As the global demand for energy rises, power system networks are teetering on the verge of collapsing owing to a compromise in system stability. During system disturbances, the network's inability to supply adequate reactive power causes instability and eventual collapse. As such, optimized generation scheduling during system disturbances can improve the utilization of the power plants while lowering power loss, improving voltage regulation, reducing branch loading, and ensuring the secure operation of system equipment. Since power systems have conflicting and multiple objectives, this study proposes a multiobjective optimal power flow incorporating three objective functions: generation cost, power loss, and the maximum value of the line Voltage Collapse Proximity Index. The Multiobjective Particle Swarm Optimization Algorithm is used to minimize these objectives on the IEEE 30-bus system for different case studies in normal, contingency, and stressed system conditions. Fuzzy Decision Theory is utilized for obtaining the best compromise solutions amongst a set of Pareto optimal solutions. The results show that the voltage stability of the system is improved by an average of 63.09% during system disturbances with multiobjective optimization. Simultaneous optimization of the three objective functions provides the most voltage stable condition for all system conditions, preventing possible collapse.

Green energy supply can be achieved by integrating intermittent renewable energy resources into the electrical distribution network. The intermittent nature of solar power generation presents significant technical challenges for integration that affect the network reliability and stability in relation to the grid power quality and voltage profile. Maximum utilization of photovoltaic in the electrical distribution network requires siting and sizing optimization. Distribution and transmission lines incur voltage drops and power losses due to their reactive and resistive properties. Application of evolutionary optimization techniques is adopted for optimal photovoltaic distributed generations placement in an electrical distribution network. Improved network voltage profile and system reliability was achieved by the application of particle swarm optimization algorithm to minimize the system’s power losses in a radial distribution network-IEEE 33-bus system. This was achieved through a MATLAB code implementation, with validation of the solution techniques and the developed model realized through a genetic algorithm case study. The active and reactive total loads linked to the network test system were 3.720 MW and 2.310 MVAr, accordingly. The conversion of solar power was modeled at a constant power factor with cut-off solar radiation ≥ 4.0 kWh/m2/day under normal operating conditions. As an initial configuration, active and reactive power losses were found as 211.02 kW and 143.04 kVAr without photovoltaic distributed generation at 0.85 pf, respectively. Integration of solar distributed generations at optimal location and capacity resulted in reduction of the network power losses by 57.98% reactive and 61.60% active. Improvement in voltage profile attained was 8.46%, while the ASAI network reliability index value before integrating solar source was 0.99734 p.u. but improved by 1.82% on installation. In conclusion, the system’s power losses reduced as acceptable voltage profile was maintained for sustained distribution network reliability.

Early detection of incipient bearing faults in induction motors has proven crucial in predictive maintenance, helping avoid machine downtime and costly repairs. The main challenge is collecting sufficient data for deep learning models since faults are a rare occurrence. This paper investigates the efficacy of a transfer learning approach for induction motor bearing fault diagnosis using simulated vibration data. Healthy and faulty bearings of different severities were simulated in MATLAB for various noise magnitudes. A Squeeze and Excitation Residual Network (SE-ResNet), previously trained on a large dataset for bearing faults of a Permanent Magnet Synchronous Motor (PMSM), is used as a feature extractor. By leveraging pre-trained knowledge, the model's weights were fine-tuned using Bayesian Optimization, aiming to mitigate the data scarcity issue while maintaining accurate fault classification. The model's performance was compared against three hybrid architectures incorporating Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) layers. The study aims to assess the impact of adding recurrent layers to capture temporal dependencies within simulated vibration signals. Contrary to expectations, the hybrid models did not improve the classification accuracy compared to the standalone pre-trained SE-ResNet. The test accuracy remained the same for all the models at 97.297% whereas the computational cost increased for the hybrid models. This paper analyzes these findings, highlighting the challenges of transfer learning with simulated data.

Intermittent power generating units lock on and off into the grid on availability basis. Practically, the operation of distributed generations is always done at the maximum rated power output. Wind power generation poses significant integration challenges due to its nature of intermittency that leads to serious issues of power grid reliability and stability with regards to voltage profile which affect quality of the grid power. Network reliability can be maintained by observing several reliability indices in existence including SAIDI, EENS, SAIFI, AENS and ASAI The purpose of the study was to optimize capacity and siting of wind distributed generations using Particle Swarm Optimization algorithm for improved system reliability and voltage profile by minimizing power losses in an electrical distribution network. Evolutionary optimization methods yield to results that suffer from premature convergence that require enhancement and modifications. MATLAB simulation was used to investigate the adequacy of the proposed PSO-based algorithm on IEEE standard 33-bus radial distribution network test system, and Genetic Algorithm case study was used to validate the developed model and solution techniques. The total reactive and active loads connected to the RDN test system are 2.72MVAr and 3.31MW respectively. The wind power generation was modelled as variable reactive power model for grid integration. The cut-off wind speed for power generation on average is considered to be $\geq$ 6 m/s. Calculations in this analysis yield average real power generated by wind turbine generator as 0.2115 p.u The initial network configuration, RPL and APL obtained are 139.12kVAr and 206.86kW respectively without DG at 0.83pf. On integration of wind DG of optimal size and location, the overall line power loss reduces by 67.13% and 60.44% for active and reactive power losses respectively. The wind DG installation improves the voltage profile by 8.16%. The value of ASAI reliability index before integration of the wind DGs was 0.99744 p.u. and after their installation it improves by 1.80%. In conclusion, there is minimized power losses while maintaining acceptable voltage profile in a distribution network for sustained reliability when more wind DGs are installed at optimal sites and capacities using the proposed method.

Photovoltaic generation will continue to grow with urbanization, electrification, digitalization, and de-carbonization. However, PV generation is variable and intermittent, non-inertia and asynchronous with the demand, posing significant challenges in generation dispatch, strategic spinning reserve and power system stability. Battery Energy Storage Systems (BESS) are key in enabling the integration of higher quanta of solar PV into utility power grids. Grid connected PV, BESS and PV-BESS have been modelled on MATLAB/Simulink. The control strategy of the grid connected PV inverter operates PV at MPP and ensures grid side current control to determine the amount of power delivered. These objectives have been achieved using PWM on two successive control strategies. The VSI control determines the modulation index for generating an output current that is both sinusoidal and in phase with the grid-side voltage. The Perturb & Observe based MPPT is used. Current-controlled H-bridge PWM single-stage three-phase and bi-directional two stage three phase inverters with LCL low pass filters are implemented on MATLAB/Simulink. The critical components in the control circuit are the PLL, P&O MPPT, feed-forward input power control and a Proportional Integral (PI) based grid-side current controller. A sinusoidal grid current is successfully fed to the grid from the grid connected solar PV and grid connected BESS. An FFT analysis performed on the grid side currents shows the total harmonic distortion (THD) is below the limit specified in IEEE-519.

Induction motors are widely used in different sectors for various industrial applications and their continuous operation is critical for ensuring the continuity of production processes and saving costs. Condition monitoring and fault diagnosis of induction motors have been of great interest to researchers and practitioners for many years. Deep learning, as an emerging machine learning technique, has shown great potential in this field. This paper reviews the recent research on condition monitoring and fault diagnosis of induction motors using deep learning. The paper introduces the types of faults in induction motors and mentions some traditional and shallow machine learning methods that have been used for condition monitoring and fault diagnosis. The second section summarizes the research on deep learning-based approaches for motor fault diagnosis, including various deep learning architectures and techniques such as autoencoders (AEs), deep belief networks (DBNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and convolutional neural networks (CNNs). Finally, the paper discusses the challenges encountered in using deep learning techniques, solutions to these problems and highlights opportunities for future research in this field.

The secure operation of a power system depends on the available security evaluation tools and improvement techniques to tackle the disturbances or contingencies. The main objective of the survey presented in this paper is to provide a comprehensive review to the researchers, academicians, and utility engineers on the available techniques of static security assessment and improvement in modern power systems. Various performance indices are used to express the severity of limit violations from security margins typically in transmission line loading and buses voltage magnitude under a given disturbance or contingency. The accuracy and speed of computation considering uncertainties in renewable energy generation and load demand scenarios are the fundamental requirements of any security assessment tool. Conventional power flow and machine learning approaches are explored and compared for static security assessment. Although, conventional AC power flow provides accurate result, it is computationally demanding and slow process to assess the security of a power system with uncertainties and changing future operating scenarios considering simultaneous component failures. Several machine learning techniques have been studied to make fast and sufficiently accurate assessment. The application of FACTS devices to improve static security of a power system has been reviewed. To ensure the effectiveness of FACTS devices, various sensitivity and optimization approaches have been suggested for proper placement and sizing. The increasing complexity and uncertainty in power systems due to increased penetration of renewable energy resources and the introduction of new type of loads such as electric vehicles and heating loads suggests the development and application of more robust and portable security assessment tools such as deep learning algorithms and fast responding flexible security improvement mechanisms like FACTS devices.

Educational robotic activities can be used to support teaching of science subjects. Many secondary school students have little interest in Physics as a subject and hence only a few students select it in preparation of career progression. Robotic activities can be employed to facilitate practical learning in science subjects in general with the Physics subject benefiting the most. In Kenya, there is little research on the role robotic activities can play on learning of these subjects. This study developed robotic activities from educational robots fabricated by the researcher. The activities presented learners in secondary school with diverse opportunities of enriching their learning of Physics as a Science subject. The robotic activities were integrated into Physics topics to give the teaching and learning of this subject a new approach. The integrated robot activities were then introduced to Form 2 students in workshops carried out during weekends which included 3-day activities for the students. In this study 200, form 2 students were selected randomly from 20 schools in Kangema sub-county, Murang’a county in Kenya. The students were issued with questionnaires before and after exposure to the activities through the workshops for purposes of data collection. The quantitative data obtained was analyzed using descriptive statistics and inferential statistics. The findings of the study revealed that the robotic activities had a significant impact on students’ interest in Physics. The study recommends that the government should facilitate the integration of educational robotic activities in the current secondary school Science curriculum in order to improve interest towards these subjects.

This paper presents a QV-based approach called Critical Voltage-Reactive Power Ratio (CVQR) index to assess the voltage instability tendencies of power system buses with increase in renewable energy (RE) penetration within the power system. The buses are thus ranked according to the order in which they are impacted by increase in renewable energy penetration. Simulations were performed using DIgSILENT PowerFactory and result analyses were done with MATLAB. The developed CVQR index has been employed to assess the effect of increasing RE generation on grid voltage stability. This index provides information on the voltage instability tendencies of all non-slack buses of the RE-integrated grid and the buses are ranked from the weakest to the strongest. The rankings obtained from the developed CVQR has been compared with five commonly-used indices and the result of the comparison verifies the accuracy of the proposed index. IEEE 14-bus and IEEE 39-bus New England systems have been used to evaluate the proposed CVQR index and various scenarios of RE system combinations and placements have been considered. Voltage collapse condition is indicated whenever the CVQR index associated with any bus becomes positive (CVQR > 0). This index can as well be applied to other power system networks. The overall ranking of the buses based on the CVQR index can provide insights on the most appropriate location for large inductive loads or compensating devices, which can either absorb or inject reactive power into the power system, thereby influencing the system’s voltage stability.

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.

This paper investigates the utilization of a STATCOM to enhance the LVRT capability of a 9 MW DFIG based Wind Power Plant (WPP) during grid faults. The STATCOM under investigation is tuned using the Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO) and a hybrid algorithm of both WCA and PSO. Simulations are carried out in MATLAB programming software, using SimScape toolbox. Performance analysis is done by investigating the ability of the test system to ride through voltage sags on the grid side, with incorporation of the STATCOM tuned using WCA, PSO and further with the hybrid WCA-PSO algorithm. To confirm effectiveness of the proposed algorithm, simulation results for the three scenarios are compared. Results show that LVRT capability of the Danish power system was met for L-G faults. However, without a STATCOM, the WPP could not ride through LLL-G faults. When STATCOM was incorporated, LVRT capability requirements were met. Voltage fluctuations reduce from 17% to 3%, without STATCOM and with WCA-PSO tuned model, respectively, during L-G faults. During LLL-G faults, voltage magnitude fluctuates from 60% to 25%, without STATCOM and with WCA-PSO tuned model respectively. WCA-PSO tuned STATCOM also resulted in the least voltage, active and reactive power overshoots.

Most comparison studies of MPPT algorithms in the literature are undertaken in terms of their precision, speed, robustness under rapidly changing environmental conditions, etc... In grid-connected photovoltaic (PV) systems, Comparative studies of MPPT methods in terms of THD injected into the network are of significant importance. In this paper, two conventional MPPT methods, perturb and observation (P&O) and incremental conductance (IC), and two advanced MPPT methods, fuzzy logic control (FLC) and Sliding mode control (SMC), were studied and compared in terms of currents injected THD into the grid using MATLAB/Simulink. The grid-connected PV system under consideration is a two-stage unit that includes a classical boost converter and a two-level voltage source inverter. With the FLC and SMC-based MPPT methods, the injected current's THD was 1.34% and 1.99%, respectively. The IC MPPT technique has slightly outperformed the P&O one, with a THD current of 3.48% versus 3.68%. The simulations were run under identical conditions; the superior performance of a particular MPPT technique over others is principally reflected in the dc-link voltage fluctuation.

The dynamic behavior of a PID-type fuzzy logic control depends on the appropriate choice of its scaling factors. Fixed scaling factors cannot provide adequate control performance under a wide range of operating conditions. This paper proposes a control strategy for separately excited dc motor (SEDCM) speed control based on fuzzy logic and neural networks. The function of the neural networks is to adapt the scaling factors at the inputs and output of the fuzzy logic controller. Using MATLAB/Simulink, the performance of the proposed controller is highlighted in comparison with anti-windup proportional-integral (PI) and sliding mode controllers under variable speed reference, disturbances, and armature resistance variation.

The research interest in the use robotics for education purposes has increased greatly in the last few years. However, no much consideration has been made to the benefits that the robots have in delivering content in STEM education. Educational robots have been used to support learning of STEM subjects but in the informal learning environment at different levels of education. This review assesses benefits of use of educational robots in teaching of these subjects to learners’ attitude towards the subjects and problem solving skills. In this research 25 papers were selected for the purpose of review through a process of search and review. The papers selected were analyzed based on similarity in their findings and mainly on the benefits educational robot activities towards teaching and learning of STEM subjects. The review reveals that robotic activities employed in education play an important role in enhancing STEM interest and also promoting problem solving skills. These benefits are greater to primary school learners than primary school learners and are realized greatly when the duration of experiment is not extended for longer durations. From the review it was noted that the robots have a greater impact in boys than in girls. The robots being multidisciplinary in nature can be utilized in teaching various subjects at different levels of education. The conclusions of this review will be useful as reference for future research in this field of study.

There is currently low interest in STEM subjects at secondary schools which leads to low interest in STEM career path in Kenya. Secondary schools have a great role in preparing learners for career progression through the subjects they teach and career guidance. In order for any country to meet the ever increasing need for a STEM related workforce, more learners should be motivated to pursue careers in STEM. Educational Robotics have been recognized to be vital teaching tools for practical learning about STEM topics in general. However, not enough attention has been paid to the development of robotics activities and their suitability for integration in Physics and Mathematics subjects. In this study robotic activities are developed which expose learners to many opportunities of enriching learning of Physics and Mathematics. The activities developed were then implemented through a 3-day workshop held at Murang’a University of Technology in Kenya for students and teachers in Physics and Mathematics. The activities were developed in a systematic manner and adopted an Active learning model. In order to assess the suitability, relevance and the impact of the developed activities to learning of Physics and Mathematics, 192 form 2 students and 10 teachers from Kangema Sub-County, Murang’a County in Kenya were selected purposefully. A questionnaire and an interview schedule were used to collect data from students and teachers respectively. From analysis of the data collected it was concluded that the activities were suitable and made learning of Physics and Mathematics fun and consequently improved learners interest to the subjects and their participation in class. The developed activities can therefore be integrated into the Physics and Mathematics curriculum for either in-class sessions or after-school sessions.

This paper investigates the utilization of a STATCOM to enhance the LVRT capability of wind power plants (WPPs) during grid faults. The STATCOM under investigation is tuned using the Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and a hybrid algorithm of both WCA and PSO. Simulations are conducted in MATLAB programming software, using the SimScape power system toolbox, where two test systems are investigated: a 9 MW WPP and the IEEE 39 bus test system. Performance analysis is done by investigating the ability of the WPPs to ride through grid voltage sags, with the incorporation of the STATCOM, independently tuned using WCA, PSO, and further with the hybrid WCA-PSO algorithm. To confirm the effectiveness of the proposed algorithm, simulation results for the three scenarios are compared. Results show that the LVRT capability of the German power system was met for L-G faults, for the 9 MW test system, whereas during LLL-G faults, the WPP only remained online for WCA and WCA-PSO tuned STATCOM. For the IEEE 39 bus system, the WPPs were able to ride through the LLL-G fault. In all scenarios, the WCA-PSO tuned STATCOM resulted in the least voltage, active, and reactive power overshoots.

Several recent studies have demonstrated the benefits of incorporating auxiliary functions such as power quality enhancement services in the control strategy of grid-connected photovoltaic (PV) systems. However, most studies ignore the necessity to prioritize the PV power injection into the grid, the primary role of grid-connected PV systems, and limit the compensation (the achievement of the auxiliary functions) according to the available capacity of the PV inverter. Limiting the inverter's rated capacity in terms of maximal current is crucial not only for preserving the life of the semiconductors but also for ensuring the inverter's principal role of injecting active power into the network. This paper proposes a control technique in abc reference frame that incorporates a current harmonics filtering function for two-stage grid-connected PV systems. The proposed strategy inherently prioritizes the PV power injection over current harmonics filtering. It also considers the PV inverter's rated capacity by characterizing it by its peak rated current to fulfil its two functions without exceeding the inverter limit by optimally limiting the current for harmonics compensation. In addition, a fuzzy logic-based control scheme was proposed for the need to limit the generated PV power regardless of environmental conditions. Furthermore, A comparison between the proposed control sheme and a traditional shunt active power filter (APF) in terms of grid current THD improvement was conducted under two scenarios using MATLAB/Simulink. The simulation of the first investigated scenario, where the local load demand power is less than the PV power generation, results in a grid current THD of 3.41% vs. 3.53% when applying the proposed technique and the shunt APF, respectively. In the second scenario, When the load is supplied by the grid and the PV generator, A grid current THD of 2.04% vs 5.54% are obtained using the proposed method and the shunt APF, respectively.

The main aim of this work consists of proposing a new control strategy for multifunctional grid-connected photovoltaic systems (GCPVSs) to enhance the power quality at the point of common coupling (PCC) while considering the inverter-rated capacity. In addition, an Adaptive neuro-fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) controller for a two-phase interleaved boost converter is proposed to improve the dc-link voltage oscillation of the GCPVS. The control strategy takes into account the inverter's rated capacity in terms of power, which is defined by its maximal current modulus. It limits the inverter current to prevent overrating operations, and it also manages the GCPVS's functions: active power injection, reactive power compensation, and current harmonic filtering. The Active power injection into the grid takes precedence over power quality enhancement. Then, The reactive power compensation takes priority over the filtering of nonlinear load current harmonics. The proposed strategy is applied to a grid-connected PV system through an interleaved boost converter and a three-level neutral point clamped (NPC) inverter. Various scenarios with different solar irradiation levels are investigated using MATLAB/Simulink environment. Compared with another existing control strategy in terms of grid current total harmonic distortion (THD) enhancement, the simulations results indicate the superiority of the proposed method. Furthermore, the simulation results also show that the multifunctional GCPVS can perfectly perform all its functions simultaneously with up to 16.95% reduction in grid current THD.

: In most countries, Secondary schools play a great role in preparing students in their future careers. STEM subjects are perceived to be difficult and as such, there has been low interest by the secondary school students in these subjects. This calls for reconsideration of the way teaching is done in the STEM subjects in order to make STEM careers attractive and to retain students in these fields. This study developed robotic activities and integrated them in the STEM subjects and assessed the effects of the activities to the perception and interest in these subjects. The study was conducted in the secondary schools in Murang’a County in Kenya. The target population included 200 students selected through simple random sampling method. The selected students were introduced to robotics activities integrated in Physics and Mathematics topics. Questionnaires were used to collect data and were administered to the students before and after exposure to the robotic activities integrated in the subjects. The quantitative data obtained was analyzed using descriptive statistics and inferential statistics. From the findings of this study, the robotic activities had a significant effect on students’ perception of Physics and Mathematics. The study recommends that the government should facilitate the integration of educational robotic activities in the current STEM curriculum in order to improve interest towards the STEM fields.