Articles
<![CDATA[Chaotic Mutation Immune Evolutionary Programming for Voltage Security with the Presence of DGPV ]]>
<![CDATA[Sharifah Azma Syed Mustaffa]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Mohd. Murthada Othman]]>;
<![CDATA[Mohd. Helmi Mansor]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 3: June 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v6.i3.pp721-729
<![CDATA[Due to environmental concern and certain constraint on building a new power plant, renewable energy particularly distributed generation photovoltaic (DGPV) has becomes one of the promising sources to cater the increasing energy demand of the power system. Furthermore, with appropriate location and sizing, the integration of DGPV to the grid will enhance the voltage stability and reduce the system losses. Hence, this paper proposed a new algorithm for DGPV optimal location and sizing of a transmission system based on minimization of Fast Voltage Stability Index (FVSI) with considering the system constraints. Chaotic Mutation Immune Evolutionary Programming (CMIEP) is developed by integrating the piecewise linear chaotic map (PWLCM) in the mutation process in order to increase the convergence rate of the algorithm. The simulation was applied on the IEEE 30 bus system with a variation of loads on Bus 30. The simulation results are also compared with Evolutionary Programming (EP) and Chaotic Evolutionary Programming (CEP) and it is found that CMIEP performed better in most of the cases.]]>
<![CDATA[Optimal Voltage Stability Improvement under Contingencies using Flower Pollination Algorithm and Thyristor Controlled Series Capacitor ]]>
<![CDATA[Zulkiffli Abdul Hamid]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Muhammad Amirul Adli Nan]]>;
<![CDATA[Zulkifli Othman]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v12.i2.pp497-504
<![CDATA[Recent power systems necessitate for maintaining a safe voltage stability as the number of problems such as contingencies and reactive power insufficiency are increasing. In this paper, installation and sizing of Flexible Alternating Current Transmission System (FACTS) devices have been introduced for solving the voltage stability problems under contingencies. The FACTS device to be used is Thyristor Controlled Series Capacitor (TCSC). Besides improving the voltage magnitude at all buses to a desired level, installation of TCSC at proper locations can minimize total transmission losses of the system. To conduct the sizing task, the newly developed Flower Pollination Algorithm (FPA) has been implemented as the engine for optimization. Through experimentation, the results proved that the proposed placement and sizing technique has successfully mitigated the voltage stability problems. In addition, the computation time for FPA’s convergence was tolerable with optimum results.]]>
<![CDATA[Enhanced BFGS Quasi-Newton Backpropagation Models on MCCI Data ]]>
<![CDATA[Nor Azura Md. Ghani]]>;
<![CDATA[Saadi Ahmad Kamaruddin]]>;
<![CDATA[Norazan Mohammed Ramli]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Hishamuddin Hashim]]>
<![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.pp101-106
<![CDATA[Neurocomputing is widely implemented in time series area, however the nearness of exceptions that for the most part happen in information time arrangement might be hurtful to the information organize preparing. This is on the grounds that the capacity to consequently discover any examples without earlier suppositions and loss of all-inclusive statement. In principle, the most well-known preparing calculation for Backpropagation calculations inclines toward lessening ordinary least squares estimator (OLS) or all the more particularly, the mean squared error (MSE). In any case, this calculation is not completely hearty when exceptions exist in preparing information, and it will prompt false estimate future esteem. Along these lines, in this paper, we show another calculation that control calculations firefly on slightest middle squares estimator (FFA-LMedS) for BFGS quasi-newton backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) model to lessen the effect of exceptions in time arrangement information. In the in the mean time, the monthly data of Malaysian Roof Materials cost index from January 1980 to December 2012 (base year 1980=100) with various level of exceptions issue is adjusted in this examination. Toward the finish of this paper, it was found that the upgraded BPNN-NARMA models utilizing FFA-LMedS performed extremely well with RMSE values just about zero errors. It is expected that the finding would help the specialists in Malaysian development activities to handle cost indices data accordingly.]]>
<![CDATA[Detection of fault during power swing in test system interconnected with DG ]]>
<![CDATA[Nor Zulaily Mohamad]]>;
<![CDATA[Ahmad Farid Abidin]]>;
<![CDATA[Ismail Musirin]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v16.i2.pp577-585
<![CDATA[Distance relay is prone to mal-operate during power swing, thus most of modern distance relay design is equipped with power swing blocking scheme to block the operation during power swing and reset the blocking operation whenever a fault occurs during power swing. However, the detection of fault during power swing especially for high resistance fault possess a challenging task, therefore it may cause the unblocking function to vulnerable to operate. This paper presents the development of a detection scheme for detecting fault during power swing in test system interconnected with Distributed Generation (DG). In this study, the detection scheme is proposed based on S-Transform analysis on the distance relay input voltage signal. It is demonstrated that the proposed S-Transform detection based scheme can effectively detect various type of fault during power swing includes high resistance fault, as well as able to operate correctly even with the presence of DG in the test system.]]>
<![CDATA[Multiverse optimisation based technique for solving economic dispatch in power system ]]>
<![CDATA[Muhammad Haziq Suhaimi]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Muzaiyanah Hidayab]]>;
<![CDATA[Shahrizal Jelani]]>;
<![CDATA[Mohd Helmi Mansor]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v20.i1.pp485-491
<![CDATA[Economic dispatch (ED) is one of the many important components in a power system operation. It is designed to calculate the exact amount of power generation needed to ensure a minimum cost of generation. A power system with multiple generators should be running under an economic condition. The operating cost has to be minimised for any feasible load demand. The increase of power demand is getting higher throughout the year. Economic dispatch is used to schedule and control all output of the fossil-fuel or coal-generators to satisfy the system load demand at a minimum cost. This paper presents the multiverse optimisation (MVO) for solving the economic dispatch in a power system. The proposed Multiverse optimisation engine developed in this study is implemented on the IEEE 30-Bus reliability test system (RTS). It has five generators, all of which are denoted as the control variables for the optimisation process. To reveal the superiority of MVO, a similar process was conducted using evolutionary programming (EP). Results from both techniques were compared, and it was revealed that MVO had outperformed EP in terms of reduced cost of generation for the system.]]>
<![CDATA[Modified BPNN via Iterated Least Median Squares, Particle Swarm Optimization and Firefly Algorithm ]]>
<![CDATA[Nor Azura Md. Ghani]]>;
<![CDATA[Saadi bin Ahmad Kamaruddin]]>;
<![CDATA[Norazan Mohamed Ramli]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Hishamuddin Hashim]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 3: December 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v8.i3.pp779-786
<![CDATA[There is doubtlessly manufactured artificial neural system (ANN) is a standout amongst the most acclaimed all-inclusive approximators, and has been executed in numerous fields. This is because of its capacity to naturally take in any example with no earlier suppositions and loss of all inclusive statement. ANNs have contributed fundamentally towards time arrangement expectation field, yet the nearness of exceptions that normally happen in the time arrangement information may dirty the system preparing information. Hypothetically, the most widely recognized calculation to prepare the system is the backpropagation (BP) calculation which depends on the minimization of the common ordinary least squares (OLS) estimator as far as mean squared error (MSE). Be that as it may, this calculation is not absolutely strong within the sight of exceptions and may bring about the bogus forecast of future qualities. Accordingly, in this paper, we actualize another calculation which exploits firefly calculation on the minimal middle of squares (FA-LMedS) estimator for manufactured neural system nonlinear autoregressive (BPNN-NAR) and counterfeit neural system nonlinear autoregressive moving normal (BPNN-NARMA) models to cook the different degrees of remote issue in time arrangement information. In addition, the execution of the proposed powerful estimator with correlation with the first MSE and strong iterative slightest middle squares (ILMedS) and molecule swarm advancement on minimum middle squares (PSO-LMedS) estimators utilizing reenactment information, in light of root mean squared blunder (RMSE) are likewise talked about in this paper. It was found that the robustified backpropagation learning calculation utilizing FA-LMedS beat the first and other powerful estimators of ILMedS and PSO-LMedS. As a conclusion, developmental calculations beat the first MSE mistake capacity in giving hearty preparing of counterfeit neural systems.]]>
<![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[Network intrusion detection system using immune-genetic algorithm (IGA) ]]>
<![CDATA[Hamizan Suhaimi]]>;
<![CDATA[Saiful Izwan Suliman]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Afdallyna Harun]]>;
<![CDATA[Roslina Mohamad]]>;
<![CDATA[Murizah Kassim]]>;
<![CDATA[Shahrani Shahbudin]]>
<![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.pp1059-1065
<![CDATA[Network security is an important aspect in maintaining computer network systems and personal information from being illegally accessed by third parties. The major problem that frequently occurs in computer network systems is the failure in detecting possible network-attacks. Apart from that, the process of recognizing the type of attack that occurs is very crucial as it will determine the elimination process that should take place to counter the intrusion. This paper proposes the application of standard Genetic Algorithm (GA) that combines with immune algorithm process to enhance the computer system’s capability in recognizing possible intrusion occurrence in a computer system. Simulation was conducted numerous times to test the effectiveness of the proposed intrusion detection system by manipulating the parameter values for genetic operators utilized in GA. The effectiveness of the proposed method is shown in the gathered results and the analysis conducted further supports and proves that Immune Genetic Algorithm (IGA) has the capability to predict the occurrence of intrusion in computer network.]]>
<![CDATA[Gravitational Search Algorithm Based Technique for Voltage Stability Improvement ]]>
<![CDATA[Mohamad Khairuzzaman Mohamad Zamani]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Mohamad Sabri Omar]]>;
<![CDATA[Saiful Izwan Suliman]]>;
<![CDATA[Nor Azura Md. Ghani]]>;
<![CDATA[Nur Azwan Mohamed Kamari]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v9.i1.pp123-130
<![CDATA[Voltage instability problem has been known as a significant threat to power system operation since its occurrence can lead to power interruption. This phenomenon can be due to uncontrollable load increment, line and generator outage contingencies or unplanned load curtailment. Optimal reactive power dispatch involving reactive power support can be one of the options for improving voltage stability of a power system, which also requires optimization process. Optimal sizing and location can of reactive power support can avoid the system from experiencing over-compensated or under-compensated phenomena. The presence of optimization techniques has helped solving non-optimal phenomenon, nevertheless some setbacks have also been experienced in terms of inaccuracy and stuck in local optima. This paper presents the application of Gravitational Search Algorithm (GSA) technique in attempt to solve optimal reactive power dispatch problem in terms of reactive power support for voltage stability improvement. Optimization process tested on IEEE 14-bus Reliability Test System (RTS) has revealed its superiority with significant promising results in terms of voltage stability improvement in the test system. ]]>
<![CDATA[Design of a Small Renewable Resource Model based on the Stirling Engine with Alpha and Beta Configurations ]]>
<![CDATA[Faisal Zahari]]>;
<![CDATA[Muhammad Murtadha Othman]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Amirul Asyraf Mohd Kamaruzaman]]>;
<![CDATA[Nur Ashida Salim]]>;
<![CDATA[Bibi Norasiqin Sheikh Rahimullah]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v8.i2.pp360-367
<![CDATA[This paper presents the conceptual design of Stirling engine based Alpha and Beta configurations. The performances of Stirling engine based Beta configuration will be expounded elaborately in the discussion. The Stirling engines are durable in its operation that requires less maintenance cost. The methodology for both configurations consists of thermodynamic formulation of Stirling Cycle, Schmidt theory and few composition of flywheel and Ross-Yoke dimension. Customarily, the Stirling engine based Beta configuration will operate during the occurrence of low and high temperature differences emanating from any type of waste heat energy. A straightforward analysis on the performance of Stirling engine based Beta configuration has been performed corresponding to the temperature variation of cooling agent. The results have shown that the temperature variation of cooling agent has a direct effect on the performances of Stirling engine in terms of its speed, voltage and output power. ]]>
