Professor Farhad Shahnia

Electric vehicle proliferation necessitates efficient power conversion systems with minimal harmonic distortion. Among the established solutions for electric vehicles’ charging infrastructure, 12-pulse thyristor-based controlled rectifiers integrated with phase-shifting transformers offer superior harmonic performance; however, their advanced design requires iterative refinements to address nonlinear magnetic behavior, loss mechanisms, and complex trade-offs among harmonic mitigation, transformer rating, volume, cost, and thermal limits. This study presents an explainable artificial intelligence (XAI)-enhanced framework that moves beyond simulation-driven, trial-and-error design toward informed data-driven decision-making supported by interpretable feature-importance analysis. The proposed framework transforms black-box ensemble models into transparent decision-support tools, enabling engineers to better understand the influence of key design parameters on grid-current total harmonic distortion (THD). An ANSYS TwinBuilder–Maxwell 3D co-simulation is employed to generate 4750 operating points linking the thyristor firing angle, output filter capacitance, and load resistance to the grid-current THD for training 15 machine learning algorithms. The evaluated machine learning models demonstrate that tree-based ensemble methods provide the highest predictive performance, with CatBoost achieving R² = 0.964 and RMSE = 0.358. SHAP-based explainability analysis identifies the firing angle as the dominant control parameter (54% feature contribution), followed by load resistance (34%) and filter capacitance (12%). Validation against an independent ANSYS Maxwell–Twin Builder co-simulation at a representative operating point resulted in a prediction error of 1.18%, demonstrating good agreement between the surrogate model and the simulation within the investigated parameter space.

Integrating solar power into electricity grids requires accurate short-term forecasting of the global horizontal irradiance to accurately predict the expected solar power generation. This paper compares five tree-based machine learning models against a Persistence baseline for multi-resolution forecasting in arid climates. A 13-year dataset from Basra, Iraq, has been employed in this study for verification purposes, and the models are tested across various very-short- to short-term forecasting horizons of 5, 10, 15, 30, and 60 min. Unlike most existing studies that focus on single forecasting horizons or mixed climatic conditions, this work systematically benchmarks multi-resolution irradiance forecasting under distinct sky conditions in a hot arid environment using a strict anti-data-leakage framework. To avoid data leakage in these models, feature engineering has used only lagged inputs. The dataset has been split into three groups for training, validation, and testing (respectively 70, 15, and 15% of the entire available dataset). The models were then tested separately under clear, partly cloudy, and cloudy skies. Numerical studies prove that picking the best model depends heavily on the forecast horizon. For very-short-term predictions, the Persistence model was competitive (RMSE = 21.32 W/m2), while the Gradient Boosting model proved slightly more accurate (RMSE = 17.65 W/m2). For the 60 min horizon, the boosting models took a clear lead. The HistGradientBoosting model resulted in a 67% reduction in the RMSE compared to the Persistence baseline. Also, the top-performing model changed depending on the weather and the time scale. Gradient Boosting was the clear winner for short-term clear sky forecasts, while XGBoost handled the longer horizons. Partly cloudy skies showed a rotating mix of different boosting algorithms taking the lead. However, studies show that when skies were fully overcast, complex machine learning models fail to capture chaotic patterns, making the simple Persistence baseline a necessary reliability safeguard. The results reveal that no single model consistently dominates all forecasting horizons and weather conditions, highlighting the necessity of adaptive model selection for operational solar forecasting. These findings highlight the importance of horizon- and weather-adaptive model selection for operational solar forecasting. Rather than relying on a single universal algorithm, grid operators in arid regions can improve forecasting reliability by dynamically selecting models based on prevailing sky conditions and forecast horizons.

The decarbonization of transport and energy sectors requires rapid deployment of electric vehicle (EV) fast charging infrastructure integrated with distributed energy resources (DERs). Although joint planning of fast charging stations (FCSs), photovoltaics (PV), and energy storage systems (ESSs) has been widely studied, many existing frameworks rely on simplified operating assumptions and do not capture temperature-driven variations in asset performance or weather-load interactions. This paper proposes a two-stage stochastic planning framework that explicitly incorporates ambient temperature effects and aligns investment decisions with regulatory incentives. The model embeds temperature-dependent physical constraints for EV energy consumption, charging power limits, EV/ESS charging efficiency, ESS discharging efficiency, and ESS degradation. Two performance-based incentive mechanisms are incorporated to monetize system-supportive behavior, namely self-sufficiency and peak shaving. Uncertainty is modeled using a vine copula scenario generation method that preserves nonlinear and tail dependencies among solar irradiance, ambient temperature, and electrical load. Case studies on a modified IEEE 33-bus system across three climatic zones demonstrate the importance of temperature-aware planning. Under cold climate conditions, traditional planning models underestimate required FCS capacity by up to 67% and lead to substantial fleet energy deficits under realistic operation conditions. Repeating the study over a larger IEEE 69-bus system with 50% higher EV penetration verifies the scalability of the proposed technique and confirms the findings that even mild climate locations may experience significant fleet energy deficits when temperature effects are ignored, and that unserved energy may occur under extreme cold. The proposed framework yields investment plans that maintain operational adequacy and improve economic performance through effective incentive capture.
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•A temperature-aware stochastic planning framework for FCSs with DERs is proposed.•Regulatory reward models monetize self-sufficiency and peak shaving.•A vine copula captures dependencies among weather variables and electrical load.•Traditional models significantly underestimate FCS needs in cold climates.

The trend in the power electronics industry toward higher power density and efficiency has brought high-frequency transformers (HFTs) to the forefront of critical applications, including isolated DC–DC converters, electric vehicle chargers, and solid-state transformers. This paper focuses on the leakage inductance of HFTs and presents a systematic comparative framework that evaluates five surrogate modeling and hybrid optimization approaches for the rapid and accurate estimation of leakage inductance. A comprehensive parametric dataset was constructed, comprising 1210 finite element analysis simulations conducted via finite element analysis in the ANSYS Maxwell 2024 R1 environment, varying the number of winding turns, primary winding thickness, and secondary winding thickness of the HFT. All five methods were trained and evaluated on the same dataset under identical conditions. The comparative evaluation demonstrates that the proposed hybrid Gray Wolf optimizer–artificial neural network (GWO-ANN) framework achieved the highest prediction accuracy (R2 = 0.9832, MSE = 0.01780, MAE = 0.0935 µH) and the fastest convergence among all tested approaches. The generalization capability of the proposed model was confirmed through blind validation tests across six geometric configurations spanning the full range of the design space, yielding a maximum prediction error of 8.15% and an average error of 2.14%. The functional validity of the proposed parameters was further tested in a third validation layer using MATLAB/Simulink R2024b transformer circuit studies, demonstrating a theoretical efficiency of 96.06%. This three-layer validation approach proves both the parametric and functional reliability of the proposed framework for HFT designs.

The uninterrupted operation of critical and distributed loads, such as traffic lights in urban environments, during power outages presents a significant challenge for city management and infrastructure authorities. Even brief power disruptions due to energy crises can trigger cascading issues, including traffic congestion, delays, accidents, and delays in emergency response vehicles, such as fire trucks and ambulances. Consequently, guaranteeing a reliable supplementary power supply for these essential loads is becoming increasingly crucial.
This study offers a comparative analysis of existing power supply solutions, including traditional grid connections, renewable energy systems, and hybrid configurations that incorporate energy storage technologies. A case study focusing on traffic lights at major intersections in Qazvin City demonstrates the practical application and efficacy of the proposed methods in real-world scenarios. The investigation evaluates the performance of a solar energy system during short-term outages, emphasizing the impact of seasonal variations in solar radiation and ambient temperature. Response strategies to planned electricity interruptions are examined, with recommendations formulated to improve system reliability for critical loads. The results indicate that the highest operational continuity occurs during outages around 9 AM, while capacity diminishes at night, predominantly due to the system’s dependence on battery storage. Monthly data projections suggest operation times of approximately 4–8 h at 50% battery charge and 5–9 h at 80% charge.
In this optimization problem, the mutual goals of traffic and electrical performance were simultaneously considered as a multi-objective framework. Moreover, the optimal selection of candidate intersections for solar system deployment was performed using the TOPSIS method in both the conventional and iterative modes. The iterative approach, which integrates the influence of prior selections into subsequent decision-making, yielded higher accuracy and more consistent results. To enhance the robustness of the analysis, a sensitivity analysis was conducted by varying one or two parameters simultaneously within the TOPSIS framework. This process ensured that the results were not only reliable but also resilient to parameter fluctuations, providing a precise and dependable foundation for strategic decision-making in critical urban-energy applications.
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•Presents a comprehensive assessment of solar-powered traffic light systems’ performance during short-term outages, considering seasonal and monthly variations.•Introduce green wave strategy and multi-objective optimization for practical intersection selection based on real-world data.•Implements a fused impact of electrical and traffic parameters within an iterative TOPSIS framework for hierarchical prioritization of intersections for solar deployment.

Surface anomalies such as dust accumulation and bird droppings on photovoltaic (PV) panels can significantly reduce their energy production and lead to inefficient maintenance decisions. This paper proposes a vision-based deep learning framework for the automatic detection of PV panel surface conditions and validates the detected anomalies using real inverter-level energy production data. Unlike conventional studies focusing solely on detection performance, the proposed approach introduces a unified and physically interpretable framework that directly links image-based anomaly detection with inverter-level energy performance and decision-oriented PV maintenance. An EfficientNetB3-based model is trained using a two-stage transfer learning strategy on a publicly available Kaggle dataset and evaluated using standard classification metrics. The trained model is then deployed and validated at a 1 MW solar power plant located at Karaman, Türkiye. Classification results obtained from field images are systematically linked with inverter-associated hourly energy production measurements. Following panel cleaning and natural rainfall, an approximately 12.5% increase in inverter-level hourly energy production is observed for the analyzed PV group (120 panels, ~270 Wp), corresponding to an increase from 23.2 to 26.1 kWh. In addition, the study introduces an energy–water–sustainability-aware cleaning decision framework tailored for arid and semi-arid regions where water scarcity and deep groundwater extraction present critical constraints. The framework defines a quantitative decision rule in which panel cleaning is performed only when the expected recoverable energy exceeds the energy cost of water extraction and cleaning. Overall, the proposed approach enables accurate surface anomaly detection while supporting sustainability-aware, resource-efficient and data-driven maintenance decisions for PV power plant operation.

Onshore, fixed-bottom offshore, and floating offshore wind turbines (FOWTs) represent distinct technological pathways for large-scale wind energy deployment, yet comparative assessments often emphasize descriptive characteristics rather than quantified performance trade-offs. This study presents a structured comparison of these three wind energy systems across technology maturity, levelized cost of energy (LCOE), environmental impacts, and socio-regulatory constraints. A harmonized literature synthesis of recent techno-economic data (2018–2024) reveals that onshore wind remains the lowest-cost option (typical LCOE: 30–55 USD/MWh), while fixed-bottom offshore wind exhibits higher but declining costs (60–100 USD/MWh) driven primarily by installation and foundation expenses. Floating offshore wind systems currently show the highest LCOE (90–160 USD/MWh), dominated by platform and mooring costs, yet demonstrate the strongest long-term cost-reduction potential due to access to superior wind resources and deep-water sites. Environmental and social analyses indicate increasing public acceptance challenges for onshore projects, while offshore and floating systems face regulatory complexity and higher capital risk rather than social opposition. The study’s contribution lies in a balanced, criterion-based framework that exposes cost drivers, maturity gaps, and deployment constraints across wind technologies, highlighting floating offshore wind as a transitional technology whose competitiveness depends on platform standardization and supply-chain learning effects. The analysis demonstrates that cost parity between floating and fixed-bottom offshore wind is not turbine limited but platform- and financing-driven, with regulatory clarity having a comparable impact to capital expenditure (CAPEX) reductions.

In light of the growing demand for sustainable energy and the diminishing viability of conventional fossil fuels, wind energy has become a compelling alternative particularly in wind-rich regions such as Morocco. Among the various variable-speed wind turbine technologies, the Doubly Fed Induction Generator (DFIG) stands out for its efficiency and cost-effectiveness. However, traditional control methods for DFIGs, including Field-Oriented Control and Direct Power Control (DPC), are hindered by limitations such as sensitivity to parameter variations, torque and flux oscillations, and fluctuating switching frequencies that contribute to harmonic distortion. To address these challenges, this study introduces an enhanced power control strategy for DFIG-based Wind Energy Conversion Systems (WECS), incorporating the Bald Eagle Search (BES) algorithm into the FOC framework. The proposed method dynamically adjusts the reference values of active and reactive power in real time, guided by system performance metrics such as power error and flux stability. Inspired by the bald eagle’s hunting strategy, the BES algorithm enables rapid convergence, high adaptability, and reduced computational complexity. Simulation results validate the effectiveness of the proposed BES-based controller, showing superior performance compared to conventional FOC and Fuzzy Logic Controller methods particularly in terms of harmonic distortion and dynamic response. The Total Harmonic Distortion (THD) values achieved are 2.69% for FOC, 2.52% for FLC, and 2.51% for the BES-based method, highlighting its potential to enhance power quality and operational stability under varying wind and grid conditions.

This paper presents a novel approach to optimizing vehicle-to-grid (V2G) enhanced energy management in microgrid systems through machine learning-based forecasting. The proposed system utilizes the Random Forest algorithms to predict energy consumption and renewable energy generation patterns, enabling intelligent decision-making for V2G operations. The proposed methodology incorporates temporal features including hourly, daily, and monthly patterns to create accurate 24-hour forecasts for both load demand and renewable energy generation. The developed V2G optimization strategy then uses these forecasts to make informed decisions about the charge and discharge timing of electric vehicles, maintaining a balance between immediate grid requirements and anticipated future needs. The performance of the proposal is evaluated using real-world microgrid data and demonstrates significant improvements against traditional V2G management approaches. The studies demonstrate that the proposed model uses battery cycles more efficiently, prevents unnecessary energy transfers, and reduces battery degradation by minimizing excessive charging. The numerical studies show that the proposed technique maintains the energy deficit at a lower rate by 6.4% fewer charge-discharge operations. Furthermore, the proposed approach relies on easily accessible data rather than difficult-to-obtain weather variables, enhancing its practicality and ease of implementation. This makes the system more applicable in real-world scenarios without requiring complex meteorological data collection and enables the proposed system to adapt to varying renewable energy generation patterns and consumption behaviors, particularly suitable for microgrids with high renewable energy penetration. This research contributes to the growing intelligent energy management systems field and provides a practical framework for implementing machine learning in V2G applications.

This paper introduces a new four-switch, high-voltage, high-step-up converter employing two transformers. The topology enables Zero-Voltage Switching (ZVS) across all primary switches for operating conditions ranging from no load to full load. A voltage-quadrupler and a voltage-doubler rectifier are used on the secondary sides of the transformers, enabling reduced turn-off current for the voltage-quadrupler diodes and Zero-Current Switching (ZCS) turn-off for the voltage-doubler diodes, thereby ensuring high efficiency across diverse load levels. Notably, the voltage stress experienced by the voltage-multiplier diodes is significantly lower than the output voltage, thereby rendering the converter exceptionally suitable for high-voltage applications such as electron beam welding (EBW). The voltage gain surpasses that of the conventional phase-shift full-bridge (PSFB) converter, permitting a lower transformer turns ratio and thus reducing winding resistivity. The removal of the substantial output inductor leads to a lighter and more compact design, eliminating insulation concerns associated with inductor windings. This paper details the operation of the proposed converter, supported by experimental results from a 500-W prototype with a 150-V input and 2-kV output, which confirm its high performance and operational advantages.