The South African rail sector is a key contributor to the national economy, boosting gross domestic product (GDP) and creating jobs. However, serious malfunctions often jeopardize the reliability of locomotives such as the Class 8E locomotive, leading to lost output and longer lead times. Accurate forecasting of the catenary line voltage is essential to ensure timely activation of protective mechanisms and maintain the safe operation of electric traction systems under undervoltage conditions. To reduce unscheduled downtime in the 8E locomotives, this study proposes a framework that analyzes the impact of clustering methods and hyperparameter settings on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Real-time operational data, including line current, ambient temperature, oil temperature, and line voltage, were gathered on the 8E locomotive at Impala Platinum Mine in Rustenburg, South Africa (SA), between August and October 2024. Three distinct clustering methods, namely, subtractive clustering (SC), grid partitioning (GP), and fuzzy c-means (FCM), along with other key hyperparameters, resulting in a total of 24 developed submodels, were examined and analyzed. The performance of the developed models was analyzed using 7 renowned statistical metrics. With a clustering radius of 0.3, the ANFIS-SC model delivered improvements of 28.45% (MAPE), 28.64% (MAE), 20.80% (SD), 27.53% (CVRMSE), 28.11% (RMSE), and 27.50% (Theil’s U) compared to its ANN counterparts. In addition, better performance was obtained compared to the PSO-based ANFIS model. The study demonstrates the potential of the proposed model as a reliable tool for catenary line voltage in the Class 8E locomotive rail sector in SA.
Integrating electric vehicle (EV)-charging infrastructure presents environmental advantages, particularly in curbing carbon emissions within the transport sector and promoting sustainable energy solutions. However, the ascending adoption of EVs transforms the operational dynamics of low-voltage distribution networks by introducing bidirectional power flows that challenge conventional overcurrent protection schemes. Traditional protection systems cannot effectively manage the complexities of variable load conditions and bidirectional energy transfers, specifically Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operational modes. These scenarios require the development of advanced, dynamic, and real-time protection mechanisms that are robust against challenging, faulty scenarios and cybersecurity threats. This study introduces an adaptive protection scheme that utilises digital overcurrent relays, LoRa-enabled sensors, a battery management system (BMS) and a central protection unit (CPU). This integrated framework dynamically recalibrates relay settings based on real-time grid conditions, ensuring optimal protection coordination during both G2V and V2G operations by employing a new optimisation algorithm called the transit search algorithm (TSA) and comparing the result to the water cycle algorithm (WCA). To assess the effectiveness of the proposed adaptive approach, simulations were performed on a 33-bus IEEE benchmark network, investigating a variety of fault scenarios and operation grid scenarios. The results indicate that the proposed system significantly mitigates relay miscoordination and reduces fault clearance durations, thus enhancing reliable protection in distribution networks with high EV penetration.
Virtual power plants (VPPs) can aggregate distributed resources across various nodes to participate and collaborate in electricity market trading. Unlike traditional standalone generators or load aggregators, this study leverages the dual role of VPPs as producers and consumers. It introduces a natural risk–hedging mechanism and proposes a price-acceptance bidding strategy for VPPs in the day-ahead spot market, which primarily relies on electricity price forecasts. This strategy is compared with scenarios where distributed resources or traditional generators/load aggregators bid independently. The analysis focuses on the success rate of market participation and the actual financial returns. The findings indicate that the proposed strategy based on the natural risk–hedging mechanism substantially enhances the resilience of VPPs in managing market risks and effectively mitigates the negative impacts of price volatility and forecasting errors on their economic benefits.
Multilevel inverters (MLIs) based on IGBT switches have gained prominence in AC power applications due to their capability to reduce harmonic distortion while offering cost-effective operation. They are widely adopted in power electronic systems; however, under high-stress conditions, power switches are prone to faults that can impair system performance. Hence, effective identification of faulty switches is crucial. This study focuses on detecting single and multiple switch open-circuit faults (OCFs) in reduced device count (RDC) MLI. A machine learning (ML)-based diagnostic framework is proposed, which utilizes only the output voltage signals for fault analysis. From these signals, three key features are extracted: standard deviation, half-cycle moving average, and total harmonic distortion for fault classification. Several ML classifiers are evaluated and benchmarked against recent approaches, with the decision tree (DT) model achieving the highest accuracy of 99.84% under a 70:30 training-to-testing split. The proposed method accurately identified both single and multiple switch OCFs in RDC-MLI within 10-30 ms. The complete diagnostic system is implemented and validated in the MATLAB/Simulink environment.
This article proposes a battery-based hybrid power flow controller (B-HPFC), offering enhanced flexibility for power flow regulation and energy storage. First, the structure of B-HPFC is given, where the cascaded H-Bridge (CHB) is integrated with the phase-shifting transformer (PST). The batteries are connected to the modules of CHB. Doing so, the power flow can be adjusted by tuning the PST and CHB while the batteries can be charged or discharged by tuning CHB. Then, the hierarchical control method is given. The power flow control strategy operates as the outer loop, and the battery control strategy operates as the inner loop. Finally, the proposed structure and control method are verified by the hardware in the loop prototype. The results show that the power flow can be controlled smoothly while the SOC of batteries can be balanced.
In order to accurately evaluate and tap the mutual-aid capacity potential of interconnected power stations under the scenario of peak-to-peak compensation between new energy output and load demand, this paper uses Copula function to describe the correlation structure of wind, light, and load from the perspective of source–load matching, quantify the complementary degree of residual power and new energy output after source–load matching, and determine the feasible interval of mutual aid. On this basis, a space–time mutual-aid capacity optimization model with the goal of minimizing the total operating cost of the interconnected area is constructed. The model takes the principle of priority mutual aid in the station area and comprehensively considers the internal and external energy interaction constraints such as the transaction cost of purchasing and selling electricity with the superior power grid, the two-way power constraints of the tie line, the transformer capacity, and the renewable energy output constraints. Finally, the model is solved efficiently by the CPLEX solver of MATLAB. The simulation results of the example show that the proposed method can automatically establish cross-regional mutual power channels in the period of significant complementarity and significantly improve the renewable energy consumption level and overall operation economy of the station area while ensuring load power supply.
DC series arc faults (DC SAF) in photovoltaic (PV) systems can lead to electrical fires and electric shock hazards. Therefore, DC SAF modeling and detection is a significant process for ensuring the safety of PV panels and is necessary for producing PV systems in actual applications. Using real data, for the first time, this study presents a DC SAF modeling technique based on machine learning (ML) algorithms. Considering the unpredictable and nonlinear nature of such arcs and the application of ML in solving nonlinear and complex problems, multilayer perceptron, radial basis function, and support vector machine algorithms are used to model DC SAF in PV systems. The performance of proposed ML-based approaches is compared with well-known traditional models by using error indices, which are computed using a test data set. Finally, comprehensive evaluations and results of modeling demonstrate that proposed models based on ML methods remarkably improved modeling accuracy and generalization capability in DC SAF modeling.
Wind energy conversion systems (WECSs) require robust and efficient control strategies to ensure optimal energy conversion. This study proposes a nonlinear and resilient control approach using a fractional-order proportional integral- and fractional-order proportional derivative (FOPI-FOPD) controller for direct power regulation of a doubly fed induction generator (DFIG)-based WECS. To meet the control objectives, two cascaded FOPI-FOPD controllers were designed, resulting in 12 parameters requiring precise tuning. To optimize these parameters, the Gazelle optimization algorithm (GOA) was employed, targeting the minimization of key performance-based cost functions: mean error (ME), mean absolute error (MAE), mean-square error (MSE), and integral time absolute error (ITAE). These functions integrate dynamic response criteria such as overshoot, rise time, and settling time. Simulation results highlight the effectiveness of the GOA-tuned FOPI-FOPD controller, particularly when using ITAE as the optimization criterion. The controller significantly reduces power ripples by 86.13% in active power and 75.66% in reactive power. It also improves transient response by reducing rise time by 0.035 ms, settling time by 0.3 ms, and completely eliminating overshoot. Moreover, the proposed strategies lower the current total harmonic distortion (THD) by approximately 21.43% compared to the basic strategy. The proposed ITAE-GOA-FOPI-FOPD technique ensures system stability and enhances performance across various operating conditions.
One renewable energy (RE) source that shows promise for producing electrical energy is wind energy (WE). The coordination between the grid and WE conversion systems has become necessary due to high wind power penetration into the grid and varying wind speeds (VWSs). When incorporated into the grid, wind systems encounter challenging scenarios, including voltage fluctuations, power loss, and the troublesome dynamics of RE sources. Conventional PI control systems and fuzzy logic controllers (FLCs) face difficulties in resolving these problems. Applying hybrid artificial neural networks (ANN) will enhance the efficiency of the VWS system. The suggested controller can facilitate uninterrupted power transmission between generators and the grid, enabling a seamless connection with the grid. Here, it can all be facilitated by the constant voltage and power source supplied by a suggested controller. The training of hybridized ANNs with model predictive control (MPC) can minimize computing demands and device version errors. For ANN-MPC, the WE systems for DC microgrids are optimal. Simulink simulations in MATLAB/Simulink are conducted using the suggested hybrid ANN controller. The proposed ANN can consistently achieve better voltage balance and accuracy across various loading cases compared to conventional FLC and PID controllers. The outcomes demonstrate this. The outcomes of these simulations verify the efficiency of the ANN-based strategy. With an accuracy rate of 92.6% and a performance rate of 95.8%, the proposed hybrid ANN-MPC model outperforms similar current methods, as demonstrated by the experimental results.
Recent advancements in electric vehicles (EVs) and modern power systems offer broad opportunities for integrating renewable energy solutions. Solar photovoltaic (PV) systems, in particular, inherently avoid harmonic injection at the source due to the absence of alternating current (AC) power. However, consistently extracting maximum power from PV panels remains a technical challenge-especially under partial shading conditions where conventional algorithms struggle to locate the global maximum on the P-V curve. The recently introduced Jaya optimization algorithm has demonstrated improved performance through its reduced control variables and lower computational demand. Despite these advantages, its random nature often results in wide output fluctuations during transient periods, leading to limited exploitation near the global maximum. To overcome these drawbacks, this article introduces an enhanced Jaya algorithm designed to improve exploitation efficiency while tracking the global maximum power point (MPPT). A Luo DC-DC converter is employed due to its low output ripple, making it suitable for stable power conversion. Extensive simulations and experimental tests were conducted using 4S and 6S PV array configurations rated at 240 W and 360 W, respectively. The proposed method was benchmarked against seven other contemporary optimization algorithms and proved superior-achieving MPPT within 0.1 s and maintaining efficiency above 99% under all shading conditions. Further validation through statistical indices such as IAE, ITAE, ISE, and ITSE confirms the proposed approach's robustness and suitability for real-time, fast renewable energy applications.
With the continued growth in energy consumption, the installed capacity of clean energy, represented by wind power, is steadily increasing. However, the precise modeling of newly built wind farms is challenging due to a lack of data. Additionally, the dynamic updates of data associated with the wind farm’s operating conditions and the difficulty in capturing time-varying features further complicate accurate wind power forecasting. In response to these challenges, this paper proposes a wind power prediction method tailored for the data-scarce scenario of newly constructed wind farms. To prevent over-reliance on single-source domain data, a similarity measurement method combining Mahalanobis distance and dynamic time warping (DTW) is used to establish a multisource transfer learning-based pretrained model using a dilated convolutional neural network–bidirectional long short-term memory (DCNN–BiLSTM) network. Furthermore, to better capture the influence of time-varying scenario data on prediction accuracy, an online adaptive module-based prediction method is introduced to enhance the model’s generalization ability. Additionally, the elastic online deep learning (EODL) method is applied to address the issue of concept drift in dynamic streaming data, enabling quick adaptation to changes in data distribution. The proposed method is validated using data from a wind farm cluster in Northwestern China, demonstrating its superior ability to filter source domain data and provide more accurate power predictions.
This study proposes an improved version of the wild horse optimizer (WHO) for the optimal allocation and sizing of distributed generators (DGs) and capacitor banks (CBs) to promote the system's susceptibility. The proposed method, namely, improved WHO (IWHO), aims to improve the performance of the system not only in terms of power loss, voltage deviation index (VDI), and voltage stability index (VSI) as in most previous studies, but also in terms of generation cost and total emissions. Five operational cases are carried out on four different systems, the IEEE 33-bus, 69-bus, 118-bus standard radial distribution systems and the real 78-bus Egyptian distribution system, to demonstrate the best performance of the proposed technique. In addition, two multiobjective functions are implemented to compare with the original WHO and other existing optimization techniques. Based on the statistical analysis, the simulation results prove that the proposed IWHO provides the best results for flexible operations, especially for large-scale complex systems. After the optimal integration of DGs and CBs, the power loss was reduced up to 94.18%, 98.53%, 92.05%, and 93.87%; the cost was reduced by 43.23%, 43.77%, 14.68%, and 99.99%; and the emissions were reduced by 99.96%, 99.99%, 76.01%, and 61.20% for 33-bus, 69-bus, 118-bus radial systems and the real 78-bus system, respectively. It is observed that the IWHO algorithm also gives recognized enhancements in conflicting objective functions such as technical, economic, and environmental objectives.
One of the most beneficial and effective methods for reducing the power losses of the distribution networks (DNs) is using distributed generations (DGs). The issue of optimal placement and sizing of DGs is a challenge that needs to be investigated carefully, as an improper location and sizing lead to a negative effect on the DN. In this work, an IEEE 33-bus is used as a test system for optimal placement and sizing of four DGs, three of them being photovoltaic (PV) sources and the fourth is a wind turbine (WT). The environmental data (irradiance, temperature, and wind speed) of Baghdad city (latitude: 33.29°, longitude: 44.38°) are used for training the artificial neural networks (ANNs) to forecast the day ahead values of the environmental variables for calculating the power production of PVs and WT. Particle swarm optimization (PSO) technique is used to optimize the location and sizing of the DGs. The operation cost of the system is optimized using genetic algorithm (GA) depending on the optimized sizing and placement of the DGs. Four electrical vehicles charging stations (EVCSs) are interconnected to the implemented DN with considering the uncertainty of hourly charging power demand using the queuing model. The optimal cost of the EVCSs is determined by using fuzzy logic system (FLS) to optimize the energy management of the daily power dispatch and peak power shifting to meet the peak power production of the DGs. The power losses are minimized by 50%, enhancing the voltage profile of the distribution system, and the operation cost is minimized by 19%. The annual operation cost saving of EVCSs is found to be 44.3%.
This paper investigates the application of reinforcement learning (RL) techniques for optimizing proportional–integral–derivative (PID) controller parameters in gas turbine speed control systems. The research employs the Rowen mathematical model as the foundational framework and introduces a novel approach utilizing twin-delayed deep deterministic policy gradient (TD3) algorithms. The methodology integrates machine learning with classical control theory to address the persistent challenges of maintaining optimal turbine speed during both transient startup phases and steady-state operations. Implementation was conducted using a simulation environment based on MATLAB/Simulink, with the General Electric 5001M heavy-duty gas turbine serving as the reference system. The RL agent was designed to interact with the simulated environment, continuously refining controller parameters to minimize performance metrics including integral error values, rise time, and settling characteristics. Comparative analysis between the proposed TD3-optimized PID controller and conventional tuning methods demonstrates significant performance enhancements across multiple control criteria. The optimized system achieved notable reductions in settling time, overshoot magnitude, and steady-state error, while also demonstrating improved disturbance rejection capabilities under variable load conditions and sensor noise.
With the increasing integration of renewable energy sources into the power system, challenges such as wind curtailment and operational flexibility are becoming more prominent. Therefore, this paper proposes a low-carbon optimised strategy for integrated energy system (IES) that considers the efficient use of hydrogen energy and the flexible operation of carbon capture power plant (CCPP)–methane reactor (MR)–hydrogen-doped combined heat and power (HCHP) combination. First, a model for the efficient utilisation of hydrogen energy containing wind power to hydrogen, hydrogen to thermoelectricity, gas-mixed hydrogen and hydrogen to methane was established. Secondly, the co-ordination mechanism among CCPP, HCHP and MR is explored, and the flexibility improvement of CCPP and HCHP is introduced by the liquid storage tank (LST) and Kalina cycle, respectively, and the joint CCPP-MR-HCHP flexible operation model is constructed. Finally, the integrated demand response (IDR) of electricity and heat is introduced, and a novel low-carbon optimisation model of the IES is established by integrating low-carbon and economic considerations. The simulation part of the example set up different scenarios for comparison, and the results showed that the introduction of an efficient hydrogen energy utilisation model can effectively improve the level of wind power consumption and reduce the total system cost and carbon emissions by about 11.35% and 24.73%, respectively. In addition, the proposed CCPP-MR-HCHP model can significantly improve the operational flexibility of the system, reducing the total system cost and carbon emissions by approximately 8.51% and 11.06%, respectively, compared to traditional operating modes.
Reliable electricity access is crucial for sustainable development, yet Bangladesh’s coastal regions face challenges due to an unreliable grid. The off-grid hybrid system based on renewable energy is recommended in the existing research for Bhasan Char, optimized through the application of HOMER Pro software with the components being solar PV (48.3 kW), wind turbine (40 kW), diesel generator (50 kW), battery storage (91 strings), and a system converter (34.7 kW). Five different system configurations were analyzed, and Case 1 was the most cost-effective with a net present cost (NPC) of 25.87 million Bangladeshi taka (BDT), cost of energy (COE) of 16.29 BDT/kWh, and operating cost of 958,523 BDT/year. The system also offers a high renewable fraction (93.9%), low emissions (7651 kg CO2/year), and payback period of 2.74 years. In addition, sensitivity analysis and heatmap correlation using Python were also utilized to compare system performance under various situations. Results show a low-cost and clean model that uses low fossil fuel but is highly economically feasible. The study submits an expandable model for off-grid coastal areas’ sustainable electrification that is consistent with Bangladesh’s energy security and conservation policies.
Accurate estimation of neutral current (I-n) in industrial three-phase power systems is critical for harmonic suppression, equipment protection, and operational safety. This study proposes an ensemble regression framework optimized by a multiobjective genetic algorithm (GA) using 12,328 real-field measurements based on 29 electrical characteristics (P, Q, S; I-rms; U-rms; PF, dPF; I-THD, etc.). The GA simultaneously determines the selection and weights of the base learners (SVR, ANN, GPR, RF, GBR, XGB, DT, and GPR-RQ), improving eight performance metrics together: RMSE, MAE, SMAPE, MdAPE, R-2, EVS, maximum error, and PBIAS. Comparative analyses show that GA achieves high accuracy in 10-fold cross-validation compared to PSO, SA, random search, and average voting strategies (e.g., R-2 = 0.9972, RMSE = 1.83, and SMAPE = 10.31%); unseen test data maintained competitive overall performance (e.g., R-2 = 0.9820; SMAPE = 56.17%). In noise robustness, R-2 = 0.9933 was achieved in target-injected disturbance scenarios. Optimization reached Pareto convergence in approximately 50 generations. In the explainability analysis, SHAP and LIME outputs showed significant differences (p < 0.05) in 28 out of 29 variables; despite low inter-method correlation (Pearson approximate to -0.022), they provided complementary insights. The results demonstrate that the GA-XAI-supported ensemble provides high accuracy, interpretability, and applicability for I-n prediction. To the best of our knowledge, this study presents the first I-n prediction framework that statistically compares SHAP and LIME when used together with a GA-optimized ensemble and reports the process in a reproducible MATLAB script. We translate these distinctions into a practical protocol: SHAP for global monitoring and policy and LIME for case-level triage, thus enabling practitioners to confidently leverage complementary XAI signals during operations.
This research offers a hands-on examination of using Jordan's naturally abundant, high-purity Wadi Rum silica sand (SiO2 > 99%) as an affordable material for thermal energy storage (TES) in concentrated solar power (CSP) systems aimed at home-scale applications. During the 3 days of continuous testing, the setup achieved a peak heat transfer rate of 18.7 kW, heating water by 27 degrees C, reaching a top outlet temperature of 54.9 degrees C. The silica sand proved to be an effective thermal reservoir, attaining internal temperatures between 67 degrees C and 69 degrees C. On average, the system produced 11.2 kWh of thermal energy per day, with an overall efficiency of 50.2%, while cutting daily CO2 emissions by about 2.07 kg. The economic assessment showed a payback time of just 1.49 years, which reduced to 1.04 years with a 30% subsidy. Altogether, the findings confirm that Wadi Rum silica sand offers a practical, sustainable, and financially attractive pathway for thermal storage, directly advancing Jordan's drive toward a cleaner and more self-reliant energy future.
Under the carbon neutrality framework, the traditional coal chemical industry requires the upgrade and transformation of industrial parks to reduce carbon emissions while maintaining economic benefits. This study establishes a green electricity–hydrogen coupled coal chemical system and proposes a robust optimization model incorporating uncertainties in wind and solar power. First, a model for green electricity-driven coal chemical production is developed based on thermodynamic principles, considering material and energy flows. Second, utilizing vine copula theory and Markov transition matrices, a confidence interval-based uncertainty set is constructed to characterize the stochastic nature of renewable energy. Finally, a robust optimization model integrating system dynamics and uncertainty sets is formulated, implemented on the MATLAB–YALMIP platform, and solved using the CPLEX solver. Results show that the proposed uncertainty set enhances wind–solar variability capture (correlation 0.0253 higher than the polyhedral uncertainty set). The system achieves about 1.2-Mt CO2 yr−1 reduction and annual revenue between 0.48 and 3.45 billion CNY (average 1.42 billion CNY), proving both robustness and economic advantage. In terms of economic assessment, the model not only overcomes the limitations of wind–solar data acquisition but also enables reasonable evaluation under diverse scenarios. This work provides novel insights into the green transformation and economic assessment of the coal chemical industry and contributes to economic budgeting and benefit evaluation for other types of industrial parks.
In recent years, managing power in the electrical systems that utilize intelligent infrastructures has become a modern solution for operators. This technology enables more effective control and improves the overall performance of electrical networks. Accordingly, this paper focused on economic and technical power management in an intelligent electrical distribution network (IEDN) with demand response programs (DRPs) at day-ahead. The proposed approach is implemented in IEDN by the bilayer optimization approach considering the contribution of the electrical distribution company (EDC) and consumers. In the first layer, implementation of the DRPs such as local power generation (LPG) by battery storage systems (BSSs), power load curtailment (PLC) program, and power load shifting (PLS) program is scheduled for minimizing bills of consumers. On the other side, in the second layer optimization, income of EDC is maximized and power losses of IEDN are minimized considering scheduled load demand in the first layer optimization. The optimization in both the layers is modeled as multiobjective functions, and optimization of consumers’ bills is done subject to power prices in EDC. The effect of the suggested approach is examined on technical metrics such as voltage profile and peak-to-average ratio (PAR) index. The improved grasshopper optimization algorithm (IGOA) and Shannon entropy decision-making method are used for solving bilayer optimization approach and multiobjective functions. In the end, the results reveal the optimal values of the objective functions of each layer, based on a comparative examination of different case studies, thereby considering consumer engagement.
