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Rule-based energy management strategies for a hybrid microgrid using grey wolf optimizer

10.11591/ijape.v15.i2.pp858-879
Sarmid Shakir Abdulsattar , Chee Wei Tan , Shahrin Ayob , Yasir Shakir Abdulsattar , Ahmed Tijjani Dahiru , Chin Kim Gan , Kwan Yiew Lau
This study utilizes grid-connected microgrids using photovoltaics (PVs) and wind turbines (WTs) in a residential system. For improved reliability, the system uses battery storage and diesel generators (Dgen). The proposed system uses supervisory controllers (as a rule-based energy management system) for energy management strategy implementations. The essence of using the grey wolf optimizer (GWO) is to strategize the rule-based energy management system in the proposed microgrid operations. The primary objectives are to achieve a low levelized cost of energy (LCOE) and determine the optimal number of microgrid components. The performance of the GWO is compared with three other optimization algorithms, namely, antlion optimizer (ALO), particle swarm optimizer (PSO), and cuckoo search algorithm (CSA), for benchmarking purposes. The findings indicate that the proposed GWO supersedes ALO, PSO, and CSO in energy cost reduction by 30.3% (0.0448 $/kWh), 65.6% (0.0971 $/kWh), and 120% (0.1774 $/kWh), respectively. The suggested algorithm selects the optimum number of the system’s components, which is 46 PV modules, 30 wind turbines, and 10 units of batteries. An improved GWO-based algorithm based on hybridization with gradient descent algorithms is envisaged to implement a customer-centered energy management that can ensure customer satisfaction and further reduce energy cost.
Volume: 15
Issue: 2
Page: 858-879
Publish at: 2026-06-01

Dual random optimized pulse width modulation controller for three-phase voltage source inverter driven brushless DC motor

10.11591/ijpeds.v17.i2.pp966-980
Halidu Abdul Mumin , Solomon Nunoo , Joseph Cudjoe Attachie
Brushless DC (BLDC) motors are widely employed in modern power electronic applications due to their high efficiency and dynamic performance. However, conventional pulse width modulation (PWM) techniques often generate concentrated harmonic components, leading to acoustic noise, torque ripple, and reduced inverter efficiency. This paper proposes an artificial neural network–assisted dual random pulse width modulation (ANN-DRPWM) strategy to enhance the output quality of a three-phase voltage source inverter driving a BLDC motor. In the proposed approach, supervised ANN training enables dual randomization of the carrier and modulation signals, effectively dispersing harmonic energy while maintaining improved DC-link voltage utilization. A passive LC filter is subsequently integrated to further suppress residual harmonics and ensure compliance with harmonic standards. The system is modeled and simulated in MATLAB/Simulink and evaluated against conventional sinusoidal PWM and flying capacitor multilevel inverter (FCMLI) techniques. Results demonstrate that the proposed ANN-DRPWM method achieves a post-filter total harmonic distortion (THD) of 2.17%, along with a 6-9% improvement in inverter efficiency and a noticeable reduction in torque ripple. Overall, the proposed strategy offers an efficient and intelligent modulation solution for high-performance BLDC motor drives, suitable for applications such as electric vehicles, renewable energy systems, and industrial drives.
Volume: 17
Issue: 2
Page: 966-980
Publish at: 2026-06-01

Analysis of CCS implementation in Indonesia’s coal fired power plants, economic optimization, and potential impact on Java-Bali grid for future decarbonization

10.11591/ijape.v15.i2.pp927-941
Anggit Raksajati , Sanggono Adisasmito , Veri Hendrayawan
This study aims to evaluate impact of retrofitting carbon capture and storage (CCS) technology on coal fired power plants (CFPP) in Indonesia. Using a representative 3×330 MW CFPP, the integration of CCS increases the levelized cost of electricity (LCoE) to 124 USD/MWh. Key cost components include CO₂ capture (21.7%), energy penalty from steam extraction (18.5%), and CO₂ transport and injection (16.7%). Sensitivity analysis indicates that CCS becomes financially viable under a high carbon cap (0.9 tCO₂/MWh) and a carbon tax of 76 USD/tCO₂. Meanwhile, International carbon markets offer a potential revenue at 75 USD/tCO₂ can fully offset CCS costs. Additionally, CAPEX grants can reduce LCoE to 12.4%, serving to mitigate upfront investment for CCS deployment. Within the Java-Bali grid, CFPP account for 58.8% of the generation mix with 41% aged 10-20 years using predominantly subcritical technology while 28% are over 20 years old and follow natural retirement being replaced by renewable energy. CCS retrofitting is more economically and technically viable for mid aged plants with newer technologies and lower emission intensities, supporting grid stability with limited renewable base load availability. This strategy also serves as a transitional pathway toward long term renewable integration until the LCoE of PV+BESS falls below 50 USD/MWh.
Volume: 15
Issue: 2
Page: 927-941
Publish at: 2026-06-01

Self tuning of output scaling factor for type-2 interval fuzzy controllers

10.11591/ijape.v15.i2.pp480-491
Mouna Ghanai , Kheireddine Chafaa , Ali Medjghou , Nadia Bounouara
In this study presents a self-tuning type-2 fuzzy logic controller framework, which operates on the principle of continuously adjusting the controller structure by modifying the controller gain (scaling factor) as a function of the error and its rate of change, in order to achieve optimal control performance. The proposed structure is both simple and robust, with real-time gain adaptation facilitated by two type-2 fuzzy systems; the first one containing the rules of control task for speed regulation, and the second one containing the rules for the adaptation of the scaling factor. Both systems have the same inputs error and its variation. This work specifically focuses on tuning the output scaling factor, which is considered equivalent to the controller gain. The effectiveness of the proposed approach is evaluated through its application to the control of an induction machine, a system known for its complexity and strong nonlinearity. Simulation results demonstrate that the fuzzy controller significantly enhances performance, even under challenging operating conditions such as low-speed regimes.
Volume: 15
Issue: 2
Page: 480-491
Publish at: 2026-06-01

Performance degradation analysis of induction motors using Simulink and hybrid method

10.11591/ijape.v15.i2.pp525-534
Kamrai Janprom , Sittadach Morkmechai , Natchanun Prainetr , Supachai Prainetr
Voltage unbalance faults (VUF) have a significant adverse impact on the performance and operational lifespan of induction motors. This paper presents a hybrid method that integrates multi-sensor analysis to evaluate induction motor behavior under different levels of electrical fault conditions. The research methodology comprises the development of a three-phase induction motor model in MATLAB/Simulink, combined with experimental monitoring of current, voltage, rotational speed, acoustic signals, and torque. The collected data are analyzed using linear regression to quantify performance degradation. The results indicate that increasing fault severity correlates with reductions in motor efficiency and operational stability. Furthermore, a hybrid technique incorporating modulation analysis of acoustic signals derived from vibration and resonance is proposed to improve the accuracy of efficiency and loss estimation. This approach outperforms conventional methods and demonstrates strong potential for industrial applications, as it effectively mitigates the negative effects of voltage supply faults.
Volume: 15
Issue: 2
Page: 525-534
Publish at: 2026-06-01

Mathematical modelling and automated control strategies for sugarcane crushing system of sugar factory

10.11591/ijape.v15.i2.pp554-564
Govind Singh Jethi , Sandeep Sunori , Surya Kant , Pradeep Juneja
Mathematical models form the basis of automation and digitalization. Control and optimization of industrial processes are important for increasing productivity and efficiency, especially in the sugar industry. This research focuses on modeling and controlling the juice extraction process, which is an important activity in sugar production. The mathematical model is obtained by creating a variable based on simple equations where the cane level in the Donnelly channel is the input and the juice output. The model captures the complexity of the process and provides a solid basis for the design of control systems. Two advanced control concepts: H-infinity control and model control (MPC) were used in MATLAB to meet the criteria. While H-infinity control provides performance in the presence of uncertainty and disturbances, MPC optimizes control performance by predicting future results. This paper observes and compares the results of two control systems to analyze their performance. This comparison highlights the advantages and limitations of each method. The research results are of great importance for increasing the efficiency and reliability of industrial processes in the sugar industry.
Volume: 15
Issue: 2
Page: 554-564
Publish at: 2026-06-01

Wind direction based aggregation of wind power plants under exact wind speeds

10.11591/ijape.v15.i2.pp818-830
Ali M. S. Al-Bayati , Huda Hamza Abdulkhudhur
Modeling of a wind power plant (WPP) containing numerous wind turbines in a highly detailed manner requires a substantial computational cost. Further, the response and dynamic behavior of the WPP systems are significantly influenced by the dynamic nature of wind speed. This paper presents a methodology of aggregating WPP systems with consideration for wind speed directions. To attain a realistic aggregated model, an algorithm for calculating actual exact wind speed at each wind turbine within the WPP was proposed considering different wind speed directions. Furthermore, the best wind speed direction for a fixed site area that produces a minimum wind energy losses inside the WPP was also assessed and reported. The results revealed the importance of employing the exact wind speed calculations within the WPP to ensure that the aggregated WPP model accurately represents real-world conditions. The results of this paper highlighted the role of wind direction in determining the response of WPPs and provide guidance on maximizing the WPP throughput during the year under the prevailing wind speed direction at the site.
Volume: 15
Issue: 2
Page: 818-830
Publish at: 2026-06-01

Development of groundwater level sensor for internet of things-based peatland fire monitoring

10.11591/ijape.v15.i2.pp915-926
Abdul Muid , Nina Siti Aminah , Maman Budiman , Mitra Djamal
Monitoring groundwater levels in peatlands is crucial for mitigating forest and land fires, which frequently occur in tropical regions. Current peatland groundwater level monitoring systems generally have limitations in their sensing range. Additionally, the lack of electricity and internet infrastructure around the monitoring sites presents another challenge. To address these issues, in this study, we developed a groundwater level sensor based on a float integrated with an optocoupler as a rotary encoder to monitor changes in water levels in real-time. The system is connected to an internet of things (IoT) platform through a LoRa communication module, powered by solar panels, enabling continuous wireless data transmission over a large range. To overcome the sensing range limitation, we developed a rotary encoder with a pulley driven by a long rope. Testing was conducted using transparent pipes with varying water levels to evaluate the sensor's performance. Experimental results showed that the sensor could measure water levels with a resolution of ±3.33 mm. This research provides a technique for measuring groundwater levels with an extended measurement range. Additionally, it produces a peatland fire risk monitoring system based on groundwater level parameters. It is expected can support peatland fire mitigation efforts more efficiently and effectively.
Volume: 15
Issue: 2
Page: 915-926
Publish at: 2026-06-01

Impact of synchronous condensers on voltage stability in systems with high renewable energy penetration

10.11591/ijape.v15.i2.pp760-769
Juan Esteban Rodríguez Quiroga , Mario A. Rios
The rapid integration of renewable energy sources (RES) poses significant challenges to power system reliability, particularly regarding voltage stability and reduced loadability margins. This study investigates the impact of synchronous condensers as a mitigation strategy to enhance stability in grids with high renewable penetration. The research objective is to evaluate how these devices influence loadability margins while considering the inherent stochastic nature of RES. The methodology employs PV curves for static voltage stability assessment, utilizing the 2m+1 point estimate method (PEM) to model uncertainty with high computational efficiency. This approach allows for the calculation of statistical indicators, including mean values, standard deviations, and confidence intervals for loadability margins. Simulations were conducted on the IEEE reliability test system (RTS) using NEPLAN360 software. The results demonstrate that the deployment of synchronous condensers (SCs) significantly improves voltage stability by increasing load margins and reducing the standard deviation of uncertainty. Conclusions indicate that these devices are effective reactive power compensators that provide a more robust operational environment against RES variability. Future research will focus on the optimal sizing and placement of these compensators to further maximize grid security.
Volume: 15
Issue: 2
Page: 760-769
Publish at: 2026-06-01

Sliding mode control of a solar powered switched-inductor based quadratic DC-DC converter for sustainable EV battery charging application

10.11591/ijape.v15.i2.pp712-723
Jawahar Marimuthu , Edward Rajan Samuel Nadar
The growing demand for sustainable transportation and fast charging solutions requires efficient power conversion technologies for solar electric vehicles or electric vehicles (SEVs/EVs). A non-isolated solar-powered switched-inductor quadratic DC-DC converter is proposed here to achieve high voltage gain in a practical way under reduced stress on power devices. A switched-inductor network blended with CCM operation avoids the extremely high duty cycles and high electromagnetic interference in conventional boost converters. A sliding mode control (SMC) strategy is applied here to improve robustness against parameter variations, ensure stable operation against dynamic load variations, and extract maximum power during solar-powered charging operation. This makes the topological platform proposed in this study especially suitable for a wide variety of applications, such as for SEVs and fast-charging applications of EVs. Detailed MATLAB/Simulink analyses along with a laboratory-scale prototype verify the performance of the converter under practical operation conditions and confirm the high efficiency of 91-96% at varied irradiance, low voltage ripple of 0.5-1.5% of output voltage and input current ripple of 5-12% of input current, reduced switching losses of 1-4%, and suitability of the presented converter for renewable-energy-based transportation systems.
Volume: 15
Issue: 2
Page: 712-723
Publish at: 2026-06-01

Analyzing the ability of capacitor energy in a modular multilevel converter to support inertia in an AC system

10.11591/ijape.v15.i2.pp646-662
Dunya Sh. Wais , Huda A. Abbood
Flexible DC transmission systems based on modular multilevel converters have the potential to support the inertia of AC power grids by using sub-module capacitor energy storage. However, existing studies generally believe that the inertia provided by flexible DC systems is limited by their energy storage time constants, which is weaker than that of synchronous motors, and lacks quantitative indicators to measure their support strength. Introducing the flexible-DC equivalent inertia constant (FDEIC) as a precise metric for assessing inertia support under different management schemes, this research presents a new analytical framework based on frequency responses. Results show that the inertial response is influenced by control bandwidth, DC-voltage dynamics, and circulating-current behaviour. A more generalized multi-terminal FDEIC is created to account for the impact of raised total capacitor energy, and the theory is further expanded to cover DC grids with more than one terminal. A three-terminal flexible DC grid simulation model is built in the PSCAD environment, and the simulation results verify the effectiveness of the proposed quantitative analysis method.
Volume: 15
Issue: 2
Page: 646-662
Publish at: 2026-06-01

Super-twisting sliding mode control for enhanced performance of grid-connected PV systems with H-bridge multilevel inverter

10.11591/ijape.v15.i2.pp464-479
CH. Venkata Amarnadh , T. Vijay Muni , T. Anuradha Devi , Rakesh Teerdala , M. Kiran Kumar , Kambhampati Venkata Govardhan Rao
This paper presents an enhanced control strategy for a grid-connected photovoltaic (PV) system employing a novel H-bridge multilevel inverter (MLI). The key contribution of this work lies in replacing the conventional proportional-integral (PI) controller with a super-twisting sliding mode controller (STSMC) for DC-link voltage regulation. Unlike earlier approaches that suffer from slow response, steady-state errors, and limited robustness under varying solar and temperature conditions, the proposed STSMC ensures faster transient response, finite-time convergence, and strong disturbance rejection without the chattering problem of classical sliding mode controllers. Another distinctive aspect of this study is the integration of STSMC with direct model predictive control (DMPC) for grid current regulation, enabling accurate reference current generation and improved synchronization. The novel H-bridge MLI topology further enhances system efficiency by reducing the number of switches while producing a seven-level output with lower total harmonic distortion (THD). Simulation results demonstrate that the proposed strategy achieves superior performance compared to the conventional PI-based system, with improvements in voltage stability, current quality, and reduced THD. These findings confirm the novelty and effectiveness of the proposed control scheme for reliable and efficient PV grid integration.
Volume: 15
Issue: 2
Page: 464-479
Publish at: 2026-06-01

Moth flame optimization based super twisting sliding mode MPPT controller for grid connected PV system

10.11591/ijape.v15.i2.pp703-711
Ujwala Gajula , Gouthami Eragamreddy , N. Malla Reddy , Remala Geshma Kumari , Veeranjaneyulu Gopu
Maximizing energy extraction while maintaining the stability of solar photovoltaic (PV) systems requires an effective and robust control strategy. This study proposes a novel control approach by integrating a super twisting sliding mode controller (STSMC) with the moth-flame optimization (MFO) algorithm to enhance battery energy management, power quality, and maximum power point tracking (MPPT) in grid-connected PV systems. The proposed MFO-STSMC controller combines the robustness of sliding mode control with the adaptive optimization capabilities of MFO, resulting in improved MPPT accuracy, reduced oscillations, and enhanced resilience to environmental disturbances and nonlinearities. Simulation results validate that the proposed method significantly outperforms conventional MFO-PI controllers, achieving accurate MPPT tracking under varying irradiance and temperature conditions, and ensuring stable operation. Moreover, the total harmonic distortion (THD) is reduced to 0.17% with MFO-STSMC, compared to 0.72% with MFO-PI, highlighting substantial improvement in power quality. The system is modeled and validated using MATLAB/Simulink, confirming the effectiveness of the proposed strategy in enhancing energy efficiency and grid stability.
Volume: 15
Issue: 2
Page: 703-711
Publish at: 2026-06-01

Adaptive P&O algorithm for fast and acurate maximum power point tracking for PV system

10.11591/ijape.v15.i2.pp590-599
Fathurrahman Fathurrahman , Rika Sri Utami , Akhyar Akhyar , Khairun Saddami
In this study, we proposed an adaptive perturb and observe (P&O) algorithm designed for efficient maximum power point tracking (MPPT) in photovoltaic (PV) systems. This method addresses key challenges in solar energy systems, including variability in solar irradiation and partial shading conditions. The proposed method introduced a dynamic and adaptive in adjusting the step size of the P&O as it nears the maximum power point (MPP), enhancing tracking precision and reducing energy losses. To show the ability of the proposed, we compared it with the conventional P&O and GWO & P&O. The proposed adaptive P&O MPPT algorithm consistently maintains near ideal tracking efficiency of ≈99.7% across various irradiance scenarios, significantly outperforming conventional P&O, which drops to 74.45% under partial shading. Overall, it achieves an average efficiency of 99.71%, surpassing hybrid P&O GWO (99.52%) and conventional P&O (91.30%), demonstrating superior reliability and energy harvesting performance. The results indicated that the proposed could reduce power deviations and obtain greater accuracy in detecting MPP. The study confirms the method's potential for optimizing energy extraction and suggests further refinement for broader applicability. This advancement represents a significant step in enhancing the reliability and efficiency of PV systems in both grid-connected and off-grid applications.
Volume: 15
Issue: 2
Page: 590-599
Publish at: 2026-06-01

Adaptive telematics integration for enhanced EV fleet management and data acquisition

10.11591/ijape.v15.i2.pp808-817
Kavitha Kumaraswamy , Pasumarthi Usha , S. Ashok Kumar , Deekshitha Arasa , Suganthi Neelagiri
Telematic control units (TCUs) and on-board diagnostics (OBD-II) systems are commonly used to monitor vehicles and enable real-time communication. However, traditional OBD-II systems provide limited data, making it difficult to accurately detect faults and analyze performance, especially in hybrid, flex-fuel, and electric vehicles. A TCU is an embedded system installed in vehicles that enables wireless communication with external networks. This paper introduces a standalone device designed to seamlessly integrate with electric vehicles (EVs) by utilizing TCU capabilities to enhance data acquisition. The TCU uses a combination of sensors to collect important real-time vehicle data, such as GPS location, battery charge level, and voltage levels. The collected data is processed to generate meaningful insights that support decision-making and system optimization. The proposed system uses the TCU as a core component to transmit real-time data to a fleet management system (FMS). By providing enhanced data to the FMS, the system improves diagnostic accuracy, strengthens EV safety monitoring, and enables more efficient fleet management across diverse vehicle types. This approach allows deeper monitoring of EVs and improves overall fleet efficiency. The framework offers a cost-effective and scalable solution for advanced monitoring and optimization of electric vehicle fleets.
Volume: 15
Issue: 2
Page: 808-817
Publish at: 2026-06-01
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