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Recognition of power quality (PQ) troubles is a critical task in the electrical power system. All previous works solve this problem using two‐step methodology: Feature extraction and classification steps with each step using its own techniques, and this consumes a computation time. The purpose of this study is to utilise a novel artificial intelligence (AI) technique for recognition of PQ events. The proposed AI technique is the long short‐term memory (LSTM) network, which detects and classifies the PQ events in one step. This technique extracts amplitude, disturbance duration and total harmonic distortion from the captured waveform, and the LSTM uses its own rules to classify the PQ events. Many simple PQ events such as interruption, sag, flicker, swell and surge or complex PQ events such as sag plus harmonics and swell plus harmonics are generated using MATLAB programming environment to evaluate the performance of LSTM. Also, real‐time measurements are collected from an industrial substation and are used to ensure the effectiveness of the proposed LSTM technique. A comparison with other techniques is conducted and the results verify the good performance of LSTM in classifying the PQ problems. [ABSTRACT FROM AUTHOR]

Power system utilities need to replace the conventional measuring and monitoring devices to the phasor measurement units (PMUs)-based wide area monitoring system (WAMS) to be able to move from conventional to smart grids. The challenge of this action is the cost of WAMS. Many researchers have tried to deal with this challenge by optimal allocation of PMUs to find the minimum number that fulfils the full observability of the system. The WAMS is not only PMUs but also it comprises communication infrastructures (CI) such as communication links, switches and phasor data concentrators. This paper proposes an optimization algorithm to solve the problem based on two strategies. In the first strategy, the objective function is to find the minimum number of PMUs, i.e. conventional objective function. While in the second strategy, the objective function is the minimum cost of the WAMS, i.e. cost of PMUs and their CI. The objective function in the two strategies is solved under normal operating condition and under contingencies such as failure of PMU and outage of network lines. The proposed artificial electric field algorithm (AEFA) is applied using two strategies on IEEE standard networks: 14-bus, 30-bus, 57-bus and 118-bus. The results show that the number of PMUs using two strategies is not the same and this means that the second strategy should be chosen to solve this problem. The results are also compared with other optimization algorithms, and they depict that the AEFA has the efficiency and superiority to find minimum cost of WAMS and lesser distance path of establishing communication links with higher observability. [ABSTRACT FROM AUTHOR]

Phasor measurement unit (PMU) is among the most important measurement devices in modern power systems. It measures the voltage and current phasors which have both magnitude and phase angle using a common timing reference. These measurements are utilized in real time applications of electrical power systems. The main drawback of PMUs is its high cost so if PMUs are used to collect or send online real data from a system to a data centre or a decision maker, the used number of PMUs should be minimum with full observability of system measurements. This paper proposes a flower pollination algorithm (FPA) to find the optimal number and locations of PMUs in power systems. The optimization objectives of the work are the minimization of PMUs number, the achievement of complete observability of power system states, and the maximization of measurement redundancy. Existence of zero-injection buses in the system decreases the number of PMUs that is required to fulfil the complete observability of system states. Furthermore, additional constraints for remaining system full observable following failure of single PMU and single line outage are also included to increase the system reliability. The performance and efficiency of the proposed FPA is tested on IEEE standard networks such as 14-bus, 30-bus, 57-bus and 118-bus and new England 39-bus network. The obtained simulation results approve the ability of the proposed optimization method to find the optimal allocation of PMUs in different cases with fulfilling complete observability and reliability of electric power systems. The superiority of FPA is also verified by comparing its results of other optimization algorithms. [ABSTRACT FROM AUTHOR]

This article introduces a problem solution of optimal allocation and hourly scheduling of capacitor banks (C-Bs) using the sine cosine optimization algorithm (SCA) to maximize the cost saving and enhance the system reliability. The impact of reliability enhancement reveals in the objective function as a reduction of energy not supplied cost. The optimal location and size of C-Bs are determined based on two strategies; strategy 1, loss sensitivity factor is used to select the candidate buses after that optimal size of C-Bs is determined by SCA while in strategy 2, the optimal location and size of C-Bs are determined by SCA. The proposed algorithm is tested on IEEE 33 and 69-bus radial distribution systems with different scenarios of single and multi-C-Bs installation. Also the daily load variations have been considered to optimally schedule switched C-Bs steps. The simulation results using Matlab programing environment have been verified using the non-parametric Wilcoxon signed rank statistical test. The superiority of proposed algorithm is also ensured by comparing its results with other optimization methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid AC/DC microgrid design based on distribution FACTS devices. The objective of this design is to enhance the microgrid operation performance such as stabilize the buses voltage, reduce the feeder losses, improve the power factor and mitigate the harmonic distortion using the distribution FACTS devices. In the proposed design of microgrid which includes PV, fuel cell, battery, micro gas turbine, wind turbine, and diesel engine within microgrid, Switched Filter Compensator (SFC) is presented as AC-side D-FACTS device and Green Plug Filter Compensator (GPFC) is introduced as DC-side D-FACTS devices. Optimization search method based particle swarm optimization (PSO) is developed to optimize the control gains of D-FACTS controllers to achieve an effective integration of distributed energy resources technologies within MGs and to achieve higher microgrid performance. The results shown are generated from MATLAB simulation of the presented microgrid, where comparisons of with and without FACTS modules are presented in grid connected and islanding operation modes. Results show superiority and improved performance using the presented distribution FACTS. [ABSTRACT FROM AUTHOR]

Microgrids are small scale energy grids that can provide adequate energy supply to cover regional demand by integrating renewable energy generation and storage technologies. This paper develops a high performance dynamic model of a microgrid system comprising a wind turbine, a PV, a fuel cell, a micro gas turbine generator, an energy storage, electric loads with variable load profile and flexible AC transmission system (FACTS) devices. The FACTS devices based on static VAR compensators have been employed as a supervisory controller. Key performance indicators such as microgrid power losses, buses voltage deviations, buses power factor, buses voltage total harmonic distortion and voltage-frequency deviation are used to evaluate the performance of this microgrid in grid-connected and islanding modes. The results obtained from the Matlab/Simulink environment show that the proposed microgrid design with SVC has the ability to meet its special requirements such as bus voltages stabilization, reduction of feeder losses, power factor enhancement and mitigation of total harmonic distortion using SVC in grid-connected and islanding modes. [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper presents a new approach for simulating electrical power flow in building. [•] The approach handles the shortage in existing building simulation programs. [•] The approach represents the building as a hierarchical group of electrical networks. [•] Two case studies and a comparison with EnergyPlus are introduced. [•] Results show the accuracy and the capability of the presented approach. [ABSTRACT FROM AUTHOR]

In this study, a probabilistic planning approach is proposed for optimally allocating different types of distributed generator (DG) (i.e. wind‐based DG, solar DG and non‐renewable DG) into a harmonic polluted distribution system so as to minimise the annual energy losses and reduce the harmonic distortions. The proposed planning methodology takes into consideration the intermittent nature of the renewable resources, load profile and the technical constraints of the system. The objective function is the total system annual power loss. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, total harmonic distortion (THD) limits and maximum penetration limit of DG units. The optimisation process is achieved using the genetic algorithm optimisation method. This proposed approach has been applied to a typical rural distribution system with different scenarios including all possible combinations of distributed energy resources. The simulation results using Matlab programming environment show that significant reductions in the energy losses and THD are achieved for all the proposed scenarios. Also simulation results depict that the proposed method is robust and computationally efficient. [ABSTRACT FROM AUTHOR]

Abstract: Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. This paper presents a new approach for power system disturbances identification and classification. The concept of linear Kalman filter together with discrete wavelet transform (DWT) is used to extract two parameters; the amplitude and the slope from the captured voltage or current waveform. DWT is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The two parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to identify the class to which the waveform belongs. To prove the ability of the new approach for classifying power system disturbances, detailed digital simulation and experimental results involving various types of power quality events are presented. The results depict that the proposed technique has the ability to accurately identify and classify PQ disturbances. [Copyright &y& Elsevier]

Abstract: This paper presents a hybrid technique for characterizing power quality (PQ) disturbances. The hybrid technique is based on Kalman filter for extracting three parameters (amplitude, slope of amplitude, harmonic indication) from the captured distorted waveform. Discrete wavelet transform (DWT) is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The three parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform. This hybrid technique can classify two simultaneous PQ events such as sag and harmonic or swell and harmonic. Several simulation and experimental data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances. [Copyright &y& Elsevier]

Microgrid (MG) is not only undergoing rapid upsurge and an effective combination of distributed generations (DGs)-based renewable energy sources, but it also fulfills a significant function in upgrading grid infrastructure. It is necessary for DGs, during and after any disturbances in the voltage, to stay connected in order to enhance the voltage, most importantly guaranteeing the stability of the power system. In this present context, the behavior of the distributed generations control systems has a significant impact on the MG performance. This paper investigates the effect of different control schemes of the inverter-based DG unit on the stable operation of MG after the occurrence of faults that lead to islanding events. In this paper, an appropriate control strategy is proposed to enhance the fault ride-through of MG. Within the scope of the study, the MG consists of a conventional diesel generator and an inverter-based DG unit. The MG also contains several load types; RLC load as a static load and induction motor as a dynamic load. The MATLAB Simulink is used to model the MG and its inverter control schemes. In order to assure MG stability, the variations of the voltage and frequency are possessed as key references. Results indicate that the control strategies of the inverter have a strong influence on the stability of MG. Moreover, the MG configuration with the proposed FRT resists fault effects for longer durations and this enhances the stability. [ABSTRACT FROM AUTHOR]

The relationship between water and energy is a strong one characterized as having integration and coupling as two important features. While energy is responsible for delivering water to the end-users, it needs energy in order to be generated, and water. In this paper, a thorough review is presented regarding the different relationships between water and energy in terms of (i) the significance of the close relationship between water and energy by means of water/energy generation and consumption. Water consumption, water cooling and heating must be taken into account in order to avoid the obstacles related to future use of water for energy generation; (ii) the measuring and monitoring technologies for the energy-water nexus, focusing attention on the variables that are interrelated in the water and energy sectors. In addition, the consequences of finding several parameters, unknown variables and unclear dependencies in measuring of energy usage in the applications of water usage should also be taken into account. Innovative developments including nanotechnology, biotechnology, and wireless networks, as sensor technologies, may resolve the challenges of sensing; (iii) the different key performance indication tools for assessing and quantifying this nexus by analyzing and categorizing recent case studies of the water energy nexus and applicable evaluation methods; and (iv) the different research dimensions conducted on this nexus. Hopefully, this review will contribute to the development of this nexus adding value to the field while reducing duplication efforts. [ABSTRACT FROM AUTHOR]

Microgrid (MG) gains higher potential and plays a key role in grid infrastructure upgrade. Although MG have many benefits, its performance during fault caused islanding conditions still needs more attentive investigations. In this context, the MG load types and the behavior of the distributed generations (DGs) control system have the significant impact on the stability of MG. This article analyzes the influence of various control techniques of the inverter DG unit on the dynamic performance MG after fault-provoked islanding conditions. To enhance the MG stability and the quality of voltage waveform, an adapted MG configuration equipped with a low-cost switched modulated power filter compensator (MPFC) is proposed. Using MPFC alongside the inverter DG unit gives adequate solution to overcome the shortcomings of the inverter and the flexibility of the interfacing control scheme. The MG structure equipped with MPFC is simulated using Matlab/Simulink software package. A wide perspective on the simulation results indicates that the MG stability is highly vulnerable to the inverter DG control techniques. Further, the MG may lose its stable operation due to some load type characteristics. The proposed MG structure with the MPFC can withstand longer fault durations and give better stability performance especially with induction motor (IM) loads. Moreover, the MG equipped with the MPFC has the ability to keep the voltage total harmonic distortion (THD) within the acceptable limits. [ABSTRACT FROM AUTHOR]

• This paper proposes a methodology to identify the decision of microgrid islanding. • The methodology is two stages: feature extraction and classification stages. • Nine extracted features are calculated using DFT and symmetrical components. • KNN algorithm is used to classify the islanding inception. • Simulation and experimental data are used to assess the methodology with comparisons. This paper proposes a methodology to identify the decision of microgrid islanding. This methodology is two stages: feature extraction and classification stages. In the feature extraction, instantaneous three phase voltage and current measurements are processed using discrete Fourier transform and the symmetrical components of voltage, current and the voltage times current of the second order harmonic are extracted. These nine extracted features are inputs to the second stage that is based on a machine learning algorithm called K-nearest neighbor technique (KNN). In KNN, a small part of the processed data is trained to get the predictor model that is used to the residual data to identify the islanding decision. A MATLAB simulation model is implemented to simulate the intentional and unintentional islanding events and a lab experiment is set up to make these islanding events practically. In addition, the impact of different types of distributed generation are studied to assess their impacts on the performance of the proposed methodology. Simulation and experimental results show that the proposed methodology has the ability to identify the correct islanding decision with the presence of noise with high accuracy and small detection time. In addition, the methodology has the capability to distinguish between islanding and non-islanding. The comparison with other algorithms shows a higher accuracy, dependability, security and a smallest islanding detection time. [Display omitted] [ABSTRACT FROM AUTHOR]