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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Mohd Saiful Azimi Mahmud
Universiti Teknologi Malaysia Skudai
Control & Systems Engineering Electrical & Electronics Engineering

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Modelling and Parameters Identification of a Quadrotor Using a Custom Test Rig Mohammad Shafiq Mohammad Ashraf; Mohamad Shukri Zainal Abidin; Mohd Saiful Azimi Mahmud; Muhammad Khairie Idham Abd Rahman; Zakarya Mohammed Nasser Saleh Motea
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 9, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.641 KB) | DOI: 10.11591/ijpeds.v9.i2.pp865-872

Abstract

Quadrotor by nature is a very unstable system and flying it without any feedback control algorithm is deemed impossible. However, before designing the control system, system identification need to be conducted as the accuracy of the control system depends highly on the accuracy of the model. Therefore, this paper explained the design of the quadrotor model with an “X” configuration using the Euler-Newton model. Two types of test rig were designed to measure the thrust coefficient, torque coefficient and throttle command relation parameter needed in the model. Other parameter such as moment of inertia was also being measured by separating the quad rotor model into several sections: Motors, Electronics Speed Controllers (ESC) and Central Hub. All parameters needed in the designed quad rotor model has been successfully identified by measuring the parameters using the custom-built quad rotor and test rigs. The parameters found in this paper will be used in designing the control system for the quadrotor.
Artificial neural network based short term electrical load forecasting Oon Yi Her; Mohd Saiful Azimi Mahmud; Mohamad Shukri Zainal Abidin; Razman Ayop; Salinda Buyamin
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i1.pp586-593

Abstract

In power generation, a 24-hour load profile can vary significantly throughout the day. Therefore, power generation must be adjusted to reduce money loss due to excess generation. This paper presents a short-term load forecasting (STLF) system design using artificial neural network (ANN). As ANN come in many different configurations, this paper analyzes the best ANN configuration via trial-and-error method. To train the ANN, historical load data from 2016 to 2018 of power south energy cooperative (AEC) is used. A simple feedforward ANN type with one hidden layer is implemented, where 48 neurons are used at the input layer. For hidden layer, an arbitrary 50 neurons are chosen and 24 neurons at output layer are used to generate a day ahead 24-hour load profile. To measure the best activation function for SLTF application, four non-linear activation functions will be tested and the best activation function is used to create two and three hidden layer ANN architecture. Finally, the performance of the two new networks will be compared against one hidden layer model. From the obtained result, the best performing model is found as two hidden layers ANN with Tanh as its hidden layer activation function with 8.9% of testing mean absolute percentage error (MAPE).
Co-Authors Mohamad Shukri Zainal Abidin Mohammad Shafiq Mohammad Ashraf Muhammad Khairie Idham Abd Rahman Oon Yi Her Razman Ayop Salinda Buyamin Zakarya Mohammed Nasser Saleh Motea