Articles
<![CDATA[Investigation of distributed generation units placement and sizing based on voltage stability condition indicator (VSCI) ]]>
<![CDATA[Arvind Raj]]>;
<![CDATA[Nur Fadilah Ab Aziz]]>;
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Nur Ashida Salim]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 3: September 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v10.i3.pp1317-1323
<![CDATA[Voltage instability in power distribution systems can result in voltage collapse throughout the grid. Today, with the advanced of power generation technology from renewable sources, concerns of utility companies are much being focused on the stability of the grid when there is an integration of distributed generation (DG) in the system. This paper presents a study on DG units placement and sizing in a radial distribution network by using a pre-developed index called Voltage Stability Condition Index (VSCI). In this paper, VSCI is used to determine DG placement candidates, while the value of power losses is used to identify the best DG placement. The proposed method is tested on a standard 33-bus radial distribution network and compared with existing Ettehadi and Aman methods. The effectiveness of the method is presented in terms of reduction in power system losses, maximization of system loadability and voltage quality improvement. Results show that VSCI can be utilized as the voltage stability indicator for DG placement in radial distribution power system. The integration of DG is found to improve voltage stability by increasing the system loadability and reducing the power losses of the network.]]>
<![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[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[Ant Lion Optimizer for Solving Unit Commitment Problem in Smart Grid System ]]>
<![CDATA[Izni Nadhirah Sam’on]]>;
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Zuhaina Zakaria]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 1: October 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v8.i1.pp129-136
<![CDATA[This paper proposed the integration of solar energy resources into the conventional unit commitment. The growing concern about the depletion of fossil fuels increased the awareness on the importance of renewable energy resources, as an alternative energy resources in unit commitment operation. However, the present renewable energy resources is intermitted due to unpredicted photovoltaic output. Therefore, Ant Lion Optimizer (ALO) is proposed to solve unit commitment problem in smart grid system with consideration of uncertainties .ALO is inspired by the hunting appliance of ant lions in natural surroundings. A 10-unit system with the constraints, such as power balance, spinning reserve, generation limit, minimum up and down time constraints are considered to prove the effectiveness of the proposed method. The performance of proposed algorithm are compared with the performance of Dynamic Programming (DP). The results show that the integration of solar energy resources in unit commitment scheduling can improve the total operating cost significantly. ]]>
<![CDATA[Prediction of solar irradiance using grey wolf Optimizer-Least-Square support vector machine ]]>
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Nur Fadilah Ab Aziz]]>;
<![CDATA[Hasmaini Mohamad]]>;
<![CDATA[Norfishah Ab Wahab]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v17.i1.pp10-17
<![CDATA[Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer- Least-Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. ]]>
<![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[Fault classification in smart distribution network using support vector machine ]]>
<![CDATA[Ong Wei Chuan]]>;
<![CDATA[Nur Fadilah Ab Aziz]]>;
<![CDATA[Zuhaila Mat Yasin]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Norfishah A. Wahab]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v18.i3.pp1148-1155
<![CDATA[Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. ]]>
