The integration of diverse load types, such as large-scale distributed generators and charging stations, presents considerable challenges to distribution networks. The Unified Power Flow Controller (UPFC) provides multiple benefits, including its compact dimensions, cost efficiency, seamless regulation, and swift response. When deployed in distribution networks, the UPFC facilitates the creation of a Distribution Unified Power Flow Controller (DUPFC) system. The DUPFC system enhances medium voltage distribution network regulation but also affects power quality, fault characteristics, and relay protection accuracy. To address these issues, a power flow equivalent circuit model for the medium voltage distribution loop network was developed. The operating principle and steady-state model of the UPFC were investigated, based on distribution loop network parameters and power flow distribution characteristics. A UPFC control strategy was designed to create a suitable DUPFC system for the medium voltage distribution loop network, ensuring coordination between UPFC protection and the distribution system's protection mechanisms. This system accomplishes rapid fault isolation and automatic load transfer. Finally, a simulation model based on a typical 20 kV distribution loop network structure was selected to validate the effectiveness of the UPFC control strategy and the DUPFC protection system in MATLAB/Simulink.
The high-efficient cooling structure is one of the key basic problems in the design of the highly integrated in-wheel motor (IWM) drive system. A 15 kW IWM drive system with the spiral cooling structure is taken as the research object in this article. First, the thermal characteristics under different conditions are analyzed based on the modeling and the heat source determination of the IWM drive system. Second, the highest temperature of insulation and permanent magnet and temperature difference of insulation are taken as optimization objectives. The inlet flow rate, the inlet and outlet diameters, the channel numbers, the channel height, and the channel ribs thickness of cooling structure are taken as optimization parameters to optimize based on the designed experiments, the multivariate linear regression equation and PSO method. Finally, the optimization results are verified by comparing the temperature field before and after optimization. The results show that the optimized structure can achieve better effect. The highest temperature of insulation, the highest temperature of permanent magnet and the temperature difference of insulation of IWM decreased by 9.27%, 9.52%, and 20.93%, respectively. This research work can provide some ideas and methods for the optimization design of cooling structure of IWM drive system.
In this article, the aim is to investigate the torque ripple and propose control strategies to reduce the torque ripple in Interior Permanent Magnet Synchronous Machine (IPMSM). Frequently, zero current in dx-axis is used to reduce the effect of reluctance torque. This is unsuitable for the IPMSM, due to the cross-coupling effect between the qx- and dx-axis. In order to overcome the cross-coupling effect problem, firstly, a novel modeling of reluctance torque is proposed, which allows the elaboration of control strategies by injecting current either in the qx-axis to suppress the effects of the cogging torque or in the dx-axis, to nullify the reluctance torque together with the cogging torque or just the reluctance torque. For this purpose, machine parameters and a pre-made lookup table are necessary. The results demonstrated the effectiveness of each strategy. The torque ripple can be totally or partially reduced according to the chosen strategy. The strategies have different current values, consequently, the copper losses in the machine will be different, as shown and discussed in the paper.
Electric vehicles (EVs) are now considered to be a cutting-edge form of transportation. However, the electrical grid infrastructure is not sufficiently developed to meet the rising charging demand for electric vehicles. Therefore, it is impossible to rely entirely on grid-generated electricity. This article discusses a control approach for extremely rapid charging of EV batteries driven by a hybrid DC microgrid, consisting of isolated, Photovoltaic (PV), Wind Turbine, Fuel Cell and Energy Storage Systems. An adaptive sliding mode controller is designed to provide the appropriate power for charging and discharging EVs and energy storage units (ESUs) under a variety of power generation and demand scenarios. The proposed adaptive sliding mode controller operates in a decentralized manner to maintain power distribution amongst the microgrid DC-link EVs and the ESUs. In order to validate the effectiveness of the sliding mode controller in battery charging applications, a comprehensive comparison between fuzzy logic and adaptive sliding mode control is conducted. The proposed controller operates exceptionally well in the presence of unpredictable operating point variations such as rapid changes in EV charging load, fluctuations in solar irradiation, unexpected wind conditions, and so on. Finally, the proposed controller is verified by both simulation and experimental studies.
This paper presents the design and analysis of a micro-transformer model aimed at achieving high efficiency in photovoltaic applications. The proposed micro-transformer structure consists of two circular planar coils made of copper mounted on PCB (FR4) and Si substrates. The Mohan formula is employed to calculate the micro-transformer's geometric parameters, ensuring alignment with flyback converter specifications. Consequently, the inductance, quality factors, and coupling factor variation parameters versus frequency, and the impact of the gap between the coils (500, 750, and 1000 mu m) on them were analyzed. The simulation findings unequivocally demonstrated that a judicious reduction in gap thickness to 500 mu m resulted in a remarkable enhancement of the coupling factor by an impressive 91%. Moreover, finite element-based software was used to analyze the thermal, magnetic, and electric performance of the micro-transformer across varying gap sizes. The investigation led to the determination of the optimal gap thickness that achieves adequate temperature, current, and magnetic flux distribution in the micro-transformer. Finally, the simulation circuit of a photovoltaic system that incorporates a flyback converter including the proposed micro-transformer was performed. Overall, the results revealed that the proposed micro-transformer model with its circular planar coil design offers high efficiency in PV applications.
Load frequency management (LFM) work has become more difficult due to hybrid power systems. These power systems are responsible for increased power quality issues, exacerbating control problems. Frequency changes and power quality issues are the central concerns caused by these modern power systems. Frequency deviations occur mainly due to variations in generation, consumption, or both, and one of the critical reasons is the low inertia of the modern renewable penetrated hybrid power systems. So, to handle all these issues related to frequency and many more, the need for the best controller arises, which can easily handle any nonlinearities. This article comprehensively analyzes various load frequency control (LFC) architectures in various power system configurations. It focuses on the frequency management features of various power systems, including microgrids that use renewable energy and electric vehicles, with a complete understanding of current development states. Different controllers and tactics underwent comparative investigations. The detailed tabular analysis of frequency control architectures provides a deep insight into the LFC assessment of various power system configurations. The importance of better controllers for LFC planning in complicated power system domains is also emphasized in this work.
Renewable energy technologies (RET) are expanding over the globe to fulfill the world's overall energy requirement. The main issue following the adoption of RET into power system network is an electrical islanding. It causes voltage, frequency, current, phase angle, power and harmonic content deviation outside the allowable limits, which may be hazardous to both connected apparatus and customers. It should be noticed within 2 s, according to Distributed Generation (DG) IEEE 1547 interconnection specifications. This research examines several islanding recognition strategies for improving the solidity of utility-connected DG. It will assist potential scholars in choosing the optimum islanding recognition technique with less non-detection zone (NDZ). The comparative assessment of various detection parameters analyzed in terms of NDZ, power quality issues and number of DG networks used. Finally, the sensitivity analysis of twenty passive methods carried out for low power mismatch cases like load switching and different fault cases, and the best methods for future islanding detection are recommended.
Electricity produced by a hybrid renewable energy system (HRES) is reliable, economical, and environmentally benign. However, effective management of the load-source side is essential to enhance the reliability, affordability, and eco-friendliness of HRES. Therefore, an integrated load-source side management for techno-economic-environmental (TEE) performance improvement of HRES is developed in this paper. The factors considered are namely technical (renewable energy portion, excess energy factor, and unmet load), environmental (particulate matter and carbon emission), and economical (annualized cost of system, total net present cost, and cost of energy). To solve the HRES size optimization problem, a marine predators algorithm (MPA) is used for the first time. HRES is investigated without Load side management (LSM), with LSM, and with LSM integrated with source-side management (SSM). LSM is carried out using the peak shifting technique, which shifts time-movable loads (TMLs) to off-peak hours depending on excess energy availability, taking into account consumer comfort violations and customer behavior uncertainty. For SSM, a comparatively better energy management strategy (EMS) than the load following, cycle charging, and generator order is proposed, which improves the TEE performance of HRES. The integrated load-source side management saves 5% in NPC and 0.008 $/kWh in COE, respectively. To come up with a configuration that is more feasible, a sensitivity analysis for the HRES's component costs and macroeconomic variables is carried out. Furthermore, the optimal configuration COE is slightly higher than that of the most recent study in the literature. By contrasting the MPA result with the HRES PSO result, the accuracy of the MPA result is also confirmed.
In an era increasingly focused on sustainability, the adoption of renewable energy stands as a promising avenue for fostering local economic growth. This study presents a novel approach, merging advanced fault mitigation techniques and machine learning, to assess the economic impact of renewable energy systems (RES) at the local level. Leveraging random forest, support vector machines (SVM), and gradient boosting, customized algorithms are deployed for regression analysis and defect identification. Hyperparameter optimization ensures optimal performance, with a linear regression meta-learner facilitating the fusion of predictions. An advanced anomaly detection component effectively identifies and rectifies errors within RES. Performance evaluation metrics, including an root mean square error (RMSE) of 2.18 and an overall system efficiency of 98%, underscore the success of the fault mitigation strategy. Precision, recall, and F1-score metrics further highlight its robustness. This comprehensive framework not only provides precise estimates of the financial impact of renewable energy adoption but also enhances the reliability of RES through sophisticated fault mitigation. Empowering decision-makers with actionable insights, it facilitates sustainable energy planning, effective policy implementation, and the establishment of resilient energy systems.
The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies.
Modification of the particle swarm optimization (PSO) method is proposed with an opposition-based learning strategy to find the optimal solution to electrical power dispatch problems. The objective of the proposed algorithm is to address the combined economic and emissions dispatch problem (CEED) of thermal power plants. This problem includes constraints such as the valve point effect, prohibited zones of operation, and ramp rate limits. In order to assess its performance, the proposed algorithm is first evaluated using a set of benchmark functions. Later, three thermal generating systems having 6, 10, and 40 units respectively are regarded as the test systems to validate the proposed method. The proposed method is tested, and a comparison of the results are made with popular optimization techniques reported in the literature such as PDE, MODE, NSGA II, and MOSSA. Promising results have been obtained with opposition-based PSO in comparison with their current equivalents. A comparison was made between the fuel cost, emissions, and CPU time of the proposed method with the two other PSO variants: inertia factor PSO (IFPSO) and constriction factor PSO (CFPSO). The results showed a decline in the overall cost by approximately 3.73% and a decrease in CPU time by as much as 2.6 s. Furthermore, the obtained predictions consistently exhibit a high level of accuracy, typically approaching 100%.
Energy Disaggregation is the efficient technique to detect the energy profile of individual electric load by disaggregating the overall power consumption. The benefits of energy disaggregation are not only limited to residents but also helps to improve the building efficiency through load identification process. The idea of this paper is to provide a widespread review of energy disaggregation and present a scheme for non-intrusive load monitoring at a building level model. As the scheme involves a simple regression model with better accuracy, it is less complex to achieve energy disaggregation for the real time load identification in educational institution. Various metrics involved in the assessment of model performance like accuracy, standard errors values and computation time were evaluated and demonstrated for validity. A broad investigation is done for building level energy disaggregation and probable elucidations were discussed for future research initiation.
Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.
The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.
This paper presented control methods for the reduction of the ripple torque and the control of speed in the residential of BLDC and different applications using ZSI. Photovoltaic (PV) systems' renewable energy is regarded as the input of BLDC motors. The ZSI has different benefits such as low cost in addition to improved efficiency. In addition, control of the speed torque ripple is achieved with the consumption of the ZSI. To improve the control performance, the Functional Order Proportional Integral Derivative (FOPID) controller can be utilized. Here, the FOPID controller parameters can be optimized through the consumption of the Emperor Penguin optimization (EPO) algorithm. The errors of torques and speeds are decreased with the consumption of the ZSI with the EPO algorithm. Therefore, to realize the purposes, EPO can be proposed to standardize the FOPID frameworks of the motors of BLDC controllers of the speeds and torque. These presented methods are carried out from the Simulink/MATLAB in addition it is compared among the previous methods of PSO, SSA, also and FA. The performance can be evaluated with two different conditions, constant irradiance conditions, and different irradiance conditions.
The unpredictability of solar and wind energy sources affects contemporary power networks and adds frequency variations. An appropriate intelligent controller is necessary to balance electricity between generation and demand. As a result, in this research, we present a sine-cosine adaptive improved equilibrium optimization (SCaIEO) method tuned to Adaptive Type 2 Fuzzy PID Controller (AT2FPID) for frequency management of cutting-edge power systems. The efficiency of the SCaIEO approach is assessed by comparing it to the original equilibrium optimization (EO) and other comparable algorithms for the test function. Moreover, engineering applications of the SCaIEO technique are carried out by constructing an AT2FPID controller to manage the frequency of power systems that include renewable energy and dispersed sources. First, we show that SCaIEO outperforms EO, Particle Swarm Optimization, Genetic Algorithm, Moth Flame Optimization, and Gravitational Search Algorithm in the PID controller (Gravity Search Algorithm). The AT2FPID is next evaluated, and the SCaIEO AT2FPID controller's dominance is proven by equating the outcome to the original, PID Type 1 Fuzzy PID, Type 2 Fuzzy PID controller.
In this study, an effective solution is given to overcome the drawbacks of backstepping command (BC) for a doubly-fed induction generator based on multi-rotor wind power system, where both fractional calculus and non-linear surface were used for this purpose. This suggested approach differs from the classical BC method and demonstrates strong efficacy, displaying resilience against both external and internal factors of the system. This command was suggested to command the machine-side inverter to increase power quality and reduce steady-state error (SSE). Therefore, the characteristics of this suggested BC were verified compared with the conventional BC method using MATLAB in different operating situations. This proposed strategy demonstrates a distinctive performance in improving system characteristics, as evidenced by the reduction rates compared to the BC technique. Active power undulations were minimized by 47.36% in the first test and 41.74% in the second test compared to BC. Also, The SSE value of reactive power was minimized by approximately 41.66% and 45.83% in both tests compared to the BC strategy. Moreover, the value of harmonic distortion improved significantly compared to the BC, with high ratios estimated at 68.57% and 55.30% in the same testes. These ratios indicate that the current quality is high when the suggested command is used. This indicates that in the future, this control method is poised to become one of the leading solutions trusted in the field of control.
An enhanced controller is proposed to investigate grid-connected photovoltaic systems employing cascaded two-level inverters. The primary focus is on optimizing power output, achieved through the development, modeling, and testing of photovoltaic systems using the suggested upgraded controller. This advanced controller, specifically a Fractional Order PID (FOPID) controller, incorporates the Ant-Lion Optimizer (ALO) algorithm for enhanced performance compared to conventional controllers. The FOPID controller's reliability is emphasized, and further improvements are pursued by optimizing gain parameters through the prescribed method. Power supply under both schemes is examined, and the controller's performance in extracting maximum power under specified operating conditions is deemed satisfactory. To validate the proposed approach, practical implementation is conducted using the MATLAB/Simulink platform. The effectiveness of proposed and conventional methodologies for analyzing the id and iq currents is assessed. A comparison is drawn between the suggested approach and current methods such as the base controller, GSA, and ABC methodologies. In comparison to the existing methods, the suggested FOPID controller demonstrates superior performance in maintaining the DC link voltage, achieving an efficiency of 94%, while the GSA method reaches 91.5%, the ABC method achieves 91%, and the base controller achieves 90%.
Power electronic converters are regarded as indispensable aspects of modern electric power systems, owing their eminence as optimal power interface and power flow regulators. Hence, the design of an efficient converter is vital to support wide-scale expansion in the application of both Renewable Energy Sources and Electric Vehicles. Thereby, a novel high gain K. S. Kavin (KSK) converter based on interleaved architecture, exhibiting lower voltage stress across switches is proposed in this research work. The suggested converter design is well suited for Photovoltaics (PV) power generation, as it offers higher step-up gain with lower input current ripples. Moreover, mathematical analysis and modeling of suggested KSK converter with different operational modes are also covered in this article. Additionally, the Radial Basis Function Neural Network based on Machine Learning is employed as a Maximum Power Point Tracking technique to optimize power generated by the PV system. Additionally, Internet of Things (IoT) is used for real-time monitoring of PV parameters. The viability of the suggested KSK converter is demonstrated through MATLAB simulation and laboratory prototype implementation, while the control of the developed 1000 W prototype is entrusted up on the FPGA Spartan 6E controller. Consequently, achieved exceptional 98.6% efficiency of the proposed KSK converter lends credence to its excellence over other published converter topologies.
The inspection of overhead power transmission line and assets is an essential aspect to improve the overhead power transmission efficiency and to ensure an uninterrupted power supply. This article has mainly focused on the progression and fabrication of indigenous quadcopter/Unmanned Aerial Vehicle (UAV) for carrying autonomous operations in a coordinated movement along the overhead transmission towers for capturing the images and videos of transmission insulators and assets. A custom based dataset of power line insulators is created by using the quadcopter for overcoming the data scarcity and to perform Deep Learning (DL) assessment for (i) inadequate data for training and (ii) power line insulator detection and faults. The experimental results showcase that, the suggested DL architecture identifies power line insulators and associated faults, such as cracks, broken disk and missing top caps etc. With a detection speed of 56.8 frames/sec and an accuracy of 94.1%, the proposed DL technique has much promise for intelligent examination of power grid insulators. Ecological Footprint assessment of different power line inspection methods are also examined in this study.
