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Anticipating failures is vital for maintaining a reliable power supply. Advanced measurement devices in the grid generate vast data that contains valuable information on grid operations. Initial signatures of an incipient failure are often reflected in this data in the form of electrical waveform distortions. Conventional protection schemes are not equipped to analyze these distortions and anticipate failures. There is a considerable research gap for a simple yet robust and universal failure anticipation and diagnosis scheme. This paper proposes a universal Failure Anticipation and Diagnosis Scheme (FADS) to detect incipient failures in AC distribution grids. The method comprises three short stages, helping the operator make an informed decision. In the first stage, the FADS scheme leverages the fundamental properties of electrical sinusoid waveforms to detect distortions. In the second stage, the distortion data is processed through pre-determined thresholds set in accordance with the system's regular operation. In the third stage, depending on the system, the FADS uses the extent of the violations of these thresholds and ranks the severity of the danger posed to grid operations. The classification helps determine if the waveform distortions are the signature of an incipient failure. The proposed FADS method's reliability, robustness and effectiveness are evaluated in incipient failure conditions of field events modelled in real-time simulations on standardized IEEE distribution feeders. The FADS is a high-speed distortion detector, is quite sensitive, and the method has high selectivity because of its nature.

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

Wildfires on average burn at 1472°F. To determine how a ductile‐iron pipeline would perform in ultra‐high temperatures, a sample pipe assembly was tested. [ABSTRACT FROM AUTHOR]

Key Takeaways: Hamilton, Ont., has an older, elaborate water distribution system, with high levels of nonrevenue water (NRW). The city was determined to reduce its NRW, so it conducted a leak detection pilot study to explore advanced technologies that could improve detection efforts. With a 95.6% accuracy rate from the pilot test results, the city implemented a full‐scale, proactive leak detection program. [ABSTRACT FROM AUTHOR]

Many heuristics for network reconfiguration rely on the systematic applying of the branch-exchange technique. In this work, two novel genetic operators for crossover and mutation have been developed that are based on the referred technique. The chromosome's codification to use these operators is straightforward and is not required any additional knowledge of graph theory to achieve the feasibility of individuals. As one of their main novelties, the methodology shows how can be employed a local improvement step, used commonly in the single-objective optimization, in the multi-objective optimization. This step increases the convergence of the optimization with populations of much reduced size. The proposed methodology is tested by solving several examples of the literature, including or not the local improvement step. The comparison of the results with the best solutions published for these examples shows the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Key Takeaways: Supervisory control and data acquisition (SCADA) provides water utilities with data for regulatory compliance, monitoring and alarm management of life safety systems, and protecting public health. Utilities often make the mistake of taking a passive approach to managing a SCADA system, not giving it the special attention and detailed planning it requires to stay current. A SCADA master plan can fit any organization's size, complexity, and budget. [ABSTRACT FROM AUTHOR]

Introduction. Most distribution networks are unbalanced and therefore require a specific solution for load flow. There are many works on the subject in the literature, but they mainly focus on simple network configurations. Among the methods dedicated to this problem, one can refer to the load flow method based on the bus injection to branch current and branch current to bus voltage matrices. Problem. Although this method is regarded as simple and complete, its drawback is the difficulty in supporting the transformer model as well as its winding connection types. Nevertheless, the method requires the system per unit to derive the load flow solution. Goal. In the present paper, our concern is the implementation of distribution transformers in the modeling and calculation of load flow in unbalanced networks. Methodology. Unlike previous method, distribution transformer model is introduced in the topology matrices without simplifying assumptions. Particularly, topology matrices were modified to take into account all winding types of both primary and secondary sides of transformer that conserve the equivalent scheme of an ideal transformer in series with an impedance. In addition, the adopted transformer models overcome the singularity problem that can be encountered when switching from the primary to the secondary side of transformer and inversely. Practical value. The proposed approach was applied to various distribution networks such as IEEE 4-nodes, IEEE 13-nodes and IEEE 37-nodes. The obtained results validate the method and show its effectiveness.

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

This study explores the feasibility of using impedance relays in electrical distribution systems, a context where their application is not as common as in transmission systems. Given the dynamic nature and complex topology of medium-voltage distribution systems, this work proposes an innovative methodology integrating clustering algorithms and metaheuristic techniques to optimize the placement of impedance relays and enhance system reliability and resilience. Using CYME simulation and the Ant Colony Optimization (ACO) method, case studies were designed to validate the effectiveness of the proposed methodology. The results demonstrated that strategic placement of impedance relays reduces failure frequency and significantly improves the system’s response to such failures. This approach allows for a more efficient configuration and quicker response, which is crucial for maintaining continuity and quality of power supply. A detailed analysis of system behavior under various fault scenarios illustrates the robustness and adaptability of the proposed solution, marking a significant advancement in the protection and optimization of electrical distribution systems.

In contrast to “frank” pathogens, like Salmonella entrocolitica, Shigella dysenteriae, and Vibrio cholerae, that always have a probability of disease, “opportunistic” pathogens are organisms that cause an infectious disease in a host with a weakened immune system and rarely in a healthy host. Historically, drinking water treatment has focused on control of frank pathogens, particularly those from human or animal sources (like Giardia lamblia, Cryptosporidium parvum, or Hepatitis A virus), but in recent years outbreaks from drinking water have increasingly been due to opportunistic pathogens. Characteristics of opportunistic pathogens that make them problematic for water treatment include: (1) they are normally present in aquatic environments, (2) they grow in biofilms that protect the bacteria from disinfectants, and (3) under appropriate conditions in drinking water systems (e.g., warm water, stagnation, low disinfectant levels, etc.), these bacteria can amplify to levels that can pose a public health risk. The three most common opportunistic pathogens in drinking water systems are Legionella pneumophila, Mycobacterium avium, and Pseudomonas aeruginosa. This report focuses on these organisms to provide information on their public health risk, occurrence in drinking water systems, susceptibility to various disinfectants, and other operational practices (like flushing and cleaning of pipes and storage tanks). In addition, information is provided on a group of nine other opportunistic pathogens that are less commonly found in drinking water systems, including Aeromonas hydrophila, Klebsiella pneumoniae, Serratia marcescens, Burkholderia pseudomallei, Acinetobacter baumannii, Stenotrophomonas maltophilia, Arcobacter butzleri, and several free-living amoebae including Naegleria fowleri and species of Acanthamoeba. The public health risk for these microbes in drinking water is still unclear, but in most cases, efforts to manage Legionella, mycobacteria, and Pseudomonas risks will also be effective for these other opportunistic pathogens. The approach to managing opportunistic pathogens in drinking water supplies focuses on controlling the growth of these organisms. Many of these microbes are normal inhabitants in biofilms in water, so the attention is less on eliminating these organisms from entering the system and more on managing their occurrence and concentrations in the pipe network. With anticipated warming trends associated with climate change, the factors that drive the growth of opportunistic pathogens in drinking water systems will likely increase. It is important, therefore, to evaluate treatment barriers and management activities for control of opportunistic pathogen risks. Controls for primary treatment, particularly for turbidity management and disinfection, should be reviewed to ensure adequacy for opportunistic pathogen control. However, the major focus for the utility’s opportunistic pathogen risk reduction plan is the management of biological activity and biofilms in the distribution system. Factors that influence the growth of microbes (primarily in biofilms) in the distribution system include, temperature, disinfectant type and concentration, nutrient levels (measured as AOC or BDOC), stagnation, flushing of pipes and cleaning of storage tank sediments, and corrosion control. Pressure management and distribution system integrity are also important to the microbial quality of water but are related more to the intrusion of contaminants into the distribution system rather than directly related to microbial growth. Summarizing the identified risk from drinking water, the availability and quality of disinfection data for treatment, and guidelines or standards for control showed that adequate information is best available for management of L. pneumophila. For L. pneumophila, the risk for this organism has been clearly established from drinking water, cases have increased worldwide, and it is one of the most identified causes of drinking water outbreaks. Water management best practices (e.g., maintenance of a disinfectant residual throughout the distribution system, flushing and cleaning of sediments in pipelines and storage tanks, among others) have been shown to be effective for control of L. pneumophila in water supplies. In addition, there are well documented management guidelines available for the control of the organism in drinking water distribution systems. By comparison, management of risks for Mycobacteria from water are less clear than for L. pneumophila. Treatment of M. avium is difficult due to its resistance to disinfection, the tendency to form clumps, and attachment to surfaces in biofilms. Additionally, there are no guidelines for management of M. avium in drinking water, and one risk assessment study suggested a low risk of infection. The role of tap water in the transmission of the other opportunistic pathogens is less clear and, in many cases, actions to manage L. pneumophila (e.g., maintenance of a disinfectant residual, flushing, cleaning of storage tanks, etc.) will also be beneficial in helping to manage these organisms as well.

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.

Many heuristics for network reconfiguration rely on the systematic applying of the branch-exchange technique. In this work, two novel genetic operators for crossover and mutation have been developed that are based on the referred technique. The chromosome's codification to use these operators is straightforward and is not required any additional knowledge of graph theory to achieve the feasibility of individuals. As one of their main novelties, the methodology shows how can be employed a local improvement step, used commonly in the single-objective optimization, in the multi-objective optimization. This step increases the convergence of the optimization with populations of much reduced size. The proposed methodology is tested by solving several examples of the literature, including or not the local improvement step. The comparison of the results with the best solutions published for these examples shows the effectiveness of the method.

Future power distribution networks will include a significant amount of renewable distributed generation (DG). To increase the system power factor (SPF) and voltage profile (VP) by including DG in the distribution networks, this research proposes a unique approach backed by genetic algorithms (GA) and monte carlo simulation (MCS). Models of constant power (P), constant current (I), and constant impedance (Z) loads (ZIP–LMs). A 75-bus distribution network has been used to test the suggested methodology. From the perspective of minimizing the real power loss of the system, this paper considered the DG consolidated in the distributed networks (DNs) with ZIP–LMs for the enhancement of the SPF and VP by applying a hybrid GA–MCS. The people who work on the subject of integrating renewable energy sources with grids will also benefit from this study. [ABSTRACT FROM AUTHOR]

The continuity of power supply is affected by many factors; the weather conditions to which power system components are exposed are considered the most effect. A new methodology to assess the reliability of distribution system considering the impact of different weather conditions is presented in this paper. The proposed method considers possible future changes in the weather patterns that might occur differently from what it was in the past. Modified sets of reliability indices are presented for mathematically predicting reliability performance based on historical reliability data under specific past weather conditions combined with forecasted future weather conditions. The proposed method is applied to the distribution system components of RBTS; the results demonstrate the significance of considering the forecast of weather conditions in reliability assessment. [ABSTRACT FROM AUTHOR]

Available, effective methods of virus detection adequately address the risks in treating water for potable reuse. [ABSTRACT FROM AUTHOR]

Understanding how to use different types of process control tests to maintain optimal corrosion control conditions can guide operators in their response. [ABSTRACT FROM AUTHOR]

Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices' parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink® allows for verifying the proposed methodology's behavior and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a novel modular voltage control algorithm for optimal scheduling of a distribution system's load tap changers to minimize the number of tap changes while maintaining a voltage deviation (VD) around a desired target. To this end, a bi-objective optimal voltage regulation (OVR) problem is addressed in two distinct stages. First, the operational constraint on the load tap changer is removed to form a single-objective OVR problem relating to the voltage. The solution obtained in this stage is ultimately utilized to determine the penalty value assigned to the distance from the optimal (solely in terms of voltage) control value. In the second stage, the optimal scheduling problem is formulated as a minimum-cost-path problem, which can be efficiently solved via dynamic programming. This approach allows the identification of optimal scheduling that considers both the voltage-related objective as well as the number of load tap changer switching operations with no added computational burden beyond that of a simple voltage optimization problem. The method imposes no restriction on the load tap changer's operation and is tested under two different target functions on the standard IEEE-123 test case. The first attains a nominal voltage with a 0.056 p.u. voltage deviation and the second is the well-known conservation voltage reduction (CVR) case with a 0.17 p.u. voltage deviation. The method is compared to an evolutionary-based algorithm and shows significant improvement in the voltage deviation by a factor of 3.5 as well as a computation time acceleration of two orders of magnitude. The paper demonstrates the effectiveness and potential of the proposed method as a key feature in future cutting-edge OVR methods. [ABSTRACT FROM AUTHOR]

The proliferation of renewable energy sources and their impact on power systems makes it necessary to conduct studies to ensure the proper operation of the power system. This paper investigates recent research on the impact of renewable energy sources on voltage stability and new methods used to assess this stability. The effects of solar and wind power generation both individually and collectively are studied, along with other inverter models through which renewable energy sources are connected to transmission systems or distribution networks. In addition, the results are highlighted by categorizing them into a deterministic and probabilistic approach. [ABSTRACT FROM AUTHOR]

Copyright of EMERG: Energy. Environment. Efficiency. Resources. Globalization is the property of Romanian National Committee of World Energy Council 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 paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCNs superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.

With the use of cutting-edge demand management at the system or home level and powerful network reconfiguration tools, smart grids are expected to introduce advanced hardware and software resources to strengthen the operation of power systems. This article describes a Modified Gradient-Based Optimization (MGBO) algorithm for Distributed Generation (DG) and capacitors integration in distribution feeders. To increase the variety of the produced searching individuals, the suggested MGBO combines the basic Gradient Searching Method (GSM) and Local Escape Mechanism (LEM) with a binomial crossover strategy. This combined cross-over strategy upgrades the forthcoming searching individuals to be more random. The LEM assists in evading local optima, whereas the GSM guides the searching scan to promising regions and facilitates its convergence to the optimum solution. The suggested MGBO method is designed and implemented to improve the performance of radial distribution networks by reducing technical power losses while taking into account the peak loading. Its relevance is tested on a practical radial 59-bus Cairo distribution feeder in Egypt and a large-scale radial 135-bus distribution feeder. The proposed MGBO is compared with the original GBO, Manta ray foraging optimization (MRFO) and honey badger algorithm (HBA). The whole comparison of the suggested MGBO with the original GBO and the newly developed optimization algorithms demonstrates that the suggested MGBO derives the best performance in all of the cases studied. For the practical radial 59-bus Cairo distribution feeder in Egypt, the proposed MGBO shows great improvement of 18.40%, 20.17%, and 2.29% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively. For the large-scale radial 135-bus distribution feeder, the proposed MGBO shows great improvement of 46.92%, 62.94%, and 67.87% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively.

DC microgrids (DCMGs), typically envisioned with high bandwidth communications, may well expand their application range by considering additional autonomous control strategies, especially to increase resiliency in contingency situation. These strategies are based on the droop control methods, enabling voltage regulation and proportional load sharing based only on local measurements. Control challenges arise when coordinating the output of multiple DCMGs, each composed of several distributed energy resources through bus-to-bus transactional converters. This paper proposes an autonomous control strategy for transactional converters when multiple DCMGs are connected through a common bus. This control strategy seeks to match the external bus voltage with the internal bus voltage, balancing power. Three case scenarios are considered: standalone operation of each DCMG, excess generation on one DCMG, and generation deficit in one DCMG. Results using Sandia National Laboratories’ Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared. These results demonstrated that the proposed control strategy supported the advantage of seamless operation with and without local generation-load mismatches and expanded the range of autonomous operation between DCMGs.

There has been a significant growth in the distributed energy resources (DERs) connected to the distribution networks in recent years, increasing the need for modeling the distribution networks in detail in conjunction with the sub-transmission/transmission networks. This paper models a real distribution/ sub-transmission network using a three-phase/three-sequence co-simulation. One of the modeled distribution feeders has a high penetration of DERs with significant reverse power flow and is modeled including the secondary network. Custom user-defined models are used to represent the solar photovoltaic (PV) units on the feeder including advanced controls and abnormal voltage responses from IEEE 1547–2018 standard. The co-simulation framework developed supports power flow/steady state as well as dynamic analysis. Using this developed framework, this paper studies the impact of balanced and unbalanced faults applied to the distribution and sub-transmission networks. The impacts of the faults on the feeder with the high penetration of DERs are studied in terms of the solar PV units tripping due to under/overvoltages and the resulting change in the feeder-head flow. It is seen that the detailed modeling of the distribution network is needed for accurately capturing the response from the distribution-connected DERs during fault events both on the distribution as well as sub-transmission networks.

Nowadays, many modern electrical power systems are designed for transmission and distribution using underground systems. Such systems minimize visual impact, reduce congestion, and increase security and reliability. However, the technology utilized has increased the capacitance effect, which can lead to the system being more susceptible to the occurrence of the ferroresonance phenomenon. Ferroresonance can cause dielectric or thermal damage and consequently makes the system susceptible to faults in the electrical power equipment and installations. This paper analyzes the ferroresonance phenomenon after switching maneuvers in a real underground distribution feeder to detect possible conditions of ferroresonance. The analysis is carried out in a typical underground distribution system with distribution transformers connected in wye-grounded/wye-grounded (Ynyn), and modeled using the ATP/EMTP software.

This paper proposes a strategy for protection coordination in distribution networks with DGs penetration. The main objective of the present work is to employ the potential of Multi-Agent Systems (MASs), which have recently been given considerable attention in the context of smart grids. A modified scheme is proposed to minimize the number of agents while preserving the necessities of protection coordination under different contingencies. A new structure is defined for relay agent (RA) to operate in centralized or decentralized modes. Each RA contains communication and protection units, to detect an appropriate mode according to the required time to clear the fault and maintain coordination with other agents. IEC-61850 standard including GOOSE and SMV protocols used to send necessary commands and share data. The proposed protection scheme has 8 stages, which removes the fault from the network with the help of services and functions defined on the proposed structure of the relay agent. These steps include gathering network information, sending commands, and updating relay settings. In this regard, impacts of DGs are analyzed to show how they would change the operation of protective relays and their corresponding zones. Results of this research ensure the proper performance of protective devices taking into account appropriate settings and configurations.

Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification [ABSTRACT FROM AUTHOR]

Various methods and processes are applied in order to manage losses in water distribution systems. In order to apply these methods and achieve the expected benefits, the basic data in the system should be measured and monitored regularly. In this study, a new model was developed to analyze and evaluate the current levels of basic data required for the sustainable execution of water loss management processes. For this, a total of 18 basic components covering water loss management were considered. A scoring structure has been developed to evaluate the current status and application level of these components. In this scoring structure, the components are scored gradually to define the current level of application. The developed model was applied to three water utilities and tested with real field data. The current situation, weaknesses and strengths of each administration are defined within the scope of basic data. Targets to be achieved gradually for each component were determined based on these analyzes. It is thought that this study will constitute a reference for decision makers and technical personnel in administrations, especially in water loss management. [ABSTRACT FROM AUTHOR]

A pattern recognition-based plan for detecting islanding and non-islanding conditions is presented in this paper. This plan is used in distribution systems in the presence of synchronous generator-based distributed generation resources with doubly fed induction generators (DFIGs), a generating principle widely used in wind turbines. The main idea behind the suggested plan is the measurement of total magnetomotive force (MMF) in the SG air gap less than 2 s based on IEEE std.1547 after the occurrence of islanding and non-islanding events. First, a mathematical morphology (MM) filter was used to lessen the possible noise in a total MMF signal sampled in SGs. Then, spatial features like variation, energy, median, rms and mean values of the output magnitude of MM filter were computed. Finally, to distinguish the islanding condition from the non-islanding conditions, support vector machine (SVM) classifier with radial basis function (RBF) kernel was taught based on the above-mentioned features. Different scenarios were considered in various loading conditions in distribution systems for the occurrence of islanding and non-islanding conditions and the determination of the ability of the suggested plan. The outcomes show the method suggested with the SVM (RBF kernel) classifier has much better results in comparison with some other methods. [ABSTRACT FROM AUTHOR]

The project taught EWEB staff valuable lessons in planning and implementing an emergency water supply program, as well as in coordinating with public agency partners and drilling new wells inside city limits. In accordance with the plan, EWEB developed a mobile water distribution system (distribution trailers) that can use stored water in reservoirs, existing groundwater wells, treated surface water, and neighboring utility interconnections. Emergency Water Sites Beginning in 2017, EWEB decided to expand its emergency water supply system by establishing permanent water distribution sites using groundwater as the source. [Extracted from the article]

Key Takeaways: Modeling tools can simulate possible disasters to water distribution systems, including pipe breaks, power outages, and changes to water source availability. Resilience analysis can help water utilities compare emergency response strategies for disasters that could affect their water distribution systems. Socioeconomic and environmental factors are useful to identify priority areas within a water distribution system to improve equitable resilience. [ABSTRACT FROM AUTHOR]

Water and wastewater laboratories have many similarities, but they also have unique differences that allow them to generate data that protect public health and the environment. [ABSTRACT FROM AUTHOR]

A fast‐growing segment of automation, the Industrial Internet of Things can transform water utility operations, but it requires wise deployment, planning, and security infrastructure. [ABSTRACT FROM AUTHOR]

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi 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.)

In Brazil, the regulatory agency defines how much revenue each distribution utility will have to cover the technical losses cost. This amount is defined by an estimation of technical losses via power flow with systems modeled in detail through data provided yearly by each utility. In this process, some assumptions and simplifications are considered to reduce required data and to set some limits in technical losses. Therefore, some network inefficiencies are not considered in the results. However, these parameters are under scrutiny by the utilities as they could result in revenue losses. In this context, this paper evaluates if these assumptions are well balanced through large-scale tests in a real network composed by 724 feeders. The study was conducted in Open Distribution System Simulator (OpenDSS), the same software adopted by the Brazilian regulatory agency, and shows which parameters have more impact in the technical losses calculation. From the results, it is possible to verify a high impact on technical losses with the adoption of standards for transformer losses and also a great relevance of the load model adopted. [ABSTRACT FROM AUTHOR]

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids. [ABSTRACT FROM AUTHOR]

Introduction. Most distribution networks are unbalanced and therefore require a specific solution for load flow. There are many works on the subject in the literature, but they mainly focus on simple network configurations. Among the methods dedicated to this problem, one can refer to the load flow method based on the bus injection to branch current and branch current to bus voltage matrices. Problem. Although this method is regarded as simple and complete, its drawback is the difficulty in supporting the transformer model as well as its winding connection types. Nevertheless, the method requires the system per unit to derive the load flow solution. Goal. In the present paper, our concern is the implementation of distribution transformers in the modeling and calculation of load flow in unbalanced networks. Methodology. Unlike previous method, distribution transformer model is introduced in the topology matrices without simplifying assumptions. Particularly, topology matrices were modified to take into account all winding types of both primary and secondary sides of transformer that conserve the equivalent scheme of an ideal transformer in series with an impedance. In addition, the adopted transformer models overcome the singularity problem that can be encountered when switching from the primary to the secondary side of transformer and inversely. Practical value. The proposed approach was applied to various distribution networks such as IEEE 4-nodes, IEEE 13-nodes and IEEE 37-nodes. The obtained results validate the method and show its effectiveness. [ABSTRACT FROM AUTHOR]

From system assessment to recovery, a water utility's staff went beyond the call of duty to respond to the most destructive structural wildfire in Colorado history. [ABSTRACT FROM AUTHOR]

The paper presents the definitions of powers and currents associated with systems characterized by non-sinusoidal sources and/or non-linear loads in the time domain. An expression for the required static element for reactive power compensation is developed. The analysis is carried out for two cases: theoretical and experimental. The Excel spread sheet is used to analyze the theoretical case to demonstrate the validity of the method. Furthermore, a circuit is designed, for the experimental case, using a microcontroller board for online measurement and performing the time domain analysis. The microcontroller is programmed to sample voltage and current waveforms, analyze current into components, calculate the value of the required passive element and display it using a Liquid Crystal Display. The microcontroller can be used to control STATCOM devices to achieve maximum obtainable compensation. This presents a fast online method (3 to 5 cycles) to enhance the power quality of the system.

The introduction of distributed generation (DG) into distribution systems is expanding due to carbon neutral policies. DG with intermittent output characteristics brings frequent voltage variations in distribution systems. Generally, a step voltage regulator (SVR) is installed in the long distribution line and controls its voltage, which has limits of stable voltage regulations due to the slow tap-changing rate, switching losses, and short life span. To solve those problems, this paper proposes an IGBT-based SVR using zero-voltage switching. It is verified that the stable operation can be obtained through simulation by PSCAD/EMTDC 4.6 software and empirical experiments.

The resilience is the most dominant factor in any disaster condition which has been approached by various techniques in literature. Some of the methods consider only the power availability and grid states for the resilience maximization. However, they suffer to achieve higher performance in voltage distribution. To solve this issue, an efficient Power Centric Resilience Maximization Model (PCRMM) is presented in this paper. The model monitors the incoming voltage and generates adaptive configuration for the distribution system by considering number of grids, their states, emergency units, essential units, service sectors and so on. By considering all these factors, the method computes the resilience support for different micro grids according to the available voltage. Also, the method computes the functional support for the cycle and based on that dynamic configurations are generated. According to the configuration, the distribution system would trigger the voltage supply to various micro grids to feed voltage to the selected units. The proposed method improves the performance of power distribution with least voltage loss.

A DC microgrid is an efficient way to combine diverse sources; conventional droop control is unable to achieve both accurate current sharing and required voltage regulation. This paper provides a new adaptive control approach for DC microgrid applications that satisfies both accurate current sharing and appropriate voltage regulation depending on the loading state. As the load increases in parallel, so do the output currents of the distributed generating units, and correct current sharing is necessary under severe load conditions. The suggested control approach raises the equivalent droop gains as the load level increases in parallel and provides accurate current sharing. The droop parameters were checked online and changed using the principal current sharing loops to reduce the variation in load current sharing, and the second loop also transferred the droop lines to eliminate DC microgrid bus voltage fluctuation in the adaptive droop controller, which is different and inventive. The proposed algorithm is tested using a variety of input voltages and load resistances. This work assesses the performance and stability of the suggested method using a linearized model and verifies the results using an acceptable model created in MATLAB/SIMULINK Software Version 9.3 and using Real-Time Simulation Fundamentals and hardware-based experimentation.

The reduction in GHG emissions has become one of the most important objectives in Europe. One of these clean technologies in the quest for large-scale decarbonisation is the blending of hydrogen and natural gas (H2NG). The significance of this article lies in conducting a technical–economic analysis that relies on simulating annual consumption patterns. This study focuses on the utilization of H2NG at different proportions within a distribution network in Bucharest, Romania’s largest city. The paper is a fallow up of another paper and presents a case study for the injection through all four entry points in the city of different percentages of hydrogen and the dynamic of the mix through the gas distribution network. The study found that the variation in H2 in the network is high and depends on the entry point levels and consumption. An economic analysis was conducted on seasonal real consumption, which confirmed the investment’s profitability.

In this paper, an optimization approach based on an arithmetic optimization algorithm (AOA) is proposed for specifying the optimal allocation of distribution generations/generators (DGs) and capacitor banks (CBs) in radial distribution systems. The AOA is a new population-based meta-heuristic algorithm that is essentially based on using basic arithmetic operators in mathematics. The proposed approach is employed to specify the optimum placement, capacity, and power factor of DGs and CBs to decrease the distribution systems’ total power loss and voltage deviation. To state the performance of the proposed approach, DGs and CBs are placed in IEEE 33-bus and 69-bus systems separately or together. When only DGs are used and the parameters of location, capacity, and power factor of DGs are determined simultaneously, the total active power loss reductions in the IEEE 33-bus and 69-bus systems are achieved at 94.42% and 98.03%, respectively. When the results of other optimization algorithms are examined, it is seen that better results are obtained with AOA.