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In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions. [ABSTRACT FROM AUTHOR]

This study proposes an under-frequency load-shedding (UFLS) scheme based on a binary Archimedes Optimization Algorithm (BAOA) and the Weighted Sum Method (WSM) to maintain the stability of an islanded distribution system. These methods consider stability indices and load priorities to ensure effective load shedding during frequency deviations. The BAOA determines the optimal load shedding based on the stability index and power mismatch that minimizes the impact on critical loads while maintaining system stability in an islanded distribution system. The WSM determines the rank of the load to be shed based on four criteria: the load priority, the load category, the stability index, and the load size. Each load is assigned a weight based on its priority. These weight variables determine the order in which loads are shed during frequency deviations. The effectiveness of the proposed UFLS was tested on an 11 kV Malaysian distribution network with two mini hydro distributed generation systems. A comparative study was conducted based on five result outputs, including the number of loads shed, the size of the loads shed, the frequency undershoot, the frequency overshoot, and the time taken to achieve a stable frequency in three cases: base load, peak load, and peak load with photovoltaics (PV). The proposed UFLS showed the best results for 11 of 15 outputs (73.3%) for islanding events and 9 of 15 outputs (60%) for overloading events. The voltage profile and stability index, also, were improved after the proposed UFLS was applied. [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]

Optimal coordination of directional over-current relays (DOCRs) is a crucial task in ensuring the security and reliability of power system network. In this paper, a hybridization of an improved particle swarm optimization and linear programming (IPSO-LP) is proposed to solve DOCRs coordination problem. The considered decision variables in the optimization are plug setting current, time multiplier setting, type of relay, and type of curve. By considering these parameters in the optimization, the best relay operating time can be determined. Furthermore, the proposed technique also considered the continuous values of pick-up current setting (PSC) and time setting multiplier (TMS). Test on the IEEE 8 bus system has proven the effectiveness of the technique, where an improvement between 2.36% and 45.8% of total relay operating time can be observed as compared to other techniques in literature. In addition, the obtained settings of the DOCRs from the proposed technique have been verified using industrial software to make sure no setting that cause violation to the DOCRs operation. [ABSTRACT FROM AUTHOR]

The exponential increase in the frequency and intensity of high impact low probability weather‐related events have pivoted the paradigm of research pertaining to power systems towards resilience. Power system is considered as a critical infrastructure, directly linked to the nation's economy, security and health. Therefore, recent researchers have proposed several techniques to enhance the resilience of power systems. In those techniques, critical loads have been considered independent in nature and different metrics have been proposed to evaluate the resilience of the network. To enhance the resiliency, this paper incorporated distributed generators in the distribution network and critical loads are modelled interdependently. Furthermore, a novel resilience metric is proposed in this paper to evaluate the resilience of a distribution system. The proposed model is formulated as a mixed integer second‐order cone programming problem and the efficacy of the proposed model is evaluated on IEEE 33‐ and 69‐bus systems. The competence of the proposed resilience metric is evaluated after comparison with existing resilience metrics. [ABSTRACT FROM AUTHOR]

Modern societies these days are more dependent on electrical energy and they expect a continuous supply as per demand. In this regard, the complex power system is designed to supply electrical energy with a certain level of quality and continuity though it is still susceptible to vandalism, natural disasters, and extreme weather. The black sky event where the power grid goes down is more of a possibility nowadays than ever due to more frequent severe weather events. This in turn has increased the need to study resilience in the context of the power system. This study presents a comprehensive review of the literature on power system resilience from various perspectives. First, well-developed power system safety concepts are discussed and critically reviewed in view of large-scale power outages. Then, the various definitions and confounding features of resilience in the power system domain are presented. Subsequently, several frameworks, resilience curves, and quantitative metrics proposed in recent years for power system resilience are investigated, followed by a summary of hardening strategies. Next, a case study is presented to illustrate how the resilience of a 69-bus system is assessed against a hurricane. Finally, the study highlights challenges and proposes several future works to achieve a resilient power grid. [ABSTRACT FROM AUTHOR]

Modern societies these days are more dependent on electrical energy and they expect a continuous supply as per demand. In this regard, the complex power system is designed to supply electrical energy with a certain level of quality and continuity though it is still susceptible to vandalism, natural disasters, and extreme weather. The black sky event where the power grid goes down is more of a possibility nowadays than ever due to more frequent severe weather events. This in turn has increased the need to study resilience in the context of the power system. This study presents a comprehensive review of the literature on power system resilience from various perspectives. First, well‐developed power system safety concepts are discussed and critically reviewed in view of large‐scale power outages. Then, the various definitions and confounding features of resilience in the power system domain are presented. Subsequently, several frameworks, resilience curves, and quantitative metrics proposed in recent years for power system resilience are investigated, followed by a summary of hardening strategies. Next, a case study is presented to illustrate how the resilience of a 69‐bus system is assessed against a hurricane. Finally, the study highlights challenges and proposes several future works to achieve a resilient power grid. [ABSTRACT FROM AUTHOR]

Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. The proposed ANN model is tested on IEEE 33-bus, IEEE 69-bus, and IEEE-118 bus systems. The test results indicate the efficiency of the proposed technique in three aspects: processing time, simple structure, and high accuracy. [ABSTRACT FROM AUTHOR]

Renewable Energy Resources (RERs) are a promising source of energy with hardly any pollution. Due to the intermittent nature from the output power of the nondispatchable RER and the load variations in the distribution network, it is important to frequently perform Network Reconfiguration (NR) to minimize the power loss and improve the network's voltage profile. Finding the optimal NR while simultaneously considering the dispatchable RER integration is important but challenging because of the complex combinational nature of the problem, and therefore, it is commonly solved by meta-heuristic techniques. However, the conventional meta-heuristic techniques involve random initializations and normally generate many nonfeasible solutions, which obstruct the search process. With the aim of improving the accuracy and consistency of the solution, this study proposes a two-stage method using biogeography-based optimization to attain the NR simultaneously with the RER placement and sizing for the sake of minimizing power loss and voltage deviation. The first stage simplifies the distribution network and then utilizes the simplified form to provide a better set of initial solutions and to maintain the network radiality. Thereafter, the second stage finds the final NR and RER locations and sizes. The simulations are carried out on 33-bus, 69-bus, and 118-bus systems, and the results are compared with previously published methods and some well-known optimization methods. In addition, load variations and RER's uncertainty are also considered. The obtained results show that the proposed method outperforms the existing methods. The results also indicate the significance of the hourly NR in reducing power loss. [ABSTRACT FROM AUTHOR]

Deregulation in the electrical industry has led utility companies to ensure high quality of power supply at the customer side. It is of utmost importance for utility companies to operate at maximum efficiency and minimise voltage deviation and power losses. Distributed network reconfiguration (DNR) and integration of distributed generation (DG) are commonly employed to mitigate power loss and voltage deviation. DNR is a complex combinatorial problem which requires radiality verification. Implicit radiality verification increases computational overhead and may lead to local optima. Whereas, improper selection of DG size poses direct consequences on the distribution network mainly on increased voltage deviation and power losses. Therefore, simultaneous optimal integration of DNR and DG is considered in this study to improve the overall performance of the distribution network. Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near-optimal solution for NR and DG size. Four cases are considered to validate the effectiveness of the proposed technique including investigation on small, medium, and large-scale distribution network. The results show that the proposed technique is able to consistently attain near optimal-solutions. [ABSTRACT FROM AUTHOR]

Deregulation in the electrical industry has led utility companies to ensure high quality of power supply at the customer side. It is of utmost importance for utility companies to operate at maximum efficiency and minimise voltage deviation and power losses. Distributed network reconfiguration (DNR) and integration of distributed generation (DG) are commonly employed to mitigate power loss and voltage deviation. DNR is a complex combinatorial problem which requires radiality verification. Implicit radiality verification increases computational overhead and may lead to local optima. Whereas, improper selection of DG size poses direct consequences on the distribution network mainly on increased voltage deviation and power losses. Therefore, simultaneous optimal integration of DNR and DG is considered in this study to improve the overall performance of the distribution network. Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near‐optimal solution for NR and DG size. Four cases are considered to validate the effectiveness of the proposed technique including investigation on small, medium, and large‐scale distribution network. The results show that the proposed technique is able to consistently attain near optimal‐solutions. [ABSTRACT FROM AUTHOR]

An electrical power system is considered as a critical infrastructure (CI), the epicenter of a nation's economy, security, and health. It is interlinked with other CIs such as gas and water supplies and transportation and communication systems. A failure in the power system will immensely affect the functionality of these CIs. Therefore, enhancing power system resilience is crucially needed to ensure continuous operation of these CIs. One of the possible approaches to improve the resilience in a power system is by integrating microgrids in the power system. Microgrids have proven to have self-healing and resilient capabilities in such extreme events which inflict damage out of the conventional scope of failures. Operational flexibility and controllability make microgrids a viable solution for resilience enhancement. This paper reviews the concept of resilience in power systems and the functions of microgrids in enhancement of resilience. The most current studies in improving power system resilience through microgrids are reviewed by highlighting their advantages and limitations. [ABSTRACT FROM AUTHOR]

Microgrid (MG) with optimal operating (OC) and capital cost (CC) is highly required. This study presents dual‐mode energy management system (DM‐EMS) operation with the objective of minimising the overall OC of the MG. DM‐EMS determines the best mode to operate battery and fuel cell at a particular time and its duration to operate MG with minimum cost. The CC of an MG is mainly depending on battery size and battery life. In this study, novel battery sizing method by using the proposed life cycle cost (LCC) function is introduced. Considering the impact of battery size and life in the OC and CC of an MG, the proposed DM energy management strategy and novel battery sizing are concurrently optimised by using the proposed LCC function. The proposed strategy is validated using an MG system consisting of wind, battery storage and fuel cell. The results show that better optimal OC and CC with an accurate optimal battery capacity of an MG are achieved. In addition, analysis on the optimal battery size variations for different battery level [state of charges (SOCs)] is conducted. Changes in OCs and CC for the different range of initial battery SOC are also assessed. [ABSTRACT FROM AUTHOR]

Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100% accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location. [ABSTRACT FROM AUTHOR]

• A combined protection strategy to incorporate the FCL deployment and DOCR settings to alleviate the miscoordination at high faulted nodes irrespective of DG location and network operating state. • Monte Carlo Simulation (MCS) is implemented to predict the locations where the fault currents exceed the CB thermal limits due to essential network growth. • The relay settings are optimized as one setting group to maintain the coordination in the network dynamic operating state, i.e., Grid (On/Off). • The multi-objective hybrid optimization technique is formulated to minimize the FCL implementation cost and DOCR coordination settings by considering user-defined characteristics. • The proposed hybrid LP is considered with the SSA algorithm to solve PCP by linearizing the relay characteristics equation. Despite providing increased reliability and power quality in meeting the load demand, the integration of DGs has imposed challenges on the protection system. High penetration of DGs changes the overall network impedance and increases the fault current level in the network. Consequently, some buses are exposed to critical conditions, violate the circuit breakers (CB) handling capacity, and affect the existing relay coordination settings. To mitigate these adverse impacts, fault current limiters (FCL) can be a potential solution to minimize the short circuit current within permissible switchgear-rated limits. Due to its expensive cost, it is crucial to ensure the optimal FCL placement and sizing, and at the same time maintain the effectiveness of the protection performance during various network operation states. This research formulates FCL and directional overcurrent relay (DOCR) settings as a combined protection coordination scheme optimized by a multi-objective hybrid optimization technique. The proposed formulation aims to determine the minimal FCL sizing with minimum investment cost to satisfy the relay coordination constraints at high fault-level buses, irrespective of DG locations and network operating state. User-defined relay characteristics (UDC) are employed to intensify the DOCR coordination performance and obtain minimum operating time, where the conventional inverse relay constants (A&B) are optimized with (TSM & Ipu). The combined UDC relay settings and FCL problem formulation solution attained by MO-hybrid optimization can lessen the escalated electromagnetic stresses, costly switchgear replacement, and relay maloperation. The performance of the proposed technique is assessed by implementing it on the radial (IEEE-33 Bus) and meshed (IEEE 30-Bus) DNs with optimized FCL sizing and relay settings. The optimal results demonstrate the effectiveness of the proposed technique in maintaining the relay coordination performance in the presence of DGs and FCL under an ON/OFF grid connection. [ABSTRACT FROM AUTHOR]

Network reconfiguration is a well-known technique for the distribution system to reduce power losses. However, the reconfiguration technique, by itself, could only minimize power losses up to a certain point. Further power losses reduction could be realized via the application of distributed generation (DG). However, the integration of DG to the distribution system at a non-optimal value could instead increase power losses and voltage fluctuation. Therefore, it is vital to develop an effective optimization strategy to determine the optimal output of the DG and simultaneously ensure optimal configuration. This paper presents a simultaneous optimal network reconfiguration with optimal DG output to minimize power losses and improve the voltage profile. Different objectives are discussed in this paper: (1) to minimize power losses, (2) to improve voltage profile index, (3) to maximize DG output. Evolutionary programming, particle swarm optimization, firefly, and gravitational search algorithm methods have been applied for optimal distribution network reconfiguration with optimal DG output. To evaluate the possibilities of the suggested method, simulations using MATLAB software are carried out on an IEEE 33-bus radial distribution system. The obtained outcomes prove the efficiency of the proposed strategy to find an optimal network configuration and optimal output of DG units. [ABSTRACT FROM AUTHOR]

Power losses in a distribution system are commonly minimised via optimal network reconfiguration (NR). Previously, research on NR was focused on planning, where the final configuration reporting the lowest power losses being the main goal. However, power losses during switching operations from the original state to the optimal state of configuration were not considered. This study discusses the optimal switching path for minimising power losses when reconfiguring a network. The simultaneous optimal NR and distributed generation (DG) output was also proposed. The proposed methodology involves: (i) optimal NR and DG output simultaneously and (ii) optimal switching path to convert the network from the initial configuration to the final configuration obtained from (i). The selected optimisation technique in this study is the firefly algorithm. The proposed method was tested using IEEE 33-bus, 69-bus, and 118-bus radial distribution networks, while also accounting for static and dynamic loads. The results confirmed the effectiveness of the proposed method in determining the optimal path of switching operations, as well as the optimal network configuration and optimal output of DG units. [ABSTRACT FROM AUTHOR]

Power losses in a distribution system are commonly minimised via optimal network reconfiguration (NR). Previously, research on NR was focused on planning, where the final configuration reporting the lowest power losses being the main goal. However, power losses during switching operations from the original state to the optimal state of configuration were not considered. This study discusses the optimal switching path for minimising power losses when reconfiguring a network. The simultaneous optimal NR and distributed generation (DG) output was also proposed. The proposed methodology involves: (i) optimal NR and DG output simultaneously and (ii) optimal switching path to convert the network from the initial configuration to the final configuration obtained from (i). The selected optimisation technique in this study is the firefly algorithm. The proposed method was tested using IEEE 33‐bus, 69‐bus, and 118‐bus radial distribution networks, while also accounting for static and dynamic loads. The results confirmed the effectiveness of the proposed method in determining the optimal path of switching operations, as well as the optimal network configuration and optimal output of DG units. [ABSTRACT FROM AUTHOR]

In this paper a new ramp-rate control strategy based on exponential smoothing (ES) method is proposed. Different from conventional ES, the proposed method varies the smoothing parameter ‘ σ ’ according to PV ramp-rate. The proposed smoothing parameter is used to determine the switching status of the battery energy storage (BES). It is also used in determining the amount of power to be injected or absorbed by the BES in order to smooth the PV output power. The proposed ramp-rate control strategy is compared with moving average (MA) and conventional exponential smoothing (CES) methods. It has been found that MA and CES method exhibit memory effect that caused BES to operate all the time. The proposed ramp-rate control strategy overcomes this limitation by operating the BES only during significant fluctuation. As a result, the size of the BES capacity can be reduced and increasing its life span. [ABSTRACT FROM AUTHOR]

Reconfiguring the link between buses is a crucial task to enhance the distribution system performance. Reconfiguration is a complex combinatorial process due to numerous feasible solutions. Therefore, to consistently find global optimum solutions within a short span of time is a challenging task. One of the factors that cause time consumption in finding optimal network configurations is the elimination of non‐radiality network solutions during the optimisation process. To address this issue, this work proposes to store pre‐determined network radiality solutions in a database. These sets of solutions are used in the network reconfiguration optimisation by a discrete evolutionary programming and a discrete evolutionary particle swarm optimisation techniques. These optimisation methods are based on a multi‐objective problem which minimises power loss, voltage deviation, and a number of switching actions. Moreover, the quality of the solutions is measured in terms of computational time and consistency. To demonstrate the efficiency of the proposed technique, a comparative assessment is carried out on 33‐bus and 118‐bus distribution systems. It is found that the proposed technique outperforms other existing methods in terms of quality of the solutions. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The benefits of distributed generation (DG) based on renewable energy sources leads to its high integration in the distribution network (DN). Despite its well-known benefits, mainly in improving the distribution system reliability and security, there are challenges encountered from a protection system perspective. Traditionally, the design and operation of the protection system are based on a unidirectional power flow in the distribution network. However, the integration of distributed generation causes multidirectional power flows in the system. Therefore, the existing protection systems require some improvement or modification to address this new feature. Various protection strategies for distribution system have been proposed so that the benefits of distributed generation can be fully utilized. This paper reviews the current progress in protection strategies to mitigate the impact of distributed generation in the distribution network. In general, the reviewed strategies in this paper are divided into: (1) conventional protection systems and (2) modifications of the protection systems. A comparative study is presented in terms of the respective benefits, shortcomings and implementation cost. Future directions for research in this area are also presented. [ABSTRACT FROM AUTHOR]

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location. [ABSTRACT FROM AUTHOR]

This paper presents a load shedding scheme (LSS) based on system frequency and voltage stability (VS). In the proposed scheme, the amount of power imbalance is determined using a rate of change of frequency, and the load shedding priority is carried out based on a VS index. The load with the highest tendency for voltage collapse (based on the index value) is given first priority to be shed. Thus, by shedding the most sensitive load, the distribution system can be saved from power collapse and then the system can return to its nominal state after load shedding. The proposed LSS is validated through simulation using PSCAD/EMTDC software on a Malaysian distribution network that consists of two mini-hydro generators. The simulation results show that the proposed scheme manages to shed the optimal amount of load when compared to conventional and adaptive frequency LSS. Moreover, apart from frequency stability, the proposed scheme also shows a significant improvement in the voltage profile of the islanded system. [ABSTRACT FROM AUTHOR]

This paper presents a load shedding scheme for a distribution network connected to mini hydro generations. The proposed scheme uses the rate of change of frequency ( ROCOF) to estimate the power imbalance and voltage stability ( VS) to prioritize the load that is to be shed. The load with the most critical voltage stability (which is based on a special index) is given the first priority to be disconnected from the network in order to avoid voltage collapse. By performing load shedding based on the combination of the ROCOF and VS techniques, a better system frequency and enhanced voltage magnitudes can be achieved. The performance of the proposed scheme is validated on existing Malaysian network interconnected with a mini hydro generator. Simulation results show that the proposed scheme not only successfully stabilizes system frequency, but also simultaneously stabilizes the voltage magnitude of the buses within an acceptable limit. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs). However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS) scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP), Binary genetic algorithm (BGA), and Binary particle swarm optimization (BPSO), to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques. [ABSTRACT FROM AUTHOR]

This paper presents a novel ‘mix-mode’ energy management strategy (MM-EMS) and its appropriate battery sizing method for operating the microgrid at the lowest possible operating cost. The MM-EMS is developed by combining three proposed operating strategies, namely “continuous run mode”, “power sharing mode” and “ON/OFF mode” for a 24 h time period. The objective functions for the proposed strategies are solved using linear programming (LP) and mixed integer linear programming (MILP) optimization methods. A sizing method using the particle swarm optimization (PSO) technique to determine the optimal energy capacity of battery energy storage (BES) in kWh is also presented. Since the size of the BES influences the microgrid's operating cost, the energy management strategy (EMS) and BES capacity are simultaneously optimized. The proposed MM-EMS and battery sizing method were first validated. Then, the variation of optimal battery capacity for different battery state of charge (SOC) levels is analyzed. The variation of microgrid's associated costs for different battery's initial state of charge (SOC) levels is analyzed as well. Finally, a recommendation on the choice of initial SOC level during the start of the day for the economic operation of microgrid is also suggested. [ABSTRACT FROM AUTHOR]

Fast and effective fault location in distribution system is important to improve the power system reliability. Most of the researches rarely mention about effective fault location consisting of faulted phase, fault type, faulty section and fault distance identification. This work presents a method using support vector machine to identify the faulted phase, fault type, faulty section and distance at the same time. Support vector classification and regression analysis are performed to locate fault. The method uses the voltage sag data during fault condition measured at the primary substation. The faulted phase and the fault type are identified using three-dimensional support vector classification. The possible faulty sections are identified by matching voltage sag at fault condition to the voltage sag in database and the possible sections are ranked using shortest distance principle. The fault distance for the possible faulty sections isthen identified using support vector regression analysis. The performance of the proposed method was tested on an unbalanced distribution system from SaskPower, Canada. The results show that the accuracy of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) sources have recently become more significant in the distribution system due to the increasing demand of electricity energy. Choosing the most suitable size of DG is an essential step to ensure tremendous effect on its operation and power system control. An optimal DG size will result in the reduction of electrical power loss as well as the cost of operation. Power loss reduction can also be achieved through optimal network reconfiguration. By considering optimal network reconfiguration and DG sizing, the effectiveness and efficiency of the system can be improved while having lower power loss in the power system. Therefore, this paper presents the simultaneous method to identify the optimal network reconfiguration and DG sizing using evolutionary programming. The main goal of this work was to reduce the power loss and increase the voltage profile, while at the same time fulfill the operating constraints. The developed method was tested on 33-bus and 69-bus system with different operating conditions. The test results showed a great improvement of power loss reduction and overall voltage profile of the system. The implementation of reconfiguration and DG technique in 33-bus and 69-bus systems which were operated simultaneously had shown tremendous improvement in terms of the number of iterations to reach the optimal value of power losses and DG sizing. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a new under-frequency load shedding technique based on the combination of random and fixed priority of loads. It has been observed that placing all of the loads in the distribution system with fixed priority results in un-optimum load shedding. On the other hand, designing the load priority with a combination of random and fixed priority provides the technique with some sort of flexibility in achieving the optimal load shedding. The validation of the proposed scheme on different scenarios proves that the proposed technique is capable of achieving the optimal load shedding and recovering frequency to nominal value without any overshoot. [ABSTRACT FROM AUTHOR]

High penetration of distributed generation resources (DGR) in distribution network provides many benefits in terms of high power quality, efficiency, and low carbon emissions in power system. However, efficient islanding detection and immediate disconnection of DGR is critical in order to avoid equipment damage, grid protection interference, and personnel safety hazards. Islanding detection techniques are mainly classified into remote, passive, active, and hybrid techniques. From these, passive techniques are more advantageous due to lower power quality degradation, lower cost, and widespread usage by power utilities. However, the main limitations of these techniques are that they possess a large non detection zones and require threshold setting. Various signal processing techniques and intelligent classifiers have been used to overcome the limitations of passive islanding. Signal processing techniques, in particular, are adopted due to their versatility, stability, cost effectiveness, and ease of modification. This paper presents a comprehensive overview of signal processing techniques used to improve common passive islanding detection techniques. A performance comparison between the signal processing based islanding detection techniques with existing techniques are also provided. Finally, this paper outlines the relative advantages and limitations of the signal processing techniques in order to provide basic guidelines for researchers and field engineers in determining the best method for their system. [ABSTRACT FROM AUTHOR]

The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems. [ABSTRACT FROM AUTHOR]

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method based on Evolutionary Programming (EP) and Genetic Algorithm (GA) to identify the switching operation plan for feeder reconfiguration and distributed generation size simultaneously. The main objectives of this paper are to gain the lowest reading of real power losses, upgrade the voltage profile in the system as well as satisfying other operating constraints. Their impacts on the network real power losses and voltage profiles are investigated. A comprehensive performance analysis is carried out on IEEE 33-bus radial distribution systems to prove the efficiency of the proposed methodology. The test result on the system showed the power loss reduction, and voltage profile improvement of the EP is superior to the GA method. [ABSTRACT FROM AUTHOR]

Mini hydro power plants (MHPP) are gaining attraction as a cost effective source for rural electrification in developing countries due to its environmental friendly operation. These MHPP plants suffer from the speed control problem due to continuous load variation. PID controllers fail to provide optimum speed control due to its limitation of parameter tuning. Alternatively, Fuzzy logic control is commonly used for implementing intelligent control for the nonlinear system. This paper presents a new fuzzy logic based governor for frequency/speed regulation of MHPP for implementation in distribution network. For verification, the response of load frequency control using fuzzy based governor and PID based governor is compared. The simulation results show that fuzzy based governor ensures robust control by improving the frequency response as well as dynamic response in comparison to PID based governor. Furthermore, fuzzy based governor enables the generator to continuously supply power even at 25% load variation whereas generator with PID based governor fails to supply power even at 20% load variation. [ABSTRACT FROM AUTHOR]

An enhanced fault location method based on voltage sag profiles for distribution networks is presented in this paper. In other previous methods, only a single measurement has been used. The proposed method can be used with any number of measurements in the network, making it more general. A new ranking approach that addresses multiple possibilities of the faulted section is proposed. Different case studies with various numbers of measurements are performed on a large 11-kV network with the main feeder consisting of 42 buses to validate the method. The test results show that there is an improvement in terms of accuracy in detecting the faulted section in the first attempt for each additional measurement. Therefore, by utilizing the average value of each measurement, a better accuracy of fault location can be achieved. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a reliable and fast load flow solution by using a real-coded genetic algorithm (RCGA), bus reduction technique and sparsity technique. The proposed load flow solution firstly used reduction technique to eliminate the load buses. Then, the power flow problem is solved for the generator buses only using real-coded GA to calculate the phase angles. Thus, the load flow problem becomes a single objective function, where the voltage magnitudes are specified resulted in reduced computation time for the solution. Once the phase angle has been calculated, the system is restored by calculating the voltages of the load buses in terms of the calculated voltages of the generator buses. A sparsity technique is used to reduce the computation time further as well as the storage requirements. The proposed load flow solution also can efficiently solve the load flow problems for ill-conditioned power systems whereas the conventional RCGA alone fails to solve these systems. The proposed method was demonstrated on 14-bus IEEE, 30-bus IEEE and 300-bus IEEE, and a practical system 362-busbar Iraqi National Grid. The proposed solution has reliable convergence, a highly accurate solution and much less computing time for on-line applications. The method can conveniently be applied for on-line analysis and planning studies of large power systems. [Copyright &y& Elsevier]

This paper presents a compendious review for the evaluation and description of the mathematical modelling of the affected components in wind turbines which cause the scattering of communication signals. The impact of an adjacent wind farm operation on telecommunication signals is that it induces electromagnetic interference (EMI) in radar, television and radio signals, resulting from the complex rotating blade's geometry of the wind turbines. Thus, altering the quality of the reflected signal, especially the capability of the radar detection. In all the modelling studies, the radar cross section (RCS) model of a wind turbine's blade is found to be the most complex, due to its huge computational burden. However, clutter filtering is another interesting technique, which employs the Doppler signal processing to obviate the huge computational task in RCS. In this case, the rotating blades of the wind turbine produce Doppler echoes, which in turn are used to estimate the model of the blade by modelling the echo of the scattering points. Therefore, this review succinctly compiles the basic steps of theoretical analysis and simulations of the impact of wind turbines on communication signals, and the remedies to minimize the impact. [ABSTRACT FROM AUTHOR]

Abstract: Hydro power generation is the oldest generation and provides the largest contribution among the renewable energy types of generation. In distribution system, most of the distributed generation (DG) is small scale hydro generation of which utilizes the natural flowing water of the river. This generation requires governor and excitation control unit to control and sustain the power generation when subjected to any changes of load behavior. More advanced control strategy is critically expected when considering the recent interest in distribution system to perform islanding operation of DG. Many of the literature have clearly highlighted this issue, but only a few have discussed on the islanding operation of small hydro generation. This paper therefore reviews this topic and relates the discussion with the controller designed for other type of turbines interfaced with synchronous generator. To strengthen the knowledge on islanding operation, the background of islanding is also presented in this paper. [Copyright &y& Elsevier]

Abstract: Fault location for distribution networks with multiple laterals would requires additional information such as from fault indicators and protective devices. As SCADA systems to provide such information are limited in 11kV or lower voltage distribution networks, effective fault location method that only use information from a measurement at primary substation is needed. This paper presents the application of calculated non-linear voltage sag profiles and voltage sag measurement at primary substation to locate a fault in distribution networks. The proposed method firstly identifies the faulted section. From the indentified section, fault distance is calculated. The method has been tested under different fault scenarios that include various fault resistance, loading variation and data measurement errors. The results indicate the possibility of using this method to support automatic fault management system. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of reusability aspects in producing load-flow analysis software application. The reusability in the algorithm and codes are obtained by applying matrix partitioning approach for Newton method and sequential approach for Fast Decoupled method. The software is built by using component-based development and object oriented programming methodologies. These methodologies made the developed load-flow analysis software flexible for future enhancement. Adding and changing codes and algorithm of the software can be done without affecting much the existing codes and algorithms. By maximizing the reusability aspects, the cost and time of software maintenance for updating can be reduced. [ABSTRACT FROM AUTHOR]

Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV power was predicted at an hour ahead on yearly basis for three different PV plants based on polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over a four-year period. Wind speed, module temperature, ambiance, and solar irradiation were among the input characteristics taken into account. Each PV plant power output was the output parameter. A deep learning method (RNN-LSTM) was developed and evaluated against existing techniques to forecast the PV output power of the selected PV plant. The proposed technique was compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering and FCM) and machine learning (ANN, SVR, SVR (PCA)) methods. Furthermore, different LSTM structures were also investigated, with recurrent neural networks (RNN) based on 2019 data to determine the best structure. The following parameters of prediction accuracy measure were considered: RMSE, MSE, MAE, correlation (r) and determination (R2) coefficients. In comparison to all other approaches, RNN-LSTM had higher prediction accuracy on the basis of minimum (RMSE and MSE) and maximum (r and R2). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m2, 19.78 W/m2 and 39.2 W/m2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants. [ABSTRACT FROM AUTHOR]

A microgrid is a developed form of a distribution system, which is integrated with a set of different types of distributed generation (DG) to supply local demand. In spite of that microgrids have many advantages as they increase reliability, raise efficiency, decrease feeder losses and voltage sag correction. However, there are many technical challenges faced, one of them is the protection of microgrid. Conventional protections have been made for radial distribution systems configuration. Where supplying source has one direction and the power flow is defined. The DG penetration converts the distribution network to a multi sources system causes a bidirectional power flow. Also, the most of DG uses a DC to AC converter which limit the fault current level. Therefore, a suitable protection scheme for microgrid ought to be designed to protect a microgrid from any disturbances may occur for both modes of operation grid-connected and islanded. The purpose of this paper is to summarize the challenges and problems facing microgrid protection. As well as the most strategies to date are presented with a discussion of their basic principles of operation to solve these problems. Finally, some conclusions and suggestions for microgrid protection in the future are presented. [ABSTRACT FROM AUTHOR]

• Analysis of the lines shielding failure and back flashover performance. • Investigating the impact of each flashover parameter by multivariate analysis. • Simulated fault waveforms exhibit similar pattern with recorded field waveforms. • The average peak voltage difference between the simulated and actual data is 6 %. • Obtained waveforms are useful for designing lightning fault classification system. Lightning overvoltage causes outages of transmission lines (TLs) in tropical countries. Identifying and analyzing the key factors responsible for tripping the lines can improve the performance of the lines. In this work, the flashover patterns due to direct lightning strikes on 275 kV double circuit TLs and towers in Malaysia was evaluated with Electromagnetic Transient Program (EMTP-RV). Three parameters that include lightning current magnitude, power frequency angle and tower footing resistance were analyzed on the line performance during direct lightning strikes. The simulated results found that two key parameters, lightning current at 270 kA and footing resistance at 50 Ω, reveal the highest impact on the insulator flashover. This finding was also verified from multivariate analysis results which is rarely found in the previous works in this field. As a key contribution in this work and in validation to the model, the simulated fault waveform patterns and voltage magnitudes were compared with the actual site data obtained from six different locations in Peninsular Malaysia. The analyzed results show a similar waveform pattern with an overall average voltage magnitude difference of 6.0 % before and after fault. The model is useful to electric utilities for analyzing transient performance of TLs. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary: Voltage source converters are important elements of grid‐connected microgrid, as these integrate distribution generators of microgrid with utility grid. Often due to lack of efficient and flexible control, these converters do not perform as per expectations. Many control methods based on dq current control theory had been developed for grid‐connected microgrids inverters to control power flow between microgrid and grid. However, all these controllers used phase locked loop systems for grid synchronization purposes, which possess slow dynamics and transient response. To overcome these issues, in this article, an improved real and reactive power control method for grid‐connected hybrid microgrid's bidirectional voltage source converter is proposed which is based on the dq current control theory without using phase locked loop system and Park transformation. The proposed power controller has possessed better dynamic performance compared with the conventional power controllers consisted of phase locked loop system. In addition, due to the elimination of phase locked loop system and park transformation not only the computational burden is reduced with the implementation of the proposed power controller, but also steady state performance is enhanced. The control hardware‐in‐loop real time simulation is carried out using real time digital simulator to validate the performance of the proposed power controller during transient state, steady state and power transfer mode by modeling a grid‐connected hybrid microgrid. From the simulation results, it has been observed that the settling time has improved to 0.08 second compared to 0.15 second of conventional controller, power ripples are significantly reduced, and total harmonic distortion of converter output current obtained is 2.226%. [ABSTRACT FROM AUTHOR]

An exponential increase in diverse load demand in the last decade has influenced the integration of more power plants into the power system. This increases the fault current due to the bidirectional flow of current, resulting in unwanted tripping of the relays if not properly coordinated. Therefore, it is imperative to ensure the installation of relays in the grid being able to sense the fault current from any direction (i.e. upstream or downstream). This can be accomplished by introducing an optimal directional overcurrent relay (DOCR) coordination scheme into the system. This paper presents an in-depth review of the applications of various optimization techniques for optimal coordination of directional overcurrent relays (DOCRs) in integrated power networks. The review highlights the advantages and limitations of techniques implemented to mitigate the DOCR coordination issues. Furthermore, potential research directions for optimal DOCR coordination are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Integration between supplies for stationary power and vehicles is potentially useful for increasing the efficiency and the reliability of energy generation systems. Solid oxide fuel cell is one matured technology, which is suitable for a polygeneration system and provides an integration of supply for stationary power and vehicles. However, a combination of solid oxide fuel cell with photovoltaic thermal and thermoelectric generation increases the complexity of a polygeneration system. The system needs a management strategy for dispatching the energies produced. Therefore, in this work, a fuzzy energy management strategy was applied for this polygeneration system by considering two different configurations: an off-grid system with electric vehicle supply and an on-grid system with hydrogen vehicle supply. A two-stage fuzzy energy management strategy considering optimization and management of multi-parameters of the polygeneration components was considered. The evaluation of the optimum fuzzy was analyzed based on energy, economic, and environmental criteria. From the results obtained, the optimal strategy increased the reliability, energy, and system cost savings by 22.05%, 22.4%, and 32.58%, respectively. Moreover, the optimum management reduced the power loss of the polygeneration system by about 48.82%, which was achieved by the configuration with electric vehicles supply and off-grid connection. [ABSTRACT FROM AUTHOR]

Common power system restoration planning strategy is based on a 'build up' approach, where a blackout system is sectionalized among several islands for parallel restoration prior to resynchronization. In order to speed up the resynchronization of the islands, each island must have similar energizing times. However, there is a huge number of possible combinations of islands that can be formed. Thus, this paper proposes a method to determine optimal islands that have similar energizing times. The method involves identifying transmission lines that should not be connected to form the islands. The proposed method is based on the combination of heuristic and discrete optimization methods. The heuristic technique is proposed to find initial solution that is close to the optimal solution. This solution will guide the optimization technique, which is the discrete Artificial Bee Colony optimization method, to find the optimum solution. The proposed method also considers restoration constraints including black start generator availability, load-generation balance, and the maintenance of acceptable voltage magnitude within each island. The proposed method is validated via simulation using IEEE 39, 118-bus and 89-bus European systems. The advantage of the proposed method in terms of restoration time is demonstrated through a comparison with other literature. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is commonly used to identify the fault type in power transformers. However, the available DGA methods have certain limitations because every method depends on the concentration of the dissolved gases. Therefore, in this work, hybrid feature selection-artificial intelligence-gravitational search algorithm (GSA) techniques were proposed to determine the fault type of power transformers based on DGA data. The artificial intelligence (AI) methods applied include support vector machine and artificial neural network. Both AI methods were optimised by GSA to enhance the accuracy of the results. Feature selections using stepwise regression and robust regression were applied to utilise only significant gases. The accuracy of the results was tested with various ratios of testing and training data. Comparison of the results using the proposed method with other optimisation methods and the previous works was performed to validate the performance of the proposed technique. It was observed that the proposed hybrid feature selection-AI-GSA technique yields reasonable accuracy although fewer types of dissolved gases were used. Therefore, the proposed method can be recommended for the application of automated power transformer fault type detection based on DGA data in practice. [ABSTRACT FROM AUTHOR]