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Reliable performance of the electricity distribution networks is critical to avoid outages and disruptions which may lead to substantial economic losses. Some of the important components of the electricity distribution networks include overhead cables, utility poles, cross-arms, substations, and transformers. These components are subjected to deterioration with aging and pose risks of failure creating safety concerns, specially under extreme weather conditions. Efficient maintenance and the use of accurate condition assessment techniques to monitor the integrity of the aforementioned components are key factors to be considered in managing the electricity distribution assets. This article presents a review of different condition assessment techniques employed for monitoring the electricity distribution infrastructure. The conventional inspection techniques and advancements in non-destructive testing methods are discussed outlining the relative advantages and disadvantages. Other than the normal failures of distribution infrastructure due to component ageing, unexpected and less frequent extreme weather events can have significant impacts on the resilience of the electrical power infrastructure. Thus, the effects of extreme weather conditions on the reliability of the electricity distribution networks and the failure rates of assets are reviewed in this study. Further, the maintenance and decision-making approaches implemented in the power distribution networks are also discussed. [ABSTRACT FROM AUTHOR]

Power system state estimation is a useful technique that enables the system to be monitored when sufficient measurements are not available. Although it has been practiced for a long time, distribution system state estimation (DSSE) is still challenging today and is being studied from various perspectives. This is because distribution systems are large, complex, and hard to be monitored entirely using adequate measuring devices. In this study, a novel approach is proposed for DSSE, and it is demonstrated that it is possible to improve conventional state estimation results by using proper statistical models of energy consumption behaviors. For that purpose, a feeder in the Civanlar test system is analyzed by adapting real energy consumption data into a virtual consumption region with 10 465 residents created in this study. It is observed that estimated bus voltage amplitude values are improved as a result of the analyses carried out for 16 scenarios in total, which consist of four seasons and two time periods. The scenarios are grouped into two cases, base system and system with solar energy generation, each containing eight scenarios. The obtained results are significant in terms of showing that it is possible to improve DSSE results by using a statistical approach. [ABSTRACT FROM AUTHOR]

The number and location of switching devices (e.g., circuit breakers and sectionalizers) should be optimally determined in power distribution systems to reduce system interruptions and associated costs. However, existing mathematical optimization algorithms, such as classic and metaheuristic methods, cannot solve the optimal switch placement problem for large-scale systems. In this paper, a scalable model is proposed based on machine learning methods to determine the optimal number and location of switching devices according to system conditions. This paper proposes employing ensemble learning methods and explainable artificial intelligence tools to build an accurate data-driven model. Consequently, power distribution operators can determine the optimal number and location of circuit breakers, remote-controlled sectionalizers, and manual switches in large-scale systems without mathematical optimization algorithms. To validate its accuracy and scalability, the proposed model and a classic-based model are implemented on a real power distribution system in Fars province. The numerical results demonstrate that the proposed data-driven model can find a solution close to the globally optimal solution quickly, using a limited range of system data.

The number and location of switching devices (e.g., circuit breakers and sectionalizers) should be optimally determined in power distribution systems to reduce system interruptions and associated costs. However, existing mathematical optimization algorithms, such as classic and metaheuristic methods, cannot solve the optimal switch placement problem for large-scale systems. In this paper, a scalable model is proposed based on machine learning methods to determine the optimal number and location of switching devices according to system conditions. This paper proposes employing ensemble learning methods and explainable artificial intelligence tools to build an accurate data-driven model. Consequently, power distribution operators can determine the optimal number and location of circuit breakers, remote-controlled sectionalizers, and manual switches in large-scale systems without mathematical optimization algorithms. To validate its accuracy and scalability, the proposed model and a classic-based model are implemented on a real power distribution system in Fars province. The numerical results demonstrate that the proposed data-driven model can find a solution close to the globally optimal solution quickly, using a limited range of system data. [ABSTRACT FROM AUTHOR]

This article examines the research carried out regarding the failure rate in electricity distribution systems. It introduces a comprehensive framework for managing failure rates in power distribution systems. This framework highlights that studies on failure rates in power distribution systems can be categorized into three distinct groups: modifying asset management activities in order to reduce failure rate, evaluate and control threats and risks, emergency measures after failure. In this article, all the studies conducted on the failure rate of electricity distribution systems are listed and presented, and categorized in the form of a comprehensive and conceptual framework. The relation of each category with the failure rate is explained and by studying the process of studies, the research gaps and the roadmap of future studies in the field of failure rate in electricity distribution systems are determined. [ABSTRACT FROM AUTHOR]

Ensuring reliable electricity supply to consumers in the face of destruction of critical energy infrastructure and shortage of generating capacities in Ukraine requires the development of distribution systems and their management systems. The purpose of the study was to substantiate the development of models for ensuring the optimal structure of electrical distribution networks under the conditions of connecting micro-networks according to the reliability criterion in conditions of limited investments. The paper uses a method for assessing the structural reliability of complex electrical systems with microgrid structures and forms a rational power distribution of such structures according to the criterion of optimising the reliability indicators of the studied electric power system. A mathematical optimisation model based on a computational system was proposed, designed to solve non-convex problems with minimising integral reliability indicators, considering financial constraints and the investment efficiency curve. Based on the research, the possibilities of optimisation using the BARON solver available on the NEOS server were examined. The results of the model's performance are demonstrated using examples, considering the parameters of distribution system objects and their limitations on network components. An algorithm and programme for solving the problem of targeting power flows of microgrid structures in multi-node regional power systems are proposed. Algorithms and scenarios for the response of dispatching services are developed, provided that investments are limited, which will ensure the survivability of the power system as a whole. It is established that the development of rational electricity flows of microgrid structures will minimise the under-supply of electric energy by specific load nodes and determine their shares in covering the demand of the energy island in conditions of power shortage. The findings can be used in the operational management of power systems. [ABSTRACT FROM AUTHOR]

Neutral-to-ground voltage (NTGV) abnormalities in secondary distribution systems (SDS) pose significant power quality (PQ) challenges, including safety hazards, power losses, and equipment damage. Despite their importance, these abnormalities remain relatively understudied. Accurate classification of NTGV events is crucial for effective mitigation strategies. Existing research primarily relies on machine learning (ML) models trained on manually extracted features from simulated or real-world signals. This paper introduces a novel end-to-end deep learning approach that leverages Gate Recurrent Units (GRU) to bypass manual feature extraction, directly utilizing real-world signals from three NTGV event categories: ground fault, lightning strike, and normal conditions. This is first time that GRU has been used for NTGV classification using raw data. The model's generalizability is assessed through 5-fold cross-validation. A comparative analysis with baseline models and traditional ML techniques demonstrates the proposed model's superior performance and computational efficiency due to its ability to directly process raw data.

Copyright of Electric Drive is the property of Electric Drive Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.

Abstract Power distribution utilities effort to ensure the quality of energy to their consumers and the reliability of their power distribution system. It is necessary that maintenance activities are planned with the aim of maintaining or improving reliability indicators in supply to consumers. In this paper, a computational model based on integer nonlinear multiobjective programming is presented to improve the maintenance of equipment in the power distribution system. Since it is a reliability-centered approach, a probabilistic failure model is first used to obtain equipment reliability values at each time point through fuzzy inference. Three objective functions are optimized: i) minimizing maintenance cost, ii) minimizing failure frequency, and iii) maximizing equipment reliability. The optimization problem is also formed by three sets of constraints: i) individual and collective continuity indicators; ii) task execution time; and iii) maintenance limit for each type of equipment. Lichtenberg's algorithm is used to solve the model. A case study is performed for a feeder section consisting of twenty-eight distribution equipment. The results obtained using the Pareto constraints show scenarios that can help maintenance teams to make decisions and develop the preventive maintenance planning. Adding constraints on the duration and frequency of collective interruptions indicators improves the power quality of the distribution system; however, it requires an increase in investment by 36%.

To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems.

Several countries have encouraged the installation of photovoltaic (PV) systems in urban areas to contribute to the decarbonization goals of the electric power system. At the same time, consumers have adopted PV systems to reduce their electricity bills. While grid-following PV inverters offset active power demand, they can decrease the power factor at the point of interconnection with the grid, subsequently leading to financial penalties imposed by distribution utilities. Additionally, utilities must maintain power factor values above a predefined threshold to maintain acceptable levels of power losses at the transmission level. This paper examines low power factor penalty schemes for distribution utilities and consumers with PV systems. In such an analysis, an optimization approach is used to minimize the costs of penalties associated with low power factor during a consumer’s billing period. This approach makes it possible to reduce the number of low power factor penalties, thus reducing the amount of electricity bills to be paid by consumers. The decision variable in this context is the power factor of the PV inverters. A case study is presented that considers the financial penalties in a city in the metropolitan area of Sao Paulo, Brazil, with various levels of PV penetration in the distribution system. The results show that while the penalties for consumers are low, distribution utilities would incur more significant penalties or require additional investments to maintain the power factor at the values imposed by electric transmission companies. This analysis aims to help regulatory agencies evaluate penalty schemes to reduce electrical losses in the distribution system.

We consider the problem of decentralized control of reactive power provided by distributed energy resources for voltage support in the distribution grid. We assume that the reactance matrix of the grid is unknown and potentially time-varying. We present a decentralized adaptive controller in which the reactive power at each inverter is set using a potentially heterogeneous droop curve and analyze the stability and the steady-state error of the resulting system. The effectiveness of the controller is validated in simulations using a modified version of the IEEE 13-bus and a 8500-node test system.

Faults in power distribution feeders cause damage to power utilities due to the deterioration of reliability and power quality indexes and the displacement of field maintenance teams to replace or repair power grid equipment. Additionally, consumer units have energy supply interruptions for an undetermined time. Studies in specialized literature usually detect, classify, and locate faults after they occur. In contrast, preventing faults by estimating areas vulnerable to them is crucial to mitigate all inconveniences and additional costs after they occur. Tree vegetation is an essential factor contributing to faults. In this sense, an enhanced method for tree vegetation mapping by areas is developed using multilayer perceptron neural networks trained on high-resolution images from Google Earth. A geographic space is incorporated to estimate the regions vulnerable to failures due to tree vegetation. Geographically weighted spatial analysis is applied from local variables aggregated by areas. Spatial data analysis is used to real faults and tree vegetation data from a medium-sized Brazilian city via QGIS and R programming environments. As a result, thematic maps are produced with the areas whose feeders are vulnerable to faults, where there is a moderate positive correlation by regions between the faults in distribution transformers and tree vegetation in the northeast and southwest areas of the city under study.

Research activities in the field of development of electrical power and electrical equipment include the development of new approaches to assessing the reliability indicators of electrical equipment elements and in-house power supply systems in general. The study has examined methods for assessing the reliability of electrical equipment in intra-shop power supply systems using the example of a workshop network section, including its main elements: power transformer, low-voltage cable lines, distribution points, circuit breakers, magnetic starters, contactors, switches. The reliability parameters of the circuit are calculated relative to the distribution cabinet of the power (DCp) and the distribution point of the power (DPp); regarding each connection of DCp and DPp. The methods under consideration are recommended to be used to clarify the frequency and timing of maintenance and repairs of electrical equipment of the in-shop power supply system, as well as to analyze the reliability of operation and identify the least reliable sections of network diagrams. The presented calculation using the logical-probabilistic method by constructing a fault tree is advisable to use to estimate the frequency of power loss of DCp and DPp, as well as individual connections. For the studied circuits, graphical dependences of the probability of failure-free operation of electrical equipment and the occurrence of a failure over time have been constructed.

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This letter proposes online algorithms for dynamic matching markets in power distribution systems. These algorithms address the problem of matching flexible loads with renewable generation, with the objective of maximizing social welfare of the exchange in the system. More specifically, two online matching algorithms are proposed for two generation-load scenarios: (i) when the mean of renewable generation is greater than the mean of the flexible load, and (ii) when the condition (i) is reversed. With the intuition that the performance of such algorithms degrades with increasing randomness of the supply and demand, two properties are proposed for assessing the performance of the algorithms. First property is convergence to optimality (CO) as the underlying randomness of renewable generation and customer loads goes to zero. The second property is deviation from optimality, which is measured as a function of the standard deviation of the underlying randomness of renewable generation and customer loads. The algorithm proposed for the first scenario is shown to satisfy CO and a deviation from optimality that varies linearly with the variation in the standard deviation. We then show that the algorithm proposed for the second scenario satisfies CO and a deviation from optimality that varies linearly with the variation in standard deviation plus an offset under certain condition.

The overhead power distribution system (OPDS) is vulnerable to strong winds, such as hurricanes. Due to the challenges of including tree damage risks to the OPDS, tree failures are usually ignored in the risk assessment of the OPDS against strong winds. In the present study, an integrated damage modeling and assessment framework for the OPDS is proposed considering tree failure risks. The geographical information of trees surrounding the OPDS is extracted from satellite images using computer vision techniques, including CNN-based (convolutional neural network) image classifier and sliding window approach. The tree failure risk models are developed using tree geographical information in conjunction with tree height data, tree allometry and finite element analysis. With further integration of the conditional probability failure of poles under fallen tree impacts, the pole's failure probability considering the combined wind and fallen trees is obtained using series system reliability analysis. The failure probability of the pole is obtained using physics-based modeling facilitated by Bayesian regularisation neural network (BRNN) algorithm. The poles and wires are connected for system reliability assessment using connectivity-based theory. When the wind direction is 300 ° counterclockwise from the east and the wind speed is 57 m/s, tree-failure can introduce 68.6% differences in OPDS' failure probabilities compared with that without consideration of fallen trees. [ABSTRACT FROM AUTHOR]

Power distribution systems (PDS) comprise essential electrical components and infrastructure that facilitate the delivery of electrical energy from a power transmission system to end users. Typically, the topology of distribution systems is radial, so that power goes from the substations to end users through main lines or feeders. However, the expansion of new feeders to accommodate new users and ever-growing energy demand have led to higher energy losses and deterioration of the voltage profile. To address these challenges, several solutions have been proposed, including the selection of optimal conductors, allocation of voltage regulators, utilization of capacitor banks, implementation of distributed generation, and optimal reconfiguration. Although reconfiguring the network is the most cost-effective approach, this solution might not be sufficient to completely minimize technical losses and improve system performance. This paper presents a novel approach that combines optimal distribution network reconfiguration (ODNR) with optimal conductor selection (OCS) to minimize power losses and enhance the voltage profiles of PDS. The key contribution lies in the integration of the ODNR and OCS into a single MILP problem, ensuring the attainment of globally optimal solutions. The proposed model was tested with benchmark 33-, 69-, and 85-bus test systems. The results allowed us to conclude that the combined effect of ODNR and OCS presents better results than when any of these approaches are applied either separately or sequentially. [ABSTRACT FROM AUTHOR]

Significant opportunity for savings in energy and investment through improved performance of power distribution systems exists in the optimal placement and rating of capacitors, a conventionally cost-effective and popular reactive power compensating technology. A novel optimal capacitor planning (OCP) procedure is proposed for large-scale utility power distribution systems, which is exemplified on an existing utility circuit of approximately 4,000 buses. An initial sensitivity analysis is employed to intelligently reduce OCP computation time and maintain quality of optimal configurations. Three optimization objectives are considered, including the minimization of total system active power losses, standard deviation of node voltages, and investment in total capacitor power rating. Eight multi-objective optimization methods that employ the non-dominated sorting algorithm III (NSGA-III) concept are compared to determine individual merits. Differences between the methods include the incorporation of a penalty constraint for voltage violations and the automatic readjustment of load-tap-changing (LTC) transformer tap settings for proposed capacitor re-configurations concurrently within the optimizer, which ensures peak system performance and fair comparison to the reference case. A multi-step model conversion process was developed with OpenDSS to enable the OCP procedure to be generally applicable to real large-scale utility circuits. OCP is performed for three example sub-circuits served by a substation with a 48MW, 9Mvar peak load, which represents the most extreme case and offers the best opportunity for savings. Example configurations from the resulting Pareto sets through a pseudo-weight vector approach are also analyzed through a systematic procedure of comparison between the most extreme configuration types to inform configuration selection.

Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.

The concept of risk assessment is an important tool in the asset integrity management of power distribution systems. This manuscript presents a risk-based asset integrity management (RBAIM) methodology for the optimization of power distribution assets using a time series analysis approach. This approach deals with time series forecasting on risk assessment for low-voltage-level (400/230 V) failures using the Python programming language and considering historical low-voltage (LV) fuse failure data from a case study over 44 months, starting from 2019. The proposed approach is deployed in a power distribution utility located in a densely populated area of Colombo district, Sri Lanka. The authors proposed a methodical approach for the identification of priority components for asset maintenance and repair ranking based on the risk index percentage value to enhance the predictiveness of potential defects and estimate the risk of potential failures. The results show that the proposed time series forecasting methodology for RBAIM is useful for power distribution utility asset owner organizations for continuous monitoring, the evaluation of asset conditions, and the implementation of proper maintenance and repair strategies to enable assets to perform at their optimal level. The proposed RBAIM methodology enables practicing engineers to assure the asset integrity of power distribution utilities. [ABSTRACT FROM AUTHOR]

One of the best ways to address urgent sustainability issues like global warming, depletion of fossil fuel reserves, and emissions of greenhouse gases is to use electric vehicles (EVs). By reducing environmental damage and lowering emissions that contribute to climate change, incorporating electric vehicles into the distribution system will benefit public health. A microgrid is a tiny power network that collaborates with groups of loads as well as distribution generators, powerful software solutions, and other elements utilizing devices connected to the grid that function as a single controlled entity. Last but not least, the study states various ways to mitigate the effects of electric vehicles on distribution system power quality and get rid of harmonics. [ABSTRACT FROM AUTHOR]

The traditional current protection uses the RMS value of the current to compare with a setting-value to identify faults, which is the typical protection scheme for the transmission lines of the power distribution systems. The current is calculated by the fast Fourier transform (FFT) algorithm. As the calculation of the RMS current requires a long time, the speed of the current quick break protection is reduced. In this regard, a fast current protection algorithm based on the discrete setting value is presented in this paper to solve this problem. The main emphasis is placed on the construction of the discrete sequence of setting-value and the protection criterion. The protection startup criterion and action criterion are constructed based on comparisons between discrete sequence of setting-value and sampling values of the current fault component. The operation characteristics and reliability of the fast current protection are analyzed. The performance of the novel fast current protection algorithm is compared with that of the conventional current protection through simulations based on PSCAD/EMTDC. The results verified that the novel fast current protection algorithm has good operation characteristics under different fault conditions.

• False data injection attack (FDIA) is addressed in power distribution systems. • A model-data-driven method tackles threshold setting in model-based FDIA detection. • The proposed method uses WLS and UKF estimated states to detect and locate FDIAs. • Unseen FDIA strategies and feeder topology changes are also addressed. Defending against false data injection attacks (FDIAs) in cyber-physical power systems is crucial. Detection in power distribution systems is complex due to load variations, uncertainties, and fewer meters. Defense strategies include model-driven and data-driven approaches, but model-based methods can trigger false alarms due to threshold setting issues. The current research proposes a novel data-driven method to address threshold setting issues in detecting and localizing FDIAs in power distribution systems. First, a dataset is created by recording estimated measurement values using an unscented Kalman filter and weighted least squares across various attack scenarios. These estimated measurements are then fed into a deep artificial neural network (ANN) for binary classification to detect attacks. The output, along with the estimated measurements, is used by another ANN to localize the corrupted meter zone. This deep learning-based approach improves threshold setting over the common chi-square method. Results show that the proposed deep learning method for FDIA detection and localization outperforms a recently proposed ensemble of shallow models. The area under the curve value increases by about 5% with lower training time. The approach is also effective against previously unseen attack strategies and different feeder topologies. [ABSTRACT FROM AUTHOR]

The development of new technologies and uses of electrical energy, such as distributed energy resources, pose new challenges and business opportunities for the electricity industry. In favor of making better use of information so that companies can efficiently manage their assets (minimizing investment and operating costs while improving service quality) and users rationally managing their demand (making efficient use of available energy resources). In this sense, Chile is experiencing a paradigm shift in the electricity sector, mainly seeking to improve energy efficiency. In this work, a survey of particularities and problems of the Electricity Distribution Systems is carried out so that these are considered as inputs in the Transmission Planning models used in Chile, specifically of the Zonal Transmission. [ABSTRACT FROM AUTHOR]

This paper introduces a new framework of deep reinforcement learning based protective relay design in power distribution systems with many distributed energy resources (DERs). With increasing penetration of power electronically-interfaced resources, conventional overcurrent relays’ performance is rendered less effective due to the two-way uncertainties in power flow patterns. In this paper, a machine learning-based protective relay that is designed for adaptively deciding the threshold for relay action is proposed. The particular algorithm used is an Long Short-Term Memory (LSTM) enhanced deep neural network that is highly accurate, communication-free and easy to implement. The proposed relay design is tested in OpenDSS simulation on the IEEE 34-node test feeder and a collection of large synthetic feeders in Austin, Texas area. By designing adaptability upfront, the proposed relay is shown to substantially improve the performance of relay in terms of failure rate, robustness, and response speed, in particular in scenarios with high level of distributed energy resources.

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this article, we propose two dynamic sparsity-based state estimation approaches for distribution systems: 1) locally weighted matrix completion (LW-MC), and 2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified. [ABSTRACT FROM AUTHOR]

This paper introduces a current differential protection scheme, appropriate for application in medium voltage active distribution systems, where it is desired to keep the greatest possible number of loads and DG units energized during a fault. Conventional two-terminal percentage current differential relays are used to form successive, time-current-coordinated, differential protection zones. Multiple time-delayed differential elements in each protection zone guarantee coordination with the zone's lateral protection devices, as well as between successive differential protection zones. Sensitive time-delayed differential elements protect against relatively high-resistance faults, while instantaneous differential elements minimize protection speed whenever possible. Additional emergency differential elements deal with post-fault topology changes and breaker failure conditions enhancing the overall scheme's performance. The proposed scheme is applied to a model of real medium voltage distribution system with distributed generation, considering a ring topology operation. A detailed simulation-based study proves the applicability and enhanced performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

The power distribution system’s fault root cause classification is an important but challenging problem. Traditional classifiers fail to achieve high accuracy and good generalization performance due to data insufficiency. A large volume of unlabeled data is available, which can be utilized to improve classification performance. This paper proposes a novel classifier called Robust Semi-Supervised Prototypical Network (RSSPN) based on Prototypical Network architecture and semi-supervised learning to address this issue. The proposed method can mine information from unlabeled data to improve the generalization ability and classification accuracy. Furthermore, RSSPN adopts the idea of meta-learning to obtain the “few-shot learning” ability for identifying new fault classes using very few samples encountered during the operation and update online. Experiments have been conducted on a dataset consisting of 1152 labeled samples belonging to 12 different classes and 10000 unlabeled samples. The accuracy of the proposed method is significantly better than the traditional classifiers. [ABSTRACT FROM AUTHOR]

The active participation of demand response (DR) resources into the wholesale market price formation and load dispatch process has the potential to stimulate demand-side flexibility. However, it is challenging for a market entity to utilize the DR resources for practical use. This is because day-ahead wholesale market-clearing prices are uncertain, and DR resources are heterogeneous. Furthermore, DR participation may lead to violations of the distribution system’s operational constraints. In this article, we propose an approach for an aggregator/load-serving entity (LSE) to profitably bid aggregated DR resources into the day-ahead wholesale market. The LSE requires an optimal bidding strategy that reflects the price elasticity of the aggregated retail loads to participate in the wholesale market operations. In the proposed approach, the LSE executes load curtailment and load shifting contracts with DR resources, where DR resources are remunerated for their participation at pre-contracted incentive prices. Then, the LSE aggregates the DR flexibility and optimally bids it in the day-ahead wholesale market. The proposed approach is validated using the IEEE-123-bus test system. It is demonstrated that the LSE can successfully generate economic bids for its participation in the day-ahead market by optimal management of DR resources and without violating the network’s operating constraints.

Distribution systems play an important role, delivering the electric power of generation system to individual consumers. Distribution system reconfiguration (DSR) is a large-scale combinatorial optimization problem. For the last 45 years, the DSR problem has been widely studied; nowadays, DSR, combined with new challenges, is being highly investigated, as researchers aim to reach a better solution. This paper presents a complete review and classification of the most significant works to date, providing a literary framework for DSR specialists. A categorization of solution methods, case studies, and novelties of the most relevant works regarding DSR are provided. In order to establish a complete background, not only traditional approaches, but also those involving uncertainty, reliability, electricity market, power quality, distributed generation, capacitor placement, and switching time in DSR are highlighted. This framework can help researches to improve previous formulations and methods and can propose more efficient models to better exploit the existing infrastructure.

Safeguarding the nation’s electrical infrastructure and personnel against natural and man-made disasters, and ensuring a continuous, reliable, and resilient supply of energy are among the top priorities for the electric industry. In recent years, wildfires have been remarkably threatening the safety and security of electric power grids demanding innovative frameworks for ensured resilience. Our proposed approach focuses on mitigation of wildfire disruptions that, once occur, can jeopardize the well-being of electrical equipment and the safety of the personnel. The proposed framework first offers a comprehensive wildfire characterization package that can spatiotemporally monitor and analyze the wildfire behaviors, i.e., wildfire intensity, arrival time, and binding paths from ignition points to electrical equipment. This allows power system operators to make proactive decisions before the fire approaches the electrical elements, e.g., power distribution lines. Next, a decision support tool for wildfire management in power grids is proposed such that various local generation resources,—i.e., distributed renewable energy resources and energy storage systems, can be effectively employed to mitigate the wildfire impacts on the power grid. The proposed integrated solution technology ensures a significant reduction in power outages and enhances the safety and resilience of the power grid and the operating personnel. [ABSTRACT FROM AUTHOR]

The concept of risk assessment is an important tool in the asset integrity management of power distribution systems. This manuscript presents a risk-based asset integrity management (RBAIM) methodology for the optimization of power distribution assets using a time series analysis approach. This approach deals with time series forecasting on risk assessment for low-voltage-level (400/230 V) failures using the Python programming language and considering historical low-voltage (LV) fuse failure data from a case study over 44 months, starting from 2019. The proposed approach is deployed in a power distribution utility located in a densely populated area of Colombo district, Sri Lanka. The authors proposed a methodical approach for the identification of priority components for asset maintenance and repair ranking based on the risk index percentage value to enhance the predictiveness of potential defects and estimate the risk of potential failures. The results show that the proposed time series forecasting methodology for RBAIM is useful for power distribution utility asset owner organizations for continuous monitoring, the evaluation of asset conditions, and the implementation of proper maintenance and repair strategies to enable assets to perform at their optimal level. The proposed RBAIM methodology enables practicing engineers to assure the asset integrity of power distribution utilities.

Automated guided vehicles (AGV) or mobile robots (MR) are being used more and more in modern factories, logistics, etc. To extend the work-time of the robot, kinetic energy recovery systems are implemented to store the braking or lifting energy. In most applications, the energy storage system is a Li-ion battery, which is therefore subjected to increased stress and is also oversized. Super-Capacitors can be used in combination to solve this issue. In this paper, different power distribution systems are analysed and compared, using both single or hybrid storage systems (battery and super-capacitor combined). The comparison is both qualitative, using general system characteristics, and quantitative, using an efficiency/power density Pareto front analysis. [ABSTRACT FROM AUTHOR]

Despite many studies have been published on non-technical loss management, there is a lack of solutions focused in the financial return of utilities’ actions. This paper aims to contribute to fill up this gap, presenting a new approach that maximizes the financial return of utilities when selecting consumers for field inspections. The predicted return of an inspection is calculated considering its operational cost versus the potential of energy recovery and the tariff value. The potential of energy recovery, in turn, is calculated considering the probability of non-technical loss existence and the forecast of unmeasured energy. The Rotation Forest algorithm is utilized to indicate the non-technical loss existence, while the eXtreme Gradient Boosting algorithm is utilized for energy recovery forecast. The proposed approach was applied in a real database of 261,489 consumers from a Brazilian utility. Then, 338 new field inspections were performed on unlabeled consumers in order to corroborate results in a real application. Achieved results showed an increase of up to 11.5 times in the financial return of field inspections when applying the proposed approach. Results could also be used to determine the optimal number of inspections in the utility, for which the financial return is maximum. [ABSTRACT FROM AUTHOR]

Wildfires have been growingly recognized as a prominent threat in regions with high temperatures during the summer. Power distribution systems, especially those passing through forest regions, are exposed and highly vulnerable to wildfires. This article provides a general formulation to enhance the operational resilience of power distribution networks equipped with renewable energy resources, e.g., wind and solar energy, micro turbines as well as energy storage systems when exposed to progressive wildfires. The wildfire incident is characterized comprehensively and the dynamic heat balance equations of power distribution branches are used to model the impacts of wildfires on overhead line conductors. A mixed-integer quadratic optimization formulation is applied to optimally operate and coordinate all local energy resources to reduce load outages and enhance the system resilience. The applied framework is evaluated on the IEEE 33-node test system. Comprehensive sensitivity analyses are conducted to assess the efficacy of the applied framework, where the numerical results reveal the resilient operation of power distribution networks in the face of wildfire emergencies. [ABSTRACT FROM AUTHOR]