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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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

Highlights • A novel polygeneration system for homes and vehicles power supplies. • Investigate the best configuration considering grid connections and vehicle types. • Comprehensive analysis based on energy, economic and environmental. • A multi-criteria reliability considering more than the electricity performance. • Introduce a scenario of selling the excess hydrogen to cope a high energy cost. Abstract This study proposes a design of polygeneration system based on solid oxide fuel cell to supply electricity, hot water, cooling, and hydrogen. This system also integrates the stationary supply for electric and hydrogen cars. The polygeneration system is developed based on energy, economic and environment simulation models by taking into account its application for the residential building. Four system configurations were designed based on the grid connection and the vehicle type and subsequently evaluated to determine the performance of the system in regard to the criteria such as efficiency, reliability, primary energy saving, cost saving as well as carbon dioxide reduction. Moreover, a strategy of selling the available hydrogen was also considered to analyze the competitiveness of the proposed system with the conventional separated system. Depending on these criteria, analysis of fuel cell size with respect to the coverage of demands was also conducted. The proposed system achieved primary energy savings, cost saving and emission reduction of about 73%, 50% and 70% respectively. The hydrogen selling strategy has a significant effect in reducing energy cost close to 51% for the configuration with electric vehicle station. [ABSTRACT FROM AUTHOR]

A direct power control (DPC) approach is proposed in this study for a grid-tied photovoltaic (PV) voltage source inverter (VSI) to regulate active and reactive power flow directly in between utility grid and microgrid (MG) by controlling point of common coupling (PCC) voltage. The proposed PCC voltage modulated (PVM) theory-based DPC method (PVMT-DPC) is composed of nonlinear PVM, nonlinear damping, conventional feedforward, and feedback PI controllers. For grid synchronization rather than employing phase-locked-loop (PLL) technology, in this study, direct power calculation of the PCC voltage and current is adopted. Subsequently, at PCC, the computed real and reactive powers are compared with reference powers in order to generate the VSI's control signals using sinusoidal pulse width modulation (SPWM). Because of the absence of the PLL and DPC method adoption, the suggested controller has a faster convergence rate compared to traditional VSI's power controllers. Additionally, it displays nearly zero steady-state power oscillations, which assure that MG's power quality is improved significantly. To validate the proposed PVMT-DPC method's performance, real-time simulations are conducted via real-time digital simulator (RTDS) for a variety of cases. The obtained results demonstrate that using the proposed PVMT-DPC approach, PV VSI can track the reference power within 0.055 s where the output power has low steady-state oscillations and output current has lower total harmonic distortion (THD) of 1.68%. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market." The correction addresses errors in the original publication, specifically changing certain phrases. The correction also includes a revised section on the Adaptive Neuro-Fuzzy Inference System (ANFIS), explaining the differences between the Mamdani-based FIS approach and the Sugeno approach. The authors state that the scientific conclusions of the article are unaffected, and the correction has been approved by the Academic Editor. [Extracted from the article]

This paper proposes a network splitting strategy following critical line outages based on N-1 contingency analysis. Network splitting is the best option for certain critical outages when the tendency of severe cascading failures is very high. Network splitting is executed by splitting the power system network into feasible set of islands. Thus, it is essential to identify the optimal splitting solution (in terms of minimal power flow disruption) that satisfies certain constraints. This paper determines the optimal splitting solution for each of the critical line outage using discrete evolutionary programming (DEP) optimization technique assisted by heuristic initialization approach. Heuristic initialization provides the best initial cutsets which will guide the optimization technique to find the optimal splitting solution. Generation-load balance and transmission line overloading analysis are carried out in each island to ensure the steady state stability is achieved. Load shedding scheme is initiated if the power balance criterion is violated in any island to sustain the generation-load balance. The proposed technique is validated on the IEEE 118 bus system. Results show that the proposed approach produces an optimal splitting solution with lower power flow disruption during network splitting execution. [ABSTRACT FROM AUTHOR]

Islanding detection is important for the protection of any distribution system supported by distributed energy resources. In this work, a number of islanding and non-islanding events are simulated to prioritise 16 different passive parameters on the basis of performance analysis. Among the parameters tested, the rate of change of frequency over reactive power (df/dq) attains the highest accuracy and consequently, a new passive technique is proposed that employs df/dq as discriminative feature. An extended study is carried out to further test the ability of the technique to differentiate the islanding from the non-islanding events of similar signatures, such as load variation, capacitor switching, faults and induction motor starting. The simulation results confirm that the technique is proficient enough to identify islanding from non-islanding events that cause minute disturbances in the power supply network. [ABSTRACT FROM AUTHOR]

Islanding detection is important for the protection of any distribution system supported by distributed energy resources. In this work, a number of islanding and non‐islanding events are simulated to prioritise 16 different passive parameters on the basis of performance analysis. Among the parameters tested, the rate of change of frequency over reactive power (df/dq) attains the highest accuracy and consequently, a new passive technique is proposed that employs df/dq as discriminative feature. An extended study is carried out to further test the ability of the technique to differentiate the islanding from the non‐islanding events of similar signatures, such as load variation, capacitor switching, faults and induction motor starting. The simulation results confirm that the technique is proficient enough to identify islanding from non‐islanding events that cause minute disturbances in the power supply network. [ABSTRACT FROM AUTHOR]

The inclusion of wind energy in a power system network is currently seeing a significant increase. However, this inclusion has resulted in degradation of the inertia response, which in turn seriously affects the stability of the power system's frequency. This problem can be solved by using an active power reserve to stabilize the frequency within an allowable limit in the event of a sudden load increment or the loss of generators. Active power reserves can be utilized via three approaches: (1) de-loading method (pitching or over-speeding) by a variable speed wind turbine (VSWT); (2) stored energy in the capacitors of voltage source converter-high voltage direct current (VSC-HVDC) transmission; and (3) coordination of frequency regulation between the offshore wind farms and the VSC-HVDC transmission. This paper reviews the solutions that can be used to overcome problems related to the frequency stability of grid- integrated offshore wind turbines. It also details the permanent magnet synchronous generator (PMSG) with full-scale back to back (B2B) converters, its corresponding control strategies, and a typical VSC-HVDC system with an associated control system. The control methods, both on the levels of a wind turbine and the VSC-HVDC system that participate in a system's primary frequency control and emulation inertia, are discussed. [ABSTRACT FROM AUTHOR]

The integration of plug-in electric vehicles (PEVs) in residential distribution networks demands a significant amount of electrical load where random and uncoordinated charging affects the quality and performance of the distribution network. Random and uncoordinated charging may increase the peak demand and can increase stress on critical network assets such as line, transformer, and switching devices. Moreover, the charging of PEVs in a low network reduces the voltage of the system below the lower limit. On the other hand, using PEVs as storage in the V2G mode can improve the network condition. Therefore, it is critical to properly manage the charging and discharging operation of PEVs. This paper proposes a multi-objective-based charging and discharging coordination of PEVs with the operation of the capacitor and on-load tap changer (OLTC). With the proposed strategy, the distribution network is operated safely, and charging is ensured for all PEVs connected to the network. The main consideration of this research is to reduce the daily power loss, operational cost, and voltage deviation of the system. The metaheuristic optimization binary firefly algorithm (BFA) has been applied to coordinate PEV charging and discharging as well as capacitor and OLTC operation in the system. A modified IEEE 31 bus 23 kV distribution system is used to implement the proposed strategy. From the obtained results, it is found that the combined PEV charging and discharging coordination with capacitor and OLTC operation reduces the power loss and cost by 34.16% and 12.68%, respectively, with respect to uncoordinated charging and enhances the voltage condition of the network. [ABSTRACT FROM AUTHOR]

In grid-connected inverters, a feedforward controller accurately compensates for a large number of the harmonic components of the injected current. This controller is fast and directly tackles the grid dynamic voltage disturbances without doing any further harmonic analysis. A feedforward controller was introduced to a three-phase grid connection with a wye configuration; however, for the widespread delta connections, such a scheme is less established. Moreover, the nonsinusoidal profile of the grid voltage waveform makes the online conversion of line-to-line values into phase values impossible. In this paper, implicit zero-sequence discontinuous pulse width modulation (IZDPWM) based feedforward control is implemented on the two-level grid-connected inverter. Regardless of the grid topology, the IZDPWM-based controller ensures sinusoidal current injection to the grid under balanced, unbalanced, and distorted conditions. [ABSTRACT FROM PUBLISHER]

• This study uses partial discharge data measured from cable joints as the dataset. • Partial discharge data was used to train 4 convolutional neural networks. • The system was trained with clean data but tested with data overlapped with noise. • Novel data augmentation was proposed to improve performance under noisy condition. • Proposed data augmentation improved classification performance by 15.83–29.05%. Insulation fault diagnosis is essential because faulty insulation will lead to the interruption of power delivery. Partial discharge (PD) pattern classification is widely used in insulation diagnosis to identify incipient fault before catastrophic failure occurs. PD classification systems improved significantly due to the emergence of advanced machine learning techniques such as deep learning algorithms. However, classification systems trained in ideal noise-free condition suffer severe performance degradation when tested on-site where the condition is nonideal due to the presence of noise contamination. In this work, a novel data augmentation technique was implemented where simulated noise was added to create augmented data. The augmented data was used in tandem with ideal condition PD data during the training phase of four well known convolutional neural networks (CNN). For a realistic performance evaluation, the classification system was trained with ideal noise-free data and augmented data but tested with data overlapped with actual measured noise representing nonideal condition via K-fold cross-validation. The results showed that the proposed data augmentation technique improved the performance of CNN under nonideal condition by 15.83%–29.05% without compromising its performance under ideal condition. [ABSTRACT FROM AUTHOR]

This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multi-objective optimizations include cost and active power loss, cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multi-objective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton-Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered. [ABSTRACT FROM AUTHOR]

• An hour ahead forecasting of power output for three different PV systems. • A hybrid deep learning algorithm (SSA-RNN-LSTM) is proposed. • The proposed model is better than RNN-LSTM, GA-RNN-LSTM and PSO-RNN-LSTM. • The model is robust for three different PV systems over four years data. The integration of photovoltaic energy into a grid demands accurate power output forecasting. In this research, an hour ahead prediction of power output is performed on an annual basis over real data period (2016–2019) for three different PV systems based on polycrystalline, monocrystalline, and thin-film technologies. The solar radiation, ambient temperature, module temperature and wind speed are the considered input parameters, while the power output of each PV system is the output parameter. A hybrid deep learning (DL) method (SSA-RNN-LSTM) is proposed for an hour ahead prediction of output power for each PV system. The proposed technique is compared with GA-RNN-LSTM, PSO-RNN-LSTM and RNN-LSTM. The considered forecasting accuracy measurement parameters are RMSE, MSE, MAE and coefficient of determination (R2). The findings elaborate that SSA-RNN-LSTM has shown better forecasting accuracy with the lowest (RMSE and MSE), highest (R2) and highest convergence speed compared to other methods. The proposed model has shown testing (RMSE and MAE) of (19.14% and 21.57%), (15.4% and 10.81%) and (22.9% and 25.2%) lower than RNN-LSTM for polycrystalline, monocrystalline and thin-film PV systems respectively. Furthermore, the proposed model is found more robust in predicting the power output for three different PV systems over four years data period. [ABSTRACT FROM AUTHOR]

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques. [ABSTRACT FROM AUTHOR]

This research aims to evaluate the performance of grid-connected photovoltaic systems based on three PV technologies along with a composite PV system installed at the rooftop of the engineering tower building, University of Malaya, Kuala Lumpur, Malaysia. The grid-connected PV systems are based on poly-crystalline (p-s i), mono-crystalline (m-s i), and thin-film (amorphous silicon (a-s i)) technologies. The performance evaluation is based on the monthly and annual data that is monitored from January 2016 to December 2019. A comprehensive analysis is conducted on eleven performance parameters such as; performance ratio, capacity factor, array yield, final yield, PV array efficiency, PV system efficiency, inverter efficiency, AC energy, array losses, system, and the overall losses. Results show that p-s i based PV system performs better with high annual average (array yield (1309.7 h), array efficiency (12.17%), and system efficiency (11.33%)) accompanied by less degradation in eleven parameters as compared to a-s i and m-s i PV systems. Moreover, the performance ratios of p-s i and a-s i PV systems are found higher than the values reported in some of the existing literature studies, subjected to similar and different climatic conditions. The results also indicate that environmentally the composite PV system has the potential to avoid 28143.7 kg of CO 2 emissions in four years. This research is expected to deliver valuable statistics to individuals and organizations about the real performance of grid-integrated PV systems in Malaysia, including other tropical climate regions in the world. • Analysis is based on four years monitoring data of three different PV systems. • The annual average PR for a-s i and p-s i modules is 91.3% and is higher than PRs in 25 studies with similar and different climates. • P-s i has higher array efficiency, system efficiency and array yield of 12.17%, 11.33% and 1309.7 h. • P-s i have less degradations in all the performance parameters than a-s i and m-s i. • The combined PV system has caused a total reduction of 28143.7 kg CO 2 with an annual average of 7035.9 kg CO 2. [ABSTRACT FROM AUTHOR]

This article proposes a unified power-flow controller (UPFC) to suppress subsynchronous resonance occurred in a hybrid power-generation system connected to an infinite bus through a series-capacitor compensated line. The hybrid power-generation system contains a steam-turbine generator (STG) and an offshore wind farm (OWF) based on doubly-fed induction generator (DFIG). The d–q axis equivalent-circuit model under three-phase balanced loading conditions is derived to establish the complete system model including the STG set, the DFIG-based OWF, the series-capacitor compensated line, the UPFC, etc. A damping controller of the proposed UPFC is designed by using pole-assignment approach based on modal control theory to effectively damp out unstable STG modes of the studied system. Both small-signal stability and transient simulation results of the studied system are systematically performed. The simulation results show that the proposed UPFC joined with the designed damping controller can effectively suppress unstable STG modes of the studied power system. [ABSTRACT FROM AUTHOR]

Detection and fault‐tolerant control (FTC) of faults in the early stage is desirable in improving efficiency. An implementation strategy is proposed for the individual controllers that work collectively in induction motor (IM) drive by interswitching from one form of a control strategy to another. The interswitching occurs between voltage by frequency (V/f) open‐loop control, closed‐loop (V/f) control, sensorless vector control and sensor vector control. Optimal performance capabilities are attained with vector control, whereas V/f is a setup that is affordable but with increased speed. In this study, the faults are open and short circuits winding faults, speed sensor failures and stator winding faults. When the severity of the fault is high, an embedded protection entity interrupts the motor. Daubechies 10 wavelet is used due to its significant vanishing moments compared to the other types of Daubechies as fault index with the stator current of 1 kW IM. A novel enhanced model reference adaptive system is employed for sensorless vector control to assess the motor speed. Both the simulation and experiment (using the F28335 DSP controller) indicate that the framework is effective in detecting the fault, ensuring the robustness of the FTC scheme and proving the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This research proposes a four-level T-type inverter that is suitable for low-power applications. The presented topology outranks other types of inverters in terms of a smaller number of semiconductor devices, absence of passive components such as clamping diodes and flying capacitors, low switching and conduction losses, and high efficiency. The proposed topology is free from voltage deviation and unbalanced voltage occurrences that are present in other multilevel converters having clamping diodes or flying capacitors. The proposed inverter can extend to N levels using unequal dc-link voltage sources for medium-voltage application. The inverter employs the simple fundamental frequency staircase modulation technique. Moreover, this paper presents a current commutation strategy to prevent the occurrences of short circuit and minimizing the number of required switching devices and switching transitions, resulting in improving the efficiency of the inverter. This paper also analyses the theoretical converter losses showing lower switching and conduction losses when compared to existing four-level inverters. The experimental validation of the proposed inverter shows its operating feasibility and a low output voltage THD. [ABSTRACT FROM AUTHOR]

Submarine cables are indispensable for offshore wind farms (OWFs) connected to a power grid. The submarine cable can cause certain degrees of impact on the system performance due to its different lengths, characteristics, electrical parameters, etc. This paper employs the power-system simulation software of Power System Simulator for Engineering (PSS/E) for modeling a future-scheduled OWF, i.e., a large-scale doubly-fed induction generator-based OWF of 200 MW, connected to Jang-Bin distribution substation of Taiwan Power System through five feeders, five circuit breakers, two step-up power transformers, a commercial submarine cable, and an underground cable. This paper simulates and compares the electrical quantities of the studied OWF with different operating capacities under various lengths of three types of commercial submarine cable. The transient surge peak voltages at the common bus of the OWF and the bus of Jang-Bin distribution substation subject to the switching of one of the five circuit breakers of the studied OWF under different lengths of the three types of commercial submarine cable are also compared using the power-system simulation software of ATPDraw in Alternative Transients Program (ATP) version of Electromagnetic Transients Program (EMTP). [ABSTRACT FROM AUTHOR]

This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances. [ABSTRACT FROM AUTHOR]

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization. [ABSTRACT FROM AUTHOR]

An addendum is presented to the article "Transient Faults in Wind Energy Conversion Systems: Analysis, Modelling Methodologies and Remedies" which appeared in the 2018 issue.

This paper presents an in-depth review of classical and state-of-the-art models for analysing the transient stability in wind energy conversion systems. Various transient simulation models for a number of wind turbine generator (WTG) configurations are introduced, under different disturbances. The mitigation is achieved, by manipulating the generator speed and power electronics control, whereas the protection is implemented using conventional, intelligent or digital relays for the safety of sensitive components, in case of transient fault occurrence. The various control systems in WECS are basically employed to transform and regulate the varying frequency, owing to the stochastic nature of wind speed, to the standard 50-Hz or 60-Hz frequency for coupling to an existing electrical utility grid. It has been observed that the control and protection schemes in wind energy systems are concurrently applied. Transient faults in WECSs are a dominant power quality problem especially in the doubly-fed induction generator (DFIG), and often classified as overcurrent or overvoltage transients. These transients are measured using the transient stability index and analysed using the EMTDC/PSCAD software. In addition, the inertia of the rotating masses of wind turbine generators is often characterized by a transient torque, which generates oscillations in power systems. [ABSTRACT FROM AUTHOR]

This paper proposes a unified approach based on modal control theory to design damping controllers of a thyristor-controlled series capacitor (TCSC) for stabilizing the inherent common-mode torsional interactions occurred in a series-capacitor compensated power system. The studied power system includes two nonidentical turbine-generator sets connected to an infinite bus through a common series-capacitor compensated transmission line. The employed TCSC is properly connected in parallel with a part of the series-capacitor bank of the compensated transmission line. The damping controllers of the TCSC employ the speed deviations of both turbine-generator sets as input signals to stabilize unstable torsional modes of the studied system by increasing the damping of these modes. Both frequency-domain approach based on eigenvalue analysis and time-domain scheme based on nonlinear-model simulations under different disturbance conditions are systematically performed. It can be concluded from the simulated results that the employed TCSC joined with the damping controllers designed by using modal control theory can effectively stabilize the common-mode torsional oscillations of the studied series-capacitor compensated system. [ABSTRACT FROM AUTHOR]