Papers by AIDIL AZWIN ZAINUL ABIDIN
Takagi-Sugeno Fuzzy Controller for Vehicle to Grid (V2g) Load Frequency Control
Price prediction has now become an important task in the operation of electrical power system. In... more Price prediction has now become an important task in the operation of electrical power system. In short term forecast, electricity price can be predicted for an hour-ahead or day-ahead. An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour. It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than singlesettlement system (real time). Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by three stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.
A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting
Predicting price has now become an important task in the operation of electrical power system. Da... more Predicting price has now become an important task in the operation of electrical power system. Day-ahead prediction provides forecast prices for a day ahead that is useful for daily operation and decision-making. The main challenge for day ahead price forecasting is the accuracy and efficiency. Lower accuracy is produced due to the nature of electricity price that is highly volatile compared to load series. Hence, some researchers have developed complex procedures to produce accurate forecast while considering significant features and optimum parameters. Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for day-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by two stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.
International Journal of Integrated Engineering, Sep 1, 2019
Price prediction is important to market members in deregulated electricity environment to provide... more Price prediction is important to market members in deregulated electricity environment to provide a better maintenance scheduling, developing investment, medium term planning, as well as decision-making. However, forecasting electricity price is a challenging task due to the volatility of price series with unexpected price spikes at any point of series. In addition, medium term forecast is more challenging than short-term price forecast, due to limited
Phase comparison protection for distribution networks with high PV penetration
With the availability and reduced cost of renewable energy sources such as Solar PV, distributed ... more With the availability and reduced cost of renewable energy sources such as Solar PV, distributed generation (DG) becomes an increasingly common practice in harnessing these energy sources. However, a high level of penetration of the energy sources in the distribution network will cause issues such as reverse power flow and the disruptions to the coordination of existing protection system. This paper looks on how reverse power flow will affect the tripping times of overcurrent relays and its effect on the coordination of the protection system. This paper also investigates on the possibility of using the phase comparison protection method to improve the coordination of the protection system in a high PV penetration environment.
Synchronous Generator System Identification Via Dynamic Simulation Using Pss/E: Practical Insights
Study on the Electrical Equivalent Circuit Model of Lightning Fault Cause in Power Grid System
2022 IEEE International Conference on Power Systems Technology (POWERCON)
Turkish Journal of Electrical Engineering and Computer Sciences
To determine the nonlinear autoregressive model with exogenous inputs (NARX) parameter values is ... more To determine the nonlinear autoregressive model with exogenous inputs (NARX) parameter values is not an easy task, even though NARX is reported to successfully identify nonlinear systems. Apart from the activation functions, number of layers, layer size, learning rate, and number of epochs, the number of delays at the input and at the feedback loop need to also be determined. The layer recurrent network (LRN) is seen to have the potential to outperform NARX. However, not many papers have reported on using the LRN to identify nonlinear systems. Therefore, it is the aim of this paper to investigate and analyze the parametric evaluation of the LRN and NARX in identifying 3 different types of nonlinear systems. From the 3 nonlinear systems, the satellite's attitude state space is more complex compared to the sigmoid and polynomial equations. To ensure an unbiased comparison, a general guideline is used to select the parameter values in an organized manner. The LRN and NARX performance is analyzed based on the training and architecture parameters, mean squared errors, and correlation coefficient values. The results show that the LRN outperformed NARX in training quality, needs equal or fewer parameters that need to be determined through heuristic processes and equal or lower number of epochs, and produced a smaller training error compared to NARX, especially when identifying the satellite's attitude. This indicates that the LRN has the capability of identifying a more complex and nonlinear system compared to NARX.
A Review on Induction Motor Speed Control Methods
In power system protection, the need to know the load<br> current together with the fault l... more In power system protection, the need to know the load<br> current together with the fault level detected by a relay is important.<br> This is due to the fact that the relay is required to isolate the<br> equipment being protected if a fault is present and keep the breaker<br> associated with it closed if the current level is lower than the<br> maximum load level. This is not an issue for a radial system. This is<br> not the same however in a looped power system. In a looped power<br> system, the isolation of an equipment system will contribute to a<br> topology change. The change in the power system topology will then<br> influence or change the maximum load current and the fault level<br> detected by each relay. In this paper, a method of data collection for<br> changing topology using matlab and sim-power will be presented.<br> The method will take into consideration the change in topology and<br> collec...
A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting
2016 IEEE International Conference on Power and Energy (PECon), 2016
Predicting price has now become an important task in the operation of electrical power system. Da... more Predicting price has now become an important task in the operation of electrical power system. Day-ahead prediction provides forecast prices for a day ahead that is useful for daily operation and decision-making. The main challenge for day ahead price forecasting is the accuracy and efficiency. Lower accuracy is produced due to the nature of electricity price that is highly volatile compared to load series. Hence, some researchers have developed complex procedures to produce accurate forecast while considering significant features and optimum parameters. Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. So far, no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for day-ahead price prediction. All the models are examined on the Ontario power market; which is reported as among the most volatile market worldwide. A huge number of features are selected by two stages of optimization to avoid from missing any important features. The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.
Journal of Telecommunication, Electronic and Computer Engineering, 2018
Predicting electricity price has now become an important task for planning and maintenance of pow... more Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatil...
Forecasting price has now become an essential task in the operation of electrical power system. P... more Forecasting price has now become an essential task in the operation of electrical power system. Power producers and customers use short term price forecasts to manage and plan for bidding approaches, and hence increase the utility’s profit and energy efficiency. This paper proposes a novel method of Least Square Support Vector Machine (LSSVM) with Bacterial Foraging Optimization Algorithm (BFOA) to predict daily electricity prices in Ontario. The selection of input data and LSSVM’s parameters held by BFOA are proven to improve accuracy as well as efficiency of prediction. A comparative study of the proposed method with previous researches was conducted in term of forecast accuracy. The results indicate that (1) the LSSVM with BFOA outperforms other methods for same test data; (2) the optimization algorithm of BFOA gives better accuracy than other optimization techniques. In fact, the proposed approach is less complex compared to other methods presented in this paper.
International Journal of Integrated Engineering, 2019
Price prediction is important to market members in deregulated electricity environment to provide... more Price prediction is important to market members in deregulated electricity environment to provide a better maintenance scheduling, developing investment, medium term planning, as well as decision-making. However, forecasting electricity price is a challenging task due to the volatility of price series with unexpected price spikes at any point of series. In addition, medium term forecast is more challenging than short-term price forecast, due to limited
Indonesian Journal of Electrical Engineering and Computer Science, 2018
Predicting electricity price has now become an important task in power system operation and plann... more Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.
Agricultural produce Sorting and Grading using Support Vector Machines and Fuzzy Logic
2009 IEEE International Conference on Signal and Image Processing Applications, 2009
Abstract Agriculture sector was accorded a very different treatment in the Ninth Malaysia Plan (... more Abstract Agriculture sector was accorded a very different treatment in the Ninth Malaysia Plan (9MP) where this sector is being revitalized to become a part of the economic growth engine. The Information and Communication Technology (ICT) application is going to be ...
Cross phase polarization algorithm for fault direction determination using zero crossing method to determine phase difference
2009 3rd International Conference on Energy and Environment (ICEE), 2009
... Using zero crossing method to determine phase difference Aidil Azwin Bin Zainul Abidin, Agile... more ... Using zero crossing method to determine phase difference Aidil Azwin Bin Zainul Abidin, Agileswari Ramasamy. ... Tests shall be done in order to see if the method could give a consistent reading in any non-balanced faults(Faults which are not three phase in nature). II. ...
Determination of overcurrent time delay using fuzzy logic relays
2009 Innovative Technologies in Intelligent Systems and Industrial Applications, 2009
... Farrukh Hafiz Nagi Dr. Universiti Tenaga Nasional Km7 Jalan Kajang puchong 43009 Kajang. Izha... more ... Farrukh Hafiz Nagi Dr. Universiti Tenaga Nasional Km7 Jalan Kajang puchong 43009 Kajang. Izham Bin Zainal Abidin, Dr., Universiti Tenaga Nasional Km7 Jalan Kajang puchong 43009 Kajang. ... 12-16. 1992. [5] Hossien Askarian Abyaneh, Majid Al-Dabbagh, Hossien ...
DSP based overcurrent relay using fuzzy bang–bang controller
Microelectronics Reliability, 2011
... DSP based overcurrent relay using fuzzy bangbang controller. Yin Lee Goh a , Corresponding A... more ... DSP based overcurrent relay using fuzzy bangbang controller. Yin Lee Goh a , Corresponding Author Contact Information , E-mail The Corresponding Author , Agileswari K. Ramasamy a , E-mail The Corresponding Author , Farrukh Hafiz Nagi b and Aidil Azwin Zainul Abidin a. ...
DSP based fuzzy and conventional overcurrent relay controller comparisons
Microelectronics Reliability, 2013
ABSTRACT Fuzzy logic control uses linguistic approach to solve complicated rules and ambiguous sy... more ABSTRACT Fuzzy logic control uses linguistic approach to solve complicated rules and ambiguous systems. This control strategy can be used to improve the overall performance of an overcurrent relay for power system protection compared to conventional relay. It is essential for a relay to work efficiently to trip the circuit breakers in the presence of faults and at the same time proficient to coordinate well with the networks to avoid mal-operation. There are two different types of fuzzy logic control strategies proposed for the relay, the Fuzzy Logic Controller (FLC) and Fuzzy Bang-Bang Controller (FBBC). The FBBC is the same as the conventional FLC except that the defuzzification method uses largest of maxima (LOM). Comparisons between the fuzzy controllers and conventional relay are based on IEC 255-3 standard. These relays are implemented on a DSP TMS320F2812 and their performance is evaluated which is based on operation time, DSP’s execution time and grading margin. The results obtained show a significant performance improvement compared to conventional relay.
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Papers by AIDIL AZWIN ZAINUL ABIDIN