My research interests include Finite Element Method modelling, condition monitoring of transformers, Renewable Energy, Data Analysis and Mathematical modelling
This Special Issue on “Advanced Technologies of Renewable Energy Sources (RESs)” seeks high-quali... more This Special Issue on “Advanced Technologies of Renewable Energy Sources (RESs)” seeks high-quality works focusing on cutting-edge advancements in renewable energy technologies. Topics include, but are not limited to, the following: - Innovations in solar photovoltaics and thermal systems; - Next-generation wind turbine design and efficiency enhancement; - Biomass and biofuel production technologies; - Hydroelectric power systems and improvements; - Geothermal energy utilization and technological advancements; - Energy storage solutions for intermittent RESs; - Smart grid integration and management of RESs; - Hybrid renewable energy systems; - Techno-economic analysis of renewable energy technologies; - Policy and strategic planning for promoting RES adoption. We encourage authors to submit original research articles, comprehensive review articles, and insightful short communications. Planned papers may be announced with a title and short abstract (around 100 words) submitted to the Editorial Office.
Lightning impulse testing is performed as part of the factory acceptance routine tests in power t... more Lightning impulse testing is performed as part of the factory acceptance routine tests in power transformers to check the integrity of the insulation and to ensure that transformer insulation performs according to the design specifications. Lightning impulse testing is a high voltage test and the pass criterion depends on a variety of parameters being met, mainly based on the lightning impulse waveforms. In the event of a test incident, the resultant waveform can be used to classify the fault and through experiential expertise, the waveform can also be used to estimate the location of the fault within the transformer. In this paper, real-life lightning impulse waveforms resulting from a test incident are presented and used to classify the failure as well as the location of the fault.
The transformer, an essential component in the electric grid, plays a prominent role in transmitt... more The transformer, an essential component in the electric grid, plays a prominent role in transmitting electricity between circuits while adjusting voltage levels. Nevertheless, transformers oftentimes are confronted with issues like temperature elevation and excessive noise generation. Traditional transformer monitoring approaches are laborious and lack accuracy. In this case, the objective of this work is to develop an experimental Internet of Things (loT) system adequate for monitoring and assessing the transformer's performance with respect to current, voltage, power, efficiency, sound, temperature, and humidity using sensors. The data collection and processing are managed by the ESP32 microcontroller, which thereafter communicates the collected information to the Arduino Ooud through a Wi-Fi connection for real-time data visualization. To evaluate the transformer parameters, three scenarios are examined i.e., with Bench Meter, Multimeter and loT sensors. Temperature measurements employ both a traditional thermometer and the designed loT PTlOO temperature sensor. Sound levels emitted by the transformer are measured through a dedicated sound sensor. The obtained sensor data is then displayed within the Arduino Ooud dashboard. The results attained fr om this work demonstrate the effectiveness of the proposed loT system. It displays not only lowered measurement errors but also significant time savings when examining various parameters. It should be noted that the utilized size of the unit in this study is used as a pilot study to develop loT systems for pole-mounted distribution transformers. Keywords-transfo rmer health monitoring, In ternet of Things (lo 1), Performance evaluation, Sensor-based monitoring, Real time data visualization I.
Accurate measurement of transformer lumped parameters is crucial for efficiency, performance opti... more Accurate measurement of transformer lumped parameters is crucial for efficiency, performance optimization, and fault detection. This study proposes an IoT system to address the issue of often computationally aided and nonphysical measurements. The system automates real-time measurement and monitoring of parameters such as resistance, inductance, and capacitance using temperature, current, and voltage sensors. Data is wirelessly transmitted to a central unit, enhancing modeling and simulation of transformer behavior, aiding analysis and validation of mathematical models. The system allows continuous circuit analysis, timely anomaly detection, and intervention, aiding efficiency improvement. loT data assists design optimization, informed decision-making, and proactive failure analysis by detecting deviations from expected values and potential transformer failures. By integrating loT into larger electrical systems, engineers can enhance system integration, stability, and performance characteristics .
Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review, 2024
Wireless network technologies, including 3G, 4G, and 5G, are transforming telecommunications infr... more Wireless network technologies, including 3G, 4G, and 5G, are transforming telecommunications infrastructure globally. However, the adoption and effectiveness of these technologies vary significantly across regions and industries, posing unique challenges and opportunities for Small and Medium Enterprises (SMEs). Understanding the critical factors influencing network deployment and optimization in different contexts is essential for telecom companies and business leaders. This systematic review aims to evaluate the infrastructure, performance, and strategic implications of wireless network technologies (3G, 4G, and 5G) across multiple industries and geographic regions, providing insights for SMEs and telecom companies on adopting these technologies to enhance operational efficiency and competitiveness. A comprehensive search of academic databases, including Google Scholar, Web of Science, and SCOPUS, was conducted using keywords such as "wireless network," "3G," "4G," "5G," "evaluation," and "infrastructure." Studies were selected based on pre-established eligibility criteria, and a risk of bias assessment was performed using the Newcastle-Ottawa Scale. Statistical synthesis and sensitivity analyses were conducted to identify key trends and challenges. A total of 121 studies were included, with the majority focusing on 5G technology (42%) and its infrastructure. Key findings highlight the importance of network densification, high-speed connectivity, and lowlatency applications, particularly in urban regions. The analysis also revealed significant regional disparities in infrastructure deployment, with developing countries facing challenges in expanding coverage and integrating advanced technologies. Industry-specific customization of wireless networks is essential for sectors such as manufacturing, healthcare, and retail. Wireless network technologies present vast opportunities for SMEs, but their successful implementation requires addressing regional infrastructure gaps and tailoring solutions to industry-specific needs. Telecom companies must prioritize strategic investments in network densification, scalability, and security to fully leverage the benefits of 5G. The findings of this review provide actionable insights for business leaders and policymakers aiming to optimize wireless technology deployments for enhanced performance and competitiveness.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Electric Transformers make up some of the most critical components of a power system. Their relia... more Electric Transformers make up some of the most critical components of a power system. Their reliableoperation is critical for an efficient power grid as well as economic viability for the transformer operators andowners. Power transformers, like other High Voltage (HV) electrical equipment experience aging andinsulation degradation due to chemical, mechanical and electrical forces during their operation. PartialDischargers (PD) are localized electrical discharges that develop within insulation systems of HV electricalequipment such as transformers. In transformers, PD occurs in different forms and various locations andinsulation both internal and external of the transformer. PD originates as small pulses but tends to increase insize and intensity which can result in complete insulation failure. Monitoring partial discharges has proven toprovide valuable information on the state of the insulation systems of the power transformer, allowingtransformer operators to make calculated decisions for the maintenance and life of plant plans. This systematicliterature review aims to systematically examine the use of machine learning techniques in classifying PD intransformers to present a complete indicator to allow for future research in the field. The systematic reviewsurveyed a total of 81 research literature published from 2010 to 2023 that fulfilled a specific methodologywhich was developed as part of this study. The results revealed that supervised learning has been the mostwidely used Artificial Intelligence (AI) algorithm utilized, primarily in the form of Support Vector Machine(SVM). Regarding PD, the survey revealed that most researchers tend to use numerous types of PD andcompare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence ofPD is widely used versus the use of actual power transformers. Most of the literature tends to not specify thephysical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.
Induction motors are ever-present in various commercial and industrial processes owing to their h... more Induction motors are ever-present in various commercial and industrial processes owing to their high-power capacity, dependability, and low manufacturing costs. However, during their operation their susceptible to heat, mechanical stress, electrical stress, and corrosion. These stresses do not affect the operation of induction motor, however, in a long run it may develop into a major fault which can cause additional maintenance costs and unscheduled downtime, resulting in overall production loss, high financial loss, and sometimes serious human injuries. This work investigates the performance of an induction motor based on various faults conditions. A MATLAB/Simulink platform is used to develop an induction motor model. The performance of induction motor is evaluated on the three-phase line connected to it since implementing a fault in a line affects the performance induction motor. The faults that were tested were no fault condition and faults between AB, AC, BC, AG, CG, BG as well as ABCG. Training dataset was extracted from the sinusoidal waveforms of this various fault conditions. The data is given to the classification learner app in MATLAB for learning. Furthermore, this data set imported to workspace for the training purposes in the classification learner app. Different SVM learning algorithms are trained to deduce the highest learning algorithm in prediction time. The highest SVM learning algorithm according to the results is cubic and Fine Gaussian SVM learning algorithms which shows 100% accuracy on prediction time. The confusion matrix is then used to compare the true predicted and the false predicted classes.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2023 31st Southern African Universities Power Engineering Conference (SAUPEC), Jan 24, 2023
Induction motors are ever-present in various commercial and industrial processes owing to their h... more Induction motors are ever-present in various commercial and industrial processes owing to their high-power capacity, dependability, and low manufacturing costs. However, during their operation their susceptible to heat, mechanical stress, electrical stress, and corrosion. These stresses do not affect the operation of induction motor, however, in a long run it may develop into a major fault which can cause additional maintenance costs and unscheduled downtime, resulting in overall production loss, high financial loss, and sometimes serious human injuries. This work investigates the performance of an induction motor based on various faults conditions. A MATLAB/Simulink platform is used to develop an induction motor model. The performance of induction motor is evaluated on the three-phase line connected to it since implementing a fault in a line affects the performance induction motor. The faults that were tested were no fault condition and faults between AB, AC, BC, AG, CG, BG as well as ABCG. Training dataset was extracted from the sinusoidal waveforms of this various fault conditions. The data is given to the classification learner app in MATLAB for learning. Furthermore, this data set imported to workspace for the training purposes in the classification learner app. Different SVM learning algorithms are trained to deduce the highest learning algorithm in prediction time. The highest SVM learning algorithm according to the results is cubic and Fine Gaussian SVM learning algorithms which shows 100% accuracy on prediction time. The confusion matrix is then used to compare the true predicted and the false predicted classes.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In the Southern African Development Community (SADC), transmission lines facilitate power utiliti... more In the Southern African Development Community (SADC), transmission lines facilitate power utilities in distributing a considerable amount of electricity from generation stations to end users. Long conductors used in transmission lines can cover great distances. However, they are susceptible to various environmental and weather conditions which can have adverse effects such as power outages, and equipment malfunctions, posing a serious threat to the reliability of the system. Hence constant monitoring of the performance characteristics of this system is crucial. The main objective of this work is to propose an Artificial Intelligent (AI) based technique, which is the Artificial Neural Network (ANN) to detect and classify faults on a transmission line. In contrast to the most common approach viz. the protective relay system, a five-layer feed-forward-back propagation neural network architecture is proposed in this work to detect and classify faults on a 230kV transmission line system. A set of 12 fault conditions have been predefined viz. No fault, AB, AC, BC, ABC, AG, BG, CG, ABG, ACG, BCG and ABCG conditions. The results indicate that the proposed ANN approach with Levenberg- Marquardt (LM) algorithm, 5-Layers and TANSIG transfer function yield an output of 0.8927, 0.8882, 0.905 and 0.8938 in the training, validation, testing and overall accuracy respectively. To corroborate these results, a comparative study of the proposed network and other neural networks was also carried out.
A high-voltage and medium-voltage cable distribution network is an extensive section of the distr... more A high-voltage and medium-voltage cable distribution network is an extensive section of the distribution route. The electrical cable system remains the most important channel or brain connection that allow all other dynamic section to function. Since the electrical cables system is entrenched often it is challenging and costly to install new ones. When the cable ages, a degradation of the electrical and mechanical properties of the insulating material occurs triggering irreversible changes in the chemical construction and hence reducing the service life of the cable. When the conductor operating temperature increases, there is a subsequent speeding up of the XLPE aging process, and consequently a reduction in the life of the cable. The key objective of this paper is to present a model for determining the thermal aging of XLPE HV cables. In addition, this paper examines the thermal aging of high voltage XLPE cable for HV distribution network, using consistent load. Based on the cable properties such as the insulation temperature, thermal aging is determined using the Thermal Aging model and Arrhenius law. In this work, the model is developed based on this premise to predict the lifetime of a 132kV, single core, 1000mm 2 Cross-linked polyethene (XLPE) cable. This paper specifically implements the lifetime model of a high-voltage cable using thermal aging. The results show that when an XLPE cable is operated at a lower temperature of 70°C is likely to survive many years (+50 years) in service, than when it is operating at a normal cable operating temperature of 90°C.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021
The presence of harmonic currents due to the switching action of inverters has spread the realiza... more The presence of harmonic currents due to the switching action of inverters has spread the realization of the prospective rapid depletion of the transformer's intended lifetime owing to increased losses and temperature rise. With the prospect of reduced transformer service lifetime under harmonic conditions, there is a growing interest by manufacturers to explore design procedures that enables transformers to operate reliably under harmonic conditions. The current work examines K-rated transformers with regards to harmonic indexes in particularly with respect to K-Factor, Harmonic loss factor (HLF) and Factor K for derating the transformer. The transformer considered in this study has a rated 630kVA. Based on a supplied harmonic current spectrum, a comparative study is carried out to calculate the additional winding Eddy losses using the K-Factor and HLF methods. The study reveals that when delivering this harmonic spectrum, the investigated unit must be de-rated to 86.6% of its nominal power rating.
2021 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), 2021
The modern electricity society has seen an increase in the integration of renewable energy source... more The modern electricity society has seen an increase in the integration of renewable energy sources into the power grid. This has significantly improved the electricity supply and sustainability. However, there are technical challenges related to the power quality (PQ) analysis for grid integrated systems. In this paper, we propose a PQ detection technique using the discrete wavelet transform (DWT) and support vector machine (SVM). The DWT analysis technique is used to extract statistical features, which are used as input to train the SVM classifier. The parameters of the SVM are optimised using the harris hawks optimisation (HHO) algorithm. Various scenarios of cases which may affect the quality of network performance are investigated. These events include the voltage sag, voltage swell, notch, transient fault conditions and sudden load increment. The proposed method is validated using the modified practical Eskom network. The presented results show that the proposed scheme correctly classified different cases.
This Special Issue on “Advanced Technologies of Renewable Energy Sources (RESs)” seeks high-quali... more This Special Issue on “Advanced Technologies of Renewable Energy Sources (RESs)” seeks high-quality works focusing on cutting-edge advancements in renewable energy technologies. Topics include, but are not limited to, the following: - Innovations in solar photovoltaics and thermal systems; - Next-generation wind turbine design and efficiency enhancement; - Biomass and biofuel production technologies; - Hydroelectric power systems and improvements; - Geothermal energy utilization and technological advancements; - Energy storage solutions for intermittent RESs; - Smart grid integration and management of RESs; - Hybrid renewable energy systems; - Techno-economic analysis of renewable energy technologies; - Policy and strategic planning for promoting RES adoption. We encourage authors to submit original research articles, comprehensive review articles, and insightful short communications. Planned papers may be announced with a title and short abstract (around 100 words) submitted to the Editorial Office.
Lightning impulse testing is performed as part of the factory acceptance routine tests in power t... more Lightning impulse testing is performed as part of the factory acceptance routine tests in power transformers to check the integrity of the insulation and to ensure that transformer insulation performs according to the design specifications. Lightning impulse testing is a high voltage test and the pass criterion depends on a variety of parameters being met, mainly based on the lightning impulse waveforms. In the event of a test incident, the resultant waveform can be used to classify the fault and through experiential expertise, the waveform can also be used to estimate the location of the fault within the transformer. In this paper, real-life lightning impulse waveforms resulting from a test incident are presented and used to classify the failure as well as the location of the fault.
The transformer, an essential component in the electric grid, plays a prominent role in transmitt... more The transformer, an essential component in the electric grid, plays a prominent role in transmitting electricity between circuits while adjusting voltage levels. Nevertheless, transformers oftentimes are confronted with issues like temperature elevation and excessive noise generation. Traditional transformer monitoring approaches are laborious and lack accuracy. In this case, the objective of this work is to develop an experimental Internet of Things (loT) system adequate for monitoring and assessing the transformer's performance with respect to current, voltage, power, efficiency, sound, temperature, and humidity using sensors. The data collection and processing are managed by the ESP32 microcontroller, which thereafter communicates the collected information to the Arduino Ooud through a Wi-Fi connection for real-time data visualization. To evaluate the transformer parameters, three scenarios are examined i.e., with Bench Meter, Multimeter and loT sensors. Temperature measurements employ both a traditional thermometer and the designed loT PTlOO temperature sensor. Sound levels emitted by the transformer are measured through a dedicated sound sensor. The obtained sensor data is then displayed within the Arduino Ooud dashboard. The results attained fr om this work demonstrate the effectiveness of the proposed loT system. It displays not only lowered measurement errors but also significant time savings when examining various parameters. It should be noted that the utilized size of the unit in this study is used as a pilot study to develop loT systems for pole-mounted distribution transformers. Keywords-transfo rmer health monitoring, In ternet of Things (lo 1), Performance evaluation, Sensor-based monitoring, Real time data visualization I.
Accurate measurement of transformer lumped parameters is crucial for efficiency, performance opti... more Accurate measurement of transformer lumped parameters is crucial for efficiency, performance optimization, and fault detection. This study proposes an IoT system to address the issue of often computationally aided and nonphysical measurements. The system automates real-time measurement and monitoring of parameters such as resistance, inductance, and capacitance using temperature, current, and voltage sensors. Data is wirelessly transmitted to a central unit, enhancing modeling and simulation of transformer behavior, aiding analysis and validation of mathematical models. The system allows continuous circuit analysis, timely anomaly detection, and intervention, aiding efficiency improvement. loT data assists design optimization, informed decision-making, and proactive failure analysis by detecting deviations from expected values and potential transformer failures. By integrating loT into larger electrical systems, engineers can enhance system integration, stability, and performance characteristics .
Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review, 2024
Wireless network technologies, including 3G, 4G, and 5G, are transforming telecommunications infr... more Wireless network technologies, including 3G, 4G, and 5G, are transforming telecommunications infrastructure globally. However, the adoption and effectiveness of these technologies vary significantly across regions and industries, posing unique challenges and opportunities for Small and Medium Enterprises (SMEs). Understanding the critical factors influencing network deployment and optimization in different contexts is essential for telecom companies and business leaders. This systematic review aims to evaluate the infrastructure, performance, and strategic implications of wireless network technologies (3G, 4G, and 5G) across multiple industries and geographic regions, providing insights for SMEs and telecom companies on adopting these technologies to enhance operational efficiency and competitiveness. A comprehensive search of academic databases, including Google Scholar, Web of Science, and SCOPUS, was conducted using keywords such as "wireless network," "3G," "4G," "5G," "evaluation," and "infrastructure." Studies were selected based on pre-established eligibility criteria, and a risk of bias assessment was performed using the Newcastle-Ottawa Scale. Statistical synthesis and sensitivity analyses were conducted to identify key trends and challenges. A total of 121 studies were included, with the majority focusing on 5G technology (42%) and its infrastructure. Key findings highlight the importance of network densification, high-speed connectivity, and lowlatency applications, particularly in urban regions. The analysis also revealed significant regional disparities in infrastructure deployment, with developing countries facing challenges in expanding coverage and integrating advanced technologies. Industry-specific customization of wireless networks is essential for sectors such as manufacturing, healthcare, and retail. Wireless network technologies present vast opportunities for SMEs, but their successful implementation requires addressing regional infrastructure gaps and tailoring solutions to industry-specific needs. Telecom companies must prioritize strategic investments in network densification, scalability, and security to fully leverage the benefits of 5G. The findings of this review provide actionable insights for business leaders and policymakers aiming to optimize wireless technology deployments for enhanced performance and competitiveness.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Electric Transformers make up some of the most critical components of a power system. Their relia... more Electric Transformers make up some of the most critical components of a power system. Their reliableoperation is critical for an efficient power grid as well as economic viability for the transformer operators andowners. Power transformers, like other High Voltage (HV) electrical equipment experience aging andinsulation degradation due to chemical, mechanical and electrical forces during their operation. PartialDischargers (PD) are localized electrical discharges that develop within insulation systems of HV electricalequipment such as transformers. In transformers, PD occurs in different forms and various locations andinsulation both internal and external of the transformer. PD originates as small pulses but tends to increase insize and intensity which can result in complete insulation failure. Monitoring partial discharges has proven toprovide valuable information on the state of the insulation systems of the power transformer, allowingtransformer operators to make calculated decisions for the maintenance and life of plant plans. This systematicliterature review aims to systematically examine the use of machine learning techniques in classifying PD intransformers to present a complete indicator to allow for future research in the field. The systematic reviewsurveyed a total of 81 research literature published from 2010 to 2023 that fulfilled a specific methodologywhich was developed as part of this study. The results revealed that supervised learning has been the mostwidely used Artificial Intelligence (AI) algorithm utilized, primarily in the form of Support Vector Machine(SVM). Regarding PD, the survey revealed that most researchers tend to use numerous types of PD andcompare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence ofPD is widely used versus the use of actual power transformers. Most of the literature tends to not specify thephysical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.
Induction motors are ever-present in various commercial and industrial processes owing to their h... more Induction motors are ever-present in various commercial and industrial processes owing to their high-power capacity, dependability, and low manufacturing costs. However, during their operation their susceptible to heat, mechanical stress, electrical stress, and corrosion. These stresses do not affect the operation of induction motor, however, in a long run it may develop into a major fault which can cause additional maintenance costs and unscheduled downtime, resulting in overall production loss, high financial loss, and sometimes serious human injuries. This work investigates the performance of an induction motor based on various faults conditions. A MATLAB/Simulink platform is used to develop an induction motor model. The performance of induction motor is evaluated on the three-phase line connected to it since implementing a fault in a line affects the performance induction motor. The faults that were tested were no fault condition and faults between AB, AC, BC, AG, CG, BG as well as ABCG. Training dataset was extracted from the sinusoidal waveforms of this various fault conditions. The data is given to the classification learner app in MATLAB for learning. Furthermore, this data set imported to workspace for the training purposes in the classification learner app. Different SVM learning algorithms are trained to deduce the highest learning algorithm in prediction time. The highest SVM learning algorithm according to the results is cubic and Fine Gaussian SVM learning algorithms which shows 100% accuracy on prediction time. The confusion matrix is then used to compare the true predicted and the false predicted classes.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2023 31st Southern African Universities Power Engineering Conference (SAUPEC), Jan 24, 2023
Induction motors are ever-present in various commercial and industrial processes owing to their h... more Induction motors are ever-present in various commercial and industrial processes owing to their high-power capacity, dependability, and low manufacturing costs. However, during their operation their susceptible to heat, mechanical stress, electrical stress, and corrosion. These stresses do not affect the operation of induction motor, however, in a long run it may develop into a major fault which can cause additional maintenance costs and unscheduled downtime, resulting in overall production loss, high financial loss, and sometimes serious human injuries. This work investigates the performance of an induction motor based on various faults conditions. A MATLAB/Simulink platform is used to develop an induction motor model. The performance of induction motor is evaluated on the three-phase line connected to it since implementing a fault in a line affects the performance induction motor. The faults that were tested were no fault condition and faults between AB, AC, BC, AG, CG, BG as well as ABCG. Training dataset was extracted from the sinusoidal waveforms of this various fault conditions. The data is given to the classification learner app in MATLAB for learning. Furthermore, this data set imported to workspace for the training purposes in the classification learner app. Different SVM learning algorithms are trained to deduce the highest learning algorithm in prediction time. The highest SVM learning algorithm according to the results is cubic and Fine Gaussian SVM learning algorithms which shows 100% accuracy on prediction time. The confusion matrix is then used to compare the true predicted and the false predicted classes.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In the Southern African Development Community (SADC), transmission lines facilitate power utiliti... more In the Southern African Development Community (SADC), transmission lines facilitate power utilities in distributing a considerable amount of electricity from generation stations to end users. Long conductors used in transmission lines can cover great distances. However, they are susceptible to various environmental and weather conditions which can have adverse effects such as power outages, and equipment malfunctions, posing a serious threat to the reliability of the system. Hence constant monitoring of the performance characteristics of this system is crucial. The main objective of this work is to propose an Artificial Intelligent (AI) based technique, which is the Artificial Neural Network (ANN) to detect and classify faults on a transmission line. In contrast to the most common approach viz. the protective relay system, a five-layer feed-forward-back propagation neural network architecture is proposed in this work to detect and classify faults on a 230kV transmission line system. A set of 12 fault conditions have been predefined viz. No fault, AB, AC, BC, ABC, AG, BG, CG, ABG, ACG, BCG and ABCG conditions. The results indicate that the proposed ANN approach with Levenberg- Marquardt (LM) algorithm, 5-Layers and TANSIG transfer function yield an output of 0.8927, 0.8882, 0.905 and 0.8938 in the training, validation, testing and overall accuracy respectively. To corroborate these results, a comparative study of the proposed network and other neural networks was also carried out.
A high-voltage and medium-voltage cable distribution network is an extensive section of the distr... more A high-voltage and medium-voltage cable distribution network is an extensive section of the distribution route. The electrical cable system remains the most important channel or brain connection that allow all other dynamic section to function. Since the electrical cables system is entrenched often it is challenging and costly to install new ones. When the cable ages, a degradation of the electrical and mechanical properties of the insulating material occurs triggering irreversible changes in the chemical construction and hence reducing the service life of the cable. When the conductor operating temperature increases, there is a subsequent speeding up of the XLPE aging process, and consequently a reduction in the life of the cable. The key objective of this paper is to present a model for determining the thermal aging of XLPE HV cables. In addition, this paper examines the thermal aging of high voltage XLPE cable for HV distribution network, using consistent load. Based on the cable properties such as the insulation temperature, thermal aging is determined using the Thermal Aging model and Arrhenius law. In this work, the model is developed based on this premise to predict the lifetime of a 132kV, single core, 1000mm 2 Cross-linked polyethene (XLPE) cable. This paper specifically implements the lifetime model of a high-voltage cable using thermal aging. The results show that when an XLPE cable is operated at a lower temperature of 70°C is likely to survive many years (+50 years) in service, than when it is operating at a normal cable operating temperature of 90°C.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021
The presence of harmonic currents due to the switching action of inverters has spread the realiza... more The presence of harmonic currents due to the switching action of inverters has spread the realization of the prospective rapid depletion of the transformer's intended lifetime owing to increased losses and temperature rise. With the prospect of reduced transformer service lifetime under harmonic conditions, there is a growing interest by manufacturers to explore design procedures that enables transformers to operate reliably under harmonic conditions. The current work examines K-rated transformers with regards to harmonic indexes in particularly with respect to K-Factor, Harmonic loss factor (HLF) and Factor K for derating the transformer. The transformer considered in this study has a rated 630kVA. Based on a supplied harmonic current spectrum, a comparative study is carried out to calculate the additional winding Eddy losses using the K-Factor and HLF methods. The study reveals that when delivering this harmonic spectrum, the investigated unit must be de-rated to 86.6% of its nominal power rating.
2021 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), 2021
The modern electricity society has seen an increase in the integration of renewable energy source... more The modern electricity society has seen an increase in the integration of renewable energy sources into the power grid. This has significantly improved the electricity supply and sustainability. However, there are technical challenges related to the power quality (PQ) analysis for grid integrated systems. In this paper, we propose a PQ detection technique using the discrete wavelet transform (DWT) and support vector machine (SVM). The DWT analysis technique is used to extract statistical features, which are used as input to train the SVM classifier. The parameters of the SVM are optimised using the harris hawks optimisation (HHO) algorithm. Various scenarios of cases which may affect the quality of network performance are investigated. These events include the voltage sag, voltage swell, notch, transient fault conditions and sudden load increment. The proposed method is validated using the modified practical Eskom network. The presented results show that the proposed scheme correctly classified different cases.
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Papers by Bonginkosi Thango
- Innovations in solar photovoltaics and thermal systems;
- Next-generation wind turbine design and efficiency enhancement;
- Biomass and biofuel production technologies;
- Hydroelectric power systems and improvements;
- Geothermal energy utilization and technological
advancements;
- Energy storage solutions for intermittent RESs;
- Smart grid integration and management of RESs;
- Hybrid renewable energy systems;
- Techno-economic analysis of renewable energy
technologies;
- Policy and strategic planning for promoting RES
adoption.
We encourage authors to submit original research articles, comprehensive review articles, and insightful short communications. Planned papers may be announced with a title and short abstract (around 100 words) submitted to the Editorial Office.
- Innovations in solar photovoltaics and thermal systems;
- Next-generation wind turbine design and efficiency enhancement;
- Biomass and biofuel production technologies;
- Hydroelectric power systems and improvements;
- Geothermal energy utilization and technological
advancements;
- Energy storage solutions for intermittent RESs;
- Smart grid integration and management of RESs;
- Hybrid renewable energy systems;
- Techno-economic analysis of renewable energy
technologies;
- Policy and strategic planning for promoting RES
adoption.
We encourage authors to submit original research articles, comprehensive review articles, and insightful short communications. Planned papers may be announced with a title and short abstract (around 100 words) submitted to the Editorial Office.