Articles
<![CDATA[Hybrid islanding detection method based on the rate of change of frequency and load impedance ]]>
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Bibi Norasiqin Sheikh Rahimullah]]>;
<![CDATA[Kanendra Naidu]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v10i6.3246
<![CDATA[Interconnection of distributed generation (DG) in distribution system will result in formation of islands in the event of loss of main supply. This scenario is harmful to the power system, hence quick detection is critical to halt the formation of islands. Among the common passive and active detection methods available, the hybrid detection method is identified as the most reliable method. This paper proposes a new hybrid method using the combination of passive and active technique which is the rate of change of frequency (ROCOF) and load impedance, respectively. The passive method works when the value of ROCOF exceeds the threshold value which is set at 0.3Hz/s. The active method works when it detects low value of ROCOF and immediately inject a pre-specified load into the system to increase the ROCOF value up to its threshold value. Simulation study on different case studies is carried out on distribution test system to evaluate the performance of the proposed method. Results show that this method is effective in detecting any events that could result in islanding.]]>
<![CDATA[Implementation of artificial intelligence for prediction performance of solar thermal system ]]>
<![CDATA[Mohd Danish Irfan Mohd Sufian]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Zuhaila Mat Yasin]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 3: September 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v13.i3.pp1751-1760
<![CDATA[A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and to compare which artificial neural network (ANN) algorithms and other artificial intelligence (AI) methods have the most reliable predictor for STS performance. In order to gauge the performance of the STS, this research aims to implement AI for predicting STS performance by comparing the ANN technique with other methods. Three different training algorithms which are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Bayesian regularization (BR) are considered in this research. This research will identify acceptable parameters and the best AI technique to use in predicting the STS performance. Previous research on STS demonstrates that the efficiency of STS has been estimated using different input parameters. The results show that the prediction of the LM training algorithm is the best for STS performance.]]>
<![CDATA[Graphical user interface based model for transmission line performance implementation in power system ]]>
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Nur Fadilah Ab Aziz]]>;
<![CDATA[Nur Azzammudin Rahmat]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i1.pp92-100
<![CDATA[Transmission line is one of the important elements in the process of power transfer from the source of generation to the consumer. In order to analyze the performance of a transmission line, it has to be represented by an equivalent model with suitable circuit parameters at a per phase basis. The line models are used to measure voltages, currents and the amount of power flow depending on the line length. Transmission line performance is determined by the voltage regulation and its efficiency under their normal operating conditions. In this study, a systematic approach was developed in order to assists the lecturers in teaching this important topic to the students despite so many complicated mathematical equations involved in the calculation. With the aid of Graphical User Interface (GUI), the performance of transmission line can be determined and monitored due to the change of line parameters. The results obtained could assist the lecturers in delivering the concept of engineering in a more systematic approach. On top of that, it could also assist the power system utility in planning the transmission line that needed to be installed in the system.]]>
<![CDATA[Optimal Charging Schedule Coordination of Electric Vehicles in Smart Grid ]]>
<![CDATA[Wan Iqmal Faezy Wan Zalnidzam]]>;
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Hazlie Mokhlis]]>;
<![CDATA[Zuhaila Mat Yasin]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v11.i1.pp82-89
<![CDATA[The increasing penetration of electric vehicle (EV) at distribution system is expected in the near future leading to rising demand for power consumption. Large scale uncoordinated charging demand of EVs will eventually threatens the safety operation of the distribution network. Therefore, a charging strategy is needed to reduce the impact of charging. This paper proposes an optimal centralized charging schedule coordination of EV to minimize active power losses while maintaining the voltage profile at the demand side. The performance of the schedule algorithm developed using particle swarm optimization (PSO) technique is evaluated at the IEEE-33 Bus radial distribution system in a set time frame of charging period. Coordinated and uncoordinated charging schedule is then compared in terms of active power losses and voltage profile at different level of EV penetration considering 24 hours of load demand profile. Results show that the proposed coordinated charging schedule is able to achieve minimum total active power losses compared to the uncoordinated charging.]]>
<![CDATA[Effect of SVC installation on loss and voltage in power system congestion management ]]>
<![CDATA[Nur Zahirah Mohd Ali]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Hasmaini Mohamad]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v14.i1.pp428-435
<![CDATA[In this paper, a new hybrid optimization technique is proposed namely Adaptive Embedded Clonal Evolutionary Programming (AECEP). This idea comes from the combination part of the clone in an Artificial Immune System (AIS) and then combined with Evolutionary Programming (EP). This technique was implemented to determine the optimal sizing of Flexible AC Transmission Systems (FACTS) devices. This study focused on the ability of Static Var Compensator (SVC) is used for the optimal operation of the power system as well as in reducing congestion in power system. In order to determine the location of SVC, the previous study has been done using pre-developed voltage stability index, Fast Voltage Stability Index (FVSI). Congested lines or buses will be identified based on the highest FVSI value for the purpose of SVC placement. The optimizations were conducted for the SVC sizing under single contingency, where SVC was modeled in steady state analysis. The objective function of this study is to minimize the power loss and improve the voltage profile along with the reduction of congestion with the SVC installation in the system. Validation on the IEEE 30 Bus RTS and IEEE 118 Bus RTS revealed that the proposed technique managed to reduce congestion in power system.]]>
<![CDATA[Impact of Distributed Generation on the Fault Current in Power Distribution System ]]>
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Izni Nadhirah Sam’ón]]>;
<![CDATA[Norziana Aminudin]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Hasmaini Mohamad]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v6.i2.pp357-367
<![CDATA[Monitoring fault current is very important in power system protection. Therefore, the impact of installing Distributed Generation (DG) on the fault current is investigated in this paper. Three types of fault currents which are single line-to-ground, double line-to-ground and three phase fault are analyzed at various fault locations. The optimal location of DG was identified heuristically using power system simulation program for planning, design and analysis of distribution system (PSS/Adept). The simulation was conducted by observing the power losses of the test system by installing DG at each load buses. Bus with minimum power loss was chosen as the optimal location of DG. In order to study the impact of DG to the fault current, various locations and sizes of DG were also selected. The simulations were conducted on IEEE 33-bus distribution test system and IEEE 69-bus distribution test system. The results showed that the impact of DG to the fault current is significant especially when fault occurs at busses near to DG location.]]>
<![CDATA[Power system restoration in distribution network using minimum spanning tree - Kruskal’s algorithm ]]>
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Wan Iqmal Faezy Wan Zalnidzham]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Shahrani Shahbudin]]>;
<![CDATA[Zuhaila Mat Yasin]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i1.pp1-8
<![CDATA[Events such as natural and manmade interference, line, transformer and feeder outages that occur in electric power distribution system negatively impact the continuity of power supply, thus affecting the power demand supply as well as customer’s satisfaction. In that cases, the restoration of power needs to be carried out immediately in order to guarantee the system’s reliability. The power flow path identification is considered as a difficult task especially in a huge system due to large number of switches. Kruskal’s algorithm is presented in this paper to find the minimum power flow path in a power distribution network. The comparison of performance between presented Kruskal’s algorithm and Binary Particle Swarm Optimization (BPSO) was made in solving a problem regarding network reconfiguration. The proposed load restoration approach is tested on IEEE 33-bus single feeder radial distribution system using MATLAB software. From the results, it is found that the presented Kruskal’s algorithm was able to search for the minimal power flow path that contribute to loss reduction for power restoration after the occurrence of fault.]]>
<![CDATA[Implementation of graphical user interface to observe and examine the frequency and rotor angle stability of a power system due to small disturbances ]]>
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Mohamad Salehan Ab. Samah]]>;
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Nur Fadilah Ab Aziz]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v17.i2.pp606-614
<![CDATA[The aim of this research is to anticipate the stability status of a power system when the system is exposed to a change in frequency and rotor angle due to small disturbances. The proposed study was implemented on the IEEE Reliability Test System 1979 (IEEE RTS-79) which contains 24 buses, 38 transmission lines and 32 generators. Steady state stability limit of a system refers to the maximum amount of power that is permissible through the system without loss of its steady state stability. This research proposes the development of a Graphical User Interface (GUI) to observe the frequency and rotor angle stability due to the effect of small disturbances using the One Machine Infinite Bus (OMIB) technique. This proposed technique could ease the power system utility especially the power system operation to observe and examine the system frequency and rotor angle stability due to small disturbances. The findings from this research has proven that the proposed technique to observe the frequency and rotor angle stability due to small disturbances has successfully been developed using a GUI.]]>
<![CDATA[IMLANNs for Congestion Management in Power System ]]>
<![CDATA[Nur Zahirah Mohd Ali]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Saiful Izwan Suliman]]>;
<![CDATA[Hadi Suyono]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v11.i2.pp630-636
<![CDATA[In this paper, Integrated Multi-Layer Artificial Neural Networks (IMLANNs) model has been developed for congested line prediction in a power system. The master characteristic of an ANN is the superiority to achieve complicated input-output mappings through a learning procedure, without exhaustive programming efforts. The IMLANNs model was developed to predict the congested lines in a power system. Before the IMLANNs model is developed, a case study was selected to receive an early result in power system load current during normal condition and contingency based on heavily loaded term. In order to optimize the architecture of the neural network and minimize the computational effort, but those state variables with major impact on the power system are selected as inputs. A pre-developed index, namely Fast Voltage Stability Index (FVSI) is employed as a benchmark to identify the locations declared as congested lines. This indicator was produced which aims for an analytic thinking, sustainable power system when an excessive load was imposed on the power system network. In addition, voltage collapse can be identified when the index is approaching 1.000 or unity. The value of FVSI is chosen as the targeted output in the IMLANNs model. The strength of the proposed IMLANNs model has been validated on the IEEE 30- Bus RTS. Results obtained from the study demonstrated that the proposed IMLANNs is feasible for congested line prediction, which in turns beneficial to power system operators in the planning unit of a utility.]]>
