Articles
<![CDATA[The Application of Modified Least Trimmed Squares with Genetic Algorithms Method in Face Recognition ]]>
<![CDATA[Nur Azimah Abdul Rahim]]>;
<![CDATA[Nor Azura Md. Ghani]]>;
<![CDATA[Norazan Mohamed]]>;
<![CDATA[Hishamuddin Hashim]]>;
<![CDATA[Ismail Musirin]]>
<![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.pp154-158
<![CDATA[Severely occluded face images are the main problem in low performance of face recognition algorithms. In this paper, we apply a new algorithm, a modified version of the least trimmed squares (LTS) with a genetic algorithms introduce by [1]. We focused on the application of modified LTS with genetic algorithm method for face image recognition. This algorithm uses genetic algorithms to construct a basic subset rather than selecting the basic subset randomly. The modification in this method lessens the number of trials to obtain the minimum of the LTS objective function. This method was then applied to two benchmark datasets with clean and occluded query images. The performance of this method was measured by recognition rates. The AT&T dataset and Yale Dataset with different image pixel sizes were used to assess the method in performing face recognition. The query images were contaminated with salt and pepper noise. The modified LTS with GAs method is applied in face recognition framework by using the contaminated images as query image in the context of linear regression. By the end of this study, we can determine this either this method can perform well in dealing with occluded images or vice versa.]]>
<![CDATA[Modeling Baseline Energy Using Artificial Neural Network – A Small Dataset Approach ]]>
<![CDATA[Wan Nazirah Wan Md Adnan]]>;
<![CDATA[Nofri Yenita Dahlan]]>;
<![CDATA[Ismail Musirin]]>
<![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.pp662-669
<![CDATA[In this work, baseline energy model development using Artificial Neural Network (ANN) with resampling techniques; Cross Validation (CV) and Bootstrap (BS) are presented. Resampling techniques are used to examine the ability of the ANN model to deal with a small dataset. Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to evaluate the model accuracy. For this analysis, R is calculated for the entire data set (R_all) and separately for training set (R_train), validation set (R_valid) dan testing set (R_test). The closer R to 1, the higher similarities between targeted and predicted output. The total of two different models with several number of neurons are developed and compared. It can be concluded that all models are capable to train the network. Artificial Neural Network with Bootstrap Cross Validation technique (ANN-BSCV) outperforms Artificial Neural Network with Cross Validation technique (ANN-CV). The 3-6-1 ANN-BSCV, with R_train = 0.95668, R_valid = 0.97553, R_test = 0.85726 and R_all = 0.94079 is selected as the baseline energy model to predict energy consumption for Option C IPMVP.]]>
<![CDATA[Chaotic Local Search Based Algorithm for Optimal DGPV Allocation ]]>
<![CDATA[Sharifah Azma Syed Mustaffa]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Mohd. Murtadha Othman]]>;
<![CDATA[Mohamad Khairuzzaman Mohamad Zamani]]>;
<![CDATA[Akhtar Kalam]]>
<![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.pp113-120
<![CDATA[The advent of advanced technology has led to the increase of electricity demand in most countries in the world. This phenomenon has made the power system network operate close to the stability limit. Therefore, the power utilities are looking forward to the solution to increase the loadability of the existing infrastructure. Integration of renewable energy into the grid such as Distributed Generation Photovoltaic (DGPV) can be one of the possible solutions. In this paper, Chaotic Mutation Immune Evolutionary Programming (CMIEP) algorithm is used as the optimization method while the chaotic mapping was employed in the local search for optimal location and sizing of DGPV. The chaotic local search has the capability of finding the best solution by increasing the possibility of exploring the global minima. The proposed technique was applied to the IEEE 30 Bus RTS with variation of load. The simulation results are compared with Evolutionary Programming (EP) and it is found that CMIEP performed better in most of the cases.]]>
<![CDATA[Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation ]]>
<![CDATA[Mohamad Khairuzzaman Mohamad Zamani]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Halim Hassan]]>;
<![CDATA[Sharifah Azwa Shaaya]]>;
<![CDATA[Shahril Irwan Sulaiman]]>;
<![CDATA[Nor Azura Md. Ghani]]>;
<![CDATA[Saiful Izwan Suliman]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v9.i2.pp365-372
<![CDATA[This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.]]>
<![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.]]>
