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Distributed generation (DG) has rapidly increased due to many technical, environmental and economical benefits. One of the DG application challenges is to find a proper area to incorporate the DG associated to a particular location. In this study, a central photovoltaic distributed generation (PVDG) topology is proposed to distribute the optimal sizes to the optimal locations. Uncertainties of load demand and renewable power generation are also taken into consideration of the optimisation problem. This study determines the deterministic and probabilistic penetration limits based on the distribution network topologies, considering the PVDG significant impact on active power losses reduction and voltage profiles improvement. The effectiveness of the proposed topology was validated on 33- and 69-bus distribution networks adopting Monte Carlo simulation method, Newton-Raphson load flow method and biogeography based optimisation. From the results, the voltage profiles, active power loss reduction, DG capacity required and penetration limit have shown better performances on the central PVDG topology over the bus dedicated PVDG topology.

In this study, the effect of sugar palm nanocrystalline cellulose (SPNCC) loading (0.00–0.10 wt %) on the electrical resistance, resistivity, and conductivity of SPS/SPNCC (SPS – sugar palm starch) nanocomposite films were evaluated. The experiments were conducted using the four-probe method and Ohm’s law, resistivity and conductivity equations were utilized to obtain the electrical properties. The results revealed that the resistivity values of SPS/SPNCC films were found to be in the range of 3.1 · 102 to 1.5 · 104 (Ω · cm).

With the modernization of power grids, the network optimal utilization is essential to ensure that voltage profile at each bus is maintained within an acceptable range, voltage stability of the system is enhanced, power losses in lines are minimized, reliability and security of system are improved and etc. These can be achieved by introducing reactive power compensation devices such as Flexible Alternating Current Transmission System (FACTS) devices, Custom Power (CP) devices, synchronous condenser, capacitor bank and etc in distribution or transmission networks. Optimal location and sizing of the reactive power compensation devices are significantly important to ensure sufficient investment onto this device. Recently, most of conducted studies had focused on the techniques for determining the optimal location and sizing of various reactive power compensation devices in the power system using various indices proposed in the literature to access the power loss, voltage stability, voltage profile and line loadability. However, no review paper had discussed on the application of the existing indices adopted in the available techniques for solving the optimal location and sizing problems for all types of reactive power compensation devices. In this paper, current literature survey on optimal location and sizing of reactive power compensations had been discussed which includes analytical, conventional, metaheuristic and hybrid based approaches. The main objectives are to reduce power losses, to mitigate voltage deviations, to increase voltage stability and to improve reliability and security of the system.

Islanding detection needs are becoming a pivotal constituent of the power system, since the penetration of distributed generators in the utility power system is continually increasing. Accurate threshold setting is an integral part of the island detection scheme since an inappropriate threshold might cause a hazardous situation. This study looked at the islanding conditions as well as two transient faults, such as a single line to ground fault and a three-phase balance fault, to assess the event distinguishing ability of the proposed method. Therefore, the goal of this research was to determine the threshold of the island if the distributed generator (DG) capacity is greater than the connected feeder load, which is the over-frequency island condition, and if the DG capacity is less than the connected feeder load, which is the under-frequency island condition. The significance of this research work is to propose a new island detection threshold setting method using the slip angle and acceleration angle that comes from phasor measurement unit (PMU) voltage angle data. The proposed threshold setting method was simulated in the PowerWorld simulator on a modified IEEE 30 bus system equipped with DG. There are three different interconnection scenarios in the test system and the performance of the proposed method shows that getting the island threshold for all the scenarios requires a single time step or 20 mile seconds after incepting an island into the network. In addition, it can distinguish between the real islanding threshold and the transient faults threshold.

Rigorous protection of wind power plants is a critical aspect of the electrical power protection engineering. A proper protection scheme must be planned thoroughly while designing the wind plants to provide safeguarding for the power components in case of fault occurrence. One of the conventional protection apparatus is overcurrent relay (OCR), which is responsible for protecting power systems from impending faults. However, the operation time of OCRs is relatively long and accurate coordination between these relays is convoluted. Moreover, when a fault occurs in wind farm-based power system, several OCRs operate instead of a designated relay to that particular fault location, which could result in unnecessary power loss and disconnection of healthy feeders out of the plant. Therefore, this article proposes a novel genetic algorithm (GA)-based optimization technique for proper coordination of the OCRs in order to provide improved protection of the wind farms. The GA optimization technique has several advantages over other intelligent algorithms, such as high accuracy, fast response, and most importantly, it is capable of achieving optimal solutions considering nonlinear characteristics of OCRs. In this article, the improvement in protection of wind farm is achieved through optimizing the relay settings, reducing their operation time, time setting multiplier of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. The developed algorithm is tested in simulation for a wind farm model under various fault conditions at random buses and the results are compared with the conventional nonlinear optimization method. It is found that the new approach achieves significant improvement in the operation of OCRs for the wind farm and drastically reduces the accumulative operation time of the relays.

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn.

This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers.

One of the challenging issues for a grid-connected embedded generation is to find a suitable technique to detect an islanding problem. The technique must be able to differentiate islanding from other grid disturbances and disconnect distributed generation (DG) rapidly to prevent from safety hazards, power quality issues, equipment damage, as well as voltage and frequency instability. This study proposes a Slantlet transform as a signal processing method to extract the essential features to distinguish islanding from other disturbances. A ridgelet probabilistic neural network (RPNN) is utilized to classify islanding and grid disturbances. A modified differential evolution (MDF) algorithm with a new mutation phase, crossover process, and selection mechanism is proposed to train the RPNN. The results of the proposed technique show its capability and robustness to differentiate between islanding events and other grid disturbances.

In this paper, a new islanding detection technique is proposed for a three-phase grid connected photovoltaic inverter system using the multi-signal analysis method. The proposed strategy is divided into two steps: first step, all possible grid faults, switching transients and islanding events are simulated and the essential detection parameters are measured. By means of the Slantlet Transform theory, the energy, mean value, minimum, maximum, range, standard deviation and log energy entropy at any decomposition level of Slantlet Transform for parameter detection is computed and the best of them are selected as input data of second step. Second step, an advanced machine learning based on Ridgelet Probabilistic Neural Network is utilized to predict islanding and none islanding states. In order to train Ridgelet Probabilistic Neural Network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is proposed. The results depicting the effectiveness of the proposed method are explained and outcomes are drawn.

The need for inexpensive and sustainable electricity has become an exciting adventure due to the recent rise in the local population and the number of visitors visiting the Banana Islands. Banana Islands is a grid-isolated environment with abundant renewable energy, establishing a hybrid renewable energy-based power system may be a viable solution to the high cost of diesel fuel. This paper describes a dual-flow optimization method for electrifying the Banana Islands, a remote island in Sierra Leone. The study weighs the pros and cons of maintaining the current diesel-based power setup versus introducing a hybrid renewable energy system that takes backup component analysis into account. Hybrid Optimization of Multiple Energy Resources (HOMER) software is used in the first optimization to optimally design the various system configurations based on techno-economic and environmental characteristics. A Multi-Attribute Decision-Making (MADM) Model that takes into account in the second optimization, the Combinative Distance-based Assessment System (CODAS) algorithm, and various methods of assigning weights to the attributes is used to rank the best configuration. The results show that the hybrid renewable energy system is a better option for electrifying the Banana Islands than the current stand-alone system. The Analytical Hierarchy Process (AHP) method of weight assignment was found to be superior to the Entropy method. Biogas generator-assisted hybrid configurations outperformed diesel generatorassisted hybrid configurations. With an optimum design of 101 kW PV, 1 wind turbine, 50 kW biogas, 86 batteries, and a 37.8 kW converter, the PV-wind-biogas-battery system is rated as the best configuration. It has a net present cost (NPC) of $487,247, a cost of energy (COE) of $0.211/kWh, and CO_2 emission of 17.5 kg/year. Sensitivity analyses reveal that changes in the rate of inflation and the cost of storage have a significant effect on the overall cost of the configuration., 論文

Photovoltaic (PV) systems encounter substantial losses throughout their lifespan due to the different derating factors of PV modules. Those factors mainly vary according to the geographical location and PV panel characteristics. However, the available literature does not explicitly concentrate on the technical and economic impact of the derating factors within the PV system. Owing to that necessity, this study performs a comprehensive analysis of various PV loss parameters followed by a techno-economic assessment of derating factors using the average value on a grid-connected and optimally tilted PV system located in Hatiya, Bangladesh. Some criteria linked to the derating factors such as PV degradation and ambient temperature are further explored to analyze their impact on the aforementioned power system. Simulation results show that PV power generation would vary around 12% annually, subject to a 10% variation in the derating factor. Again, a 10% difference in the derating factor changes the net present cost (NPC) by around 3% to 4%. The system provides the best technical performance concerning annual PV production, power trade with the grid, and the renewable fraction at a higher value of the derating factor since it represents a lower impact of the loss parameters. Similarly, the financial performance in terms of the NPC, levelized cost of energy (LCOE), and grid power exchange cost is found to be lower when the derating factor value is higher., 論文

The integration of renewable and clean solar energy through photovoltaic (PV) technologies is increasing rapidly all over the world. Well-planned and maximum usage of sunlight elevates the efficiency of PV panels. Due to the different geographical conditions of the earth, solar radiation is not the same for all locations. Optimum tilt angle helps the solar panels tap the highest solar irradiance, consequently, provides maximum PV electricity. In this work, a method of determining optimum tilt angle is presented, which is implemented in MATLAB environment. Eight sites of Okinawa prefecture of Japan are selected to measure the optimum tilt angles, which render the highest incident global solar radiation over the year. Results show that optimum tilt angle ranges from 1 degree to 58 degrees in Okinawa. It is also observed that optimum tilt angle based PV panel orientation obtains a significant increase in solar radiation on its tilted surface.

This work describes a methodology to evaluate a hybrid microgrid's energy resilience comprising a photovoltaic, battery, and diesel generator. This paper aims to figure out the optimized dispatched energy for different interruption scenarios with an economic analysis. It was observed that expanding power flexibility is conceivable by adding diesel generator with the microgrid model. Two different interruption scenarios have been developed by a power cut of 8 h and 72 h considering the major fault event. The result shows that the microgrid survives both outages through optimized energy dispatch, and the microgrid could spare $86,026 for the short time outage and for the prolonged outage, it could save $99,491 in energy over the 20-year project lifetime.

The need to solve the protection compromised currently preventing the smooth coexistence of both conventional fossil generation sources and the renewable green wind farm energy resources (WFER) on the same transmission line during a short circuit fault is the motivation for this study as the solution for meeting the pending future energy sustainability problems. The fast rate of global fossil-fuel reserves depletion, price instability, and climatic impact from the greenhouse gas (GHG) emission levels considered. A novel hybrid Wavelet-Machine Learning (W-ML) special protection scheme with the adoption of extracted 1-cycle wavelet decomposed transient fault signals features in Matlab/Simulink. The result from the supervised learning in the Waikato environment for knowledge analysis (WEKA) software indicated the best performance from Nearest-Neighbours (Lazy.IBK) classifier algorithm with 99.86 % classification for single-line-to-ground (SLG) faults, RMS error value of 0.0322 and instantaneous tripping time. The protection compromise is addressed for the effective future network coexistence.

This paper presents a novel intelligence-based recurrent hopfield neural network (HNN) for solving the non-linear power flow equations. The proffered method is an energy function-based approach formulated using power residuals of the system. The dynamics associated with the neural networks are minimized by intelligence-based technique to determine the unknown parameters such as voltage magnitude (V) and phase angle (δ) of the system. A hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) has been used to minimize the dynamics of HNN and its stability is proved in Lyapunov sense of notion. The effectiveness of the method is tested on IEEE 14-bus system and the results obtained are compared to the conventional newton raphson method. Moreover, the stability indices such as voltage stability load index, line stability index, fast voltage stability index and line stability factor pertaining to the assessment of stability under the contingency case of N-1-1-1 was evaluated using the presented load flow analysis technique to study the stability of the system.

Electrical power utilities nowadays are facing various challenges with emerging technologies that leads to increase in demand on electrical power. Due to the aging grid infrastructure, existing power transmission networks are forced to operate at near critical point which could cause tripping in transmission lines and potentially lead to cascading outages and blackout. During this point, voltage at some buses drop below the lower voltage limit and the system power loss is significantly high. Constructing new power system networks or expanding existing networks is a must to accommodate the impact of ever-increasing demand. However, this consumes a lot of time and money. This can be alleviated by integrating Flexible Alternating Current Transmission System (FACTS) devices in the existing transmission system. In order to ensure investment feasibility, the location of FACTS devices in the network is essential. In this study, several analytical approaches using loss sensitivity factor (LSF), real power performance indicator (PI), voltage profile index (VPI) and power loss index (PLI) is carried out to examine the optimal location of Static Synchronous Compensator (STATCOM) in the power system. The proposed analytical approaches were examined using the IEEE 14 bus and IEEE 30 bus benchmark test systems in DigSILENT PowerFactory 18.

In this study, the design of proportional integral derivative (PID) PSS (PIDPSS) is proposed for damping oscillations in single machine infinite bus (SMIB) power system in MATLAB/SIMULINK. A new metaheuristics method called Farmland Fertility Algorithm (FFA) inspired by nature is proposed to optimize the PIDPSS parameters via minimizing robust time domain performance indices called Integral of Squared Time multiplied by Squared Error (ISTSE) objective function. The robustness of the ISTSE index was tested by comparing it with four common time integral indices, also, the FFA PIDPSS was compare with Conventional PIDPSS (CPIDPSS) and Invasive Weed Optimization (IWO) algorithm for plausible application in. The phasor simulation results shows that the proposed ISTSE, the speed deviation SMIB transient response in terms of rise time, settling time, peak time were all remarkably improved by an amount of 35.16%, 40.57%, 77.1% and 1% respectively by the proposed FFA method compare to the IWO method.

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

This paper proposes an alternative approach to extract transformer's winding parameters of resistance (R), inductance (L), capacitance (C) and conductance (G) based on Finite Element Method (FEM). The capacitance and conductance were computed based on Fast Multiple Method (FMM) and Method of Moment (MoM) through quasi-electrostatics approach. The AC resistances and inductances were computed based on MoM through quasi-magnetostatics approach. Maxwell's equations were used to compute the DC resistances and inductances. Based on the FEM computed parameters, the frequency response of the winding was obtained through the Bode plot function. The simulated frequency response by FEM model was compared with the simulated frequency response based on the Multi-conductor Transmission Line (MTL) model and the measured frequency response of a 33/11 kV, 30 MVA transformer. The statistical indices such as Root Mean Square Error (RMSE) and Absolute Sum of Logarithmic Error (ASLE) were used to analyze the performance of the proposed FEM model. It is found that the simulated frequency response by FEM model is quite close to measured frequency response at low and mid frequency regions as compared to simulated frequency response by MTL model based on RMSE and ASLE analysis.

This paper is present a novel approach for solving the pending under-reach problem encountered by distance relay protection scheme in the 3rd zones protection coverage for a midpoint STATCOM compensated transmission lines. The propose transmission line model is develop in Matlab for analyzed feature extraction using Discrete Wavelet multiresolution analysis approach. Extracted feature from standard deviation and entropy energy contents of SLG transient faults current at location beyond the integrated STATCOM used for machine learning algorithm model building using WEKA software. The Naïve Bayes classifier model perform best with robustness prediction and detection of faults with quick convergence even with less training data. The outperformance of the proposed classifier has been 100 % for the relay algorithm modification for under-reach problem elimination in 3rd zones protection coverage.

This paper looks at an islanded complementary power system in Sierra Leone’s South-eastern region. It presents a method for assessing or evaluating the performance of an existing complementary hybrid energy system (Bo-Kenema power network) in an urban environment, taking seasonal variability into consideration. The proposed method attempts to assess the feasibility of establishing either a new hybrid system without current sources or a hybrid system that includes both existing and new sources. A comprehensive techno-economic-environmental optimization analysis was carried out in which 13 scenarios were evaluated and compared. With a net present cost (NPC) of $50.4 million (M) and a cost of energy (COE) of $0.211/kWh, Case 1 (Solar photovoltaics (PV)/generator 1 (G1)/generator 2 (G2)/generator 3 (G3)/battery/hydro/converter) was found to be the most cost-effective and sustainable scenario. The sensitivity analysis performed on Case 1 (optimum scenario) revealed that the total system cost was heavily reliant on turbine efficiency, PV and battery capital prices. Furthermore, adding two extra high-efficiency turbines (75%) to the optimum scenario eliminated the PV component and reduced the COE and NPC by 38.9% (from $0.211/kWh to $0.129/kWh) and 39.1% (from $50.4M to $30.7M) respectively, while increasing CO2 emissions by 49.9%. This elimination led to a change in the configuration of Case 1 (G1/G2/G3/hydro). The preference of the governing authorities determines whether the previous or later optimal design is to be implemented. By implementing this strategy, the optimum scenario can provide cost-effective and long-term electricity for the selected region.

This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.

In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.

Smart grid system protection can be divided into wide area network, WAN, and substation area protections. The IEC 61850 Ed.1 standard contains models for SV and GOOSE messages that can be used in intra-substation protection. This paper investigates the WAN and substation area protections based on a travelling wave with IEC 61850 that have not been used in a product since 2016. The high sampling frequency for processing the travelling wave may block the communications channel and reduce security. In order to address this problem, an approach is proposed that is based on using real-time travelling wave signal packing with lossless compression and travelling wave feature extraction. The performance of these techniques has been tested using an OPNET modeler, while their validity has been tested through computer simulation. The application of WLAN based on transient travelling wave for real-time protection has also been investigated. The lower sampling, or transformed, data can be used for the applications of transient waves in digital substations. The comparison protection based on travelling wave between the Ethernet and second-generation WLAN results has also been discussed. An IED design is proposed for fast tripping that can be used in WAN protection based on the travelling wave. This work paves the way for other research to start developing fast protection systems based on travelling wave with IEC 61850.

The increasing penetration of alternative energy sources into the integrated energy systems often influences the voltage stability (VS) of the entire system. However, developing a technique that comprehensively analyze the energy flow in this environment is a major challenge. This paper presents a novel heuristic-based recurrent type Hopfield Neural Network (h-HNN) planning tool for VS assessment of power system; towards reducing the computational cost of conventional power flow (PF) method. The proposed approach is a Jacobian-less, energy function-based approach, which was formulated using power residuals of the system. The dynamics of neural networks were governed by the differential equations of energy function, which would be minimized by the heuristic particle swarm optimization-gravitational search algorithm to deduce the unknown parameters of voltage magnitude and phase angle. The proposed technique was coded in MATLAB and its effectiveness was tested on IEEE 14-, 30-, and 57- buses, as well as a 1354-bus test system. The obtained results were compared with well-known PF techniques, and the robustness was demonstrated for ill-conditioned network. A composite severity index was proposed to rank the critical contingency of the energy network. Then, the VS assessment was performed in IEEE 14-bus system under severe contingency conditions and improvement of VS is observed under the penetration of distributed energy sources (DES). During the case of DES placement, (i) the voltage profile of the system is maintained within the acceptable range of 0.95 to 1.05 pu and (ii) the VS of the system evaluated using stability indices are enhanced by an amount of 16.54 % to 88.16 %. The application results indicate that the proposed method is useful for electric energy utilities to assess the state of the system under monitoring process.

In order to successfully launch Electric vehicles (EVs) over the actual world, EVs battery chargers have a crucial role to predict the charging time. This work presents the design process of a universal EV charger, The proposed charger is able of providing a controllable and constant charging voltage for a various EVs, which is composed of three levels of charging, 650 V/100 A DC for bus or lorry, single-phase 120 V/16 A AC for motorcycle, and three-phase 240 V/60 A for saloon car battery charging. The output voltage ranges are 40 V–60 V, 400 V–500 V, or 500 V–600 V. The power system of this work consists of two converters; First one, is for the three-phase pulse-width modulated (PWM) of a bridge rectifier with an output of 850 V DC unregulated voltage, while the second converter is for the three-phase DC–DC converter. To satisfy the voltage control and the isolation between Grid To Vehicle (G2V), a three-phase transformer has been exploited within the DC/DC converter. The primary output circuit achieved the charge levels one and two, while the whole circuit output can charge level three or the DC charge. For efficient and secure battery charging, voltageoriented control (VOC) technique is proposed. The unity power factor for the PWM-based rectifier and SPWM are designed and evaluated using MATLAB/Simulink block sets. The total harmonic distortion (THD) for the input current is less than 5%, while the overall simulation-based efficiency is more than 97%. A prototype and simulation results validate that the proposed EV charger is robust, accurate, and applicable.

This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.

The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed lead to the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High Voltage Transmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration.

This paper presents the improved analysis of reliability for battery storage used in power system. The reliability assessment of this paper includes the evaluation of reliability of the system components, battery module and power electronic components. Battery storage is considered as one of energy storage and energy source that commonly used in power system. The evaluation of the reliability of power systems utilising with the storage batteries is performed by using the Markov chain process. The computation of the reliability is conducted by referring to the generated reliability block begins from power supply system. Every part of the system is evaluated regarding two specific states that are in normal or failure mode. By using the Markov method, the system unavailability and failure frequency can be computed.This paper presents the improved analysis of reliability for battery storage used in power system. The reliability assessment of this paper includes the evaluation of reliability of the system components, battery module and power electronic components. Battery storage is considered as one of energy storage and energy source that commonly used in power system. The evaluation of the reliability of power systems utilising with the storage batteries is performed by using the Markov chain process. The computation of the reliability is conducted by referring to the generated reliability block begins from power supply system. Every part of the system is evaluated regarding two specific states that are in normal or failure mode. By using the Markov method, the system unavailability and failure frequency can be computed.

There are three types of multilevel inverter proposed in the early years, namely neutral point clamped or diode clamp multilevel inverter, flying capacitor and cascaded H-bridge multilevel inverter. However, due its modular structure and replicating ability, the cascaded H-bridge multilevel inverter is much more popular and widely improved and modified into various cascaded multilevel inverter topology versions, particularly in the attempts to reduce the total number of switches in the topology design. One aspect that escapes much of the researchers and inventors attention is the effect of the number of conducting switches along the current loop of the inverter before it reaches the output terminals. In this paper, a comparison has been made on the effect of the number of conducting switches during inverter operation between a 41-level classic H-bridge multilevel inverter and a 41-level inverter topology with only one bidirectional switch. Both circuits are tested using Matlab Simulink platform. The results presented show that for higher load power and increased load current, the cascaded H-bridge inverter output is reduced and distorted.

A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

This paper presents an investigation on the resonant oscillations of an 11 kV layer-type winding transformer under standard and chopped lightning impulse overvoltage conditions based on calculated parameters. The resistances, inductances and capacitances were calculated in order to develop the transformer winding equivalent circuit. The impulse overvoltages were applied to the high voltage (HV) winding and the resonant oscillations were simulated for each of the layers based on different electrostatic shield placement configurations. It is found that the placement of grounded shields between layer 13 and layer 14 results in the highest resonant oscillation and non-linear initial voltage distribution. The oscillation and linear stress distributions are at the lowest for shield placement between the HV and low voltage (LV) windings.

The world energy sector nowadays are moving from fossil fuel energy generation towards renewable energy systems. Hence the rise of battery cells technology and with it the multilevel inverter. Three originally main multilevel inverter categories are cascaded H-bridge inverter, flying capacitor inverter and neutral point clamped inverter. Due to its modular structure and easily configured into multi-sources systems such as photovoltaic array, the cascaded H-bridge inverter is thriving in the renewable energy sector. However the downside of cascaded H-bridge inverter is the need for large number of switches, and this is evident in three phase design. The other problem is the amount of switches that have to be turned on during operation are high, thus resulting in voltage drops accumulations internally, causing losses and increase inefficiency. In this paper, a single battery bank, three phase multilevel inverter general topology with only three conducting ideal switches during operation is proposed. A 41-level version of the topology is built and simulated in Matlab Simulink platform. The result shows that the proposed inverter topology has a minimal output THD level and low internal voltage losses.

Infrastructure development in project management process tend to consume more energy and waste more natural resources where higher emission of carbon dioxide and pollution of the surrounding environment other than. Infrastructure consumed a large amount of large amount of energy and materials, which is comparable to cities in countries like Malaysia. This paper reviewed that the role of construction industry specifically for green building development is significant. Though the field of project management is grabbing importance yet certain related issues like land availability and ignorance of people along with certain technical insufficiencies are to be investigated in countries like Malaysia. Energy saving techniques or methods like cogeneration and trigeneration is highlighted and discussed as it is discovered as a key which worked as a backup source of energy supply to avoid interruption.

Background:Dengue infection is associated with joints pain mimicking disease onset symptom of rheumatoid arthritis (RA). However, there is lack of epidemiological studies on exposure to dengue infection and risk of future RA.Objectives:We investigated the relationship between exposure to dengue infection and risk of developing different subsets of RA, defined by the presence of anti-citrullinated peptide antibody (ACPA) in the multi-ethnic Malaysian population.Methods:Serum samples from 1,235 RA cases (i.e. 516 Malay, 254 Chinese, 405 Indians and 60 others/mixed-ethnicity) and 1,624 epidemiological matched population-based controls (i.e. 1,023 Malay, 208 Chinese, 297 Indians and 96 others/mixed-ethnicity) were assayed for presence of dengue IgG antibody using World Health Organization recommended ELISA kits. Positive results of dengue IgG antibodies indicates previous exposure to dengue infection(s). We performed chi-square and Mann-Whitney U analysis to determine the association of ever-exposed dengue infection with ACPA-positive/ACPA-negative RA and to investigate the antibody frequency and levels among the studied populations.Results:We observed high occurrence of dengue IgG antibody in the overall RA cases (79.7%) and matched controls (77.3%), with no significant differences detected between the ACPA subsets of RA. Ethnicity stratification analysis revealed a decrease risk of developing ACPA-positive RA in the Indian patients with positive dengue IgG antibody (OR=0.59, 95% CI=0.37-0.94, p=0.03), and in particular patients with elevated level of dengue IgG antibody (OR=0.44, 95% CI=0.25-0.78, pFigure 1.Comparison of mean dengue IgG antibody level between ever-exposed dengue infection RA cases, stratified by ACPA status. Comparison of median dengue IgG antibody level between the ever-exposed dengue infection ACPA-positive RA and normal controls in the four ethnic groups. The red line indicates the mean level of dengue IgG antibody levelConclusion:Our findings demonstrated that exposure to dengue infection do not increase the risk of developing future RA in the multi-ethnic Malaysian population. The inverse associations observed in the Indian ethnic group are in line with the other studies investigating exposure to viral infection and risk of RA.References:[1]Sherina et al (2017) Low levels of antibodies against common viruses associate with anti-citrullinated protein antibody-positive rheumatoid arthritis; implications for disease aetiology. Arthritis Research & Therapy 2017, 19:2169[2]Gissel García et. al. (2011) Long-term persistence of clinical symptoms in dengue-infected persons and its association with immunological disorders. International Journal of Infectious Diseases 15 (2011) e38–e43Acknowledgements:The authors would like to thank the Director General of Health, Ministry of Health Malaysia for supporting this study. The authors are also indebted to participants for their kind participation. This study was financially supported by the Ministry of Health, Malaysia (JPP-IMR 17-025) and the short-term research grant by UniKL RCMP (str16037).Disclosure of Interests:None declared

High total cooling load (COLA) relatively during peak and mid-peak as compared to off-peak hour’s period requires more operating equipment of chiller plant (CHIP) to be operated during that period. The more operating equipment working, the more energy consumed is required to provide cooling thermal for COLA. High energy consumption of CHIP operation during peak and mid-peak hour lead high electricity cost due to higher energy charge charged by utility for every per kWh energy consumed by a CHIP operation. Therefore, this study aims to determine potential energy saving of CHIP operation by using optimal charging of ice thermal energy storage (ITES), then provide cooling thermal (COMA) for total COLA through discharging operation. Cooling thermal of ITES was charged based on maximum required COLA during peak and mid-peak hour. In this study, an optimization technique of linear programming was used to charge ITES optimally during off-peak hour. Analysis was conducted to compare the energy consumption of CHIP operation by using ITES discharging operation with the current practice of CHIP operator. This analysis showed that, the potential energy saving based on this strategy by using optimization technique is 27.89%.

Fast protection of distribution substation based on traveling wave is becoming a reality today. The transient traveling wave is mostly used in protection to determine the faulted line and the fault location. The advantages of signal of traveling wave includes immunity to system oscillation, transition resistance, CT saturation, and neutral point operation modes. In this article, a detailed study for analyzing the performance of using WLAN for real time protection based on the transient traveling wave. The high sampling frequency needed for traveling wave causes blockage in communication in the process bus. Now a packing of multi samples with suitable compression techniques in Merging Unit (MU), or using feature extraction with Hilbert–Huang transform with WLAN compliance with IEC 61850 has been tested. Further, this article presents the modeling and simulation of a WLAN communication network for an automation system using the precepts of IEC 61850, which is currently becoming a trend in Substation Automation System (SAS) specification. This article also investigates the impact of impulsive and interface noise on WLAN performance. Finally, the article advocates the wide application of traveling waves in the digital substations based on the IEC 61850 protocol.

This paper presents a double-stator permanent magnet generator (DSPMG) integrated with a novel magnetic gear structure which is proposed to be used as a direct drive generator for low speed applications. Torque transmission is based on three rotors consisting of prime permanent magnet poles on the middle rotor and field permanent magnet poles on the inner and outer rotors, respectively. The proposed machine combines the function of a triple rotor magnetic gear and electrical power generator. The operating principle of the generator is discussed, and its performance characteristics are analyzed using 2-dimensional finite-element method (2D-FEM). Analysis results about its magnetic gear ratio, transmission torque, cogging torque and electrical power performance are reported. The 2-D finite element analysis results verify the proposed generator design.

Integration of Distributed Renewable Generation (DRG) to the future Smart Grid (SG) is one of the important considerations that is highly prioritized in the SG development roadmap by most of the countries including Malaysia. The plausible way of this integration is the enhancement of information and bidirectional communication infrastructure for energy monitoring and controlling facilities. However, urgency of data delivery through maintaining critical time condition is not crucial in these facilities. In this paper, we have surveyed state-of-the-art protocols for different Wireless Sensor Networks (WSNs) with the aim of realizing communication infrastructure for DRG in Malaysia. Based on the analytical results from surveys, data communication for DRG should be efficient, flexible, reliable, cost effective, and secured. To meet this achievement, IEEE802.15.4 supported ZigBee PRO protocol together with sensors and embedded system is shown as Wireless Sensor (WS) for DRG bidirectional network with prospect of attaining data monitoring facilities. The prospect towards utilizing ZigBee PRO protocol can be a cost effective option for full integration of intelligent DRG and small scale Building-Integrated Photovoltaic (BIPV)/Feed-in-Tariff (FiT) under SG roadmap (Phase4: 2016–2017) conducted by Malaysia national utility company, Tenaga Nasional Berhad (TNB). Moreover, we have provided a direction to utilize the effectiveness of ZigBee-WS network with the existing optical communication backbone for data importing from the end DRG site to the TNB control center. A comparative study is carried out among developing countries on recent trends of SG progress which reveals that some common projects like smart metering and DRG integration are on priority.