Optimizing the control parameters of an islanded microgrid with active load integration presents a challenging operational research problem since current methodologies frequently fail to reach the ideal balance or symmetry between transient response, stability, and efficiency. The conventional methods, such as the canonical Particle Swarm Optimization (PSO), have settling time and voltage ripple minimization constraints, indicating possible improvement scopes. This research addresses this gap by employing advanced metaheuristic algorithms such as Accelerated Particle Swarm Optimization (APSO), Accelerated Particle Swarm Optimization with variable alpha (APSO alpha), Accelerated Particle Swarm Optimization with Normal Distribution (APSO_G), Rayleigh Distribution Accelerated Particle Swarm Optimization (RDAPSO), Rayleigh Distribution Accelerated Particle Swarm Optimization with variable alpha (RDAPSO alpha), and the Dragonfly Algorithm (DA). The algorithms were tested for their performance by using CEC Standard Benchmark functions from 2017, 2019, and 2022, providing a basis for rigorous and symmetrical testing and validation. The optimized RDAPSO alpha algorithm showed a significant reduction in voltage ripple, which was reduced from 4 V to 0.47 V, with an 88.25% reduction. It also showed a 46.32% improvement in settling time, which was reduced from 184.2 ms to 98.9 ms compared to PSO. A detailed statistical analysis was conducted to enhance the reliability and symmetry of the outcomes using Multivariate Analysis of Variance (MANOVA), the Mann-Whitney U test, the Friedman test, and the Bonferroni test. The results show that RDAPSO alpha offers a significant edge over the rest of the algorithms, with improvements that can be declared statistically superior in optimizing microgrids with improved symmetry in performance.

This paper presents a machine-learning based strategy for accurate identification of commutation failures instances. A commutation-failure identification (CFI) block has been proposed and implemented, which utilizes the DC current and the valve-side AC currents for accurate detection. The well-established CIGRE benchmark model, implemented in PSCAD/EMTDC, is employed as the test system. Extensive simulations are performed under diverse fault scenarios, and a comprehensive dataset is generated under both commutation failure and normal operating conditions. Six different machine-learning classifier models are trained using a sufficient portion of the generated dataset and are subsequently evaluated against the remaining test dataset. The performance of the classification is assessed though confusion matrices and several statistical metrics including Accuracy, Precision, Recall and F1-Score for the base case study. Several other studies including the reduced feature cases, noisy validation scenario, five-fold cross validation, and hyperparameter sensitivity analysis have been performed to validate the effectiveness and reliability of the proposed ML-assisted approach. The results demonstrate that the proposed framework can reliably distinguish commutation failure events from normal operating conditions with high accuracy. Among all these classifier models, the Random Forest classifier has been identified as the most suitable model to classify the commutation failure instances with an Accuracy of 99.53% and Recall score of 99.63% for the base case. This accurate classification of commutation failure will ensure enhanced reliability of an HVDC system.

A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots.

Increased adoption of the electric vehicle (EV) needs the proper charging infrastructure integrated with suitable energy management schemes. However, the available literature on this topic lacks in providing a comparative survey on different aspects of this field to properly guide the people interested in this area. To mitigate this gap, this research survey is an effort to provide know-how about the important aspects of the overall EV charging setup with 239 relevant references. It reviews the achievements of energy management systems in terms of improving fuel consumption efficiency and reducing carbon dioxide emissions in EV charging systems. State-of-the-art and most up-to-date standards of EV technology and charging infrastructure are presented. EV charging schemes based on standard grid and renewable energy resources are introduced with a brief comparison of the standard grid and photovoltaic-grid charging systems. Moreover, this article describes centralized and decentralized control configurations of EV charging. A comparative survey of different energy management algorithms is presented while highlighting the benefits and drawbacks of each algorithm. The work presented in this review will help frame future research needs. It is anticipated that the material gathered in this article will become a valuable source of information for the researchers working on this area of study.

The rapid expansion of solar photovoltaic (PV) capacity in Africa presents huge opportunities for energy independence but is hindered by urgent operating and maintenance challenges. Effective fault detection and diagnosis (FDD) is key, but is hampered by the double challenges of scarce labeled fault data and the need for interpretable models that can be trusted by local technicians. This paper proposes and validates a hybrid Artificial Intelligence (AI) model to overcome these limitations. The model proposed here employs an unsupervised Dense Autoencoder for anomaly detection with near-perfect accuracy (AUC of 1.0000) using only healthy operation data, entirely circumventing the challenge of data unavailability. This is augmented with a feature-engineered Random Forest classifier that diagnoses the specific fault type with 90% overall accuracy and good precision on all individual fault classes. To enable trust and actionability, the model’s predictions are explained by SHapley Additive exPlanations (SHAP), which confirms the model learns physically meaningful relationships from the data. Validated in a high-fidelity simulation of the 1.3 MW Phakalane PV plant in Botswana, our framework demonstrates a data-efficient, interpretable and highly accurate solution for improving PV reliability in the African context.

An improved framework for measurement noise reduction of nonlinear PMDC motor using variants of extended Kalman filter (EKF) is presented in this paper. Simulatory as well as experimental testing and validation of presented developments has also been performed. The nonlinearities like hard dead zone and friction have been incorporated in the PMDC motor model. Position as well as velocity measurement scenarios have been considered. Firstly, the noise corrupted measurement is invoked in standard EKF that perform prediction and correction to generate the best possible reduced noise estimate of the true measurement. One drawback standard EKF is that it ignores the effect of noise in the physical system and setting process and measurement covariance values in a vague manner that cause inaccurate estimates. In order to remedy this problem, an adaptive variant of EKF is introduced that utilizes the weighting coefficients and forgetting factor in order to set covariance parameters accurately and hence measurement noise reduction and estimation results get relatively accurate. The propositions are tested for angular position and velocity applications through simulation as well as practical experimentation. The results indicate that the adaptive AEKF provides quantitative improvements over the traditional EKF significantly by adaptively adjusting noise covariance matrices. In addition, it is observed that AEKF produce smaller root mean square errors in state estimation, enhance convergence speed, and demonstrate higher tolerance to unforeseen disturbances. These improvements make AEKF particularly valuable in applications such as PMDC machines, navigation systems, robotics, and sensor fusion, where precise and reliable state estimation is critical.

The increasing sophistication of grid-connected photovoltaic (GCPV) systems necessitates advanced fault detection and diagnosis (FDD) methods to ensure operation efficiency and security. In this paper, a novel two-stage hybrid AI architecture is analyzed that couples an autoencoder using Long Short-Term Memory (LSTM) for unsupervised anomaly detection with an RF classifier for focused fault diagnosis. The architecture is critically compared to that of a baseline-only RF baseline on a synthetic dataset. The results of this two-stage hybrid AI show a strong overall accuracy of (83.1%). The hybrid model’s first stage trains only on unlabeled healthy data, reducing the reliance on extensive and often unavailable labeled fault datasets. This design has the safety-critical advantage of marking unfamiliar faults as anomalies instead of committing to a misclassification. By integrating anomaly detection with classification, the architecture enables early stage screening of faults and targeted categorization, even in data-scarce scenarios. This offers a scalable, interpretable solution suitable for deployment in real-world GCPV systems where robustness and early detection are critical. While the method exhibits reduced sensitivity to subtle or recurring faults, it demonstrates strong reliability in confidently detecting distinct and significant anomalies. Additionally, the approach improves interpretability, facilitating clearer identification of performance constraints such as the autoencoder’s moderate fault sensitivity (AUC = 0.61). This study confirms the hybrid approach as a very promising FDD solution, in which the architectural advantages of safety and maintainability offer a more worthwhile proposition to real-world systems than incremental improvements in a single accuracy measure.

Dissolved Gas Analysis (DGA) of power system transformers has emerged as one of the most effective transformer health diagnosing tools by analyzing the gases dissolved in the insulating oil. There are various traditional DGA techniques like Key Gas Method, Roger’s Ratio, IEC ratio, Dornenburg’s Ratio, and Duval Triangle method. However, these techniques have limitations such as inconsistent results, the inability to detect low-energy faults, and reliance on expert knowledge due to complex interpretation. To overcome these limitations, this paper introduces an integrated fuzzy logic system that enhances DGA interpretation by combining the diagnostic strengths of Key Gas Method, Roger’s Ratio, IEC ratio, and Duval Triangle methods. To obtain a final, human-readable diagnosis, the output of each technique is incorporated into a higher-level fuzzy inference system once each is modeled separately with fuzzy logic, having known membership functions and rule bases. To test this model, oil samples of known results of different transformers are used and compared to the results given by the proposed fuzzy inference system. The proposed method is easier and more feasible for practical use since it not only improves fault detection accuracy and reliability but also allows for easier interpretation by non-specialists. This study makes an additional contribution to a higher-level, more effective, and more accurate method for transformer fault detection by overcoming the interpretational difficulties and weaknesses of conventional DGA approaches.

Ripples in the electromagnetic torque of electric vehicle (EV) motors due to poor stator voltage and control cause jerky movements, equipment failure, discomfort for passengers and drivers, and damage to the associated civil works. This paper presents the implementation of Finite Control Set Model Predictive Control (FCSMPC) for a high-level modified W-type inverter (MWI) driving a three-phase induction motor (IM), along with validation of its performance. The proposed control strategy aims to minimize motor torque ripples and has been tested under various driving torque patterns. The results demonstrate a significant reduction in torque ripples—down to less than 1%—and acceptable levels of total harmonic distortion (THD), as verified through quality analysis of the stator currents. Moreover, a comparative assessment of voltage profiles for the electromagnetic torque and rotor speed curves has been presented for nine cases of simultaneous variations in multiple motor parameters; the results indicate that the MWI-fed motor has the best performance and the lowest sensitivity to the variations.

The rising demand for renewable energy has recently spurred notable advancements in hybrid energy systems that utilize solar and wind power. The Hybrid Solar Wind Energy System (HSWES) integrates wind turbines with solar energy systems. This research project aims to develop effective modeling and control techniques for a grid-connected HSWES. The goal is to optimize power tracking efficiency in an electrically linked solar photovoltaic system combined with a wind-powered Doubly Fed Induction Generator (DFIG). The back-to-back ( $$\beta t \beta$$ ) converters’ DC link is connected to this to integrate the solar photovoltaic (PV). The study controls the rotor and grid converters via a vector control technique. This study aims to optimize power extraction efficiency and hybrid system integration with electrical grids by applying the Maximum Power Point Tracking (MPPT) technique to solar and wind systems. Combining the control strategy with the optimization algorithm makes our work new and compelling. We utilized this technology with a focus on optimization to evaluate how our system performs when applying optimization techniques to the control strategies. We optimized the solar system using the conventional Perturb and Observe (P & O) method and the metaheuristic Particle Swarm Optimization (PSO) technique. Our primary objective was to validate the effectiveness of the optimization process in enhancing the control strategy. The paper investigates the applications of Particle Swarm Optimization (PSO) and Perturb and Observe (P & O) algorithms in solar photovoltaics under constant and real-time sunlight. The hybrid Indirect Speed Controller MPPT algorithms are utilized for step-up and step-down wind speeds. An HSWES simulation is used to confirm the effectiveness and efficiency of the recommended regulation technique. The suggested control approach is simulated using the Sim Power of the MATLAB/Simulink platform. The advantage of the established techniques lies in their capacity to swiftly and precisely monitor the ideal power output of the HSWES. The comprehensive simulations conducted provide compelling evidence for the efficacy of the proposed system in attaining optimal efficiency and stability, propelling the progress of sustainable energy solutions.

Trending electric vehicles with different battery technologies need universally compatible and fast chargers. Present semiconductor technology is not suitable for designing high-power-rating converters. The increasing demand for high-capacity electric vehicle chargers requires efficient and optimum advanced material technology. This research presents next-generation material-based smart ultra-fast electric vehicle charging infrastructure for upcoming high-capacity EV batteries. The designed level 4 charger will be helpful for charging future heavy-duty electric vehicles with battery voltages of up to 2000 V. The designed infrastructure will be helpful for charging both EVs and heavy-duty electric trucks with a wide range of power levels. Wireless sensor-based smart systems monitor and control the overall charging infrastructure. The detailed design analysis of the proposed charger using the Simscape physical modeling tool is discussed using mathematical equations.

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

This research aims to optimize the size and location of biomass-based distributed generator (BMDG) units to enhance the voltage profile, reduce electrical losses, maximize cost savings, and decrease emissions from power distribution systems. Biomass-based distributed generator (BMDG) systems offer numerous advantages to enhance the efficiency of power distribution systems. However, achieving these benefits relies on determining the optimal size and position of the BMDGs. To achieve these objectives, the metaheuristic technique called particle swarm optimization (PSO) is employed to find the optimal placement and size of BMDGs. The proposed model was validated on MATLAB's IEEE-33 bus radial distribution system (RDS), confirming the aforementioned benefits. Comparative analysis between the PSO-based technique and other algorithms from previous research revealed better results with the proposed method. The results indicate that optimal placement and sizing of BMDG units have led to a reduction of more than 67.68% in active power losses and 65.90% in reactive power losses compared to the base case. Additionally, the reduction in active power loss was 40.44%, 11.39%, 42.85%, 1.81%, 0.85%, 29.83%, 5.82% and 28.38% more than artificial bee colony, backtracking search optimization algorithm, moth-flame optimization, Coordinate control, artificial Hummingbird algorithm, variable constants PSO (VCPSO), artificial gorilla troops optimizer (AGTO), and a jellyfish search optimizer respectively. Furthermore, the reactive power losses were reduced by 38.33% and 15.68% compared to VCPSO and AGTO respectively. Furthermore, this study revealed a cost reduction of 6.38% when compared to the AGTO and 1.30% when compared to the AHA. Moreover, the voltage profile of the power distribution system was improved by 7.28%. The presented methodology has demonstrated promising results for BMDGs in RDS across various applications.

A brushless DC (BLDC) motor is likewise called an electrically commutated motor; because of its long help life, high productivity, smaller size, and higher power output, it has numerous modern applications. These motors require precise rotor orientation for longevity, as they utilize a magnet at the shaft end, detected by sensors to maintain speed control for stability. In modern apparatuses, the corresponding, primary, and subsidiary (proportional-integral) regulator is broadly utilized in controlling the speed of modern machines; however, an ideal and effective controlling strategy is constantly invited. BLDC motor is a complex system having nonlinearity in its dynamic responses which makes primary controllers in efficient. Therefore, this paper implements metaheuristic optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA); moreover, a chaotic map and weight factor are also being applied to modify LFWOA (i.e., CMLFWOA) for optimizing the PI controller to control the speed of BLDC motor. Model of the brushless DC motor using a sensorless control strategy incorporated metaheuristic algorithms is simulated on MATLAB (Matrix Laboratory)/Simulink. The Integral Square Error (ISE) criteria is used to determine the efficiency of the algorithms-based controller. In the latter part of this article after implementing these mentioned techniques a comparative analysis of their results is presented through statistical tests using SPSS (Statistical Package for Social Sciences) software. The results of statistical and analytical tests show the significant supremacy of WOA on others.

The optimal integration of Photovoltaic (PV) systems into an electric grid is dependent upon the total output power of the PV system. To optimize the output power of a PV system, the modules must be positioned at an optimal tilt angle (OTA) to maximize the absorption of solar radiations. This research focused on a mathematical model to optimize incident solar radiation. The proposed model is used to determine the OTA and evaluate its impact on the optimum configuration and power output capacity using MATLAB for six cities located in different temperature zones across Pakistan. The isotropic and anisotropic models have been used to calculate the total solar radiations (HT) on a sloped surface. During the summer season, all four selected models present similar findings in terms of the monthly average daily solar radiations on the tilted surface. During the winter season, the anisotropic models performed better than the isotropic models. The anisotropic model achieved a 14.82% energy increase in January and a 0.16% increase in June compared to the isotropic model. We present monthly and annual OTA calculated from the anisotropic model. The OTA has been determined for the HT across slope values ranging from 0 degrees to 90 degrees with a 1 degrees resolution. The monthly OTA using anisotropic model for the Faisalabad, Lahore, Multan, RYK, Islamabad and Karachi ranges from 7 degrees to 54 degrees, 7 degrees to 53 degrees, 6 degrees to 52 degrees, 5 degrees to 52 degrees, 10 degrees to 58 degrees and 1 degrees to 50 degrees and the annual OTA for cities has been calculated to be 30.5 degrees, 30.25 degrees, 29.33 degrees, 28.66 degrees, 33.34 degrees and 25.5 degrees respectively. Utilizing the OTA calculated from the selected model, a PV array with a rated power of 52.200 kW has been used to analyze the system performance. The annual average output power at the monthly OTA results in gains of 8.83%, 9.40%, 9.78%, 9.77%, 9.82%, and 9.91% compared to the annual OTA. This research study is particularly beneficial for researchers and the industry in deploying PV systems across different climatic zones of Pakistan, intending to maximize output power while minimizing energy costs.

This work presents an energy management scheme (EMS) based on a rule-based grasshopper optimization algorithm (RB-GOA) for a solar-powered battery-ultracapacitor hybrid system. The main objective is to efficiently meet pulsed load (PL) demands and extract maximum energy from the photovoltaic (PV) array. The proposed approach establishes a simple IF-THEN set of rules to define the search space, including PV, battery bank (BB), and ultracapacitor (UC) constraints. GOA then dynamically allocates power shares among PV, BB, and UC to meet PL demand based on these rules and search space. A comprehensive study is conducted to evaluate and compare the performance of the proposed technique with other well-known swarm intelligence techniques (SITs) such as the cuckoo search algorithm (CSA), gray wolf optimization (GWO), and salp swarm algorithm (SSA). Evaluation is carried out for various cases, including PV alone without any energy storage device, variable PV with a constant load, variable PV with PL cases, and PV with maximum power point tracking (MPPT). Comparative analysis shows that the proposed technique outperforms the other SITs in terms of reducing power surges caused by PV power or load transition, oscillation mitigation, and MPP tracking. Specifically, for the variable PV with constant load case, it reduces the power surge by 26%, 22%, and 8% compared to CSA, GWO, and SSA, respectively. It also mitigates oscillations twice as fast as CSA and GWO and more than three times as fast as SSA. Moreover, it reduces the power surge by 9 times compared to CSA and GWO and by 6 times compared to SSA in variable PV with the PL case. Furthermore, its MPP tracking speed is approximately 29% to 61% faster than its counterparts, regardless of weather conditions. The results demonstrate that the proposed EMS is superior to other SITs in keeping a stable output across PL demand, reducing power surges, and minimizing oscillations while maximizing the usage of PV energy.

Severe pollution-induced flashovers on insulators present a pressing challenge to power system safety. The frequent failure of high-voltage insulators, particularly in the polluted environments of Pakistan, poses a critical concern. This paper investigates the impact of insulator profile on reducing pollution flashovers, testing two designs as per IEC standard 60383 and simulated using the Finite Element Method in COMSOL Multiphysics®. The test results revealed that deep under-ribs insulators exhibited a 5.008% reduction in flashover voltage, while alternating shed insulators experienced a 3.233% decrease in polluted conditions compared to clean conditions. Notably, under both clean and polluted conditions, alternating shed insulators consistently outperformed deep under-ribs insulators, with a 25.377% higher flashover voltage in clean conditions and a 27.400% superiority in polluted conditions. Computational analysis through the Finite Element Method in COMSOL Multiphysics shows a consistent pattern in potential distribution with increasing insulator count, but the presence of a pollution layer introduces spikes in the electric field distribution, validating experimental results. These findings highlight the superior performance of alternating shed insulators, especially in polluted environments.

Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.

This research work proposes an Improved Supervised Learning (ISL)-based Deep Neural Network (DNN) for accurately forecasting the load demand of Electric Vehicles (EVs). This work incorporates Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), Fully Connected (FC), and Convolutional Neural Network (CNN) architectures. The proposed ISL technique enhances prediction performance by refining the training process with additional features and information. Using a real-world EV charging dataset from Boulder City, USA, the simulations demonstrate consistent improvements in the GRU, LSTM, RNN, FC, and CNN models with the proposed ISL technique. Further, the proposed technique reduces the Normalised Root Mean Square Error (NRMSE) and Normalised Mean Absolute Error (NMAE) values. The accurate load demand predictions facilitated by the proposed models with ISL have significant implications for the planning and management of EV charging stations. This enables stakeholders to optimize resource allocation, effectively plan infrastructure capacity, and ensure the sustainable and reliable operation of grids in the face of increasing EV adoption. By leveraging deep learning architectures and incorporating the ISL technique, this research contributes to advancing load forecasting models for EVs, providing practical solutions for efficient management and planning in the evolving electric mobility landscape.

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.