Samila Mat Zali
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Preferred name
Samila Mat Zali
Official Name
Samila, Mat Zali
Alternative Name
Zali, Samila Mat
Mat Zali, Samila
Zali, S. M.
Mat Zali, S.
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Scopus Author ID
6506906142

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Varying the energisation condition to mitigate sympathetic inrush current

2023-12-01 , Nadhirah N.F. , Hana Abdull Halim , Nurhakimah Mohd Mukhtar , Samila Mat Zali

Transformers are generally easy to access and can contribute significantly to entire power system. When a transformer is turned on for the first time, it produces a magnetising inrush current which acts as a starting current. Energisation of transformer has a substantial impact on inrush current and transformer that are connected in parallel. Sympathetic inrush current is a phenomenon that appears when a transformer is switched-on in network whereas the other transformers that was earlier energised. Besides, when sympathetic inrush phenomena occur, the peak and period fluctuate significantly. In this paper, the transformers will be energised in three different ways and each condition will be explored in depth. The operation time of the transformer’s energisation whether it is energised simultaneously or at different times are tested and analysed in terms of their characteristics. It is performed using power system computer aided design (PSCAD) software, starting with a develop model of the energisation and then generate the outcomes. The results of the simulation demonstrate that energising the transformer in different ways can give different effect on the sympathetic inrush current, as well as the variables that affect it and methods for reducing it

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Analysis of fault detection and classification in photovoltaic arrays using neural network-based methods

2025-06 , Arizadayana Zahalan , Samila Mat Zali , Ernie Che Mid , Noor Fazliana Fadzail

Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.

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Identifiability Evaluation of Crucial Parameters for Grid Connected Photovoltaic Power Plants Design Optimization

2021-01-01 , Tekai Eddine Khalil Zidane , Mohd Rafi Adzman , Mohammad Faridun Naim Tajuddin , Samila Mat Zali , Ali Durusu , Saad Mekhilef , Chun-Lien Su , Yacine Terriche , Joseph M. Guerrero

This paper aims to assess the impact of different key factors on the optimized design and performance of grid connected photovoltaic (PV) power plants, as such key factors can lead to re-design the PV plant and affect its optimum performance. The impact on the optimized design and performance of the PV plant is achieved by considering each factor individually. A comprehensive analysis is conducted on nine factors such as; three objectives are predefined, five recent optimization approaches, three different locations around the world, changes in solar irradiance, ambient temperature, and wind speed levels, variation in the available area, PV module type and inverters size. The performance of the PV plant is evaluated for each factor based on five performance parameters such as; energy yield, sizing ratio, performance ratio, ground cover ratio, and energy losses. The results show that the geographic location, a change in meteorological conditions levels, and an increase or decrease in the available area require the re-design of the PV plant. A change in inverter size and PV module type has a significant impact on the configuration of the PV plant leading to an increase in the cost of energy. The predefined objectives and proposed optimization methods can affect the PV plant design by producing completely different structures. Furthermore, most PV plant performance parameters are significantly changed due to the variation of these factors. The results also show the environmental benefit of the PV plant and the great potential to avoid green-house gas emissions from the atmosphere.

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An integrated of hydrogen fuel cell to distribution network system: Challenging and opportunity for D-STATCOM

2021-11-01 , Khaleel M.M. , Mohd Rafi Adzman , Samila Mat Zali

The electric power industry sector has become increasingly aware of how counterproduc-tive voltage sag affects distribution network systems (DNS). The voltage sag backfires disastrously at the demand load side and affects equipment in DNS. To settle the voltage sag issue, this paper achieved its primary purpose to mitigate the voltage sag based on integrating a hydrogen fuel cell (HFC) with the DNS using a distribution static synchronous compensator (D-STATCOM) system. Besides, this paper discusses the challenges and opportunities of D-STATCOM in DNS. In this paper, using HFC is well-designed, modeled, and simulated to mitigate the voltage sag in DNS with a positive impact on the environment and an immediate response to the issue of the injection of voltage. Furthermore, this modeling and controller are particularly suitable in terms of cost-effectiveness as well as reliability based on the adaptive network fuzzy inference system (ANFIS), fuzzy logic system (FLC), and proportional–integral (P-I). The effectiveness of the MATLAB simulation is confirmed by implementing the system and carrying out a DNS connection, obtaining efficiencies over 94.5% at three-phase fault for values of injection voltage in HFC D-STATCOM using a P-I controller. Moreover, the HFC D-STATCOM using FLC proved capable of supporting the network by 97.00%. The HFC D-STATCOM based ANFIS proved capable of supporting the network by 98.00% in the DNS.

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A Review of Fuel Cell to Distribution Network Interface Using D-FACTS: Technical Challenges and Interconnection Trends

2021-09-01 , Khaleel M.M. , Mohd Rafi Adzman , Samila Mat Zali , Graisa M.M. , Ahmed A.A.

Today, the worldwide public interest in reducing power quality issues and greenhouse gas emissions is a key driver to study fuel cells (FCs) connected to distribution network systems (DNs) based on distributed flexible alternating-current transmission systems (D-FACTS). DNs will need to develop a better performance on Power Quality (PQ) while providing a more efficient energy technology. This study reviewed in-depth the interface of DN to FC systems. By focusing on the FC interface and the associated technical challenges, this review may help reduce the risk of DNs, minimizing the consumption of fossil fuels for power generation, lowering the emission of hazardous gases while dramatically increasing electrical power loads, improving PQ and stability. Besides, the study deliberated aspects of FC power technology with DNs interfacing based on D-FACTS. Specifically, the discussion encompassed the configuration structures of FC power technology and DNs connection based on D-FACTS, technical challenges of DNs, and its trends to determine the diagnosis, integration, and optimization for FC power technology.

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Double sigmoid activation function for fault detection in wind turbine generator using artificial neural network

2025-06 , Noor Fazliana Fadzail , Samila Mat Zali , Ernie Che Mid

The activation function has gained popularity in the research community since it is the most crucial component of the artificial neural network (ANN) algorithm. However, the existing activation function is unable to accurately capture the value of several parameters that are affected by the fault, especially in wind turbines (WT). Therefore, a new activation function is suggested in this paper, which is called the double sigmoid activation function to capture the value of certain parameters that are affected by the fault. The fault detection in WT with a doubly fed induction generator (DFIG) is the basis for the ANN algorithm model that is presented in this study. The ANN model was developed in different activation functions, namely linear and double sigmoid activation functions to evaluate the effectiveness of the proposed activation function. The findings indicate that the model with a double sigmoid activation function has greater accuracy than the model with a linear activation function. Moreover, the double sigmoid activation function provides an accuracy of more than 82% in the ANN algorithm. In conclusion, the simulated response demonstrates that the proposed double sigmoid activation function in the ANN model can effectively be applied in fault detection for DFIG based WT model.

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Stator winding fault detection of induction generator based wind turbine using ANN

2020-07 , Noor Fazliana Fadzail , Samila Mat Zali , M. A. Khairudin , Noor Hasnizam Hanafi

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

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Optimal design of photovoltaic power plant using hybrid optimisation: A case of South Algeria

2020-06-01 , Zidane Tekai Eddine Khalil , Mohd Rafi Adzman , Mohammad Faridun Naim Tajuddin , Samila Mat Zali , Durusu, Ali , Mekhilef, Saad

Considering the recent drop (up to 86%) in photovoltaic (PV) module prices from 2010 to 2017, many countries have shown interest in investing in PV plants to meet their energy demand. In this study, a detailed design methodology is presented to achieve high benefits with low installation, maintenance and operation costs of PV plants. This procedure includes in detail the semi-hourly average time meteorological data from the location to maximise the accuracy and detailed characteristics of different PV modules and inverters. The minimum levelised cost of energy (LCOE) and maximum annual energy are the objective functions in this proposed procedure, whereas the design variables are the number of series and parallel PV modules, the number of PV module lines per row, tilt angle and orientation, inter-row space, PV module type, and inverter structure. The design problem was solved using a recent hybrid algorithm, namely, the grey wolf optimiser-sine cosine algorithm. The high performance for LCOE-based design optimisation in economic terms with lower installation, maintenance and operation costs than that resulting from the use of maximum annual energy objective function by 12%. Moreover, sensitivity analysis showed that the PV plant performance can be improved by decreasing the PV module annual reduction coefficient.

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PV array and inverter optimum sizing for grid-connected photovoltaic power plants using optimization design

2021-06-11 , Zidane Tekai Eddine Khalil , Samila Mat Zali , Mohd Rafi Adzman , Mohammad Faridun Naim Tajuddin , Durusu A.

This paper aims to select the optimum inverter size for large-scale PV power plants grid-connected based on the optimum combination between PV array and inverter, among several possible combinations. Inverters used in this proposed methodology have high-efficiency conversion in the range of 98.5% which is largely used in real large-scale PV power plants to increase the financial benefits by injecting maximum energy into the grid. To investigate the PV array-inverter sizing ratio, many PV power plants rated power are considered. The proposed method is based on the modelling of several parts of the PV power plant taking into account many design variables and constraints. The objective function is the levelized cost of energy (LCOE) and the optimization is performed by a multi-verse algorithm. The optimization method results in an optimum inverter size that depends on the PV plant rated capacity by providing an optimum number of inverters required in the installation site. The optimum sizing ratio (Rs) between PV array and inverter were found equal to 0.928, 0.904, and 0.871 for 1 MW, 1.5 MW, and more than 2 MW, respectively, whereas the total power losses reached 8% of the total energy generation during the PV power plant operational lifetime.

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