of the most critical factors of a network operating system that is supported. Several things become essential parameters in determining an index that complements the distribution system, namely SAIFI and SAIDI. This research is able to know about the high levels obtained by the distribution system at the Wates substation. With this analysis, it is expected that this will become a reference for improving service quality in the coming year. Based on the results of calculations and analyses that have been done, it can be said that the distribution system at the substation has been reliable, because all feeders have met the standards set by the SPLN, IEEE, and WCS, especially the WT 05 feeder meets the standard.

Optimal Reconstruction of Power Distribution Network based on Reliability and Economy

Great significance shall be attached to the research on how to improve reliability of user-end power supply with limited expense by coordinating reliability and economic efficiency of power grid, so that to meet the increasing demand on reliability of electric power, and achieve considerable economic effectiveness. In this paper, process of power distribution network reliability calculation based on minimum-path failure mode and effect analysis mode is expounded, and a demonstration on calculation of power distribution network reliability is provided. Furthermore, cost-benefit analysis, which includes calculations of cost and benefit of reliability, is introduced to reflect reliability efficiency through power interruption cost calculation. At the end of this paper the cost-benefit Analyses is applied on the optimal reconstructed power distribution network.

Reliability evaluation of distribution network considering controllable distributed generation, battery swapping station and controllable switches

Distributed generation (DG) and battery swapping station (BSS) access to the distribution network has brought impact to the reliability of distribution network. The method to assess the reliability of power distribution network with DG and BSS is proposed. First, according to the island formation strategy with DG, the case of island unable to form due to DG power shortage is analysed. Then, considering the energy storage characteristics of BSS while DG power shortage, the island formation strategy with DG and BSS is proposed and the island formation probability can be calculated. Finally, the correctness of algorithm is verified by using the IEEE three-feeder distribution system.

Reliability evaluation of distribution systems with mobile energy storage systems

The development of battery energy storage system (BESS) facilitates the integration of renewable energy sources in the distribution system. Both distribution generation and mobile BESS (MBESS) can enhance the reliability of the distribution system. MBESS can facilitate the island operation of microgrids. This study proposes a set of methodologies to assess the reliability of power distribution network with the penetrations of MBESS and intermittent distribution sources. First, an analytic approach based on Markov models is applied for assessing the reliability analysis of the MBESS in distribution system. Then the method is verified by Monte Carlo simulation method and extended to a more complex distribution system. The model of MBESS is developed based on the real operation data. Case study on the IEEE test system has successfully verified the effectiveness of the proposed approach.

A 6(10) kV Cable Section Reliability Assessment Model Including Power Distribution Network Configurations and Relay Protection Circuits

Urban power distribution network reliability depends on the resource of 6 (10) kV cables, which are its longest element. In the context of long-term operation of the distribution network, it is necessary to know when the cable network must be renewed to avoid damage, with due account of network configurations and regimes. The relevance of retrospective analysis and developong forecast methods is pointed out in the reliability concepts of the electricity industry. A number of statistical models are known. However, the reliability indicators of their operation in the distribution network must be assessed comprenehsively to improve the functioning of the existing cable network, taking into account their conditions, network configuration, and relay protection circuits. A model based on the Monte-Carlo method has developed in the LabVIEW software environment so as to estimate the probability of no-failure operation of an urban area cable section, and/or to identify renovations required to reduce the failure rate. The model permits testing different combinations of the failure rate parameters, thus producing “rich” statistical material. Cable line failure rates and urban distribution network configurations are obtained via retrospective analysis. The developed model can be applied to such cable networks.

The Research of 10 kV Feeder Automation Based on the Upper PC System

As the basis of power distribution network automation, the feeder automation plays an important role in the distribution network. The traditional feeder automation system has a long fault isolation time, the frequent switching and outgoing and then overlap power filter troubleshooting repeatedly. 10 kv feeder automation system in the thesis is controlled by the upper PC computer system and the lower machine system, and the lower machine system's intelligent controller collects the operation information of the voltage and current of the line being monitored in real time with AC-sampling technology, and through the GPRS wireless communication network will field data is passed to the remote control center. When the short circuit faults occur, the controller will immediately disconnect the switch, and the incorrect information will be sent to the remote control center, the remote control center to collect incorrect information from all the controllers, then provide the fault judgment and control strategy through relevant logic. Practices have proved that the system could improve the level and reliability of power distribution network effectively.

THE POWER DISTRIBUTION NETWORK EXPANSION PLANNING BASED ON STACKELBERG MINIMUM WEIGHT K-STAR GAME

The reliability of power distribution network is important. For high reliability, it is necessary for some nodes to have backup connections to other feeders in the network. The substation operator wants to expand the network such that some nodes have k redundant connection lines (i.e., k redundancy) in case the current feeder line does not work. The corporation is given this task to design the expansion planning to construct new connection lines. The substation operator will choose the minimum charged k redundant connection lines based on both of the existing network and the expansion network, which is designed by the corporation. The existing network has the cost for the redundant connection due to the operational expense. The corporation proposes the design with its own price, which may include the operational expense and the construction expense. Thus, for the corporation, how to assign the low price on the connection lines while maximizing the revenue becomes a Stackelberg minimum weight k-star game for the power distribution network expansion. A heuristic algorithm is proposed to solve this Stackelberg minimum weight k-star game for the power distribution network expansion, using three heuristic rules for price setting in a scenario by scenario fashion. The experimental results show that the proposed algorithm always outperforms the greedy algorithm which is natural to k-star game in terms of corporation revenue. Compared to the greedy algorithm, the proposed algorithm improves up to 60.7% in the corporation revenue in the chosen minimum weight k-star, which is the minimum charged k connection lines. The average improvement is 7.5%. This effectively handles k redundancy in the power distribution network expansion while maximizing the corporation revenue.

On the Application of Cooperative Communications in Renewable Energy Sources for Maximizing the Reliability of Power Distribution Networks

In this paper, the efficiency of cooperative communications in power distribution networks is investigated, where a number of renewable energy sources (RESs) are deployed. Specifically, any medium voltage (MV) b us of the power distribution network is considered as a wireless relay node where the information gathered from each MV bus can be reliably transmitted to a control station for further processing. Therefore, the power d istribution network can be equivalently considered as a cooperative communication network. The average bit error rate of the proposed cooperative diversit y network is studied and a closed-form lower bound is analytically derived. Numerical results corroborate the tightness of the proposed bound compared to the Monte Carlo simulations. The proposed wireless communication scheme enables the increase of the RES penetration level in distribution networks, improving so RES exploitation.